www.sciencemag.org/cgi/content/full/science.aaa3650/DC1
Supplementary Materials for
Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and
pathways
Elizabeth T. Cirulli, Brittany N. Lasseigne, Slavé Petrovski, Peter C. Sapp, Patrick A. Dion, Claire S. Leblond, Julien Couthouis, Yi-Fan Lu, Quanli Wang, Brian J. Krueger, Zhong Ren,
Jonathan Keebler, Yujun Han, Shawn E. Levy, Braden E. Boone, Jack R. Wimbish, Lindsay L. Waite, Angela L. Jones, John P. Carulli, Aaron G. Day-Williams, John F. Staropoli, Winnie W. Xin,
Alessandra Chesi, Alya R. Raphael, Diane McKenna-Yasek, Janet Cady, J. M. B. Vianney de Jong, Kevin P. Kenna, Bradley N. Smith, Simon Topp, Jack Miller, Athina Gkazi,
FALS Sequencing Consortium, Ammar Al-Chalabi, Leonard H. van den Berg, Jan Veldink, Vincenzo Silani, Nicola Ticozzi, Christopher E. Shaw, Robert H. Baloh, Stanley Appel, Ericka Simpson,
Clotilde Lagier-Tourenne, Stefan M. Pulst, Summer Gibson, John Q. Trojanowski, Lauren Elman, Leo McCluskey, Murray Grossman, Neil A. Shneider, Wendy K. Chung, John M. Ravits,
Jonathan D. Glass, Katherine B. Sims, Vivianna M. Van Deerlin, Tom Maniatis, Sebastian D. Hayes, Alban Ordureau, Sharan Swarup, John Landers, Frank Baas, Andrew S. Allen, Richard S. Bedlack,
J. Wade Harper, Aaron D. Gitler, Guy A. Rouleau, Robert Brown, Matthew B. Harms, Gregory M. Cooper, Tim Harris,* Richard M. Myers, David B. Goldstein
*Corresponding author. E-mail: [email protected]
Published 19 February 2015 on Science Express DOI: 10.1126/science.aaa3650
This PDF file includes: Materials and Methods
Supplementary Text
Figs. S1 to S4
Tables S1 to S5
References
Other supplementary material for this manuscript includes the following: Table S6 (Excel format)
Correction (26 March 2015): Data were changed throughout the supplement PDF and table S6 to reflect a reduction of 5 in the case sample size; the data now correspond to the final version of the main article.
2
Materials and Methods Samples
Subjects for whole exome analysis were drawn from IRB-approved genetic studies of
ALS subjects at Consortium member institutions: the Columbia University Medical
Center (which included Coriell samples), University of Massachusetts at Worchester,
Stanford University (which included contributions from Emory University School of
Medicine, the Johns Hopkins University School of Medicine, and the University of
California, San Diego), Massachusetts General Hospital Neurogenetics DNA Diagnostic
Lab Repository, Duke University, McGill University (which included contributions from
Saint-Luc and Notre-Dame Hospital of the Centre Hospitalier de l'Université de Montréal
(CHUM) (University of Montreal), Gui de Chauliac Hospital of the CHU de Montpellier
(Montpellier University), Pitié Salpêtrière Hospital, Fleurimont Hospital of the Centre
Hospitalier Universitaire de Sherbrooke (CHUS) (University of Sherbrooke), Enfant-
Jésus Hospital of the Centre hospitalier affilié universitaire de Québec (CHA) (Laval
University), and Montreal General Hospital and Montreal Neurological Institute and
Hospital of the McGill University Health Centre), and Washington University in St.
Louis (which included contributions from Houston Methodist Hospital, Virginia Mason
Medical Center, University of Utah, and Cedars Sinai Medical Center). Subjects for
follow-up sequencing came from the same centers plus the University of Pennsylvania
and University of Amsterdam. Genotypes for the 51 genes used in the replication analysis
were also provided for FALS exomes sequenced as previously described(17). All patients
were diagnosed according to El Escorial revised criteria as suspected, possible, probable,
or definite ALS by neuromuscular physicians. Subjects were considered sporadic if no
first or second-degree relatives had been diagnosed with ALS or died of an ALS-like
syndrome. Details are presented in Table S1.
