Personality Genetics and Health in
Super-Seniors
by
Jessica Marit Turner Nelson
B.A. and B.Sc., University of Regina, 2007
Thesis Submitted in Partial Fulfillment of the
Requirements for the Degree of
Master of Science
in the
Department of Biomedical Physiology and Kinesiology
Faculty of Science
Jessica Marit Turner Nelson 2015
SIMON FRASER UNIVERSITY
Fall 2015
ii
Approval
Name: Jessica Marit Turner Nelson
Degree: Master of Science
Title: Personality Genetics and Health in Super-Seniors
Examining Committee: Chair: Dr. Parveen Bawa Professor
Dr. Angela Brooks-Wilson Senior Supervisor Professor
Dr. Andrew Wister Supervisor Professor
Dr. Robert Holt External Examiner Professor Molecular Biology and Biochemistry Simon Fraser University
Date Defended/Approved: December 17th, 2015
iii
Ethics Statement
iv
Abstract
Healthy aging is a complex phenotype, and genetic factors that contribute to long term
good health are not well understood. Longevity and health are associated with lifestyle
choices. Behaviour is governed by personality; therefore, variation in personality-related
genes may affect healthy aging. Five candidate genes involved in the physiology of
personality and personality disorders were identified from the literature: COMT, DRD4,
MAOA, SLC6A4, and TH. Single nucleotide polymorphisms and variable number of
random repeat polymorphisms were genotyped in DNA from 493 European-ancestry
healthy oldest-old and 431 European-ancestry middle-aged controls. Tests for allelic
associations were conducted, with stratification by sex. No associations remained
significant after correction for multiple tests. Variants tested in these candidate genes were
not associated with long-term good health in this sample. Either genetic variation in these
genes does not influence healthy aging, or true effects exist that are too small to be
detected in this study.
Keywords: Healthy Aging; Personality; Oldest-old; Genetic association; Longevity; Candidate Gene Design
v
Dedication
For my own super grandparents, for teaching me how
to bring integrity, dedication and joy to everything I do.
vi
Acknowledgements
Firstly, I would like to acknowledge my supervisor, Dr. Angela Brooks-Wilson, for this
wonderful opportunity. I appreciate all your guidance and counsel, and in particular, your
constant upbeat encouragement. I have learned so much from you throughout this
experience, related to this endeavor and beyond. To my committee member, Dr. Andrew
Wister, thank you for all thoughtful advice and efforts in helping to make this project a
success.
I would also like to extend thanks to my lab, the Cancer Genetics group. In particular
Stephen Leach for reacquainting me with the lab and helping with technical difficulties,
Ruth Thomas for seamlessly knowing all the facts in the Super-Senior Study, and
Samantha Jones for editing and assistance in the lab. My thanks to the many students
and volunteers in the lab that have helped to support my learning. I’m additionally grateful
for the statistical assistance that Andy Leung has provided.
I would also like to acknowledge the contribution of the Super-Seniors and controls who
volunteered for the Super-Senior Study, without them this work would not have been
possible.
To my parents, family and friends, thank you for your endless support and understanding
throughout this project. Finally, my eternal gratitude to Shawn Williams and Kelly
Santbergen for seeing this through with me from beginning to end.
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Table of Contents
Approval .......................................................................................................................... ii Ethics Statement ............................................................................................................ iii Abstract .......................................................................................................................... iv Dedication ....................................................................................................................... v Acknowledgements ........................................................................................................ vi Table of Contents .......................................................................................................... vii List of Tables .................................................................................................................. ix List of Figures.................................................................................................................. x List of Acronyms ............................................................................................................. xi
Chapter 1. Background ............................................................................................. 1 1.1. Aging ...................................................................................................................... 1
1.1.1. The Aging Population ................................................................................ 1 1.1.2. Limitation in the Aging Field ....................................................................... 2 1.1.3. Chronic Disease and Disability .................................................................. 2 1.1.4. Behavioural Impact on Health .................................................................... 5
1.2. Personality .............................................................................................................. 6 1.2.1. Historical Perspectives on Personality ....................................................... 6 1.2.2. The Big Five .............................................................................................. 7 1.2.3. Heritability of Personality ........................................................................... 9 1.2.4. Stability of Personality ............................................................................. 10 1.2.5. Personality Gender Differences ............................................................... 11 1.2.6. Personality Profile of the Very Old and Very Healthy ............................... 11 1.2.7. Limitation in the Personality Field ............................................................ 12 1.2.8. The Impact of Personality on Behaviour .................................................. 14
1.3. Biochemistry and Physiology of Personality .......................................................... 15 1.3.1. Age Related Changes.............................................................................. 15 1.3.2. Personality Domains and Relevant Structures ......................................... 16
Extraversion ........................................................................................................... 16 Neuroticism ............................................................................................................ 18 Conscientiousness ................................................................................................. 20 Agreeableness ....................................................................................................... 20 Openness ............................................................................................................... 21
1.3.3. Neurotransmitters and Personality ........................................................... 21 1.4. The Genome and Genetic Variation ...................................................................... 23
1.4.1. Basic Structures ...................................................................................... 23 1.4.2. Microsatellites and Minisatellites .............................................................. 25 1.4.3. Single Nucleotide Polymorphisms (SNPs) ............................................... 27
1.5. Candidate Genes .................................................................................................. 28 1.5.1. Catechol-O-Methyltransferase (COMT) ................................................... 28 1.5.2. Dopamine Receptor D4 Gene (DRD4) ..................................................... 30 1.5.3. Monoamine Oxidase A (MAOA) ............................................................... 31 1.5.4. Sodium Chloride Dependent Transporter (SLC6A4) ................................ 34 1.5.5. Tyrosine Hydroxylase (TH) ...................................................................... 37
