i
Copy Number Variation in Neurodevelopmental Disorders
by
Anath Christopher Lionel
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Molecular Genetics University of Toronto
© Copyright by Anath Christopher Lionel 2014
ii
Copy Number Variation in Neurodevelopmental Disorders
Anath Christopher Lionel
Doctor of Philosophy
Molecular Genetics University of Toronto
2014
Abstract
Recent research on genetic etiologies of different neurodevelopmental conditions such as Autism
Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and schizophrenia
has highlighted a key role for rare copy number variants (CNVs). The study of such rare
deletions and duplications in the genomes of patients has proven to be a powerful strategy for
rapid identification of novel risk genes. Intriguingly, there is an emerging pattern of certain genes
being implicated by rare CNVs across clinically distinct disorders. Findings of genetic overlap
are consistent with comorbidity and shared traits often observed among these conditions and hint
at common underlying biological processes or pathways such as synaptic development. This
trend of overlapping genetic loci predisposing to different phenotypes has not been
systematically investigated in an unbiased fashion on a genome-wide scale.
In this thesis, I describe high resolution microarray analysis of three newly characterized
Canadian cohorts of individuals with different neurodevelopmental disorders (ASD, ADHD and
schizophrenia) in a uniform manner and with two main objectives. First, within each cohort, rare
CNVs were detected and used to identify new genetic risk loci for each disorder. My
prioritization strategy focused on rare CNVs of two types: those of de novo origin and those
significantly enriched in multiple unrelated patients compared to controls. Second, overlapping
iii
rare CNVs among the three cohorts were further investigated to assess cross-disorder genetic
overlap and pinpoint shared risk genes.
These analyses confirmed regions previously implicated in genetic risk for neurodevelopmental
disorders, and also revealed novel CNV loci such as deletions affecting NRXN3 in ASD,
MACROD2/FLRT3 in ADHD and the 2q13 region in schizophrenia. Cross-disorder rare CNV
comparisons highlighted several shared risk genes including ASTN2/TRIM32 in ASD and ADHD
and GPHN in ASD and schizophrenia. Follow-up clinical and molecular characterization of
these CNVs revealed factors modulating their penetrance including gender and isoform-specific
effects. These results provide support for the role of rare CNVs in the genetic etiologies of ASD,
ADHD and schizophrenia; provide evidence for shared susceptibility genes for different
neuropsychiatric disorders; and identify new risk genes to guide clinical genetic testing as well as
the development of molecular therapeutics.
iv
Acknowledgments
It is a great pleasure to thank the many wonderful people I have been privileged to work with,
and who have helped me during my time in graduate school.
Firstly, I thank my supervisor Dr. Stephen Scherer for his confidence in me, and for his
mentorship, guidance, advice and financial support during my PhD journey. He has been an
outstanding role model exemplifying research leadership and ‘big picture’ scientific insights, the
initiation and strengthening of powerful collaborative partnerships and effective scientific
communication and writing. My graduate training has been enriched by experiencing first hand
these key facets of research and by the many opportunities and resources that have been provided
in his lab. I will always be proud to have been his graduate student. I am also grateful to the
members of my supervisory committee, Dr. Lucy Osborne and Dr. Gary Bader for their patient
guidance, helpful suggestions and encouragement which have kept me on track during my
research and have been particularly instrumental during key graduate school milestones
including the reclassification exam and completion of this thesis. I am thankful to the other
members of my PhD examination committee including Dr. Paul Pavlidis, Dr. Sean Egan and Dr.
Berge Minassian for their insightful questions and helpful suggestions.
I would like to thank several past and present members of the Scherer academic lab and the
Centre for Applied Genomics (TCAG). In particular, I am indebted to Dr. Christian Marshall for
his research prowess and for the guidance provided, in his own inimitable and humorous way,
from my first days as a rotation student right up to the end of my PhD over the course of many
different projects. I have learned much about genetic research, Canada and life from him. I have
also had the privilege of working closely with Bhooma Thiruvahindrapuram, Dr. Kristiina
Tammimies, Dr. Daisuke Sato, Dr. Andrea Vaags, Dr. Daniele Merico and Matthew Gazzellone
and I am grateful for the invaluable contributions of their excellent scientific skills, support and
kindness to my research training and graduate experience. I thank the other graduate students in
the Scherer lab: Dr. Andrew Carson and Dr. Layla Parker-Katiraee for their guidance during my
early days in the lab and my contemporaries Dr. Andy Pang, Matthew Gazzellone and Lia
v
D’Abate for their friendship, encouragement, frequent coffee breaks and abundant laughter.
Other current and former members of TCAG that I would like to thank for their assistance at
different times during my research training and friendships that have enriched my time in the lab
include Sanjeev Pullenayegum, Dr. Mary Ann George, Jennifer Howe, Jenny Kaderali, Dr.
Richard Wintle, Dr. Lars Feuk, Dr. Hameed Khan, Dr. Susan Walker, Dr. Ryan Yuen, Dr.
Mohammed Uddin, Dr. Mehdi Seno, Dr. Aparna Prasad, Dr. Chao Lu, Lynette Lau, Dr. Tara
Paton, Elango Cheran, Robert Ziman, Joe Whitney, Deepthi Rajagopalan, Dr. John Wei, Dr.
Zhuozhi Wang, Dennis Chen, Divya Mandyam, Vibha Raghavan and Jeff MacDonald.
It has been a great pleasure and an inspirational experience to work closely with my many
clinical collaborators, of whom those at the Centre for Addiction and Mental Health (Dr. Anne
Bassett, Dr. Gregory Costain, Dr. John Vincent, Dr. Abdul Noor), the Hospital for Sick Children
(Dr. Russell Schachar, Dr. Jennifer Crosbie, Dr. James Stavropoulos, Dr. Wendy Roberts, Dr.
Melissa Carter, Dr. Gregory Handrigan and the Autism Research Unit team) and Toronto
General Hospital (Dr. Candice Silversides) deserve special mention. My research work would
not have been possible without funding and support from different sources including
NeuroDevNet, the SickKids foundation, Autism Speaks and the Ontario Ministry of Education
and Training.
I would also like to mention and thank friends who have been an important source of support and
inspiration during my PhD including those from my undergraduate days - in particular, Sherwin
Abraham, Selvaprabhu Nadarajah and Kaushik Gopal - and those I met during graduate school
including Niladri Chattopadhyay, Kirill Zaslavsky, Santhosh Dhanraj, Bhaskar Chanda, Vivek
Mahadevan and Brige Chugh. I thank my sisters Achsah and Amrita Lionel, and my relatives in
India and elsewhere for their support. Finally, I would like to dedicate this thesis to my parents
Jesuraj and Ranjini Lionel, whose constant encouragement, love and prayers have helped me
through tough times, and have contributed immeasurably to everything I have achieved, and to
the person that I am today. I hope I have made them proud.
vi
Table of Contents
Acknowledgments.......................................................................................................................... iv
Table of Contents ........................................................................................................................... vi
List of Tables ................................................................................................................................. xi
List of Figures ............................................................................................................................... xii
List of Abbreviations ................................................................................................................... xiii
Chapter 1 Introduction .....................................................................................................................1
1.1 Autism Spectrum Disorder (ASD) .......................................................................................3
1.1.1 Clinical profile of ASD ............................................................................................3
1.1.2 Evidence for the genetic etiology of ASD ...............................................................3
1.1.3 Common genetic variant findings in studies of ASD risk .......................................4
1.1.4 Rare genetic variant findings in studies of ASD risk ...............................................5
1.2 Attention Deficit Hyperactivity Disorder (ADHD) .............................................................8
1.2.1 Clinical profile of ADHD ........................................................................................8
1.2.2 Evidence for the genetic etiology of ADHD............................................................9
1.2.3 Common genetic variant findings in studies of ADHD risk ..................................10
1.2.4 Rare genetic variant findings in studies of ADHD risk .........................................10
1.3 Schizophrenia .....................................................................................................................11
1.3.1 Clinical profile of schizophrenia ............................................................................11
1.3.2 Evidence for the genetic etiology of schizophrenia ...............................................12
1.3.3 Common genetic variant findings in studies of schizophrenia risk .......................13
1.3.4 Rare genetic variant findings in studies of schizophrenia risk ..............................13
1.4 Phenotypic and Genetic Overlap Across NDDs ................................................................15
vii
1.4.1 Comorbidity and clinical connections between ASD and ADHD .........................15
1.4.2 Comorbidity and clinical connections between ASD and schizophrenia ..............18
1.4.3 Comorbidity and clinical connections between ADHD and schizophrenia ...........20
1.4.4 Evidence for shared genetic risk factors across NDDs ..........................................20
1.5 Thesis Rationale and Overview .........................................................................................29
Chapter 2 Thesis Overview and Methodology ..............................................................................34
2 35
2.1 Thesis Overview ................................................................................................................35
2.2 Uniform CNV Analysis Workflow ....................................................................................37
2.2.1 Microarray genotyping...........................................................................................37
2.2.2 Uniform quality control measures .........................................................................37
2.2.3 CNV detection from microarray data ....................................................................40
Chapter 3 CNV Analysis of ASD Cohort Reveals Novel Risk Gene NRXN3...............................42
3 43
3.1 Abstract ..............................................................................................................................43
3.2 Introduction ........................................................................................................................44
3.3 Results ................................................................................................................................44
3.3.1 Candidate risk loci identified by rare CNVs in ASD cohort .................................44
3.3.2 Follow-up of rare exonic deletions at the NRXN3 locus ........................................48
3.3.3 Clinical information from ASD probands with NRXN3 deletions and their families ...................................................................................................................51
3.4 Discussion ..........................................................................................................................54
3.5 Materials and Methods .......................................................................................................56
3.5.1 Study subjects and methodology ...........................................................................56
Chapter 4 Rare Copy Number Variant Discovery and Cross-Disorder Comparisons Identify Risk Genes for ADHD ..............................................................................................................57
viii
4 58
4.1 Abstract ..............................................................................................................................58
4.2 Introduction ........................................................................................................................59
4.3 Results ................................................................................................................................59
4.3.1 Detection of rare CNVs in ADHD cohort ..............................................................59
4.3.2 Loci implicated by de novo CNVs in ADHD cases ...............................................65
4.3.3 Rare inherited CNVs at loci previously implicated in ADHD and other NDDs ...66
4.3.4 Overlap between rare CNV findings in the ADHD and ASD cohorts ..................67
4.4 Discussion ..........................................................................................................................69
4.5 Materials and Methods .......................................................................................................73
4.5.1 Study subjects in ADHD cohort ............................................................................73
Chapter 5 Pathogenic Rare Copy Number Variants in Community-Based Schizophrenia Suggest a Potential Role for Clinical Microarrays ....................................................................75
5 77
5.1 Abstract ..............................................................................................................................77
5.2 Introduction ........................................................................................................................78
5.3 Results ................................................................................................................................81
5.3.1 Clinically significant and novel large rare structural variants in schizophrenia ....81
5.3.2 Prevalence of clinically significant CNVs in community sample of schizophrenia .........................................................................................................82
5.3.3 Large rare CNVs of unknown significance in schizophrenia ................................82
5.3.4 Genome-wide CNV burden in schizophrenia ........................................................83
5.3.5 Very rare, smaller CNVs identifying genes of interest in schizophrenia ..............84
5.3.6 Functional networks revealed by pathway enrichment analysis ............................85
5.4 Discussion ..........................................................................................................................98
5.5 Materials and Methods .....................................................................................................104
ix
5.5.1 Schizophrenia sample ascertainment and assessment..........................................104
5.5.2 Control sample for formal analyses .....................................................................105
5.5.3 CNV prioritization, assessment of pathogenicity and prevalence .......................106
5.5.4 Experimental validation of CNVs ........................................................................107
5.5.5 CNV burden analyses ..........................................................................................107
5.5.6 Functional network from gene association analysis ............................................108
Chapter 6 Gephyrin Deletions in Cross-Disorder Risk for Autism, Schizophrenia and Seizures ...................................................................................................................................109
6 110
6.1 Abstract ............................................................................................................................110
6.2 Introduction ......................................................................................................................110
6.3 Results ..............................................................................................................................112
6.3.1 Detection and inheritance testing of rare microdeletions at the GPHN locus .....112
6.3.2 Frequency comparison of GPHN deletions in cases and controls .......................116
6.3.3 Clinical features of individuals with exonic GPHN deletions .............................116
6.4 Discussion ........................................................................................................................120
6.5 Materials and Methods .....................................................................................................124
6.5.1 Study subjects ......................................................................................................124
6.5.2 CNV validation and inheritance testing ...............................................................124
Chapter 7 Summary and Future Research Directions ..................................................................125
7 126
7.1 Summary and Implications ..............................................................................................126
7.1.1 Implications for clinical genetic testing and molecular diagnostics ....................127
7.1.2 Implications for genetic counseling of patients and their families ......................128
7.1.3 Implications for screening, diagnosis and treatment of NDDs ............................129
7.1.4 Implications for development of novel therapeutics ............................................130
x
7.2 Future Research Directions ..............................................................................................132
7.2.1 Follow-up of rare CNV findings in clinical genetic cohorts................................132
7.2.2 Functional characterization of rare CNVs and risk genes ...................................141
7.2.3 Higher resolution genomic studies of NDDs .......................................................149
References ....................................................................................................................................151
xi
List of Tables
Table 1.1. Examples of genomic regions implicated by rare CNVs across different NDDs 23
Table 1.2. Examples of genes implicated by rare CNVs and/or SNVs across NDDs 25
Table 3.1. Examples of risk loci implicated by rare CNVs in ASD probands 46
Table 3.2. Examples of risk loci implicated by rare exonic CNVs across 3 NDD cohorts 47
Table 4.1. De novo and rare inherited CNVs at candidate loci in ADHD probands 60
Table 4.2. Clinical phenotypes of ADHD families with rare CNVs of interest 63
Table 5.1. Very rare, clinically significant CNVs in unrelated schizophrenia probands 86
Table 5.2. Large rare CNVs of uncertain pathogenicity in unrelated probands 89
Table 5.3. Rare autosomal CNV burden in 420 unrelated schizophrenia probands 92
Table 5.4. Candidate genes for schizophrenia overlapped by very rare (<500 kb) CNVs 93
Table 6.1. Genetic and phenotypic details of probands with GPHN deletions 115
Table 7.1. Clinical diagnostic cohorts for screening of rare ASTN2/TRIM32 CNVs 135
Table 7.2. Genetic and clinical information for individuals with rare ASTN2 deletions 138
Table 7.3. Results of CNV enrichment analysis of ASTN2/TRIM32 locus 140
xii
List of Figures
Figure 1.1. Reports in literature of exonic NRXN1 deletions across a range of NDDs 28
Figure 1.2. Rationale for cross-NDD comparison of rare CNVs 31
Figure 2.1. Overview of thesis project 36
Figure 2.2. Uniform quality control measures 39
Figure 2.3. Uniform CNV analysis workflow 41
Figure 3.1. Rare exonic NRXN3 deletions in ASD probands and controls 49
Figure 3.2. Pedigrees of the families of ASD probands with NRXN3 deletions 50
Figure 4.1. Pedigrees of ADHD families with rare CNVs of interest 62
Figure 4.2. Rare CNVs at the ASTN2/TRIM32 locus in ADHD and ASD probands 68
Figure 5.1. Overview of study design and workflow 80
Figure 5.2. Functional map of schizophrenia from pathway enrichment analysis 96
Figure 5.3. Overlap of schizophrenia pathway analysis with known ASD genes 97
Figure 6.1. Genomic locations of exonic GPHN deletions in the NDD probands 113
Figure 6.2. Pedigrees of individuals with GPHN deletions 114
Figure 7.1. Exonic ASTN2/TRIM32 deletions in clinical and control cohorts 137
Figure 7.2. Impact of ASTN2 deletions on gene expression in lymphoblast cell lines 142
Figure 7.3. Exon conservation analysis of ASTN2 and TRIM32 144
Figure 7.4. Amino acid conservation analysis of ASTN2 protein 145
Figure 7.5. Relative expression levels of ASTN2 transcript isoforms 146
Figure 7.6. Expression profile of ASTN2 across human brain development 147
xiii
List of Abbreviations
ADHD Attention Deficit Hyperactivity Disorder
ASD Autism Spectrum Disorder
BAP Broader Autism Phenotype
CNV Copy Number Variant
DGV Database of Genomic Variants
DSM The Diagnostic and Statistical Manual of Mental Disorders
FISH Fluorescence In Situ Hybridization
ICD International Classification of Diseases
ID Intellectual Disability
NDD Neurodevelopmental Disorder
NGS Next Generation Sequencing
qPCR Quantitative Polymerase Chain Reaction
SNP Single Nucleotide Polymorphism
SNV Single Nucleotide Variant
WES Whole Exome Sequencing
WGS Whole Genome Sequencing
1
Chapter 1
Introduction
Parts of this chapter are adapted, with permission for use from Elsevier, from the following
published book chapter:
Marshall CR, Lionel AC, Scherer SW, Chapter 2.2 - Copy Number Variation in Autism
Spectrum Disorders, The Neuroscience of Autism Spectrum Disorders, Academic Press, San
Diego, 2013, Pages 145-154, ISBN 9780123919243
2
Psychiatric and neurodevelopmental disorders (NDDs) are among the most challenging problems
faced by modern medicine and account for nearly one-third of disability worldwide (Collins et
al., 2011; Sullivan et al., 2012a). Major NDDs such as autism spectrum disorder (ASD),
schizophrenia, intellectual disability (ID) and attention deficit hyperactivity disorder (ADHD)
cause significant suffering to patients and their families, and impose burdens on healthcare
systems and society at large. For many years, the underlying causes of NDDs appeared
intractable but research breakthroughs in psychiatric genetics over the past decade have yielded
unprecedented and long awaited insights on NDD causation (Sullivan et al., 2012a). While
scientific evidence for the genetic underpinnings of NDD etiology has accumulated over the past
century, pinpointing the precise genes responsible has proven to be extremely challenging.
Recent advances in genomics technology have facilitated a landmark accomplishment in the
quest for NDD risk genes: the discovery and characterization of copy number variants (CNVs)
(Cook and Scherer, 2008). While these deletions and duplications of specific DNA fragments are
present in every human genome as part of the natural spectrum of genetic variation, there is
substantial evidence that multiple rare pathogenic CNVs at certain specific genomic locations
contribute to medical conditions (Girirajan et al., 2011; Lee and Scherer, 2010; Weischenfeldt et
al., 2013). The study of these rare CNVs in the genomes of patients has proven to be a powerful
strategy for the rapid identification of novel NDD risk genes and has revealed etiological links
between clinically distinct NDDs (Cook and Scherer, 2008; Malhotra and Sebat, 2012;
Merikangas et al., 2009). Despite recent progress, most NDD cases are still of unknown or
idiopathic causation and the continued elucidation of their genetic architecture is imperative to
guide molecular diagnostics, clinical intervention and therapeutic development. The goal of my
doctoral thesis research is to identify novel candidate risk genes for ASD, ADHD and
schizophrenia, and to systematically investigate cross-disorder genetic overlap to pinpoint shared
risk genes. In this introductory chapter, I present an overview of the current genetic knowledge
for these three NDDs, their emerging clinical and genetic links and the rationale for a cross-
disorder CNV investigation aimed at novel NDD risk gene discovery and characterization.
3
1.1 Autism Spectrum Disorder (ASD)
1.1.1 Clinical profile of ASD
Autism is the prototypic form of a spectrum of conditions previously known as pervasive
developmental disorder (PDD), but now more widely referred to as autism spectrum disorder
(ASD). ASD is characterized by challenges in communication, impaired social function,
repetitive behaviors, and restricted interests with onset before the age of three. Signs and
symptoms can present with a range of severity and in a variety of combinations often along with
developmental delay / intellectual disability (DD/ID), sleep difficulties, seizure, and other
neurological and behavioral issues (Anagnostou et al., 2014). The Diagnostic and Statistical
Manual of Mental Disorders edition 4 (DSM-IV) defined three major subtypes within ASD
including autism, Asperger syndrome and pervasive developmental disorder not otherwise
specified (PDD-NOS). However, this sub-categorization has not been found to be linked to
replicable differences in etiology or developmental outcomes (Devlin and Scherer, 2012).
Consequently, as of May 2013, the DSM-V has subsumed and replaced the previous categorical
subgroups with a single umbrella term: “autism spectrum disorder”(Lai et al., 2013).
The latest epidemiological data estimate the population prevalence of ASD to be approximately
1 in 88 children (Autism and Developmental Disabilities Monitoring Network, 2012). Incidence
appears to be independent of ancestry and demographics, with similar rates being found on a
global scale when the same diagnostic tools are used (Fombonne, 2009). A feature of ASD is a
gender bias, with males four times more likely than females to receive a diagnosis (Werling and
Geschwind, 2013). However there is variability as it may rise to 11:1 when considering Asperger
disorder and fall to 1:1 when considering severe syndromic cases (Gillberg et al., 2006).
1.1.2 Evidence for the genetic etiology of ASD
Family studies have provided strong evidence for the contribution of complex genetic factors in
ASD etiology. Early studies showed a recurrence risk in siblings of ASD probands of 8% – 10%
(Szatmari et al., 1998; Zwaigenbaum et al., 2005) with more recent studies observing as many as
25% of siblings affected (Constantino et al., 2010; Ozonoff et al., 2011). The latter observation
implies that risk in siblings of an ASD child is roughly 20 times higher than the general
4
population risk. A recent study looking at the differential in ASD diagnosis in siblings found the
recurrence rate was approximately twice in full siblings compared to that among half siblings
(Constantino et al., 2013) further demonstrating the large genetic component to ASD etiology.
Familial studies cannot distinguish between genetic and environmental factors; however twin
studies point to a substantial genetic contribution that cannot be explained solely by the
environment. Estimates of heritability from twin studies range from 37 to 90% (Bailey et al.,
1995; Hallmayer et al., 2011; Le Couteur et al., 1996; Rosenberg et al., 2009; Steffenburg et al.,
1989). The most recent twin study showed much lower heritability (37%) compared to earlier
studies when using a strict autism cutoff, although the confidence interval was wide (8–84%)
(Hallmayer et al., 2011). When considering a broader spectrum of related cognitive or social
abnormalities, upwards of 92% of monozygotic (MZ) twins were concordant for phenotype in
contrast with ~ 10% in dizygotic (DZ) twins (Bailey et al., 1995; Constantino et al., 2010). This
difference in concordance between MZ and DZ twins suggests ASD is a genetic disorder (Risch
et al., 1999) and that the phenotype likely extends to a subclinical, broader autism phenotype
(BAP) (Losh et al., 2008).
1.1.3 Common genetic variant findings in studies of ASD risk
The role of common genetic variation (e.g. single nucleotide polymorphisms (SNPs) with minor
allele frequency > 5% in the general population) in the genetic etiology of ASD has been the
subject of considerable research and debate (Buxbaum et al., 2010; Devlin et al., 2011). There is
evidence from quantitative genetic studies in support of contributions to ASD heritability from
common variation taken together on a genome-wide scale (Klei et al., 2012). However, there
have been difficulties in pinpointing the precise locations of such contributory variants. The
standard approach for investigation of common genetic variation in disease is the genome wide
association (GWA) study. Aided by the development of SNP based genotyping microarrays,
GWA studies utilize unbiased and simultaneous frequency testing of multiple SNPs across the
genome to detect those enriched in cases relative to controls. Although highly successful in
revealing novel disease genes and biological pathways in complex conditions such as
Alzheimer’s disease and type II diabetes (Manolio, 2013; McCarthy and Hirschhorn, 2008), the
GWA approach has produced mixed results when applied to ASD. To date, there have been five
such major studies (Anney et al., 2012; Anney et al., 2010; Wang et al., 2009; Weiss et al., 2009;
5
Xia et al., 2013) and significant associations have been reported at different genomic regions
including 1p13.2, 5p14.1, 5p15.2 and 20p12.1. However, there has been no inter-study
replication as each of these findings were reported only by a single study, and targeted attempts
at validation have failed (Curran et al., 2011). In addition, the individual variant findings exhibit
very weak effect sizes and may explain only a modest part of the genetic heritability of this
disorder (Anney et al., 2012; Poot, 2013).
1.1.4 Rare genetic variant findings in studies of ASD risk
The quest for specific genes underlying ASD risk has been far more successful in the domain of
rare genetic variation (variants present at < 1% in the general population) compared with
common variation (Abrahams and Geschwind, 2008; Devlin and Scherer, 2012; Huguet et al.,
2013). The presence of ASD symptoms in rare genetic syndromes which have clear causal links
to a single genetic locus and follow simple Mendelian rules of inheritance revealed the first risk
genes for the disorder. Over 20 such syndromic genes have now been reported in connection
with autism (Abrahams et al., 2013), prominent examples of which include fragile X syndrome
(FMR1 mutations in ~ 1–2% of ASD cases), tuberous sclerosis (TSC1 and TSC2 mutations in ~
1% of ASD cases) and Rett syndrome (MECP2 mutations in ~ 0.5% of ASD cases) (Abrahams
and Geschwind, 2008). Karyotyping studies have observed microscopically visible abnormalities
in close to 5% of ASD cases and individuals with such events often exhibit dysmorphology
and/or severe ID (Shen et al., 2010; Xu et al., 2004). Although large structural changes have
been found on all chromosomes, most are so rare that association with ASD is difficult to prove.
One of the exceptions, and the most common karyotypic change found in individuals with ASD
(at a frequency of ~ 1%), is a 15q11–13 duplication (of the maternal allele) at the Prader–
Willi/Angelman syndrome region (Baker et al., 1994).
The combined observations of evidence for a strong genetic contribution to ASD from family
and twin studies, the increase in cytogenetically visible abnormalities in ASD cases compared to
controls, and the lack of consistently reproducible common variants associated with ASD led to
the hypothesis that rare submicroscopic variants in the form of CNVs contribute to the genetic
architecture of ASD. Early CNV studies using low-resolution bacterial artificial chromosome
(BAC)- CGH (Jacquemont et al., 2006) and SNP (Szatmari et al., 2007) arrays provided some of
the first evidence that CNVs contribute to ASD risk. Subsequently, a series of large studies with
6
high-resolution microarrays has further probed the role of CNVs in ASD (Bucan et al., 2009;
Christian et al., 2008; Gai et al., 2012; Glessner et al., 2009; Levy et al., 2011; Marshall et al.,
2008; Morrow et al., 2008; Pinto et al., 2010; Rosenfeld et al., 2010; Sanders et al., 2011; Sebat
et al., 2007; Weiss et al., 2008). The design of these studies varies but typically involves
computational CNV prediction from microarray intensity data, followed by rare variant detection
in the probands through filtering out CNVs seen to be present in control individuals or in
databases of copy number polymorphisms in the general population such as the Database of
Genomic Variants (Marshall and Scherer, 2012).
Microarray studies of ASD probands and their parents have identified significant enrichment of
de novo CNVs in ASD (5% - 10% of trios) compared to 1 % of control trios (Levy et al., 2011;
Marshall et al., 2008; Pinto et al., 2010; Sanders et al., 2011; Sebat et al., 2007). This is one of
the most exciting and consistently replicated findings in psychiatric genetics and it is now clear
that these highly penetrant new mutations are an important source of autism causality in
individuals who carry them (Krumm et al., 2014; Ku et al., 2013; Ronemus et al., 2014). In
addition to identifying specific genes underlying ASD etiology, de novo mutations provide a
mechanism by which such a complex, early-onset disorder with reduced reproductive fitness
remains frequent in the population (Veltman and Brunner, 2012). The incidence of de novo
mutations in spermatogenesis increases with age (Hehir-Kwa et al., 2011; Kong et al., 2012),
providing a molecular explanation for the strong epidemiological links observed between
advancing paternal age with ASD risk in children (Hultman et al., 2011). Some studies have
reported a higher rate of de novo CNVs in simplex families (only 1 child with ASD) relative to
multiplex families (two or more children with ASD) (Marshall et al., 2008; Sebat et al., 2007),
although this is not always the case (Pinto et al., 2010). Individuals with multiple de novo
variants often present with more complex and syndromic forms of ASD (Marshall et al., 2008;
Pinto et al., 2010).
Most de novo mutations, with the exception of rare instances of germline mosaicism, are
sporadic events which occur in a single individual in a family. Thus, they are insufficient on their
own to explain the high heritability of ASD evident from family studies. Indeed, rare inherited
CNVs, which often implicate the same genes as rare de novo events, are potentially risk factors
in some cases (Krumm et al., 2013; Matsunami et al., 2013; Salyakina et al., 2011). Although
many inherited CNVs act in an apparently dominant manner with varying penetrance (Fernandez
7
et al., 2010; Vaags et al., 2012), some transmission is clearly recessive in consanguineous ASD
families with rare homozygous deletions (Morrow et al., 2008). To date, upwards of 10% of
ASD probands have been seen to have rare CNVs of etiological significance. Microarray testing
is now recommended and widely used for ASD patients in clinical diagnostic laboratories in
Canada and other countries (Anagnostou et al., 2014; Miller et al., 2010a; Shen et al., 2010).
CNV analysis focused on rare variants in general, both de novo and inherited, in research and
clinical diagnostic settings has led to discovery of dozens of ASD susceptibility loci (Betancur,
2011). Some of these CNVs are large recurrent microdeletion and microduplication events, each
reported by several independent groups. These include 1q21.1 duplications (Brunetti-Pierri et al.,
2008; Szatmari et al., 2007), 7q11.23 duplications (Cooper et al., 2011; Sanders et al., 2011),
16p11.2 deletions (Kumar et al., 2008; Marshall et al., 2008; Weiss et al., 2008) and 15q13.3
deletions (Ben-Shachar et al., 2009; Sanders et al., 2011) among others (Malhotra and Sebat,
2012). CNVs of this type are created by non-allelic homologous recombination (NAHR) at
segmental duplications flanking CNV breakpoints and typically overlap several genes (Liu et al.,
2012). For some large, multi-genic microdeletions, specific genes that drive the ASD phenotype
have been pinpointed through identification of smaller deletions and point mutations at the same
locus. Examples include SHANK3 within 22q13.3 terminal deletions of Phelan-McDermid
syndrome (Durand et al., 2007; Moessner et al., 2007) and MBD5 within the 2q23.1
microdeletion syndrome (Talkowski et al., 2011).
In addition to multi-genic CNVs, genome-wide findings from high resolution microarrays have
implicated single gene deletions and duplications in ASD. Many of these mutations have
highlighted genes encoding neuronal synaptic complex proteins, and disruption of synaptic
homeostasis is now recognized as one of the key molecular mechanisms underlying ASD
(Bourgeron, 2009; Ramocki and Zoghbi, 2008; Toro et al., 2010). The first synaptic genes to be
implicated were neuroligins and neurexins, which are neuronal transmembrane proteins that bind
to each other and function as major organizers of excitatory glutamatergic synapses (Sudhof,
2008). The detection of rare functional mutations in X-linked genes for neuroligins 3 and 4
(NLGN4X and NLGN3) (Jamain et al., 2003; Laumonnier et al., 2004) was followed by discovery
of rare mutations in ASD individuals affecting all three members of the neurexin gene family:
NRXN1 (Szatmari et al., 2007), NRXN2 (Gauthier et al., 2011) and NRXN3 (Vaags et al., 2012).
Other synaptic risk gene findings from ASD CNV studies include scaffolding proteins such as
8
those from the SHANK and contactin gene families. In addition to synapse development and
function, additional biological processes highlighted by pathway analyses of genes impacted by
rare CNVs include ubiquitin degradation (Glessner et al., 2009), axon targeting and neuron
motility (Gilman et al., 2011), as well as the GTPase/Ras signaling (Pinto et al., 2010) and
TSC/SHANK (Sakai et al., 2011) networks.
Key insights into ASD risk genetics, first derived from rare CNV findings, have been reinforced
by rare SNV results from recent whole-exome (Iossifov et al., 2012; Neale et al., 2012; O'Roak
et al., 2011; O'Roak et al., 2012; Sanders et al., 2012) and whole-genome sequencing studies
(Jiang et al., 2013; Michaelson et al., 2012). These studies estimate that de novo SNVs contribute
to ASD etiology in 10% to 20% of cases and report significantly higher rates of such mutations
in ASD probands compared to either their unaffected siblings or to controls (Krumm et al., 2014;
Ronemus et al., 2014). This enrichment was particularly strong for de novo loss-of function
(nonsense and frameshift) events which resulted in prematurely truncated proteins. While no
single gene was mutated in more than 1% of cases, several genes were seen to be affected by rare
de novo SNVs across unrelated probands including SCN2A, CHD8, DYRK1A and GRIN2B. In
accordance with earlier results from CNV studies, analysis of pathways enriched for de novo
SNVs in ASD revealed postsynaptic scaffolding proteins to be particularly prominent. Other
gene-sets of note included WNT signaling, chromatin remodeling and known targets of FMR1,
the key protein in fragile X syndrome.
1.2 Attention Deficit Hyperactivity Disorder (ADHD)
1.2.1 Clinical profile of ADHD
First described as a distinct disorder in 1902 by George Still, Attention Deficit Hyperactivity
Disorder (ADHD) has a core triad of phenotypes including inattention, hyperactivity and
impulsiveness (Dalsgaard, 2013). ADHD is estimated to be prevalent in around 4% of school-
age children across the world (Polanczyk et al., 2007). Symptoms of this disorder often arise in
pre-school years before the age of seven, and can persist through adulthood in around half of
cases (Biederman et al., 2008). Skewed gender ratios (estimates range from 3:1 to 8:1) have been
reported for ADHD with boys far more likely than girls to be affected (Biederman et al., 2002).
Phenotype severity is also different between the sexes, with boys tending to be more hyperactive,
9
and girls more inattentive (Cuffe et al., 2005). Considerable clinical heterogeneity is observed
and the latest DSM version (DSM-5) recognizes four major ADHD subtypes: inattentive
presentation (restrictive), predominantly inattentive presentation, predominantly
hyperactive/impulsive presentation, and combined presentation (Dalsgaard, 2013). Longitudinal
studies spanning childhood to adolescence have shown that ADHD symptoms and subtypes often
change over time, with hyperactive symptoms more prevalent during early childhood and
attention problems more prominent during late childhood and adolescence (Elia et al., 2012b;
Larsson et al., 2011). Additional comorbidities such as depression, anxiety, learning difficulties
and other psychiatric conditions such as conduct and mood disorders are often present in
individuals with ADHD (Geissler and Lesch, 2011). They are also at heightened risk for
substance abuse, school failure, motor vehicle accidents and impaired workplace productivity
(Kessler et al., 2009). Several pharmacological treatments are available and widely used but are
hampered by varying effectiveness and the presence of side effects (Jensen et al., 2005). As a
result, considerable impairment often remains despite treatment.
1.2.2 Evidence for the genetic etiology of ADHD
Converging evidence from family, adoption and twin studies indicate a strong genetic
component underlying ADHD etiology. First degree relatives of children with ADHD are
significantly more likely to have ADHD compared with relatives of non-ADHD control
individuals (Thapar et al., 2007). Epidemiological investigations of parents (Faraone et al.,
2000), siblings (Manshadi et al., 1983) and offspring (Biederman et al., 1995) of adults with
ADHD have also confirmed the familial nature of this disorder. Studies of children with ADHD
who were adopted revealed that their biological parents were 8-10 times more likely to have
ADHD compared with the adoptive parents (Sprich et al., 2000). Cumulative findings from
dozens of pediatric twin studies report a heritability estimate of around 70% for ADHD
(Posthuma and Polderman, 2013). Adult twin studies have been fewer in number and have
generally reported lower heritability estimates of around 30% - 50% (Kan et al., 2013; Larsson et
al., 2013a). One possible reason for the discrepancy between adult and pediatric twin studies
could be that self-rating scales often used to diagnose ADHD in adults are a poorer measure of
the condition than ratings from parents and teachers that are utilized for pediatric diagnosis
(Franke et al., 2012).
10
1.2.3 Common genetic variant findings in studies of ADHD risk
Simulant based medication such as methylphenidate and amphetamine have been effective in
treating ADHD symptoms. These drugs are known to modify neurotransmission. Consequently,
the first investigations on genetic risk for ADHD focused on testing of SNPs within genes
involved in neurotransmission for association with the disorder (Gizer et al., 2009; Kebir et al.,
2009) These hypothesis based candidate gene studies have found some evidence for a role of
common variants within dopaminergic (e.g. DRD4, DRD5, DAT1) and serotonergic (e.g. 5HTT,
HTR1B) genes in ADHD (Thapar et al., 2013). However, SNPs with reported associations have
very low odds ratios (ranging from 1 to 1.3) and could potentially only account for a very small
percentage (~3%) of ADHD risk (Kuntsi et al., 2006). The candidate gene association approach
suffers from poor replication of results and other methodological issues such as potential bias
during selection of candidate genes, insufficient sample sizes, measurement errors and other
confounding factors (Kebir et al., 2009). In an effort to screen genome-wide for common variant
risk factors in an unbiased, hypothesis free manner, several GWA studies of ADHD have been
conducted (Neale et al., 2008; Neale et al., 2010a; Neale et al., 2010b; Yang et al., 2013). None
of these studies, including a large meta-analysis of close to 3,000 cases (Neale et al., 2010b),
have yielded results that pass the genome-wide threshold for statistical significance.
1.2.4 Rare genetic variant findings in studies of ADHD risk
The paucity of conclusive findings with major effects on ADHD risk from studies of common
genetic variation suggests that rare genetic risk factors could contribute to the high heritability of
the disorder. Previous reports on high incidence of ADHD traits in the phenotype spectrum of
individuals with large microdeletion syndromes also support a role for rare genetic variation. For
instance, around 30% of 22q11.2 DS patients (Arnold et al., 2001), and 70% of individuals with
the 7q11.23 microdeletion (Williams Beuren syndrome) present with ADHD symptoms (Pober,
2010). Following the success of genome-wide microarray based studies in detecting rare
pathogenic CNVs in ASD and schizophrenia, similar investigations of ADHD were launched.
There have been several studies of CNVs in pediatric ADHD cohorts (Elia et al., 2010; Elia et
al., 2012a; Jarick et al., 2014; Langley et al., 2011; Lesch et al., 2011; Lionel et al., 2011;
Stergiakouli et al., 2012; Williams et al., 2012; Williams et al., 2010; Yang et al., 2013)
11
including one from my thesis work ((Lionel et al., 2011) and Chapter 4 of this thesis) and one
CNV scan of adults with the disorder (Ramos-Quiroga et al., 2014). In four of these studies,
significantly higher numbers of rare CNVs were observed in ADHD cases compared to controls
(Stergiakouli et al., 2012; Williams et al., 2012; Williams et al., 2010; Yang et al., 2013). The
genome-wide CNV burden findings in two of these publications were primarily driven by
enrichment for large (> 500 kb) multi-genic duplications at 16p13.11 (Williams et al., 2010) and
15q13.3 (Williams et al., 2012) regions in individuals with ADHD. Although the other studies
did not find a genome-wide difference in rare CNV numbers between individuals with ADHD
and controls, rare CNV findings highlighted specific genes and biological networks. For
instance, the largest CNV study to date, performed on more than 3,500 individuals with ADHD,
noted a strong enrichment for rare CNVs at loci affecting metabotropic glutamate receptor
(GRM) genes and their interaction partners in 10% of cases, suggesting a possible role for
selective GRM agonists in ADHD treatment (Elia et al., 2012a). Results from two other studies
featuring independent cohorts of German children highlighted involvement of the parkinson
protein 2 (PARK2) and neuropeptide Y (NPY) genes (Jarick et al., 2014; Lesch et al., 2011). An
intriguing trend observed across different studies was enrichment for rare deletions and
duplications in ADHD individuals at genes previously implicated in rare CNV studies of other
NDDs such as ASD and schizophrenia (Elia et al., 2010; Lionel et al., 2011; Williams et al.,
2012; Williams et al., 2010).
Although such efforts are currently underway, to date there have been no publications describing
whole-exome or whole-genome sequencing of individuals with ADHD to discover rare SNVs.
1.3 Schizophrenia
1.3.1 Clinical profile of schizophrenia
Schizophrenia was initially identified and clinically delineated under the name “dementia
praecox” by Emil Kraepelin in 1899, based on longitudinal study of several patients exhibiting
severe decline in behavior and cognition (Jablensky, 2010). Currently, a diagnosis of
schizophrenia is based on a core triad of symptom domains (Moore et al., 2011). The first
comprises symptoms of psychoses featuring delusions and hallucinations. The second set of
symptoms, often closely associated with psychotic episodes, concerns disorganization of
12
behavior and thought processes (Moore et al., 2011). In addition to these two commonly reported
phenotypes, schizophrenia is often characterized by a third set of symptoms involving reduced
emotional expression, deficits in speech and social function and occupational deterioration
(Costain and Bassett, 2012). The evolution and severity of symptoms are highly heterogeneous
(Moore et al., 2011) and there are often shared clinical features with other psychiatric disorders
such as schizoaffective disorder, delusional disorder and psychotic bipolar disorder (Gejman et
al., 2011). The course of illness is often chronic and marked by fluctuating patterns and frequent
relapses despite modern treatments (Gejman et al., 2011). On average, the life-expectancy of
patients is reduced by 20 to 25 years (Moore et al., 2011). Thus, schizophrenia is a complex
psychiatric condition that results in a severe burden on patients and their families. The lifetime
prevalence estimate for schizophrenia is around 1%. While typical onset is during early
adulthood (17 to 30 years of age), the disorder can also arise in childhood or in the elderly age
range in a small subset of cases (Costain and Bassett, 2012). In contrast to ASD, there is no
striking gender bias in prevalence. However, the average age of onset in males is lower by
around 5 years than that in females (Mulle, 2012).
1.3.2 Evidence for the genetic etiology of schizophrenia
Consistent evidence from twin, family and adoption studies support a predominantly genetic
model of schizophrenia susceptibility. Twin studies conducted in different countries have
consistently found the concordance rate for schizophrenia in monozygotic twins (40%-50%) to
be notably higher than that of 6%-10% in dizygotic twins (Cannon et al., 1998; Cardno et al.,
1999; Franzek and Beckmann, 1998; Klaning et al., 1996). These findings support a high
heritability estimate of around 80% for schizophrenia, second only to ASD among major
psychiatric disorders (Gejman et al., 2011; Sullivan et al., 2003). Familial clustering is a
characteristic of schizophrenia, as siblings and children of individuals with schizophrenia have a
risk for the disorder, almost 10-fold times higher than the general population (Gejman et al.,
2011; Shih et al., 2004). Higher risk for psychosis was observed in foster homes for adopted
children whose biological parents had schizophrenia, but not in offspring of non-schizophrenic
biological parents who were raised by adoptive parents with psychosis (Gejman et al., 2011;
Ingraham and Kety, 2000; Kety et al., 1994).
13
1.3.3 Common genetic variant findings in studies of schizophrenia risk
Initial attempts to probe the genetic basis of schizophrenia focused on targeted case vs. control
frequency comparisons of SNPs within handpicked genes of known biological function. Such
candidate gene association studies were often prone to false positive findings which suffered
from poor reproducibility, inadequate sample size and failure to correct for multiple testing
(Smoller, 2013). The development of whole-genome microarray technology in the mid 2000’s
enabled simultaneous genome-wide interrogation of millions of SNPs. Subsequent large-scale
GWA meta-analyses on thousands of patients have highlighted the involvement of common
genetic variation (SNPs with allele frequency > 1%) at a handful of loci in risk for schizophrenia
(O'Donovan et al., 2008; Purcell et al., 2009; Shi et al., 2009; Stefansson et al., 2009). These
include genes such as ZNF804A and TCF4, and the major histocompatibility complex (MHC)
region on chromosome 6. Although there is evidence, above the genome-wide threshold for
significance, for their involvement across multiple studies, the odds ratios for the risk alleles at
each of these regions are very small. rs1344706, a SNP within ZNF804A, a gene encoding a zinc
finger protein, was the first genetic variant reported to have a significant association with
schizophrenia on a genome wide level (O'Donovan et al., 2008). This finding has been
subsequently replicated by other studies, with odds ratios consistently around 1.1 (Riley et al.,
2010; Williams et al., 2011b). The cumulative evidence to date is clear that there are no common
genetic variants of large effect size for schizophrenia (Costain and Bassett, 2012; Mulle, 2012).
1.3.4 Rare genetic variant findings in studies of schizophrenia risk
The high heritability of schizophrenia, taken together with the low number of common risk allele
findings and their small odds ratios, implies that rare genetic risk factors with much larger effect
sizes also contribute to the complex genetic architecture of this disorder (Bray et al., 2010;
Costain and Bassett, 2012; Mulle, 2012). Two such high-penetrance rare mutations have been
known for more than a decade: a translocation disrupting the DISC1 gene and the 22q11.2
deletion syndrome (22q11.2 DS). The former was discovered in a large Scottish pedigree where
it co-segregated with schizophrenia and other psychiatric illness such as bipolar disorder (Millar
et al., 2000). Recent functional work has revealed that DISC1 is an important functional hub in
several biological pathways of relevance to schizophrenia including neuronal migration,
neuronal progenitor cell proliferation and neural cell signaling (Mao et al., 2009).
14
22q11.2 DS, also known as velocardiofacial syndrome or DiGeorge syndrome, features a large
multi-genic microdeletion at the 22q11.2 region and represents the most common genomic
disorder in humans with a prevalence of 1 in 3000-4000 live births (Costain and Bassett, 2012).
These deletions have very large effect sizes and account for ~ 1% of all schizophrenia cases
since individuals with them have a 20-fold increase in risk for schizophrenia relative to the
general population (Bassett and Chow, 2008). In addition to psychoses, 22q11.2 deletions are
characterized by a heterogeneous phenotype spectrum including congenital heart defects,
seizures and ID (Bassett et al., 2011). Usually de novo in origin, 22q11.2 DS arises by NAHR of
flanking segmental duplication regions. In a small number of individuals (5% - 10%), the
deletion is inherited from a parent with a milder neuropsychiatric phenotype (Costain et al.,
2011).
In the years following discovery of the two examples mentioned above, rapid advances in SNP-
based and CGH-based microarrays have accelerated the search for other rare structural variants
involved in schizophrenia risk. The first CNV studies led to the discoveries of large rare
deletions enriched (3-fold) in schizophrenia probands relative to the general population (Walsh et
al., 2008), elevated rates of rare de novo CNVs (8-fold) in individuals with sporadic non-familial
forms of the disorder (Xu et al., 2008) and risk genes involved in glutamate signaling (Wilson et
al., 2006). Other than 22q11.2 DS, no two patients had recurrent mutations in common in these
early studies, likely due to their small sample sizes (Mulle, 2012). Subsequent studies featuring
more sizeable cohorts, including work from my thesis research ((Costain et al., 2013) and
Chapter 5), have confirmed the increased burden of large rare CNVs in patients, and have
reported on recurrent deletions and duplications at specific genomic regions (Buizer-Voskamp et
al., 2011; International Schizophrenia Consortium, 2008; Kirov et al., 2009; Rees et al., 2013;
Stefansson et al., 2008; Stewart et al., 2011; Vacic et al., 2011; Xu et al., 2009). Examples of
such CNVs that are significantly enriched in schizophrenia cases include 1q21 deletions
(International Schizophrenia Consortium, 2008; Stefansson et al., 2008), 1q21 duplications
(Levinson et al., 2011; Vacic et al., 2011), 2q13 deletions (Costain et al., 2013), 3q29 deletions
(Levinson et al., 2011; Mulle et al., 2010), 15q11.2 deletions (Kirov et al., 2009; Stefansson et
al., 2008), 15q13.3 deletions (International Schizophrenia Consortium, 2008; Levinson et al.,
2011), 16p11.2 duplications (Levinson et al., 2011; McCarthy et al., 2009), 17p12 deletions
(Kirov et al., 2009; Vacic et al., 2011) and 17q12 deletions (Moreno-De-Luca et al., 2010; Vacic
15
et al., 2011). These large multi-genic rare CNV findings show higher inter-study consistency
(Girard et al., 2012) and much larger odds ratios (Mulle, 2012) relative to GWAS results.
The initial observation (Xu et al., 2008) of a significantly higher de novo CNV rate in
schizophrenia probands relative to control individuals has also been replicated by additional trio-
based studies (Kirov et al., 2012; Malhotra et al., 2011). Strikingly, and just like the situation in
ASD, these findings highlight the presence of rare de novo CNVs in 5% - 10% of schizophrenia
cases as opposed to just 1% of control individuals. In addition to the large multi-genic CNVs
described above, de novo deletions and duplications affecting single genes have also been
identified. Recurrent de novo CNVs shared by unrelated patients affected genes encoding
synaptic proteins such as DLG2, EHMT1 and NRXN1 (Kirov et al., 2012; Rujescu et al., 2009).
Further support for the pathogenic role of de novo mutations in schizophrenia is provided by
recent exome sequencing studies. Significantly higher rates of rare de novo SNVs has been
detected in schizophrenia probands relative to controls, with especially strong enrichment for the
subset of such events which were nonsense mutations (Girard et al., 2011; Xu et al., 2012; Xu et
al., 2011). In addition to highlighting recurrent de novo SNVs across unrelated patients in genes
such as LAMA2, DPYD, TRRAP and VPS39, the sequencing results also revealed de novo SNVs
at genes previously implicated by de novo CNV findings including NRXN1, CIT and two genes
within the 22q11.2 DS region: DGCR2 and TOP3B (Awadalla et al., 2010; Xu et al., 2012).
Statistical analyses integrating significant results from the different de novo CNV, de novo SNV
and GWAS studies of schizophrenia have been performed recently (Gilman et al., 2012). Several
cohesive gene networks related to synaptic function, axon guidance, neuronal cell mobility and
chromosomal remodeling were pinpointed as key underlying molecular mechanisms of
schizophrenia (Gilman et al., 2012).
1.4 Phenotypic and Genetic Overlap Across NDDs
1.4.1 Comorbidity and clinical connections between ASD and ADHD
Historically, the major psychiatric diagnostic classification systems, including The Diagnostic
and Statistical Manual (DSM) and the International Classification of Diseases (ICD), did not
allow concurrent diagnoses of both ASD and ADHD in the same individual (Gargaro et al.,
2011). A diagnosis of ASD was considered sufficient to ‘trump’ and exclude a diagnosis of
16
ADHD (Martin et al., 2014). Although there are differences between the two conditions in terms
of core symptoms, ages of onset and recommended treatment regimens, there is substantial
evidence in support of overlapping pathophysiology for ASD and ADHD (Gargaro et al., 2011;
Murray, 2010; Russell and Pavelka, 2013; Taurines et al., 2012).
Firstly, ADHD and ASD are both childhood-onset neurodevelopmental conditions with similar
impairments in developmental and cognitive domains and a markedly higher prevalence in males
compared with females (Cooper et al., 2014). Both disorders share a range of developmental
comorbidities (Cooper et al., 2014) including intellectual disability (Ahuja et al., 2013; Matson
and Shoemaker, 2009), reading difficulties (El Zein et al., 2013; Miranda et al., 2013), dyslexia
(Russell and Pavelka, 2013), language delay (Bruce et al., 2006; Mody and Belliveau, 2013),
deficits in executive functions (Rosenthal et al., 2013; Tseng and Gau, 2013), and motor
problems such as developmental coordination disorder (Fournier et al., 2010; Martin et al.,
2010).
Secondly, co-occurrence of comorbid ASD and ADHD symptoms have been repeatedly
observed in individuals from both population-based and clinic-based studies. Several studies
have reported the presence of ADHD symptoms in autistic individuals (de Bruin et al., 2007;
Holtmann et al., 2007; Leyfer et al., 2006; Simonoff et al., 2008; Sinzig et al., 2009; Yoshida and
Uchiyama, 2004), and based on the lowest and highest reported prevalence rates, 30% – 80% of
ASD children meet criteria for ADHD (Murray, 2010; Rommelse et al., 2010). Other studies
have investigated the occurrence of autistic traits in ADHD cohorts and have found such traits to
be present in around 18% – 30% of individuals with ADHD (de Bruin et al., 2007; Grzadzinski
et al., 2011; Kochhar et al., 2011; Kotte et al., 2013; Mulligan et al., 2009; Reiersen et al., 2007).
While most studies investigating clinical overlap of ASD and ADHD have focused on pediatric
cohorts, similar trends have also been seen in adult patients (Anckarsater et al., 2006; Hofvander
et al., 2009).