All samples known to be carriers of the C9orf72 expansion were excluded from all
analyses. There were 883 case exomes used in the discovery phase that were not screened
for this variant. Additionally, prior to exome sequencing, some samples were screened
for variants in known ALS genes and were only sequenced if they were found to be
negative for a mutation in that gene. The number of pre-screened discovery samples for
3
each gene were 430 for SOD1, 334 for TARDBP and FUS, and 143 for VAPB, DCTN1,
ANG, FIG4, OPTN, VCP, UBQLN2, EWSR1, DAO, SQSTM1, SETX, and TAF15. The
543 exomes used in the replication stage were also screened for variants in TARDBP,
FUS, SOD1, VCP, PFN1, and TUBA4A prior to use in this study.
Control samples were sequenced as part of other studies at Duke University,
HudsonAlpha, and McGill University and were not enriched for (but not specifically
screened for) ALS or other neurodegenerative disorders. Control samples were matched
to case samples in terms of similar capture kits and coverage levels (Tables S2 and S3).
Except for the exome cases used in the replication phase, all samples used within each
analysis subset were processed using identical pipelines.
Only genetically European ethnicity samples were included in the analysis. Samples were
also screened with KING(55) to remove duplicate samples between the custom capture
and exome datasets and to remove second-degree or higher relatives in the exome
datasets; exomes with incorrect sexes according to X:Y coverage ratios were removed, as
were contaminated samples according to VerifyBamID(56).
Sequencing
Sequencing of DNA was performed at Duke University, McGill University, Stanford
University, HudsonAlpha, and University of Massachusetts, Worcester. Samples were
either exome sequenced using the Agilent All Exon (37MB, 50MB or 65MB) or the
Nimblegen SeqCap EZ V2.0 or 3.0 Exome Enrichment kit or whole-genome sequenced
using Illumina GAIIx or HiSeq 2000 or 2500 sequencers according to standard protocols
(see Table S2). Follow-up custom capture sequencing was performed using the same
methods as the exome sequencing with the Nimblegen SeqCap EZ Choice to an average
coverage of 144.60x within the capture regions, with an average of 99.37% bases covered
at least 5x.
Case and control samples were processed at Duke University (discovery Duke and
McGill/Stanford datasets and replication custom capture dataset), HudsonAlpha
(discovery HudsonAlpha dataset) and University of Massachusetts, Worcester
(replication exome dataset) as follows. The Illumina lane-level fastq files were aligned to
4
the Human Reference Genome (NCBI Build 37) using the Burrows-Wheeler Alignment
Tool (BWA)(57). We then used Picard software (http://picard.sourceforge.net) to remove
duplicate reads and process these lane-level SAM files, resulting in a sample-level BAM
file that is used for variant calling. We used GATK to recalibrate base quality scores,
realign around indels, and call variants(58). The Duke, McGill/Stanford, custom capture
and replication exome variants were required to have a quality score (QUAL) of at least
20 (30 for replication exomes), a genotype quality (GQ) score of at least 20, and at least
10x coverage. Additionally, Duke, McGill/Stanford and custom capture variants were
required to have a quality by depth (QD) score of at least 2 and a mapping quality (MQ)
score of at least 40. For Duke and McGill/Stanford exomes and custom capture samples,
indels were also required to have a maximum strand bias (FS) of 200 and a minimum
read position rank sum (RPRS) of -20. Other variants were restricted according to VQSR
tranche (calculated using the known SNV sites from HapMap v3.3, dbSNP, and the Omni
chip array from the 1000 Genomes Project): the cutoffs for Duke, McGill/Stanford and
custom capture variants were a tranche of 99.9% for SNVs in genomes and exomes and
99% for indels in genomes; the cutoffs for HudsonAlpha were a 99% tranche for SNVs
and 95% tranche for indels; and the cutoff for the replication exomes was a 97% tranche
for SNVs and indels. Variants were excluded if they were determined to be sequencing,
batch-specific or kit-specific artifacts; they were also excluded in the Duke,
McGill/Stanford and custom capture datasets if marked by EVS as being failures(59).
Variants were annotated to Ensembl 73 using SnpEff.