1.6. Thesis Objective ................................................................................................... 39
viii
Chapter 2. Methods.................................................................................................. 40 2.1. Study Participants ................................................................................................. 40 2.2. Literature Search and Gene Selection .................................................................. 40 2.3. SNP Selection ...................................................................................................... 42 2.4. SNP Genotyping ................................................................................................... 42 2.5. SNP Analysis ........................................................................................................ 42 2.6. VNTR Genotyping ................................................................................................ 43 2.7. VNTR Analysis ..................................................................................................... 45
Chapter 3. Results ................................................................................................... 46 3.1. Literature Search and Gene Selection Results ..................................................... 46 3.2. SNP Selection Results .......................................................................................... 50 3.3. SNP Genotyping Results ...................................................................................... 52 3.4. SNP Quality Control Results ................................................................................. 52 3.5. VNTR Genotyping Results .................................................................................... 54 3.6. VNTR Quality Control Results .............................................................................. 60 3.7. Association Results .............................................................................................. 61
Chapter 4. Discussion ............................................................................................. 69 4.1. Interpretation of Results ........................................................................................ 69
4.1.1. Relevant Association Studies .................................................................. 71 4.1.2. Potential Causes of Variation in Published Association Studies for
Candidate Genes ..................................................................................... 73 4.2. Limitations of Design ............................................................................................ 76
4.2.1. Candidate Gene Selection ....................................................................... 76 4.2.2. Marker Selection ...................................................................................... 77 4.2.3. Power and Rejection of the Null Hypothesis ............................................ 78 4.2.4. Control Group .......................................................................................... 80 4.2.5. Sample Size ............................................................................................ 81 4.2.6. Lack of Personality Testing ...................................................................... 81
4.3. Recommendations ................................................................................................ 82 4.3.1. Candidate Gene Based Design ............................................................... 82 4.3.2. Super-Senior Study Design ..................................................................... 87
4.4. Future Developments ........................................................................................... 88 4.4.1. Towards Integrative Approaches ............................................................. 88 4.4.2. Personality and Personalized Medicine Interventions for Longevity ......... 89
References 91
ix
List of Tables
Table 2.1. Summary of program choice for genes and markers .............................. 43
Table 2.2. Primers and PCR conditions used for amplification of VNTRs ................ 44
Table 3.1. Summary of potential candidate genes found through literature search, including polymorphisms and results of association studies ................................................................................................... 47
Table 3.2. SNPs Selected to Represent the Five Final Candidate Genes ............... 51
Table 3.3. Average VNTR allele sizes and standard deviations .............................. 60
Table 3.4. Results from Pearson Chi-Square tests of SNP and Score Tests of VNTRs, for the Combined Sample and when Stratified by Sex .......... 62
Table 3.5. Adjusted p-values Using False Discovery Rate (FDR)* .......................... 67
x
List of Figures
Figure 1.1. General Gene Structure ......................................................................... 24
Figure 1.2. Genomic structure of COMT................................................................... 28
Figure 1.3. Genomic structure of DRD4 ................................................................... 30
Figure 1.4. Genomic structure of MAOA................................................................... 31
Figure 1.5. Genomic structure of SLC6A4 ................................................................ 34
Figure 1.6. Genomic structure of TH ........................................................................ 37
Figure 3.1. Quality control for SNPs and VNTRs ...................................................... 53
Figure 3.2. CEPH Family #1341 showing Mendelian Segregation of the DRD4 VNTR ..................................................................................................... 54
Figure 3.3. DRD4 VNTR allele bins .......................................................................... 55
Figure 3.4. Examples of common calling problems in GeneMapper v5 .................... 57
Figure 3.5. Bin sets used in GeneMapper v5 to call each VNTR .............................. 59
Figure 4.1. Candidate gene network ........................................................................ 84
Figure 4.2. Serotonin receptors and associated BDNF, TPH1 and TPH2 network .................................................................................................. 85
xi
List of Acronyms
5-HTT 5-hydroxytryptamine Transporter
ADHD Attention Deficit Hyperactivity Disorder
BC British Columbia
BLSA Baltimore Longitudinal Study of Aging
CEPH Centre d’Etude du Polymorphisme Humain
CEU Northern European Utah Population
CMMT Centre for Molecular Medicine and Therapeutics
COMT Catechol-O-Methyltransferase
DRD4 Dopamine Receptor D4
DSM-V Diagnostic and Statistical Manual of Mental Disorders, Version 5
FBST Full Bayesian Significance Test
FDR False Discovery Rate
GWAS Genome Wide Association Studies
HWE Hardy-Weinberg Equilibrium
HDL High-Density Lipoprotein
MAOA Monoamine Oxidase A
MAOAH Monoamine Oxidase A High Activity
MAOAL Monoamine Oxidase A Low Activity
MSGSC Michael Smith Genome Sciences Centre
NEO-FFI NEO-Five Factor Inventory
OCD Obsessive Compulsive Disorder
PCR Polymerase Chain Reaction
PHAC Public Health Agency of Canada
SES Socio-Economical Status
SLC6A4 Sodium Chloride Dependent Transporter
SNP Single Nucleotide Polymorphism
SVS8 Golden Helix SVS suite 8
TCI Temperament and Character Inventory
TH Tyrosine Hydroxylase
TPQ Tri-Personality Questionnaire
UN United Nations
xii
VNTR Variable Number Tandem Repeat
1
Chapter 1. Background
1.1. Aging
1.1.1. The Aging Population
Canada’s population is aging. In 1960, Canadians over 65 years in age constituted only
8% of the population. By 2009 that rose to 14%, and by 2036 Statistics Canada predicts they will
comprise 23%-25% [1]. The two main driving factors behind this growth are reduction in total
fertility rates and increase in life expectancy [2].
The total fertility rate, which is defined as the average number of children a woman would
bear if she survived through the end of her reproductive age span [3], has been declining globally
for a number of reasons. Increased education and reduced child mortality are the major
contributing factors to the decline [4], while other social programs such as family planning play a
smaller role [4]. The United Nations (UN) has projected that the global fertility rate will decline
from the current rate of 2.5 children per woman to 2.2 by 2045-2050 [2].