Thirdly, family and twin studies suggest a heritable etiological link between ASD and ADHD
(Rommelse et al., 2010). ASD symptom levels were seen to be elevated in siblings of ADHD
patients, indicating that ASD symptoms are familial traits within ADHD families (Mulligan et
al., 2009; Nijmeijer et al., 2009; Rommelse et al., 2010). Phenotype correlation analyses suggest
that enrichment of ASD traits in brothers of ADHD probands represented shared genetic rather
17
than environmental factors (Mulligan et al., 2009; Rommelse et al., 2010). These trends were
observed even after probands and siblings with a full diagnosis of ASD were excluded from
study participation (Mulligan et al., 2009; Nijmeijer et al., 2009). Other studies examining the
correlation of ASD and ADHD symptoms in pairs of twins have found evidence for shared
genetic risk (Rommelse et al., 2010). These findings of overlap were consistently seen in both
pediatric (Lichtenstein et al., 2010; Lundstrom et al., 2011; Ronald et al., 2008; Taylor et al.,
2013) and adult twin studies (Lundstrom et al., 2011; Reiersen et al., 2008) and were robust
across different methods of phenotype assessment (Rommelse et al., 2010). Longitudinal
analysis of symptoms in twins found that early ADHD traits were predictive of later
communication difficulties and development of ASD (Taylor et al., 2013).
Fourthly, neuroimaging studies have detected underlying commonalities among brain
mechanisms implicated in ASD and ADHD. Consistent findings across both disorders include
disruption of both resting and active brain networks, especially within the frontostriatal (Gargaro
et al., 2011) and precuneus regions (Di Martino et al., 2013). Comparisons of EEG profiles of
children with a diagnosis of ADHD and additional autistic features, against profiles from ADHD
children without such features, also provide evidence for comorbidity of these disorders (Clarke
et al., 2011).
The presence of comorbid ADHD and ASD traits in patients has important implications for
prognosis and severity of psychopathology and can influence clinical treatment options.
Individuals with both ADHD and autistic traits were observed to be more severely impaired,
with higher rates of psychopathological, neuropsychological and interpersonal deficits compared
with ADHD individuals who had no autistic traits (Anckarsater et al., 2006; Kotte et al., 2013;
Rommelse et al., 2011; Sprenger et al., 2013). Individuals with traits from both disorders were
also seen to share other phenotypic characteristics such as being more likely to report with co-
morbid oppositional defiant disorder, conduct disorder, anxiety symptoms, lower full-scale IQ,
working memory deficits, language disorder and motor disorder (Cooper et al., 2014; Mulligan et
al., 2009). These findings indicate that individuals with comorbid ASD and ADHD require more
intensive medical and psychosocial support (Mulligan et al., 2009). Drugs such as
methylphenidate and atomoxetine effectively address the ADHD symptoms in individuals with
comorbid ASD and ADHD traits (Mahajan et al., 2012; Murray, 2010; Taurines et al., 2012) and
their use is now widespread (Dalsgaard et al., 2013b) although a lower daily dose is sometimes
18
required than that used for ADHD individuals without autistic symptoms (Taurines et al., 2012).
Unfortunately, these drugs do not have a substantial effect on ASD symptoms on their own
(Posey et al., 2006; Santosh et al., 2006). As a result, in patients with comorbid ASD and ADHD
traits, there is a particular need for integration of pharmaceutical interventions with behavioral,
educational and social support as well as therapies such as Cognitive Behavioral Therapy and
neurofeedback for optimal treatment of the composite symptoms (Mahajan et al., 2012;
Rommelse et al., 2011).
1.4.2 Comorbidity and clinical connections between ASD and
schizophrenia
Although currently perceived as clinically distinct disorders, schizophrenia and autism were
regarded to be part of the same psychiatric condition prior to the 1970s, with autism considered
to represent manifestation of schizophrenia at an early stage (de Lacy and King, 2013; Stone and
Iguchi, 2011). In fact, autistic behavior such as social withdrawal and developmental delay were
initially classified under “childhood schizophrenia” in the first two editions of the DSM: DSM-I
and DSM-II (Baribeau and Anagnostou, 2013). Differences in ages of onset, treatment methods
and phenotypic heterogeneity between autism and schizophrenia led to their subsequent
demarcation as distinct and non-overlapping conditions in DSM-III and DSM-IV. As a result,
autism could not be diagnosed in an individual if symptoms of schizophrenia such as psychoses,
hallucinations and delusions were also present (de Lacy and King, 2013). However, recent
epidemiological, imaging and genetic studies have highlighted biological and clinical links
between these conditions and suggest overlapping etiologies (Baribeau and Anagnostou, 2013;
de Lacy and King, 2013; King and Lord, 2011; Owen et al., 2011; Pelletier and Mittal, 2012;
Rapoport et al., 2009; Stone and Iguchi, 2011). Both conditions are recognized as
neurodevelopmental abnormalities with several overlapping deficits in motor, cognitive and
social communication domains (Barneveld et al., 2011; Eack et al., 2013; Sheitman et al., 2004;
Vannucchi et al., 2013). Consequently, the DSM-5 released in 2013 now permits concurrent
diagnosis of ASD and schizophrenia in patients (American Psychiatric Association, 2013).
Comorbidity and clinical links to ASD are particularly striking in childhood onset schizophrenia
(COS), a relatively rare form of schizophrenia. COS is characterized by identical diagnostic
criteria as adult-onset schizophrenia but with onset of symptoms before the age of 13 (Baribeau
19
and Anagnostou, 2013). COS cases often suffer from more severe symptoms and poorer
prognostic outcomes compared to individuals with adult-onset schizophrenia (Clemmensen et al.,
2012). Several studies on COS cohorts have found traits related to ASD to be co-morbid in
around 30% – 50% of individuals (Rapoport et al., 2009; Reaven et al., 2008; Sporn et al., 2004;
Waris et al., 2013). Traits related to ASD often arose 3 – 5 years before the psychotic symptoms
of COS. The unaffected siblings of children with comorbid ASD/COS symptoms were also
found to have a higher rate of ASD symptoms compared to the unaffected siblings of children
with only COS but no ASD traits (Sporn et al., 2004). These findings indicate a heritable
etiology for the comorbid ASD traits observed in COS (Rapoport et al., 2009). Neuroimaging
studies incorporating volumetric, structural and functional data have revealed some
commonalities between COS and ASD (Baribeau and Anagnostou, 2013; Rapoport et al., 2009).
Reduction of volume has been seen in both conditions, particularly in the white matter regions of
the corpus callosum and cingulum (Baribeau and Anagnostou, 2013). Exaggerated grey matter
loss during adolescence has also been reported (Baribeau and Anagnostou, 2013).
In addition to findings in COS, clinical connections to ASD are also observed in individuals with
more typical adult-onset schizophrenia. Relative to controls, individuals with a childhood
diagnosis of ASD were much more likely to experience psychotic symptoms in adolescence
(Bevan Jones et al., 2012; Sullivan et al., 2013) and adulthood (Joshi et al., 2013; Unenge
Hallerback et al., 2012). A longitudinal study over a 30-year timespan found that 35% of
individuals presenting with autism in childhood were later diagnosed with schizophrenia or
schizophrenia spectrum disorder in adulthood (Mouridsen et al., 2008). The overlap between
these conditions has important clinical consequences since the presence of childhood autistic
traits appears to correlate with more severe schizophrenia symptoms and negative long-term
outcomes in adult patients (Esterberg et al., 2008; Vannucchi et al., 2013). Results from family
studies have further underscored the etiologic link between ASD and schizophrenia. The
presence of schizophrenia in parents was correlated with an elevated risk for ASD in their
children in different Swedish cohorts (Sullivan et al., 2012b). The same study also found
evidence for higher risk for ASD in children with a schizophrenic sibling (in independent cohorts
from Sweden and Israel) (Sullivan et al., 2012b). Other large population studies have found
schizophrenia to be more common in parents of children with autism compared to parents of
control children (Daniels et al., 2008; Jokiranta et al., 2013; Larsson et al., 2005).
20
1.4.3 Comorbidity and clinical connections between ADHD and
schizophrenia
Retrospective investigations have discovered ADHD symptoms to be prevalent in around 15 –
17% of individuals with schizophrenia (Gomez et al., 1981; Peralta et al., 2011). This rate is
higher than the 5% prevalence of ADHD in the general population (Polanczyk et al., 2007).
ADHD was also the most prevalent childhood-onset neurodevelopmental condition detected in a
case series of teens at high risk for psychosis (Mazzoni et al., 2009). Individuals with comorbid
ADHD and schizophrenia had increased rates of suicidal behavior and worse performance in
neuropsychological tests relative to individuals with a single condition (Donev et al., 2011).
Further highlighting the links between these disorders, two Spanish studies found ADHD to be
the most prevalent neurodevelopmental phenotype observed in first degree relatives (siblings and
offspring) of individuals with schizophrenia (de la Serna et al., 2011; de la Serna et al., 2010).
Complementing the aforementioned work on ADHD symptoms in individuals with
schizophrenia and their relatives, other studies have examined the risk for developing
schizophrenia in individuals with ADHD. Children with ADHD are 4 times more likely to
develop schizophrenia (Dalsgaard et al., 2013a; Kim-Cohen et al., 2003) or report a psychotic
episode (Biederman et al., 2006) in adulthood. A large Swedish epidemiological study of more
than 60,000 individuals with ADHD reported substantially increased risk for both schizophrenia
(6-fold increase) and bipolar disorder (24-fold) in adulthood among individuals with a childhood
ADHD diagnosis (Larsson et al., 2013b). This increased risk for psychosis was also seen in first
degree relatives of the ADHD proband group but not in second degree relatives, suggesting that
co-occurrence of ADHD and schizophrenia could be due to shared genetic risk factors.
1.4.4 Evidence for shared genetic risk factors across NDDs
Consistent with the comorbidity and overlapping clinical traits often observed across different
NDDs, there is emerging evidence for the contribution of shared genetic risk factors to etiologies
of these conditions. This intriguing cross-disorder genetic overlap has been noted for both
common and rare forms of genetic variation. In an effort to investigate shared common genetic
variation, the Cross-Disorder Group of the Psychiatric Genomics Consortium (PGC) conducted
a GWAS meta-analysis of genome wide SNP data from 33,332 cases across five major
21
psychiatric disorders (ADHD, ASD, bipolar disorder, major depressive disorder and
schizophrenia) and 27,888 ancestry-matched controls (Cross-Disorder Group of the Psychiatric
Genomics Consortium, 2013). This was the largest genome-wide analysis of psychiatric illness
to date and provided the first key evidence that individual and aggregate molecular genetic risk
factors are shared between childhood-onset and adult-onset psychiatric disorders that are treated
as distinct categories in clinical practice (Cross-Disorder Group of the Psychiatric Genomics
Consortium, 2013; Smoller, 2013). Four SNPs exceeded the threshold for genome-wide
significance, three of which were involved in shared genetic risk across all five disorders. These
SNPs were within ITIH3 (3p21), AS3MT (10q24) and CACNB2 (10p12). The fourth SNP, within
CACNA1C (12p13.3), contributed to risk for schizophrenia and bipolar disorder. The presence of
two calcium channel genes (CACNA1C and CACNB2) within the top hits and a subsequent
pathway analysis highlighted voltage-gated calcium channel signaling as a key biological
contributor to psychopathology and a potential therapeutic target. The study also revealed other
intriguing shared risk loci for ASD and schizophrenia such as the microRNA mir137 (1p21.3)
and the genes TCF4 (18q21.1) and PCGEM1 (2q32). Earlier GWAS studies with smaller cohorts
have also found evidence for common alleles conferring risk across traditional diagnostic
boundaries in psychiatry (Hamshere et al., 2013; Williams et al., 2011a).
The overlap of genetic risk factors across neuropsychiatric disorders has been especially striking
among rare CNV findings (Carroll and Owen, 2009; Guilmatre et al., 2009; Malhotra and Sebat,
2012; Pescosolido et al., 2013; Rosenfeld et al., 2010; Sahoo et al., 2011). The first such
discoveries involved rare microdeletions and microduplications of sizes large enough (> 500 kb)
to be detected by early low-resolution microarray analyses. These genomic events are often
characterized by recurrent breakpoints determined by flanking segmental duplications, which
mediate CNV formation via non-allelic homologous recombination. In-depth clinical
characterization of individuals with microdeletions and microduplications has established that
several of these events are genetic syndromes, associated with certain core characteristics amidst
a heterogeneous spectrum of NDD phenotypes (Table 1.1). For example, 16p11.2 microdeletions
are the second most prevalent pathogenic CNV (after 22q11.2 microdeletions) in NDD
individuals referred for clinical microarray testing (Cooper et al., 2011; Kaminsky et al., 2011;
Moreno-De-Luca et al., 2013). Initially discovered in ASD cohorts (Kumar et al., 2008; Marshall
et al., 2008; Weiss et al., 2008), follow-up studies have revealed a trend for macrocephaly and
22
obesity in individuals with this microdeletion, in addition to a varied assortment of traits such as
seizures, speech and motor delays and congenital anomalies (Bochukova et al., 2010; Fernandez
et al., 2010; Jacquemont et al., 2011; Moreno-De-Luca et al., 2013; Walters et al., 2010).
Individuals with the reciprocal 16p11.2 microduplication are at higher risk for schizophrenia,
microcephaly and low BMI (Jacquemont et al., 2011; McCarthy et al., 2009). A similar scenario
of certain mirror phenotypes between deletions and duplications amidst considerable phenotypic
heterogeneity is observed at the 1q21.1 locus where microdeletions and microduplications
present with microcephaly and macrocephaly respectively, together with developmental delay,
congenital anomalies and facial dysmorphisms in both syndromes (Brunetti-Pierri et al., 2008).
23
Table 1.1. Examples of genomic regions implicated by rare CNVs across different NDDs
Genomic segment Disorders References
1q21.1 deletion ASD, EPY, ID, SZ Brunetti-Pierri 2008, Mefford 2008, Stefansson 2008,
Kirov 2009, ISC 2008, Levinson 2011
1q21.1 duplication ASD, SZ Brunetti-Pierri 2008, Costain 2013
2q13 deletion ADHD, ASD, ID, SZ Yu 2012, Costain 2013
2q13 duplication ASD, SZ Yu 2012, Costain 2013
2q21.1 deletion ADHD, ID, EPY Dharmadhikari 2012
2q21.1 duplication ASD, ADHD, EPY Dharmadhikari 2012
3q29 deletion ASD, ID, SZ Willatt 2005, Mulle 2010, Levinson 2011
7q11.23 deletion ASD, EPY, ID Fusco 2014
7q11.23 duplication ADHD, ASD, SZ Dixit 2013,Sanders 2011, Kirov 2012, Mulle 2013
15q11.2 deletion ASD, EPY, ID, SZ De Kovel 2010, Cooper 2011, Kirov 2009, Levinson
2011
15q11-q13 duplication ADHD, ASD, SZ Pinto 2010, Lionel 2011, Costain 2013
15q13.3 deletion ASD, SZ Ben-Shachar 2009, Costain 2013
16p13.11 deletion ID, SZ Cooper 2011, Ingason 2011, Mefford 2009
16p13.11 duplication ADHD, SZ Williams 2010, Costain 2013
16p11.2 deletion ASD, SZ Weiss 2008, Marshall 2008, Pinto 2010, Costain 2013
16p11.2 duplication ADHD, ASD, SZ Pinto 2010, Lionel 2011, McCarthy 2009
17q12 deletion ASD, EPY, ID, SZ Moreno-De-Luca 2010, Cooper 2011, Kaminsky 2011
22q11.2 deletion ASD, SZ Pinto 2010, Bassett 2008
22q11.2 duplication ASD, ID Lo-Castro 2009, Pinto 2010
Abbreviations: EPY, epilepsy; ID, Intellectual Disability; SZ, schizophrenia.
24
Since each of the genomic microduplications and microdeletions include several genes, it is not
clear if the spectrum of neurocognitive symptoms associated with them reflects pleiotropic
effects of individual genes or is a result of aberrant dosage of multiple genes (Pescosolido et al.,
2013). Cross-NDD genetic overlap has also been consistently observed in more recent studies
with higher resolution microarrays and sequencing to identify smaller CNVs and SNVs affecting
single genes (Table 1.2). These findings provide gene-level specificity and have been particularly
informative for investigations of the biological pathways involved in etiology of different NDDs.
Such efforts have indeed found evidence for shared underlying molecular mechanisms across
diverse conditions. Independent analyses of functional gene networks impacted by rare CNVs in
ASD (Gilman et al., 2011) and schizophrenia (Gilman et al., 2012) have revealed significant
overlap with convergence around proteins involved in synaptic function. Cristino et al. (2013)
performed a systematic, genome-wide investigation of candidate genes reported for ASD,
schizophrenia, ADHD and ID (from CNV, SNP and sequencing studies). They found evidence
for co-occurrence of genetic factors implicated in these different disorders in the same molecular
pathways and functional domains, with synaptic transmission as a prominent example of such
overlap (Cristino et al., 2013).
25
Table 1.2. Examples of genes implicated by rare CNVs and/or SNVs across different NDDs
Gene Disorders References
ANK3 ADHD, ASD, ID Bi 2012, Sanders 2012, Iqbal 2013
ARHGEF9 EPY, ID Kalscheuer 2009, Lesca 2011
ARID1B ASD, ID Halgren 2012
ASTN2/TRIM32 ADHD, ASD,
BPD, EPY, ID, SZ
Vrijenhoek 2008, Glessner 2009, Grozeva 2010, Lionel 2011, Van-
Silfhout 2013, Lionel 2014
AUTS2 ADHD, ASD,
EPY, ID Elia 2009, Beunders 2013
BCORL1 ASD, ID, SZ Xu 2012, Schuurs-Hoeijmakers 2013
BIRC6 ASD, SZ Xu 2012
C2CD3 ASD, SZ Xu 2012
CNTNAP2 ADHD, ASD,
EPY, SZ Bakkaloglu 2008, Friedman 2008, Elia 2009, Poot 2010
CSMD3 ASD, SZ Floris 2008, Malhotra 2011
DISC1 ASD, BPD, SZ Millar 2000, Williams 2009, Costain 2013
DLGAP2 ASD, SZ Marshall 2008, Guilmatre 2009, Pinto 2010
DPP6 ADHD, ASD, SZ Marshall 2008, Xu 2008, Elia 2009, Pinto 2010, Liao 2013
DPYD/mir137 ASD, ID, SZ Willemsen 2009, Carter 2011, Xu 2011, Van-Silfhout 2013
EHMT1 ASD, ID, SZ Kleefstra 2006, Kirov 2012, Talkowski 2012
FOXP1 ASD, ID Hamdan 2010, Girirajan 2011, Talkowski 2012
GABRG1 ADHD, ASD Vincent 2006, Lionel 2011
GPHN ASD, EPY, SZ ISC 2008, Lionel 2013
GPRIN3 ASD, SZ Xu 2012
GRIK2 ASD, ID Motazacker 2007
GRIN2B ASD, BPD, ID, SZ Awadalla 2010, Endele 2010, Talkowski 2012
GRM7 ADHD, SZ Walsh 2008, Elia 2009, Costain 2013
H2AFV ASD, SZ Xu 2012
26
HECTD1 ASD, SZ Xu 2012
ILRAPL1 ASD, ID Piton 2008, Pinto 2010
IMMP2L ADHD, ASD Elia 2009
MACROD2/FLRT3 ADHD, SZ Lionel 2011, Costain 2013
MBD5 ASD, BPD, EPY,
ID Talkowski 2011, Hodge 2013
MEF2C ASD, EPY, SZ Le Meur 2009
MYH10 ASD, SZ Xu 2012
NRXN1 ADHD, ASD,
EPY, ID, SZ Szatmari 2007, Rujescu 2009, Bradley 2010, Duong 2012
PARK2 ASD, ADHD, SZ Elia 2009, Jarick 2012, Glessner 2009, Vacic 2011
PIK3C3 ID, SZ Costain 2013, Van-Silfhout 2013
PTCHD1/PTCHD1AS ADHD, ASD, ID Noor 2010, Filges 2010, Lionel 2011
SCN2A ASD, EPY, ID Ogiwara 2009, Sanders 2012, Jiang 2013
SHANK2 ASD, ID Berkel 2010, Leblond 2012
SHANK3 ASD, EPY, ID, SZ Durand 2007, Moessner 2007, Gauthier 2010, Lesca 2012
SMCHD1 ASD, SZ Xu 2012
SYN1 ASD, EPY Garcia 2004, Fassio 2011
SYNGAP1 ASD, EPY, ID Carvill 2013, Hamdan 2009, Pinto 2010
TCF4 ASD, EPY, ID, SZ Amiel 2007, Talkowski 2012, Van-Silfhout 2013
TOP3B ASD, SZ Xu 2012
VPS39 ASD, SZ Xu 2012
ZNF804A ASD, SZ Steinberg 2011, Griswold 2012, Talkowski 2012, Costain 2013
Abbreviations: EPY, epilepsy; ID, Intellectual Disability; SZ, schizophrenia.
27
An excellent example of a gene involved in shared risk for different NDDs is NRXN1, which
encodes the neurexin 1 protein, with critically important roles in synaptic cell adhesion and
neurotransmitter secretion (Missler et al., 2003). The first observation of NXRN1 mutations in
ASD was a 300 kb hemizygous de novo deletion that eliminated several NRXN1 exons an ASD
individual (Szatmari et al., 2007). Subsequent studies have found exonic deletions in several
ASD cases (Ching et al., 2010; Glessner et al., 2009; Kim et al., 2008; Pinto et al., 2010; Sanders
et al., 2011; Shen et al., 2010; Zahir et al., 2008). Independent studies have also reported rare de
novo and inherited deletions affecting exons of NRXN1 in cases with schizophrenia (International
Schizophrenia Consortium, 2008; Kirov et al., 2009; Kirov et al., 2008; Rujescu et al., 2009;
Vrijenhoek et al., 2008; Walsh et al., 2008), intellectual disability (Ching et al., 2010; Friedman
et al., 2006; Guilmatre et al., 2009), bipolar disorder (Zhang et al., 2009), ADHD (Bradley et al.,
2010), Tourette syndrome (Sundaram et al., 2010) and autosomal-recessive Pitt-Hopkins
syndrome (Zweier et al., 2009). On the other hand, such deletions are absent or extremely rare in
control individuals (Ching et al., 2010). However the association of NRXN1 deletions with
disease risk often does not reach statistical significance within individual studies due to small
sample numbers and rarity of the deletions. It is only by performing a meta-analysis across
several studies (Figure 1.1) that we observe a highly significant enrichment of exonic NRXN1
deletions (p = 2.4 x 10-18) in cases versus controls. Nrxn1 knockout mice exhibit cognitive
impairment, behavioral changes and aberrant synaptic function, providing additional evidence
for a causal link between NRXN1 and NDD etiology (Etherton et al., 2009).
28
Figure 1.1. Reports in literature of exonic NRXN1 deletions across a range of NDDs
Overview of rare CNVs detected at 2p16.3 locus, overlapping NRXN1 by different published CNV studies of NDDs. The blue and red bars denote deletions and duplications, respectively. Genomic coordinates and information about transcript isoforms are from Genome Build 36 (hg18).
29
The overlapping CNV findings described above were derived from incidental observations and
the overall trend of cross-disorder overlap became apparent upon comparing results from
independent CNV scans of different NDDs. Very few studies have systematically and uniformly
assessed different NDD cohorts simultaneously for the presence of CNVs at overlapping risk loci
(Guilmatre et al., 2009; Moreno-De-Luca et al., 2010). In one such effort, Guilmatre et al.
investigated microarray data from 743 individuals with NDDs (247 with ID, 260 with ASD and
236 with schizophrenia) and 236 control individuals for shared risk CNVs. This study was not a
genome-wide microarray scan but rather used a targeted approach (quantitative multiplex PCR of
short fluorescent fragments) restricted to 28 specific candidate loci. These regions were selected
based on previous implication by rare deletions or duplications in individuals with NDDs
including NRXN1, CNTNAP2, SHANK3 and the 16p11.2 and 15q13 regions among others.
Guilmatre et al. observed overlapping CNVs in cases at 39.3% of the 28 selected loci. These risk
CNVs were detected in 4.2% of the schizophrenia cases, 6.2% of the ASD cases and 5.3% of the
ID cases. In contrast, only 0.4% of control individuals were seen to have CNVs at the risk loci,
thus demonstrating a significant excess of CNVs in each disorder cohort compared with controls
(p =.01 for schizophrenia, p < .001 for ASD and p = .001 for ID). Several CNVs in NDD cases
for whom parental DNA was available were seen to be of de novo origin, and overlapped genes
with roles in neurotransmission or in synapse formation and maintenance. The authors concluded
that their observations provided additional support for a causative or contributory role of rare,
and often de novo, CNVs to NDD risk and highlighted the existence of shared biological
pathways underlying etiology of clinically distinct NDDs.
1.5 Thesis Rationale and Overview
The composite genetic picture of NDD risk emerging from the myriad human genetics
investigations over the past decade is highly complex (Sullivan et al., 2012a). Conditions such as
ASD, schizophrenia, ADHD exhibit high heritability and clearly possess substantial genetic
underpinnings. The general failure of common variant studies in identifying specific risk genes
of large effect sizes and consistent reproducibility has led to a paradigm shift in thinking from
the older “common disorder-common variant” model. The new “common disorder-rare variant”
model has been bolstered by numerous rare variant studies investigating CNVs and SNVs. The
exciting results from these studies represent considerable progress in defining specific risk genes
30
and pathways. However, these findings also highlight challenges going forward. Although
several genomic loci have been consistently implicated in NDD risk, no single locus accounts for
more than 1% of cases and it is now evident that hundreds of risk loci are likely involved, each
responsible for a small subset of individuals with the disorder. The majority of cases still lack a
known genetic cause and the continued cataloguing of bona fide risk loci is essential to expand
the scope of molecular diagnostics, enable more effective clinical intervention and facilitate
therapeutic development The intriguing trend of shared risk genes for clinically distinct but often
overlapping NDDs offers both insight into common causal mechanisms and a promising research
avenue for identifying novel candidate genes.
Prior to the work described in this thesis, no unbiased, simultaneous, genome-wide comparisons
of rare CNVs across different NDDs have been reported. Combining CNV data from multiple
disorder cohorts in such a manner has the advantage of providing increased statistical power
necessary for detection and interpretation of very rare genetic events (Figure 1.2). Such a
strategy has the potential to reveal novel candidate genes contributing to cross-disorder risk for a
spectrum of NDDs and thus explain the genetic basis of shared clinical features often observed
across conditions. For instance, if a rare deletion of a gene is observed in a single individual from
a large cohort of ASD patients but is absent in control individuals, it is difficult to ascertain
whether this CNV is connected to ASD etiology or is merely a benign rare variant. Using a
cross-order analysis, if the same gene is also found to be affected by CNVs and/or point
mutations in unrelated individuals from other NDD cohorts such as schizophrenia or ADHD, this
provides additional support for a likely contributory role for this gene to NDD risk. Follow-up
in-depth phenotyping of individuals with such rare mutations affecting a particular gene can
inform genotype-phenotype correlations and enable detection of subtle shared clinical features
among these individuals.
31
Figure 1.2. Rationale for cross-NDD comparison of rare CNVs
32
In Chapter 2, I describe the overview of my thesis project, which involves genome-wide CNV
analysis of three newly characterized cohorts of Canadian individuals with one of three different
NDDs: ASD, ADHD and schizophrenia. My first objective, using intra-cohort analyses, was to
identify new genetic risk loci for each disorder by focusing on de novo CNVs and rare CNVs
significantly enriched in multiple unrelated patients compared to controls. My second objective
was to perform a cross-cohort analysis to detect overlapping rare CNVs among the three
disorders and pinpoint shared risk genes. The CNV calling methodology used for uniform
analysis of the different case cohorts is also described.
In Chapter 3, I present results of a genome wide CNV analysis for the ASD cohort. As an
illustration of my first objective, I outline the discovery of rare de novo and inherited exonic
deletions affecting the NRXN3 gene in multiple unrelated individuals with ASD. This gene, at
chromosome 14q24, encodes a key synaptic structural protein belonging to the neurexin protein
family, whose other members such as NRXN1 and NRXN2 are well-established ASD risk
factors. These deletions exhibited location-dependent penetrance effects, with those overlapping
multiple isoforms of the gene resulting in a more severe phenotype than those affecting a single
isoform. Follow-up characterization of individuals with NRXN3 deletions revealed clinical
complexity including a heterogeneous spectrum of ASD-related phenotypes and variable
expressivity.
In Chapter 4, I present results of a genome-wide CNV analysis for the ADHD cohort. The
availability of parental DNA for these individuals was used to investigate de novo CNV rate in
ADHD for the first time. This was found to be lower than published rates for ASD and
schizophrenia, and individuals with de novo CNVs had complex phenotypes featuring other
neurodevelopmental comorbidities in addition to their ADHD symptoms. Rare inherited CNVs
were observed in 19 of 248 (7.7%) individuals with ADHD, which either overlapped previously
implicated ADHD loci (e.g. DRD5 and 15q13 microduplication) or identified new candidate
susceptibility genes (e.g. MACROD2/FLRT3, CPLX2 and PTPRN2). Genome-wide rare CNV
findings in the ADHD cohort were compared with those from the ASD cohort described in
Chapter 3. Deletions of neuronal ASTN2 and ASTN2-intronic TRIM32 genes at chromosome
9q33.1 yielded the strongest association with ADHD and ASD, but numerous other shared
candidate genes (such as CHCHD3, MACROD2, and the 16p11.2 region) were also revealed.
33
In Chapter 5, I present results of a genome-wide CNV analysis on the schizophrenia cohort. Rare
CNVs greater than 500 kb in size were highly enriched in individuals with schizophrenia as
compared to a matched control cohort. CNVs at chromosome 2q13, which had been previously
implicated in ASD, were found to be significantly associated with schizophrenia for the first
time. Several new candidate genes for schizophrenia were revealed by studying overlapping rare
CNVs in multiple unrelated individuals. Functional gene-mapping analyses revealed significant
enrichment of exonic deletions in the schizophrenia cohort that impacted biological pathways
previously highlighted in ASD literature including neurodevelopmental and synaptic processes.
In Chapter 6, as an illustration of my second objective, I describe the discovery of exonic GPHN
deletions in individuals from ASD and schizophrenia cohorts. GPHN, at chromosome 14q23.3,
codes for gephyrin, a key scaffolding protein in the neuronal postsynaptic membrane, responsible
for clustering and localization of glycine and GABA receptors at inhibitory synapses. Follow-up
screening of a large clinical microarray dataset revealed additional individuals with GPHN
deletions, who presented with a range of neurodevelopmental diagnoses including ASD,
schizophrenia or seizures. Inheritance testing revealed that the majority of such deletions arose
de novo. All of the deletions shared a common region of overlap affecting a functionally
important G-domain of the gephyrin protein.
In Chapter 7, I summarize my key findings and discuss their implications for genetic screening
and counseling, psychiatric diagnosis and drug development. I conclude with a discussion of
possible extensions and future directions for this research. These include follow-up screening of
candidate genes in clinical microarray datasets; in-depth functional characterization of candidate
genes and molecular mechanisms of disease development; and the higher resolution
investigations of NDDs through whole-genome sequencing approaches.
34
Chapter 2
Thesis Overview and Methodology
35
2.1 Thesis Overview
An overview of my thesis project is presented in Figure 2.1. The three patient cohorts used for
the genome-wide CNV analysis were recruited by my clinical collaborators from different sites
across Canada. The phenotyping protocols used and the composition of the patient cohorts are
described in Chapter 3 (ASD cohort), Chapter 4 (ADHD cohort) and Chapter 5 (schizophrenia
cohort). The microarray genotyping, CNV analysis and prioritization of rare CNVs were
conducted in a uniform manner for all patients as described in section 2.2. My first objective was
to discover new candidate risk genes within each disorder dataset. For this purpose, I prioritized
those rare CNVs that affected recurrent genetic regions across multiple unrelated patients within
a cohort, or affected genes previously implicated in literature for NDD risk. In order to address
my second objective of discovering genes involved in shared genetic risk across different NDDs,
I compared the rare CNV findings from all three cohorts. Genes at which rare CNVs were
detected in 2 or more of the cohorts are listed in section 2.3. Follow-up replication studies and
in-depth clinical characterization were performed for two of the novel cross-NDD risk genes
discovered: ASTN2 (Chapter 3 and Chapter 7) and GPHN (Chapter 6).
36
Figure 2.1. Overview of thesis project
Abbreviations used: ASD, Autism Spectrum Disorder; ADHD, Attention Deficit Hyperactivity Disorder; SCZ, schizophrenia
37
2.2 Uniform CNV Analysis Workflow
2.2.1 Microarray genotyping
DNA samples from the patients across all 3 cohorts were sent from clinical sites to The Centre
for Applied Genomics (TCAG), Toronto. Samples were genotyped on the Affymetrix Genome-
Wide Human SNP Array 6.0 at the TCAG microarray facility with standard protocols as
provided by the manufacturer. The completion of genotyping experiments for all study samples
at the same facility using identical protocols was designed to minimize laboratory related
artefacts. The Affymetrix 6.0 is a widely used microarray platform that features comprehensive
coverage of the genome with more than 1.8 million probes (McCarroll et al., 2008). Half of these
are at known SNPs (SNP probes) and provide genotype data in addition to intensity data while
the other half are copy number specific probes (CN probes) which provide only intensity data
(McCarroll et al., 2008). The availability of both genotype and CNV data from this microarray is
particularly useful for the different quality control measures described in the next section.
2.2.2 Uniform quality control measures
To ensure high quality microarray data for CNV analysis, different quality control measures
were performed (Figure 2.2). In accordance with recommended quality control guidelines by
Affymetrix, microarrays which did not have values of the contrast QC metric > 0.4 were
excluded from analysis. The contrast QC metric measures the quality of each microarray
experiment by assessing uniformity of probe hybridization and testing for major defects in the
microarray chip. In order to reduce error due to sample mix-up prior to genotyping, the gender of
the individual genotyped on each array was determined using intensities from X and Y
chromosome probes. Those experiments for which the gender indicated by the microarray
contradicted the known gender of the individual were discarded. In order to minimize CNV
artefacts due to batch effects, suitable reference batches for CNV analysis were selected based on
previously validated approaches from the Scherer laboratory (Pinto et al., 2011). Briefly, pair-
wise Pearson correlations of median normalized intensities were calculated between different
arrays to pick suitable analysis batches with Pearson correlation values > 0.88 (Pinto et al.,
2011).
38
For individuals with parental DNA available for microarray genotyping, as was the case for
some complete trios in the ADHD and ASD cohorts, the PLINK tool set (Purcell et al., 2007)
was used to compute Mendelian error rate of genotypes from SNP probes on the Affymetrix 6.0
platform. Those trios in which Mendelian errors were observed for more than 1% of SNP probes
were considered to have potential issues with non-paternity or sample mix-ups and were
excluded from analysis. The PLINK toolset was also used to systematically check the degree of
genetic relatedness between cases to ensure that recurrent CNV findings across multiple
individuals were not due to hidden relatedness. Pair-wise identity by descent (IBD) was
calculated for every pair of cases from genotypes of microarray SNP probes on the Affymetrix
6.0 platform. Those pairs of samples with PI_HAT values [defined as P(IBD = 2) + 0.5 × P(IBD
= 1)] greater than 0.1, corresponding to first cousins or closer, were further investigated for
relatedness and only one sample from each pair was used in the final dataset. To accurately
estimate ancestry for individuals in patient cohorts, genotypes of 1,120 genome-wide unlinked
SNPs were clustered by the program STRUCTURE (Pritchard et al., 2000) together with
genotypes from 270 HapMap samples, which were used as references of known ancestry during
clustering. Samples were assigned to one of the following three: “European”, “East Asian”,
“African” using a threshold of coefficient of ancestry exceeding 0.9.
39
Figure 2.2. Uniform quality control measures
The different quality control filters used, together with their associated metrics and thresholds, are depicted. As an illustration of the relative impact of these sequential filters during data cleaning, the number of arrays excluded from analysis in the ADHD project (Chapter 4) were 6 with Contrast QC < 0.4, 3 with gender mismatches and 6 (2 trios) due to high Mendelian errors. A total of 594 arrays (248 from probands and 346 from parents) passed the quality control filters and were used for further analysis in the ADHD project (Chapter 4).
40
2.2.3 CNV detection from microarray data
Microarray data from all individuals in the clinical cohorts were analyzed for CNVs on a
genome-wide scale using a uniform workflow (Figure 2.3). To maximize sensitivity and
specificity of CNV detection, three different algorithms were used to call CNVs from the
microrray data: Birdsuite (Korn et al., 2008), iPattern (Pinto et al., 2011), and the Affymetrix
Genotyping Console. For each of these algorithms, CNVs were required to be supported by five
or more consecutive array probes. Subsequent analyses focused on “stringent” CNV calls,
defined as those CNVs detected by at least two of three algorithms. All subsequent analyses
focused on these stringent CNVs, which have very high positive validation rates (> 90%) by
independent experimental methods such as quantitative PCR (qPCR) and fluorescence in situ
hybridization (FISH) (Costain et al., 2013; Lionel et al., 2011; Marshall et al., 2008; Pinto et al.,
2010; Prasad et al., 2012; Silversides et al., 2012). Within CNV calls from each individual,
overlapping calls from Birdsuite and iPattern were merged with the outside probe boundaries and
designated as stringent calls. Singleton calls from Birdsuite or iPattern, which overlapped with a
Genotyping Console call from the same individual, were also included in the stringent set.
Samples with number of calls greater than three times the SD from the mean number of calls for
an analysis batch were excluded. Rare CNVs were defined for individuals from the three clinical
cohorts by comparing the total dataset of stringent CNVs against those CNVs identified in two
large population-based control cohorts comprising 2357 individuals of European ancestry from
Ontario (Stewart et al., 2009) and Germany (Krawczak et al., 2006). CNVs were identified in
these control individuals using an identical microarray platform and CNV analysis strategy as
described above for the clinical cohorts. A 50% reciprocal overlap criterion was used; i.e. a case
CNV that was at least 50% unique by length when compared to every CNV in controls was taken
to be rare. In order to identify novel NDD risk genes, those rare CNVs that affected the same
gene across multiple individuals were prioritized for follow-up after confirmation of microarray
calls as true positives. When parental samples were available, as was the case for some of the
probands in the ADHD (Chapter 4) and ASD (Chapter 3), inheritance testing was performed in
order to detect de novo CNVs, which were also prioritized for risk gene discovery. Experimental
validation and inheritance testing of rare CNVs was performed using SYBR Green based qPCR.
41
Figure 2.3. Uniform CNV analysis workflow
42
Chapter 3
CNV Analysis of ASD Cohort Reveals Novel Risk Gene NRXN3
Parts of this chapter are adapted, with permission for use from Cell Press, from the following
published journal article:
Vaags AK*, Lionel AC*, Sato D, Goodenberger M, Stein QP, Curran S, Ogilvie C, Ahn JW,
Drmic I, Senman L, Chrysler C, Thompson A, Russell C, Prasad A, Walker S, Pinto D, Marshall
CR, Stavropoulos DJ, Zwaigenbaum L, Fernandez BA, Fombonne E, Bolton PF, Collier DA,
Hodge JC, Roberts W, Szatmari P, Scherer SW. Rare deletions at the neurexin 3 locus in autism
spectrum disorder. American Journal of Human Genetics; 2012; 90(1):133-41. *Joint first
authors
I performed CNV analysis of ASD cohort and controls, prioritized rare CNVs for follow-up,
discovered NRXN3 deletions, as well as drafted and revised the manuscript. Dr. A.K. Vaags
followed up on clinical information for ASD probands with NRXN3 deletions and their families,
performed inheritance testing, and drafted and revised the manuscript.
43
3.1 Abstract
Autism spectrum disorder (ASD), a common NDD with an estimated prevalence rate of nearly
1% of children, is characterized by impairment in reciprocal social interaction, communication
deficits and a repetitive pattern of behavior. In recent years, several studies have highlighted the
role of rare copy number variants (CNVs) in the genetic etiology of ASD. The study of these rare
deletions and duplications in the genomes of ASD patients has proven to be a powerful tool for
the identification of novel candidate genetic loci for further investigation by targeted gene
sequencing and functional studies. This chapter describes the results from a genome-wide CNV
scan of more than 700 unrelated, newly characterized Canadian ASD patients. Rare exonic
CNVs were detected in the ASD cases that were absent in more than 2,000 ancestry-matched
population based controls and overlapped previously implicated ASD loci (e.g. PTCHD1, 15q11-
q13, 16p11.2), or identified new candidate susceptibility loci for ASD (e.g. NRXN3). Follow-up
investigation of the neurexin 3 (NRXN3) locus in other clinical cohorts revealed additional rare
inherited and de novo deletions overlapping this gene in individuals with ASD. In-depth clinical
characterization of the families of the individuals with these deletions revealed complexities of
penetrance and expressivity at this locus. These findings provide the final piece of evidence for
the involvement of all three members of the synaptic neurexin protein family in ASD risk.
44
3.2 Introduction
As described in Chapter 1, the emerging picture of ASD risk genetics is highly complex. Despite
strong evidence that ASD has genetic underpinnings, the specific risk genes involved in the
disorder have been hard to pinpoint until recently. The advent of whole-genome microarray
technology has highlighted the role of rare CNVs affecting certain genetic regions in a subset of
ASD cases. Rare CNV detection and characterization has proven to be a powerful tool for the
identification of novel candidate genetic loci for further investigation by targeted gene
sequencing and functional studies. We conducted and present here the results of a genome-wide
CNV scan of more than 700 unrelated, newly characterized Canadian ASD patients.
3.3 Results
3.3.1 Candidate risk loci identified by rare CNVs in ASD cohort
To identify candidate risk genes for ASD using the rare CNV data, the following categories of
variants were prioritized: de novo CNVs, rare CNVs overlapping the same genetic region in
unrelated ASD probands and rare CNVs affecting regions previously implicated in risk for ASD
or other NDD. Table 3.1 shows examples of rare CNV results from each of these categories.
DNA was available from parents of some of the probands for inheritance testing and this enabled
the detection of de novo variants. One de novo deletion was observed to affect several exons of
the autism susceptibility candidate 2 (AUTS2) gene on chromosome 7. Recent studies have
provided support for the role of AUTS2 deletions in ASD and ID (Beunders et al., 2013;
Nagamani et al., 2013). The largest such investigation by Beunders et al. (2013) detected such
de novo deletions at a frequency of 1 in 2,000 in cohorts of ID and ASD individuals but did not
find similar events in more than 15,000 controls. Modeling of the deletion in zebrafish revealed
recapitulation of phenotypes often observed in human patients with such deletions including
microcephaly and craniofacial defects. These deficits in the zebrafish knockdown models were
rescued by introduction of the human mRNA, providing further evidence for the causal role of
such deletions. Another de novo CNV previously implicated in NDD risk was a 22q11.2
duplication. Although marked by variable penetrance and lower severity than the reciprocal
45
22q11.2DS, this gain has been reported in connection with ASD and ID (Lo-Castro et al., 2009;
Pinto et al., 2010).
Examples of genetic regions implicated by CNVs previously reported in connected with ASD
include 15q11-13 duplications in two individuals and a 16p11.2 deletion in one individual. As
discussed in Chapter 1, these genetic syndromes often feature ASD as a core phenotype amidst a
spectrum of other varying clinical traits. The PTCHD1/DDX53 region at Xq22 is another bona
fide ASD risk locus affected by a deletion in this cohort. After initial discovery (Marshall et al.,
2008), several other studies have reported deletions at this gene in ASD (Noor et al., 2010;
Sanders et al., 2011) and ID (Filges et al., 2011) Several rare exonic CNVs in the ASD patient
cohort , including CNVs at the ASTN2/TRIM32 locus in three individuals, were observed to
overlap findings from the ADHD cohort presented in Chapter 4 (Table 3.2) and rare exonic
deletions at the synaptic GPHN gene were observed in one individual from each of the ASD and
schizophrenia cohorts. Overlapping rare CNVs between ASD and ADHD are discussed in
Chapter 4, the GPHN deletions are discussed in Chapter 6 and further details about the
ASTN2/TRIM32 locus are presented in Chapters 4 and 7.
One intriguing novel candidate risk gene implicated by overlapping rare CNVs in multiple ASD
probands was neurexin 3 (NRXN3), which is located at chromosome 14 and has two major
transcript isoforms (Figure 3.1).Rare exonic deletions affected the first exon of the longer alpha
isoform in one patient and several terminal exons of both isoforms of the gene in the second
proband. NRXN3, together with the other two members of the neurexin gene family (NRXN1 on
chromosome 2 and NRXN2 on chromosome 11) has been shown to have important roles in
synaptic cell adhesion and neurotransmitter secretion (Missler et al., 2003). While there were no
previous reports of NRXN3 in connection with ASD, the other members of the gene family have
well-established roles in NDD risk (Reichelt et al., 2012; Sudhof, 2008). As described in Chapter
1, rare exonic deletions within NRXN1 are among the most consistently observed findings from
CNV investigations of ASD and other NDDs. A truncating NRXN2 mutation has been reported
in a patient with ASD and was inherited from a father with severe language delay and family
history of schizophrenia (Gauthier et al., 2011). Given the above evidence, the NRXN3 locus was
further investigated as described in the next section.
46
Table 3.1. Examples of risk loci implicated by rare CNVs in ASD probands
Sample Sex CNV Locus Size (kb) Inh Genes Previous reports1
A. Loci implicated by de novo CNVs in ASD probands
SK0485 F Loss 7q11.22 330 D AUTS2 ASD, ID
124498 M Loss 12q24.33 108 D POLE,PGAM5,PXMP2,ANKLE2
149151 M Gain 22q11.2 2,985 D 64 genes ASD, ID
B. Rare CNVs at loci previously implicated in ASD
153706 M Gain 15q11-13 6,267 U > 100 genes ASD
114963L F Gain 15q11-13 5,639 U > 100 genes ASD
121851 M Loss 16p11.2 610 M 28 genes ASD
91548 M Loss Xp22 124 M PTCHD1/DDX53 ASD, ID
C. Overlapping rare CNVs in unrelated ASD probands
64119 M Loss 9q33.1 90 M ASTN2, TRIM32 ASD, ID, SZ
128963 M Loss 9q33.1 116 M ASTN2, TRIM32 ASD, ID, SZ
136929 M Gain 9q33.1 45 M ASTN2 ASD, ID, SZ
F1-003 M Loss 14q24.3 63 M NRXN3
F2-003 M Loss 14q24.3 292 P NRXN3
Abbreviations: Inh, Inheritance; P, Paternal; M, Maternal; D, De novo; U, Unknown;
1 This column lists the neuropsychiatric disorder(s) in connection to which each locus has been reported by previous studies : ADHD: Attention deficit hyperactivity disorder, ASD: Autism spectrum disorder, ID: Intellectual disability, SZ: Schizophrenia.
47
Table 3.2. Examples of risk loci implicated by rare exonic CNVs across 3 NDD cohorts
Genetic
locus Gene(s)
ASD
dataset1
ADHD
dataset1
Schizophrenia
dataset1
7q32.2 CHCHD3 1 gain 1 gain -
9q33.1 ASTN2 & TRIM32 2 losses,
1 gain 2 losses
12q24.3 ANKLE2,POLE, PGAM5,PXMP2 1 loss 1 gain -
14q23.3 GPHN 1 loss - 1 loss
16p11.2 28 genes 1 loss 1 gain 3 gains
20p12.1 MACROD2 & FLRT3 1 loss 2 losses -
Xp22.1 PTCHD1/TRIM32 1 loss 1 loss
48
3.3.2 Follow-up of rare exonic deletions at the NRXN3 locus
Following the detection of the rare exonic NRXN3 deletions in two individuals with ASD (F1-
003 and F2-003 in Figures 3.1 and 3.2), this gene was screened for additional CNVs in other
ASD cohorts including 447 Canadian patients and two groups of individuals referred for clinical
microarray testing to the diagnostic laboratories at the Mayo Clinic, Rochester, Minnesota (n =
1,796) and at Guy’s Hospital, London, UK (n = 1,368). Additional exonic NRXN3 deletions were
identified in one individual from each of the diagnostic laboratory cohorts (F3-003 and F4-003 in
Figures 3.1 and 3.2). Following independent experimental validation of the NRXN3 deletions for
each of the four ASD patients, detailed clinical information was obtained from their referring
physicians together with DNA samples from the parents and several of the immediate relatives of
the probands. Inheritance testing revealed the deletions to be of de novo origin in family 4,
paternally inherited in families 2 and 3 and maternally inherited in family 1(Figure 3.2).
The NRXN3 locus described here are very rare genetic events and not often observed in the
general population. In the 15,122 control individuals examined for CNVs affecting this gene,
only four possessed exonic NRXN3 deletions (Figure 3.1). Taken together, the frequency of
exonic deletions at the NRXN3 locus is significantly higher in ASD cases than in controls
(4/4,322 cases versus 4/15,122 controls; Fisher’s exact test, one-tailed p = 0.039). Three of the
control deletions affected only the alpha isoform while the fourth affected only the beta isoform
(Figure 3.1). Thus, while three of the four deletions in the cases affect both of the known NRXN3
isoforms (alpha and beta), none of the control deletions do so. This difference is also statistically
significant Fisher’s exact test, (one-tailed p = 0.0055). These findings parallel what is observed
in NRXN1, for which deletions, while extremely rare, have been reported in a few control
individuals (Ching et al., 2010; Rujescu et al., 2009). CNVs present in control individuals might
be indicative of reduced penetrance of NXRN3 CNVs (perhaps modified by the non-deleted
allele or other modifier genes), the presence of CNVs within control individuals in regions where
they do not destroy NRXN3 function, a lack of rigorous testing of the neuropsychiatric phenotype
in the controls, or false-positive microarray CNV calls in the controls, since DNA was not
available for their independent experimental confirmation.
49
Figure 3.1. Rare exonic NRXN3 deletions in ASD probands and controls
The deletions detected at chromosome 14 affecting NRXN3 exons in four ASD probands and four control individuals are shown in red. NRXN3 has two major transcript isoforms – the longer alpha isoform and the shorter beta isoform. Genomic coordinates and isoform information are from hg18 genome build.
50
Figure 3.2. Pedigrees of the families of ASD probands with NRXN3 deletions
Black-filled symbols represent ASD-affected individuals, gray-filled symbols represent broader autism phenotype (BAP)-affected individuals, and unfilled symbols represent apparently unaffected individuals. Probands are marked with an arrow. Clinical diagnosis and segregation of the NRXN3 deletions are shown. Children are placed left to right in birth order from eldest to youngest. Abbreviations are as follows: ADHD, attention deficit hyperactivity disorder; OCD, obsessive compulsive disorder; and NA, not assessed.
51
3.3.3 Clinical information from ASD probands with NRXN3 deletions and
their families
Family 1 (Proband with ASD diagnosis; 63 kb maternally inherited deletion):
In Family 1, the male proband (F1-003) was conceived naturally to a 33 year-old (yro) mother
(F1-001) and 35 yro father (F1-002; Figure 3.2). The pregnancy was uncomplicated, although the
mother contracted pneumonia at 7 months of gestation. The proband was delivered by
spontaneous vaginal delivery at 38 weeks with a nuchal cord and birth weight 7 lbs 3 oz. The
parents first noted advanced speech and memorization of detail, as well as social difficulties at
12-24 months of age. At age 9, he was identified as “gifted” following assessment for learning
disabilities. Between ages 9-14, there were concerns of depression. A family stressor, at age 10,
may have contributed to suicidal ideation. At 13 he was diagnosed with Asperger syndrome. He
had difficulties with aggression, anger, transitions and stimulation, including bright lights and
small spaces. His non-verbal cognitive abilities were assessed at age 16 with the Leiter
International Performance Scale - Revised (Leiter-R) measure. He performed above age
expectations compared to his same-aged peers (92nd percentile). In addition, The Vineland
Adaptive Behavior Scales (VABS) was completed with age appropriate outcomes for
communication (39th percentile) and daily living skills (34th percentile), but socialization skills
were severely delayed (below the 1st percentile). Oral and Written Language Scales (OWLS)
testing indicated he had above average (93rd percentile) receptive language and listening
comprehension, but he declined to complete measures of expressive language (Oral Expression
Scale). The Autism Diagnostic Interview- Revised (ADI-R) combined with clinical assessment
was consistent with a diagnosis of Asperger syndrome. The proband reported that his mind races
and he has difficulty sleeping through the night. There have been recurrent episodes of
aggressive outbursts and depression for which he received ineffective treatment with risperidone.
Following complaints of recurrent headaches, he was diagnosed with bilateral chronic migraine
headaches with episodic visual symptoms, likely due to migrainous aura. At 16 yro, he attended
university part-time while maintaining a part-time job.