Predisposition analysis
This study first analyzed whole exome sequence data for discovery purposes and then
performed follow-up custom capture sequencing of 51 genes and interrogated these 51
genes in additional case exomes. We analyzed the discovery samples in separate groups
to control for differences in sequencing methods and coverage levels. The Duke analysis
used genomes and Nimblegen and Agilent 65MB exomes with at least 90% of the
consensus coding sequence (CCDS) bases covered to at least 10x, the HudsonAlpha
analysis used Nimbelgen exomes with at least 90% of the CCDS bases covered to at least
5
10x, and the McGill/Stanford University analysis used genomes and Agilent 37MB and
50MB exomes with 75% of the CCDS covered at least 10x.
Our study focused on gene-based collapsing analyses. First, the number of bases with at
least 10x coverage was calculated for each CCDS exon plus 10 bp into each intron for
each sample. Because differences in coverage can cause biased results, exons with
coverage differences (cutoff tailored to each analysis (see Table S3)) between cases and
controls were excluded from analysis.
For each gene, each sample was then indicated as carrying or not carrying a qualifying
variant. Qualifying variants were defined for dominant (one qualifying variant per gene;
minor allele frequency (MAF) cutoff of 0.05% internally and 0.005% in ExAC) and
recessive (two qualifying variants per gene, including homozygous and potentially
compound heterozygous samples as carriers; MAF cutoff 1%) models. These allele
frequency thresholds used a leave-one-out method for the combined sample of cases and
controls in each analysis group (where the MAF of each variant was calculated using all
samples except for the sample in question). Variants were also required to pass this MAF
cutoff in the publically available ExAC global frequencies; HudsonAlpha analyses
additionally required a MAF below 0.01 in the 1000 Genomes Data (50, 60). We
performed analyses of CCDS genes using three methods to identify qualifying variants:
1) all non-synonymous and canonical splice variants (coding model), 2) all non-
synonymous coding variants except those predicted by PolyPhen-2 HumVar(13) to be
benign (not benign model), and 3) only stop gain, frameshift and canonical splice variants
(loss-of-function [LoF] model). Qualifying variants were identified using Analysis Tools
for Annotated Variants (http://humangenome.duke.edu/software) at Duke and an in-house
pipeline at HudsonAlpha.
The total number of cases and controls with qualifying variants in each gene for each
model were calculated, and a Cochran-Mantel-Haenszel (CMH) test was performed in R
to generate a combined, stratified p-value across all three discovery groups. Genes were
only considered if they were assessed in all three discovery cohorts and had more than
one case or control sample with a genetic variant meeting the inclusion criteria for the
genetic model being tested. A Breslow-Day test was applied to assess homogeneity of
6
effects across different groups, and p>0.05 was required for the gene to be considered.
The adjusted alpha after correcting for the number of genes tested over all six genetic
models is p
7
samples(59). Linear regression analysis was performed to analyze age at onset, Firth
logistic regression was used to analyze the site of onset, and Cox Proportional-Hazards
Regression was used to analyze survival in R(62). These analyses always included sex,
analysis group and EIGENSTRAT axes (which were calculated for EIGENSTRAT-
pruned whites only and were created using the genotypes for variants from the Illumina
HumanCore chip that overlap exons and were not found to be influenced by sequencing
or genotyping method) as covariates, and the analysis of survival additionally included
age at onset as a covariate and required at least 1% of cases to have variants in a given
gene for it to be included in the analysis (with the exception of previously reported ALS
genes, which we analyzed separately and included regardless of the number of carriers).
Cell Culture, Transfection, and Reagents
All cell lines were grown in Dulbecco’s modified Eagle’s medium supplemented with
10% fetal calf serum (FBS) and maintained at 5% CO2/37oC. Plasmid were transfected
using lipid-based reagents (Lipofectamine 2000). Lentiviruses were made in 293T cells
and used to infect 293T cells followed by selection on puromycin.
Immunoprecipitation and Proteomic Analysis
AP-MS and CompPASS analysis using the (Comparative Proteomics Analysis Software
Suite) were performed as previously described(63, 64). Briefly, 107 cells were lysed in
lysis buffer [50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA, 1% NP-40 and
supplemented with protease inhibitors (Roche)] for 30 min on ice to obtain whole cell
extracts. Lysates were incubated with 30 l of anti-HA resin (Covance) and after
extensive washing with lysis buffer, proteins were eluted with HA peptide prior to
trichloroacetic acid precipitation, trypsinization, and stage tipping. Samples were ran in
technical duplicate on an LTQ Velos (Thermo) mass spectrometer, and spectra search
with Sequest prior to target-decoy peptide filtering, and linear discriminant analysis(65).