Increased life expectancy is caused by two factors. First, there has been an increased life
expectancy at birth which has added to the potential number of people in the aging population.
This was not initially intuitive as an element of increased life expectancy as there was an increase
in children and a reduction in the proportion of older adults in the population [2]. Second, there is
an increase in the proportion of older adults as late life survival continues to rise [2]. The UN World
Aging report predicts that life expectancy will rise to 83 years of age by 2045-2050 from the current
78 years in developed regions [2]. This trend is also seen in less developed regions with the
current expectancy at 68 years and future expectancy at 75 years by 2045-2050 [2]. As our
population ages it is important to define what aging is.
2
1.1.2. Limitation in the Aging Field
While intuitively the definition of aging seems clear, there is no consensus definition and
researchers often use their own inclusion criteria for their studies. Criteria such as the age senior
status is achieved, who is the oldest-old, what defines healthy seniors, and which co-morbidities
are allowable, are some common measures that can be very different between studies. Depp and
Jeste looked at the definitions used in aging studies and found little agreement on what successful
aging is and that definitions used by researchers were often driven by their specific research
question [5]. This lack of defined characteristics for seniors is problematic, and is a potential
source of discrepancy between studies, since slightly different definitions will produce different
results that are not comparable or reproducible [6].
A further problem in the field is the lack of available controls for the successful aging
subjects. Ideal controls for this group usually do not escape the high mortality rates of this cohort
or are difficult to enroll due to disability or illness. As such, aging studies use a variety of
populations for their control group which also makes comparisons between studies hard to
evaluate [6]. Despite problems with definitions and controls, studying the very old and very healthy
can give researchers valuable insight into the development and management of chronic disease
and disability.
1.1.3. Chronic Disease and Disability
While the increase in life expectancy has been a remarkable feat for science and social
programs alike, it has created problems in caring for an aging population. Disability and chronic
disease are barriers in the aging population that cause early unemployment, poverty, increased
health care costs, stress and resource demands on family members, and decreased quality of
life. A meta-analysis from Marengoni et al. showed that “major consequences of multimorbidity
are disability and functional decline, poor quality of life, and high health care costs” [7].
Unsurprisingly, research has consistently linked chronic disease in elderly with poor quality of life,
such that the greater the number of chronic diseases, the poorer the reported quality of life [8].
Additional research by Campolina et al. found that the more years elderly spent free of chronic
disease, the more years they gained living free of disability and the greater their life expectancy
[9].
3
Chronic disease and disability are common in the senior population. The Chief Public
Health Officer’s Report on the State of Public Health in Canada 2010 found that 89% of Canadian
seniors (defined as Canadians over the age of 65) had at least one chronic condition [10]. Seniors
with more than one chronic condition were further divided into younger (65-79) and older seniors
(80+), with disease prevalence of 25% and 37%, respectively [10]. The likelihood of developing a
chronic disease also increased with age; 71% of seniors aged 65 to 74 reported having at least
one chronic condition, increasing to 80% in the 75 to 84 cohort [11]. Notably, increases in chronic
conditions are not substantial in seniors over 85 years of age [11].
There are many types of chronic conditions in the senior population; arthritis, heart
disease, diabetes, cancer, dementia and pulmonary disease are the most common complaints.
Arthritis is the most prevalent, although it does not increase the overall mortality in the group. In
2006, 23% of seniors were living with some form of heart disease, 21% had diabetes and 56%
had novel developments of cancer [10].
More alarming still are reports that indicate diabetes is often under-diagnosed in the senior
population; Franse et al. suggests that a third of seniors with diabetes remain undiagnosed, with
Leong et al. suggesting that number is closer to 40% [12,13]. Furthermore, the annual cost of
health care per patient increases as diabetic patients age. Patients under 65 years of age have
an annual cost of $4,267 CDN, however that cost doubles in a senior population, to $8,902 CDN
[14]. This increase in cost is largely the consequence of worsening kidney function and poor
glycemic control [14].
Dementia is steadily increasing in the senior population. The Alzheimer Society of Canada
recorded 103,728 new dementia cases for seniors in 2008 and projected that to increase 2.5
times by 2038 [15], with Alzheimer disease accounting for 50% of new dementia cases [15].
Annual costs per patient in Canada vary from $4,164 CDN for individuals with mild Alzheimer
disease to $48,756 CDN for those with severe diagnoses [16].
Chronic Obstructive Pulmonary Disease (COPD) is the ninth leading cause of death
globally [17] with a 27% risk of developing COPD by the time an individual is 80 [18]. This is a
decrease from its position as fourth leading cause in 2008 [19]. Maleki-Yazdi et al. reported an
annual cost per patient for COPD of $4,147 CDN [20].
4
Cancer is the major cause of death for Canadians. The Canadian Cancer Society reports
that cancer death accounted for 30% of the total recorded deaths in Canada for 2011 and are
projecting 196,900 new cases in 2015 [21]. Moreover, 89% of these new cases are predicted to
affect Canadians over the age of 50. The Public Health Agency of Canada (PHAC) reported
malignant cancers to be the 8th leading direct cost to the Canadian health care system for 2008,
with hospital care, physician and drug costs totaling 3.8 billion dollars [22]. Accounting for indirect
costs, dollars lost due to illness, injury and premature death, the cost rose to 4.4 billion [22].
Cardiovascular disease, which encompasses heart and cerebovascular disease, was the
second major cause of death for Canadians in 2011, accounting for 25.5% of total recorded
deaths [23]. Heart disease was reported to affect 22% of seniors over the age of 75 and that
percentage rose to 27.5% in seniors over the age of 85. Additionally, 7% of Canadians over the
age of 75 reported living with the effects of stroke [24]. The cost of cardiovascular disease is
profound. The PHAC listed it as the most directly burdensome disease on the heath care system,
with direct costs of 11.7 billion dollars in 2008 [22].