F1-003 is one of three children of non-consanguineous parents of Irish and English descent. The
mother has self-reported high energy levels, social difficulties, anxiety, as well as auditory and
language processing difficulties. She did not meet criteria for ASD or sub-clinical autism/broader
52
autism phenotype (BAP) upon clinical interview assessment. Within the extended maternal
family, there is one 3rd degree relative with an ASD diagnosis, as well as several 2nd and 3rd
degree relatives with suspected ASD characteristics. The father, who is apparently healthy, has a
brother with Down syndrome and a 2nd and 3rd degree relative with suspected ASD
characteristics.
The proband’s deletion-negative brother (F1-004) has a normal 46,XY karyotype and normal
FMR-1 repeat. He had a diagnosis of attention deficit hyperactivity disorder (ADHD) at age 12;
testing with the Autism Diagnostic Observation Schedule (ADOS: Module 4) at age 14 did not
meet formal diagnostic criteria for an ASD. He performed at age expectations upon completion
of the Leiter-R test (63rd percentile). His receptive and expressive language skills were above age
expectations (OWLS, 88th percentile) and adaptive behavior was adequate in measures of
communication, daily living skills and socialization as assessed by VABS.
The proband’s sister (F1-005) has been diagnosed with Down syndrome and possesses a
47,XX,+21 karyotype and normal FMR-1 repeat. A research assessment at age 11 revealed she
was below the 1st percentile for her age on the Leiter-R. Her receptive and expressive language
skills also fell below the 1st percentile on the OWLS assessment. She showed mild to moderate
deficits across her adaptive behavior profile for her age. She met criteria for ASD on the ADOS:
Module 2 and ADI-R Behavior dimension only, but did not meet clinical criteria when assessed
by a developmental pediatrician. Her behavior was consistent with her Trisomy 21 status, and
complicated the diagnostic process. At age 16, heightened anxiety as well as increased obsessive
and impulsive behaviors prompted a second clinical assessment. The obsessive and repetitive
components of her behavior were the most challenging and interfered with her adaptive
functioning but an overall diagnosis of non-ASD was given.
Family 2 (Proband with ASD diagnosis; 292 kb paternally inherited deletion):
Family 2 includes trizygous triplets: the proband (F2-003) and an affected sister (F2-004)
inherited the NRXN3 deletion from their father, while an unaffected brother (F2-005; Figure 3.2)
did not inherit the deletion. The triplets were conceived naturally to a 23 yro mother (F2-001)
and 30 yro father (F2-002) and were delivered at 31 weeks of gestation at a weight of 3 lbs, 12
oz. for the proband, and 3 lbs, 4 oz. for the affected sister. Multiple medical interventions were
undertaken at birth due to the premature delivery. The proband was born with a right-side cranial
53
hemorrhage and the affected sister was also suspected to have suffered a cranial hemorrhage at
birth. All of the siblings have continuing difficulty with asthma, while the proband suffers from
juvenile arthritis and the affected sister has had multiple ear and respiratory infections.
The proband was diagnosed with an ASD through clinical assessment at 3 years, 5 months of
age. During testing for a research protocol at age five years, 8 months, he met the criteria for an
autism diagnosis utilizing the ADI-R and ADOS-Module 1. Non-verbal communication ability
and receptive and expressive language skills could not be assessed, as the proband was unable to
complete the IQ (Leiter-R) and language (OWLS) tests. Testing with the Peabody Picture
Vocabulary Test (PPVT-4) indicated the proband was extremely low functioning in receptive
vocabulary, as well as in his adaptive behavioral profile (VABS-II, below 1st percentile).
The ASD sister (F2-004) was diagnosed at 3 years, 9 months. As part of a research protocol, she
was assessed at the age of five years, 8 months to have autism using the ADI-R and ADOS-
Module 1. Her non-verbal communication ability was assessed with Leiter-R and found to be in
the low average range for her age group (12th percentile), while her VABS-II assessment
indicated extremely low-functioning (below 1st percentile). Assessment of receptive and
expressive language was attempted with the OWLS measure, but could not be completed.
Receptive vocabulary testing (PPVT-4) could also not be completed. The unaffected brother (F2-
005) was assessed at the age of 4 years, 10 months with a Social Communication Questionnaire
(SCQ), which indicated he did not have ASD (total score: 3, non-ASD).
These children were born to non-consanguineous parents of French Canadian descent; neither
has an ASD diagnosis. The mother has asthma and was assessed to not have a BAP, i.e. she was
not non-alexithymic on the Toronto Alexithymia Scale (TAS-20; total score: 29), while the
father is color blind and was also assessed be non-BAP (TAS-20 total score: 41). Clinical
information does not exist on male deletion carrier F2-006.
Family 3 (Proband with ASD diagnosis; 336 kb paternally inherited deletion):
F3-003 was diagnosed to have autism in early childhood and clinical testing with an ADI-R at
age 13 confirmed this finding. He had issues with aggression, self-harm, obsessive behaviors,
persecutory and delusional ideas, suicidal ideation and sleep problems. The father who also
54
carried the NRXN3 deletion was assessed by clinical review to have BAP as well as learning
disability, depression and alcohol-related issues.
Family 4 (Proband with ASD diagnosis; 247 kb de novo deletion):
In Family 4, the male proband (F4-003) was born at 38 weeks, weighing 8 lbs, 4 oz. The
pregnancy was complicated by insulin controlled gestational diabetes and hypertension. He was
delivered via cesarean section due to maternal hemorrhaging. The neonatal course was
uneventful. During 19 to 24 months of age, the parents first noted their son had difficulty being
consoled, developed temper tantrums, started to bang his head, developed difficulty making eye
contact, and started to develop some stiffness and rigidity. Between age 2-3, he could not keep to
a schedule, developed extreme reactions to taste and touching, and had trouble falling asleep. At
2 years, 10 months he received a clinical diagnosis of ASD, speech language delays and sleep
onset disorder. At 4 year, 6 months, a Caregiver-Teacher Report Form for Ages 1.5-5 was
completed. His scores endorsed internalizing/externalizing problems and the DSM-Oriented
Scales for Boys was in the clinical range for pervasive developmental problems (above the 97th
percentile). At 6 years, 10 months he had near normal speech, no longer banged his head nor had
oppositional defiance. His main concerns included high levels of anxiety and temper tantrums in
unfamiliar situations. An individualized educational plan was in place for math, reading, and
writing and he had a one-on-one educational assistant. Although he was in good general health,
he received occupational therapy for difficulty with thumb flexion and hypotonia of the hands
and chest.
3.4 Discussion
These findings support the hypothesis that hemizygous deletions involving NRXN3 may be
involved in the manifestation of an ASD or sub-clinical autism / BAP phenotype. Perhaps most
compelling, the deletion in Family 4 arose de novo, lending strong support for a causative
relationship between the loss of NRXN3 and the development of ASD.
Family 1 carried a deletion that overlaps exon 1 NRXN3 alpha, which may predispose affected
individuals to a mild ASD or Asperger syndrome phenotype in the absence of confounding
genomic changes, such as trisomy 21, as was observed in the Down syndrome-affected sister. As
55
NRXN3 alpha is translated from a start codon located in exon 2 (Rowen et al., 2002; Tabuchi
and Sudhof, 2002), it is possible that full-length NRXN3 alpha could be produced despite the
deletion. The effect of deletion of exon 1 may be to decrease transcription of the gene or
diminish the stability of messenger RNA. Interestingly, the same CNV deletion is present within
the mother who does not have a formal ASD diagnosis, but who self-identified as possessing
several ASD-like characteristics, including anxiety, social difficulties as well as auditory and
language processing difficulties. Thus, there may be variable expressivity of the NRXN3 deletion
and/or the necessity for as yet, unidentified, contributing genetic or environmental factors to
cause a clinical ASD phenotype.
In contrast, Family 2, 3 and 4 carried a deletion that removes multiple exons of both the alpha
and beta isoforms of NRXN3. The children carrying these deletions presented with a more severe
form of ASD than that identified in Family 1 wherein only exon 1 of the alpha isoform was
involved. This observation coupled with the absence of deletions affecting both isoforms of
NRXN3 in our controls could imply that the loss of the alpha isoform in addition to the beta
isoform may result in a more severe ASD phenotype. It is perhaps especially interesting to note
that the apparently unaffected carrier father and paternal grandfather in Family 2 carry the same
deletion and missense mutations as the ASD-affected children, yet have not been diagnosed with
an ASD or BAP. Conversely, the proband of Family 3 presented with autism while the carrier
father was diagnosed as having the BAP. The complexities of penetrance and variable
expressivity of NRXN3 in ASD may be partially explained by gene dosage balance and
functional redundancies of the neurexin gene family. Although all three neurexin genes have
similar exonic nucleotide sequences, exon-intron structure and patterns of alternative splicing for
mRNAs, NRXN1 and NRXN3 have the greatest sequence and protein identity. Expression
patterns vary between the neurexin genes and their alpha and beta isoforms. NRXN3 is expressed
throughout the brain, while other isoforms show differential locations and intensities of
expression (Ullrich et al., 1995).
Ultimately, to fully understand genotype and phenotype correlations will require knowledge of
the diploid expression patterns of the neurexin genes, other interacting molecules, and perhaps
non-genetic factors that might impede their homeostasis. The discovery of rare NRXN3 deletions
in ASD presented here provides a set of reference cases for ascertainment and comparison to
56
other patients, both enabling the molecular diagnosis of autism and providing potential new
targets for therapeutic intervention.
3.5 Materials and Methods
3.5.1 Study subjects and methodology
The cohort of 711 ASD patients used for the CNV analysis described in this chapter was
recruited from four different Canadian sites: The Hospital for Sick Children, Toronto, Ontario;
McMaster University, Hamilton, Ontario; Memorial University of Newfoundland, St. John’s,
Newfoundland and the University of Alberta, Edmonton, Alberta. ASD diagnoses were made
using uniform criteria common to all the sites and were based on a combination of two widely
used and clinically validated diagnostic tools: the Autism Diagnostic Interview-Revised (ADI-R)
and/or the Autism Diagnostic Observation Schedule (ADOS) (Risi et al., 2006). Approval was
obtained for this study from the research ethics boards at The Hospital for Sick Children
(Toronto). All participants provided informed written consent. DNA from the ASD probands,
and from their parents and immediate family members if available, was genotyped and analyzed
for CNVs using the methodology outlined in Chapter 2.
57
Chapter 4
Rare Copy Number Variant Discovery and Cross-Disorder
Comparisons Identify Risk Genes for ADHD
Parts of this chapter are adapted, with permission for use from AAAS, from the following
published journal article:
Lionel AC, Crosbie J, Barbosa N, Goodale T, Thiruvahindrapuram B, Rickaby J, Gazzellone M,
Carson AR, Howe JL, Wang Z, Wei J, Stewart AF, Roberts R, McPherson R, Fiebig A, Franke
A, Schreiber S, Zwaigenbaum L, Fernandez BA, Roberts W, Arnold PD, Szatmari P, Marshall
CR, Schachar R, Scherer SW. Rare copy number variation discovery and cross-disorder
comparisons identify risk genes for Attention Deficit Hyperactivity Disorder (ADHD).
Science Translational Medicine; 2011; 3(95):95ra75.
I designed and implemented CNV analysis of ADHD cases and controls and cross-disorder
comparison with ASD, prioritized rare CNVs for follow-up, coordinated experimental validation
and collection of information for clinical interpretation of CNVs and drafted and revised the
manuscript.
58
4.1 Abstract
Attention deficit hyperactivity disorder (ADHD) is a common and persistent condition
characterized by developmentally atypical and impairing inattention, hyperactivity and
impulsiveness. This chapter describes the identification of de novo and rare copy number
variations (CNVs) in 248 unrelated ADHD patients using million feature genotyping arrays. De
novo CNVs were detected in 3 (1.7%) of the 173 ADHD patients for whom DNA was available
from both parents. These CNVs affected brain-expressed genes: DCLK2, SORCS1/SORCS3 and
MACROD2. Rare inherited CNVs, that were absent in 2,357 controls and that either overlapped
previously implicated ADHD loci (for example DRD5 and 15q13 micro-duplication), or
identified new candidate susceptibility genes (ASTN2, CPLX2, ZBBX, PTPRN2) were detected in
19 of 248 (7.7%) ADHD probands. Among these de novo and rare-inherited CNVs there were
also examples of genes (e.g. ASTN2, GABRG1, CNTN5) previously implicated by rare CNVs in
other neurodevelopmental conditions including autism spectrum disorder (ASD). To further
explore the overlap of risks in ADHD and ASD, the rare CNV findings in the ADHD cohort
were compared with those from a newly collected cohort of individuals with a primary diagnosis
of ASD. Deletions of the neuronal ASTN2 and the ASTN2-intronic TRIM32 genes yielded the
strongest association with ADHD and ASD, but numerous other shared candidate genes (such as
CHCHD3, MACROD2 and the 16p11.2 region) were also revealed. These results provide support
for a role for rare CNVs in ADHD risk and reinforce evidence for the existence of common
underlying susceptibility genes for ADHD, ASD and other neuropsychiatric disorders.
59
4.2 Introduction
As outlined in Chapter 1, despite substantial evidence for ADHD having a genetic basis,
common variant approaches such as GWA have failed to find specific risk genes of large effect
sizes. This could suggest that rare genetic variants have a role in the etiology of the disorder. In
this study of a well-characterized Canadian cohort, we sought to explore the etiologic role of rare
inherited CNVs in ADHD, as well as to determine whether de novo CNV mutations might also
contribute. Our primary study design emphasized the use of identical high-resolution genotyping
microarrays and detection algorithms for all cases and controls, yielding a robust discovery data
set. We also compared our ADHD-CNV data with CNVs from the ASD cohort described in
Chapter 3, which was genotyped with the same microarray platform and analysis design (Chapter
2) to test whether neuropsychiatric risk genes might have pleiotropic effects across—or be
expressed as traits within—disorders.
4.3 Results
4.3.1 Detection of rare CNVs in ADHD cohort
In this study, 248 unrelated ADHD probands (73 female and 175 male) collected in Canada were
genotyped using the Affymetrix single nucleotide polymorphism (SNP) 6.0 microarray and
assessed for CNVs using the methods described in Chapter 2. In 173 of these families, DNA was
genotyped in both parents. Concurrently, 2,357 population-based controls including 1,234 from
Ontario (Stewart et al., 2009) and 1,123 from Germany (Krawczak et al., 2006) were assessed for
CNVs using the same array platform and calling strategy as the cases. A dataset of rare,
stringently defined CNVs was obtained from the ADHD probands that spanned a minimum of
five array probes, had support from two or more calling algorithms and were not found in
controls (less than 50% overlap by length). Using quantitative PCR (qPCR), 33 / 34 (97.1%) rare
stringent calls that were tested were validated as true positives, including all 23 CNVs in Table
4.1. De novo CNVs and rare inherited CNVs which overlapped genetic loci previously
implicated in ADHD or in other NDDs were prioritized to obtained novel ADHD candidate gene
loci (Table 4.1, Figure 4.1 and Table 4.2).
60
Table 4.1. De novo and rare inherited CNVs at candidate loci in ADHD probands
Sample Sex CNV Locus Size (kb) Inh Genes Previous reports1
A. Loci implicated by de novo CNVs in ADHD probands
27696.3 M Loss 4q31.3 33 D DCLK22 Epilepsy
30600.3 M
Gain 10q25.1 242 D SORCS32 BPD
Gain 10q25.1 318 D SORCS12 BPD
113400.3 M Loss 20p12.1 109 D MACROD23 ASD
B. Rare CNVs at loci previously implicated in ADHD
27027.3 M Gain 4p16.1 482 P DRD5,WDR1,SLC2A9 ADHD
27060.3 M Gain 5q35.2 131 M CPLX2 ADHD
63900.3 F Gain 7q32.3 159 M CHCHD3 ADHD
19896.3 M Gain 7q36.3 1,023 M PTPRN2,WDR60,NCAPG2,ESYT2 ADHD
19929.3 F Gain 11q13.4 147 U4 5 genes ADHD
19752.3 F Gain 15q13 3,666 M 15q13 locus (16 genes) ADHD, ASD, SZ
19839.3 M Gain 16p11.2 789 P 16p11.2 locus (40 genes) ADHD, ASD, SZ
C. Overlapping rare CNVs in unrelated ADHD probands
22512.3 F Loss 3q26.1 165 P ZBBX ADHD
19365.3 F Loss 3q26.1 22 U5 ZBBX ADHD
57300.3 M Gain 6p24.2 262 M TFAP2A,GCNT2,C6orf218 ASD
19698.4 F Loss 6p24.2 92 M GCNT2 ASD
19761.3 M Loss 9q33.1 177 P ASTN2, TRIM32 ASD, BPD, SZ
19812.3 M Loss 9q33.1 148 U4 ASTN2, TRIM32 ASD, BPD, SZ
108300.3 M Loss 20p12.1 542 M MACROD2, FLRT3 ASD
61
D. Rare CNVs at loci implicated in other NDDs
87600.3 F Loss 4p16 97 M STK32B mild ID
27048.3 M Loss 4p12 64 M GABRG1 ASD
125700.3 M Loss 11q22.1 49 P CNTN52(intronic) ADHD, ASD, SZ
89700.3 M Gain 12q24.33 211 P ANKLE2,POLE,PGAM5,PXMP2 ASD
27075.3 M Loss Xp22.11 388 M DDX53, upstream of PTCHD1 ASD, ID
Abbreviation used: Inh, Inheritance; P, Paternal; M, Maternal; D, De novo; U, Unknown;
1 This column lists the neuropsychiatric disorder(s) in connection to which each locus has been reported by previous studies : ADHD: Attention deficit hyperactivity disorder, ASD: Autism spectrum disorder, BPD: Bipolar disorder, ID: Intellectual disability, SZ: Schizophrenia.
2 Paralogs of these genes have been implicated by previous studies in the neuropsychiatric disorders listed in the last column.
3Loci highlighted in bold had overlapping rare CNVs in both the ADHD and the ASD datasets
4DNA was not available from the father for testing.
5DNA was not available from the mother for testing.
62
Figure 4.1. Pedigrees of ADHD families with rare CNVs of interest
(A to D) Pedigrees of ADHD families with variants of interest, which were absent in controls and (A) were de novo or (B) occurred at loci previously implicated in ADHD or (C) were present in two unrelated ADHD probands or (D) overlapped loci previously implicated in other NDDs. Circles and squares denote females and males, respectively, whereas arrows highlight the index proband in each family. Black filled objects indicate ADHD diagnosis, unfilled symbols signify unaffected family members, gray symbolizes individuals with non-ADHD neuropsychiatric conditions, and stripes represent individuals with some ADHD traits, but not having a definitive ADHD diagnosis. N/A denotes individuals from whom no DNA was available for testing. Detailed overview of phenotypes is presented in Table 4.2. Proband in family 30600 also has a diagnosis of bipolar disorder.
63
Table 4.2. Clinical phenotypes of ADHD families with rare CNVs of interest
Family Proband phenotype Maternal phenotype Paternal phenotype Sibling phenotype
27696 ADHD-I;
possible seizure disorder
(No CNV) neg.
(No CNV) neg.
(No CNV) sister: LD,ODD brother: mild tics
30600 ADHD-C
BPD (@ age 15) (No CNV)
neg. (No CNV)
ADHD
(No CNV) brother: ADHD, OCD
traits
113400 ADHD-I; LD (No CNV)
depression, inattention (No CNV)
neg. (No DNA)
2 brothers: Unknown
27027 ADHD-C, SAD
(No CNV) anxiety, depression, post-traumatic stress
disorder
(CNV present) ADHD
(No CNV) Sister: neg.
27060 ADHD-I, transient tic
disorder, LD (CNV present)
Depression (No CNV)
neg.
(No DNA) Brother:
ODD traits, OCD traits
63900 ADHD-I; ODD (CNV present)
unknown (No CNV) unknown
(No DNA) 2 brothers: Unknown
19896 ADHD-HI; ODD (CNV present)
depression (No CNV)
neg.
(CNV present) Brother:
ADHD, anxiety, ODD, query Asperger’s, learning problems
19929 ADHD-I, LD (No CNV)
anxiety, depression, learning problems
(No DNA) ADHD traits, learning problems, CD, problems with law, alcohol
and drugs
No siblings
19752 ADHD, LD, GAD Major depressive episode @ age 15
(CNV present) depression, learning
problems
(No CNV) query learning problems, query
ADHD, query ODD
(No DNA) sister and brother: neg.
for both
19839 ADHD-C; LD (No CNV) Anxiety
(CNV present) neg.
(No DNA) brother: LD. (No CNV) brother: LD
22512 ADHD-C, LD (No CNV)
neg.
(CNV present) query ADHD; anxiety/depressive
traits
(No DNA) sister: ADHD traits,
anxiety trait
19365 ADHD-I, ODD, sleep
disorder (No DNA)
neg. (No CNV)
neg.
(No DNA) 2 brothers: eldest brother - ADHD, language delay
57300 ADHD-I, dysthymia, LD, Crohn's disease
(CNV present) depression
(No CNV) learning problems, ADHD
No siblings
19698 ADHD-I, SAD, LD (CNV present)
neg.
(No DNA) depression, learning problems,
problems with law, alcohol abuse, drug abuse
(CNV present) brother: LD, depressive
traits
19761 ADHD-C, LD (No CNV)
neg. (CNV present)
anxiety (as a teen) (No DNA)
sister: unknown
19812 ADHD-C (No CNV)
neg. (No DNA)
neg. (CNV present) brother: neg.
108300 ADHD-C; LD;
depressive disorder-NOS
(CNV present) anxiety, depression
(No CNV) Anxiety, depression, ADHD traits
(CNV present) Sister: ADHD traits
64
87600 ADHD-C, ODD (CNV present)
anxiety; depression; inattentive traits
(No CNV) ADHD, ODD, learning problems
No siblings
27048 ADHD-C, ODD, sleep
disorder
(CNV present) anxiety, query learning
problems
(No CNV) ADHD
(No CNV) sister: Neg.
125700 ADHD, LD (No CNV)
neg. (CNV present) ADHD traits
(No DNA) brother: LD, query ODD.
(No CNV) sister: LD
89700 ADHD- C; LD (No CNV)
anxiety; traits of hyperactivity
(CNV present) query ADHD and LD, traits of
ODD and OCD
(No DNA) brother: language delay;
learning problems, ADHD traits
27075 ADHD, LD (CNV present)
anxiety, depression (No CNV)
neg.
(No DNA) 2 sisters: anxious traits in
eldest
Abbreviations: ADHD-C: (ADHD combined type), ADHD-I (ADHD inattentive type), ADHD-HI (ADHD hyperactive/impulsive type), BPD: Bipolar disorder, CD: Conduct disorder, GAD: Generalized anxiety disorder, LD: Learning disability, OCD: Obsessive-compulsive disorder, ODD: Oppositional defiant disorder; SAD: Separation anxiety disorder. “neg.” indicates individuals who did not have a diagnosis for, or any symptoms/traits of, any neuropsychiatric disorder, “CNV present” and “No CNV” indicate presence and absence of CNV respectively as determined by qPCR or FISH testing, “No DNA” indicates samples which could not be tested for CNV status due to unavailability of DNA. “Unknown” indicates individuals whose phenotype information is not known
65
4.3.2 Loci implicated by de novo CNVs in ADHD cases
Using data from probands and both parents (trio data), the de novo CNV rate in ADHD probands
was determined to be ~2% (3/173) (Table 4.1 and Figure 4.1). De novo CNVs were validated by
either qPCR or fluorescence in situ hybridization (FISH) after confirming parentage in all trios
with array genotypes. In one family, a 33kb de novo deletion at 4q31.3 was detected in a male
proband (27696.3) with ADHD and anxiety traits who also exhibited seizure-like symptoms. The
deletion eliminates two exons of the DCLK2 (doublecortin-like kinase 2) gene, which is
expressed in both proliferating neural cells and post-mitotic neurons (Tuy et al., 2008).
Mutations in the paralogous gene DCX have been associated with epilepsy and periodic limb
movements (Parisi et al., 2010), and the mouse double-knockout model for DCX and DCLK2
exhibits spontaneous seizures and a disorganized hippocampal network with aberrant positioning
of excitatory and inhibitory neurons (Kerjan et al., 2009). The DCLK1 region was one of the
stronger signals in a GWA study of 958 ADHD probands, but did not meet genome-wide
significance (Neale et al., 2008).
In one male ADHD proband (30600.3) of a second family, a pair of adjacent de novo CNVs at
10q25.1 duplicated regions of sizes 242 kb and 318 kb, overlapping the genes SORCS3 and
SORCS1, respectively. SORCS3 and SORCS1 are paralogs encoding sortilin-related VPS10
domain-containing receptor proteins, which are involved in intracellular sorting of surface
membrane proteins and are highly expressed in the developing and mature central nervous
system (Hermey et al., 2004). Upon phenotypic follow-up it was discovered that the proband,
who had met criteria for ADHD at age 8, was later diagnosed with bipolar disorder at age 15.
Indeed, variants at SORCS2 (a paralog of SORCS3 and SORCS1 mapping to 4p16.1) have been
shown by GWA studies to be potential risk factors for both ADHD (Lesch et al., 2008) and
bipolar disorder (Baum et al., 2008; Ollila et al., 2009). Neither the proband’s father nor brother
possessed these CNVs, although both had a diagnosis of ADHD, but not bipolar disorder. Thus,
the de novo duplications may contribute to the bipolar disorder phenotype of the proband.
Lastly, a 109 kb de novo exonic deletion was detected in a male ADHD proband (113400.3)
overlapping MACROD2 at 20p12.1. Further support for the potential pathogenicity of this locus
comes from the presence of a rare, maternally inherited deletion exonic to MACROD2 and
FLRT3, a neuronal cell adhesion gene intrinsic to MACROD2, in another ADHD proband
66
(108300.3) in this study, and from reports of rare CNVs at this locus in two previous ADHD
CNV scans (Elia et al., 2010; Williams et al., 2010). While little is known about its function, the
MACROD2 gene is expressed in the brain and represented the strongest association in a recent
GWA analysis of ASD (Anney et al., 2010).
4.3.3 Rare inherited CNVs at loci previously implicated in ADHD and
other NDDs
Among rare inherited CNVs that overlapped loci previously reported in genetic studies of
ADHD (Table 4.1 and Figure 4.1) were large inherited duplications at 16p11.2 and 15q13 in
ADHD probands 19839.3 and 19752.3, respectively, consistent with reports of ADHD being a
frequent phenotypic component in patients with microdeletions and duplications at these two loci
(Miller et al., 2009; Shinawi et al., 2010). Other genes of interest implicated by overlap with rare
duplications included DRD5 (at 4p16.1) and PTPRN2 (at 7q36.3). Genetic variants within the
dopamine receptor subtype D5 gene (DRD5) have been associated with ADHD (Barr et al.,
2000b) and in this study, a duplication encompassing DRD5 was transmitted to a male proband
(27027.3) from a father having a presumptive ADHD diagnosis. The duplication at 7q36.3 was
transmitted to two brothers with ADHD (19896.3) from their mother and overlaps PTPRN2,
which encodes a protein tyrosine phosphatase. This genetic locus has been associated with
ADHD traits (Lasky-Su et al., 2008) and with behavioral and learning disturbances in mice with
deletion of the PTPRN2 homolog (Nishimura et al., 2009).
Other CNVs overlapping loci previously reported in ADHD CNV scans (Elia et al., 2010; Lesch
et al., 2011) were detected at ZBBX (two exonic deletions) (Table 4.1 and Figure 4.1), CPLX2,
CHCHD3 and 11q13.4. ZBBX was implicated previously by a de novo deletion in a male ADHD
proband (Lesch et al., 2011), and separately by a CNV gain disrupting ZBBX (Elia et al., 2010).
The observation of CNVs at Complexin 2 (CPLX2) across multiple studies is intriguing given its
co-localization with the SNAP-25 protein in the SNARE complex (McMahon et al., 1995). The
SNAP25 gene has been implicated in ADHD via candidate gene association testing (Barr et al.,
2000a; Kustanovich et al., 2003) and Cplx2 knockout mice exhibited behavioral deficits and
cognitive abnormalities (Glynn et al., 2003).
67
Overlap was also observed of rare CNVs in the ADHD cohort with those previously implicated
in other neuropsychiatric disorders, notably ASD, including deletions at genes CNTN5,
GABRG1, GCNT2 and STK32B (Table 4.1 and Figure 4.1). There is evidence from multiple
CNV studies for the involvement of contactins in neuropsychiatric disorders (Burbach and van
der Zwaag, 2009). Moreover, disruption of the GABRG1 (Vincent et al., 2006) and GCNT2 (van
der Zwaag et al., 2009) genes have previously been reported in ASD.
4.3.4 Overlap between rare CNV findings in the ADHD and ASD cohorts
To explore further the hypothesis that rare CNVs absent in controls and present in both ASD and
ADHD patients could represent putative candidates for genetic risk across both conditions, the
rare CNV findings in the ADHD cohort were compared with those in the ASD cohort described
in Chapter 3. Usage of an identical microarray platform and CNV detection methodology
(Chapter 2) for the ASD and ADHD cases and the controls allowed for a robust comparison.
Genetic loci highlighted by the occurrence of rare CNVs in both the ASD and ADHD datasets
included those previously mentioned (16p11.2 locus, MACROD2 and CHCHD3) as well as the
12q24.33 region and the X-linked DDX53/PTCHD1 locus.
The most intriguing finding was a significant enrichment of rare CNVs at 9q33.1 overlapping
ASTN2 and TRIM32 in four ASD (~1%) and two ADHD probands (~1%) (Figure 4.2) (frequency
of 6/597 in cases vs. 0/2,357 in controls - Fisher's exact test two tailed p-value = 6.68 x 10-5).
ASTN2 encodes astrotactin 2, a neuronal membrane protein exhibiting abundant expression in the
cerebellum in both the fetal and adult brain (Wilson et al., 2010). Three of the ASD probands
met criteria for ADHD as measured by the Conners’ hyperactivity questionnaire, while the fourth
showed signs of hyperactivity, but was below the threshold for ADHD diagnosis (Figure 4.2).
These findings highlight the potential contribution of this locus to the presence of ADHD
symptoms in ASD. While exceptionally rare CNVs at this locus have been reported in earlier
CNV studies of ASD (Glessner et al., 2009), bipolar disorder (Grozeva et al., 2010), intellectual
disability (Bernardini et al., 2010) and schizophrenia (Vrijenhoek et al., 2008) (Figure 4.2), this
is the first report of CNVs at this locus in ADHD. Following the publication of this ADHD study
(Lionel et al., 2011), newer studies have also identified exonic ASTN2/TRIM32 deletions in two
individuals with Tourette syndrome (Fernandez et al., 2012) and nine individuals with ID (Vulto-
van Silfhout et al., 2013)
68
Figure 4.2. Rare CNVs at the ASTN2/TRIM32 locus in ADHD and ASD probands
Overview of rare CNVs detected at the 9q33.1 locus, overlapping ASTN2 and/or TRIM32 in six unrelated male probands in this study (two ADHD and four ASD). Variants reported at this locus from other CNV studies of different neuropsychiatric disorders are also shown. The blue and red bars denote deletions and duplications, respectively. No CNVs were seen at this locus in 2,357 control individuals. Genomic coordinates and information about transcript isoforms are from Genome Build 36 (hg18). Pedigrees of four ASD probands with maternally inherited CNVs overlapping ASTN2 and/or TRIM32 are shown. Black filled symbols represent individuals with an ASD diagnosis, whereas black filled symbols with white stripes symbolize ASD probands who also met criteria for ADHD.
69
4.4 Discussion
These data delineate several rare CNVs identified in ADHD cases, which were not found in
controls, suggesting they may have a contributory role in the clinical phenotype observed in
these individuals. The rate of de novo CNVs observed in the ADHD cohort is ~1.7% (3/173),
just slightly higher than the typical ~1% that has been reported in non-disease control trios (Levy
et al., 2011; Sanders et al., 2011; Xu et al., 2008), but less than the 5 - 10% reported in ASD
(Marshall et al., 2008; Pinto et al., 2010; Sebat et al., 2007) and schizophrenia (Xu et al., 2008).
The similar rate of de novo CNV observed in ADHD cases and controls implies that de novo
events may not simply equate to pathogenicity in ADHD. However, one ADHD proband was
identified with seizure-like symptoms, who was carrying a de novo CNV disrupting DCLK2.
There was also another ADHD proband with de novo CNVs disrupting SORCS1 and SORCS3,
who developed bipolar disorder in adolescence. In both cases, the clinical presentation was
consistent with what might be predicted based on the annotated function of these genes. These
data, therefore, suggest that the contribution of de novo CNV to ADHD is likely small, but that
careful analysis of phenotype and genes affected (and comparison to other cases and controls)
may potentially be clinically revealing. Below, potential scenarios are provided based on case
descriptions for the probands carrying these two de novo CNVs (DCLK2 and SORCS1/SORCS3),
and a third case study of a male with a rare, maternally inherited X-linked CNV (at the PTCHD1
locus). These examples and others from this study support the need for both extensive and
longitudinal phenotype data including family histories in analyses such as these, particularly
when considering subjects exhibiting additional medical complications. Moreover, in all
instances, potential genotype-phenotype correlations will benefit from much larger sample sizes.
Case 1 (27696.3) was first diagnosed at age 7 with ADHD, with combined subtype and sub-
threshold anxiety symptoms that did not warrant separate diagnoses. At age 14, he continued to
show ADHD and social difficulties and the patient and his mother reported episodes during
which the patient stared blankly and was unresponsive to events going on around him, including
someone calling his own name. He also reported having unusual sensory experiences such as
hearing noises (e.g., high-pitched screeches, whispers) and smelling odors. The family was
concerned that he might have psychosis due to schizo-affective disorder in a maternal uncle and
schizophrenia plus depression in the maternal grandfather. There was no family history of
70
epilepsy or evidence of delusional or disordered thoughts, or mood symptoms. Neurological
examination and EEG found no abnormality. The patient was found to have a de novo deletion at
4q31.3 affecting DCLK2, a gene whose predicted function may account for his seizure-like
episodes.
Case 2 (30600.3), a male, was diagnosed with ADHD, combined subtype, at age 8. There were
no concerns regarding his mood. Family history was positive for ADHD in father and brother,
bipolar disorder and possible schizophrenia in a second degree relative (maternal aunt), possible
childhood schizophrenia in a maternal cousin, and alcoholism in multiple paternal and maternal
second degree relatives. At age 15 the patient was diagnosed with bipolar II disorder, after
several depressive episodes and a single manic episode characterized by inflated self-esteem,
grandiosity, hypersensitivity to environmental stimuli, rapid thought patterns, rapid speech, and
decreased need for sleep. He continued to meet diagnostic criteria for ADHD, combined subtype.
At age 18, both ADHD and bipolar disorder persisted with some escalation of impairment and
symptoms. Subsequent to the third assessment at age 18, de novo duplications at 10q25 were
identified affecting SORCS1 and SORCS3. Variants at SORCS2 have been associated with
bipolar disorder. The existence of these two CNVs in this patient may therefore have influenced
the presentation of bipolar disorder.
Case 3 (27075.3), a male, was diagnosed with ADHD at age 7. He was noted to have poor social
skills and anxious traits. He did not have a history of language delay, social isolation or
obsessive preoccupations. Subsequent to the initial assessment, a community psychiatrist
diagnosed depression, generalized anxiety disorder and learning disability. Assessment at ages
15 and 17 revealed continued ADHD and anxious traits, but no depression. His parents reported
that he preferred to be alone or with adults. Upon entering university, he began to flourish
academically but his anxiety increased with the heightened academic demands. At each
assessment point, his preference for social isolation was noted, but no formal diagnosis of ASD
was made, as he did not meet full criteria. Microarray analysis revealed a maternally inherited
deletion at the PTCHD1 locus at Xp22.11. This CNV has recently been implicated in ASD (Noor
et al., 2010) and could potentially contribute to, and explain the social interaction deficits in this
individual.
71
Overall, rare-inherited CNVs were detected at loci previously reported in ADHD or in other
NDDs in 8% of the ADHD sample, suggesting that they may be risk factors for ADHD and/or
associated neuropsychiatric phenotypes in the individuals carrying them. CNVs involving
previously implicated-ADHD loci such as DRD5 and 15q13 micro-duplications are most
compelling for further genomic analyses, but new candidate susceptibility genes such as CPLX2,
ZBBX and PTPRN2 were also discovered.
Rare-inherited CNVs were also identified in genes such as ASTN2, GABRG1 and CNTN5, which
are affected by rare CNVs in other NDDs. The apparent overlap of rare-inherited CNVs in the
ADHD cohort with those in cases with other NDDs, in particular ASD, supports the idea that
variants at a common set of genes could be involved in the etiology of several neuropsychiatric
disorders. To further explore this possibility, rare CNVs in a new cohort of unrelated individuals
having a primary diagnosis of ASD were compared to the CNV findings in the ADHD cohort.
Deletions of ASTN2 and the ASTN2-intronic TRIM32 genes yielded the strongest association
with ADHD and ASD. GWA (Lesch et al., 2008) and linkage studies (Arcos-Burgos et al., 2004;
Bakker et al., 2003; Romanos et al., 2008) provide additional evidence for involvement of the
9q33 region in ADHD. GWA studies have also suggested a role for this region in schizophrenia
(Glessner et al., 2010; Wang et al., 2010) and bipolar disorder (Wang et al., 2010).
ASTN2 has recently been shown to play a vital role in the developing mammalian brain by
forming a complex with its paralog astrotactin 1 (ASTN1) and regulating its expression on the
surface of young cerebellar neuroblasts (Wilson et al., 2010). Although ASTN2 and ASTN1 are
highly homologous, their protein products have been found to function together in a
complementary yet non-redundant manner (Wilson et al., 2010). In vitro studies, using
antibodies against ASTN1, have confirmed its role as a receptor for neuronal migration along
astroglial fibers by facilitating glial-neuron binding (Fishell and Hatten, 1991; Zheng et al.,
1996). Mice deficient in ASTN1 exhibit slowed neuronal migration, altered Purkinje cell
morphology and impairments in movement and co-ordination (Adams et al., 2002). TRIM32
encodes a TRI-partite Motif containing E3 ubiquitin ligase protein, which has been reported to
have a role in deciding the fate of neuronal stem cell lineages (Schwamborn et al., 2009).
Homozygous mutations in this gene have also been reported in a consanguineous family with
Bardet Biedl syndrome and cognitive impairment (Chiang et al., 2006). The observation of
deletions spanning both ASTN2 and TRIM32 in ASD and ADHD patients, could indicate that
72
disruption of both genes is associated with a higher risk for neuropsychiatric disorder in general
rather than risk for any specific disorder.
There has been recent debate about the possibility that intellectual disability, rather than ADHD
accounts for the large, rare variants previously observed in ADHD (Elia et al., 2011). It is
unlikely that low IQ could account for the rare CNVs reported in the current study, since ADHD
probands with below average IQ were excluded (defined as both a verbal and performance IQ
score below 80) and because the IQ of those subjects with rare CNVs did not differ significantly
from that of the total sample (Unpaired two-tailed t-test p-value = 0.07).
One intriguing finding that emerges from CNV studies of neuropsychiatric conditions such as
ADHD and ASD is that there seem to be common genes and pathways implicated in several
disorders. As detailed in Chapter 1, there is considerable clinical overlap between ADHD and
ASD. Recent genomic hypotheses put forth to explain clinical overlap and pleiotropy suggest
that different human disease-phenotype groups might arise from overlapping molecular causation
(Oti et al., 2008; Rzhetsky et al., 2007). This could involve different functional domains of a
single protein, the interaction between different proteins (such as a ligand and receptor), the
interaction of proteins in a multi-protein complex, or different steps in a cellular pathway. Crespi
and colleagues have also suggested that rare duplications and deletions at dosage sensitive genes
could be responsible for diametric models of neuropsychiatric disorders, e.g. considering
schizophrenia and autism as opposite conditions along a spectrum of social-brain phenotypes
from hypo-development in autism to hyper-development in schizophrenia (Crespi et al., 2010).
Given the extent and types of genes that were identified in this study, it is likely that some or all
of these mechanisms contribute to ADHD etiology. Moreover, all of these issues are exacerbated
in complex and apparently heterogeneous disorders like ADHD, where the genetic liability of
other risk alleles, protective genetic modifiers (both rare and common), and other factors like
gender can influence expression and penetrance of neuropsychiatric phenotypes.
The importance of obtaining detailed longitudinal phenotype data on the ADHD index cases, and
on their family members is illustrated by this work. In the three case studies (27696.3, 30600.3,
27075.3) discussed above and others described in Table 4.1, the original ascertainment diagnosis
of ADHD initially predominated the interpretation of the CNV association results, but these
interpretations were broadened upon receiving new clinical assessments as the participants
73
reached adolescence. Ascertainment issues will always present challenges in genetic studies of
neuropsychiatric disorders. One example relevant to this work is that the DSM-IV will not allow
a diagnosis of ADHD to a child with ASD, even though data suggest upwards of 50% of children
with ASD would otherwise meet criteria for ADHD. Ultimately then, these new results will serve
as a conservative starting point for much larger studies (such as the follow-up investigation of
the ASTN2/TRIM32 locus presented in Chapter 7) exploring the role of de novo and rare CNVs in
ADHD, ADHD-related disorders and other associated clinical comorbidities.
4.5 Materials and Methods
4.5.1 Study subjects in ADHD cohort
Participants were 175 boys (71%) and 73 girls (29%) of age 5 to 17 years old (mean = 9.5,
minimum = 5.6, maximum = 16.9) who were referred for assessment of attention, learning and
behavior problems to The Hospital for Sick Children, Toronto. Participants were included if they
met criteria for ADHD based on the results of a semi-structured diagnostic interview of the
participant’s parent(s) and of the proband’s teacher, and did not meet any of the exclusion
criteria specified by DSM-IV (mental retardation, pervasive developmental disorder, autism or
co-morbid psychiatric disorder that could better account for the disorder). The Parent Interview
for Child Symptoms (PICS) (Ickowicz et al., 2006) is similar to the Schedule for Affective
Disorders and Schizophrenia (KSADS) (Ambrosini, 2000) with an enhanced module for ADHD
and other disruptive behavior disorders. The PICS was conducted by a social worker with a
master of social work (MSW), a clinical nurse specialist or a clinical psychologist. Reliability of
ADHD diagnoses was assessed through videotaped interviews in 48 cases and was found to be
high (interclass correlation for total symptom score = 0.93). The Teacher Telephone Interview
(TTI) (Charach et al., 2009) is a semi-structured interview conducted by an interviewer with at
least a Master’s degree in psychology. Reliability of the TTI was assessed using audiotapes and
found to be high (interclass correlation for total symptom score = 0.93). Intelligence and
academic attainment were assessed by a clinical psychologist. To receive a best estimate
diagnosis of ADHD arrived at through consensus between the assessing psychiatrist and
psychologist, the participant had to present with impairing and developmentally atypical
symptoms before age 7, meet DSM-IV criteria based on PICS and/or TTI, exhibit evidence of
symptoms and impairment both at home and at school, not present with any of the exclusion
74
criteria for ADHD as stated in DSM-IV. Individuals were excluded if they had an IQ of less
than 80 on both the verbal and performance subscales of the WISC. The mean full-scale IQ for
the entire sample was 102.12 (SD = 12.74) and 107.57 (SD = 16.25) for the subsample with a
rare CNV of interest (Table 4.1). Presumptive psychiatric status of each proband’s parents and
siblings was established by family history. Parents and children over age 12 years provided
consent and younger participants gave assent. The protocol was approved by the Research Ethics
Board of the Hospital for Sick Children, Toronto. Microarray genotyping, CNV analysis and
prioritization were performed using the uniform methodology presented in Chapter 2.
75
Chapter 5
Pathogenic Rare Copy Number Variants in Community-Based
Schizophrenia Suggest a Potential Role for Clinical Microarrays
Parts of this chapter are adapted, with permission for use from Oxford Press, from the following
published journal article:
Costain G*, Lionel AC*, Merico D, Forsythe P, Russell K, Lowther C, Yuen T, Husted J,
Stavropoulos DJ, Speevak M, Chow EW, Marshall CR, Scherer SW, Bassett AS. Pathogenic rare
copy number variants in community-based schizophrenia suggest a potential role for clinical
microarrays. Human Molecular Genetics; 2013; 22(22):4485-501. *Joint first authors
My roles in this study were as follows: 1.Co-ordinated experimental batches for microarray
genotyping of DNA samples from schizophrenia patients; 2. Performed quality control of raw
microarray data from cases and controls as described in Chapter 2; 3.Designed and implemented
uniform CNV analysis strategy for cases and controls; filtered and curated final rare CNV dataset
used for all downstream analysis in the study; 4.Assisted with other aspects of data analysis and
interpretation (including annotation of genetic results, CNV burden calculation, interpretation of
gene-set enrichment analysis, and prioritization of specific CNV regions for follow-up in
additional control datasets); 5. Prioritized rare CNV loci for qPCR validation, coordinated and
interpreted results of validation experiments; 6. Drafted and revised the manuscript.
The roles of Dr. G. Costain were as follows: 1. Assisted with primary data collection (recruiting
subjects, performing comprehensive phenotypic assessments, and arranging for the collection of
blood or saliva samples for DNA extraction); 2. Checked, verified, and updated clinical data
(including but not limited to diagnosis, age at onset, family history, and syndromal status) and
assisting with manual curation of CNV data; 3. Created Table 5.1, Table 5.2, and Table 5.3, and
performing the systematic review of all genes included in these tables (>800 in total); 4. Assisted
with and coordinated other aspects of data analysis (including but not limited to the relatedness
calculations, quantitative burden analyses, and the blinded review of all large rare CNVs); 5.
Assisted with data interpretation (particularly in relation to findings concerning the burden of
large, rare CNVs and smaller CNVs implicating candidate genes for schizophrenia); and, 6.
Drafted and revised the manuscript.
76
77
5.1 Abstract
Individually rare, large copy number variants (CNVs) contribute to genetic vulnerability for
schizophrenia. Unresolved questions remain, however, regarding the anticipated yield of clinical
microarray testing in schizophrenia. Using high resolution genome-wide microarrays and
rigorous methods, we investigated rare CNVs in a prospectively recruited community-based
cohort of 459 unrelated adults with schizophrenia and estimated the minimum prevalence of
clinically significant CNVs that would be detectable on a clinical microarray. Blinded review by
two independent clinical cytogenetic laboratory directors of all large (>500 kb) rare CNVs in
cases and well-matched controls showed that those deemed clinically significant were highly
enriched in schizophrenia (16.4-fold increase, p<0.0001). In a single community catchment area,
the prevalence of individuals with these CNVs was 8.1%. Rare 1.7 Mb CNVs at 2q13 were
found to be significantly associated with schizophrenia for the first time, compared with the
prevalence in 23,838 population-based controls (42.9-fold increase, p=0.0002). Additional novel
findings that will facilitate the future clinical interpretation of smaller CNVs in schizophrenia
include: (i) a greater proportion of individuals with two or more rare exonic CNVs >10 kb in size
(1.5-fold increase, p=0.0134) in schizophrenia; (ii) the systematic discovery of new candidate
genes for schizophrenia; and (iii) pathway enrichment analysis highlighting a differential impact
in schizophrenia of rare exonic deletions involving diverse functions, including
neurodevelopmental and synaptic processes (4.7-fold increase, p=0.0060). These findings
suggest consideration of a potential role for clinical microarray testing in schizophrenia, as is
now the suggested standard of care for related developmental disorders like autism.
78
5.2 Introduction
Schizophrenia is a complex neuropsychiatric disease that affects up to 1% of the general
population and shows evidence for neurodevelopmental origins and genetic heterogeneity
(Costain and Bassett, 2012). There is compelling evidence that individually rare, large copy
number variants (CNVs), especially 22q11.2 microdeletions, play a role in the genetic causation
of schizophrenia (Bassett et al., 2010b). As for other major NDDs, such as autism and
intellectual disability, findings from recent genome-wide case-control studies of CNVs support a
multiple rare variant model of causation in schizophrenia (Bassett et al., 2010b; Cook and
Scherer, 2008; Costain and Bassett, 2012). In general, rare and large (e.g., >500 kb) CNVs, often
overlapping multiple genes, are more likely to have a clinically relevant phenotype (Cook and
Scherer, 2008). In schizophrenia, unresolved questions remain regarding the anticipated yield
today on clinical microarray testing of large rare CNVs of potential clinical relevance (Bassett et
al., 2010b; Cooper et al., 2011). Most case-control sampling schemes to date have not been
epidemiologic in nature, and therefore cannot provide reliable estimates (Bassett et al., 2010b).
This uncertainty about yield may contribute to the low rate of uptake of clinical genetic testing in
schizophrenia: only 5 of 38,779 samples processed at Signature Genomics were from patients
with this common and serious mental illness (Sahoo et al., 2011).
Smaller rare CNVs may implicate individual risk genes for schizophrenia (Lionel et al., 2013;
Rujescu et al., 2009), but the clinical interpretation of these variants is more challenging. A
practical consideration for an adult-onset condition like schizophrenia is the typical absence of
parental samples to determine de novo / inheritance status. High resolution genome-wide data
and systematic approaches (both quantitative and descriptive/qualitative) to identify putative
candidate genes are needed to inform a more sophisticated approach to the clinical assessment of
CNVs in schizophrenia. The existence of overarching genetic networks, whose disruption may
contribute to expression of schizophrenia, is a particularly tantalizing possibility (Kirov et al.,
2012; Raychaudhuri et al., 2009).
We prospectively ascertained and systematically assessed a community-based sample of
Canadian patients with schizophrenia to address the above issues. All subjects underwent direct
clinical screening assessments for potential syndromic features using a standardized protocol that
included review of available lifetime medical records and assessment of physical features
79
(Bassett et al., 2010a; Fung et al., 2008; Silversides et al., 2012). Cases were compared to an
unrelated epidemiological control sample of comparable (European) ancestry (Ontario
Population Genomics Platform; OPGP) (Silversides et al., 2012). We used a high resolution
genome-wide microarray and proven analytic methods for CNV detection and evaluation (Figure
5.1) (Lionel et al., 2011; Marshall et al., 2008; Pinto et al., 2010; Silversides et al., 2012). An
independent, blinded review process identified large rare CNVs deemed "Pathogenic" or of
"Uncertain clinical significance; likely pathogenic" in cases and controls, based on well-
established American College of Medical Genetics (ACMG) guidelines for CNV interpretation
(Kearney et al., 2011). A representative epidemiologic subsample from a single catchment area
(Bassett et al., 2010a) allowed us to estimate for the first time the minimum collective prevalence
of individuals with large, clinically significant CNVs in schizophrenia. To shed light on the
contribution of smaller CNVs to the genetic architecture of schizophrenia, we investigated the
total burden of such CNVs and the potential support for a “multiple hit” model of causation. We
also developed a systematic approach for the discovery of new candidate genes for
schizophrenia, which included pathway enrichment analysis to identify relevant genetic
networks.
80
Genotyping on Affymetrix 6.0 SNP array
CNV detection
• Comprehensive examination of genes for evidence of relevant expression in human and model animal (mouse, zebrafish) systems
• Overlap with previous genetic findings in SZ• Recurrent and overlapping rare CNVs identifying familiar and novel SZ risk genes• Experimental validation of CNVs (quantitative PCR or clinical microarray)
454 discovery sample SZ cases 416 OPGP controls
Stringent CNVs (SZ cases, OPGP controls)
CNV frequency (rarity) assignment using data from 2,357 independent controls2
CNVs present in > 0.1 % controls2
(<50% unique sequence)
Single algorithm only< 5 consecutive probes
< 10 kb in size
459 adult cases with schizophrenia (SZ)
5 cases with 22q11.2 deletions
Global CNV burden
Stringent rare CNVs (SZ cases, OPGP controls)
Rare CNVs present in both SZ and OPGP control datasets
Stringent very rare CNVs in SZ cases only3
Stringent rare CNV burden analysis1
(SZ vs. OPGP and syndromic SZ vs. non-syndromic SZ)
1CNV burden analysis focused on comparing the subset of unrelated SZ cases of European ancestry (n = 420) with ancestry-matched unrelated adult OPGP controls (n=416).2Population-based adult controls of European ancestry (n=2,357), used in previous studies of autism and attention deficit disorder and independent to OPGP controls, were genotyped on the Affymetrix 6.0 platform and analyzed for CNVs in an identical manner to the SZ cases and OPGP controls.3Included CNVs 4–10 kb in size (n=7) that overlapped CNVs > 10kb in size overlapping SZ candidate genes
CNVs present in any control individual2
(<50% unique sequence)Stringent very rare CNVs (SZ cases, OPGP controls)
Figure 5.1. Overview of study design and workflow
81
5.3 Results
5.3.1 Clinically significant and novel large rare structural variants in schizophrenia
Two clinical cytogenetic laboratory directors conducted independent blinded examinations of all
rare (present in < 0.1% of 2357 population controls) CNVs 500 kb–6.5 Mb in size in
schizophrenia cases of European ancestry and OPGP controls. This revealed a significant
enrichment in schizophrenia of individuals with ‘Pathogenic’ or ‘Uncertain clinical significance;
likely pathogenic’ (collectively termed ‘clinically significant’) large rare autosomal CNVs [16 of
420 versus 1 of 416; P < 0.0001, odds ratios (OR) 16.44 (95% CI 2.17–124.51)]. In
schizophrenia cases, there were 16 CNVs considered clinically significant at eight loci (Table
5.1). All were very rare (i.e., present in none of 2357 population controls used to adjudicate both
the schizophrenia case and OPGP control samples), including six CNVs at four loci novel to
schizophrenia: 2q13 (Yu et al., 2012), 3q13.31 (Molin et al., 2012), 5p15.33-p15.32 (Zhang et
al., 2005) and 10q11.22-q11.23 (Stankiewicz et al., 2012). None of the corresponding individuals
with schizophrenia had a pediatric diagnosis of moderate or severe intellectual disability,
multiple major congenital anomalies, epilepsy or autism. Notably, five typical 2.6 Mb 22q11.2
deletions in cases (none in controls) were a priori excluded to demonstrate that these findings are
not being driven by those established variants. The single large rare CNV in OPGP control
individuals deemed to be clinically significant was a typical 2.6 Mb 22q11.2 duplication. There
were no significant differences in demographics, years of education, age at onset, family history
of schizophrenia, self-reported history of special education or syndromic designation in these
cases with large, rare, clinically significant CNVs when compared with subjects with no large
rare CNVs 500 kb–6.5 Mb in size. There were also eight very large (>6.5 Mb) anomalies in
schizophrenia cases (none in OPGP controls) that were considered clinically significant (Table
5.1), including a 10 Mb loss (deletion) overlapping FOXP2 (Feuk et al., 2006).