Protein Assembler was used to convert spectral counts to average protein spectral
matches (APSMs), which takes into account peptides, which match more than one protein
in the database. Peptides were identified with a false discovery rate of < 1.0% and the
protein false discovery rate was
8
used: Activation Type – Collision induced dissociation; Minimum Signal Required -
2000.0; Isolation width (m/z) - 1.00; Normalized Collision Energy - 35.0; Default Charge
State – 2; Activation Q - 0.250; Activation Time (ms) - 10.000. Peptide data (APSMs)
were uploaded into the CompPASS algorithm housed within the CORE environment. For
CompPASS analysis, we employed a stats table of 170 unrelated bait proteins analyzed in
an analogous manner, including deubiquitinating enzymes and autophagy
components(63, 64). The CompPASS system identifies high confidence candidate
interacting proteins (HCIPs) based on the normalized weighted D (NWD)-score, which
incorporates the frequency with which they identified within the stats table, the
abundance (APSMs, average peptide spectral matches) when found, and the
reproducibility of identification in technical replicates, and also determines a z-score
based on APSMs(63, 64). Proteins with NWD-scores >1.0 are considered HCIPs,
although we also note that some proteins that may be bona fide interacting proteins may
not reach the strict threshold set by a NWD-score of > 1.0.
For IP of endogenous NEK1, VAPB or ALS2 in NSC-34 cells expressing either HA-
ALS2, HA-VAPB or FLAG-NEK1, respectively, ~0.5 mg of lysate was incubated with
0.5 µg of the indicated antibody (anti-HA Abcam ab9110, anti-Nek1 Bethyl Labs A304-
570A, anti-FLAG Sigma F1804, anti-ALS2 Sigma SAB4200137, anti-VAPB Bethyl
Labs A302-894A) or control IgG (Cell Signaling Technologies) overnight at 4°C. Protein
G resin (25 µl) was then added to the IP reaction and incubated for a further 2 hours at
4°C. Beads were washed three times with lysis buffer. After washing, 4x SDS loading
buffer was added and the samples were boiled for 5 min. Samples were separated on a
SDS-PAGE gel prior to immunoblot analysis according to standard procedures using
primary antibodies at 1:1000 overnight at 4°C, HRP-conjugated secondary antibodies
(Promega) at 1:5000 for 1 hour at room temperature, and chemiluminescent detection
(PerkinElmer).
Supplementary Text
FALS Sequencing Consortium
Other FALS Sequencing Consortium Members are as follows:
Orla Hardiman1, Russell L McLaughlin1, Letizia Mazzini2, Ian P Blair3, Kelly L Williams3, Garth A Nicholson4, Safa Al-Sarraj5, Andrew King5, Emma L Scotter5, Simon
9
Topp5, Claire Troakes5, Caroline Vance5, Sandra D'Alfonso6, Stefano Duga7, Lucia Corrado8, Anneloor LMA ten Asbroek9, Daniela Calini10, Claudia Colombrita11, Antonia Ratti11, Cinzia Tiloca12, Zheyang Wu13, Seneshaw Asress14, Meraida Polak14, Frank Diekstra15, Wouter van Rheenen15, Eric W Danielson16, Claudia Fallini16, Pamela Keagle16, Elizabeth A Lewis16, Jason Kost17, Gianni Sorarù18, Cinzia Bertolin18, Giorgia Querin18, Barbara Castellotti19, Cinzia Gellera19, Viviana Pensato19, Franco Taroni19, Cristina Cereda20, Stella Gagliardi20, Mauro Ceroni21, Giuseppe Lauria22, Jacqueline de Belleroche23, Giacomo P Comi24, Stefania Corti24, Roberto Del Bo24, Martin R Turner25, Kevin Talbot25, Hardev Pall26, Karen E Morrison27, Pamela J Shaw28, Jesús Esteban-Pérez29, Alberto García-Redondo29, José Luis Muñoz-Blanco30. 1Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Republic of Ireland. 2ALS Center, Department of Neurology, 'A. Avogadro' University of Eastern Piedmont, Novara, Italy. 3Australian School of Advanced Medicine, Macquarie University, Sydney, NSW 2109, Australia. 4Australian School of Advanced Medicine, Macquarie University, Sydney, NSW 2109, Australia; Northcott Neuroscience Laboratory, ANZAC Research Institute, Sydney, NSW 2139, Australia. 5Centre for Neurodegeneration Research, King’s College London, Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, London, SE5 8AF, UK. 6Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), ‘‘A. Avogadro’’ University, 28100 Novara, Italy. 7Humanitas University, Via Manzoni 113, 20089 Rozzano (Mi) – Italy; Humanitas Clinical and Research Center, Via Manzoni 56, 20089 Rozzano (Mi) – Italy. 8Department of Medical Sciences, 'A. Avogadro' University of Eastern Piedmont, Novara, Italy. 9Department of Neurogenetics and Neurology, Academic Medical Centre, Amsterdam, The Netherlands. 10Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy. 11Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy; Department of Pathophysiology and Transplantation, 'Dino Ferrari' Center - Università degli Studi di Milano, Milan 20122 Italy. 12Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy; Doctoral School in Molecular Medicine, Department of Sciences and Biomedical Technologies, Universita` degli Studi di Milano, Milan 20122, Italy. 13Worcester Polytechnic Institute, Worcester, MA 01609, USA.