The cost of these chronic conditions is substantial. The Certified General Accountants
Association of Canada estimates that by 2050 public health care costs will consume 10.9% of our
country’s gross domestic product. This is a marked increase from the 6.8% spent in 2007 [25].
Further, this increase in health care costs is underestimated, as it does not account for all the
informal care delivered by family members [26]. The Alzheimer Society of Canada reported
opportunity costs (the wage caregivers could have made in the workforce) from informal
caregiving in 2008 to be close to $5 billion with projections of that number increasing to $55 billion
by 2038 [15]. The Canadian Institute for Health Information reported in 2011 that seniors with
more than three chronic conditions had three times as many health care visits compared with
those under three visits [11]. Multiple chronic conditions in seniors are expensive, not only to the
public health care system but also to family members.
Keeping the senior population free of morbidities such as dementia, diabetes,
cardiovascular disease and cancer would greatly reduce this financial burden, as people free of
comorbidities are not only less burdensome on the health care system but are also more
productive members of society and have a better quality of life [27]. Regrettably, factors behind
why one individual ages with few comorbidities and another with many are not fully understood.
Behavioural research is one of the few areas where research has made gains into correlations
between healthy behaviours and longevity.
5
1.1.4. Behavioural Impact on Health
Unsurprisingly, certain behaviours such as abstaining from smoking, maintaining weight
in the healthy range, and being physically active [28], have been associated with healthy aging.
Factors that influence health are known as determinants of health. These determinants can be
used as potential predictors for individuals and help identify preventative measures to correct
unhealthy behaviours.
Determinants of health are often used to look at the general health of a population of
interest. For example, one study found that the best determinants for general Canadian self-
reported health were socioeconomic status, marriage status and unmet healthcare needs [29].
Determinants from one population; however, do not always apply to another population.
Denton and Walters found that high income, working full-time, caring for family, and social
support were stronger predictors of general health in women compared to men [30]. This
emphasizes the need to look at a specific population for health determinants, and the need to
have a well defined group.
For a summary of determinants of healthy aging, Depp and Jeste conducted a meta-
analysis and found that healthy aging was most strongly correlated with being free of disability or
having good functional status. Other good determinants that were correlated were arthritis status,
hearing problems, type of daily activities, diabetes and smoking status. Physical exercise, systolic
blood pressure, self reported health, depression history and global cognitive functioning, were
found to only be moderate predictors of healthy aging. Interestingly, some of the best accepted
predictors of health, such as socioeconomic status, education, marriage status and ethnicity, were
found to be limited in their correlation to healthy aging [5].
In subsequent years, studies have reproduced the correlation with smoking status,
arthritis, and physical activity [31]. McKee and Schuz highlighted the importance of physical
activity and smoking status as determinants of healthy aging. They further proposed that
perception of control is an important moderator of health determinants, giving an example of self-
efficacy moderating the correlation between physical activity and health [32].
There have been few studies looking at gender differences in determinants of healthy
aging; however, Yates et al. followed a group of male, young-senior physicians and found that
6
there were three strong predictors of longevity in this group. If sedentary lifestyle, obesity and
diabetes were present, there was only a 14% chance that a subject would reach 90 years of age.
In relation to the meta-analysis results of Depp and Jeste, Yates et al. showed that the chance of
longevity was reduced with each subsequent diagnosis of a chronic condition, such that one
condition decreased the odds to 67%, but four conditions decreased it to 12% [33].
Plainly, there are predictors of healthy aging. Physical activity, absence of chronic disease
and disability, smoking status, and arthritis are determinants that continually show up in the
literature. With the exception of arthritis, the remaining three determinants are modifiable; they
can be changed or prevented through behavioural modifications. Of particular interest is why
some people naturally abstain from smoking, exercise regularly and take preventative measures
against disease and others do not.
These healthy behaviours have been shown to be associated with personality. Individuals
who are high in extraversion, conscientiousness and emotional stability are better able to adhere
to exercise behaviours, and individuals high in conscientiousness are less likely to take part in
risky health behaviours [34,35]. Personality acts as a modifier of behaviour and is itself an
important predictor of health.
1.2. Personality
1.2.1. Historical Perspectives on Personality
The Diagnostic and Statistical Manual of Mental Disorders (DSM-V) defines personality
as “enduring patterns of perceiving, relating to, and thinking about the environment and oneself
that are exhibited in a wide range of social and personal contexts” [36]. Personality is not an
objective physical trait that is easily measured. Measurements can only be done indirectly through
either covert or overt observations, and/or reports on an individual’s complex behaviour [37]. As
such, there are many personality theories, resulting from the variety of schools of thought within
the psychology field; however, the focus of this project will be on the prevailing trait theory of
personality.
Lewis Goldberg’s condensed history of trait theory attributes Sir Francis Galton in 1884
as one of the first scientists to categorize personality. He formed the Lexical Hypothesis, which
7
postulates that descriptive individual differences are encoded in our natural language. This would
later be refined to a set number of personality-related terms by scientists during the 1930’s, most
notably Gordon Allport, Henry Odber and later in the early 1960’s by Warren Norman [38]. During
the 1960s Trait Theory lost popularity in favour of the then fashionable Behaviorism model [39].
Trait theory, however, would regain a place in personality theory and become the dominant model
for personality theorist. Within trait theory there were two camps of theorist, those who thought
that there were a few broad traits and those who thought there were many narrowly specified
traits. Time and consensus has merged these two camps into a hierarchy model, where there are
broad domains that are constructed from more specialized traits [40].