A novel finding was the detection in three of 420 schizophrenia cases of European ancestry of
very rare, recurrent 1.7 Mb CNVs at 2q13 (Table 5.1), a significant enrichment compared with
the prevalence (n = 4) in 23 838 population-based controls (Costain et al., 2013) [P = 0.0002; OR
42.87 (95% CI 9.56–192.14)]. To our knowledge, this CNV has not been previously reported in
other genotyped schizophrenia cohorts [e.g., the International Schizophrenia Consortium dataset
82
(International Schizophrenia Consortium, 2008)]. None of the three individuals in our case
sample with these 2q13 CNVs had any of the pediatric diagnoses [developmental delay, multiple
congenital anomalies and/or autism spectrum disorder (ASD)] previously reported for samples
assessed at clinical laboratories (Cooper et al., 2011; Yu et al., 2012). Two of the three
individuals reported a history of learning difficulties in school, but all had IQs within the average
range. For the one schizophrenia proband with a positive family history, we were able to confirm
co-segregation of the 2q13 duplication with schizophrenia. Of the 10 genes overlapped by these
2q13CNVs, three are promising candidates for schizophrenia: the neurodevelopmental facilitator
ANAPC1 (Paridaen et al., 2009), the neuronal apoptosis regulator BCL2L11 (Youle and Strasser,
2008), and the TAM receptor component and multiple sclerosis risk gene MERTK (Binder and
Kilpatrick, 2009) (Table 5.1).
5.3.2 Prevalence of clinically significant CNVs in community sample of schizophrenia
We used our schizophrenia catchment sample to estimate the minimum prevalence of large (500
kb–6.5 Mb) rare clinically significant CNVs that would be detectable on a clinical microarray. In
the 248 unrelated case subjects in this catchment area with CNV data, the number of individuals
with these clinically significant CNVs, including two with 22q11.2 deletions, was 15 (6.0%, 95%
CI 3.7–9.8%) (Table 5.1). Nine (60.0%) of these 15 individuals were in the non-syndromic
subgroup (i.e., were a priori found to not meet our established criteria for syndromic features). If
the five anomalies >6.5 Mb were included (Table 5.1), the estimated prevalence would be 8.1%
(95% CI 5.2–12.2%). Notably, none of these 20 variants had been detected prior to study
participation. We estimate that there are a total of 370 unrelated adults with schizophrenia in this
catchment area (i.e., n = 122 not included in this study, in addition to the n = 248 with CNV
data). A conservative estimate of the minimum prevalence of clinically significant CNVs in this
catchment area, based on the assumption of no clinically significant variants in the estimated n =
122 unrelated individuals unavailable for study, would thus be 20 in 370 or 5.4% (95% CI 3.5–
8.3%).
5.3.3 Large rare CNVs of unknown significance in schizophrenia
Table 5.2 shows the remaining large rare CNVs in schizophrenia cases deemed to be of
‘Uncertain clinical significance (no sub-classification)’ (termed ‘variants of unknown
83
significance’; VUS), or of ‘Uncertain clinical significance; likely benign’ or ‘Benign’
(collectively termed ‘benign’). Of these, VUS [33 of 420 versus 12 of 416; P = 0.0012, OR 2.87
(95% CI 1.46–5.64)], but not benign CNVs, were significantly enriched in schizophrenia cases
of European ancestry compared to OPGP controls. There were three novel loci where there were
overlapping large rare CNVs in unrelated schizophrenia cases: 6q11.1 (two gains), 7p21.3 (one
gain, one loss) and 12q21.31 (one gain, one loss) (Table 5.2). All were VUS, except for the
smaller of the 6q11.1 gains (Table 5.2), making the latter a less likely candidate region for
schizophrenia. The 12q21.31 CNVs overlap LIN7A, an intriguing candidate gene (Figure 5.2)
that interacts with DLG1, DLG2 and GRIN2B, and is implicated in postsynaptic density
functions (Kirov et al., 2012). There was also a 2.4 Mb gain at 15q12–q13.1 (a VUS), nested
within the typical 15q11–q13 duplication region, that overlapped just five genes, including three
gamma-aminobutyric acid (GABA) receptor component genes (Table 5.2; Figure 5.2) (Bassett,
2011). Other large rare CNVs in single case subjects overlapped CNVs previously reported in
schizophrenia or other neuropsychiatric disorders, involving genes of interest including RYR2,
MYT1L, LRP1B, IL1RAPL1 and ZNF804A (Table 5.2) (Kirov et al., 2012; Melhem et al., 2011;
Steinberg et al., 2011; Vrijenhoek et al., 2008; Xu et al., 2008). Interestingly, several genes we
had annotated as candidates were subsequently implicated in recent next-generation sequencing
studies of schizophrenia (Need et al., 2012; Xu et al., 2011) (Table 5.2).
5.3.4 Genome-wide CNV burden in schizophrenia
The proportion of subjects with one or more large (500 kb–6.5 Mb), rare (present in <.1% of
population controls) autosomal CNVs was significantly greater in the schizophrenia cases than in
the OPGP controls [62 of 420 versus 21 of 416; P < 0.0001, OR 3.26 (95% CI 1.95–5.45)]
(Table 5.3). As described above, this finding was driven by clinically significant CNVs and
VUS, and not benign variants. We a priori excluded 22q11.2 deletions and variants > 6.5 Mb
from these to demonstrate that this enrichment is robust to the exclusion of variants that might
reasonably be discovered without the use of chromosomal microarray technology. Case–control
differences were most marked for large rare exonic gains [38 of 420 versus 11 of 416; P <
0.0001, OR 3.66 (95% CI 1.85–7.27)] (Table 5.3). Within the schizophrenia sample, the
subgroup a priori designated as ‘syndromic’ using previously published clinical screening
criteria for adults (Fung et al., 2008; Silversides et al., 2012) was significantly enriched for
subjects with one or more large rare CNVs only when restricting to exonic losses [7 of 73
84
syndromic cases versus 12 of 347 non-syndromic cases; P = 0.0220, OR 2.96 (95% CI 1.12–
7.80)] (Table 5.3).
The schizophrenia group was also significantly enriched for subjects with two or more rare
CNVs > 10 kb in size that overlapped exons [122 of 420 versus 89 of 416; P = 0.0109, OR 1.50
(95% CI 1.10–2.06)] (Table 5.3). Further restricting to very rare CNVs (i.e., those found in none
of the 2357 adjudication controls) strengthened these results [78 of 420 versus 44 of 416; P =
0.0012, OR 1.93 (95% CI 1.30–2.87)]. In contrast, as expected (6,18,19), the overall CNV
profile (unrestricted, e.g., by rarity or size) was similar for schizophrenia cases and OPGP
controls. The results were also non-significant for subjects with one or more, or with two or
more, rare CNVs unrestricted by exonic status (Table 5.3). The results for all analyses were
similar if subjects of non-European ancestry were included.
5.3.5 Very rare, smaller CNVs identifying genes of interest in schizophrenia
Table 5.4 shows 15 candidate genes identified, independent of gene enrichment mapping, using a
strategy to determine very rare CNVs that overlapped the same gene in two or more
schizophrenia cases and in no controls. Five unrelated schizophrenia cases had loss CNVs
overlapping the RBFOX1 gene (previously A2BP1) (Melhem et al., 2011) and four had intronic
loss CNVs overlapping the SOX5 gene (Walsh et al., 2008). Arguably, the most persuasive of
candidates may involve genes of smaller genomic extent (< 200 kb), where CNVs overlap exons;
five genes met these conservative criteria: DNM1L, HIST3H3, JAK2, LIMS1 and PPP3CC.
Singleton very rare CNVs (Table 5.4) implicated other potential candidates, including DISC1,
GRK4, GRM4, PIK3C3 and RELN with overlapped exons, GRIK1, GRIN2A, and GRM7, with
introns overlapped only, and individual genes within large CNVs (Tables 5.1 and 5.2), e.g.,
IL1RAPL1 and TRPM1 (Melhem et al., 2011; Xu et al., 2008). Several of these candidates
(DISC1, IL1RAPL1, JAK2, PIK3C3, RELN and TRPM1) converged with functional gene
enrichment mapping results using loss CNVs (Figure 5.2). There was also convergence with
multiple genes implicated in two recent next-generation sequencing studies of schizophrenia
(Need et al., 2012; Xu et al., 2011), including 10 genes (CACNA2D1, GRIN2A, GRM7, JAK2,
NRXN1, PIK3C3, PTGER3, PTPRT, SLC1A1 and USH2A) implicated by multiple rare single
nucleotide variants (SNVs) (Need et al., 2012), and one gene (PTPRM) implicated by a de novo
rare SNV (Xu et al., 2011) (Table 5.4).
85
5.3.6 Functional networks revealed by pathway enrichment analysis
Figure 5.2 summarizes the pathway enrichment analysis results. For 70 (2.9%) of 2456 pathways
tested (Silversides et al., 2012), we found a significant enrichment of very rare, exonic loss
CNVs in schizophrenia cases compared with OPGP controls (OR 1.9, 95% CI 1.0–3.6),
involving 73 support genes (Figure 5.2). Considering just the three functional clusters
comprising eight pathways most clearly related to neurodevelopmental and synaptic processes
(Figure 5.2), the effect was greater (OR 4.7, 95% CI: 1.3–26.0), involving 19 support genes for
schizophrenia (Figure 5.2). The other clusters having a less obvious functional scope may
suggest more novel aspects of a gene network for schizophrenia (Figure 5.2). Notably, there was
a significant degree of functional cluster overlap in a formal cross-disease comparison of this
schizophrenia network with known genes for ASD (Figure 5.3). This included not only the eight
pathways annotated as neurodevelopmental and synaptic, but also novel clusters of pathway
enrichment analysis involved in cell motility, signal transduction and transcription (Figure 5.3).
86
Table 5.1. Very rare, clinically significant CNVs in unrelated schizophrenia probands
Subject CNV characteristics Candidate gene(s)
Case
# Catchment
area
Cytoband Start Size (bp) CN Flanking segmental
duplications
# of genes
Large, very rare CNVs <6.5 Mb in size, excluding 22q11.2 deletionsb (n=16 subjects)
2 ● 1q21.1 144,472,163 1,839,252 gGain ● 16
BCL9, GJA5, GJA8, PDZK1e,f, PRKAB2
3 ● 1q21.1 144,643,825 1,653,983 gGain ● 14
8 ● 2q13 111,105,101 1,727,363 Loss ● 10
ANAPC1, BCL2L11, MERTK
7c ● 2q13 111,105,101 1,727,363 Gain ● 10
9 2q13 111,105,101 1,727,363 Gain ● 10
13c ● 3q13.31 115,308,450 2,062,410 Loss 7 DRD3, GAP43, LSAMP, ZBTB20
17 ● 5p15.33-p15.32 1,864,574 3,687,431 Loss 9 IrxA cluster (IRX1, IRX2, IRX4), NDUFS6
40c ● 10q11.22-q11.23 45,905,767 5,423,684 Gain ● 52 CHAT, ERCC6, GDF2, GPRIN2, MAPK8,
SLC18A3
48 ● 15q11-q13 20,224,763 6,498,447 dGain ● 115
GABA receptor gene clusterf (GABRB3, GABRA5, GABRG3), MAGEL2, NDN, UBE3A
49 ● 15q11-q13 21,192,955 5,680,224 dGain ● 106
50 ● 15q11-q13 21,192,955 5,015,049 dGain ● 101
52 ● 15q13.2-q13.3 28,608,929 1,690,584 Loss ● 10 CHRNA7f, TRPM1f
87
55c ● 16p11.2 29,474,810 624,599 Gain ● 28
DOC2A, MAPK3, PRRT2, QPRT, SEZ6L2, TBX6
57c 16p11.2 29,474,810 624,599 Gain ● 28
56 ● 16p11.2 29,474,810 659,635 Gain ● 38
58 16p11.2 29,474,810 659,635 Gain ● 38
Very rare CNVs >6.5 Mb in size (n=8 subjects; one subject also appears above)
247c 6p25.3-p25.1 94,661 6,836,704 gLoss 41 FOXC1, GMDS, NRN1, TUBB2B
206 ● 7q22.2-q31.1 105,304,955 10,037,597 Loss ● 37 COG5, DOCK4, FOXP2, GPR85, IMMP2L,
LAMB1, NRCAM, PIK3CG, PNPLA8
115c ● 8p23.3-p23.1 148,062 6,828,275 gLoss ● 25 ANGPT2, CLN8, CSMD1f, DLGAP2, MCPH1
277c ● 19p13.3-p13.2 2,705,548 9,595,341 gGain 280 Various, including DNMT1, DOCK6
173 ● X chr (47, XXX) - - Gain - 975
Various, including IL1RAPL1f, SYN1
278 X chr (47, XXX) - - Gain - 975
57c X chr (47, XXY) - - Gain - 975
279c ● Y chr (47, XYY) - - gGain - 34 IL9R, SPRY3, SRY, TSPY, UTY, VAMP7
Case #, case number for subjects (n=454) with schizophrenia (n=2 were not of European ancestry: cases 278, 279); Catchment area, subjects originating from the only community mental health clinic in a specific catchment area of ~150,000 people (�), see text for details; Cytoband, cytogenetic location of CNV; CNV start, hg18 (NCBI Build 36.1, March 2006); CNV size, in base pairs; CN, type of copy number aberration; Flanking segmental duplication(s), known flanking segmental duplication(s) (from the UCSC Genome Browser hg18 version) that cover at least 20% of the CNV length (�); # of genes, number of known genes overlapped by CNV as annotated in the Database of Genomic Variants (http://projects.tcag.ca/variation/; September 2011); Candidate gene(s), selected based on reported neuropsychiatric/neurodevelopmental phenotype identified from systematic searches of human (e.g., Online Mendelian Inheritance in Man; http://www.omim.org/) and model organism (e.g., Mouse Genome Informatics; http://www.informatics.jax.org/) databases (for CNVs overlapping >250 genes, all genes were not systematically searched). aCNVs assessed independently by two clinical cytogenetic lab directors to be "Pathogenic" or of "Uncertain clinical significance; likely pathogenic" according to established guidelines (Kearney et al., 2011). bRecurrent 1.5 to 3 Mb 22q11.2 deletions (n=5, all meeting syndromic criteria) are not shown; of these, 2 are from the community catchment population, and 1 is not of European ancestry. Bolded cytoband indicates a novel association with schizophrenia cMet criteria for syndromic subgroup
88
dMaternal origin of 15q11-q13 gain per results from Multiplex Ligation-dependent Probe Amplification analysis to determine methylation status of the imprinted gene SNRPN eGene overlapped by 1.8 Mb gain in Case 2 only fGene(s) overlapped by one or more rare CNVs in other unrelated probands with schizophrenia in our sample: PDZK1, 826 kb exonic gain; GABA receptor gene cluster, 2.4 Mb exonic gain; CHRNA7, 54 kb exonic loss; TRPM1, 28 kb exonic gain (also implicated in a previous CNV study of schizophrenia (Kirov et al., 2009); CSMD1, 65 kb exonic, and 13 kb, 17 kb, 92 kb, and 117 kb intronic, losses (also implicated in previous CNV studies of schizophrenia (Kirov et al., 2012; Melhem et al., 2011); IL1RAPL1, 3.1 Mb exonic loss and 60 kb intronic loss (also implicated in a previous CNV study of schizophrenia (Melhem et al., 2011). gPreviously reported by our group: 1q21 gains (Dolcetti et al., 2013), 6p25 loss (Caluseriu et al., 2006), 8p23 loss (Bassett et al., 2010a), 19p13 gain (Bassett et al., 2010a), XYY (Bassett et al., 2010a).
89
Table 5.2. Large rare CNVs of uncertain pathogenicity in unrelated schizophrenia probands
Subject CNV characteristics Candidate gene(s)
Case
# Catchment
area
Cytoband Start Size (bp) CN Very rare
Flanking segmental
duplications
# of genes
VUS
1 1q21.1 143,677,127 826,295 Gain ● 21 ● PDE4DIPe, PDZK1c, PEX11B
4 1q43 235,349,594 726,228 Gain ● 1 ● RYR2b
5 ● 2p25.3 1,377,419 745,146 Gain ● 3 ● MYT1Lb
6 2q11.1 94,691,614 735,772 Gain ● ● 8 ● KCNIP3
10 ● 2q22.1 139,898,997 1,322,193 Loss ● 1 ● LRP1Bb,e
11 2q32.1 185,043,646 790,869 Loss ● 1 ● ZNF804Ac
12 3p12.1 83,940,054 1,038,151 Loss 1 ● -
14 ● 4q25-q26 113,634,704 783,442 Gain ● 10 ● ANK2e, NEUROG2
15 ● 4q33-q34.1 171,556,613 2,693,129 Loss ● 1 ● -
16 ● 4q34.3-q35.1 179,878,817 4,343,697 Gain ● 6 ● ODZ3
18 ● 5p15.1 15,134,588 628,993 Gain ● 1 ● -
19 5p13.2 37,294,917 520,350 Gain 2 ● -
20 5q13.3-q14.1 74,706,210 2,764,437 Gain ● 20 ● OTP, PDE8B, SV2Ce
22 ● 6q11.1 61,944,399 1,031,545 Gain ● 2 ● KHDRBS2, MTRNR2L9
23 6q11.1 61,944,399 502,986 Gain 1 MTRNR2L9
24 ● 6q14.3 86,804,893 577,156 Gain ● 0 -
25 ● 6q16.2-q16.3 98,969,135 1,408,033 Gain ● 9 ● POU3F2
26 ● 7p21.3 9,580,685 819,384 Loss 1 ● PER4
27 ● 7p21.3 9,573,572 1,080,196 Gain ● 1 ●
28 7q31.31 118,515,093 1,204,095 Gain 1 ● KCND2
29 ● 8p23.3 439,294 733,309 Gain ● ● 2 ● -
30 8p11.21-p11.1 43,132,979 772,981 Gain 2 HGSNAT
90
31 8q12.1 60,901,679 766,789 Gain ● 2 ● CA8, RAB2A
32 ● 8q21.3 89,566,045 1,073,381 Gain ● 0 -
33 9p21.1 30,791,693 549,623 Loss ● 0 -
34 9p13.3-p13.2 36,173,229 862,436 Loss ● 5 ● CLTA, GNE, PAX5e
35 9p12 41,440,153 535,035 Gain ● ● 6 -
36 ● 9p11.2 44,936,301 570,215 Gain ● ● 0 -
37 9q12 65,370,766 902,773 Loss ● ● 1 -
38 ● 9q21.11-q21.12 72,107,555 964,557 Loss ● 4 ● KLF9, TRPM3e
39 ● 10p11.22 33,636,052 651,356 Gain ● 1 ● NRP1
42 ● 11q22.1 97,224,044 626,127 Loss ● 0 -
43 ● 12p11.1 34,206,228 539,538 Gain 0 -
39 ● 12q21.31 79,255,527 568,008 Gain ● 6 ● LIN7A
44 ● 12q21.31 79,679,461 1,397,367 Loss ● 5 ●
45 13q13.1-q13.2 32,108,632 2,434,026 Loss ● 5 ● KLe, NBEAe, PDS5B
46 ● 13q21.32-q21.33 66,742,848 993,434 Gain ● 0 -
47 14q21.1-q21.3 40,497,844 2,771,925 Gain ● 1 ● LRFN5
51 ● 15q12-q13.1 23,766,255 2,437,700 Gain ● ● 5 ● GABRB3c, GABRA5c, GABRG3c
36 ● 15q13.2 28,153,539 564,254 Gain ● ● 7 CHRFAM7A
53 16p13.11 14,805,302 1,498,712 Gain ● 24 ● NDE1, NOMO3, NTAN1
54 16p13.11 15,032,942 1,388,198 Gain ● 20 ●
59 ● 16p11.1-p11.2 34,324,072 816,656 Gain 4 -
60 17p13.1 10,552,154 607,279 Gain ● 5 ● -
61 ● 19q12 32,648,879 2,916,074 Loss ● 10 ● C19orf12e, ZNF536
62 20p12.1 14,305,685 1,108,941 Loss ● 1 ● MACROD2b,c
63 ● 22q12.1 25,696,811 1,304,432 Loss ● 6 ● PITPNB
64d ● Xp21.3-p21.2 27,619,068 3,129,173 Loss ● ● 11 ● IL1RAPL1b,c
65 Xq11.1 63,283,656 721,753 Gain ● ● 3 ● -
66 ● Xq21.31 86,334,886 839,884 Gain ● 1 ● -
91
Case #, case number for subjects (n=454) with schizophrenia (n=6 were not of European ancestry: Cases 35, 37, 43, 60, 278, 279); Catchment area, subjects originating from the only community mental health clinic in a specific catchment area of ~150,000 people (�), see text for details; Cytoband, cytogenetic location of CNV; CNV start, hg18 (NCBI Build 36.1, March 2006); CNV size, in base pairs; CN, type of copy number aberration; Very rare, not found in 2,357 population controls and absent in OPGP controls (�), see text for details; Flanking segmental duplication(s), known flanking segmental duplication(s) (from the UCSC Genome Browser hg18 version) that cover at least 20% of the CNV length (�); # of genes, number of known genes overlapped by CNV as annotated in the Database of Genomic Variants (http://projects.tcag.ca/variation/; September 2011); VUS, variant of unknown significance (�) as assessed independently by two clinical cytogenetic lab directors (variants not so annotated were considered likely benign and not reportable clinically); Candidate gene(s), selected based on reported neuropsychiatric/neurodevelopmental phenotype identified from systematic searches of human (e.g., Online Mendelian Inheritance in Man; http://www.omim.org/) and model organism (e.g., Mouse Genome Informatics; http://www.informatics.jax.org/) databases. There were no such candidate genes identified for 11 genic CNVs. aFive large CNVs in schizophrenia cases of European ancestry that were present in the OPGP controls, using a 50% reciprocal overlap criterion are not shown: 3p14.2 gain in Case 263, 9q12 loss in Case 172, 11q11 gain in Case 41, and two 14q32.33 losses in Cases 156 and 280 bGene implicated in a previous CNV study of schizophrenia: RYR2 (Kirov et al., 2012), MYT1L (Vrijenhoek et al., 2008), LRP1B (Xu et al., 2008), ZNF804A (Steinberg et al., 2011), MACROD2 (Kirov et al., 2012), IL1RAPL1 (Melhem et al., 2011) cGene(s) overlapped by one or more additional rare CNVs in unrelated probands with schizophrenia in our sample: PDZK1, 1.8 Mb exonic gain (Table 5.1); GABA receptor gene cluster (GABRB3, GABRA5, GABRG3), 5.0 Mb, 5.7 Mb, and 6.5 Mb exonic gains (Table 5.1); MACROD2, 14 kb gain and 15 kb, 25 kb, and 63 kb losses; IL1RAPL1, X chromosome aneuploidies (Table 5.1) and 60 kb intronic loss dFemale subject, therefore judged to be a VUS instead of "Uncertain clinical significance; likely pathogenic" eGene implicated in a previous next-generation sequencing study of schizophrenia: PDE4DIP (Xu et al., 2011), LRP1B (Need et al., 2012), ANK2 (Need et al., 2012), SV2C (Need et al., 2012), PAX5 (Need et al., 2012), TRPM3 (Need et al., 2012), KL (Need et al., 2012), NBEA (Need et al., 2012), C19orf12 (Need et al., 2012).
92
Table 5.3. aRare autosomal CNV burden in 420 unrelated schizophrenia probands Schizophrenia cases vs. OPGP controls Schizophrenia cases
OPGP controls
All schizophrenia cases
Analysis Non-syndromic
cases Syndromic
cases Analysis
(n=416) (n=420) (n=347) (n=73) n (%) n (%) p OR (95% CI) n (%) n (%) p OR (95% CI)
Large rare CNVs (>500 kb)
Subjects with one or more large rare CNVs
Loss or gain 21 (5.05) 62 (14.76) <0.0001 3.26 (1.95) (5.45) 46 (13.26) 16 (21.92) NS 1.84 (0.97) (3.47) Exonic loss or gainc 17 (4.09) 57 (13.57) <0.0001 3.69 (2.11) (6.45) 42 (12.10) 15 (20.55) NS 1.88 (0.98) (3.61) Exonic lossc 7 (1.68) 19 (4.52) 0.0180 2.77 (1.15) (6.66) 12 (3.46) 7 (9.59) 0.0220 2.96 (1.12) (7.80) Exonic gain 10 (2.40) 38 (9.05) <0.0001 4.04 (1.98) (8.22) 30 (8.65) 8 (10.96) NS 1.30 (0.57) (2.97)
All rare CNVs (any size)
Subjects with one or more rare CNVs
Loss or gainb 372 (89.42) 376 (89.52) NS 1.01 (0.65) (1.57) 310 (89.34) 66 (90.41) NS 1.12 (0.48) (2.63) Exonic loss or gainc 249 (59.86) 264 (62.86) NS 1.14 (0.86) (1.50) 217 (62.54) 47 (64.38) NS 1.08 (0.64) (1.83)
All rare CNVs (any size)
Subjects with two or more rare CNVs
Loss or gainb 264 (63.46) 282 (67.14) NS 1.18 (0.88) (1.56) 232 (66.86) 50 (68.49) NS 1.08 (0.63) (1.85) Exonic loss or gainc,d 89 (21.39) 121 (28.81) 0.0134 1.49 (1.08) (2.04) 97 (27.95) 24 (32.88) NS 1.26 (0.73) (2.17)
aRare = CNVs with <0.1% prevalence in 2,357 population controls of European ancestry (used to adjudicate both schizophrenia cases and OPGP controls). Rare CNVs <10 kb or >6.5 Mb in size, 22q11.2 deletions and all sex chromosome CNVs were excluded CNV burden was measured using proportion of subjects with one or more, or with two or more, rare CNVs; measuring rare CNV burden (all sizes) for cases and controls using total number of genes overlapped by CNVs or total genomic extent of CNVs showed similar results. bResults were similarly non-significant for rare CNVs involving losses only and gains only, and for non-exonic losses and non-exonic gains cFive syndromic subjects of European ancestry in the schizophrenia group with typical 1.5 to 3 Mb 22q11.2 deletions (i.e., exonic losses) were not included in the study. NS = non-significant (p>0.05). Significant results are shown in bold font
93
Table 5.4. Candidate genes for schizophrenia overlapped by very rare (<500 kb) CNVs
Candidate gene Cytoband Gene size
(nt) Case CNV start CNV size
(bp) CN Exonic
Very rare CNVs overlapping the same CNS-related gene in two or more unrelated schizophrenia cases
CAMTA1 1p36.31 984,383 67 7,063,417 33,667 Loss �
68 7,545,052 44,523 Loss
DNM3 1q24.3 571,237 87 170,223,915 47,814 Loss �
168 170,450,307 4,136 Loss
HIST3H3 1q42.13 481 69 226,634,235 54,736 Gain �
70 226,673,588 109,976 Gain �
COMMD1 2p15 230,403 66 62,049,330 36,475 Loss �
32 62,106,806 24,384 Loss
LIMS1 2q13 152,892 71 108,619,802 61,009 Loss �
72 108,619,802 61,583 Loss �
DPP6 7q36.2 679,607
2 153,637,309 9,260 Loss
73 153,737,928 6,935 Loss
74 154,060,964 31,082 Loss �
PPP3CC 8p21.3 100,043 75 22,265,902 166,778 Gain �
76 22,265,902 166,778 Gain �
JAK2a,b 9p24.1 142,939 7 4,527,834 486,659 Loss �
77 5,014,345 82,361 Gain �
ERC1 12p13.33 504,696 78 865,867 157,163 Gain �
50 1,255,099 369,587 Loss �
SOX5a 12p12.1 1,030,150
79 23,826,011 11,461 Loss
80 23,828,090 9,382 Loss
81 24,063,813 25,105 Loss
82 24,561,569 35,937 Loss
DNM1L 12p11.21 66,448 83 32,679,398 45,218 Gain �
84 32,679,398 47,929 Gain �
RBFOX1a 16p13.2 1,694,209
54 6,171,253 32,382 Loss
85 6,754,460 50,650 Loss �
86 6,766,601 9,285 Loss
87 6,973,749 104,964 Loss �
62 6,992,360 143,753 Loss �
PRKCAa 17q24.2 507,937 61 61,941,341 277,949 Loss �
88 62,145,527 7,081 Loss
DOK6 18q22.2 448,040 89 65,614,643 10,824 Loss
90 65,639,534 10,209 Loss
94
PTPRTb 20q12 1,117,166
91 40,639,325 86,602 Loss
92 40,778,749 187,266 Gain �
93 40,887,741 7,643 Loss
Very rare CNVs overlapping promising candidate genes for schizophrenia found in a single case
PTGER3b 1p31.2 195,456 141 71,150,238 44,186 Loss �
USH2Ab 1q41 800,503 104 213,914,685 64,806 Loss �
DISC1 1q42.2 414,458 38 230,150,208 121,262 Loss �
NRXN1a,b 2p16.3 1,114,032 170 503,371,141 18,515 Loss
GRM7a,b 3p26.1 880,417 146 7,058,814 35,895 Loss
GRK4 4p16.3 77,132 100 2,994,521 31,371 Loss �
DOK7a 4p16.2 31,177 169 3,439,241 13,595 Gain �
FGF2 4q26 71,528 198 123,980,237 58,004 Loss �
GRM4 6p21.31 111,816 95 34,175,770 43,039 Gain �
RUNX2 6p21 222,766 231 45,129,193 298,662 Loss �
GABRR1 6q15 40,274 182 89,894,102 52,079 Loss �
CALN1a 7q11.22 632,885 223 71,331,369 131,729 Loss �
CACNA2D1b 7q21.11 493,614 166 81,829,583 187,332 Gain �
SEMA3A 7q21.11 236,559 195 83,380,795 87,642 Gain �
RELN 7q22.1 517,733 131 103,178,239 90,728 Loss �
SLC1A1a,b 9p24 97,043 7 4,527,834 486,659 Loss �
ABLIM1 10q25 253,546 200 116,351,384 17,427 Loss �
RHOG 11p15.4 14,006 188 3,817,748 11,292 Loss �
MTNR1B 11q21 13,160 240 92,336,224 10,613 Loss �
WNT5B 12p13.3 30,157 50 1,255,099 369,587 Loss �
ATN1c 12p13.31 17,859 208 6,837,052 74,565 Loss �
ENO2c 12p13.31 9,246 208 6,837,052 74,565 Loss �
GRIN2Ab 16p13.2 429,347 32 9,982,679 10,953 Loss
PTPRMa,b 18p11.2 839,546 119 7,899,131 55,764 Loss �
PIK3C3b 18q12.3 126,250 132 37,880,681 27,546 Loss �
AP3D1a 19p13.3 50,564 195 2,015,619 180,285 Loss �
JAK3 19p13.11 23,251 271 17,754,374 68,190 Gain �
GRIK1 21q21.3 403,029 166 30,082,515 11,811 Loss
S100B 21q22.3 6,505 181 46,843,667 16,998 Loss �
95
Candidate gene were selected based on reported neuropsychiatric/neurodevelopmental phenotype identified from systematic searches of human (e.g., Online Mendelian Inheritance in Man; http://www.omim.org/) and model organism (e.g., Mouse Genome Informatics; http://www.informatics.jax.org/) databases; Cytoband, cytogenetic location of candidate gene; Gene size, in nucleotides; Case, subjects from discovery sample (n=454) with schizophrenia; CNV start, hg18 (NCBI Build 36.1, March 2006); CNV size, in base pairs; CN, type of copy number aberration; Exonic, CNV overlaps exon(s) of candidate gene (�). aGene implicated in a previous CNV study of schizophrenia: JAK2 (Xu et al., 2008), SOX5 (Walsh et al., 2008), RBFOX1 (Melhem et al., 2011), PRKCA (Xu et al., 2008), NRXN1 (International Schizophrenia Consortium, 2008; Levinson et al., 2011; Melhem et al., 2011; Rujescu et al., 2009; Walsh et al., 2008), GRM7 (Walsh et al., 2008), DOK7 (Xu et al., 2008), CALN1(Stewart et al., 2011), SLC1A1 (Melhem et al., 2011; Rees et al., 2013; Stewart et al., 2011), PTPRM (Walsh et al., 2008), and AP3D1 (Melhem et al., 2011). bGenes implicated in previous exome sequencing studies of schizophrenia: JAK2 (Need et al., 2012), PTPRT (Need et al., 2012), PTGER3 (Need et al., 2012), USH2A (Need et al., 2012), NRXN1 (Need et al., 2012), GRM7 (Need et al., 2012), CACNA2D1 (Need et al., 2012), SLC1A1 (Need et al., 2012), GRIN2A (Need et al., 2012), PTPRM (Xu et al., 2011), PIK3C3(Need et al., 2012). cGenes overlapped by the same CNV.
96
Figure 5.2. Functional map of schizophrenia from pathway enrichment analysis
Network of 70 pathways (circles) related by mutual overlap (lines) is depicted using the Enrichment Map Cytoscape plugin (Merico et al., 2010). Circle size is proportional to the total number of ‘support’ genes in each pathway and line thickness represents the number of genes in common between two pathways. Each support gene harbors a rare exonic loss CNV in one or more schizophrenia subjects but in no OPGP controls; only CNVs overlapping 15 or fewer genes were tested for pathway enrichment analysis. Groups of functionally related pathways are represented by the filled circles and respective labels; grey circles indicate pathways not assigned to these 13 clusters. Three major functional groups are further highlighted by darker filled circles (blue, Synapse; pink, Nervous system development; red, Axonogenesis) with the respective 19 support genes for schizophrenia shown in color. We also list (in black) other genes in these three functional clusters that were overlapped by very rare loss or gain CNVs in subjects with schizophrenia (i.e., not in 2773 controls), and that have additional supportive data.
97
Figure 5.3. Overlap of schizophrenia pathway analysis with known ASD genes
Functional map of schizophrenia from Figure 5.2 is presented with highlighting of the clusters of the pathways significantly associated to schizophrenia for which at least half the pathways were also significantly over-represented in known ASD genes (Betancur, 2011).Over-representation was tested using Fisher's Exact Test (FET) and Benjamini-Hochberg FDR for multiple test correction, with a significance threshold of 5% FDR.
98
5.4 Discussion
Derived from a clinically informed and rigorous approach, the results of this study provide new
information on the genomic architecture of schizophrenia and on the potential role of clinical
microarray testing. Adding novel data to those available from the component studies of existing
consortia reports, this study involved (i) unparalleled systematic recruitment efforts in a
community setting (Bassett et al., 2010a), (ii) consistent phenotyping of all cases using
previously published methods (Fung et al., 2008; Silversides et al., 2012), (iii) rigorous CNV
methodology (Lionel et al., 2011), (iv) exclusion of already known effects of 22q11.2 deletions,
(v) blinded adjudication of rare CNVs by two independent clinical cytogenetic laboratory
directors and (vi) clinical confirmation and return of results to all patients with clinically
significant CNVs and their families. The results strengthen etiologic and mechanistic ties of
schizophrenia to developmental disorders like autism, where clinical microarray testing is now
indicated and suggest a yield of reportable findings approaching that seen in these disorders
(Miller et al., 2010a; Shen et al., 2010). Rare, recurrent structural rearrangements at 2q13 were
implicated for the first time as significant risk factors for schizophrenia. We provide strong
evidence supporting a quantitatively greater, and qualitatively different, genome-wide burden of
rare CNVs in schizophrenia compared with controls. New candidate genes and networks relevant
to schizophrenia identified in this study will facilitate the future clinical interpretation of smaller
CNVs.
The results of this study indicate that ~1 in 13 unrelated patients with schizophrenia may harbor
a clinically significant large rare structural variant that would be detectable using methods
available in most clinical laboratories. This yield is similar to that seen in a 2010 clinically
driven study of genetic testing in ASD (Shen et al., 2010), a related neurodevelopmental
condition in which clinical microarray testing is now considered the first-tier diagnostic test
(Miller et al., 2010a). None of the patients in the schizophrenia sample studied had autism or
multiple congenital anomalies and few would meet the existing criteria for clinical microarray
testing, e.g., on the basis of mild intellectual disability. Our data also suggest that most
individuals with schizophrenia who have large rare CNVs would not have obvious
developmental (syndromal) features, with the exception of some with large exonic losses where
phenotypes tend to be more complex, as in 22q11.2 deletions. Thus, to discover most of the
99
clinically significant variants identified in this study a genome-wide microarray would be
necessary. Introduction of such genetic testing into clinical practice for an adult neuropsychiatric
disease would have implications for psychiatry and for the still pediatric-focused field of medical
genetics. As for ASD and developmental delay/intellectual disability, the availability of clinical
CNV data for schizophrenia, coupled with an improved ability to interpret these data for the
benefit of patients and families, could have scientific implications and clinical benefits as
discussed below. Further studies are needed to demonstrate the worth of such clinical genetic
testing in schizophrenia.
What are the potential benefits of identifying a ‘clinically significant’ CNV? The impetus to
offer clinical microarray testing to individuals with schizophrenia in the hope of arriving at a
partial etiologic explanation is similar to that described for other neurodevelopmental conditions
(Miller et al., 2010b). Uncertainty about penetrance and expressivity of rare variants is a
common reality of practice in medical genetics, and remains compatible with the provision of
helpful genetic counseling. With the exception of 22q11.2 deletions, most clinically significant
variants in individuals with schizophrenia today would only be discovered on a research basis.
More needs to be known about lifetime, including adult, expression of these CNVs to better
inform medical and/or psychiatric management. Nevertheless, all of these large, rare, clinically
significant variants already have implications for genetic counseling that include, but are not
limited to, reproductive implications for siblings and patients themselves (Costain et al., 2011),
and are of potential benefit to patients and families (Costain et al., 2014a; Costain et al., 2014b).
Our own efforts with this translation to clinical genetic counseling are ongoing, with an overall
positive response to date, comparable with our experience with 22q11.2 deletion syndrome
(Costain et al., 2012) and schizophrenia in general (Costain et al., 2014a; Costain et al., 2014b).
There may be, therefore, a growing obligation to offer the return of selected, clinically relevant
and clinically confirmed individual research results to patients and their families in large-scale
genome-wide studies of schizophrenia such as this (Costain and Bassett, 2013). To our
knowledge, this is currently a rare practice, even for 22q11.2 deletions.
The new candidate genes and network highlighted in this report will facilitate the clinical
interpretation of genome-wide CNVs in schizophrenia, including CNVs that are neither large nor
recurrent. We took a conservative approach to pathway enrichment analysis, using only exonic
losses in the analyses and excluding major multi-genic CNVs. The results showed that the genes
100
implicated by rare CNVs in schizophrenia are especially overrepresented by those with synaptic
and neurodevelopmental functions, consistent with other studies (Kirov et al., 2012; Walsh et al.,
2008). Moreover, the findings extended attention from such well-recognized candidates to less
obvious genes. Importantly, these were not restricted to genes with multiple functions (e.g.,
DISC1 and RELN). The pathways implicated in this study may assist with the interpretation of
smaller genic CNVs in schizophrenia. This more complex gene network for schizophrenia was
similar to that seen in autism (Pinto et al., 2010). For the first time, a formal analysis has
demonstrated that in schizophrenia there is significant over-representation of known genes for
ASD (Figure 5.3). This finding provides additional evidence in support of using data from ASD
to assist in the clinical interpretation of CNVs in schizophrenia, and vice versa. The fact that
none of the study participants had a history of autism or moderate to severe intellectual disability
supports the likelihood that CNVs shared with these pediatric conditions exhibit true pleiotropy.
Many of the implicated genes in this study show regional gene expression differences in mid-
fetal development of the brain (e.g., CHRNA7 and LIN7A in the thalamus) (Johnson et al., 2009),
a critical time for the pathogenesis of schizophrenia (Insel, 2010). We note, however, that the
prolonged maturation of the human brain provides multiple opportunities for perturbation of
gene expression through gene dosage changes.
Complementary to these results, we demonstrated enrichment in schizophrenia of not only
individuals with large rare exonic CNVs, but also those with two or more rare CNVs >10 kb that
overlapped exons. These results represent some of the first concrete evidence supporting a
‘multiple hit’ hypothesis for schizophrenia, outside of multi-genic large CNVs (Bassett et al.,
2010a; Kirov et al., 2012; Walsh et al., 2008). There is also support for regulatory (e.g., large
intronic CNVs) and epigenetic mechanisms (e.g., chromatin modification cluster in the pathway
enrichment analysis) as important contributory factors to the etiology of schizophrenia. There
remained such evidence after excluding the potentially overpowering influence of multi-genic
CNVs. The new candidate genes and network for schizophrenia identified in this report may also
help guide the future interpretation of results from whole exome and whole genome sequencing
studies (Need et al., 2012; Xu et al., 2011). Indeed, there was a notable degree of overlap
between the promising candidate genes overlapped by rare CNVs in our sample (Tables 5.2 and
5.4) and those genes containing rare SNVs in schizophrenia cases in recent next-generation
sequencing studies (Need et al., 2012; Xu et al., 2011).
101
This is the first study of genome-wide CNVs in schizophrenia to include clinical adjudication of
identified rare CNVs and to use a systematic clinically informed genetic approach to investigate
a well-characterized cohort of adults assessed a priori for certain ‘syndromic’ features (Bassett et
al., 2010a; Fung et al., 2008; Silversides et al., 2012). To minimize the potential impact of false
positives, we applied identical molecular and conservative analytic methods to unrelated cases
with schizophrenia and to a similar-sized Canadian sample of epidemiologic controls, an
unprecedented approach in neuropsychiatric disease recently validated in the study of congenital
cardiac disease (Silversides et al., 2012). The strategy emphasized CNVs likely to have an
enhanced effect size because we used a more restricted level to define rarity (< 0.1%) than that
usually employed (< 1%) (International Schizophrenia Consortium, 2008; Levinson et al., 2011;
Melhem et al., 2011). This could mean missing more common variants with a lower effect size.
Using an independent population-based control set ensured equal effects for both cases and
OPGP controls, however, and thus all comparative analyses (including the burden and gene
enrichment mapping) are robust to the potential effects of systematic Type 1 or 2 errors.
Published guidelines for CNV analysis support our multi-algorithm approach (Lionel et al.,
2011), and we report a very high positive validation rate by independent methods (100% of 58
CNVs tested, including 17 smaller than 50 kb in size) that is in keeping with our previous
experience (Lionel et al., 2011; Marshall et al., 2008; Pinto et al., 2010; Silversides et al., 2012).
All studies will be affected by the cases and controls studied, and the controls used to adjudicate
rarity of CNVs. We acknowledge a focus on individuals of European ancestry. No genome-wide
CNV results of our cases have been previously published, so there will be no sample overlap
with other datasets.
Adjudicating findings for individually rare variants is challenging—indeed, this was a major
rationale for our study and was performed through blinded assessment of large rare CNVs by
clinical laboratory directors. We also employed conservative qualitative and quantitative
methods in order to identify putative candidate genes for schizophrenia overlapped by rare CNVs
whose expression may be affected by the associated gene dosage changes. We report the details
of a gene network derived from rare CNVs overlapping exons that differentiated schizophrenia
cases from controls (Figure 5.2) to facilitate interpretation of findings of future studies. The
network was further validated by demonstrating significant overrepresentation with known genes
for ASD (Figure 5.3). Nonetheless, all clinical and research interpretations are constrained by
102
knowledge available at the time. We expect that understanding of coding regions, and non-genic
and regulatory sequences, will continue to evolve. Although the number of cases in our study
appears to be small in comparison to the combined numbers reported by large consortia, we were
sufficiently powered to (i) arrive at a confidence interval (CI) for the prevalence of clinically
significant CNVs in schizophrenia with a lower bound still >5%, and (ii) demonstrate within-
sample recurrence implicating individual genes (Table 5.4) and specific loci (Tables 5.1 and 5.2),
including the novel discovery of enrichment of CNVs at 2q13 in schizophrenia.
Our results provide clinical adjudication of previously reported CNVs for schizophrenia (Bassett
et al., 2010b; Kirov et al., 2012; Levinson et al., 2011; Melhem et al., 2011; Walsh et al., 2008).
Family studies, rare in schizophrenia, will be essential to delineate the inheritance status,
penetrance and variable neuropsychiatric expression, segregation pattern and extended
phenotype associated with those CNVs believed to underlie emerging genetic subtypes of the
disorder. The exception is 22q11.2 deletion syndrome, where these are well established (Costain
and Bassett, 2012). De novo status is neither a necessary nor a sufficient criterion for
pathogenicity, and many of the CNVs highlighted in this study may have been inherited. Many
well-established schizophrenia risk variants of major effect are found in the majority of cases to
be inherited (Sahoo et al., 2011), usually from parents with mild or no overt neuropsychiatric
phenotype (Cooper et al., 2011; Costain et al., 2011). We elected to use previously validated
criteria (Bassett et al., 2010a; Fung et al., 2008; Silversides et al., 2012) for assessing syndromic
features, shown to (i) have a good discriminant ability to identify at least one genomic disorder
(22q11.2 deletion syndrome) and (ii) be feasible for administration in a brief clinical encounter—
a key prerequisite for translation into the psychiatric clinic. There may exist more complex
clinical screening criteria with sufficient discriminant ability to identify patients with large, rare,
clinically significant CNVs. The collectively high prevalence of such variants, and the non-
significant findings for clinical variables in this study, however, support the potential universal
use of clinical microarray testing in schizophrenia. Definitively proving causality of specific
genetic variants for schizophrenia is beyond the scope of our and other genome-wide CNV
studies. Ideally, this would comprise direct functional evidence that can be related to gene
dosage effects in the developing and adult human brain. For many candidate genes implicated by
these CNV results, the functional significance has already been validated in model organisms
and/or human cellular models, as outlined in Tables 5.1 and 5.2. Such models are useful in a
103
disease where access to the target organ at precise developmental time points is challenging.
Why does the prevalence of large, rare, recurrent CNVs in schizophrenia appear to be higher in
this study than estimates reported in previous studies (Levinson et al., 2011)? First, this is a
prospective study using a consistent ascertainment, recruitment and assessment strategy for
patients with chronic schizophrenia at community clinics. Although demographic features and
age at onset were similar to those in other studies, the chronicity of the illness may have meant
that a relatively few individuals with mild forms of schizophrenia and/or evolving psychiatric
diagnoses were included. Second, although all studies may be expected to have volunteer bias,
we took an inclusive approach to recruitment. We did not exclude individuals, e.g., with learning
difficulties or residual symptoms, and obtained a surrogate consent for individuals where
necessary. Also, recruitment has persisted over several years, and particularly in our catchment
based sample we have made every effort to include all patients (e.g. waiting until their clinical
state improved, making home visits). We did not recruit from university health clinics, use
groups of patients already enrolled in and/or volunteering for other studies or preferentially
enroll patients from intact families with both parents available for study or those with familial
schizophrenia. Such factors can affect the observed prevalence of 22q11.2 deletions (Bassett et
al., 2010b) and likely that of other rare CNVs with high penetrance for severe neuropsychiatric
phenotypes. Nevertheless, given evidence for premature death in some CNV-related conditions
(Bassett et al., 2009), we would have missed individuals who died before enrollment and those
too ill to participate. Replication of our findings in an independent cohort should be a key goal of
future research in this area, and our results may encourage others to adopt our clinically driven
approach, including more epidemiologically-based strategies for patient ascertainment.
In conclusion, for the first time, we have a reliable estimate of the collective prevalence of
clinically significant CNVs in a community-based sample of schizophrenia. Most individuals
with large rare CNVs, including many of those with clinically significant CNVs, will not be
readily distinguishable from the rest of the general schizophrenia population based on
developmental (syndromic) features. The prevalence of clinically significant CNVs discovered in
community-based schizophrenia brings us a step closer to recommending clinical microarray and
other methods of genetic testing (including sequencing) for patients with schizophrenia, and may
initiate a debate among clinicians, scientists, families and policy makers. As for children with
developmental disorders found to have clinically significant CNVs, clinical genetic counseling
104
could be provided to patients with schizophrenia and their families (Costain and Bassett, 2012),
regardless of the acknowledged need for more information on key genetic parameters, such as
penetrance and variable expression across the lifespan. The complex network of genes being
discovered through studies of rare variants mirrors the complexity of the neural networks
involved. Further research efforts promise to bring order to these for schizophrenia and related
NDDs, as they have for cancer.
5.5 Materials and Methods
5.5.1 Schizophrenia sample ascertainment and assessment
We prospectively recruited 459 unrelated Canadian patients meeting DSM-IV criteria for chronic
schizophrenia or schizoaffective disorder from four community mental health clinics. The
majority (n = 248) comprised a ‘catchment sample’ that originated from the only community
mental health clinic in a catchment area of ~150 000 people (Bassett et al., 2010a). For the
current study, we excluded the five subjects with 22q11.2 deletions with genome-wide CNV data
published elsewhere (Bassett et al., 2008), two of whom were from the catchment area sample
(2). None of the remaining 454 subjects [317 (69.8%) male; mean age 44.8 (SD 12.1) years] had
previously published genome-wide CNV data, though four had previously published abnormal
karyotype results (Bassett et al., 2010a; Caluseriu et al., 2006) and two with typical 1q21.1 gains
were recently reported by us in a case series (Dolcetti et al., 2013). After description of the study
to the subjects, a written informed consent was obtained. The study was approved by local
hospital and university institutional review boards.
All subjects underwent direct clinical screening assessments for potential syndromic features
using a standardized protocol that included a review of available lifetime medical records and
assessment of physical features (Bassett et al., 2010a; Fung et al., 2008; Silversides et al., 2012).
Using the criteria previously validated for identifying adults with 22q11.2 deletion syndrome
[two or more of: global dysmorphic facial features, history of learning difficulties, abnormal
(hypernasal) voice (17)], syndromic [n = 78 (17.2%)] and non-syndromic subgroups were
designated (Silversides et al., 2012). No individual had a pediatric diagnosis of moderate or
severe intellectual disability, multiple major congenital anomalies or autism. All phenotyping
was done blind to genotype. The age at onset was considered the age at first treatment for
105
psychosis [mean 22.9 (SD 6.8) years], and a positive family history defined as having one or
more first degree relatives with a psychotic disorder [n = 70 (16.8%) of 416].
5.5.2 Control sample for formal analyses
To optimize our analyses, we used an independent Canadian control sample from the OPGP
genetic epidemiologic project comprising 416 unrelated adults [208 (50.0%) male; mean age
45.0 (SD 12.1) years] of European ancestry (Silversides et al., 2012). The OPGP controls are
independent of the 2357 population-based CNV adjudication controls described below and in
Chapter 2. The OPGP was conceived as an opportunity to establish a collection of fully
consented, Ontario based population control DNA samples, as a resource for researchers in
Ontario and elsewhere. The OPGP was constructed in two phases. The first phase involved
collaborations with the Ontario Familial Breast Cancer Registry (OFBCR) (Andrulis et al., 1997)
and the Ontario Familial Colorectal Cancer Registry (OFCCR) (Cotterchio et al., 2000).