10
14Department of Neurology, Emory University, Atlanta, GA 30322, USA. 15Department of Neurology, Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, 3508 GA Utrecht, The Netherlands. 16Department of Neurology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA. 17Department of Neurology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA.; Worcester Polytechnic Institute, Worcester, MA 01609, USA. 18Department of Neurosciences, University of Padova, Padova, Italy. 19Unit of Genetics of Neurodegenerative and Metabolic Diseases, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan 20133, Italy. 20Experimental Neurobiology Laboratory, IRCCS 'C. Mondino' National Neurological Institute, 27100 Pavia, Italy. 21Experimental Neurobiology Laboratory, IRCCS 'C. Mondino' National Neurological Institute, 27100 Pavia, Italy; Department of Neurological Sciences, University of Pavia, 27100 Pavia, Italy. 22Headache and Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan 20133, Italy. 23Neurogenetics Group, Division of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, Du Cane Road, London W12 0NN. 24Neurology Unit, IRCCS Foundation Ca' Granda Ospedale Maggiore Policlinico, Milan 20122, Italy; Department of Pathophysiology and Transplantation, 'Dino Ferrari' Center - Università degli Studi di Milano, Milan 20122 Italy. 25Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK. 26School of Clinical and Experimental Medicine, College of Medical and Dental Sciences, The University of Birmingham, Birmingham, UK. 27School of Clinical and Experimental Medicine, College of Medical and Dental Sciences, University of Birmingham, UK.; Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust UK. 28Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK. 29Unidad de ELA, Instituto de Investigación Hospital 12 de Octubre de Madrid, SERMAS, and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER U-723), Madrid, Spain. 30Unidad de ELA, Instituto de Investigación Hospital Gregorio Marañón de Madrid, SERMAS, Spain.
Acknowledgements
11
The majority of the funding for the ALS patient sequencing performed in this study was
provided by Biogen Idec. P.C.S. was supported through the auspices of Dr. H. Robert
Horvitz, an Investigator at the Howard Hughes Medical Institute in the Department of
Biology at the Massachusetts Institute of Technology. J.W.H. was supported by a
research grant from Biogen-Idec and R37 NS083524. The Shaw laboratory is supported
by grants from the Wellcome Trust and Medical Research Council Grants
089701/Z/09/Z, G0900688 and MR/L021803/1, MND Association and American ALS
Association. Funding for the FALS Sequencing Consortium was provided by the
National Institutes of Health (NIH)/National Institute of Neurological Disorders and
Stroke (NINDS) (5RO1NS050557; 5R01 NS067206; 5R01NS065847; 1R01NS073873;
5R01NS079836 R01NS065847; and R01NS073873 [J.E.L., R.H.B.]), the American ALS
Association (N.T., V.S., C.E.S., J.E.L., R.H.B.), Project MinE, the MND Association
(N.T., V.S., A.A.C, C.E.S., J.E.L.), the Angel Fund (R.H.B.), Project ALS/P2ALS
(R.H.B.), the ALS Therapy Alliance (R.H.B., J.E.L.), the Pierre L. de Bourghknecht ALS
Research Foundation (R.H.B.), a Francesco Caleffi donation (N.T., V.S.), the Medical
Research Council, the Heaton-Ellis Trust and AriSLA - Italian Research Foundation for
ALS, cofinanced with support of ‘‘5 x 1000’’—Healthcare research of the Italian
Ministry of Health (grants EXOMEFALS 2009 and NOVALS 2012 [N.T., C.G., C.