Currently there are three models that have been widely adopted and validated through
research. Eysenck’s model uses three traits to summarize personality (neuroticism, extraversion,
and psychoticism). This model focuses on the biological basis of personality [41]. The second
model is Cloninger's, consisting of four temperaments (harm avoidance, novelty seeking, reward
dependence and persistence) and three character traits (self-directedness, cooperativeness, and
self-transcendence). This model takes into account both biological and sociocultural influences
[41]. Lastly there is Costa and McCrae's model which is a taxonomy of traits with no particular
original inclination of biological or sociocultural influences [41]. This model does assume that
personality is a strictly intrinsic process, and is not overly influenced by external forces [42]. The
model is called the big five and will be the model of focus for this project.
The big five model was chosen over Eysenck's and Cloninger's due to its prevalence in
the literature and the previous use of the NEO-Five Factor Inventory (NEO-FFI) with a small
subset of participants in a related research project in our laboratory. There is little difference in
the overall traits captured by the various theories [43–46]. Bouchard and Loehlin describe the
various theories as “dividing the same pie in different ways”, for example impulsivity is listed under
neuroticism in the big five but is listed as a facet of psychoticism in Eysenck’s model [40].
1.2.2. The Big Five
The big five is divided into five factors or domains, each with a list of traits or facets. Factors
can be thought of as a continuum with extreme phenotypes at each end and average personality
somewhere in the middle. The five factors are extraversion, agreeableness, conscientiousness,
neuroticism and openness [38]. Each factor is briefly explained, with its opposite in parentheses,
and associations with general health.
8
Extraversion (Introversion) is the tendency towards positive mood, sociability and activity
[47]. The facets measured are warmth, gregariousness, assertiveness, activity, excitement-
seeking, and positive emotions [48]. Many studies have been conducted on extraversion and its
role in health. Low levels of extraversion have been associated with mothers more likely to have
a caesarean section and experience complications during labour, and depression (Johnston &
Brown, 2013). High extraversion has been associated with attention deficit hyperactivity disorder
(ADHD) [51], bipolar disorder [52], and better general health perceptions and physical functioning
[53].
Neuroticism (Emotional Stability) is characterized by a tendency to experience negative
emotions, become easily overwhelmed by stressful events and have difficulty controlling impulses
[50]. It is the only factor that is oriented towards the more negative spectrum in the naming
scheme; recent literature refers to emotional stability. This project will maintain the neuroticism
label to stay consistent with the literature. The facets in this factor include anxiousness, angry
hostility, depressiveness, self-consciousness, impulsivity, and vulnerability [48]. Neuroticism is
another factor that has been highly researched. High neuroticism has been associated biologically
with high levels of interleukin 6 (stimulates the immune response) [54], poor antibody response
[55] and low cortisol response [55,56]. High impulsivity in particular has been associated to low
high-density lipoprotein (HDL) cholesterol and high triglycerides [54]. Many mental health studies
have reported high neuroticism as risk factor, including substance abuse , panic disorder,
generalized anxiety disorder, phobias, obsessive compulsive disorder, uni-polar disorder, post-
traumatic stress disorder [57], depression [50,53], Alzheimer disease [58], low subjective physical
and mental health [50,53], and ADHD [51,59]. Further, high neuroticism has been associated with
higher reports of tremors, breathlessness, constipation, skin trouble and strokes [53].
Conscientiousness (Undirectedness) is defined as task and goal directed, planful, follows
social norms and rules and can delay gratification [60]. Facets in this factor are competence,
order, dutifulness, achievement, self-discipline and deliberation [48]. Interestingly,
conscientiousness seems to act as an antagonist to neuroticism. Physiologically, high
conscientiousness has been associated with low levels of interleukin 6 [54,61], low HDL
cholesterol and low triglycerides [62], the opposite profile of high neuroticism. Low
conscientiousness is also associated with increased substance abuse, uni-polar disorder, post-
traumatic disorder, panic disorder, phobia, generalized anxiety disorder [57], depression [53],
Alzheimer disease [58] and ADHD [51,59]. Individuals high in conscientiousness are associated
9
with better subjective physical and mental health [50], and men in particular report less depression
and better general health [53].
Openness to Experience (Closed to Experience) is defined by Chapman as having
“cognitive and behavioural flexibility, urbane or cultured tendencies, and attunement to internal
and external events and experiences” [47]. Openness to experience will be shortened to
openness for the remainder of this project. Facets consist of fantasy, esthetics, feelings, actions,
ideas, and values [48]. Intelligence is often ascribed as being a result of openness given its
association with cognitive engagement, flexibility and maintenance [58]. Costa argues that
openness is not simply a measure of intelligence given that joint factor analysis shows intelligence
and openness are separate factors. Openness is oriented to intellectual curiosity but also
culturally oriented with aesthetical sensitivities and liberal value systems [63]. Low openness is
associated with Alzheimer disease [58], depression [50], and the facets of feelings, values and
actions with Schizophrenia [52]. High openness is associated with increased creativity in ADHD
patients [51], high cortisol activity [56], low levels of interleukin 6 [61], and men reporting less
vitality but women reporting better general health perceptions and physical health, with less pain
and more vitality [53].
Agreeableness (Antagonism) is an inclination towards maintaining interpersonal harmony
[47]. This factor’s facets are trust, straightforwardness, altruism, compliance, modesty, and
tender-mindedness [48]. Chapman argues that agreeableness in itself is not a great predictor, but
it’s combinations with other factors can produce good predictors [47]. For example, hostility is a
common trait that is the combination of low agreeableness and high neuroticism and has been
associated with poor health trajectories in male veterans [64]. There have been some positive
associations found at the factor level, including a relationship between high agreeableness and
high cortical activity [56], and women reporting better physical and mental health, fewer medical
problems and less visits to general practitioners [53].