Population controls from the OFBCR and OFCCR that had completed the study questionnaires
and provided a blood sample were eligible to participate as controls in the OPGP. The second
phase involved collaboration with the Institute of Social Research at York University (Toronto,
ON, Canada). A database of information on individuals from the population of Ontario (n = 14
000) was obtained, based on a random sample of households across Ontario identified initially
on the basis of telephone directory listings. These households were contacted by means of a
mailed letter and follow-up telephone calls were made by a trained interviewer who, after
establishing eligibility within the household, randomly chose one person as the eligible control.
Individuals were eligible if they were 20–79 years of age and resided in Ontario. In both the
phases, subjects were re-consented for OPGP, and surveyed for comprehensive demographic and
health information. Blood samples were collected and sent via courier to The Centre for Applied
Genomics (TCAG) Biobanking Facility for transformation, DNA preparation and distribution.
The total OPGP collection consists of samples from 1134 males (42%) and 1556 females (58%),
ranging in age from 20 to 82 years. This study uses data from 416 of these OPGP samples of
European ancestry that were genotyped on the Affymetrix 6.0 platform. To minimize laboratory-
related artefacts, identical experimental and analytical protocols for array genotyping and CNV
analyses were followed at the same facility for the OPGP control and schizophrenia case samples
using the methodology described in Chapter 2. Each CNV identified in the schizophrenia and
OPGP cohorts was then adjudicated for rarity by comparison to those identified, using an
106
identical microarray platform and CNV analysis strategy, in two large population-based control
cohorts comprising 2357 individuals of European ancestry from Ontario and Germany
(Krawczak et al., 2006; Stewart et al., 2009). We used a conservative definition of ‘rare’ CNVs:
those present in < 0.1% of the population controls (Silversides et al., 2012).
5.5.3 CNV prioritization, assessment of pathogenicity and prevalence
We prioritized two main groups (Figure 5.1) of rare (present in < 0.1% of population-based
controls) CNVs in the schizophrenia cases for qualitative analysis: (i) large CNVs (i.e., those
defined as > 500 kb in size) and (ii) CNVs < 500 kb in size that were ‘very rare’ (i.e., not present
in the population-based control cohorts, using a 50% reciprocal overlap criterion) (Lionel et al.,
2011), and that overlapped the same CNS-related gene(s) in two or more unrelated schizophrenia
cases (Silversides et al., 2012). We also identified other very rare CNVs that overlapped
promising candidate genes for schizophrenia (as determined by known biological function and/or
previous reports), but that were found only in a single schizophrenia case.
Each large (> 500 kb) rare CNV in schizophrenia case and OPGP control individuals was
assessed independently by two experienced clinical cytogenetic laboratory directors blind to
case–control status. According to ACMG guidelines for interpretation of postnatal constitutional
CNVs (Kearney et al., 2011), each CNV event was assigned to one of the five main categories:
‘Pathogenic’, ‘Uncertain clinical significance; likely pathogenic’, ‘Uncertain clinical significance
(no sub-classification)’, ‘Uncertain clinical significance; likely benign’ and ‘Benign’.
Disagreements between the two laboratory directors in initial interpretations were subsequently
resolved through discussion to arrive at a consensus determination (while still blind to case-
control status). In the text, variants deemed to be ‘Pathogenic’ or of ‘Uncertain clinical
significance; likely pathogenic’ were collectively termed ‘clinically significant’, and those of
‘Uncertain clinical significance (no sub-classification)’ were termed ‘variants of unknown
significance’ (VUS); the remainder were termed ‘benign’.
We used our schizophrenia community catchment sample to estimate the minimum prevalence
of large, rare, clinically significant structural variants that would be detectable on a clinical
microarray. We estimated that there were 370 unrelated adults with schizophrenia in this area,
including the 248 consecutive subjects for whom genome-wide CNV data were available
(67.0%), as well as individuals who refused or were unable to consent to participation in the
107
study, and others known to but not attending the clinic or hospital system; all non-participants
were assumed to be unrelated to probands in this study or to each other.
5.5.4 Experimental validation of CNVs
Confirmatory studies of possible schizophrenia-associated CNVs used Stratagene SYBR Green
based qPCR. Each qPCR assay was performed simultaneously in triplicate for both the test
region and a control region of known copy number 2 on chromosome 7. The ratio of the average
value for the test region to that for the control region had to be > 1.3 or < 0.7 in order for the
CNV to be confirmed as a duplication or deletion, respectively. In addition, the standard error of
the ratio had to be < 1.0 on the same scale in order for the assay to be considered reliable. We
validated 58 (100.0%) of 58 CNVs tested, i.e., all 58 assays met both of these criteria for CNV
confirmation. The validated CNVs spanned a range of sizes, and included 17 that were < 50 kb.
Post-study molecular genetic results from clinical laboratories confirmed a further 11 CNVs, and
also the parental origin of the 15q11-q13 duplications. All clinically significant variants were
confirmed in a CLIA-approved laboratory and returned to patients and their families.
5.5.5 CNV burden analyses
For the CNV burden analyses, we compared the proportion of subjects of European ancestry
with one or more rare CNVs in the 420 [n = 291 (69.3%) male] unrelated schizophrenia cases
with that in the 416 [n = 208 (50.0%) male] unrelated OPGP controls (Silversides et al.,
2012).We used only autosomal CNVs because of the significant sex differences between these
groups (χ2 = 32.31, df = 1, P < 0.001). As an additional conservative step, variants that might
reasonably be discovered without use of a genome-wide microarray, i.e., rare CNVs > 6.5 Mb in
size potentially detectable by routine G-band analysis (cases of European ancestry: n = 6,
controls: n = 0; Table 5.1) and 22q11.2 deletions (cases of European ancestry: n = 4, controls: n
= 0), were not used in these formal burden calculations. Statistical analyses were performed
using SAS software. Comparisons between schizophrenia cases and OPGP controls, and between
syndromic and non-syndromic schizophrenia cases, were performed using standard two-sided
statistical tests. Odds ratios and 95% confidence intervals were calculated using standard
methods. A P-value of < 0.05 was considered to be significant. As described above, identical
experimental and analytical protocols for array genotyping and CNV analyses were followed at
the same facility for the schizophrenia case and OPGP control samples, and each CNV was then
108
adjudicated for rarity by comparison to those identified, using an identical microarray platform
and CNV analysis strategy, in two large population-based control cohorts distinct from the
OPGP controls. Thus, the rate of Type 1 and 2 errors would be expected to be the same in
schizophrenia cases and OPGP controls, with no resulting impact on the relative inter-group
CNV burden.
5.5.6 Functional network from gene association analysis
To identify biologically meaningful groups of genes (i.e., those sharing a common function)
affected by CNVs using the same samples as for the main burden analyses, we used a pathway
enrichment analysis similar to that previously published for ASD (Merico et al., 2010; Pinto et
al., 2010) and congenital cardiac disease (Silversides et al., 2012). Briefly, we first compiled
comprehensive collections of pathways (Silversides et al., 2012) and for each functional
pathway, we tested whether the prevalence of subjects with very rare CNVs was higher in
schizophrenia cases than in OPGP controls using a one-tailed Fisher’s exact test (FET) (Pinto et
al., 2010; Silversides et al., 2012). Nominal P-values were corrected for multiple testing using a
case– control class permutation procedure to estimate an empirical false discovery rate (FDR)
(Pinto et al., 2010; Silversides et al., 2012). Case–control labels were randomly permuted 5000
times and, for each P-value, the empirical FDR was computed as the average number of
pathways with equal or smaller P-value over permutations. We selected 10% as the empirical
FDR significance threshold for final results. As above, the rate of Type 1 and 2 errors would be
expected to be the same in schizophrenia cases and OPGP controls, with no resulting impact on
the pathway enrichment analysis. Finally, we highlighted the clusters of the pathways that were
significantly associated with schizophrenia where for at least half of the pathways there was also
significant overrepresentation of known ASD genes (Betancur, 2011). Over-representation was
tested using FET and Benjamini–Hochberg FDR for multiple test correction, with a significance
threshold of 5% FDR.
109
Chapter 6
Gephyrin Deletions in Cross-Disorder Risk for Autism,
Schizophrenia and Seizures
Parts of this chapter are adapted, with permission for use from Oxford Press, from the following
published journal article:
Lionel AC, Vaags AK, Sato D, Gazzellone MJ, Mitchell EB, Chen HY, Costain G, Walker S,
Egger G, Thiruvahindrapuram B, Merico D, Prasad A, Anagnostou E, Fombonne E,
Zwaigenbaum L, Roberts W, Szatmari P, Fernandez BA, Georgieva L, Brzustowicz LM, Roetzer
K, Kaschnitz W, Vincent JB, Windpassinger C, Marshall CR, Trifiletti RR, Kirmani S, Kirov G,
Petek E, Hodge JC, Bassett AS, Scherer SW. Rare exonic deletions implicate the synaptic
organizer Gephyrin (GPHN) in risk for autism, schizophrenia and seizures. Human Molecular
Genetics; 2013; 22(10):2055-66.
I performed CNV analysis of the ASD and schizophrenia cases genotyped at TCAG and
discovered the GPHN deletions, performed CNV analysis of controls, coordinated collection of
genetic data from the other clinical sites and drafted and revised the manuscript. Dr. A.K. Vaags
coordinated collection of clinical phenotype data from the individuals with GPHN deletions.
110
6.1 Abstract
The GPHN gene codes for gephyrin, a key scaffolding protein in the neuronal postsynaptic
membrane, responsible for the clustering and localization of glycine and GABA receptors at
inhibitory synapses. Gephyrin has well-established functional links with several synaptic
proteins that have been implicated in genetic risk for NDDs such as Autism Spectrum Disorder
(ASD), schizophrenia and epilepsy including the neuroligins (NLGN2, NLGN4), the neurexins
(NRXN1, NRXN2, NRXN3) and collybistin (ARHGEF9). Moreover, temporal lobe epilepsy
arising due to cellular stress such as temperature and alkalosis associated with epileptogenesis
has been linked to abnormally spliced GPHN mRNA lacking exons encoding the G-domain of
the gephyrin protein. This chapter describes the clinical and genomic characterization of six
unrelated subjects, with a range of neurodevelopmental diagnoses including ASD, schizophrenia
or seizures, who possess rare de novo or inherited hemizygous microdeletions overlapping exons
of GPHN at chromosome 14q23.3. The region of common overlap across the deletions
encompasses exons 3-5, corresponding to the G-domain of the gephyrin protein. These findings,
together with previous reports of homozygous GPHN mutations in connection with autosomal
recessive molybdenum cofactor deficiency, will aid in clinical genetic interpretation of the
GPHN mutation spectrum. This data also add to the accumulating evidence implicating neuronal
synaptic gene products as key molecular factors underlying the etiologies of a diverse range of
neurodevelopmental conditions.
6.2 Introduction
GPHN encodes the protein gephyrin, a key scaffolding molecule in the neuronal postsynaptic
membrane at inhibitory synapses (Fritschy et al., 2008; Tretter et al., 2012). After its initial
discovery by affinity chromatography experiments as co-purifying with the postsynaptic
inhibitory glycine receptor, the protein was named “gephyrin” from the Greek term for “bridge”,
in line with its proposed activity as an intermediary molecule connecting neurotransmitter
receptors to the postsynaptic microtubule cytoskeleton (Prior et al., 1992). Follow-up
experiments demonstrated that the clustering and postsynaptic localization of both major types of
inhibitory receptors (glycinergic and GABAergic) depend on direct protein-protein interactions
of gephyrin with subunits of the glycine receptor (Kirsch et al., 1993) and the GABAA receptor
111
(Essrich et al., 1998). Gephyrin clustering is essential for GABAergic synapse stability and
aberrant GPHN expression has been observed in the hippocampus and adjacent neocortex of
patients with temporal lobe epilepsy (TLE) and in a rat model of TLE (Fang et al., 2011). These
findings are in line with known deficits in GABAergic synaptic transmission in TLE (Kumar and
Buckmaster, 2006). One potential molecular explanation for gephyrin’s involvement in the
epileptic hippocampus could be abnormally spliced forms of RNA produced by cellular stress-
induced exon skipping, which can lead to altered or inactive protein (Forstera et al., 2010).
However, there have been no reports of genomic GPHN mutations in patients with epilepsy.
In addition to its potential involvement in epilepsy, gephyrin has functional links with several
synaptic proteins, mutations of which have been reported in a range of NDDs. Together with
collybistin, gephyrin forms complexes with the postsynaptic neuroligin proteins NLGN2
(Poulopoulos et al., 2009) and NLGN4 (Hoon et al., 2011). Gephyrin’s specific postsynapatic
localization and clustering are mediated by members of the presynaptic neurexin protein family
(Kang et al., 2008). This complex interplay of gephyrin with the neurexins and neuroligins not
only attests to its importance in the proper formation and function of synapses, but is also of
particular interest given the substantial evidence implicating these trans-synaptic adhesion
molecules in genetic risk for a range of neurodevelopmental conditions (Reichelt et al., 2012;
Sudhof, 2008) including Autism Spectrum Disorder (ASD), schizophrenia and epilepsy as
detailed in Chapter 1. Of specific relevance to gephyrin, rare point mutations in NLGN2 (Sun et
al., 2011) and NLGN4 (Jamain et al., 2003) have been identified in patients with schizophrenia
and ASD, respectively, while mutations in the collybistin gene (ARHGEF9) have been reported
in cases with intellectual disability (ID) and epilepsy (Kalscheuer et al., 2009; Lesca et al., 2011;
Shimojima et al., 2011). Frameshift mutations truncating NRXN1 and NRXN2 in patients with
schizophrenia and ASD, respectively, were also observed to abolish the ability of these proteins
to induce gephyrin clustering at dendrite contact sites when synaptogenic activity was tested in
neuron co-culture assays (Gauthier et al., 2011). The SHANK proteins, which act as postsynaptic
neuronal scaffolding proteins in excitatory synapses (a role analogous to that performed by
gephyrin in inhibitory synapses) have been implicated by rare mutations in genetic risk for ASD
(Berkel et al., 2010; Durand et al., 2007; Sato et al., 2012), schizophrenia (Gauthier et al., 2010)
and epilepsy (Lesca et al., 2012). Given the strong evidence supporting GPHN as a candidate
112
molecule for involvement in NDDs, the gene was screened for copy number variations (CNVs)
and point mutations in patients from ASD, schizophrenia and seizure disorder case cohorts.
6.3 Results
6.3.1 Detection and inheritance testing of rare microdeletions at the GPHN locus
The GPHN locus was screened for CNVs using high resolution microarray data from 5,384
individuals from ASD, schizophrenia and seizure disorder patient cohorts. In five of these
patients, hemizygous deletions were identified at 14q23.3 interrupting multiple exons of GPHN
(Figure 6.1 & Table 6.1). There was also an earlier report of a 183 kb deletion affecting six
exons of GPHN in a single individual from a cohort of 3,391 schizophrenia patients studied by
the International Schizophrenia Consortium (International Schizophrenia Consortium, 2008).
Additional clinical details were obtained for this patient and his family (Family 6 in Figures 6.1
and 6.2). Independent experimental validation of the array calls was performed using quantitative
PCR (qPCR) or fluorescence in situ hybridization (FISH). None of the CNVs detected at other
loci in probands from the six families were predicted to be of clinical significance when
classified based on American College of Medical Genetics guidelines for interpretation of CNVs
(Kearney et al., 2011). Parental testing for CNV status at the GPHN locus revealed that the
deletions arose de novo in three of the probands (Families 1, 3 and 5 in Figure 6.2). The deletion
in the proband from Family 2 with ASD was inherited from his father, who was reported to have
sub-clinical socialization deficits, but no formal diagnosis of ASD. The deletion was not present
in the three sisters of the proband. DNA samples were not available from the parents in Family 4
for inheritance testing. The deletion in the proband from Family 6 was inherited from his mother,
who was apparently unaffected. DNA samples were not available from the maternal grandmother
of the proband, who was reported to have schizophrenia.
113
Figure 6.1. Genomic locations of exonic GPHN deletions in the NDD probands
Red bars represent the hemizygous exonic GPHN deletions at 14q23.3 detected in the index probands of the six families presented in this chapter. Information about transcript isoforms and genomic co-ordinates corresponds to human genome Build 36 (hg18). The common region of overlap of the six deletions encompasses exons 3–5, which correspond to the G-domain of the gephyrin protein. Coordinates (hg18) of the deletions observed in control individuals are chr14:66,055,080–66,882,227, chr14:66,153,552–66,267,833 and chr14:66,287,336–66,355,274.
114
Figure 6.2. Pedigrees of individuals with GPHN deletions
Squares and circles denote males and females, respectively. Arrow indicates the index proband in each family. ‘N/A’ represents individuals for whom DNA was not available for testing. All other individuals were assayed for deletions at the GPHN locus using microarray, FISH or qPCR.
115
Table 6.1. Genetic and phenotypic details of probands with GPHN deletions Family
(proband sex) Genotype Phenotype
GPHN deletion size Exons
affected Inheritance Diagnosis
Dysmorphic features
Speech Delay
Motor Delay
Other
Family 1 (female)
357 kb 3-12 De novo ASD N Y Y -
Family 2 (male)
319 kb 2-5 Paternal ASD N N N Coarctation of aorta
Family 3 (male)
273 kb 2-5 De novo ASD & seizures
N/A Y N Intellectual disability
Family 4 (male)
134 kb 3-5 Unknown Seizures N/A N/A N/A -
Family 5 (male)
338 kb 3-11 De novo Schizophrenia N N N -
Family 6 (male)
183 kb 3-8 Maternal Schizophrenia N/A N N Learning difficulties
Abbreviations: ASD, Autism Spectrum Disorder; N/A, Not assessed
116
6.3.2 Frequency comparison of GPHN deletions in cases and controls
Deletions affecting exons of GPHN are extremely rare in the general population. Of the 27,019
control individuals examined for CNVs at this locus (Lionel et al., 2013), only three possessed
exonic GPHN deletions. CNVs present in control individuals might be indicative of variable
expression of GPHN deletions, lack of rigorous assessment of neuropsychiatric phenotype in the
controls, or false-positive calls since DNA was not available from the two SAGE control
individuals for experimental validation. Taken together, the frequency of experimentally
validated exonic deletions at the GPHN locus in cases is significantly greater than that in
controls (6 / 8,775 cases vs. 3 / 27,019 controls, respectively; Fisher’s exact test 2 sided p =
0.009). There were no exonic deletions reported at the GPHN locus in the Database of Genomic
Variants (DGV).
6.3.3 Clinical features of individuals with exonic GPHN deletions
Family 1 (Proband with ASD diagnosis; 357 kb de novo deletion):
The female proband of European ancestry was conceived naturally to a non-consanguineous 35
year old (yro) mother and 33 yro father. The pregnancy was uncomplicated and the child was
delivered at 38 weeks of gestation with a birth weight of 2,810 grams and length of 47 cm. There
are no reported medical or neuropsychiatric conditions in her parents and two older siblings.
During early development she was noted to be a “calm” child, with limited movement when laid
down. She began walking at 12 months but otherwise had slow motor development and gait
issues, which prompted ergotherapy. Upon examination by a pediatrician at two yro and again at
four yro, no dysmorphology was noted. She had language delay with no speech at 2 yro,
followed by gradual speech development by age four. She had episodes of echolalia. She
attended kindergarten for two years with an early intervention program teacher, followed by
regular elementary and secondary school with the aid of an education assistance worker. Up to
six years of age, she continued to desire swaddling and had hypersensitivity to light and sound
associated with self-injury (head banging, tearing hair). Assessment by a psychologist at 13 yro,
which included the Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic
Interview – Revised (ADI-R), resulted in a diagnosis of high-functioning ASD. She completed a
secondary school degree with the exception of mathematics. Chromosomal microarray
117
(Affymetrix 6.0) indicated that she had a deletion at cytoband 14q23.3 (chr14:66,274,499-
66,631,750 [hg18]), which was not present in the parents (Figures 6.1 and 6.2, Family 1).
Family 2 (Proband with ASD diagnosis; 319 kb paternally inherited deletion):
The male proband was conceived naturally to a 28 yro mother and 30 yro father. The proband
was born vaginally at 35 weeks gestation with a birth weight of 3,133 grams and length of 50.8
cm. He was treated for respiratory distress in hospital for nine days following delivery.
Coarctation of the aorta and bicuspid aortic valve were noted on echocardiography performed
upon re-admittance to the hospital at 18 days of life, secondary to continued respiratory distress.
He had an end-to-end anastomosis procedure at one month followed by a coarctation repair with
patch annuloplasty at four months. Post-treatment echocardiograms have been normal and he
remains asymptomatic. He exhibited mild global developmental delay early in life, with walking
at 16 months and speech emergence at 15-16 months. By the age of three years he could speak in
two word sentences. At age six he was diagnosed with ASD by a child psychologist. In
particular, he had difficulties with socialization and repetitive behaviors (specifically throat
clearing and tics). Upon examination by a clinical geneticist at 6 years 8 months, no significant
dysmorphology was noted. The proband experienced academic difficulties and required a
modified program in preschool and kindergarten. By 9 years of age, he had graduated from this
program and received no additional special education. Chromosomal microarray (Agilent 44K)
indicated he had a deletion at cytoband 14q23.3 (chr14:66,102,556-66,421,440 [hg18]), which
was paternally inherited, but not present in his siblings (Figures 6.1 and 6.2, Family 2). The
proband is the third of four children of non-consanguineous parents (Figure 6.2, Family 2). He
has one sister with anxiety, another sister with speech delay and hyperactivity, a third sister with
head-banging behaviors, and a maternal half-brother with bipolar disorder. His mother has been
diagnosed with depression and anxiety and was noted to have speech difficulties in school. In the
maternal extended family there are individuals with additional psychological concerns. The
father is apparently healthy with normal intelligence, but has some challenges with socialization.
There is significant psychiatric illness in the paternal extended family.
Family 3 (Proband with diagnosis of ASD and seizures; 273 kb de novo deletion):
The male proband was conceived naturally to a 36 yro mother and 40 yro father. The pregnancy
was uncomplicated and the proband was delivered naturally with a birth weight of 3,714 grams
118
and length of 48 cm. He crawled at 7 months and walked at 15 months. Developmental delay
was first noticed by parents at age two, two months after a serious encephalitic illness that
required hospitalization for three days. Cyclical seizures began at the time of this illness and
recurred until age six and subsequently he remained seizure free without anti-convulsant
treatment. He exhibited speech and language delay and did not talk until approximately age four.
He was diagnosed with ASD (Pervasive Developmental Disorder (PDD)) at age four by a child
psychologist on the basis of behavioral assessments provided by parents, teachers and therapists.
He was diagnosed with intellectual disability a few years later by a school psychologist. He
continues to have behavioral issues including anxiety, OCD, tics, and impulsive, sometimes
aggressive behaviors. He talks to himself frequently, and has trouble concentrating and sitting
still. The proband attended early intervention therapy from age three to six. He has received
special education and/or tutoring throughout school as well as speech therapy and occupational
therapy. He is currently in tenth grade. He has had physical therapy in the past for toe walking.
The proband tested negative for fragile X and had normal results from magnetic resonance
imaging (MRI) and computed tomography (CT) brain scans and electroencephalography (EEG).
At 15 years of age, he was referred for further genetic testing. Chromosomal microarray (Agilent
180K) indicated he had a deletion at cytoband 14q23.3 (chr14:66,148,602-66,421,440 [hg18])
(Figure 6.1, Family 3). Neither parent had the deletion when tested by FISH (Figure 6.2, Family
3). After detection of the de novo hemizygous exonic deletion in the proband, clinical metabolic
testing for Molybdenum Cofactor (MoCo) deficiency was performed and the results were
negative. The testing measured urine levels of uric acid, xanthine, hypoxanthine and uracil and
serum levels of molybdenum and uric acid.
Family 4 (Proband with diagnosis of seizures; 134 kb deletion):
The male proband was examined at five years of age for seizures of unknown type or duration.
Clinical chromosomal microarray (Agilent 44K) indicated he had a deletion at cytoband 14q23.3
(chr14:66,287,215-66,421,440 [hg18]) (Figure 6.1, Family 4). This individual was lost to follow-
up and detailed clinical information was not obtained. Samples from the parents were not
available for testing (Figure 6.2, Family 4).
Family 5 (Proband with schizophrenia diagnosis; 338 kb de novo deletion):
119
The male proband was conceived naturally to non-consanguineous parents, a 29 yro mother and
36 yro father of European ancestry. There was no evidence of developmental delay or autistic
features. The proband completed tenth grade with no reported difficulties, leaving school at age
17 years to work. At 23 years, he began to have increasing anxiety, preoccupation, and paranoia
that responded well to a few months of treatment with chlorpromazine. At age 24 years, he was
admitted to a hospital for schizophrenia. Neuropsychological testing using the Wechsler Adult
Intelligence Scale (WAIS) revealed a Full Scale IQ of 92. He improved with electroconvulsive
therapy, and was discharged after two months. Following two additional hospitalizations, he has
been relatively stable on a standard antipsychotic medication regimen with an adjuvant
antidepressant for many years. A detailed psychiatric assessment, including use of a modified
version of Structured Clinical Interviews for DSM-III-R for Axis I disorders, confirmed a
research diagnosis of chronic schizophrenia. He has no history of seizures and there is no known
history of neuropsychiatric illness or seizures in his parents or siblings. There is a significant
history of schizophrenia in the paternal extended family. There was no significant
dysmorphology noted on examination as an adult by a psychiatric geneticist. Research
chromosomal microarray (Affymetrix 6.0) indicated he had a deletion at cytoband 14q23.3
(chr14:66,267,488-66,605,185 [hg18]) (Figure 6.1, Family 5). Neither parent had the deletion
when tested by Affymetrix 6.0 microarray and qPCR (Figure 6.2, Family 5).
Family 6 (Proband with schizophrenia diagnosis; 183 kb maternally inherited deletion):
The male proband of European ancestry was recruited into the International Schizophrenia
Consortium (ISC) study (30) at age 26 yro. He had normal early milestones. During his
schooling, he had poor marks and left early after 8 years of education. The first onset of
psychiatric symptoms started at 21 years with changes in his behavior, three months before he
attended the National Service. After finishing the service he appeared withdrawn, talked little,
and claimed that “things were different” and that his genitals were changing. At age 22 he was
seen by a psychiatrist after reporting he heard voices laughing at him and people were looking at
him. He believed that he was being poisoned. He was subsequently treated with long-acting
injectable antipsychotic medication for the three years prior to recruitment into the ISC study. He
remained in a chronic condition, with similar delusions, anhedonia (loss of interest in pleasurable
activities) and severe lethargy. Chromosomal microarray testing (Affymetrix 6.0) as part of the
ISC study indicated that he had a deletion at cytoband 14q23.3 (chr14:66,287,324-66,470,381
120
[hg18]) (Figure 6.1, Family 6). Microarray testing showed the deletion to be present in the
mother, who was reported to be unaffected (Figure 6.2, Family 6). DNA was not available for
testing from the proband’s maternal grandmother, who reportedly also had schizophrenia.
6.4 Discussion
Here, we present clinical and molecular characterization of six unrelated index cases possessing
rare de novo or inherited microdeletions overlapping the GPHN gene at 14q23.3. Perhaps the
most compelling evidence for the pathogenicity of the deletions is the fact that three of the five
in families where DNA from both parents was available for inheritance testing arose as de novo
events in the probands. The deletions in the probands in Families 2 and 6 were inherited from a
parent with sub-clinical socialization deficits and an unaffected parent respectively, suggesting
variable expression of structural mutations at this locus.
To our knowledge, this is the first report of hemizygous deletions at the GPHN locus in
connection with ASD or seizure phenotypes. The deletions in probands from Families 5 and 6
add to previous evidence supporting the involvement of GPHN in genetic risk for schizophrenia.
The GPHN locus was one of the top ranking genetic loci at which runs of homozygosity were
significantly over-represented in cases versus controls in a study examining highly penetrant risk
loci in schizophrenia (Lencz et al., 2007). In addition, the statistical method Gene Relationships
Among Implicated Loci (GRAIL) employed by Raychaudhri et al. pinpointed GPHN as a key
gene highly likely to play an etiological role in schizophrenia based on its functional relatedness
to other genes implicated in risk for the disorder (Raychaudhuri et al., 2009).
The common region of overlap across the six deletions encompasses exons 3 to 5 (Figure 6.1),
corresponding to the coding segment of the G domain of the gephyrin protein. G-domain
trimerization is vital for the formation of the hexagonal gephyrin oligomer scaffolds required for
stable GABA receptor clustering in postsynaptic inhibitory neurons (Fritschy et al., 2008).
Interestingly, abnormally spliced gephyrin mRNA (with no corresponding genomic mutation),
lacking several exons corresponding to the G domain, has been isolated in the hippocampus of
patients with temporal lobe epilepsy (Forstera et al., 2010). Due to the missing G-domain exons,
these aberrant protein variants were no longer able to form trimers and were observed to act in a
121
dominant-negative manner by interacting with normally spliced gephyrin and impairing its
function, as observed by depleted GABA receptor cluster density and reduced GABAergic
postsynaptic current amplitudes (Forstera et al., 2010). The authors of this study speculated that
cellular stressors such as elevated temperature or alkalosis, which can cause or arise from seizure
activity, respectively, might induce exon-skipping in GPHN mRNA leading to a dominant-
negative effect (Forstera et al., 2010). This type of ‘environmentally’ induced mRNA mutation
might have an equivalent functional impact to dosage effects of a hemizygous genomic deletion
of the underlying GPHN locus (as is seen in our six families). However the situation in vivo may
be more complex than that observed in vitro due to the multiple isoforms and splice variants of
the gene, differences in gephyrin expression across various synapse types and factors affecting
the expression levels of the non-deleted allele. Given the high prevalence of epilepsy in ASD
(>21% in ASD individuals with an intellectual disability (ID) and 8% in those without ID)
(Amiet et al., 2008), our findings call attention to considering models (Rubenstein and
Merzenich, 2003) whereby the effects of seizures may contribute to expression of ASD, instead
of being interpreted as associated medical comorbidities.
In addition to its synaptic function in the central nervous system, gephyrin has a more wide-
ranging role in peripheral tissues in the synthesis of molybdenum cofactor (MoCo) (Feng et al.,
1998; Stallmeyer et al., 1999), the catalytically active center of molybdoenzymes (class of
molybdenum containing enzymes), whose function is essential for the survival of almost all life-
forms. Gephyrin exhibits extensive sequence conservation across different species ranging from
bacteria to humans, attesting to the strong evolutionary pressure preserving MoCo synthesis
(Stallmeyer et al., 1999). To date, there have been two reports of homozygous GPHN mutations
in humans, both in connection with MoCo deficiency, an extremely rare and sometimes lethal
autosomal recessive metabolic disease characterized by untreatable neonatal seizures with
opisthotonos, hypotonia, feeding difficulties, facial dysmorphism and intellectual disability. One
report (Reiss et al., 2001) identified homozygous deletions, which eliminated exons 2 and 3
resulting in a frameshift after only 21 codons of normal coding sequence and complete loss of
protein function, in the last of three affected infants born to Danish first-cousin parents. All three
infants exhibited symptoms of severe MoCo deficiency and died within a month after birth.
While the vast majority of cases of MoCo deficiency are caused by point mutations in one of two
other related genes involved in MoCo biosynthesis, MOCS1 and MOCS2 (Reiss and Hahnewald,
122
2011), there has been one other case linked to a homozygous point mutation in GPHN (Reiss et
al., 2011). This D580A missense change was recently reported in a female child (of first cousin
parents) of Algerian origin who exhibited a milder MoCo deficiency phenotype when compared
with the patients carrying the GPHN homozygous deletion. The parents, who were carriers of
hemizygous GPHN mutations, did not exhibit any MoCo deficiency symptoms in either of the
two families. We have attempted to see if these parents might have a neuropsychiatric
phenotype, but were unable to obtain information.
The severe symptoms of GPHN null mutations in humans are consistent with phenotypes
observed in geph–/– knockout mice embryos that were born in expected numbers without
apparent physical abnormalities but died within one day of birth (Feng et al., 1998). Knockout
neonates had breathing difficulties and a heightened startle response, becoming rigid and
exhibiting a hyperextended posture when lightly touched. They also had other phenotypic traits
similar to human MoCo deficiency patients including myoclonus and feeding difficulties. The
authors demonstrated that gephyrin was essential for both glycine receptor clustering and MoCo
synthesis. While heterozygous mutant (geph+/–) mice were apparently phenotypically normal
(Feng et al., 1998), there is some evidence from other studies for subtle gene dosage effects. The
concentration of gephyrin protein was reduced by 50% in geph+/– mice (Kneussel et al., 1999),
and reductions in GABAA receptor clustering have been observed in vitro (but not in vivo) in
neuronal cultures derived from geph+/– animals (Fischer et al., 2000). RNAi experiments that
reduced gephyrin expression by ~50% in mouse hippocampal neurons observed significant
reduction of GABA receptor cluster density (Jacob et al., 2005). These findings hint at potential
disturbances in synaptic homeostasis in hemizygous GPHN mutation carriers. Thus while
homozygous GPHN mutations abolishing both the synaptic and molybdenum cofactor
biosynthetic activity of the protein result in the severe metabolic phenotype of MoCo deficiency,
hemizygous deletions reported in this study presumably affect only the synaptic functions of the
protein, as can be inferred from the absence of MoCo deficiency symptoms in our patients and in
previously reported individuals with such mutations (Reiss et al., 2001).
Our data add to the accumulating evidence highlighting the role of dysfunctional inhibitory
signaling with ASD etiology (Coghlan et al., 2012). GPHN joins the growing list of genetic risk
loci evidently shared between ASD and schizophrenia and other NDDs, examples of which are
listed in Chapter 1. Recent research has also highlighted the involvement of CNVs in epilepsy at
123
several of the loci first implicated in ASD and schizophrenia, including 15q13.3, 16p13.11 and
15q11.2 as well as at individual genes such as CNTNAP2, NRXN1 and SHANK3 (Duong et al.,
2012; Lesca et al., 2012; Mulley and Mefford, 2011; Poot et al., 2011). There is also emerging
evidence of comorbidity and functional links between epilepsy and ASD (Amiet et al., 2008;
Brooks-Kayal, 2010), and between epilepsy and schizophrenia (Chang et al., 2011).
Our finding of different disease states arising from deletions of the same gene is in line with
previous reports of several other genetic loci being involved in genetic risk for a range of NDDs
including autism, schizophrenia and epilepsy. For example, these observations of pleiotropic
effects of mutations at the same gene are also seen in connection with mutations in pre-synaptic
neurexin and in the post-synaptic SHANK scaffolding proteins. Duong et al. recently reported
variable penetrance and pleiotropy of NRXN1 mutations within a single family – individuals with
hemizygous deletions and point mutations had differing neurodevelopmental phenotypes ranging
from ASD and epilepsy to schizophrenia and psychotic disorder (Duong et al., 2012). As with
these and other CNVs, the variable expression and penetrance of the NDD phenotypes associated
with structural mutations at the GPHN locus could be modulated by other risk alleles, protective
genetic modifiers and/or environmental factors. Differences among the translational products,
expression levels and stabilities of the pathogenic gephyrin forms, together with the multiple
types of inhibitory receptors potentially affected downstream, could also contribute to the range
of observed disorder phenotypes.
Our results enhance understanding of the GPHN mutation spectrum and will aid in the
interpretation of clinical genetic testing of NDDs. The molecular findings and clinical case
descriptions provided here will serve as a foundation for future studies elucidating a more
comprehensive correlation study of DNA, mRNA (and protein) variants at this locus with the
resulting neurodevelopmental clinical outcomes. Building on previous work (Forstera et al.,
2010), our findings potentially link genomic CNV dosage effects and epilepsy-induced mRNA
dysregulation to GPHN, possibly providing a sought after model to explore gene-environment
interactions in neuropsychiatric disease.
124
6.5 Materials and Methods
6.5.1 Study subjects
The patient cohort inspected for GPHN deletions in this study was composed of 1,158 Canadian
patients with ASD, 72 Austrian patients with ASD, 450 Canadian patients with schizophrenia
and a clinical dataset of 3,704 individuals with primary diagnoses of ASD and/or seizure
disorders that were referred for clinical microarray testing at the Mayo Clinic cytogenetics
laboratory. The Canadian ASD and schizophrenia cohorts were recruited as previously described
in Chapters 3 and 5 respectively. All samples were approved for inclusion in the study by the
appropriate local research ethics boards. The GPHN region was inspected for CNVs in
microarray data analyzed by our group (Lionel et al., 2013) from 21,345 population-based adult
controls genotyped with high-resolution arrays and published data from 2,493 controls
genotyped at the University of Washington (Itsara et al., 2009) and from 3,181 controls analyzed
by the International Schizophrenia Consortium (International Schizophrenia Consortium, 2008).
6.5.2 CNV validation and inheritance testing
Validation of deletions in probands from Families 2, 3 and 4 and in their family members from
whom DNA was available, was conducted by FISH. CNV validation in probands from Families
1, 5 and 6, and in their family members from whom DNA was available, was performed using
SYBR-Green-based real-time quantitative PCR (qPCR), for which two independent primer pairs
were designed at the GPHN locus and at the FOXP2 locus on chromosome 7 as a diploid control.
The parentage of the proband in Family 5 with the de novo GPHN deletion, was confirmed by
calculating the Mendelian error rate of Affymetrix SNP 6.0 array genotypes using the PLINK
tool set (Purcell et al., 2007). Parentage of the proband with a de novo deletion in Family 1 was
confirmed using the AmpFlSTR Identifiler PCR Amplification Kit (Life Technologies) using the
manufacturer’s instructions. Briefly, the kit employs a short tandem repeat (STR) multiplex
assay that amplifies 15 tetranucleotide repeat loci and the Amelogenin gender determining
marker in a single PCR reaction. Samples were run on an AB 3100 Genetic Analyzer using
POP4, dye set G5 and analyzed in GeneMapper v3.7.
125
Chapter 7
Summary and Future Research Directions
The following study provides an excellent illustration of several key themes of my thesis
research and I use this to illustrate possible extensions of my work. Parts of this chapter are
adapted, with permission from Oxford Press, from the following published journal article:
Lionel AC*, Tammimies K*, Vaags AK*, Rosenfeld JA, Ahn JW, Merico D, Noor A, Runke
CK, Pillalamarri VK, Carter MT, Gazzellone MJ, Thiruvahindrapuram B, Fagerberg C, Laulund
LW, Pellecchia G, Lamoureux S, Deshpande C, Clayton-Smith J, White AC, Leather S, Trounce
J, Melanie Bedford H, Hatchwell E, Eis PS, Yuen RK, Walker S, Uddin M, Geraghty MT,
Nikkel SM, Tomiak EM, Fernandez BA, Soreni N, Crosbie J, Arnold PD, Schachar RJ, Roberts
W, Paterson AD, So J, Szatmari P, Chrysler C, Woodbury-Smith M, Brian Lowry R,
Zwaigenbaum L, Mandyam D, Wei J, Macdonald JR, Howe JL, Nalpathamkalam T, Wang Z,
Tolson D, Cobb DS, Wilks TM, Sorensen MJ, Bader PI, An Y, Wu BL, Musumeci SA, Romano
C, Postorivo D, Nardone AM, Monica MD, Scarano G, Zoccante L, Novara F, Zuffardi O,
Ciccone R, Antona V, Carella M, Zelante L, Cavalli P, Poggiani C, Cavallari U, Argiropoulos B,
Chernos J, Brasch-Andersen C, Speevak M, Fichera M, Ogilvie CM, Shen Y, Hodge JC,
Talkowski ME, Stavropoulos DJ, Marshall CR, Scherer SW. Disruption of the ASTN2/TRIM32
locus at 9q33.1 is a risk factor in males for autism spectrum disorders, ADHD and other
neurodevelopmental phenotypes. Human Molecular Genetics; 2014; doi:10.1093/hmg/ddt669.
*Joint first authors
I coordinated collection of genetic data from the different molecular diagnostic sites involved in
this study, performed CNV analysis of controls and the TCAG cases, conducted the statistical
analysis of the CNV results presented in Section 7.2.1 and drafted and revised the manuscript.
Dr. K. Tammimies performed the functional characterization experiments, prepared the figures
presented in Section 7.2.2 and drafted and revised the manuscript. Dr A.K. Vaags coordinated
collection of clinical phenotype information from individuals with ASTN2/TRIM32 deletions.
126
7.1 Summary and Implications
My thesis research had two broad aims. First, in order to discover novel genes involved in NDD
risk, I performed the genome-wide CNV analysis of three newly characterized Canadian patient
cohorts of individuals with one of 3 different disorders: ASD, ADHD and schizophrenia.
Following high-confidence CNV calling using multiple computational algorithms, rare CNVs
were identified by filtering against CNV results generated from controls from the general
population using identical methodology as for the cases. Within each NDD dataset, the strategy
of rare CNV prioritization focused on de novo variants (in those ASD and ADHD probands for
whom parental DNA was available), loci affected by rare CNVs across unrelated probands and
loci previously reported in literature. In addition to replicating pathogenic CNVs previously
reported in literature, these analyses uncovered several novel risk genes for each disorder. For
ASD, discovery of de novo and rare inherited exonic deletions affecting the NRXN3 gene in
multiple unrelated probands provided the final piece of evidence for the involvement of all three
members of the synaptic neurexin protein family in risk for the disorder. Follow-up of these
findings in additional clinical datasets and phenotype characterization of individuals with such
variants revealed clinical heterogeneity and variance in phenotype severity potentially correlated
with the specific gene isoforms affected by the deletions. The rate of de novo CNVs in ADHD
was assessed for the first time and was found to be relatively lower than the established rate of
such mutations in ASD and schizophrenia. Several rare inherited variants were identified in the
ADHD dataset in unrelated patients that affected loci previously implicated in NDD risk
including ASTN2/TRIM32 and DDX53/PTCHD1.The clinical diagnostic yield of microarray
testing of schizophrenia individuals was investigated and was found to be in line with findings
from ASD, providing the first evidence for the benefits of such testing for schizophrenia.
Another novel finding in the schizophrenia dataset involved rare CNVs at the 2q13 region, thus
expanding the phenotype spectrum of this genomic locus from its previous known role in ASD
and ID. The second aim of my thesis research was to probe genetic overlap between the three
NDDs by a systematic comparison of the genome-wide rare CNV results from each disorder
cohort. In addition to confirming the phenotypic heterogeneity of established genomic
syndromes such as the 16p11.2 microduplication and 15q11.2 microduplication regions, this
investigation revealed several novel candidate genes involved in cross-disorder risk including
ASTN2/TRIM32 in ADHD and ASD and GPHN for ASD and schizophrenia. In addition to the
127
results presented here, the data from my thesis project has also facilitated publications with
collaborators describing other novel NDD risk loci such as 16q24.2 deletions (Handrigan et al.,
2013), 1q21.1 duplications (Dolcetti et al., 2013), 15q13.3 duplications (Williams et al., 2012),
2q23.1 duplications (Chung et al., 2012), 3q29 duplications (Goobie et al., 2008), AUTS2
(Beunders et al., 2013), SHANK1 (Sato et al., 2012), PTCHD1 (Noor et al., 2010), TMLHE
(Celestino-Soper et al., 2011) and DPYD (Carter et al., 2011). The findings of my thesis research
have important implications for different facets of molecular medicine including diagnostic
testing, genetic counseling, clinical screening and intervention, as well as for the development of
novel therapeutic agents.
7.1.1 Implications for clinical genetic testing and molecular diagnostics
The rare CNV findings of my thesis research are of immediate relevance to guide clinical genetic
testing. In Canada and many other countries, genome-wide microarray testing in diagnostic
laboratories is now a routine part of the clinical work-up of individuals presenting with NDDs
such as ASD, ID or developmental delay or with congenital anomalies (Anagnostou et al., 2014;
Miller et al., 2010a). Before the results from such testing can be reported to the referring
clinicians, clinical cytogeneticists interpret the CNVs detected and classify them into different
categories ranging from known pathogenic mutations to known benign variants with
intermediate categories of unknown clinical significance (Kearney et al., 2011). A key step in
this categorization process involves literature searches for publications describing the clinical
relevance of specific CNVs and the biological functions of the genes encompassed by them
(Kearney et al., 2011). Thus, research articles such as those arising from my thesis work are
instrumental in the interpretation of test results. Indeed, we have often received requests for
additional information about loci such as GPHN, NRXN3 and ASTN2/TRIM32 from
cytogeneticists who have encountered novel cases with mutations at these genes.
Based on the results of my thesis research, I envision an expansion of the scope of clinical
genetic testing. In particular, the findings from the CNV analysis of the schizophrenia cohort
presented in Chapter 5 make a case for routine clinical microarray testing in schizophrenia,
similar to what is currently standard practice for patients with ASD, ID and developmental delay.
As described in Chapter 5, the percentage of schizophrenia patients with a clinically significant
CNV (as defined by clinical cytogeneticists using consensus parameters for CNV categorization)
128
is in line with yields observed for ASD genetic testing (Miller et al., 2010a; Shen et al., 2010).
Routine employment of clinical microarray testing for schizophrenia would greatly facilitate the
discovery of additional risk genes for the disorder. Such an expansion of clinical genetic testing
into what is largely an adult onset disorder would also represent a paradigm shift in medical
genetics and will greatly broaden its current predominant focus on pediatric conditions.
7.1.2 Implications for genetic counseling of patients and their families
CNV findings highlighting specific genes in NDD risk are especially informative for genetic
counseling (De Wolf et al., 2013). Return of these genetic results, concurrent with explanations
of their implications by genetic counselors and clinical geneticists, can be beneficial to patients
and their families. For instance, the availability of these rare CNV results aids in the calculation
of recurrence risk for NDD in the family and is informative for family planning (Costain and
Bassett, 2014). If the pathogenic rare CNV is found to be of de novo origin in the proband, this
implies that the recurrence risk for siblings is equal to the population prevalence of the disorder
(~1% for schizophrenia and ASD and ~5% for ADHD). On the other hand if the mutation is
found to be inherited from a parent, the siblings will be at a much higher risk for recurrence than
the general population risk. For example, presence of 22q11.23 deletion in a parent would imply
a risk for schizophrenia of approximately 20%–25% in each child (Costain and Bassett, 2014).
The X-linked recessive inheritance of risk CNVs such as PTCHD1 deletions has specific
implications for the counseling of female carriers.
In addition to better informed reproductive decision making, genetic counseling of patients and
their families also has other important therapeutic benefits. Of particular relevance to this thesis
are the comprehensive investigations of the efficacy of genetic counseling of individuals with
schizophrenia (Costain et al., 2014b) and their family members (Costain et al., 2014a) (by my
clinical collaborators, Dr. Anne Bassett and Dr. Gregory Costain). These longitudinal studies
followed up on the rare CNV findings described in the schizophrenia cohort in Chapter 5 of this
thesis and assessed individuals for certain knowledge-based and psychological factors before and
after the receipt of their CNV results via genetic counseling. Individuals with schizophrenia
reported significant improvements after counseling in understanding of the disorder and its
recurrence risk as well as significant reductions in internalized stigma and self-blame (Costain et
al., 2014b). Similar post-counseling increases in knowledge about the condition and decreases
129
sense of stigma and fear of recurrence were also observed in family members of individuals with
the disorder (Costain et al., 2014a). Participants in both studies reported high satisfaction with
genetic counseling and strongly endorsed its necessity.
7.1.3 Implications for screening, diagnosis and treatment of NDDs
The findings from my research will enhance the detection yield in clinical diagnostic laboratories
of rare CNVs implicating specific NDD risk genes in patients. The rapid reporting of these
results to the referring clinicians will enable much faster diagnosis of NDD conditions than was
possible earlier in the absence of genetic testing. This is turn will allow for earlier and more
effective clinical intervention to benefit the patients. For example, in the case of ASD, there is
clear evidence that children diagnosed at earlier ages benefit from timely introduction to
behavioral therapy regimens (Carbone, 2013; Myers and Johnson, 2007). One particularly
effective and widely prescribed regimen is Applied Behavioral Analysis (ABA), which increases
the functional abilities of children with ASD by teaching them behavior skills using repeated
trials (Vismara and Rogers, 2010). Early administration of intensive ABA or other behavioral
interventions such as the Early Start Denver Model have been shown by several studies to
comprehensively alter the developmental trajectory of ASD and yield significant improvements
in long-term outcomes (Dawson et al., 2012; Dawson et al., 2010; Eldevik et al., 2009; Vismara
and Rogers, 2010). Although maximum benefits are obtained when these therapeutic approaches
are started at age 3 or younger, the current average age of diagnosis is between 48 months
(classic autism) and 75 months (Asperger syndrome), and ASD diagnosis is not made for half of
school-aged children before the age of 5 (Autism and Developmental Disabilities Monitoring
Network, 2012; Pringle et al., 2012). The results from genetic screening can bridge this gap
between optimal age for initiating clinical interventions and current age of diagnosis. Earlier
diagnosis and treatment are also of substantial benefit for long-term outcomes in adult-onset
NDDs such as schizophrenia (Emsley et al., 2008).
In addition to benefiting NDD screening, the rare CNV findings have important implications for
the clinical management of patients with such genetic changes. For some of the well-established
microdeletion syndromes featuring recurrent changes of relatively high prevalence such as
22q11.2 DS, consensus clinical practice guidelines have been formulated (Bassett et al., 2011).
These feature anticipatory care over the lifetime of the patient focused on the optimal medical
130
management of the clinical features that are typically known to arise in connection with the
deletion at different time points (Costain and Bassett, 2012). For other rarer CNVs that were
discovered more recently, longitudinal genotype-phenotype studies are essential to define the
phenotypes resulting from the clinical manifestation of the CNV and establish appropriate
guidelines for their clinical management. Knowledge of the biological function of key genes
affected by the CNV can inform treatment strategies. One recent study reported
pharmacogenetically guided treatment of a patient with a large 15q13.3 deletion who presented
with epilepsy, ID, schizophrenia and episodes of aggressive rage (Cubells et al., 2011). This
recurrent microdeletion has been reported in several other individuals with epilepsy, ID,
aggressive behavior, schizophrenia, and autism (Ben-Shachar et al., 2009; Miller et al., 2009).
Since one of the genes affected by the deletion, CHRNA7, encodes a7 nicotinic cholinergic
receptor (NCHR) protein, Cubells et al. hypothesized that deficits in NCHR related
neurotransmission was involved in their patient’s phenotype. Indeed, substantial improvement
was observed upon treatment with galantamine, a U.S. Food and Drug Administration (FDA)
approved modulator of NCHR neurotransmission. The presence of comorbid symptoms of
different NDDs in individuals with CNVs involved in cross-disorder risk also has relevance for
the selection of optimal pharmacological treatment. For instance, individuals with
ASTN2/TRIM32 deletions often present with both ASD and ADHD traits and specific drugs such
as methylphenidate and atomoxetine, together with behavioral therapy, have been recommended
for individuals with these comorbid symptoms (Mahajan et al., 2012; Murray, 2010; Taurines et
al., 2012). Other examples of pharmaceuticals with potential therapeutic benefit across different
NDDs include antipsychotic drugs usually prescribed for schizophrenia showing efficacy when
used for different conditions such as ASD (Zuddas et al., 2011). The use of oxytocin has shown
promise for the resolution of deficits in social cognition and mood problems in both autism and
schizophrenia (Andari et al., 2010; Feifel et al., 2010; Hollander et al., 2007).