Tiloca, V.S., J.E.L.]), the National Institute for Health Research (NIHR) Dementia
Biomedical Research Unit at South London (C.E.S., A.A.C.), Maudsley NHS Foundation
Trust (C.E.S., A.A.C.), King’s College London (C.E.S., A.C.), the Motor Neurone
Disease Research Institute of Australia (Leadership Grant to IPB and a Bill Gole
fellowship to K.L.W.), United Kingdom Medical Research Council under the aegis of
JPND – http://www.jpnd.eu (A.A.C.), the National Health and Medical Research Council
of Australia (1004670), Canadian Institutes of Health Research (208973, G.A.R.), Muscle
Dystrophy Association (153959, G.A.R.), Target ALS (C.L.T., A.D.G.), Instituto de
Salud Carlos III - ISCIII (grants EC08/00049; PI10/00092; PI12/ 03110 – Erare european
call – J.E.P., A.G.R.), FUNDELA (Spanish foundation for the development of ALS
research, J.E.P, A.G.R.) and the Mireia Barneda project ‘‘No llores, no te rindas’’ (J.E.P.,
A.G.R.), the Netherlands ALS Foundation, Telethon Genetic BioBank (GTB12001D) and
12
the Eurobiobank network. Research leading to these results has received funding from the
European Community's Health Seventh Framework Programme (FP7/2007-2013).
We acknowledge Jarod Shelton at Washington University St. Louis, Peggy Allred at
Cedars Sinai, Luis Lay and Sharon Halton at Houston Medical, Karla Figueroa at
University of Utah, Michael Baughn, Melissa McAlonis and Jonhatan W. Artates at
University of California, San Diego, and Marlyne Silver, Karen Grace, and Ken Cronin at
Duke University for recruiting subjects, preparing DNA, and maintaining clinical
databases. We acknowledge M.T. van Meegen and A.P. Prijs at University of Amsterdam
and Mei Liu and Paul Carmillo at Biogen Idec for technical assistance.
DNA panels from the NINDS Human Genetics Resource Center DNA and Cell Line
Repository (http://ccr.coriell.org/ninds) were used in this study, as well as clinical data.
The submitters that contributed samples are acknowledged in detailed descriptions of
each ALS1 sample.
We would like to acknowledge the following individuals for the contributions of control
samples: Dr. Joseph McEvoy, Dr. Anna Need, Mr. Jordan Silver and Ms. Marlyne Silver;
Dr. Deborah Koltai Attix, and Ms. Jill McEvoy, Dr. Kenneth Schmader, Dr. Shelley
McDonald, Dr. Heidi K. White, Dr. Mamata Yanamadala, and the Carol Woods and
Crosdaile Retirement Communities; Dr. Gianpiero Cavalleri, Dr. Norman Delanty, and
Dr. Chantal Depondt; Dr. William B. Gallentine, Dr. Erin L. Heinzen , Dr. Aatif M.
Husain, Ms. Kristen N. Linney, Dr. Mohamad A. Mikati, Dr. Rodney A. Radtke, Dr.
Saurabh R. Sinha, and Ms. Nicole M. Walley; Dr. Ruth Ottman; Dr. Julie Hoover-Fong,
Dr. Nara L. Sobreira and Dr. David Valle; Dr. Yong-Hui Jiang; Dr. Demetre Daskalakis;
Dr. William L. Lowe; Dr. James Burke, Dr. Christine Hulette, and Dr. Kathleen Welsh-
Bohmer; Dr. Vandana Shashi and Ms. Kelly Schoch; Mr. David H. Murdock and The
MURDOCK Study Community Registry and Biorepository; Dr. Scott M. Palmer; Dr. Zvi
Farfel, Dr. Doron Lancet, and Dr. Elon Pras; Mr. Arthur Holden and Dr. Elijah Behr; Dr.
Annapurna Poduri; Dr. M Chiara Manzini; Dr. Nicole Calakos; Dr. Patricia Lugar; Dr.