1.2.3. Heritability of Personality
Monozygotic and dizygotic twin studies summarized by Bouchard found that heritability
was estimated to be between 40-60% and displayed an additive model [40], though multiple
personality scales were used. To further break down the heritability of specific factors, Bae et al.
show in the Long Life Family Study that openness was the most heritable personality trait (49%),
followed by extraversion (32%), conscientiousness (30%), neuroticism (25%), and finally
10
agreeableness (18%) [65]. Notably, agreeableness is the least heritable trait, possibly explaining
its low predictive power. Further, Pilia et al. found in their family study that genetic effects
explained 19% of the variance in personality traits [66].
Givens et al. took the traits further and demonstrated parallel personality profiles in
offspring of long lived individuals. They found that children of centenarians displayed lower levels
of neuroticism and higher extraversion, much like the profile of their parents, when compared to
a normative population [67]. Not only are domains heritable, but there is evidence that specific
combinations could be inherited together.
Heritability links personality to genetic factors, conservatively estimated at around 40%.
Yet, there is still a large amount of personality that must therefore be explained through other
mechanisms such as environmental influences. Given these other factors could be influencing
personality the stability or personality must be addressed.
1.2.4. Stability of Personality
By its definition, personality is considered to be relatively stable. Drastic alteration of
personality from moment to moment is rare. There are, however, biological pressures that do
cause changes in personality. Chapman et al. noted that personality can change over the course
of a long period through intentional interventions or as a by-product of aging [47]. In a meta-
analysis performed by Roberts and Viechtbauer, they found that there was increasing positive
gains in personality throughout the lifetime, with exceptions of openness and the extraversion
facets contributing to social vitality, which decreased in old age [68]. These changes, however,
were small (at most 1 SD for the whole lifetime) and domains of conscientiousness and
agreeableness suffered from cohort standing [68]; where there are baseline differences between
the cohorts, in this case due to generational differences [68]. The report found that the biggest
changes in their comparison of cohorts occurred in early adulthood, from age 20-40, well below
our study population.
Aging is a biological occurrence that can cause minor shifts in personality. Personality is
not considered stable until adulthood, where it starts to stabilize in individuals around the age of
30 [66,69,70]. There is some debate as to the reasons for this stability; some argue that it is the
reflection of increasingly stable environmental factors such as mature romantic relationships and
occupation [42]. While personality is relatively stable, it does not mean that it is immutable.
11
Terracciano et al. found that while personality was stable, the stability coefficients for traits rarely
reached 1.0, which would indicate constant stability [66]. Indeed, population studies have shown
there are consistent predictable changes to personality as individuals age, such that typically
there is a decrease in extraversion, neuroticism and openness with an increase in
conscientiousness and agreeableness [71,72].
Personality can be a flexible characteristic but there are limitations on the amount of
natural change that can occur. Roberts proposed that personality has set points that people vary
around throughout their lifetime [73], and that change is limited. Roberts further found in a meta-
analysis of longitudinal studies using a test-retest correlation coefficient that personality was more
stable with advancing age [70]. While personality is not a fixed phenotype throughout the lifespan,
there is enough evidence to suggest that personality in adulthood is relatively stable and that
changes that do occur are mild. Aging is a biological force that affects personality and should be
given consideration and accounted for when necessary. Other biological factors to consider are
the affects of sex and gender.
1.2.5. Personality Gender Differences
As hinted in the description of the factors, there are clear sex and gender differences in
personality as with most other biological systems. In psychology, gender is used to describe an
individual’s perception of being male or female from their social and cultural context [74]. Sex is
used in biology and other sciences to describe the physical manifestation of being male or female
[74]. Psychology typically measures gender and biology usually measures sex.
For trait theory, studies have generally found women to score higher in the agreeableness
and neuroticism factors than men [75–78]. Men have consistently scored higher in openness
[75,76,78]. These differences between genders will be taken into account during analysis.
1.2.6. Personality Profile of the Very Old and Very Healthy
Investigations into longevity and health have revealed a particular big five personality
profile for healthy seniors, such that healthy long-lived seniors demonstrate high
conscientiousness, extraversion, openness and low neuroticism. Conscientiousness is the largest
predictive factor for longevity, with most studies reporting a protective effect [79–83]. Wilson et al
12
in particular reported that risk of death was halved in subjects reporting high conscientiousness
when compared to those with low conscientiousness [82].
Another major predictor was neuroticism. High neuroticism was consistently shown to be
detrimental to longevity [79,81,82,84]. Specifically, Shipley et al. found that individuals high in
neuroticism had a 12% increase in the risk of death from cardiovascular disease [85]. Duberstein
et al. found that high neuroticism was associated with poorer perceived health and found that this
perception became more pronounced with age [86], possibly indicating a mental health
mechanism for the decreased mortality in individuals with high neuroticism.
Additionally, extraversion was positively associated with longevity [79–81,84,86], as was
openness [80,81,86]. Terracciano in particular found that for every standard deviation gain in
either conscientiousness, emotional stability or the extraversion facet activity, there was an
increase in average life span of 2 to 3 years [79]. Agreeableness was not found to be a strong
predictor of longevity. Decidedly there is a distinctive personality profile for the very old and very
healthy.
1.2.7. Limitation in the Personality Field
Personality is not a physical trait, and we rely on indirect measurements as mentioned
previously. The predominant measurement used is the self-reported inventory, which is not
without drawbacks. It relies on subjects to complete the inventories honestly, and cannot account
for their biases or perceptions of themselves. There are, however, many other metrics that may
be used. Inventories completed by someone who knows the subject, behavioural measurements
(how frequently a particular behaviour is done), response tests (how an individual responds to a
specific stimuli), nonverbal tests (for example, report which pictures best describe you),
physiological measures (such as stress tests), or tests of ability (resisting temptation, recognizing
facial responses), are all options that could be used in conjunction with the self-reported inventory
to establish a more precise personality profile [47]. Ease and costs are factors that prevent
additional metrics from being used.