7.1.4 Implications for development of novel therapeutics
There is an urgent need for new therapeutic agents for different NDDs in general and in
particular for ASD, for which there is a particular dearth relative to ADHD and schizophrenia
(Delorme et al., 2013; Ghosh et al., 2013; Hampson et al., 2012). Currently, only two drugs are
approved by the FDA specifically for the treatment of ASD: risperidone and aripiprazole
131
(Delorme et al., 2013; McPheeters et al., 2011). These drugs are aimed at reducing symptoms of
irritability associated with ASD and as such are ineffective at resolving core disorder symptoms
including communication deficits and repetitive behaviors (McPheeters et al., 2011; Politte and
McDougle, 2013). Their therapeutical applications are also restricted due to side effects such as
weight gain and sedation. The recent progress in ASD genetic research, including findings
presented in this thesis, could potentially facilitate the development of novel knowledge-based
therapeutics targeting the core molecular etiology of the disorder (Muglia, 2011). Rare CNV and
SNV findings have implicated hundreds of risk genes in ASD (Betancur, 2011; Devlin and
Scherer, 2012). At first glance, this extensive genetic heterogeneity would seem to pose
considerable challenges for the design and targeting of new pharmaceutical therapies. However,
many of these ASD risk genes often converge onto specific pathways. The most striking in terms
of their consistency across findings from multiple independent CNV and SNV studies are gene
networks involved in the formation, maturation and function of neuronal synapses. Examples of
such synaptic ASD risk genes first discovered in my thesis research are NRXN3 (Vaags et al.,
2012) and GPHN (Lionel et al., 2013) among others. It is now evident that the disruption of
synaptic homeostasis is an overarching etiological feature of a significant subset of cases with
ASD and other NDDs (Bourgeron, 2009; Grant, 2012; Guilmatre et al., 2009; Heck and Lu,
2012; Penzes et al., 2013; Ramocki and Zoghbi, 2008; Toro et al., 2010; Waltereit et al., 2013)
and resolution of these synaptic deficits could be the gateway to experimental therapeutics
(Canitano, 2013; Delorme et al., 2013). Two particularly powerful methodologies for modeling
synaptic defects in NDDs are animal models of specific mutations seen in human patients
(Nomura and Takumi, 2012) and patient derived induced pluripotent stem cells (iPSCs)
(Brennand et al., 2011; Mackay-Sim, 2013). In addition to yielding valuable insights into the
precise molecular mechanisms involved in NDD etiology, these technologies can also be used to
test novel candidate pharmaceutical agents with the goal of reversing NDD symptoms at the
organismal and cellular levels. For instance, exciting new research has shown that mice with
overexpression of the synaptic SHANK3 gene show dramatic resolution of their seizure and
manic symptoms upon treatment with valproate, a finding of direct relevance to similar
symptoms in human patients with SHANK3 duplications (Han et al., 2013). Independent work on
functional neurons produced by the iPSC differentiation of skin fibroblasts of ASD patients with
SHANK3 deletions has revealed that the administration of insulin-like growth factor (IGF1) can
132
compensate for the deficits in excitatory synaptic transmission observed in such neurons
(Shcheglovitov et al., 2013).
7.2 Future Research Directions
7.2.1 Follow-up of rare CNV findings in clinical genetic cohorts
The rare CNV findings presented in this thesis and the specific genes highlighted by them
represent a resource that can be used to guide future genetic studies. Replication of these findings
in much larger patient cohorts would provide further evidence that these are bona fide risk loci.
Discovery of additional patients with mutations at the regions reported in this thesis would
enable better understanding of the genotype-phenotype correlation and allow more precise
definition of the clinical features of individuals with such mutations. Particularly valuable
sources of genomic data for future replication and follow-up studies are the genome-wide CNV
results generated by clinical diagnostic laboratories during the genetic testing of patients referred
to them. Since microarray testing is now routinely performed as part of the clinical workup of
cases with ASD, ID, developmental delay and congenital anomalies, clinical diagnostic labs have
access to genome wide CNV data from thousands of such patients, which can be mined for
mutations at specific genes. As an illustration of this strategy, I present here the results of a
follow-up study (Lionel et al., 2014) of the ASTN2/TRIM32 locus, which was first implicated in
ASD and ADHD by rare deletions in the work presented in Chapter 4 (Lionel et al., 2011).
This follow-up screening study utilized microarray data from 89,985 individuals referred for
clinical microarray testing to 10 different molecular diagnostic centers in Canada, Denmark,
Italy, the United Kingdom and the United States (Table 7.1). The reasons for referral for clinical
microarray testing were systematically assessed at each of the study sites and 64,114 individuals
were determined to be NDD cases based on the presence of one or more of the following
phenotypes: ADHD, ASD, behavioral disorders, cognitive impairment, developmental delay, ID,
learning disability, macrocephaly, microcephaly, neurological disorders, OCD, psychoses,
seizures and speech/language disorders. The remaining 25,871 patients in the clinical cohorts,
whose reasons for referral for genetic testing did not contain any of the NDD terms listed above,
were counted as non-NDD cases. The gender composition of each clinical cohort was also
tabulated in order to test for sex-biased effects. For the purpose of statistical testing of findings in
133
the case cohorts, high resolution microarray data from 44,085 control individuals were inspected
for exonic deletions at the ASTN2/TRIM32 regions (Lionel et al., 2014). Fisher’s one-sided exact
test was used to test for enrichment of CNVs in cases versus non-NDD cases and versus controls
with a significance threshold of p < 0.05. A challenge in combining data across multiple case and
control cohorts is the heterogeneity of microarray platforms and the resulting differences in
probe coverage. The array platforms used for the control dataset had much higher probe densities
on average, both genome-wide and in the ASTN2/TRIM32 region of interest, than those
constituting the clinical dataset. The higher resolution of the control CNV dataset relative to the
cases decreases the likelihood of spurious enrichment findings driven by false negatives in the
controls and provides a conservative estimate of the significance and effect size of the findings.
Deletions impacting exons of ASTN2 were identified in 46 individuals (Figure 7.1). In the 20
individuals with parental DNA available for inheritance testing, the deletions were maternally
inherited in 10 (50%), paternally inherited in 8 (40%) and arose de novo in two individuals
(10%). This observed rate of de novo deletions in the cases (2/20) was significantly higher (one-
sided binomial test p = 0.017) than the expected genome-wide background rate of 1% for de
novo deletions in the general population. The latter rate was derived from findings in control
individuals by previous work (Levy et al., 2011; Sanders et al., 2011; Xu et al., 2008), which
used microarrays of similar resolution to those in this study. De novo ASTN2 deletions have
recently been reported in individuals with ID (Bernardini et al., 2010; Vulto-van Silfhout et al.,
2013). The majority of the individuals with exonic ASTN2 deletions (40/46) belonged to the case
subset of 64,114 with NDD phenotypes (Table 7.2). Significant enrichment of ASTN2 exonic
deletions (p = 0.01) was observed in the NDD cases compared with the 25,871 non-NDD cases
in the clinical cohort. Inspection of microarray data from 44,085 control individuals revealed 18
exonic ASTN2 deletions and one deletion exonic solely to TRIM32. Deletions around the 3’ end
of ASTN2 (Figure 7.1) that disrupted multiple transcript isoforms of ASTN2 were significantly
enriched in NDD cases versus controls (p = 0.002) but not in non-NDD cases versus controls
(Table 7.3). A striking difference was observed in sex-specific frequencies for ASTN2 exonic
deletions among the NDD cases, with an excess of such events in male cases compared with
female cases (p = 0.003). Deletion frequencies among the controls did not exhibit a sex-specific
difference. The sex-specific difference was also observed for the deletions at the 3’ end of
ASTN2 that disrupted multiple transcript isoforms of the gene. Such deletions were enriched in
134
the male NDD cases compared with male controls (p = 0.005) but not in female NDD cases
relative to female controls.
135
Table 7.1. Clinical diagnostic cohorts used for screening of rare ASTN2/TRIM32 CNVs
Cohort1 Total # of cases
Total # exonic ASTN2 losses2
# NDD individuals (males/females)
# exonic ASTN2 losses in NDD individuals
Alberta Children’s Hospital 1,619 1 1,170 (675/495) 1
BBGRE 14,847 3 9,650 (6,486/3,164) 1
Boston Children's Hospital 7,320 6 6,623 (4,152/2,471) 5
Credit Valley Hospital 3,552 1 3,098 (2,055/1,043) 1
Hospital for Sick Children 7,411 5 4,863 (3,267/1,596) 5
Italian diagnostic labs3 6,626 3 5,568 (3,272/2,296) 3
Mayo Clinic 19,131 6 11,208 (7,282/3,926) 5
Odense University Hospital 551 2 289 (182/107) 2
Signature Genomics 26,973 13 19,690 (11,617/8,073)4 11
The Centre for Applied Genomics5 1,955 6 1,955 (1,450/505) 6
Total 89,985 46 losses 64,114 (40,438/23,676) 40 losses
Abbreviations: BBGRE, Brain and Body Genetic Resource Exchange database; NDD, Neurodevelopmental disorders.
1 Ten different molecular diagnostic sites that contributed clinical microarray data for this study. Further descriptions are available in the following references: (Ahn et al., 2013). for BBGRE data, (Chen et al., 2013) for Boston Children’s Hospital data, (Hodge et al., 2013) for Mayo Clinic data and (Rosenfeld et al., 2013)for Signature Genomics data.
2 All deletions in the clinical cohorts smaller than 6 Mb that overlapped one or more exons of ASTN2 were included in the counts above.
3.Italian cohort includes data from individuals tested at five different molecular diagnostic sites: Cremona, Pavia, San Giovanni Rotondo, Tor Vergata and Troina.
4 Sex distribution of the Signature cohort was extrapolated from that found in a sampling cross-section of the data by (Ernst et al., 2012)
5 The Centre for Applied Genomics cohort includes 415 Canadian individuals with ADHD (Lionel et al., 2011) genotyped on the Affymetrix 6.0 (n = 248) and the Affymetrix CytoScan HD (n = 167), 174 individuals with OCD genotyped on the Illumina Omni2.5M-quad, and 1,366 Canadian individuals with ASD (Sato et al., 2012) genotyped on one of the following microarray platforms: Affymetrix 6.0, Agilent 1M, Illumina 1M or Affymetrix CytoScan HD.
136
These findings suggest greater penetrance in males relative to females. Similar sex-specific
effects have been recently reported for CNVs at other autosomal loci including SHANK1
deletions in ASD risk (Sato et al., 2012) and 16p13.11 CNVs in NDD risk (Tropeano et al.,
2013). Such autosomal loci with male-biased penetrance, together with X-linked risk genes,
could help explain the skewed sex ratios observed in the prevalence of NDDs such as ADHD,
ASD and ID. Given the enrichment of exonic ASTN2 deletions in cases relative to controls, the
clinical features of individuals with such CNVs were examined for phenotypic trends. The
reasons for referral for genetic testing, together with more detailed clinical phenotypes when
available, were obtained. The major commonality among these individuals was some form of a
NDD phenotype, which was observed in 87% of ASTN2 deletion cases (n = 41). The most
common NDD diagnoses were speech/language delay (n=18), ASD (n=12), ADHD (n=9),
generalized developmental delay (DD) (n=9), anxiety (n=9), obsessive compulsive disorder
(OCD) (n=6) and learning disability (n=8) (Table 7.2). These findings are in line with the initial
observations in Chapter 4, where ASD and ADHD were the primary phenotypes observed in
connected with exonic ASTN2 deletions. Gross motor delay was present in 12 cases and fine
motor delay in 6 cases. In addition, a wide range of dysmorphic features were present in ASTN2
deletion carriers, although macrocephaly was the only feature found to be common in more than
10% of the cases (n=7). Phenotypic information was available for 12 (8 mothers and 4 fathers) of
the 17 parents with exonic ASTN2 deletions. Seventy-five percent of the paternal carriers and
50% of the maternal carriers reported some form of neurodevelopmental trait (primarily anxiety,
depression, learning disabilities, dyslexia and in some cases formal adult diagnoses of ASD or
ADHD), though usually milder than those seen in the probands.
In summary, this follow-up study replicated the initial enrichment observed for ASTN2 deletions
in Chapter 4 and confirmed ASD and ADHD as the most frequent NDD diagnoses observed in
connection with such mutations. In addition, the massive size of the new clinical datasets
provided sufficient statistical power to test additional trends such as the sex-specific gender
differences and the differing penetrance of the deletions based on location within the gene. These
data emphasize the need to characterize rare CNVs (and other genetic variants) in the context of
large case and control cohorts in order to extract meaningful genotype and phenotype data
necessary for proper clinical genetic interpretation.
137
Figure 7.1. Exonic ASTN2/TRIM32 deletions in clinical and control cohorts
Filled red bars represent deletions detected in individuals with NDD phenotypes. Empty red bars denote deletions in cases without known NDD phenotypes (from available clinical information) and in controls. Shaded gray region denotes the critical region defined by deletions that disrupt multiple isoforms of ASTN2. Numbers adjacent to the bars are the randomized sample identifiers of individuals with the deletions and correlate with information in Table 7.2. Gender information was not available for the two control individuals marked with ∗ at the bottom of the figure. Dashed purple lines intersect deletions that overlap exons shared by multiple ASTN2 isoforms and dashed green lines intersect those affecting only the long isoform. Dashed vertical black line intersects deletions that overlap an exon of TRIM32. Genomic locations and coordinates are based on hg18 (NCBI36). The three transcript isoforms of ASTN2 possessing different numbers of exons are depicted including the long isoform (NM_014010) and two shorter isoforms (NM_198186 and NM_001184735). The three other shorter isoforms of the gene (NM_198187, NM_198188 and NM_001184734) have the same number and location of exons as NM_198186 but differ slightly in the length of their first and terminal exons and UTRs.
138
Table 7.2. Genetic and clinical information for individuals with rare ASTN2 deletions
Case# Sex Site1 CNV coordinates (hg18) CNV size CNV NDD phenotypes2
1 M TCAG chr9:117,954,428-118,356,243 401,816 loss ADHD, Anx, LD, SD
2 M OUH chr9:118,055,333-118,646,904 591,572 loss DD, MD
3 M ACH chr9:118,069,649-119,679,670 1,610,022 loss Chiari I malformation
4 M MC chr9:118,130,121-119,029,857 899,737 loss Hydrocephalus, Mac
5 M HSC chr9:118,164,272-118,358,705 194,434 loss ADHD, ASD, DD, LD, Mic, MD, OCD,
SD
6 M SG chr9:118,291,060-118,661,674 370,615 loss Anx, ASD, LD, Mac, MD, OCD, SD
7 M SG chr9:118,327,395-118,595,433 268,039 loss ASD, DD
8 M CVH chr9:118,342,936-118,685,436 342,501 loss DD, seizures
9 M BCH chr9:118,358,646-118,459,563 100,918 loss Anx, DD, Mac
10 M ITA chr9:118,358,837-118,728,270 369,434 loss ASD, SD
11 M HSC chr9:118,390,436-118,524,432 133,997 loss DD, ID, LD, MD, SD
12 M SG chr9:118,395,767-118,520,501 124,735 loss ADHD, SD, CNS disorder
13 M SG chr9:118,395,767-118,595,433 199,667 loss Hydrocephalus
14 M TCAG chr9:118,407,129-118,523,510 116,382 loss Anx, ASD, SD
15 M SG chr9:118,421,170-118,683,092 261,923 loss Behavioral problems
16 M SG chr9:118,430,585-118,569,556 138,972 loss DD, MD, seizures, SD
17 M SG chr9:118,430,585-118,610,907 180,323 loss DD, MD, SD
18 M DEC chr9:118,440,935-118,584,415 143,481 loss ID, Mac
19 M MC chr9:118,459,294-118,616,407 157,114 loss Chronic static encephalopathy
20 M BCH chr9:118,469,713-118,524,312 54,600 loss DD, LD, tics
21 M TCAG chr9:118,479,893-118,627,637 147,745 loss ADHD, Anx, OCD, tics
22 M TCAG chr9:118,480,042-118,570,447 90,406 loss ADHD, ASD, Mac, OCD, SD, seizures
23 M TCAG chr9:118,481,308-118,654,031 172,724 loss Anx, OCD
24 M BCH chr9:118,488,204-118,558,274 70,071 loss ASD, seizures
25 M BCH chr9:118,488,204-118,700,657 212,454 loss -
139
26 M TCAG chr9:118,493,276-118,670,608 177,333 Loss ADHD, Anx, LD
27 M BBG chr9:118,497,759-118,661,673 163,915 Loss -
28 M MC chr9:118,502,294-118,616,407 114,114 Loss ADHD, Anx, ASD, Mac, SD
29 M SG chr9:118,530,143-118,569,556 39,414 Loss Mic
30 M MC chr9:118,541,180-118,685,465 144,286 Loss Dizziness, dysgraphia, migraines
31 M BCH chr9:118,572,937-118,637,250 64,314 Loss DCD, DD, MD, SD
32 M ITA chr9:118,580,317-118,814,591 234,275 Loss ASD, structural brain anomaly
33 M SG chr9:118,580,891-118,630,518 49,628 Loss DD
34 M HSC chr9:118,616,347-118,907,058 290,712 Loss Anx, ASD, hydrocephalus, LD, Mac,
MD, structural brain anomaly
35 M SG chr9:118,620,063-118,781,101 161,039 Loss ASD
36 M BCH chr9:118,743,266-118,991,977 248,712 Loss ASD, DD
37 M ITA chr9:118,840,027-118,935,027 95,001 Loss Behavioral problems, ID, OCD, SD
38 M SG chr9:118,874,947-119,109,618 234,672 Loss -
39 F SG chr9:118,199,243-118,248,950 49,708 Loss ADHD, DD, Mic, SD
40 F BBG chr9:118,202,805-118,227,359 24,555 Loss MD, plagiocephaly, SD
41 F HSC chr9:118,202,811-118,459,563 256,753 Loss Mac
42 F HSC chr9:118,390,436-118,524,432 133,997 Loss ID, MD, SD, seizures
43 F SG chr9:118,481,080-118,610,907 129,828 Loss -
44 F BBG chr9:118,497,759-118,661,673 163,915 Loss -
45 F OUH chr9:118,608,198-118,669,889 61,692 Loss ADHD, MD, SD
46 F MC chr9:118,728,240-118,992,036 263,797 Loss -
47 F MC chr9:118,829,818-118,890,615 60,798 Loss Septo-optic dysplasia
1. Molecular diagnostic testing site of origin of the case: ACD, Alberta Children’s Hospital; BBG, Brain and Body Genetic Resource Exchange (BBGRE); BCH, Boston Children’s Hospital; CVH, Credit Valley Hospital; DEC DECIPHER database; HSC, The Hospital for Sick Children; ITA, Italian diagnostic labs; MC, Mayo Clinic; OUH, Odense University Hospital; SG, Signature Genomics; TCAG, The Centre for Applied Genomics.
2. NDD trait abbreviations: ADHD, attention deficit hyperactivity disorder; Anx, anxiety; ASD, autism spectrum disorder; CNS, central nervous system; DCD, developmental coordination disorder; DD, developmental delay; ID, intellectual disability; LD, learning disability; Mac, macrocephaly; MD, motor delay; Mic, microcephaly; NDD; neurodevelopmental disorder; OCD, obsessive compulsive disorder; SD, speech delay. “-“indicates non-NDD cases (no NDD terms were present in their reasons for referral for clinical microarray testing)
140
Table 7.3. Results of CNV enrichment analysis of ASTN2/TRIM32 locus
Total NDD dataset Male NDD Female NDD
CNV type NDD cases
Controls p-
value1 NDD males
Male controls2
p-value1
NDD females
Female controls2
p-value1
Exonic ASTN2 losses
40 18 0.084 34 10 0.328 6 6 0.773
Exonic losses affecting only long
ASTN2 isoform 13 13 0.876 11 9 0.976 2 3 0.883
Exonic losses affecting multiple ASTN2 isoforms
27 5 0.002 23 1 0.005 4 3 0.64
Exonic losses affecting TRIM323
23 6 0.019 22 2 0.024 1 3 0.964
Abbreviations: NDD, Neurodevelopmental disorders
1. P-values are from one-sided Fisher’s exact test. Values in bold are significant at threshold of p < 0.05.
2. Gender information was available for 33,171 control individuals, and this subset was used for the sex-specific enrichment analysis.
3. All deletions which affected TRIM32 in the cases also overlapped exon(s) of ASTN2. One of the exonic TRIM32 deletions in the controls did not overlap an ASTN2 exon.
141
7.2.2 Functional characterization of rare CNVs and risk genes
Following the discovery of a rare CNV implicating a specific gene in NDD risk, experimental
and computational investigations can be performed in order to characterize the gene’s biological
role, explore causal links between the CNV and gene function and better understand the
molecular mechanisms by which the CNV influences phenotype. Examples of such
investigations include assessing the CNV’s effect on gene expression, quantifying expression of
the gene in different brain tissue types across different neurodevelopmental time points,
measuring relative expression of different transcript isoforms of the gene, and performing
comparative genetics analyses to identify evolutionarily conserved regions within the gene and
functionally important domains of its protein. As an illustration of some of these approaches, I
present here data from the functional characterization of ASTN2 following its implication in
NDD risk by exonic deletions as described in the previous section and in Chapter 4.
Since exonic deletions near the 3’ terminal end of ASTN2, which affected multiple isoforms of
the gene, were strongly enriched in NDD cases compared to controls, the functional impact of
these deletions on the gene’s expression was investigated. The expression testing was performed
in lymphoblast cell lines from six individuals with such deletions including two patients from the
follow-up study described in the previous section and their families (Figure 7.2A). Expression
was significantly lower in ASTN2 deletion carriers (Figure 7.2B) compared with expression
levels in nine individuals with two copies of ASTN2. These results suggest that the deletions
could disrupt the expression of all transcript isoforms of ASTN2 and potentially lead to complete
haploinsufficiency of the protein. This may have more severe phenotypic consequences than
those deletions affecting only the long transcript isoform. Similar trends of greater penetrance of
deletions impacting multiple isoforms of a gene, relative to those overlapping a single isoform,
were also observed for the NRXN3 deletions presented in Chapter 3 (Vaags et al., 2012) and at
other loci such as NRXN1 (Schaaf et al., 2012) and AUTS2 (Beunders et al., 2013) in connection
with risk for NDDs.
142
Figure 7.2. Impact of ASTN2 deletions on gene expression in lymphoblast cell lines
A) Pedigrees of the two families used for ASTN2 expression analysis. Arrows indicate the index probands of the pedigrees (corresponding to patients 14 and 22 in Figure 7.1 and Table 7.2). Individuals with black shading have an ASD diagnosis while the person represented by gray shading reported depression and anxiety issues. B) Relative ASTN2 expression in lymphoblast cell lines from the six individuals (3 males and 3 females) with ASTN2 deletions in the two pedigrees were compared to lymphoblast cell-lines from 9 individuals (5 males and 4 females) with two copies of ASTN2. The latter group included the three individuals without deletions in the two pedigrees as well as six additional samples with two copies of ASTN2. The lymphoblast cell lines were cultured as previously described (Seno et al., 2011). Total RNA was extracted using Qiagen RNeasy mini kit with DNase I treatment (QIAGEN, Valencia, CA, USA). cDNA was synthesized using Superscript III First strand Synthesis Supermix (Invitrogen, Carlsbad, CA, USA) with 1 µg of poly (A+) or total DNase I treated RNA as a template. The expression was measured using two different primer pairs which detected all ASTN2 isoforms. ASTN2 expression was normalized using GAPDH (dCt) and the fold change was calculated using the ∆∆Ct method. Student’s T-test was used to assess the expression difference between the two sample groups for statistical significance.
143
Given the location-dependent penetrance observed for the ASTN2 exonic deletions (Table 7.3),
the average nucleotide conservation of all exons in ASTN2 (Figure 7.3A) and TRIM32 was
assessed across vertebrates, placental mammals and primates (Figure 7.3B). Exons with coding
sequence were well-conserved across all vertebrate species. Interestingly, the conservation was
much lower between humans and other vertebrates for the exons unique to the shorter isoforms
(exons 1B and 5C in Figure 7.3A). Additionally, inspection of EST databases such as Aceview,
Ensembl and Fantom did not reveal any evidence for the presence of shorter 3’-terminal Astn2
isoforms in mouse. These observations suggest that the unique exons of the shorter isoforms (and
their alternative transcription site in exon 1B) are of more recent evolutionary origin and are
potentially derived in or just prior to the primate lineage. In support of this claim, both exons 1B
and 5C contain transposable elements, which have recently been shown to contribute novel
regulatory elements and thus give rise to new transcript isoforms (Jacques et al., 2013).
Although there was no evident difference in the average nucleotide conservation between ASTN2
coding exons, the amino acid alignment of eight ASTN2 protein orthologs revealed that the C-
terminal end exhibits a greater degree of conservation compared to the rest of the protein (Figure
7.4). This section of the protein corresponds to the 3’-terminal region of the gene exhibiting the
strongest enrichment of exonic deletions in male NDD cases (Figure 7.1) and encodes the
fibronectin type III domain (Figure 7.4). Interestingly, this domain is also found in other genes
implicated in NDDs such as the contactins (Burbach and van der Zwaag, 2009). In addition, the
data suggest that the shorter transcript isoforms are of functional importance especially during
early brain development. This claim is supported by mRNA quantification assays in which the
shorter isoforms account for forty percent of the total expression of the gene in fetal brain and
were detected in all the different brain regions tested (Figure 7.5). The evolutionary conservation
analyses suggest that the first exon shared by the shorter ASTN2 isoforms is of more recent
evolutionary origin and could have a function specific to primates. There is no indication from
previous functional work (Wilson et al., 2010) or from mouse EST databases for existence of
shorter 3’-terminal Astn2 transcript isoforms in the mouse. Taken together, the recent emergence
of the shorter isoforms comprising a functionally important domain and their widespread
expression in the human brain could indicate primate-specific involvement in neurodevelopment.
144
Figure 7.3. Exon conservation analysis of ASTN2 and TRIM32
(A) Schematic presentation of the ASTN2 transcript isoforms. The three transcript isoforms of ASTN2 possessing different numbers of exons are depicted including the long isoform (NM_014010) and two shorter isoforms (NM_198186 and NM_001184735). The three other shorter isoforms of the gene (NM_198187, NM_198188 and NM_001184734) have the same number and location of exons as NM_198186 but differ slightly in the length of their first and terminal exons and UTRs. The ‘∗’ symbol denotes exons with variable length in different isoforms. (B) Conservation profile of ASTN2 and TRIM32 exons across vertebrates, placental mammals and primates. The nucleotide conservation scores for each base present in all ASTN2 and TRIM32 exons were computed using the PhyloP program based on alignment between 46 vertebrate species including 23 placental mammals and eight primates. The average score was calculated for each exon and for the unique UTRs.
145
Figure 7.4. Amino acid conservation analysis of ASTN2 protein
The amino acid sequences of 1:1 ASTN2 orthologs from the following eight species were downloaded from Ensembl: human ASTN2 (ENSP00000354504): macaque (ENSMMUP00000039777), chimpanzee (ENSPTRP00000036394), horse (ENSECAP00000003457), mouse (ENSMUSP00000065786), dog (ENSCAFP00000005232), chicken (ENSGALP00000011381) and clawed frog (ENSXETP00000047008). Following multiple sequence alignment using ClustalW2, amino acid conservation was quantified using Scorecons server. The known protein domains and features of ASTN2 are shown in the schematic illustration above the chart. The signal peptide (SP) is marked red, trans-membrane (TM) domains are marked light blue, EGF/laminin superfamily (EGF) domains are marked light green, the MACPF domain is marked blue and the fibronectin domain type 3 (FN III) is marked dark green. The critical region with enrichment of exonic deletions affecting multiple ASTN2 isoforms in NDD cases from Figure 7.1 is shown in grey. Vertical red dashed lines correspond to the exon boundaries.
146
Figure 7.5. Relative expression levels of ASTN2 transcript isoforms
Figure on the left shows the expression of ASTN2 transcript isoforms (long, shorter and all) in eight different human brain regions. ACTB was used as a control gene. Results are from RT-PCR analysis using primers with locations as depicted by red arrows in Figure 7.3A. Abundant expression was detected for primers amplifying exons present in all isoforms (ASTN2 all) and for exons specific to the long ASTN2 isoform NM_014010 (ASTN2 long isoform) in all the brain regions. The shorter transcripts (NM_198187, NM_198188, NM_001184734 and NM_001184735) were expressed at a lower level (ASTN2 shorter isoforms).
Figure on the right shows the results from quantification of ASTN2 isoform expression levels in triplicate from adult brain and fetal brain RNA samples by qRT–PCR (standard curve method). The expression was normalized using ACTB as a housekeeping gene, and the expression ratio is relative to the expression from all ASTN2 isoforms (mean ratio+standard deviation). The results were replicated using normalization against GAPDH, a housekeeping gene. A change was observed in the expression ratios between the developmental stages. In the fetal brain, the shorter isoforms accounted for approximately 40% of the total ASTN2 expression, which decreased to 20% in the adult brain. This suggests a functional role for the shorter isoforms during early development.
147
Figure 7.6. Expression profile of ASTN2 across human brain development
The gene level expression profiles of ASTN2 across developmental time points in nine regions of the human brain; amygdala (AMY), cerebellar cortex (CBC), diencephalon (DIE), frontal cortex (FC), hippocampus (HIP), occipital cortex (OC), parietal cortex (PC), temporal cortex (TC) and ventral forebrain (VF). This analysis utilized expression data from the BrainSpan database (www.brainspan.org) (Kang et al., 2011), which contains extensive transcriptome profiles for 16 brain regions from 41 individuals. The age range of the subjects spans from 8 post-conception weeks to 40 years. The data was quantile normalized and the gene level expression was averaged across donors for each time point in the nine regions. The expression levels of ASTN2 in the different brain regions were plotted across age range using the R package ggplot2. Smooth curve lines were computed by loess using a span of 0.4.
148
The spatiotemporal expression pattern of ASTN2 in different brain regions during human
development was analyzed by utilizing comprehensive gene expression data from the BrainSpan
database (Kang et al., 2011). Overall, ASTN2 is expressed at a moderate level throughout
development and exhibits an increase in the late prenatal period and during postnatal
development in many of the brain regions (Figure 7.6). The highest level of ASTN2 expression
was observed in the cerebellar cortex (CBC), where the expression increase during infancy and
early childhood is in concordance with the expression pattern previously reported in mice
(Wilson et al., 2010). Other genes from the BrainSpan database exhibiting similar expression
patterns in the CBC were involved in synaptic transmission and plasticity. Interestingly, during
the prenatal period, ASTN2 has a dynamic spike in the expression around 12-13 gestational
weeks in the neocortical regions (frontal cortex, parietal cortex and occipital cortex) (Figure 7.6).
This expression pattern is enriched in neuronal development genes involved in axonogenesis and
neuron differentiation. The dynamic expression pattern of ASTN2 together with the biological
annotation of genes with similar expression patterns suggests that ASTN2 could have an
important role in both prenatal and postnatal brain development.
Comparison of the human brain expression profile of ASTN2 with previous work in mice
revealed both intriguing differences and similarities. The striking prenatal spike in ASTN2
expression in the neocortical regions, towards the end of the first trimester, has not been reported
in the embryonic mouse brain (Wilson et al., 2010). This finding might indicate transcriptional
regulation patterns or additional functions of the protein and/or transcript isoforms specific to
primates. Interestingly, this period in the developmental timeline of the human cerebral cortex is
marked by extensive neuronal migration, increasing axonal outgrowth and the formation of the
early synapses (Kang et al., 2011; Pescosolido et al., 2012). Several genes previously implicated
in risk for ASD (and also other NDDs) such as DOCK4, and NRCAM also exhibit increasing
cortical expression around this time-point (Pagnamenta et al., 2010; Sakurai, 2012). In
concordance with the experimental work from mouse (Wilson et al., 2010), the highest ASTN2
expression is expressed in the cerebellar cortex shortly after birth. Several studies investigating
the neuropathology of ASD (Fatemi et al., 2012) and ADHD (O'Halloran et al., 2012) have
consistently highlighted the cerebellum as a major region of interest. Reported neuroanatomical
abnormalities include disrupted neuronal migration in the cerebellar cortex (Fatemi et al., 2012)
as well as reduction in volume of the cerebellar vermis (Anagnostou and Taylor, 2011), which
149
has been linked to repetitive and stereotyped behavior in ASD (Pierce and Courchesne, 2001).
Furthermore, the number of Purkinje cells, one of the main cell types in the cerebellum, has been
found to be decreased by up to 50% in individuals with ASD (Fatemi et al., 2012). Astn2 has also
been shown to be very highly expressed in the cerebellum in general, and in the Purkinje cells in
particular, during both embryonic and postnatal development (Wilson et al., 2010).
7.2.3 Higher resolution genomic studies of NDDs
Although substantial initial progress has been made over the past decade in the discovery of the
specific genes involved in NDD risk, this monumental task is far from complete. For instance, in
ASD genetics research, it is estimated that the etiological mutations (in the form of rare CNVs,
SNVs and chromosomal abnormalities) identified to date account for around 20% of cases
(Devlin and Scherer, 2012). It is clear that the majority of NDD cases are still of idiopathic or
unknown causation. The continued elucidation of the genetic architectures of these conditions is
critical for further advances in molecular diagnostics, clinical screening and therapeutic
development. Other intriguing sources of genetic risk for NDDs remain relatively unexplored
including random monoallelic inactivation (Chess, 2012) and specific somatic mutations in
affected parts of the brain that are absent in other tissues (Insel, 2014). Additionally, the roles of
“modifier genes” and gene-gene interactions have to be systematically investigated for their
potential to explain the complex and variable phenotypes observed in the clinical presentation of
different NDDs.
While genome-wide microarrays such as those used in this thesis research have been effective at
detecting CNVs of relatively large sizes, they suffer from certain limitations. These include their
inability to detect either smaller genetic changes such as SNVs and indels or balanced events
such as inversions and translocations. The scope of CNV detection by microarrays is also limited
to those regions with sufficient probe coverage, which is often preferentially biased towards
regions with known genes at the expense of intergenic regions. As a result of these deficiencies,
broad swathes of the genetic variant spectrum are missed by microarrays, thus necessitating
higher-resolution genomics technology for NDD gene discovery. Arguably the most promising
of these new approaches is whole genome sequencing (WGS) technology. By presenting a
comprehensive and unbiased view of the total spectrum of genetic variation, WGS represents a
considerable advance from older genetic technology. WGS studies of individuals with NDD
150
have the potential to substantially increase the detection yield of genetic testing by capturing all
classes of genetic mutations within a patient in a single experiment. The first such applications of
WGS in ASD genetic research have indeed found high yields of clinically relevant mutations (in
~50% of families) and identified specific mutational hotspots missed by previous microarray and
exome sequencing studies (Jiang et al., 2013; Michaelson et al., 2012). The exponentially
increasing use of WGS in psychiatric genetic research and its ongoing transition into clinical
diagnostic laboratories will catalyze the next significant advances in uncovering the genomic
architectures of NDDs.
151
References
Abrahams, B.S., Arking, D.E., Campbell, D.B., Mefford, H.C., Morrow, E.M., Weiss, L.A., Menashe, I., Wadkins, T., Banerjee-Basu, S., and Packer, A. (2013). SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol Autism 4, 36.
Abrahams, B.S., and Geschwind, D.H. (2008). Advances in autism genetics: on the threshold of a new neurobiology. Nat Rev Genet 9, 341-355.
Adams, N.C., Tomoda, T., Cooper, M., Dietz, G., and Hatten, M.E. (2002). Mice that lack astrotactin have slowed neuronal migration. Development 129, 965-972.
Ahn, J.W., Dixit, A., Johnston, C., Ogilvie, C.M., Collier, D.A., Curran, S., and Dobson, R.J. (2013). BBGRE: brain and body genetic resource exchange. Database (Oxford) 2013, bat067.
Ahuja, A., Martin, J., Langley, K., and Thapar, A. (2013). Intellectual disability in children with attention deficit hyperactivity disorder. J Pediatr 163, 890-895 e891.
Ambrosini, P.J. (2000). Historical development and present status of the schedule for affective disorders and schizophrenia for school-age children (K-SADS). J Am Acad Child Adolesc Psychiatry 39, 49-58.
American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.) (Arlington, VA, American Psychiatric Publishing).
Amiet, C., Gourfinkel-An, I., Bouzamondo, A., Tordjman, S., Baulac, M., Lechat, P., Mottron, L., and Cohen, D. (2008). Epilepsy in autism is associated with intellectual disability and gender: evidence from a meta-analysis. Biol Psychiatry 64, 577-582.
Anagnostou, E., and Taylor, M.J. (2011). Review of neuroimaging in autism spectrum disorders: what have we learned and where we go from here. Mol Autism 2, 4.
Anagnostou, E., Zwaigenbaum, L., Szatmari, P., Fombonne, E., Fernandez, B.A., Woodbury-Smith, M., Brian, J., Bryson, S., Smith, I.M., Drmic, I., et al. (2014). Autism spectrum disorder: advances in evidence-based practice. CMAJ.
Anckarsater, H., Stahlberg, O., Larson, T., Hakansson, C., Jutblad, S.B., Niklasson, L., Nyden, A., Wentz, E., Westergren, S., Cloninger, C.R., et al. (2006). The impact of ADHD and autism spectrum disorders on temperament, character, and personality development. Am J Psychiatry 163, 1239-1244.
Andari, E., Duhamel, J.R., Zalla, T., Herbrecht, E., Leboyer, M., and Sirigu, A. (2010). Promoting social behavior with oxytocin in high-functioning autism spectrum disorders. Proc Natl Acad Sci USA 107, 4389-4394.
152
Andrulis, I.L., Boyd, N.F., and Sutherland, H. (1997). New Ontario familial breast cancer registry to facilitate genetic and epidemiologic studies. Can Fam Physician 43, 949-950.
Anney, R., Klei, L., Pinto, D., Almeida, J., Bacchelli, E., Baird, G., Bolshakova, N., Bolte, S., Bolton, P.F., Bourgeron, T., et al. (2012). Individual common variants exert weak effects on the risk for autism spectrum disorderspi. Hum Mol Genet 21, 4781-4792.
Anney, R., Klei, L., Pinto, D., Regan, R., Conroy, J., Magalhaes, T.R., Correia, C., Abrahams, B.S., Sykes, N., Pagnamenta, A.T., et al. (2010). A genome-wide scan for common alleles affecting risk for autism. Hum Mol Genet 19, 4072-4082.
Arcos-Burgos, M., Castellanos, F.X., Pineda, D., Lopera, F., Palacio, J.D., Palacio, L.G., Rapoport, J.L., Berg, K., Bailey-Wilson, J.E., and Muenke, M. (2004). Attention-deficit/hyperactivity disorder in a population isolate: linkage to loci at 4q13.2, 5q33.3, 11q22, and 17p11. Am J Hum Genet 75, 998-1014.
Arnold, P.D., Siegel-Bartelt, J., Cytrynbaum, C., Teshima, I., and Schachar, R. (2001). Velo-cardio-facial syndrome: Implications of microdeletion 22q11 for schizophrenia and mood disorders. Am J Med Genet 105, 354-362.
Autism and Developmental Disabilities Monitoring Network (2012). Prevalence of autism spectrum disorders--Autism and Developmental Disabilities Monitoring Network, 14 sites, United States, 2008. MMWR Surveill Summ 61, 1-19.
Awadalla, P., Gauthier, J., Myers, R.A., Casals, F., Hamdan, F.F., Griffing, A.R., Cote, M., Henrion, E., Spiegelman, D., Tarabeux, J., et al. (2010). Direct measure of the de novo mutation rate in autism and schizophrenia cohorts. Am J Hum Genet 87, 316-324.
Bailey, A., Le Couteur, A., Gottesman, I., Bolton, P., Simonoff, E., Yuzda, E., and Rutter, M. (1995). Autism as a strongly genetic disorder: evidence from a British twin study. Psychol Med 25, 63-77.
Baker, P., Piven, J., Schwartz, S., and Patil, S. (1994). Brief report: duplication of chromosome 15q11-13 in two individuals with autistic disorder. J Autism Dev Disord 24, 529-535.
Bakker, S.C., van der Meulen, E.M., Buitelaar, J.K., Sandkuijl, L.A., Pauls, D.L., Monsuur, A.J., van 't Slot, R., Minderaa, R.B., Gunning, W.B., Pearson, P.L., et al. (2003). A whole-genome scan in 164 Dutch sib pairs with attention-deficit/hyperactivity disorder: suggestive evidence for linkage on chromosomes 7p and 15q. Am J Hum Genet 72, 1251-1260.
Baribeau, D.A., and Anagnostou, E. (2013). A Comparison of Neuroimaging Findings in Childhood Onset Schizophrenia and Autism Spectrum Disorder: A Review of the Literature. Front Psychiatry 4, 175.
Barneveld, P.S., Pieterse, J., de Sonneville, L., van Rijn, S., Lahuis, B., van Engeland, H., and Swaab, H. (2011). Overlap of autistic and schizotypal traits in adolescents with Autism Spectrum Disorders. Schizophr Res 126, 231-236.
153
Barr, C.L., Feng, Y., Wigg, K., Bloom, S., Roberts, W., Malone, M., Schachar, R., Tannock, R., and Kennedy, J.L. (2000a). Identification of DNA variants in the SNAP-25 gene and linkage study of these polymorphisms and attention-deficit hyperactivity disorder. Mol Psychiatry 5, 405-409.
Barr, C.L., Wigg, K.G., Feng, Y., Zai, G., Malone, M., Roberts, W., Schachar, R., Tannock, R., and Kennedy, J.L. (2000b). Attention-deficit hyperactivity disorder and the gene for the dopamine D5 receptor. Mol Psychiatry 5, 548-551.
Bassett, A.S. (2011). Parental origin, DNA structure, and the schizophrenia spectrum. Am J Psychiatry 168, 350-353.
Bassett, A.S., and Chow, E.W. (2008). Schizophrenia and 22q11.2 deletion syndrome. Curr Psychiatry Rep 10, 148-157.
Bassett, A.S., Chow, E.W., Husted, J., Hodgkinson, K.A., Oechslin, E., Harris, L., and Silversides, C. (2009). Premature death in adults with 22q11.2 deletion syndrome. J Med Genet 46, 324-330.
Bassett, A.S., Costain, G., Fung, W.L., Russell, K.J., Pierce, L., Kapadia, R., Carter, R.F., Chow, E.W., and Forsythe, P.J. (2010a). Clinically detectable copy number variations in a Canadian catchment population of schizophrenia. J Psychiatr Res 44, 1005-1009.
Bassett, A.S., Marshall, C.R., Lionel, A.C., Chow, E.W., and Scherer, S.W. (2008). Copy number variations and risk for schizophrenia in 22q11.2 deletion syndrome. Hum Mol Genet 17, 4045-4053.
Bassett, A.S., McDonald-McGinn, D.M., Devriendt, K., Digilio, M.C., Goldenberg, P., Habel, A., Marino, B., Oskarsdottir, S., Philip, N., Sullivan, K., et al. (2011). Practical guidelines for managing patients with 22q11.2 deletion syndrome. J Pediatr 159, 332-339 e331.
Bassett, A.S., Scherer, S.W., and Brzustowicz, L.M. (2010b). Copy number variations in schizophrenia: critical review and new perspectives on concepts of genetics and disease. Am J Psychiatry 167, 899-914.
Baum, A.E., Akula, N., Cabanero, M., Cardona, I., Corona, W., Klemens, B., Schulze, T.G., Cichon, S., Rietschel, M., Nothen, M.M., et al. (2008). A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder. Mol Psychiatry 13, 197-207.
Ben-Shachar, S., Lanpher, B., German, J.R., Qasaymeh, M., Potocki, L., Nagamani, S.C., Franco, L.M., Malphrus, A., Bottenfield, G.W., Spence, J.E., et al. (2009). Microdeletion 15q13.3: a locus with incomplete penetrance for autism, mental retardation, and psychiatric disorders. J Med Genet 46, 382-388.
Berkel, S., Marshall, C.R., Weiss, B., Howe, J., Roeth, R., Moog, U., Endris, V., Roberts, W., Szatmari, P., Pinto, D., et al. (2010). Mutations in the SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental retardation. Nat Genet 42, 489-491.
154
Bernardini, L., Alesi, V., Loddo, S., Novelli, A., Bottillo, I., Battaglia, A., Digilio, M.C., Zampino, G., Ertel, A., Fortina, P., et al. (2010). High-resolution SNP arrays in mental retardation diagnostics: how much do we gain? Eur J Hum Genet 18, 178-185.
Betancur, C. (2011). Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res 1380, 42-77.
Beunders, G., Voorhoeve, E., Golzio, C., Pardo, L.M., Rosenfeld, J.A., Talkowski, M.E., Simonic, I., Lionel, A.C., Vergult, S., Pyatt, R.E., et al. (2013). Exonic deletions in AUTS2 cause a syndromic form of intellectual disability and suggest a critical role for the C terminus. Am J Hum Genet 92, 210-220.
Bevan Jones, R., Thapar, A., Lewis, G., and Zammit, S. (2012). The association between early autistic traits and psychotic experiences in adolescence. Schizophr Res 135, 164-169.
Biederman, J., Faraone, S.V., Mick, E., Spencer, T., Wilens, T., Kiely, K., Guite, J., Ablon, J.S., Reed, E., and Warburton, R. (1995). High risk for attention deficit hyperactivity disorder among children of parents with childhood onset of the disorder: a pilot study. Am J Psychiatry 152, 431-435.
Biederman, J., Mick, E., Faraone, S.V., Braaten, E., Doyle, A., Spencer, T., Wilens, T.E., Frazier, E., and Johnson, M.A. (2002). Influence of gender on attention deficit hyperactivity disorder in children referred to a psychiatric clinic. Am J Psychiatry 159, 36-42.
Biederman, J., Monuteaux, M.C., Mick, E., Spencer, T., Wilens, T.E., Silva, J.M., Snyder, L.E., and Faraone, S.V. (2006). Young adult outcome of attention deficit hyperactivity disorder: a controlled 10-year follow-up study. Psychol Med 36, 167-179.
Biederman, J., Petty, C.R., Fried, R., Kaiser, R., Dolan, C.R., Schoenfeld, S., Doyle, A.E., Seidman, L.J., and Faraone, S.V. (2008). Educational and occupational underattainment in adults with attention-deficit/hyperactivity disorder: a controlled study. J Clin Psychiatry 69, 1217-1222.
Binder, M.D., and Kilpatrick, T.J. (2009). TAM receptor signalling and demyelination. Neurosignals 17, 277-287.
Bochukova, E.G., Huang, N., Keogh, J., Henning, E., Purmann, C., Blaszczyk, K., Saeed, S., Hamilton-Shield, J., Clayton-Smith, J., O'Rahilly, S., et al. (2010). Large, rare chromosomal deletions associated with severe early-onset obesity. Nature 463, 666-670.
Bourgeron, T. (2009). A synaptic trek to autism. Curr Opin Neurobiol 19, 231-234.
Bradley, W.E., Raelson, J.V., Dubois, D.Y., Godin, E., Fournier, H., Prive, C., Allard, R., Pinchuk, V., Lapalme, M., Paulussen, R.J., et al. (2010). Hotspots of large rare deletions in the human genome. PLoS One 5, e9401.
Bray, N.J., Leweke, F.M., Kapur, S., and Meyer-Lindenberg, A. (2010). The neurobiology of schizophrenia: new leads and avenues for treatment. Curr Opin Neurobiol 20, 810-815.
155
Brennand, K.J., Simone, A., Jou, J., Gelboin-Burkhart, C., Tran, N., Sangar, S., Li, Y., Mu, Y., Chen, G., Yu, D., et al. (2011). Modelling schizophrenia using human induced pluripotent stem cells. Nature 473, 221-225.
Brooks-Kayal, A. (2010). Epilepsy and autism spectrum disorders: are there common developmental mechanisms? Brain Dev 32, 731-738.
Bruce, B., Thernlund, G., and Nettelbladt, U. (2006). ADHD and language impairment: A study of the parent questionnaire FTF (Five to Fifteen). Eur Child Adolesc Psychiatry 15, 52-60.
Brunetti-Pierri, N., Berg, J.S., Scaglia, F., Belmont, J., Bacino, C.A., Sahoo, T., Lalani, S.R., Graham, B., Lee, B., Shinawi, M., et al. (2008). Recurrent reciprocal 1q21.1 deletions and duplications associated with microcephaly or macrocephaly and developmental and behavioral abnormalities. Nat Genet 40, 1466-1471.
Bucan, M., Abrahams, B.S., Wang, K., Glessner, J.T., Herman, E.I., Sonnenblick, L.I., Alvarez Retuerto, A.I., Imielinski, M., Hadley, D., Bradfield, J.P., et al. (2009). Genome-wide analyses of exonic copy number variants in a family-based study point to novel autism susceptibility genes. PLoS Genet 5, e1000536.
Buizer-Voskamp, J.E., Muntjewerff, J.W., Strengman, E., Sabatti, C., Stefansson, H., Vorstman, J.A., and Ophoff, R.A. (2011). Genome-wide analysis shows increased frequency of copy number variation deletions in Dutch schizophrenia patients. Biol Psychiatry 70, 655-662.
Burbach, J.P., and van der Zwaag, B. (2009). Contact in the genetics of autism and schizophrenia. Trends Neurosci 32, 69-72.
Buxbaum, J.D., Baron-Cohen, S., and Devlin, B. (2010). Genetics in psychiatry: common variant association studies. Mol Autism 1, 6.
Caluseriu, O., Mirza, G., Ragoussis, J., Chow, E.W., MacCrimmon, D., and Bassett, A.S. (2006). Schizophrenia in an adult with 6p25 deletion syndrome. Am J Med Genet A 140, 1208-1213.
Canitano, R. (2013). Novel treatments in autism spectrum disorders: from synaptic dysfunction to experimental therapeutics. Behav Brain Res 251, 125-132.
Cannon, T.D., Kaprio, J., Lonnqvist, J., Huttunen, M., and Koskenvuo, M. (1998). The genetic epidemiology of schizophrenia in a Finnish twin cohort. A population-based modeling study. Arch Gen Psychiatry 55, 67-74.
Carbone, P.S. (2013). Moving from research to practice in the primary care of children with autism spectrum disorders. Acad Pediatr 13, 390-399.
Cardno, A.G., Marshall, E.J., Coid, B., Macdonald, A.M., Ribchester, T.R., Davies, N.J., Venturi, P., Jones, L.A., Lewis, S.W., Sham, P.C., et al. (1999). Heritability estimates for psychotic disorders: the Maudsley twin psychosis series. Arch Gen Psychiatry 56, 162-168.
Carroll, L.S., and Owen, M.J. (2009). Genetic overlap between autism, schizophrenia and bipolar disorder. Genome Med 1, 102.
156
Carter, M.T., Nikkel, S.M., Fernandez, B.A., Marshall, C.R., Noor, A., Lionel, A.C., Prasad, A., Pinto, D., Joseph-George, A.M., Noakes, C., et al. (2011). Hemizygous deletions on chromosome 1p21.3 involving the DPYD gene in individuals with autism spectrum disorder. Clin Genet 80, 435-443.
Celestino-Soper, P.B., Shaw, C.A., Sanders, S.J., Li, J., Murtha, M.T., Ercan-Sencicek, A.G., Davis, L., Thomson, S., Gambin, T., Chinault, A.C., et al. (2011). Use of array CGH to detect exonic copy number variants throughout the genome in autism families detects a novel deletion in TMLHE. Hum Mol Genet 20, 4360-4370.
Chang, Y.T., Chen, P.C., Tsai, I.J., Sung, F.C., Chin, Z.N., Kuo, H.T., Tsai, C.H., and Chou, I.C. (2011). Bidirectional relation between schizophrenia and epilepsy: a population-based retrospective cohort study. Epilepsia 52, 2036-2042.
Charach, A., Chen, S., Hogg-Johnson, S., and Schachar, R.J. (2009). Using the Conners' Teacher Rating Scale-Revised in school children referred for assessment. Can J Psychiatry 54, 232-241.
Chen, X., Shen, Y., Zhang, F., Chiang, C., Pillalamarri, V., Blumenthal, I., Talkowski, M., Wu, B.L., and Gusella, J.F. (2013). Molecular analysis of a deletion hotspot in the NRXN1 region reveals the involvement of short inverted repeats in deletion CNVs. Am J Hum Genet 92, 375-386.
Chess, A. (2012). Mechanisms and consequences of widespread random monoallelic expression. Nat Rev Genet 13, 421-428.
Chiang, A.P., Beck, J.S., Yen, H.J., Tayeh, M.K., Scheetz, T.E., Swiderski, R.E., Nishimura, D.Y., Braun, T.A., Kim, K.Y., Huang, J., et al. (2006). Homozygosity mapping with SNP arrays identifies TRIM32, an E3 ubiquitin ligase, as a Bardet-Biedl syndrome gene (BBS11). Proc Natl Acad Sci USA 103, 6287-6292.
Ching, M.S., Shen, Y., Tan, W.H., Jeste, S.S., Morrow, E.M., Chen, X., Mukaddes, N.M., Yoo, S.Y., Hanson, E., Hundley, R., et al. (2010). Deletions of NRXN1 (neurexin-1) predispose to a wide spectrum of developmental disorders. Am J Med Genet B Neuropsychiatr Genet 153B, 937-947.
Christian, S.L., Brune, C.W., Sudi, J., Kumar, R.A., Liu, S., Karamohamed, S., Badner, J.A., Matsui, S., Conroy, J., McQuaid, D., et al. (2008). Novel submicroscopic chromosomal abnormalities detected in autism spectrum disorder. Biol Psychiatry 63, 1111-1117.