Doug Marchuk; Dr. Qian Zhao; Dr. Sarah Kerns and Dr. Harriet Oster; Dr. Rasheed
Gbadegesin and Dr. Michelle Winn; Dr. Francis J. McMahon and Nirmala Akula; Dr. Eli
13
J. Holtzman; Dr. Joshua Milner; Dr. Deborah Levy; Dr. Ann Pulver; and Dr. Michael
Hauser.
The collection of control samples was funded in part by Bryan ADRC NIA P30
AG028377, NIMH Award RC2MH089915, NINDS Award RC2NS070344, NIAID
Award R56AI098588, the Ellison Medical Foundation New Scholar award AG-NS-0441-
08, an award from SAIC-Frederick, Inc. (M11-074), NIMH Award K01MH098126,
MH057314 and MH068406 to Ann E. Pulver, Sc.D. (Johns Hopkins University School of
Medicine), and the Epi4K Gene Discovery in Epilepsy study (NINDS U01-NS077303)
and the Epilepsy Genome/Phenome Project (EPGP - NINDS U01-NS053998). The
collection of control samples was supported in part by funding from the Division of
Intramural Research, NIAID, NIH, and with federal funds by the Center for HIV/AIDS
Vaccine Immunology ("CHAVI") under a grant from the National Institute of Allergy
and Infectious Diseases, National Institutes of Health, Grant Number UO1AIO67854.
The authors would like to thank the Exome Aggregation Consortium and the groups that
provided exome variant data for comparison. A full list of contributing groups can be
found at http://exac.broadinstitute.org/about.
14
Fig. S1. QQ plot of discovery results for dominant not benign model
Shown are the results for the analysis of 2,869 case and 6,405 control exomes. There
were 16,339 covered genes passing QC with more than one case or control carrier for this
test, and the genes with the top 10 associations are labeled. The lambda quantifying
inflation is 1.058 The association with SOD1 passes correction for multiple tests.
15
Fig. S2 QQ plot for the discovery results from the dominant LoF model
Shown are the results for the analysis of 2,869 case and 6,405 control exomes. There
were 9,817 covered genes passing QC with more than one case or control carrier for this
test, and the genes with the top 10 associations are labeled. The lambda quantifying
inflation is 0.956.
16
Fig. S3 Variants in NEK1 and ALS2
Dominant LoF variants are shown for NEK1 (combined dataset), and recessive coding
variants are shown for ALS2 (discovery dataset). LoF variants are filled in red, non-
synonymous variants are filled in blue, and splice variants are filled in purple. Case
variants are shown with red lines, control variants are shown with blue lines, and variants
found in both cases and controls are shown with dashed lines.
17
Fig. S4 NEK1 interacts with ALS2 and VAPB
NEK1 interacts with ALS2 and VAPB. (A,B) HEK293T cells stably expressing HA-
NEK1 or HA-NEK1K33R (K/R) were subjected to AP-MS analysis using the CompPASS
platform and proteins with a normalized weighted D (NWD)-score > 1.0 identified.
Among the 51 proteins identified with NEK1 and the 38 proteins identified with
NEK1K33R, 17 were in common (panel A). The major classes of interacting proteins found
with both bait proteins are shown in panel B. Proteins indicated with asterisks were
identified but with a sub-threshold NWD-score. (C) HEK293T cells stably expressing
either HA-ALS2 or ALS2-HA were subjected to AP-MS and NEK1 as well as the NEK1
associated protein C21orf2 were identified. APSM, average peptide spectral matches.
(D,E) NCS-34 neuronal cells expressing either HA-ALS2, HA-VAPB, or FLAG-NEK1
were subjected to immunoprecipitation using either anti-NEK1, anti-VAPB or anti-
ALS2, as indicated, to immunoprecipitate the endogenous protein and complexes then
immunoblotted with the indicated antibodies. Similarly, anti-FLAG or anti-HA
immunoprecipitations were performed to demonstrate reciprocal interactions.
18
Table S1. Patient Demographics for Discovery Samples
Total ALS Patients Analyzed 2,869
Family History of ALS 6.7% (104/1560)
Male Sex 58.8% (1687/2869)
Bulbar symptom onset 26.8% (661/2465)
If limb onset, proportion upper limb onset 52.0% (903/1735)
Cognitive impairment noted at any time 14.6% (178/1221)
Mean age at symptom onset in years (Stdev, n) 57.1 (13.0,2519)
Range of ages at symptom onset 13-90
Median disease duration in months (IQR, n) 36 (32, 678)
Because data collection varied across centers, the numerators and denominators are
shown. Disease duration was only calculated for subjects with complete follow-up and
known durations to death or full-time positive pressure ventilation. All patients analyzed
were of white ethnicity.