Selection of the proper inventory is also a consistent issue in personality. While inventories
are very similar, as previously discussed, some tend to show more positive results depending on
what relationships are under study. Studies using the big five model‘s NEO-PR-I inventory, for
example, consistently report positive associations with the HTTLPR gene when compared to
13
those that employ Eysenck's Tri-Personality Questionnaire (TPQ) inventory [87]. While it is difficult
to establish what this means, it does indicate that proper selection of which inventory to use is
important. While the big five model’s inventories seem to be used most often for personality
related to biology there could be other models that better describe personality.
In recent years, there has been an emergence of a modified big five model. Current
personality models have a distinct lack of facets or domains that would properly predict or describe
less desirable behaviour patterns. The 6-domain HEXACO theory proposes that there is another
domain called Honesty-Humility which captures ethical behaviour. This model rearranges the big
five so that the opposite of honesty-humility is referred to as the dark triad and looks to capture
psychoticism, neuroticism and Machiavellian behaviours [88]. An individual measurement on the
honesty-humility to dark triad scale has so far shown to be a good predictor of sex, power and
money behaviours [89], and it will be interesting to see what associations it may yield. While this
model and its inventories still need to be validated, it’s important to remember that while current
tools used in personality may be validated and reliable, they may not capture all aspects of
personality and in the future there could be much better measures of personality available.
Noticeably in the studies discussed so far, associations have typically been made with the
domains of personality. This is characteristic in personality association studies but presents a
problem regarding the specificity of associations [47]. Rarely are facets analyzed for their
relationships to health, behaviours or even genetics; yet, the intrinsic processes that govern facets
could potentially be vastly different. It does not seem plausible that biological processes regulating
anxiety would also control impulsivity; however, these are both facets of neuroticism. In general,
it seems that specific facets are more useful in measuring specific measures of health, like blood
pressure, whereas domains may be best suited for measurements of general overall health, like
subject health reports and disease status [90]. Additionally, there are personality profiles,
characteristic mixes of domains, which are rarely evaluated.
Kinnunen et al. found that certain personality profiles were associated with subjective
reported health and level of symptoms experienced. Those with resilient profiles (high
extraversion, agreeableness, and conscientiousness with low neuroticism) were found to report
better health, fewer symptoms and low psychological distress when compared to other profiles.
Individuals with high extraversion and neuroticism , referred to as the over-controlled profile,
reported the worst subjective health and the most symptoms [91]. These profiles are useful
14
predictors that should be evaluated more often, as they could strengthen personality’s ability to
be used as a health predictor.
1.2.8. The Impact of Personality on Behaviour
Personality can be just as effective in predicting health outcomes as the standard socio-
economical status (SES) or IQ scores. Roberts et al. found in their meta-analysis that
conscientiousness, extraversion, neuroticism and hostility were stronger predictors of mortality
when compared to SES; they noted there were mixed results for agreeableness and openness
[92]. They also found that conscientiousness, agreeableness, and neuroticism were better
predictors of divorce, and personality was similar in ability to predict occupation [92]. Weiss et al.
used a senior sample to further refine these relationships to facets showing that survival was
associated with higher impulsiveness (neuroticism), straightforwardness (agreeableness), and
most strongly with self-discipline (conscientiousness) [93]. Here, high conscientiousness and high
neuroticism stand out as good predictors of mortality.
Personality is able to predict health outcomes because it is associated with decision styles
and behaviour choices. Flynn et al. reviewed personality and decision styles in a senior population
and found that seniors who scored high in conscientiousness and openness but low in neuroticism
and agreeableness were associated with having the most active decision style, deliberative
autonomist [94]. Further, the deliberative autonomists were more likely to be female and
nondeliberative delegators were the least active in decision making and showed an opposite
personality profile [94]. Personality affects decision styles, which in turn affects behaviour choices.
As highlighted in the description of personality domains, personality is associated with
healthy behaviours. Young male militants who engaged in wellness behaviours, accident control
and less frequent risk taking were associated with higher reported conscientiousness and
agreeableness scores and lower neuroticism scores [95]. A meta-analysis of conscientiousness
by Bogg et al. showed that conscientiousness could predict a variety of health behaviours.
Predictive power of conscientiousness from strongest to weakest behaviour correlation are drug
behaviours, risky driving, excessive alcohol use, violence, tobacco use, risky sex, unhealthy
eating, suicide, and activity [60].
It has been proposed that individuals high in conscientiousness are better able to predict
and plan for future negative events, which gives them an advantage for preventing the escalation
15
of stressful situations and enhances their coping [61]. Extraversion’s optimism facet has also been
proposed to encourage healthy behaviours by guiding individuals towards adaptive coping
whereby strategies to eliminate, reduce or manage stress are utilized more frequently [96].
Personality domains are important predictors of behaviours and decision styles. If personality
affects behaviours and there is evidence of genetic heritability (as previously discussed) than
there must be a modulating biological component to personality.
1.3. Biochemistry and Physiology of Personality
Linking decision making and complex health behaviours to physiological processes is still
a developing field but there has been progress that is of interest to this project. The focus of this
section will be on specific neuro-physiology and the biochemistry that controls behavioural
processes in relation to personality and aging.
1.3.1. Age Related Changes
Aging results in many structural changes throughout the brain. In general there is a loss
of brain mass replaced by an increasing amount of cerebrospinal fluid [97]. The diminishing mass
represents both grey matter (brain tissue consisting of glial, neurons and vasculature responsible
for processing and cognition) and white matter (the myelinated axons of neuronal cells
responsible for transmission of signals from one area to another in the brain).
Sex differences, in relation to the proportion of white and grey matter, persist in aging.
Lemaitre et al. found that the rate of loss for grey and white matter was the same for men and
women but still found classic dimorphism of tissues where men showed larger compartmental
volumes with more white matter and cerebrospinal fluid while women showed larger grey matter
volumes [98]. This sexual dimorphism is also seen with cognitive performance tests, where the
elderly display the same pattern of women performing better in psychomotor speed, verbal
learning and memory task and men score higher on visuoconstruction and visual perception tasks
[99].