Chung, B.H., Mullegama, S., Marshall, C.R., Lionel, A.C., Weksberg, R., Dupuis, L., Brick, L., Li, C., Scherer, S.W., Aradhya, S., et al. (2012). Severe intellectual disability and autistic features associated with microduplication 2q23.1. Eur J Hum Genet 20, 398-403.
Clarke, A.R., Barry, R.J., Irving, A.M., McCarthy, R., and Selikowitz, M. (2011). Children with attention-deficit/hyperactivity disorder and autistic features: EEG evidence for comorbid disorders. Psychiatry Res 185, 225-231.
Clemmensen, L., Vernal, D.L., and Steinhausen, H.C. (2012). A systematic review of the long-term outcome of early onset schizophrenia. BMC Psychiatry 12, 150.
157
Coghlan, S., Horder, J., Inkster, B., Mendez, M.A., Murphy, D.G., and Nutt, D.J. (2012). GABA system dysfunction in autism and related disorders: From synapse to symptoms. Neurosci Biobehav Rev 36, 2044-2055.
Collins, P.Y., Patel, V., Joestl, S.S., March, D., Insel, T.R., Daar, A.S., Anderson, W., Dhansay, M.A., Phillips, A., Shurin, S., et al. (2011). Grand challenges in global mental health. Nature 475, 27-30.
Constantino, J.N., Todorov, A., Hilton, C., Law, P., Zhang, Y., Molloy, E., Fitzgerald, R., and Geschwind, D. (2013). Autism recurrence in half siblings: strong support for genetic mechanisms of transmission in ASD. Mol Psychiatry 18, 137-138.
Constantino, J.N., Zhang, Y., Frazier, T., Abbacchi, A.M., and Law, P. (2010). Sibling recurrence and the genetic epidemiology of autism. Am J Psychiatry 167, 1349-1356.
Cook, E.H., Jr., and Scherer, S.W. (2008). Copy-number variations associated with neuropsychiatric conditions. Nature 455, 919-923.
Cooper, G.M., Coe, B.P., Girirajan, S., Rosenfeld, J.A., Vu, T.H., Baker, C., Williams, C., Stalker, H., Hamid, R., Hannig, V., et al. (2011). A copy number variation morbidity map of developmental delay. Nat Genet 43, 838-846.
Cooper, M., Martin, J., Langley, K., Hamshere, M., and Thapar, A. (2014). Autistic traits in children with ADHD index clinical and cognitive problems. Eur Child Adolesc Psychiatry 23, 23-34.
Costain, G., and Bassett, A.S. (2012). Clinical applications of schizophrenia genetics: genetic diagnosis, risk, and counseling in the molecular era. Appl Clin Genet 5, 1-18.
Costain, G., and Bassett, A.S. (2013). Incomplete knowledge of the clinical context as a barrier to interpreting incidental genetic research findings. Am J Bioeth 13, 58-60.
Costain, G., and Bassett, A.S. (2014). Individualizing recurrence risks for severe mental illness: epidemiologic and molecular genetic approaches. Schizophr Bull 40, 21-23.
Costain, G., Chow, E.W., Ray, P.N., and Bassett, A.S. (2012). Caregiver and adult patient perspectives on the importance of a diagnosis of 22q11.2 deletion syndrome. J Intellect Disabil Res 56, 641-651.
Costain, G., Chow, E.W., Silversides, C.K., and Bassett, A.S. (2011). Sex differences in reproductive fitness contribute to preferential maternal transmission of 22q11.2 deletions. J Med Genet 48, 819-824.
Costain, G., Esplen, M.J., Toner, B., Hodgkinson, K.A., and Bassett, A.S. (2014a). Evaluating genetic counseling for family members of individuals with schizophrenia in the molecular age. Schizophr Bull 40, 88-99.
158
Costain, G., Esplen, M.J., Toner, B., Scherer, S.W., Meschino, W.S., Hodgkinson, K.A., and Bassett, A.S. (2014b). Evaluating genetic counseling for individuals with schizophrenia in the molecular age. Schizophr Bull 40, 78-87.
Costain, G., Lionel, A.C., Merico, D., Forsythe, P., Russell, K., Lowther, C., Yuen, T., Husted, J., Stavropoulos, D.J., Speevak, M., et al. (2013). Pathogenic rare copy number variants in community-based schizophrenia suggest a potential role for clinical microarrays. Hum Mol Genet 22, 4485-4501.
Cotterchio, M., McKeown-Eyssen, G., Sutherland, H., Buchan, G., Aronson, M., Easson, A.M., Macey, J., Holowaty, E., and Gallinger, S. (2000). Ontario familial colon cancer registry: methods and first-year response rates. Chronic Dis Can 21, 81-86.
Crespi, B., Stead, P., and Elliot, M. (2010). Evolution in health and medicine Sackler colloquium: Comparative genomics of autism and schizophrenia. Proc Natl Acad Sci USA 107 Suppl 1, 1736-1741.
Cristino, A.S., Williams, S.M., Hawi, Z., An, J.Y., Bellgrove, M.A., Schwartz, C.E., Costa, L.D., and Claudianos, C. (2013). Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system. Mol Psychiatry.
Cross-Disorder Group of the Psychiatric Genomics Consortium (2013). Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371-1379.
Cubells, J.F., Deoreo, E.H., Harvey, P.D., Garlow, S.J., Garber, K., Adam, M.P., and Martin, C.L. (2011). Pharmaco-genetically guided treatment of recurrent rage outbursts in an adult male with 15q13.3 deletion syndrome. Am J Med Genet A 155A, 805-810.
Cuffe, S.P., Moore, C.G., and McKeown, R.E. (2005). Prevalence and correlates of ADHD symptoms in the national health interview survey. J Atten Disord 9, 392-401.
Curran, S., Bolton, P., Rozsnyai, K., Chiocchetti, A., Klauck, S.M., Duketis, E., Poustka, F., Schlitt, S., Freitag, C.M., Lee, I., et al. (2011). No association between a common single nucleotide polymorphism, rs4141463, in the MACROD2 gene and autism spectrum disorder. Am J Med Genet B Neuropsychiatr Genet 156B, 633-639.
Dalsgaard, S. (2013). Attention-deficit/hyperactivity disorder (ADHD). Eur Child Adolesc Psychiatry 22 Suppl 1, S43-48.
Dalsgaard, S., Mortensen, P.B., Frydenberg, M., Maibing, C.M., Nordentoft, M., and Thomsen, P.H. (2013a). Association between Attention-Deficit Hyperactivity Disorder in childhood and schizophrenia later in adulthood. Eur Psychiatry.
Dalsgaard, S., Nielsen, H.S., and Simonsen, M. (2013b). Five-fold increase in national prevalence rates of attention-deficit/hyperactivity disorder medications for children and adolescents with autism spectrum disorder, attention-deficit/hyperactivity disorder, and other psychiatric disorders: a Danish register-based study. J Child Adolesc Psychopharmacol 23, 432-439.
159
Daniels, J.L., Forssen, U., Hultman, C.M., Cnattingius, S., Savitz, D.A., Feychting, M., and Sparen, P. (2008). Parental psychiatric disorders associated with autism spectrum disorders in the offspring. Pediatrics 121, e1357-1362.
Dawson, G., Jones, E.J., Merkle, K., Venema, K., Lowy, R., Faja, S., Kamara, D., Murias, M., Greenson, J., Winter, J., et al. (2012). Early behavioral intervention is associated with normalized brain activity in young children with autism. J Am Acad Child Adolesc Psychiatry 51, 1150-1159.
Dawson, G., Rogers, S., Munson, J., Smith, M., Winter, J., Greenson, J., Donaldson, A., and Varley, J. (2010). Randomized, controlled trial of an intervention for toddlers with autism: the Early Start Denver Model. Pediatrics 125, e17-23.
de Bruin, E.I., Ferdinand, R.F., Meester, S., de Nijs, P.F., and Verheij, F. (2007). High rates of psychiatric co-morbidity in PDD-NOS. J Autism Dev Disord 37, 877-886.
de la Serna, E., Baeza, I., Andres, S., Puig, O., Sanchez-Guistau, V., Romero, S., Bernardo, M., Moreno, D., Noguera, A., and Castro-Fornieles, J. (2011). Comparison between young siblings and offspring of subjects with schizophrenia: clinical and neuropsychological characteristics. Schizophr Res 131, 35-42.
de la Serna, E., Baeza, I., Toro, J., Andres, S., Puig, O., Sanchez-Guistau, V., Romero, S., Bernardo, M., and Castro-Fornieles, J. (2010). Relationship between clinical and neuropsychological characteristics in child and adolescent first degree relatives of subjects with schizophrenia. Schizophr Res 116, 159-167.
de Lacy, N., and King, B.H. (2013). Revisiting the relationship between autism and schizophrenia: toward an integrated neurobiology. Annu Rev Clin Psychol 9, 555-587.
De Wolf, V., Brison, N., Devriendt, K., and Peeters, H. (2013). Genetic counseling for susceptibility loci and neurodevelopmental disorders: the del15q11.2 as an example. Am J Med Genet A 161A, 2846-2854.
Delorme, R., Ey, E., Toro, R., Leboyer, M., Gillberg, C., and Bourgeron, T. (2013). Progress toward treatments for synaptic defects in autism. Nat Med 19, 685-694.
Devlin, B., Melhem, N., and Roeder, K. (2011). Do common variants play a role in risk for autism? Evidence and theoretical musings. Brain Res 1380, 78-84.
Devlin, B., and Scherer, S.W. (2012). Genetic architecture in autism spectrum disorder. Curr Opin Genet Dev 22, 229-237.
Di Martino, A., Zuo, X.N., Kelly, C., Grzadzinski, R., Mennes, M., Schvarcz, A., Rodman, J., Lord, C., Castellanos, F.X., and Milham, M.P. (2013). Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder. Biol Psychiatry 74, 623-632.
Dolcetti, A., Silversides, C.K., Marshall, C.R., Lionel, A.C., Stavropoulos, D.J., Scherer, S.W., and Bassett, A.S. (2013). 1q21.1 Microduplication expression in adults. Genet Med 15, 282-289.
160
Donev, R., Gantert, D., Alawam, K., Edworthy, A., Hassler, F., Meyer-Lindenberg, A., Dressing, H., and Thome, J. (2011). Comorbidity of schizophrenia and adult attention-deficit hyperactivity disorder. World J Biol Psychiatry 12 Suppl 1, 52-56.
Duong, L., Klitten, L.L., Moller, R.S., Ingason, A., Jakobsen, K.D., Skjodt, C., Didriksen, M., Hjalgrim, H., Werge, T., and Tommerup, N. (2012). Mutations in NRXN1 in a family multiply affected with brain disorders: NRXN1 mutations and brain disorders. Am J Med Genet B Neuropsychiatr Genet 159B, 354-358.
Durand, C.M., Betancur, C., Boeckers, T.M., Bockmann, J., Chaste, P., Fauchereau, F., Nygren, G., Rastam, M., Gillberg, I.C., Anckarsater, H., et al. (2007). Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are associated with autism spectrum disorders. Nat Genet 39, 25-27.
Eack, S.M., Bahorik, A.L., McKnight, S.A., Hogarty, S.S., Greenwald, D.P., Newhill, C.E., Phillips, M.L., Keshavan, M.S., and Minshew, N.J. (2013). Commonalities in social and non-social cognitive impairments in adults with autism spectrum disorder and schizophrenia. Schizophr Res 148, 24-28.
El Zein, F., Solis, M., Vaughn, S., and McCulley, L. (2013). Reading Comprehension Interventions for Students with Autism Spectrum Disorders: A Synthesis of Research. J Autism Dev Disord.
Eldevik, S., Hastings, R.P., Hughes, J.C., Jahr, E., Eikeseth, S., and Cross, S. (2009). Meta-analysis of Early Intensive Behavioral Intervention for children with autism. J Clin Child Adolesc Psychol 38, 439-450.
Elia, J., Gai, X., Hakonarson, H., and White, P.S. (2011). Structural variations in attention-deficit hyperactivity disorder. Lancet 377, 377-378; author reply 378.
Elia, J., Gai, X., Xie, H.M., Perin, J.C., Geiger, E., Glessner, J.T., D'Arcy, M., deBerardinis, R., Frackelton, E., Kim, C., et al. (2010). Rare structural variants found in attention-deficit hyperactivity disorder are preferentially associated with neurodevelopmental genes. Mol Psychiatry 15, 637-646.
Elia, J., Glessner, J.T., Wang, K., Takahashi, N., Shtir, C.J., Hadley, D., Sleiman, P.M., Zhang, H., Kim, C.E., Robison, R., et al. (2012a). Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder. Nat Genet 44, 78-84.
Elia, J., Sackett, J., Turner, T., Schardt, M., Tang, S.C., Kurtz, N., Dunfey, M., McFarlane, N.A., Susi, A., Danish, D., et al. (2012b). Attention-deficit/hyperactivity disorder genomics: update for clinicians. Curr Psychiatry Rep 14, 579-589.
Emsley, R., Chiliza, B., and Schoeman, R. (2008). Predictors of long-term outcome in schizophrenia. Curr Opin Psychiatry 21, 173-177.
Ernst, C., Marshall, C.R., Shen, Y., Metcalfe, K., Rosenfeld, J., Hodge, J.C., Torres, A., Blumenthal, I., Chiang, C., Pillalamarri, V., et al. (2012). Highly penetrant alterations of a
161
critical region including BDNF in human psychopathology and obesity. Arch Gen Psychiatry 69, 1238-1246.
Essrich, C., Lorez, M., Benson, J.A., Fritschy, J.M., and Luscher, B. (1998). Postsynaptic clustering of major GABAA receptor subtypes requires the gamma 2 subunit and gephyrin. Nat Neurosci 1, 563-571.
Esterberg, M.L., Trotman, H.D., Brasfield, J.L., Compton, M.T., and Walker, E.F. (2008). Childhood and current autistic features in adolescents with schizotypal personality disorder. Schizophr Res 104, 265-273.
Etherton, M.R., Blaiss, C.A., Powell, C.M., and Sudhof, T.C. (2009). Mouse neurexin-1alpha deletion causes correlated electrophysiological and behavioral changes consistent with cognitive impairments. Proc Natl Acad Sci USA 106, 17998-18003.
Fang, M., Shen, L., Yin, H., Pan, Y.M., Wang, L., Chen, D., Xi, Z.Q., Xiao, Z., Wang, X.F., and Zhou, S.N. (2011). Downregulation of gephyrin in temporal lobe epilepsy neurons in humans and a rat model. Synapse 65, 1006-1014.
Faraone, S.V., Biederman, J., and Monuteaux, M.C. (2000). Toward guidelines for pedigree selection in genetic studies of attention deficit hyperactivity disorder. Genet Epidemiol 18, 1-16.
Fatemi, S.H., Aldinger, K.A., Ashwood, P., Bauman, M.L., Blaha, C.D., Blatt, G.J., Chauhan, A., Chauhan, V., Dager, S.R., Dickson, P.E., et al. (2012). Consensus paper: pathological role of the cerebellum in autism. Cerebellum 11, 777-807.
Feifel, D., Macdonald, K., Nguyen, A., Cobb, P., Warlan, H., Galangue, B., Minassian, A., Becker, O., Cooper, J., Perry, W., et al. (2010). Adjunctive intranasal oxytocin reduces symptoms in schizophrenia patients. Biol Psychiatry 68, 678-680.
Feng, G., Tintrup, H., Kirsch, J., Nichol, M.C., Kuhse, J., Betz, H., and Sanes, J.R. (1998). Dual requirement for gephyrin in glycine receptor clustering and molybdoenzyme activity. Science 282, 1321-1324.
Fernandez, B.A., Roberts, W., Chung, B., Weksberg, R., Meyn, S., Szatmari, P., Joseph-George, A.M., Mackay, S., Whitten, K., Noble, B., et al. (2010). Phenotypic spectrum associated with de novo and inherited deletions and duplications at 16p11.2 in individuals ascertained for diagnosis of autism spectrum disorder. J Med Genet 47, 195-203.
Fernandez, T.V., Sanders, S.J., Yurkiewicz, I.R., Ercan-Sencicek, A.G., Kim, Y.S., Fishman, D.O., Raubeson, M.J., Song, Y., Yasuno, K., Ho, W.S., et al. (2012). Rare copy number variants in tourette syndrome disrupt genes in histaminergic pathways and overlap with autism. Biol Psychiatry 71, 392-402.
Feuk, L., Kalervo, A., Lipsanen-Nyman, M., Skaug, J., Nakabayashi, K., Finucane, B., Hartung, D., Innes, M., Kerem, B., Nowaczyk, M.J., et al. (2006). Absence of a paternally inherited FOXP2 gene in developmental verbal dyspraxia. Am J Hum Genet 79, 965-972.
162
Filges, I., Rothlisberger, B., Blattner, A., Boesch, N., Demougin, P., Wenzel, F., Huber, A.R., Heinimann, K., Weber, P., and Miny, P. (2011). Deletion in Xp22.11: PTCHD1 is a candidate gene for X-linked intellectual disability with or without autism. Clin Genet 79, 79-85.
Fischer, F., Kneussel, M., Tintrup, H., Haverkamp, S., Rauen, T., Betz, H., and Wassle, H. (2000). Reduced synaptic clustering of GABA and glycine receptors in the retina of the gephyrin null mutant mouse. J Comp Neurol 427, 634-648.
Fishell, G., and Hatten, M.E. (1991). Astrotactin provides a receptor system for CNS neuronal migration. Development 113, 755-765.
Fombonne, E. (2009). Epidemiology of pervasive developmental disorders. Pediatr Res 65, 591-598.
Forstera, B., Belaidi, A.A., Juttner, R., Bernert, C., Tsokos, M., Lehmann, T.N., Horn, P., Dehnicke, C., Schwarz, G., and Meier, J.C. (2010). Irregular RNA splicing curtails postsynaptic gephyrin in the cornu ammonis of patients with epilepsy. Brain 133, 3778-3794.
Fournier, K.A., Hass, C.J., Naik, S.K., Lodha, N., and Cauraugh, J.H. (2010). Motor coordination in autism spectrum disorders: a synthesis and meta-analysis. J Autism Dev Disord 40, 1227-1240.
Franke, B., Faraone, S.V., Asherson, P., Buitelaar, J., Bau, C.H., Ramos-Quiroga, J.A., Mick, E., Grevet, E.H., Johansson, S., Haavik, J., et al. (2012). The genetics of attention deficit/hyperactivity disorder in adults, a review. Mol Psychiatry 17, 960-987.
Franzek, E., and Beckmann, H. (1998). Different genetic background of schizophrenia spectrum psychoses: a twin study. Am J Psychiatry 155, 76-83.
Friedman, J.M., Baross, A., Delaney, A.D., Ally, A., Arbour, L., Armstrong, L., Asano, J., Bailey, D.K., Barber, S., Birch, P., et al. (2006). Oligonucleotide microarray analysis of genomic imbalance in children with mental retardation. Am J Hum Genet 79, 500-513.
Fritschy, J.M., Harvey, R.J., and Schwarz, G. (2008). Gephyrin: where do we stand, where do we go? Trends Neurosci 31, 257-264.
Fung, W.L., Chow, E.W., Webb, G.D., Gatzoulis, M.A., and Bassett, A.S. (2008). Extracardiac features predicting 22q11.2 deletion syndrome in adult congenital heart disease. Int J Cardiol 131, 51-58.
Gai, X., Xie, H.M., Perin, J.C., Takahashi, N., Murphy, K., Wenocur, A.S., D'Arcy, M., O'Hara, R.J., Goldmuntz, E., Grice, D.E., et al. (2012). Rare structural variation of synapse and neurotransmission genes in autism. Mol Psychiatry 17, 402-411.
Gargaro, B.A., Rinehart, N.J., Bradshaw, J.L., Tonge, B.J., and Sheppard, D.M. (2011). Autism and ADHD: how far have we come in the comorbidity debate? Neurosci Biobehav Rev 35, 1081-1088.
163
Gauthier, J., Champagne, N., Lafreniere, R.G., Xiong, L., Spiegelman, D., Brustein, E., Lapointe, M., Peng, H., Cote, M., Noreau, A., et al. (2010). De novo mutations in the gene encoding the synaptic scaffolding protein SHANK3 in patients ascertained for schizophrenia. Proc Natl Acad Sci USA 107, 7863-7868.
Gauthier, J., Siddiqui, T., Huashan, P., Yokomaku, D., Hamdan, F., Champagne, N., Lapointe, M., Spiegelman, D., Noreau, A., Lafrenière, R., et al. (2011). Truncating mutations in NRXN2 and NRXN1 in autism spectrum disorders and schizophrenia. Hum Genet 130, 563-573.
Geissler, J., and Lesch, K.P. (2011). A lifetime of attention-deficit/hyperactivity disorder: diagnostic challenges, treatment and neurobiological mechanisms. Expert Rev Neurother 11, 1467-1484.
Gejman, P.V., Sanders, A.R., and Kendler, K.S. (2011). Genetics of schizophrenia: new findings and challenges. Annu Rev Genomics Hum Genet 12, 121-144.
Ghosh, A., Michalon, A., Lindemann, L., Fontoura, P., and Santarelli, L. (2013). Drug discovery for autism spectrum disorder: challenges and opportunities. Nat Rev Drug Discov 12, 777-790.
Gillberg, C., Cederlund, M., Lamberg, K., and Zeijlon, L. (2006). Brief report: "the autism epidemic". The registered prevalence of autism in a Swedish urban area. J Autism Dev Disord 36, 429-435.
Gilman, S.R., Chang, J., Xu, B., Bawa, T.S., Gogos, J.A., Karayiorgou, M., and Vitkup, D. (2012). Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nat Neurosci 15, 1723-1728.
Gilman, S.R., Iossifov, I., Levy, D., Ronemus, M., Wigler, M., and Vitkup, D. (2011). Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70, 898-907.
Girard, S.L., Dion, P.A., and Rouleau, G.A. (2012). Schizophrenia genetics: putting all the pieces together. Curr Neurol Neurosci Rep 12, 261-266.
Girard, S.L., Gauthier, J., Noreau, A., Xiong, L., Zhou, S., Jouan, L., Dionne-Laporte, A., Spiegelman, D., Henrion, E., Diallo, O., et al. (2011). Increased exonic de novo mutation rate in individuals with schizophrenia. Nat Genet 43, 860-863.
Girirajan, S., Campbell, C.D., and Eichler, E.E. (2011). Human copy number variation and complex genetic disease. Annu Rev Genet 45, 203-226.
Gizer, I.R., Ficks, C., and Waldman, I.D. (2009). Candidate gene studies of ADHD: a meta-analytic review. Hum Genet 126, 51-90.
Glessner, J.T., Reilly, M.P., Kim, C.E., Takahashi, N., Albano, A., Hou, C., Bradfield, J.P., Zhang, H., Sleiman, P.M., Flory, J.H., et al. (2010). Strong synaptic transmission impact by copy number variations in schizophrenia. Proc Natl Acad Sci USA 107, 10584-10589.
164
Glessner, J.T., Wang, K., Cai, G., Korvatska, O., Kim, C.E., Wood, S., Zhang, H., Estes, A., Brune, C.W., Bradfield, J.P., et al. (2009). Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459, 569-573.
Glynn, D., Bortnick, R.A., and Morton, A.J. (2003). Complexin II is essential for normal neurological function in mice. Hum Mol Genet 12, 2431-2448.
Gomez, R.L., Janowsky, D., Zetin, M., Huey, L., and Clopton, P.L. (1981). Adult psychiatric diagnosis and symptoms compatible with the hyperactive child syndrome: a retrospective study. J Clin Psychiatry 42, 389-394.
Goobie, S., Knijnenburg, J., Fitzpatrick, D., Sharkey, F.H., Lionel, A.C., Marshall, C.R., Azam, T., Shago, M., Chong, K., Mendoza-Londono, R., et al. (2008). Molecular and clinical characterization of de novo and familial cases with microduplication 3q29: guidelines for copy number variation case reporting. Cytogenet Genome Res 123, 65-78.
Grant, S.G. (2012). Synaptopathies: diseases of the synaptome. Curr Opin Neurobiol 22, 522-529.
Grozeva, D., Kirov, G., Ivanov, D., Jones, I.R., Jones, L., Green, E.K., St Clair, D.M., Young, A.H., Ferrier, N., Farmer, A.E., et al. (2010). Rare copy number variants: a point of rarity in genetic risk for bipolar disorder and schizophrenia. Arch Gen Psychiatry 67, 318-327.
Grzadzinski, R., Di Martino, A., Brady, E., Mairena, M.A., O'Neale, M., Petkova, E., Lord, C., and Castellanos, F.X. (2011). Examining autistic traits in children with ADHD: does the autism spectrum extend to ADHD? J Autism Dev Disord 41, 1178-1191.
Guilmatre, A., Dubourg, C., Mosca, A.L., Legallic, S., Goldenberg, A., Drouin-Garraud, V., Layet, V., Rosier, A., Briault, S., Bonnet-Brilhault, F., et al. (2009). Recurrent rearrangements in synaptic and neurodevelopmental genes and shared biologic pathways in schizophrenia, autism, and mental retardation. Arch Gen Psychiatry 66, 947-956.
Hallmayer, J., Cleveland, S., Torres, A., Phillips, J., Cohen, B., Torigoe, T., Miller, J., Fedele, A., Collins, J., Smith, K., et al. (2011). Genetic heritability and shared environmental factors among twin pairs with autism. Arch Gen Psychiatry 68, 1095-1102.
Hampson, D.R., Gholizadeh, S., and Pacey, L.K. (2012). Pathways to drug development for autism spectrum disorders. Clin Pharmacol Ther 91, 189-200.
Hamshere, M.L., Stergiakouli, E., Langley, K., Martin, J., Holmans, P., Kent, L., Owen, M.J., Gill, M., Thapar, A., O'Donovan, M., et al. (2013). Shared polygenic contribution between childhood attention-deficit hyperactivity disorder and adult schizophrenia. Br J Psychiatry 203, 107-111.
Han, K., Holder, J.L., Jr., Schaaf, C.P., Lu, H., Chen, H., Kang, H., Tang, J., Wu, Z., Hao, S., Cheung, S.W., et al. (2013). SHANK3 overexpression causes manic-like behaviour with unique pharmacogenetic properties. Nature 503, 72-77.
165
Handrigan, G.R., Chitayat, D., Lionel, A.C., Pinsk, M., Vaags, A.K., Marshall, C.R., Dyack, S., Escobar, L.F., Fernandez, B.A., Stegman, J.C., et al. (2013). Deletions in 16q24.2 are associated with autism spectrum disorder, intellectual disability and congenital renal malformation. J Med Genet 50, 163-173.
Heck, D.H., and Lu, L. (2012). The social life of neurons: synaptic communication deficits as a common denominator of autism, schizophrenia, and other cognitive disorders. Biol Psychiatry 72, 173-174.
Hehir-Kwa, J.Y., Rodriguez-Santiago, B., Vissers, L.E., de Leeuw, N., Pfundt, R., Buitelaar, J.K., Perez-Jurado, L.A., and Veltman, J.A. (2011). De novo copy number variants associated with intellectual disability have a paternal origin and age bias. J Med Genet 48, 776-778.
Hermey, G., Plath, N., Hubner, C.A., Kuhl, D., Schaller, H.C., and Hermans-Borgmeyer, I. (2004). The three sorCS genes are differentially expressed and regulated by synaptic activity. J Neurochem 88, 1470-1476.
Hodge, J.C., Mitchell, E., Pillalamarri, V., Toler, T.L., Bartel, F., Kearney, H.M., Zou, Y.S., Tan, W.H., Hanscom, C., Kirmani, S., et al. (2013). Disruption of MBD5 contributes to a spectrum of psychopathology and neurodevelopmental abnormalities. Mol Psychiatry.
Hofvander, B., Delorme, R., Chaste, P., Nyden, A., Wentz, E., Stahlberg, O., Herbrecht, E., Stopin, A., Anckarsater, H., Gillberg, C., et al. (2009). Psychiatric and psychosocial problems in adults with normal-intelligence autism spectrum disorders. BMC Psychiatry 9, 35.
Hollander, E., Bartz, J., Chaplin, W., Phillips, A., Sumner, J., Soorya, L., Anagnostou, E., and Wasserman, S. (2007). Oxytocin increases retention of social cognition in autism. Biol Psychiatry 61, 498-503.
Holtmann, M., Bolte, S., and Poustka, F. (2007). Attention deficit hyperactivity disorder symptoms in pervasive developmental disorders: association with autistic behavior domains and coexisting psychopathology. Psychopathology 40, 172-177.
Hoon, M., Soykan, T., Falkenburger, B., Hammer, M., Patrizi, A., Schmidt, K.F., Sassoe-Pognetto, M., Lowel, S., Moser, T., Taschenberger, H., et al. (2011). Neuroligin-4 is localized to glycinergic postsynapses and regulates inhibition in the retina. Proc Natl Acad Sci USA 108, 3053-3058.
Huguet, G., Ey, E., and Bourgeron, T. (2013). The genetic landscapes of autism spectrum disorders. Annu Rev Genomics Hum Genet 14, 191-213.
Hultman, C.M., Sandin, S., Levine, S.Z., Lichtenstein, P., and Reichenberg, A. (2011). Advancing paternal age and risk of autism: new evidence from a population-based study and a meta-analysis of epidemiological studies. Mol Psychiatry 16, 1203-1212.
Ickowicz, A., Schachar, R.J., Sugarman, R., Chen, S.X., Millette, C., and Cook, L. (2006). The parent interview for child symptoms: a situation-specific clinical research interview for attention-deficit hyperactivity and related disorders. Can J Psychiatry 51, 325-328.
166
Ingraham, L.J., and Kety, S.S. (2000). Adoption studies of schizophrenia. Am J Med Genet 97, 18-22.
Insel, T.R. (2010). Rethinking schizophrenia. Nature 468, 187-193.
Insel, T.R. (2014). Brain somatic mutations: the dark matter of psychiatric genetics? Mol Psychiatry 19, 156-158.
International Schizophrenia Consortium (2008). Rare chromosomal deletions and duplications increase risk of schizophrenia. Nature 455, 237-241.
Iossifov, I., Ronemus, M., Levy, D., Wang, Z., Hakker, I., Rosenbaum, J., Yamrom, B., Lee, Y.H., Narzisi, G., Leotta, A., et al. (2012). De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285-299.
Itsara, A., Cooper, G.M., Baker, C., Girirajan, S., Li, J., Absher, D., Krauss, R.M., Myers, R.M., Ridker, P.M., Chasman, D.I., et al. (2009). Population analysis of large copy number variants and hotspots of human genetic disease. Am J Hum Genet 84, 148-161.
Jablensky, A. (2010). The diagnostic concept of schizophrenia: its history, evolution, and future prospects. Dialogues Clin Neurosci 12, 271-287.
Jacob, T.C., Bogdanov, Y.D., Magnus, C., Saliba, R.S., Kittler, J.T., Haydon, P.G., and Moss, S.J. (2005). Gephyrin regulates the cell surface dynamics of synaptic GABAA receptors. J Neurosci 25, 10469-10478.
Jacquemont, M.L., Sanlaville, D., Redon, R., Raoul, O., Cormier-Daire, V., Lyonnet, S., Amiel, J., Le Merrer, M., Heron, D., de Blois, M.C., et al. (2006). Array-based comparative genomic hybridisation identifies high frequency of cryptic chromosomal rearrangements in patients with syndromic autism spectrum disorders. J Med Genet 43, 843-849.
Jacquemont, S., Reymond, A., Zufferey, F., Harewood, L., Walters, R.G., Kutalik, Z., Martinet, D., Shen, Y., Valsesia, A., Beckmann, N.D., et al. (2011). Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus. Nature 478, 97-102.
Jacques, P.E., Jeyakani, J., and Bourque, G. (2013). The majority of primate-specific regulatory sequences are derived from transposable elements. PLoS Genet 9, e1003504.
Jamain, S., Quach, H., Betancur, C., Rastam, M., Colineaux, C., Gillberg, I.C., Soderstrom, H., Giros, B., Leboyer, M., Gillberg, C., et al. (2003). Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat Genet 34, 27-29.
Jarick, I., Volckmar, A.L., Putter, C., Pechlivanis, S., Nguyen, T.T., Dauvermann, M.R., Beck, S., Albayrak, O., Scherag, S., Gilsbach, S., et al. (2014). Genome-wide analysis of rare copy number variations reveals PARK2 as a candidate gene for attention-deficit/hyperactivity disorder. Mol Psychiatry 19, 115-121.
167
Jensen, P.S., Garcia, J.A., Glied, S., Crowe, M., Foster, M., Schlander, M., Hinshaw, S., Vitiello, B., Arnold, L.E., Elliott, G., et al. (2005). Cost-effectiveness of ADHD treatments: findings from the multimodal treatment study of children with ADHD. Am J Psychiatry 162, 1628-1636.
Jiang, Y.-h., Yuen, Ryan K.C., Jin, X., Wang, M., Chen, N., Wu, X., Ju, J., Mei, J., Shi, Y., He, M., et al. (2013). Detection of Clinically Relevant Genetic Variants in Autism Spectrum Disorder by Whole-Genome Sequencing. Am J Hum Genet 93, 249-263.
Johnson, M.B., Kawasawa, Y.I., Mason, C.E., Krsnik, Z., Coppola, G., Bogdanovic, D., Geschwind, D.H., Mane, S.M., State, M.W., and Sestan, N. (2009). Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron 62, 494-509.
Jokiranta, E., Brown, A.S., Heinimaa, M., Cheslack-Postava, K., Suominen, A., and Sourander, A. (2013). Parental psychiatric disorders and autism spectrum disorders. Psychiatry Res 207, 203-211.
Joshi, G., Wozniak, J., Petty, C., Martelon, M.K., Fried, R., Bolfek, A., Kotte, A., Stevens, J., Furtak, S.L., Bourgeois, M., et al. (2013). Psychiatric comorbidity and functioning in a clinically referred population of adults with autism spectrum disorders: a comparative study. J Autism Dev Disord 43, 1314-1325.
Kalscheuer, V.M., Musante, L., Fang, C., Hoffmann, K., Fuchs, C., Carta, E., Deas, E., Venkateswarlu, K., Menzel, C., Ullmann, R., et al. (2009). A balanced chromosomal translocation disrupting ARHGEF9 is associated with epilepsy, anxiety, aggression, and mental retardation. Hum Mutat 30, 61-68.
Kaminsky, E.B., Kaul, V., Paschall, J., Church, D.M., Bunke, B., Kunig, D., Moreno-De-Luca, D., Moreno-De-Luca, A., Mulle, J.G., Warren, S.T., et al. (2011). An evidence-based approach to establish the functional and clinical significance of copy number variants in intellectual and developmental disabilities. Genet Med 13, 777-784.
Kan, K.J., Dolan, C.V., Nivard, M.G., Middeldorp, C.M., van Beijsterveldt, C.E., Willemsen, G., and Boomsma, D.I. (2013). Genetic and environmental stability in attention problems across the lifespan: evidence from the Netherlands twin register. J Am Acad Child Adolesc Psychiatry 52, 12-25.
Kang, H.J., Kawasawa, Y.I., Cheng, F., Zhu, Y., Xu, X., Li, M., Sousa, A.M., Pletikos, M., Meyer, K.A., Sedmak, G., et al. (2011). Spatio-temporal transcriptome of the human brain. Nature 478, 483-489.
Kang, Y., Zhang, X., Dobie, F., Wu, H., and Craig, A.M. (2008). Induction of GABAergic postsynaptic differentiation by alpha-neurexins. J Biol Chem 283, 2323-2334.
Kearney, H.M., Thorland, E.C., Brown, K.K., Quintero-Rivera, F., and South, S.T. (2011). American College of Medical Genetics standards and guidelines for interpretation and reporting of postnatal constitutional copy number variants. Genet Med 13, 680-685.
168
Kebir, O., Tabbane, K., Sengupta, S., and Joober, R. (2009). Candidate genes and neuropsychological phenotypes in children with ADHD: review of association studies. J Psychiatry Neurosci 34, 88-101.
Kerjan, G., Koizumi, H., Han, E.B., Dube, C.M., Djakovic, S.N., Patrick, G.N., Baram, T.Z., Heinemann, S.F., and Gleeson, J.G. (2009). Mice lacking doublecortin and doublecortin-like kinase 2 display altered hippocampal neuronal maturation and spontaneous seizures. Proc Natl Acad Sci USA 106, 6766-6771.
Kessler, R.C., Lane, M., Stang, P.E., and Van Brunt, D.L. (2009). The prevalence and workplace costs of adult attention deficit hyperactivity disorder in a large manufacturing firm. Psychol Med 39, 137-147.
Kety, S.S., Wender, P.H., Jacobsen, B., Ingraham, L.J., Jansson, L., Faber, B., and Kinney, D.K. (1994). Mental illness in the biological and adoptive relatives of schizophrenic adoptees. Replication of the Copenhagen Study in the rest of Denmark. Arch Gen Psychiatry 51, 442-455.
Kim-Cohen, J., Caspi, A., Moffitt, T.E., Harrington, H., Milne, B.J., and Poulton, R. (2003). Prior juvenile diagnoses in adults with mental disorder: developmental follow-back of a prospective-longitudinal cohort. Arch Gen Psychiatry 60, 709-717.
Kim, H.G., Kishikawa, S., Higgins, A.W., Seong, I.S., Donovan, D.J., Shen, Y., Lally, E., Weiss, L.A., Najm, J., Kutsche, K., et al. (2008). Disruption of neurexin 1 associated with autism spectrum disorder. Am J Hum Genet 82, 199-207.
King, B.H., and Lord, C. (2011). Is schizophrenia on the autism spectrum? Brain Res 1380, 34-41.
Kirov, G., Grozeva, D., Norton, N., Ivanov, D., Mantripragada, K.K., Holmans, P., Craddock, N., Owen, M.J., and O'Donovan, M.C. (2009). Support for the involvement of large copy number variants in the pathogenesis of schizophrenia. Hum Mol Genet 18, 1497-1503.
Kirov, G., Gumus, D., Chen, W., Norton, N., Georgieva, L., Sari, M., O'Donovan, M.C., Erdogan, F., Owen, M.J., Ropers, H.H., et al. (2008). Comparative genome hybridization suggests a role for NRXN1 and APBA2 in schizophrenia. Hum Mol Genet 17, 458-465.
Kirov, G., Pocklington, A.J., Holmans, P., Ivanov, D., Ikeda, M., Ruderfer, D., Moran, J., Chambert, K., Toncheva, D., Georgieva, L., et al. (2012). De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry 17, 142-153.
Kirsch, J., Wolters, I., Triller, A., and Betz, H. (1993). Gephyrin antisense oligonucleotides prevent glycine receptor clustering in spinal neurons. Nature 366, 745-748.
Klaning, U., Mortensen, P.B., and Kyvik, K.O. (1996). Increased occurrence of schizophrenia and other psychiatric illnesses among twins. Br J Psychiatry 168, 688-692.
169
Klei, L., Sanders, S.J., Murtha, M.T., Hus, V., Lowe, J.K., Willsey, A.J., Moreno-De-Luca, D., Yu, T.W., Fombonne, E., Geschwind, D., et al. (2012). Common genetic variants, acting additively, are a major source of risk for autism. Mol Autism 3, 9.
Kneussel, M., Brandstatter, J.H., Laube, B., Stahl, S., Muller, U., and Betz, H. (1999). Loss of postsynaptic GABA(A) receptor clustering in gephyrin-deficient mice. J Neurosci 19, 9289-9297.
Kochhar, P., Batty, M.J., Liddle, E.B., Groom, M.J., Scerif, G., Liddle, P.F., and Hollis, C.P. (2011). Autistic spectrum disorder traits in children with attention deficit hyperactivity disorder. Child Care Health Dev 37, 103-110.
Kong, A., Frigge, M.L., Masson, G., Besenbacher, S., Sulem, P., Magnusson, G., Gudjonsson, S.A., Sigurdsson, A., Jonasdottir, A., Wong, W.S., et al. (2012). Rate of de novo mutations and the importance of father's age to disease risk. Nature 488, 471-475.
Korn, J.M., Kuruvilla, F.G., McCarroll, S.A., Wysoker, A., Nemesh, J., Cawley, S., Hubbell, E., Veitch, J., Collins, P.J., Darvishi, K., et al. (2008). Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat Genet 40, 1253-1260.
Kotte, A., Joshi, G., Fried, R., Uchida, M., Spencer, A., Woodworth, K.Y., Kenworthy, T., Faraone, S.V., and Biederman, J. (2013). Autistic traits in children with and without ADHD. Pediatrics 132, e612-622.
Krawczak, M., Nikolaus, S., von Eberstein, H., Croucher, P.J., El Mokhtari, N.E., and Schreiber, S. (2006). PopGen: population-based recruitment of patients and controls for the analysis of complex genotype-phenotype relationships. Community Genet 9, 55-61.
Krumm, N., O'Roak, B.J., Karakoc, E., Mohajeri, K., Nelson, B., Vives, L., Jacquemont, S., Munson, J., Bernier, R., and Eichler, E.E. (2013). Transmission disequilibrium of small CNVs in simplex autism. Am J Hum Genet 93, 595-606.
Krumm, N., O'Roak, B.J., Shendure, J., and Eichler, E.E. (2014). A de novo convergence of autism genetics and molecular neuroscience. Trends Neurosci 37, 95-105.
Ku, C.S., Polychronakos, C., Tan, E.K., Naidoo, N., Pawitan, Y., Roukos, D.H., Mort, M., and Cooper, D.N. (2013). A new paradigm emerges from the study of de novo mutations in the context of neurodevelopmental disease. Mol Psychiatry 18, 141-153.
Kumar, R.A., KaraMohamed, S., Sudi, J., Conrad, D.F., Brune, C., Badner, J.A., Gilliam, T.C., Nowak, N.J., Cook, E.H., Jr., Dobyns, W.B., et al. (2008). Recurrent 16p11.2 microdeletions in autism. Hum Mol Genet 17, 628-638.
Kumar, S.S., and Buckmaster, P.S. (2006). Hyperexcitability, interneurons, and loss of GABAergic synapses in entorhinal cortex in a model of temporal lobe epilepsy. J Neurosci 26, 4613-4623.
170
Kuntsi, J., Neale, B.M., Chen, W., Faraone, S.V., and Asherson, P. (2006). The IMAGE project: methodological issues for the molecular genetic analysis of ADHD. Behav Brain Funct 2, 27.
Kustanovich, V., Merriman, B., McGough, J., McCracken, J.T., Smalley, S.L., and Nelson, S.F. (2003). Biased paternal transmission of SNAP-25 risk alleles in attention-deficit hyperactivity disorder. Mol Psychiatry 8, 309-315.
Lai, M.C., Lombardo, M.V., Chakrabarti, B., and Baron-Cohen, S. (2013). Subgrouping the autism "spectrum": reflections on DSM-5. PLoS Biol 11, e1001544.
Langley, K., Martin, J., Agha, S.S., Davies, C., Stergiakouli, E., Holmans, P., Williams, N., Owen, M., O'Donovan, M., and Thapar, A. (2011). Clinical and cognitive characteristics of children with attention-deficit hyperactivity disorder, with and without copy number variants. Br J Psychiatry 199, 398-403.
Larsson, H., Asherson, P., Chang, Z., Ljung, T., Friedrichs, B., Larsson, J.O., and Lichtenstein, P. (2013a). Genetic and environmental influences on adult attention deficit hyperactivity disorder symptoms: a large Swedish population-based study of twins. Psychol Med 43, 197-207.
Larsson, H., Dilshad, R., Lichtenstein, P., and Barker, E.D. (2011). Developmental trajectories of DSM-IV symptoms of attention-deficit/hyperactivity disorder: genetic effects, family risk and associated psychopathology. J Child Psychol Psychiatry 52, 954-963.
Larsson, H., Ryden, E., Boman, M., Langstrom, N., Lichtenstein, P., and Landen, M. (2013b). Risk of bipolar disorder and schizophrenia in relatives of people with attention-deficit hyperactivity disorder. Br J Psychiatry 203, 103-106.
Larsson, H.J., Eaton, W.W., Madsen, K.M., Vestergaard, M., Olesen, A.V., Agerbo, E., Schendel, D., Thorsen, P., and Mortensen, P.B. (2005). Risk factors for autism: perinatal factors, parental psychiatric history, and socioeconomic status. Am J Epidemiol 161, 916-925; discussion 926-918.
Lasky-Su, J., Neale, B.M., Franke, B., Anney, R.J., Zhou, K., Maller, J.B., Vasquez, A.A., Chen, W., Asherson, P., Buitelaar, J., et al. (2008). Genome-wide association scan of quantitative traits for attention deficit hyperactivity disorder identifies novel associations and confirms candidate gene associations. Am J Med Genet B Neuropsychiatr Genet 147B, 1345-1354.
Laumonnier, F., Bonnet-Brilhault, F., Gomot, M., Blanc, R., David, A., Moizard, M.P., Raynaud, M., Ronce, N., Lemonnier, E., Calvas, P., et al. (2004). X-linked mental retardation and autism are associated with a mutation in the NLGN4 gene, a member of the neuroligin family. Am J Hum Genet 74, 552-557.
Le Couteur, A., Bailey, A., Goode, S., Pickles, A., Robertson, S., Gottesman, I., and Rutter, M. (1996). A broader phenotype of autism: the clinical spectrum in twins. J Child Psychol Psychiatry 37, 785-801.
Lee, C., and Scherer, S.W. (2010). The clinical context of copy number variation in the human genome. Expert Rev Mol Med 12, e8.
171
Lencz, T., Lambert, C., DeRosse, P., Burdick, K.E., Morgan, T.V., Kane, J.M., Kucherlapati, R., and Malhotra, A.K. (2007). Runs of homozygosity reveal highly penetrant recessive loci in schizophrenia. Proc Natl Acad Sci USA 104, 19942-19947.
Lesca, G., Rudolf, G., Labalme, A., Hirsch, E., Arzimanoglou, A., Genton, P., Motte, J., de Saint Martin, A., Valenti, M.P., Boulay, C., et al. (2012). Epileptic encephalopathies of the Landau-Kleffner and continuous spike and waves during slow-wave sleep types: Genomic dissection makes the link with autism. Epilepsia 53, 1526-1538.
Lesca, G., Till, M., Labalme, A., Vallee, D., Hugonenq, C., Philip, N., Edery, P., and Sanlaville, D. (2011). De novo Xq11.11 microdeletion including ARHGEF9 in a boy with mental retardation, epilepsy, macrosomia, and dysmorphic features. Am J Med Genet A 155A, 1706-1711.
Lesch, K.P., Selch, S., Renner, T.J., Jacob, C., Nguyen, T.T., Hahn, T., Romanos, M., Walitza, S., Shoichet, S., Dempfle, A., et al. (2011). Genome-wide copy number variation analysis in attention-deficit/hyperactivity disorder: association with neuropeptide Y gene dosage in an extended pedigree. Mol Psychiatry 16, 491-503.
Lesch, K.P., Timmesfeld, N., Renner, T.J., Halperin, R., Roser, C., Nguyen, T.T., Craig, D.W., Romanos, J., Heine, M., Meyer, J., et al. (2008). Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies. J Neural Transm 115, 1573-1585.
Levinson, D.F., Duan, J., Oh, S., Wang, K., Sanders, A.R., Shi, J., Zhang, N., Mowry, B.J., Olincy, A., Amin, F., et al. (2011). Copy number variants in schizophrenia: confirmation of five previous findings and new evidence for 3q29 microdeletions and VIPR2 duplications. Am J Psychiatry 168, 302-316.
Levy, D., Ronemus, M., Yamrom, B., Lee, Y.H., Leotta, A., Kendall, J., Marks, S., Lakshmi, B., Pai, D., Ye, K., et al. (2011). Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886-897.
Leyfer, O.T., Folstein, S.E., Bacalman, S., Davis, N.O., Dinh, E., Morgan, J., Tager-Flusberg, H., and Lainhart, J.E. (2006). Comorbid psychiatric disorders in children with autism: interview development and rates of disorders. J Autism Dev Disord 36, 849-861.
Lichtenstein, P., Carlstrom, E., Rastam, M., Gillberg, C., and Anckarsater, H. (2010). The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. Am J Psychiatry 167, 1357-1363.
Lionel, A.C., Crosbie, J., Barbosa, N., Goodale, T., Thiruvahindrapuram, B., Rickaby, J., Gazzellone, M., Carson, A.R., Howe, J.L., Wang, Z., et al. (2011). Rare copy number variation discovery and cross-disorder comparisons identify risk genes for ADHD. Sci Transl Med 3, 95ra75.
Lionel, A.C., Tammimies, K., Vaags, A.K., Rosenfeld, J.A., Ahn, J.W., Merico, D., Noor, A., Runke, C.K., Pillalamarri, V.K., Carter, M.T., et al. (2014). Disruption of the ASTN2/TRIM32
172
locus at 9q33.1 is a risk factor in males for autism spectrum disorders, ADHD and other neurodevelopmental phenotypes. Hum Mol Genet.
Lionel, A.C., Vaags, A.K., Sato, D., Gazzellone, M.J., Mitchell, E.B., Chen, H.Y., Costain, G., Walker, S., Egger, G., Thiruvahindrapuram, B., et al. (2013). Rare exonic deletions implicate the synaptic organizer Gephyrin (GPHN) in risk for autism, schizophrenia and seizures. Hum Mol Genet 22, 2055-2066.
Liu, P., Carvalho, C.M., Hastings, P.J., and Lupski, J.R. (2012). Mechanisms for recurrent and complex human genomic rearrangements. Curr Opin Genet Dev 22, 211-220.
Lo-Castro, A., Galasso, C., Cerminara, C., El-Malhany, N., Benedetti, S., Nardone, A.M., and Curatolo, P. (2009). Association of syndromic mental retardation and autism with 22q11.2 duplication. Neuropediatrics 40, 137-140.
Losh, M., Childress, D., Lam, K., and Piven, J. (2008). Defining key features of the broad autism phenotype: a comparison across parents of multiple- and single-incidence autism families. Am J Med Genet B Neuropsychiatr Genet 147B, 424-433.
Lundstrom, S., Chang, Z., Kerekes, N., Gumpert, C.H., Rastam, M., Gillberg, C., Lichtenstein, P., and Anckarsater, H. (2011). Autistic-like traits and their association with mental health problems in two nationwide twin cohorts of children and adults. Psychol Med 41, 2423-2433.
Mackay-Sim, A. (2013). Patient-derived stem cells: pathways to drug discovery for brain diseases. Front Cell Neurosci 7, 29.
Mahajan, R., Bernal, M.P., Panzer, R., Whitaker, A., Roberts, W., Handen, B., Hardan, A., Anagnostou, E., and Veenstra-VanderWeele, J. (2012). Clinical practice pathways for evaluation and medication choice for attention-deficit/hyperactivity disorder symptoms in autism spectrum disorders. Pediatrics 130 Suppl 2, S125-138.
Malhotra, D., McCarthy, S., Michaelson, J.J., Vacic, V., Burdick, K.E., Yoon, S., Cichon, S., Corvin, A., Gary, S., Gershon, E.S., et al. (2011). High frequencies of de novo CNVs in bipolar disorder and schizophrenia. Neuron 72, 951-963.
Malhotra, D., and Sebat, J. (2012). CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell 148, 1223-1241.
Manolio, T.A. (2013). Bringing genome-wide association findings into clinical use. Nat Rev Genet 14, 549-558.
Manshadi, M., Lippmann, S., O'Daniel, R.G., and Blackman, A. (1983). Alcohol abuse and attention deficit disorder. J Clin Psychiatry 44, 379-380.
Mao, Y., Ge, X., Frank, C.L., Madison, J.M., Koehler, A.N., Doud, M.K., Tassa, C., Berry, E.M., Soda, T., Singh, K.K., et al. (2009). Disrupted in schizophrenia 1 regulates neuronal progenitor proliferation via modulation of GSK3beta/beta-catenin signaling. Cell 136, 1017-1031.
173
Marshall, C.R., Noor, A., Vincent, J.B., Lionel, A.C., Feuk, L., Skaug, J., Shago, M., Moessner, R., Pinto, D., Ren, Y., et al. (2008). Structural variation of chromosomes in autism spectrum disorder. Am J Hum Genet 82, 477-488.
Marshall, C.R., and Scherer, S.W. (2012). Detection and characterization of copy number variation in autism spectrum disorder. Methods Mol Biol 838, 115-135.
Martin, J., Hamshere, M.L., O'Donovan, M.C., Rutter, M., and Thapar, A. (2014). Factor structure of autistic traits in children with ADHD. J Autism Dev Disord 44, 204-215.
Martin, N.C., Piek, J., Baynam, G., Levy, F., and Hay, D. (2010). An examination of the relationship between movement problems and four common developmental disorders. Hum Mov Sci 29, 799-808.