19
Table S2. Sequencing methods and groups
Kit 37MB 50MB 65MB Nimblegen Genome
Analysis group Cases/Ctrls Cases/Ctrls Cases/Ctrls Cases/Ctrls Cases/Ctrls
Duke University 0/0 0/0 0/676 1137/2915 36/423
McGill/Stanford
University
0/335 248/227 0/165 1/1 2/61
HudsonAlpha 0/0 0/0 0/0 1445/1602 0/0
20
Table S3. Number of CCDS bases covered in each analysis
Analysis group Cases Ctrls Cutoff
Duke University 32,233,687
(91%)
32,165,079
(91%)
5%
McGill/Stanford
University
28,272,224
(80%)
27,737,938
(78%)
27%
HudsonAlpha 30,969,984
(88%)
31,367,279
(90%)
20%
Replication- Duke
University custom
capture
111,821 112,423 5%
Replication-
University of
Massachusetts
exomes
102,359 N/A N/A*
Shown is the average number of bases covered at least 10x. Numbers are n(%). The
cutoff refers to the difference allowed between cases and controls in their average exonic
coverage; exons with differences above this value were not included in the analysis.
*Exomes used in the replication dataset were restricted to the same exons used in the
custom capture samples.
21
Table S4. The 51 genes chosen for targeted follow-up based on initial exome
sequencing results.
TET3
ALYREF
TAF6
NEK1
ATP6V1F
ZNF296
UBE2D2
DOCK3
TBK1
TRAF4
OPTN
GRID2IP
C16orf11
PFKFB1
BTBD11
ENAH
TBC1D30
TNNT3
TMEM55B
CYGB
CYP17A1
CEL
PCDHGA9
LGALSL
PDLIM2
LENG9
C19orf25
PAMR1
SPSB3
DNMT3A
SH3KBP1
SPG11
ZNF432
AP1G2
MADD
GPR162
ADCYAP1R1
YBX3
KCNT1
CAMK2A
LRRC73
S100A2
HAS2
ZNF837
IL5
MPL
SLC15A2
PPCS
HIVEP3
TGM3
SCEL
22
Table S5. NEK1-Interacting proteins
INTERACTOR NEK1 (APSM) NEK1K33R (APSM)
NEK1 (NWD) NEK1K33R (NWD)
ZXDC 10 4 2.75 1.56
VPS29 1 3 0.08 1.35
VPS26B 2 7 1.06 2.19
VAPA 9 4 1.5 1.04
SGPL1 21 4 1.74 0.39
RPS6KA3 4 1 3 0.12
PIPSL 2 0 2.12 0
PDF 1 3 0.12 2.71
NOSIP 2 1 1.06 0.78
NEK1 592 269 17.15 11.95
MYO5A 2 0 1.06 0
MAP4 2 1 1.06 0.78
LRP2 1 2 0.08 1.1
KIFC1 4 1 3 0.12
KIF2C 13 11 3.27 3.06
KIF2A 25 15 2.35 1.72
KIAA0562 15 8 2.77 1.78
KATNB1 2 3 1.06 1.35
KATNA1 4 1 3 0.12
JUN 4 4 1.5 1.56
CREB5 5 0 1.68 0
CEP97 2 1 2.12 1.56
CEP78 5 1 1.12 0.52
CEP290 24 8 3.75 1.78
23
CAMK2G 4 6 1.5 1.93
CAMK2D 7 10 1.19 1.7
CAMK2B 4 4 1 1.04
C21orf2 10 8 4.74 4.42
ATF7 5 1 3.35 0.12
ATF2 6 1 1.86 0.08
ANKRD27 0 3 0 2.71
ALS2 19 23 2.52 3.01
ALG11 1 0 1.5 0
VPS35 7 13 0.23 0.4
YWHAG 12 13 0.2 0.21
YWHAZ 27 23 0.2 0.19
YWHAQ 22 10 0.15 0.1
YWHAE 104 91 0.48 0.46
YWHAB 28 24 0.46 0.43
YWHAH 10 16 0.36 0.48
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Cirulli-SM.page.1-REVISED.pdfExome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways
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