Matson, J.L., and Shoemaker, M. (2009). Intellectual disability and its relationship to autism spectrum disorders. Res Dev Disabil 30, 1107-1114.
Matsunami, N., Hadley, D., Hensel, C.H., Christensen, G.B., Kim, C., Frackelton, E., Thomas, K., da Silva, R.P., Stevens, J., Baird, L., et al. (2013). Identification of rare recurrent copy number variants in high-risk autism families and their prevalence in a large ASD population. PLoS One 8, e52239.
Mazzoni, P., Kimhy, D., Khan, S., Posner, K., Maayan, L., Eilenberg, M., Messinger, J., Kestenbaum, C., and Corcoran, C. (2009). Childhood onset diagnoses in a case series of teens at clinical high risk for psychosis. J Child Adolesc Psychopharmacol 19, 771-776.
McCarroll, S.A., Kuruvilla, F.G., Korn, J.M., Cawley, S., Nemesh, J., Wysoker, A., Shapero, M.H., de Bakker, P.I., Maller, J.B., Kirby, A., et al. (2008). Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat Genet 40, 1166-1174.
McCarthy, M.I., and Hirschhorn, J.N. (2008). Genome-wide association studies: past, present and future. Hum Mol Genet 17, R100-101.
McCarthy, S.E., Makarov, V., Kirov, G., Addington, A.M., McClellan, J., Yoon, S., Perkins, D.O., Dickel, D.E., Kusenda, M., Krastoshevsky, O., et al. (2009). Microduplications of 16p11.2 are associated with schizophrenia. Nat Genet 41, 1223-1227.
McMahon, H.T., Missler, M., Li, C., and Sudhof, T.C. (1995). Complexins: cytosolic proteins that regulate SNAP receptor function. Cell 83, 111-119.
McPheeters, M.L., Warren, Z., Sathe, N., Bruzek, J.L., Krishnaswami, S., Jerome, R.N., and Veenstra-Vanderweele, J. (2011). A systematic review of medical treatments for children with autism spectrum disorders. Pediatrics 127, e1312-1321.
Melhem, N., Middleton, F., McFadden, K., Klei, L., Faraone, S.V., Vinogradov, S., Tiobech, J., Yano, V., Kuartei, S., Roeder, K., et al. (2011). Copy number variants for schizophrenia and related psychotic disorders in Oceanic Palau: risk and transmission in extended pedigrees. Biol Psychiatry 70, 1115-1121.
174
Merico, D., Isserlin, R., Stueker, O., Emili, A., and Bader, G.D. (2010). Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5, e13984.
Merikangas, A.K., Corvin, A.P., and Gallagher, L. (2009). Copy-number variants in neurodevelopmental disorders: promises and challenges. Trends Genet 25, 536-544.
Michaelson, J.J., Shi, Y., Gujral, M., Zheng, H., Malhotra, D., Jin, X., Jian, M., Liu, G., Greer, D., Bhandari, A., et al. (2012). Whole-genome sequencing in autism identifies hot spots for de novo germline mutation. Cell 151, 1431-1442.
Millar, J.K., Wilson-Annan, J.C., Anderson, S., Christie, S., Taylor, M.S., Semple, C.A., Devon, R.S., St Clair, D.M., Muir, W.J., Blackwood, D.H., et al. (2000). Disruption of two novel genes by a translocation co-segregating with schizophrenia. Hum Mol Genet 9, 1415-1423.
Miller, D.T., Adam, M.P., Aradhya, S., Biesecker, L.G., Brothman, A.R., Carter, N.P., Church, D.M., Crolla, J.A., Eichler, E.E., Epstein, C.J., et al. (2010a). Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am J Hum Genet 86, 749-764.
Miller, D.T., Shen, Y., Weiss, L.A., Korn, J., Anselm, I., Bridgemohan, C., Cox, G.F., Dickinson, H., Gentile, J., Harris, D.J., et al. (2009). Microdeletion/duplication at 15q13.2q13.3 among individuals with features of autism and other neuropsychiatric disorders. J Med Genet 46, 242-248.
Miller, F.A., Hayeems, R.Z., and Bytautas, J.P. (2010b). What is a meaningful result? Disclosing the results of genomic research in autism to research participants. Eur J Hum Genet 18, 867-871.
Miranda, A., Mercader, J., Fernandez, M.I., and Colomer, C. (2013). Reading Performance of Young Adults With ADHD Diagnosed in Childhood: Relations With Executive Functioning. J Atten Disord.
Missler, M., Zhang, W., Rohlmann, A., Kattenstroth, G., Hammer, R.E., Gottmann, K., and Sudhof, T.C. (2003). Alpha-neurexins couple Ca2+ channels to synaptic vesicle exocytosis. Nature 423, 939-948.
Mody, M., and Belliveau, J.W. (2013). Speech and Language Impairments in Autism: Insights from Behavior and Neuroimaging. N Am J Med Sci (Boston) 5, 157-161.
Moessner, R., Marshall, C.R., Sutcliffe, J.S., Skaug, J., Pinto, D., Vincent, J., Zwaigenbaum, L., Fernandez, B., Roberts, W., Szatmari, P., et al. (2007). Contribution of SHANK3 mutations to autism spectrum disorder. Am J Hum Genet 81, 1289-1297.
Molin, A.M., Andrieux, J., Koolen, D.A., Malan, V., Carella, M., Colleaux, L., Cormier-Daire, V., David, A., de Leeuw, N., Delobel, B., et al. (2012). A novel microdeletion syndrome at 3q13.31 characterised by developmental delay, postnatal overgrowth, hypoplastic male genitals, and characteristic facial features. J Med Genet 49, 104-109.
175
Moore, S., Kelleher, E., and Corvin, A. (2011). The shock of the new: progress in schizophrenia genomics. Curr Genomics 12, 516-524.
Moreno-De-Luca, A., Myers, S.M., Challman, T.D., Moreno-De-Luca, D., Evans, D.W., and Ledbetter, D.H. (2013). Developmental brain dysfunction: revival and expansion of old concepts based on new genetic evidence. Lancet Neurol 12, 406-414.
Moreno-De-Luca, D., Mulle, J.G., Kaminsky, E.B., Sanders, S.J., Myers, S.M., Adam, M.P., Pakula, A.T., Eisenhauer, N.J., Uhas, K., Weik, L., et al. (2010). Deletion 17q12 is a recurrent copy number variant that confers high risk of autism and schizophrenia. Am J Hum Genet 87, 618-630.
Morrow, E.M., Yoo, S.Y., Flavell, S.W., Kim, T.K., Lin, Y., Hill, R.S., Mukaddes, N.M., Balkhy, S., Gascon, G., Hashmi, A., et al. (2008). Identifying autism loci and genes by tracing recent shared ancestry. Science 321, 218-223.
Mouridsen, S.E., Rich, B., and Isager, T. (2008). Psychiatric disorders in adults diagnosed as children with atypical autism. A case control study. J Neural Transm 115, 135-138.
Muglia, P. (2011). From genes to therapeutic targets for psychiatric disorders - what to expect? Curr Opin Pharmacol 11, 563-571.
Mulle, J.G. (2012). Schizophrenia genetics: progress, at last. Curr Opin Genet Dev 22, 238-244.
Mulle, J.G., Dodd, A.F., McGrath, J.A., Wolyniec, P.S., Mitchell, A.A., Shetty, A.C., Sobreira, N.L., Valle, D., Rudd, M.K., Satten, G., et al. (2010). Microdeletions of 3q29 confer high risk for schizophrenia. Am J Hum Genet 87, 229-236.
Mulley, J.C., and Mefford, H.C. (2011). Epilepsy and the new cytogenetics. Epilepsia 52, 423-432.
Mulligan, A., Anney, R.J., O'Regan, M., Chen, W., Butler, L., Fitzgerald, M., Buitelaar, J., Steinhausen, H.C., Rothenberger, A., Minderaa, R., et al. (2009). Autism symptoms in Attention-Deficit/Hyperactivity Disorder: a familial trait which correlates with conduct, oppositional defiant, language and motor disorders. J Autism Dev Disord 39, 197-209.
Murray, M.J. (2010). Attention-deficit/Hyperactivity Disorder in the context of Autism spectrum disorders. Curr Psychiatry Rep 12, 382-388.
Myers, S.M., and Johnson, C.P. (2007). Management of children with autism spectrum disorders. Pediatrics 120, 1162-1182.
Nagamani, S.C., Erez, A., Ben-Zeev, B., Frydman, M., Winter, S., Zeller, R., El-Khechen, D., Escobar, L., Stankiewicz, P., Patel, A., et al. (2013). Detection of copy-number variation in AUTS2 gene by targeted exonic array CGH in patients with developmental delay and autistic spectrum disorders. Eur J Hum Genet 21, 343-346.
176
Neale, B.M., Kou, Y., Liu, L., Ma'ayan, A., Samocha, K.E., Sabo, A., Lin, C.F., Stevens, C., Wang, L.S., Makarov, V., et al. (2012). Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242-245.
Neale, B.M., Lasky-Su, J., Anney, R., Franke, B., Zhou, K., Maller, J.B., Vasquez, A.A., Asherson, P., Chen, W., Banaschewski, T., et al. (2008). Genome-wide association scan of attention deficit hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 147B, 1337-1344.
Neale, B.M., Medland, S., Ripke, S., Anney, R.J., Asherson, P., Buitelaar, J., Franke, B., Gill, M., Kent, L., Holmans, P., et al. (2010a). Case-control genome-wide association study of attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 49, 906-920.
Neale, B.M., Medland, S.E., Ripke, S., Asherson, P., Franke, B., Lesch, K.P., Faraone, S.V., Nguyen, T.T., Schafer, H., Holmans, P., et al. (2010b). Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 49, 884-897.
Need, A.C., McEvoy, J.P., Gennarelli, M., Heinzen, E.L., Ge, D., Maia, J.M., Shianna, K.V., He, M., Cirulli, E.T., Gumbs, C.E., et al. (2012). Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia. Am J Hum Genet 91, 303-312.
Nijmeijer, J.S., Hoekstra, P.J., Minderaa, R.B., Buitelaar, J.K., Altink, M.E., Buschgens, C.J., Fliers, E.A., Rommelse, N.N., Sergeant, J.A., and Hartman, C.A. (2009). PDD symptoms in ADHD, an independent familial trait? J Abnorm Child Psychol 37, 443-453.
Nishimura, T., Kubosaki, A., Ito, Y., and Notkins, A.L. (2009). Disturbances in the secretion of neurotransmitters in IA-2/IA-2beta null mice: changes in behavior, learning and lifespan. Neuroscience 159, 427-437.
Nomura, J., and Takumi, T. (2012). Animal models of psychiatric disorders that reflect human copy number variation. Neural Plast 2012, 589524.
Noor, A., Whibley, A., Marshall, C.R., Gianakopoulos, P.J., Piton, A., Carson, A.R., Orlic-Milacic, M., Lionel, A.C., Sato, D., Pinto, D., et al. (2010). Disruption at the PTCHD1 Locus on Xp22.11 in Autism spectrum disorder and intellectual disability. Sci Transl Med 2, 49ra68.
O'Donovan, M.C., Craddock, N., Norton, N., Williams, H., Peirce, T., Moskvina, V., Nikolov, I., Hamshere, M., Carroll, L., Georgieva, L., et al. (2008). Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet 40, 1053-1055.
O'Halloran, C.J., Kinsella, G.J., and Storey, E. (2012). The cerebellum and neuropsychological functioning: a critical review. J Clin Exp Neuropsychol 34, 35-56.
O'Roak, B.J., Deriziotis, P., Lee, C., Vives, L., Schwartz, J.J., Girirajan, S., Karakoc, E., Mackenzie, A.P., Ng, S.B., Baker, C., et al. (2011). Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nat Genet 43, 585-589.
177
O'Roak, B.J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B.P., Levy, R., Ko, A., Lee, C., Smith, J.D., et al. (2012). Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246-250.
Ollila, H.M., Soronen, P., Silander, K., Palo, O.M., Kieseppa, T., Kaunisto, M.A., Lonnqvist, J., Peltonen, L., Partonen, T., and Paunio, T. (2009). Findings from bipolar disorder genome-wide association studies replicate in a Finnish bipolar family-cohort. Mol Psychiatry 14, 351-353.
Oti, M., Huynen, M.A., and Brunner, H.G. (2008). Phenome connections. Trends Genet 24, 103-106.
Owen, M.J., O'Donovan, M.C., Thapar, A., and Craddock, N. (2011). Neurodevelopmental hypothesis of schizophrenia. Br J Psychiatry 198, 173-175.
Ozonoff, S., Young, G.S., Carter, A., Messinger, D., Yirmiya, N., Zwaigenbaum, L., Bryson, S., Carver, L.J., Constantino, J.N., Dobkins, K., et al. (2011). Recurrence risk for autism spectrum disorders: a baby siblings research consortium study. Pediatrics 128, e488-495.
Pagnamenta, A.T., Bacchelli, E., de Jonge, M.V., Mirza, G., Scerri, T.S., Minopoli, F., Chiocchetti, A., Ludwig, K.U., Hoffmann, P., Paracchini, S., et al. (2010). Characterization of a family with rare deletions in CNTNAP5 and DOCK4 suggests novel risk loci for autism and dyslexia. Biol Psychiatry 68, 320-328.
Paridaen, J.T., Danesin, C., Elas, A.T., van de Water, S., Houart, C., and Zivkovic, D. (2009). Apc1 is required for maintenance of local brain organizers and dorsal midbrain survival. Dev Biol 331, 101-112.
Parisi, P., Miano, S., Mei, D., Paolino, M.C., Castaldo, R., and Villa, M.P. (2010). Diffuse subcortical band heterotopia, periodic limb movements during sleep and a novel "de novo" mutation in the DCX gene. Brain Dev 32, 511-515.
Pelletier, A.L., and Mittal, V.A. (2012). An autism dimension for schizophrenia in the next Diagnostic and Statistical Manual? Schizophr Res 137, 269-270.
Penzes, P., Buonanno, A., Passafaro, M., Sala, C., and Sweet, R.A. (2013). Developmental vulnerability of synapses and circuits associated with neuropsychiatric disorders. J Neurochem 126, 165-182.
Peralta, V., de Jalon, E.G., Campos, M.S., Zandio, M., Sanchez-Torres, A., and Cuesta, M.J. (2011). The meaning of childhood attention-deficit hyperactivity symptoms in patients with a first-episode of schizophrenia-spectrum psychosis. Schizophr Res 126, 28-35.
Pescosolido, M.F., Gamsiz, E.D., Nagpal, S., and Morrow, E.M. (2013). Distribution of disease-associated copy number variants across distinct disorders of cognitive development. J Am Acad Child Adolesc Psychiatry 52, 414-430 e414.
Pescosolido, M.F., Yang, U., Sabbagh, M., and Morrow, E.M. (2012). Lighting a path: genetic studies pinpoint neurodevelopmental mechanisms in autism and related disorders. Dialogues Clin Neurosci 14, 239-252.
178
Pierce, K., and Courchesne, E. (2001). Evidence for a cerebellar role in reduced exploration and stereotyped behavior in autism. Biol Psychiatry 49, 655-664.
Pinto, D., Darvishi, K., Shi, X., Rajan, D., Rigler, D., Fitzgerald, T., Lionel, A.C., Thiruvahindrapuram, B., Macdonald, J.R., Mills, R., et al. (2011). Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants. Nat Biotechnol 29, 512-520.
Pinto, D., Pagnamenta, A.T., Klei, L., Anney, R., Merico, D., Regan, R., Conroy, J., Magalhaes, T.R., Correia, C., Abrahams, B.S., et al. (2010). Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368-372.
Pober, B.R. (2010). Williams-Beuren syndrome. N Engl J Med 362, 239-252.
Polanczyk, G., de Lima, M.S., Horta, B.L., Biederman, J., and Rohde, L.A. (2007). The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry 164, 942-948.
Politte, L.C., and McDougle, C.J. (2013). Atypical antipsychotics in the treatment of children and adolescents with pervasive developmental disorders. Psychopharmacology (Berl).
Poot, M. (2013). Towards identification of individual etiologies by resolving genomic and biological conundrums in patients with autism spectrum disorders. Mol Syndromol 4, 213-226.
Poot, M., van der Smagt, J.J., Brilstra, E.H., and Bourgeron, T. (2011). Disentangling the myriad genomics of complex disorders, specifically focusing on autism, epilepsy, and schizophrenia. Cytogenet Genome Res 135, 228-240.
Posey, D.J., Wiegand, R.E., Wilkerson, J., Maynard, M., Stigler, K.A., and McDougle, C.J. (2006). Open-label atomoxetine for attention-deficit/ hyperactivity disorder symptoms associated with high-functioning pervasive developmental disorders. J Child Adolesc Psychopharmacol 16, 599-610.
Posthuma, D., and Polderman, T.J. (2013). What have we learned from recent twin studies about the etiology of neurodevelopmental disorders? Curr Opin Neurol 26, 111-121.
Poulopoulos, A., Aramuni, G., Meyer, G., Soykan, T., Hoon, M., Papadopoulos, T., Zhang, M., Paarmann, I., Fuchs, C., Harvey, K., et al. (2009). Neuroligin 2 drives postsynaptic assembly at perisomatic inhibitory synapses through gephyrin and collybistin. Neuron 63, 628-642.
Prasad, A., Merico, D., Thiruvahindrapuram, B., Wei, J., Lionel, A.C., Sato, D., Rickaby, J., Lu, C., Szatmari, P., Roberts, W., et al. (2012). A discovery resource of rare copy number variations in individuals with autism spectrum disorder. G3 (Bethesda) 2, 1665-1685.
Pringle, B., Colpe, L.J., Blumberg, S.J., Avila, R.M., and Kogan, M.D. (2012). Diagnostic history and treatment of school-aged children with autism spectrum disorder and special health care needs. NCHS Data Brief, 1-8.
179
Prior, P., Schmitt, B., Grenningloh, G., Pribilla, I., Multhaup, G., Beyreuther, K., Maulet, Y., Werner, P., Langosch, D., Kirsch, J., et al. (1992). Primary structure and alternative splice variants of gephyrin, a putative glycine receptor-tubulin linker protein. Neuron 8, 1161-1170.
Pritchard, J.K., Stephens, M., and Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics 155, 945-959.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559-575.
Purcell, S.M., Wray, N.R., Stone, J.L., Visscher, P.M., O'Donovan, M.C., Sullivan, P.F., and Sklar, P. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748-752.
Ramocki, M.B., and Zoghbi, H.Y. (2008). Failure of neuronal homeostasis results in common neuropsychiatric phenotypes. Nature 455, 912-918.
Ramos-Quiroga, J.A., Sanchez-Mora, C., Casas, M., Garcia-Martinez, I., Bosch, R., Nogueira, M., Corrales, M., Palomar, G., Vidal, R., Coll-Tane, M., et al. (2014). Genome-wide copy number variation analysis in adult attention-deficit and hyperactivity disorder. J Psychiatr Res 49, 60-67.
Rapoport, J., Chavez, A., Greenstein, D., Addington, A., and Gogtay, N. (2009). Autism spectrum disorders and childhood-onset schizophrenia: clinical and biological contributions to a relation revisited. J Am Acad Child Adolesc Psychiatry 48, 10-18.
Raychaudhuri, S., Plenge, R.M., Rossin, E.J., Ng, A.C., Purcell, S.M., Sklar, P., Scolnick, E.M., Xavier, R.J., Altshuler, D., and Daly, M.J. (2009). Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet 5, e1000534.
Reaven, J.A., Hepburn, S.L., and Ross, R.G. (2008). Use of the ADOS and ADI-R in children with psychosis: importance of clinical judgment. Clin Child Psychol Psychiatry 13, 81-94.
Rees, E., Walters, J.T., Chambert, K.D., O'Dushlaine, C., Szatkiewicz, J., Richards, A.L., Georgieva, L., Mahoney-Davies, G., Legge, S.E., Moran, J.L., et al. (2013). CNV analysis in a large schizophrenia sample implicates deletions at 16p12.1 and SLC1A1 and duplications at 1p36.33 and CGNL1. Hum Mol Genet.
Reichelt, A.C., Rodgers, R.J., and Clapcote, S.J. (2012). The role of neurexins in schizophrenia and autistic spectrum disorder. Neuropharmacology 62, 1519-1526.
Reiersen, A.M., Constantino, J.N., Grimmer, M., Martin, N.G., and Todd, R.D. (2008). Evidence for shared genetic influences on self-reported ADHD and autistic symptoms in young adult Australian twins. Twin Res Hum Genet 11, 579-585.
Reiersen, A.M., Constantino, J.N., Volk, H.E., and Todd, R.D. (2007). Autistic traits in a population-based ADHD twin sample. J Child Psychol Psychiatry 48, 464-472.
180
Reiss, J., Gross-Hardt, S., Christensen, E., Schmidt, P., Mendel, R.R., and Schwarz, G. (2001). A mutation in the gene for the neurotransmitter receptor-clustering protein gephyrin causes a novel form of molybdenum cofactor deficiency. Am J Hum Genet 68, 208-213.
Reiss, J., and Hahnewald, R. (2011). Molybdenum cofactor deficiency: Mutations in GPHN, MOCS1, and MOCS2. Hum Mutat 32, 10-18.
Reiss, J., Lenz, U., Aquaviva-Bourdain, C., Joriot-Chekaf, S., Mention-Mulliez, K., and Holder-Espinasse, M. (2011). A GPHN point mutation leading to molybdenum cofactor deficiency. Clin Genet 80, 598-599.
Riley, B., Thiselton, D., Maher, B.S., Bigdeli, T., Wormley, B., McMichael, G.O., Fanous, A.H., Vladimirov, V., O'Neill, F.A., Walsh, D., et al. (2010). Replication of association between schizophrenia and ZNF804A in the Irish Case-Control Study of Schizophrenia sample. Mol Psychiatry 15, 29-37.
Risch, N., Spiker, D., Lotspeich, L., Nouri, N., Hinds, D., Hallmayer, J., Kalaydjieva, L., McCague, P., Dimiceli, S., Pitts, T., et al. (1999). A genomic screen of autism: evidence for a multilocus etiology. Am J Hum Genet 65, 493-507.
Risi, S., Lord, C., Gotham, K., Corsello, C., Chrysler, C., Szatmari, P., Cook, E.H., Jr., Leventhal, B.L., and Pickles, A. (2006). Combining information from multiple sources in the diagnosis of autism spectrum disorders. J Am Acad Child Adolesc Psychiatry 45, 1094-1103.
Romanos, M., Freitag, C., Jacob, C., Craig, D.W., Dempfle, A., Nguyen, T.T., Halperin, R., Walitza, S., Renner, T.J., Seitz, C., et al. (2008). Genome-wide linkage analysis of ADHD using high-density SNP arrays: novel loci at 5q13.1 and 14q12. Mol Psychiatry 13, 522-530.
Rommelse, N.N., Franke, B., Geurts, H.M., Hartman, C.A., and Buitelaar, J.K. (2010). Shared heritability of attention-deficit/hyperactivity disorder and autism spectrum disorder. Eur Child Adolesc Psychiatry 19, 281-295.
Rommelse, N.N., Geurts, H.M., Franke, B., Buitelaar, J.K., and Hartman, C.A. (2011). A review on cognitive and brain endophenotypes that may be common in autism spectrum disorder and attention-deficit/hyperactivity disorder and facilitate the search for pleiotropic genes. Neurosci Biobehav Rev 35, 1363-1396.
Ronald, A., Simonoff, E., Kuntsi, J., Asherson, P., and Plomin, R. (2008). Evidence for overlapping genetic influences on autistic and ADHD behaviours in a community twin sample. J Child Psychol Psychiatry 49, 535-542.
Ronemus, M., Iossifov, I., Levy, D., and Wigler, M. (2014). The role of de novo mutations in the genetics of autism spectrum disorders. Nat Rev Genet 15, 133-141.
Rosenberg, R.E., Law, J.K., Yenokyan, G., McGready, J., Kaufmann, W.E., and Law, P.A. (2009). Characteristics and concordance of autism spectrum disorders among 277 twin pairs. Arch Pediatr Adolesc Med 163, 907-914.
181
Rosenfeld, J.A., Ballif, B.C., Torchia, B.S., Sahoo, T., Ravnan, J.B., Schultz, R., Lamb, A., Bejjani, B.A., and Shaffer, L.G. (2010). Copy number variations associated with autism spectrum disorders contribute to a spectrum of neurodevelopmental disorders. Genet Med 12, 694-702.
Rosenfeld, J.A., Coe, B.P., Eichler, E.E., Cuckle, H., and Shaffer, L.G. (2013). Estimates of penetrance for recurrent pathogenic copy-number variations. Genet Med 15, 478-481.
Rosenthal, M., Wallace, G.L., Lawson, R., Wills, M.C., Dixon, E., Yerys, B.E., and Kenworthy, L. (2013). Impairments in real-world executive function increase from childhood to adolescence in autism spectrum disorders. Neuropsychology 27, 13-18.
Rowen, L., Young, J., Birditt, B., Kaur, A., Madan, A., Philipps, D.L., Qin, S., Minx, P., Wilson, R.K., Hood, L., et al. (2002). Analysis of the human neurexin genes: alternative splicing and the generation of protein diversity. Genomics 79, 587-597.
Rubenstein, J.L., and Merzenich, M.M. (2003). Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav 2, 255-267.
Rujescu, D., Ingason, A., Cichon, S., Pietilainen, O.P., Barnes, M.R., Toulopoulou, T., Picchioni, M., Vassos, E., Ettinger, U., Bramon, E., et al. (2009). Disruption of the neurexin 1 gene is associated with schizophrenia. Hum Mol Genet 18, 988-996.
Russell, G., and Pavelka, Z. (2013). Co-Occurrence of Developmental Disorders: Children Who Share Symptoms of Autism, Dyslexia and Attention Deficit Hyperactivity Disorder. In Recent Advances in Autism Spectrum Disorders, M. Fitzgerald, ed. (InTech).
Rzhetsky, A., Wajngurt, D., Park, N., and Zheng, T. (2007). Probing genetic overlap among complex human phenotypes. Proc Natl Acad Sci USA 104, 11694-11699.
Sahoo, T., Theisen, A., Rosenfeld, J.A., Lamb, A.N., Ravnan, J.B., Schultz, R.A., Torchia, B.S., Neill, N., Casci, I., Bejjani, B.A., et al. (2011). Copy number variants of schizophrenia susceptibility loci are associated with a spectrum of speech and developmental delays and behavior problems. Genet Med 13, 868-880.
Sakai, Y., Shaw, C.A., Dawson, B.C., Dugas, D.V., Al-Mohtaseb, Z., Hill, D.E., and Zoghbi, H.Y. (2011). Protein interactome reveals converging molecular pathways among autism disorders. Sci Transl Med 3, 86ra49.
Sakurai, T. (2012). The role of NrCAM in neural development and disorders--beyond a simple glue in the brain. Mol Cell Neurosci 49, 351-363.
Salyakina, D., Cukier, H.N., Lee, J.M., Sacharow, S., Nations, L.D., Ma, D., Jaworski, J.M., Konidari, I., Whitehead, P.L., Wright, H.H., et al. (2011). Copy number variants in extended autism spectrum disorder families reveal candidates potentially involved in autism risk. PLoS One 6, e26049.
Sanders, S.J., Ercan-Sencicek, A.G., Hus, V., Luo, R., Murtha, M.T., Moreno-De-Luca, D., Chu, S.H., Moreau, M.P., Gupta, A.R., Thomson, S.A., et al. (2011). Multiple recurrent de novo
182
CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70, 863-885.
Sanders, S.J., Murtha, M.T., Gupta, A.R., Murdoch, J.D., Raubeson, M.J., Willsey, A.J., Ercan-Sencicek, A.G., DiLullo, N.M., Parikshak, N.N., Stein, J.L., et al. (2012). De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237-241.
Santosh, P.J., Baird, G., Pityaratstian, N., Tavare, E., and Gringras, P. (2006). Impact of comorbid autism spectrum disorders on stimulant response in children with attention deficit hyperactivity disorder: a retrospective and prospective effectiveness study. Child Care Health Dev 32, 575-583.
Sato, D., Lionel, A.C., Leblond, C.S., Prasad, A., Pinto, D., Walker, S., O'Connor, I., Russell, C., Drmic, I.E., Hamdan, F.F., et al. (2012). SHANK1 Deletions in Males with Autism Spectrum Disorder. Am J Hum Genet 90, 879-887.
Schaaf, C.P., Boone, P.M., Sampath, S., Williams, C., Bader, P.I., Mueller, J.M., Shchelochkov, O.A., Brown, C.W., Crawford, H.P., Phalen, J.A., et al. (2012). Phenotypic spectrum and genotype-phenotype correlations of NRXN1 exon deletions. Eur J Hum Genet 20, 1240-1247.
Schwamborn, J.C., Berezikov, E., and Knoblich, J.A. (2009). The TRIM-NHL protein TRIM32 activates microRNAs and prevents self-renewal in mouse neural progenitors. Cell 136, 913-925.
Sebat, J., Lakshmi, B., Malhotra, D., Troge, J., Lese-Martin, C., Walsh, T., Yamrom, B., Yoon, S., Krasnitz, A., Kendall, J., et al. (2007). Strong association of de novo copy number mutations with autism. Science 316, 445-449.
Seno, M.M., Hu, P., Gwadry, F.G., Pinto, D., Marshall, C.R., Casallo, G., and Scherer, S.W. (2011). Gene and miRNA expression profiles in autism spectrum disorders. Brain Res 1380, 85-97.
Shcheglovitov, A., Shcheglovitova, O., Yazawa, M., Portmann, T., Shu, R., Sebastiano, V., Krawisz, A., Froehlich, W., Bernstein, J.A., Hallmayer, J.F., et al. (2013). SHANK3 and IGF1 restore synaptic deficits in neurons from 22q13 deletion syndrome patients. Nature 503, 267-271.
Sheitman, B.B., Kraus, J.E., Bodfish, J.W., and Carmel, H. (2004). Are the negative symptoms of schizophrenia consistent with an autistic spectrum illness? Schizophr Res 69, 119-120.
Shen, Y., Dies, K.A., Holm, I.A., Bridgemohan, C., Sobeih, M.M., Caronna, E.B., Miller, K.J., Frazier, J.A., Silverstein, I., Picker, J., et al. (2010). Clinical genetic testing for patients with autism spectrum disorders. Pediatrics 125, e727-735.
Shi, J., Levinson, D.F., Duan, J., Sanders, A.R., Zheng, Y., Pe'er, I., Dudbridge, F., Holmans, P.A., Whittemore, A.S., Mowry, B.J., et al. (2009). Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature 460, 753-757.
Shih, R.A., Belmonte, P.L., and Zandi, P.P. (2004). A review of the evidence from family, twin and adoption studies for a genetic contribution to adult psychiatric disorders. Int Rev Psychiatry 16, 260-283.
183
Shimojima, K., Sugawara, M., Shichiji, M., Mukaida, S., Takayama, R., Imai, K., and Yamamoto, T. (2011). Loss-of-function mutation of collybistin is responsible for X-linked mental retardation associated with epilepsy. J Hum Genet 56, 561-565.
Shinawi, M., Liu, P., Kang, S.H., Shen, J., Belmont, J.W., Scott, D.A., Probst, F.J., Craigen, W.J., Graham, B.H., Pursley, A., et al. (2010). Recurrent reciprocal 16p11.2 rearrangements associated with global developmental delay, behavioural problems, dysmorphism, epilepsy, and abnormal head size. J Med Genet 47, 332-341.
Silversides, C.K., Lionel, A.C., Costain, G., Merico, D., Migita, O., Liu, B., Yuen, T., Rickaby, J., Thiruvahindrapuram, B., Marshall, C.R., et al. (2012). Rare Copy Number Variations in Adults with Tetralogy of Fallot Implicate Novel Risk Gene Pathways. PLoS Genet 8, e1002843.
Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., and Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. J Am Acad Child Adolesc Psychiatry 47, 921-929.
Sinzig, J., Walter, D., and Doepfner, M. (2009). Attention deficit/hyperactivity disorder in children and adolescents with autism spectrum disorder: symptom or syndrome? J Atten Disord 13, 117-126.
Smoller, J.W. (2013). Disorders and borders: psychiatric genetics and nosology. Am J Med Genet B Neuropsychiatr Genet 162B, 559-578.
Sporn, A.L., Addington, A.M., Gogtay, N., Ordonez, A.E., Gornick, M., Clasen, L., Greenstein, D., Tossell, J.W., Gochman, P., Lenane, M., et al. (2004). Pervasive developmental disorder and childhood-onset schizophrenia: comorbid disorder or a phenotypic variant of a very early onset illness? Biol Psychiatry 55, 989-994.
Sprenger, L., Buhler, E., Poustka, L., Bach, C., Heinzel-Gutenbrunner, M., Kamp-Becker, I., and Bachmann, C. (2013). Impact of ADHD symptoms on autism spectrum disorder symptom severity. Res Dev Disabil 34, 3545-3552.
Sprich, S., Biederman, J., Crawford, M.H., Mundy, E., and Faraone, S.V. (2000). Adoptive and biological families of children and adolescents with ADHD. J Am Acad Child Adolesc Psychiatry 39, 1432-1437.
Stallmeyer, B., Schwarz, G., Schulze, J., Nerlich, A., Reiss, J., Kirsch, J., and Mendel, R.R. (1999). The neurotransmitter receptor-anchoring protein gephyrin reconstitutes molybdenum cofactor biosynthesis in bacteria, plants, and mammalian cells. Proc Natl Acad Sci USA 96, 1333-1338.
Stankiewicz, P., Kulkarni, S., Dharmadhikari, A.V., Sampath, S., Bhatt, S.S., Shaikh, T.H., Xia, Z., Pursley, A.N., Cooper, M.L., Shinawi, M., et al. (2012). Recurrent deletions and reciprocal duplications of 10q11.21q11.23 including CHAT and SLC18A3 are likely mediated by complex low-copy repeats. Hum Mutat 33, 165-179.
184
Stefansson, H., Ophoff, R.A., Steinberg, S., Andreassen, O.A., Cichon, S., Rujescu, D., Werge, T., Pietilainen, O.P., Mors, O., Mortensen, P.B., et al. (2009). Common variants conferring risk of schizophrenia. Nature 460, 744-747.
Stefansson, H., Rujescu, D., Cichon, S., Pietilainen, O.P., Ingason, A., Steinberg, S., Fossdal, R., Sigurdsson, E., Sigmundsson, T., Buizer-Voskamp, J.E., et al. (2008). Large recurrent microdeletions associated with schizophrenia. Nature 455, 232-236.
Steffenburg, S., Gillberg, C., Hellgren, L., Andersson, L., Gillberg, I.C., Jakobsson, G., and Bohman, M. (1989). A twin study of autism in Denmark, Finland, Iceland, Norway and Sweden. J Child Psychol Psychiatry 30, 405-416.
Steinberg, S., Mors, O., Borglum, A.D., Gustafsson, O., Werge, T., Mortensen, P.B., Andreassen, O.A., Sigurdsson, E., Thorgeirsson, T.E., Bottcher, Y., et al. (2011). Expanding the range of ZNF804A variants conferring risk of psychosis. Mol Psychiatry 16, 59-66.
Stergiakouli, E., Hamshere, M., Holmans, P., Langley, K., Zaharieva, I., Hawi, Z., Kent, L., Gill, M., Williams, N., Owen, M.J., et al. (2012). Investigating the contribution of common genetic variants to the risk and pathogenesis of ADHD. Am J Psychiatry 169, 186-194.
Stewart, A.F., Dandona, S., Chen, L., Assogba, O., Belanger, M., Ewart, G., LaRose, R., Doelle, H., Williams, K., Wells, G.A., et al. (2009). Kinesin family member 6 variant Trp719Arg does not associate with angiographically defined coronary artery disease in the Ottawa Heart Genomics Study. J Am Coll Cardiol 53, 1471-1472.
Stewart, L.R., Hall, A.L., Kang, S.H., Shaw, C.A., and Beaudet, A.L. (2011). High frequency of known copy number abnormalities and maternal duplication 15q11-q13 in patients with combined schizophrenia and epilepsy. BMC Med Genet 12, 154.
Stone, W.S., and Iguchi, L. (2011). Do Apparent Overlaps between Schizophrenia and Autistic Spectrum Disorders Reflect Superficial Similarities or Etiological Commonalities? N Am J Med Sci (Boston) 4, 124-133.
Sudhof, T.C. (2008). Neuroligins and neurexins link synaptic function to cognitive disease. Nature 455, 903-911.
Sullivan, P.F., Daly, M.J., and O'Donovan, M. (2012a). Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet 13, 537-551.
Sullivan, P.F., Kendler, K.S., and Neale, M.C. (2003). Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry 60, 1187-1192.
Sullivan, P.F., Magnusson, C., Reichenberg, A., Boman, M., Dalman, C., Davidson, M., Fruchter, E., Hultman, C.M., Lundberg, M., Langstrom, N., et al. (2012b). Family history of schizophrenia and bipolar disorder as risk factors for autism. Arch Gen Psychiatry 69, 1099-1103.
185
Sullivan, S., Rai, D., Golding, J., Zammit, S., and Steer, C. (2013). The association between autism spectrum disorder and psychotic experiences in the Avon longitudinal study of parents and children (ALSPAC) birth cohort. J Am Acad Child Adolesc Psychiatry 52, 806-814 e802.
Sun, C., Cheng, M.C., Qin, R., Liao, D.L., Chen, T.T., Koong, F.J., Chen, G., and Chen, C.H. (2011). Identification and functional characterization of rare mutations of the neuroligin-2 gene (NLGN2) associated with schizophrenia. Hum Mol Genet 20, 3042-3051.
Sundaram, S.K., Huq, A.M., Wilson, B.J., and Chugani, H.T. (2010). Tourette syndrome is associated with recurrent exonic copy number variants. Neurology 74, 1583-1590.
Szatmari, P., Jones, M.B., Zwaigenbaum, L., and MacLean, J.E. (1998). Genetics of autism: overview and new directions. J Autism Dev Disord 28, 351-368.
Szatmari, P., Paterson, A.D., Zwaigenbaum, L., Roberts, W., Brian, J., Liu, X.Q., Vincent, J.B., Skaug, J.L., Thompson, A.P., Senman, L., et al. (2007). Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet 39, 319-328.
Tabuchi, K., and Sudhof, T.C. (2002). Structure and evolution of neurexin genes: insight into the mechanism of alternative splicing. Genomics 79, 849-859.
Talkowski, M.E., Mullegama, S.V., Rosenfeld, J.A., van Bon, B.W., Shen, Y., Repnikova, E.A., Gastier-Foster, J., Thrush, D.L., Kathiresan, S., Ruderfer, D.M., et al. (2011). Assessment of 2q23.1 microdeletion syndrome implicates MBD5 as a single causal locus of intellectual disability, epilepsy, and autism spectrum disorder. Am J Hum Genet 89, 551-563.
Taurines, R., Schwenck, C., Westerwald, E., Sachse, M., Siniatchkin, M., and Freitag, C. (2012). ADHD and autism: differential diagnosis or overlapping traits? A selective review. Atten Defic Hyperact Disord 4, 115-139.
Taylor, M.J., Charman, T., Robinson, E.B., Plomin, R., Happe, F., Asherson, P., and Ronald, A. (2013). Developmental associations between traits of autism spectrum disorder and attention deficit hyperactivity disorder: a genetically informative, longitudinal twin study. Psychol Med 43, 1735-1746.
Thapar, A., Cooper, M., Eyre, O., and Langley, K. (2013). What have we learnt about the causes of ADHD? J Child Psychol Psychiatry 54, 3-16.
Thapar, A., Langley, K., Owen, M.J., and O'Donovan, M.C. (2007). Advances in genetic findings on attention deficit hyperactivity disorder. Psychol Med 37, 1681-1692.
Toro, R., Konyukh, M., Delorme, R., Leblond, C., Chaste, P., Fauchereau, F., Coleman, M., Leboyer, M., Gillberg, C., and Bourgeron, T. (2010). Key role for gene dosage and synaptic homeostasis in autism spectrum disorders. Trends Genet 26, 363-372.
Tretter, V., Mukherjee, J., Maric, H.M., Schindelin, H., Sieghart, W., and Moss, S.J. (2012). Gephyrin, the enigmatic organizer at GABAergic synapses. Front Cell Neurosci 6, 23.
186
Tropeano, M., Ahn, J.W., Dobson, R.J., Breen, G., Rucker, J., Dixit, A., Pal, D.K., McGuffin, P., Farmer, A., White, P.S., et al. (2013). Male-biased autosomal effect of 16p13.11 copy number variation in neurodevelopmental disorders. PLoS One 8, e61365.
Tseng, W.L., and Gau, S.S. (2013). Executive function as a mediator in the link between attention-deficit/hyperactivity disorder and social problems. J Child Psychol Psychiatry 54, 996-1004.
Tuy, F.P., Saillour, Y., Kappeler, C., Chelly, J., and Francis, F. (2008). Alternative transcripts of Dclk1 and Dclk2 and their expression in doublecortin knockout mice. Dev Neurosci 30, 171-186.
Ullrich, B., Ushkaryov, Y.A., and Sudhof, T.C. (1995). Cartography of neurexins: more than 1000 isoforms generated by alternative splicing and expressed in distinct subsets of neurons. Neuron 14, 497-507.
Unenge Hallerback, M., Lugnegard, T., and Gillberg, C. (2012). Is autism spectrum disorder common in schizophrenia? Psychiatry Res 198, 12-17.
Vaags, A.K., Lionel, A.C., Sato, D., Goodenberger, M., Stein, Q.P., Curran, S., Ogilvie, C., Ahn, J.W., Drmic, I., Senman, L., et al. (2012). Rare deletions at the neurexin 3 locus in autism spectrum disorder. Am J Hum Genet 90, 133-141.
Vacic, V., McCarthy, S., Malhotra, D., Murray, F., Chou, H.H., Peoples, A., Makarov, V., Yoon, S., Bhandari, A., Corominas, R., et al. (2011). Duplications of the neuropeptide receptor gene VIPR2 confer significant risk for schizophrenia. Nature 471, 499-503.
van der Zwaag, B., Franke, L., Poot, M., Hochstenbach, R., Spierenburg, H.A., Vorstman, J.A., van Daalen, E., de Jonge, M.V., Verbeek, N.E., Brilstra, E.H., et al. (2009). Gene-network analysis identifies susceptibility genes related to glycobiology in autism. PLoS One 4, e5324.
Vannucchi, G., Masi, G., Toni, C., Dell'osso, L., Marazziti, D., and Perugi, G. (2013). Clinical features, developmental course, and psychiatric comorbidity of adult autism spectrum disorders. CNS Spectr, 1-8.
Veltman, J.A., and Brunner, H.G. (2012). De novo mutations in human genetic disease. Nat Rev Genet 13, 565-575.
Vincent, J.B., Horike, S.I., Choufani, S., Paterson, A.D., Roberts, W., Szatmari, P., Weksberg, R., Fernandez, B., and Scherer, S.W. (2006). An inversion inv(4)(p12-p15.3) in autistic siblings implicates the 4p GABA receptor gene cluster. J Med Genet 43, 429-434.
Vismara, L.A., and Rogers, S.J. (2010). Behavioral treatments in autism spectrum disorder: what do we know? Annu Rev Clin Psychol 6, 447-468.
Vrijenhoek, T., Buizer-Voskamp, J.E., van der Stelt, I., Strengman, E., Sabatti, C., Geurts van Kessel, A., Brunner, H.G., Ophoff, R.A., and Veltman, J.A. (2008). Recurrent CNVs disrupt three candidate genes in schizophrenia patients. Am J Hum Genet 83, 504-510.
187
Vulto-van Silfhout, A.T., Hehir-Kwa, J.Y., van Bon, B.W., Schuurs-Hoeijmakers, J.H., Meader, S., Hellebrekers, C.J., Thoonen, I.J., de Brouwer, A.P., Brunner, H.G., Webber, C., et al. (2013). Clinical significance of de novo and inherited copy-number variation. Hum Mutat 34, 1679-1687.
Walsh, T., McClellan, J.M., McCarthy, S.E., Addington, A.M., Pierce, S.B., Cooper, G.M., Nord, A.S., Kusenda, M., Malhotra, D., Bhandari, A., et al. (2008). Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia. Science 320, 539-543.
Waltereit, R., Banaschewski, T., Meyer-Lindenberg, A., and Poustka, L. (2013). Interaction of neurodevelopmental pathways and synaptic plasticity in mental retardation, autism spectrum disorder and schizophrenia: Implications for psychiatry. World J Biol Psychiatry.
Walters, R.G., Jacquemont, S., Valsesia, A., de Smith, A.J., Martinet, D., Andersson, J., Falchi, M., Chen, F., Andrieux, J., Lobbens, S., et al. (2010). A new highly penetrant form of obesity due to deletions on chromosome 16p11.2. Nature 463, 671-675.
Wang, K., Zhang, H., Ma, D., Bucan, M., Glessner, J.T., Abrahams, B.S., Salyakina, D., Imielinski, M., Bradfield, J.P., Sleiman, P.M., et al. (2009). Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 459, 528-533.
Wang, K.S., Liu, X.F., and Aragam, N. (2010). A genome-wide meta-analysis identifies novel loci associated with schizophrenia and bipolar disorder. Schizophr Res 124, 192-199.
Waris, P., Lindberg, N., Kettunen, K., and Tani, P. (2013). The relationship between Asperger's syndrome and schizophrenia in adolescence. Eur Child Adolesc Psychiatry 22, 217-223.
Weischenfeldt, J., Symmons, O., Spitz, F., and Korbel, J.O. (2013). Phenotypic impact of genomic structural variation: insights from and for human disease. Nat Rev Genet 14, 125-138.
Weiss, L.A., Arking, D.E., Daly, M.J., and Chakravarti, A. (2009). A genome-wide linkage and association scan reveals novel loci for autism. Nature 461, 802-808.
Weiss, L.A., Shen, Y., Korn, J.M., Arking, D.E., Miller, D.T., Fossdal, R., Saemundsen, E., Stefansson, H., Ferreira, M.A., Green, T., et al. (2008). Association between microdeletion and microduplication at 16p11.2 and autism. N Engl J Med 358, 667-675.
Werling, D.M., and Geschwind, D.H. (2013). Sex differences in autism spectrum disorders. Curr Opin Neurol 26, 146-153.
Williams, H.J., Craddock, N., Russo, G., Hamshere, M.L., Moskvina, V., Dwyer, S., Smith, R.L., Green, E., Grozeva, D., Holmans, P., et al. (2011a). Most genome-wide significant susceptibility loci for schizophrenia and bipolar disorder reported to date cross-traditional diagnostic boundaries. Hum Mol Genet 20, 387-391.
Williams, H.J., Norton, N., Dwyer, S., Moskvina, V., Nikolov, I., Carroll, L., Georgieva, L., Williams, N.M., Morris, D.W., Quinn, E.M., et al. (2011b). Fine mapping of ZNF804A and genome-wide significant evidence for its involvement in schizophrenia and bipolar disorder. Mol Psychiatry 16, 429-441.
188
Williams, N.M., Franke, B., Mick, E., Anney, R.J., Freitag, C.M., Gill, M., Thapar, A., O'Donovan, M.C., Owen, M.J., Holmans, P., et al. (2012). Genome-wide analysis of copy number variants in attention deficit hyperactivity disorder: the role of rare variants and duplications at 15q13.3. Am J Psychiatry 169, 195-204.
Williams, N.M., Zaharieva, I., Martin, A., Langley, K., Mantripragada, K., Fossdal, R., Stefansson, H., Stefansson, K., Magnusson, P., Gudmundsson, O.O., et al. (2010). Rare chromosomal deletions and duplications in attention-deficit hyperactivity disorder: a genome-wide analysis. Lancet 376, 1401-1408.
Wilson, G.M., Flibotte, S., Chopra, V., Melnyk, B.L., Honer, W.G., and Holt, R.A. (2006). DNA copy-number analysis in bipolar disorder and schizophrenia reveals aberrations in genes involved in glutamate signaling. Hum Mol Genet 15, 743-749.
Wilson, P.M., Fryer, R.H., Fang, Y., and Hatten, M.E. (2010). Astn2, a novel member of the astrotactin gene family, regulates the trafficking of ASTN1 during glial-guided neuronal migration. J Neurosci 30, 8529-8540.
Xia, K., Guo, H., Hu, Z., Xun, G., Zuo, L., Peng, Y., Wang, K., He, Y., Xiong, Z., Sun, L., et al. (2013). Common genetic variants on 1p13.2 associate with risk of autism. Mol Psychiatry.
Xu, B., Ionita-Laza, I., Roos, J.L., Boone, B., Woodrick, S., Sun, Y., Levy, S., Gogos, J.A., and Karayiorgou, M. (2012). De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat Genet 44, 1365-1369.
Xu, B., Roos, J.L., Dexheimer, P., Boone, B., Plummer, B., Levy, S., Gogos, J.A., and Karayiorgou, M. (2011). Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat Genet 43, 864-868.
Xu, B., Roos, J.L., Levy, S., van Rensburg, E.J., Gogos, J.A., and Karayiorgou, M. (2008). Strong association of de novo copy number mutations with sporadic schizophrenia. Nat Genet 40, 880-885.
Xu, B., Woodroffe, A., Rodriguez-Murillo, L., Roos, J.L., van Rensburg, E.J., Abecasis, G.R., Gogos, J.A., and Karayiorgou, M. (2009). Elucidating the genetic architecture of familial schizophrenia using rare copy number variant and linkage scans. Proc Natl Acad Sci USA 106, 16746-16751.
Xu, J., Zwaigenbaum, L., Szatmari, P., and Scherer, S.W. (2004). Molecular Cytogenetics of Autism. Curr Genomics 5, 347-364.
Yang, L., Neale, B.M., Liu, L., Lee, S.H., Wray, N.R., Ji, N., Li, H., Qian, Q., Wang, D., Li, J., et al. (2013). Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: genome-wide association study of both common and rare variants. Am J Med Genet B Neuropsychiatr Genet 162B, 419-430.
Yoshida, Y., and Uchiyama, T. (2004). The clinical necessity for assessing Attention Deficit/Hyperactivity Disorder (AD/HD) symptoms in children with high-functioning Pervasive Developmental Disorder (PDD). Eur Child Adolesc Psychiatry 13, 307-314.
189
Youle, R.J., and Strasser, A. (2008). The BCL-2 protein family: opposing activities that mediate cell death. Nat Rev Mol Cell Biol 9, 47-59.
Yu, H.E., Hawash, K., Picker, J., Stoler, J., Urion, D., Wu, B.L., and Shen, Y. (2012). A recurrent 1.71 Mb genomic imbalance at 2q13 increases the risk of developmental delay and dysmorphism. Clin Genet 81, 257-264.
Zahir, F.R., Baross, A., Delaney, A.D., Eydoux, P., Fernandes, N.D., Pugh, T., Marra, M.A., and Friedman, J.M. (2008). A patient with vertebral, cognitive and behavioural abnormalities and a de novo deletion of NRXN1alpha. J Med Genet 45, 239-243.
Zhang, D., Cheng, L., Qian, Y., Alliey-Rodriguez, N., Kelsoe, J.R., Greenwood, T., Nievergelt, C., Barrett, T.B., McKinney, R., Schork, N., et al. (2009). Singleton deletions throughout the genome increase risk of bipolar disorder. Mol Psychiatry 14, 376-380.
Zhang, X., Snijders, A., Segraves, R., Niebuhr, A., Albertson, D., Yang, H., Gray, J., Niebuhr, E., Bolund, L., and Pinkel, D. (2005). High-resolution mapping of genotype-phenotype relationships in cri du chat syndrome using array comparative genomic hybridization. Am J Hum Genet 76, 312-326.
Zheng, C., Heintz, N., and Hatten, M.E. (1996). CNS gene encoding astrotactin, which supports neuronal migration along glial fibers. Science 272, 417-419.
Zuddas, A., Zanni, R., and Usala, T. (2011). Second generation antipsychotics (SGAs) for non-psychotic disorders in children and adolescents: a review of the randomized controlled studies. Eur Neuropsychopharmacol 21, 600-620.
Zwaigenbaum, L., Bryson, S., Rogers, T., Roberts, W., Brian, J., and Szatmari, P. (2005). Behavioral manifestations of autism in the first year of life. Int J Dev Neurosci 23, 143-152.
Zweier, C., de Jong, E.K., Zweier, M., Orrico, A., Ousager, L.B., Collins, A.L., Bijlsma, E.K., Oortveld, M.A., Ekici, A.B., Reis, A., et al. (2009). CNTNAP2 and NRXN1 are mutated in autosomal-recessive Pitt-Hopkins-like mental retardation and determine the level of a common synaptic protein in Drosophila. Am J Hum Genet 85, 655-666.