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1.3.2. Personality Domains and Relevant Structures
There are few studies that examine the relationship between physiological structure or
processes and all the personality domains. Instead, studies often look at one or two specific
personality domains and their potential influence on a specific structure or activation of certain
area. Each domain is summarized below. Neuroticism and Extraversion are the most well
researched domains and have a substantially greater amount of literature and support for specific
associations with cognitive functions and structures.
Extraversion
Many structures have been identified as being associated with extraversion. Generally,
there is greater cortical volume in the prefrontal and frontal cortex with higher scores of
extraversion. Specifically, volumes of the dorsolateral prefrontal cortex [100], medial orbitofrontal
cortex [101], temporal lobe [100], and the mid and superior frontal cortexes [100] are all positively
associated with higher extraversion. Functionally, extraverts display increased glucose
metabolism activity in the orbitofrontal cortex [102], the right putamen [103], and the middle
temporal gyrus [103], compared to introverts. Together these regions create planned activity,
including working memory [104], evaluation of outcomes [105], decision making [105], learning
[106], and facial attraction [107].
Interestingly, Bjornebekk et al., found that there was a negative association between
extraversion and inferior frontal gyrus volume, with the excitement seeking facet as the major
contributor to the association [108]. This relationship was previously established by Blankstein et
al. but only found in females [109]. Bjornebekk proposed this is related to the uninhibited pattern
of speech that is demonstrated by some extraverts, as this area is important for inhibition of
activities such as risky behaviours [108].
Anterior cingulate volume was also positively associated with extraversion [100].
Connectivity to this area was also found to have a positive association with extraversion,
particularly the facets warmth, gregariousness and positive emotions [110]. Increased activation
of the anterior cingulate has been positively associated with extraversion when comparing positive
to neutral stimulation, with facet analysis revealing that excitement seeking and warmth
contributed the most to the activation [110]. The anterior cingulate also plays a role in learning
and reward dependence [111].
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Extraversion has also been connected to the limbic system which collective manages
intrinsic drives and emotions. Cohen et al. looked at the strength of white matter connectivity
between the limbic system and striatum with varying levels of extraversion [112]. They found that
there was a positive association between the strength of connectivity and extraversion; particular
fiber tracks in the hippocampus, amygdala and ventral striatum were predicted by the novelty
seeking facet and tracks between the prefrontal cortex and striatum were predicted by the reward
dependence facet [112]. This could indicate a stronger or faster information relay to these areas
for extraverts.
The orbitofrontal cortex is a structure that is very involved in extraversion and its role in
the reward system. The reward system is a dopamergenic system that is key for cognitive control
and learning [113]. It controls the ability to observe some event or action in the environment and
make a prediction of a potential reward or outcome using negative or positive feedback [114]. The
signal begins in the ventral tegemental area of the brain, within the midbrain, which projects to
the ventral striatum’s nucleus accumbens or to the prefrontal cortex [115]. This pathway is called
the mesolimbic dopamine pathway and contains 80% of the brain’s dopamine [116]. The system
can be broken down into two phases, anticipation and receipt or outcome [114].
The nucleus accumbens is associated with anticipation of rewards. The prefrontal cortex,
in particular the ventromedial prefrontal cortex, has been associated with reward outcome [117].
While there are no major healthy aging changes in the system for reaction time, accuracy or task
performance, Vink et al. found there was a difference in activity for the ventral striatum (where the
nucleus accumbens lies) activation [114]. They found the area of reward anticipation was less
active for their older subjects, perhaps the results of learned patterns over the lifetime.
Deckersbach et al. has proposed that the orbitofrontal cortex guides motivation behaviour
and decision making, so that individuals high in orbitofrontal metabolism/activation may value
more external positive rewards than lower metabolizers [102]. Hooker et al. hypothesized that
extraverts have greater sensitivity to the type of reward or the value placed on the object instead
of a constant higher sensitivity to rewards [118]. As extraversion is related to reward-dependence,
there is a strong connection to dopamine function [118,119].
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Neuroticism
Due to its links with anxiety and depression, neuroticism has been extensively researched
for a physiological origin. Findings include a few positive associations between neuroticism and
grey matter volume, notably visual stream areas [100], ventral anterior cingulate cortex [109] ,
mid cingulate cortex [101] and the cerebellum [101]. The best understood connection is to the
anterior cingulate cortex, which will be discussed further below.
Many structures have been negativity associated with higher neuroticism scores. The
orbitofrontal cortex [100,120,121], ventrolateral prefrontal cortex [120], dorsolateral prefrontal
cortex [100,120], dorsomedial prefrontal cortex [101], precentral gyrus [101], medial temporal lobe
[101], middle frontal gyrus [121], superior frontal gyrus [121,122], inferior frontal gyrus [122], and
general cortical thickness [120,122] were all areas identified as having reduced volume.
The middle frontal gyrus is important in top-down control of executive and attentional
processes and is specifically associated with the impulsivity facet of neuroticism [121]. It is a
proposed mediator of impulsivity [121], which is generated in the orbitofrontal cortex [105]. The
facet vulnerability is negatively correlated with anterior cingulate cortex volume [108]. Many
specific areas are seemingly affected by neuroticism, but there is strong evidence of lower total
volume associated with higher neuroticism scores.
Many studies have linked higher neuroticism with lower total grey matter volume
[108,120,123]. Specific facets such as anxiety and self-conscientiousness have been found to be
the largest contributors [123], and depression, anxiety and vulnerability to stressors have been
found to contribute to lower gray matter volume, particularly in the frontotemporal region [108].
White matter is also reported as being affected by higher neuroticism [108,123], Jackson
et al. saw that there was a large white matter decline in an aging brain for subjects reporting
higher neuroticism scores when compared to their low scoring equivalents [120]. Xu et al. further
found that there were specific white matter tracks that showed worse integrity in high neuroticism
individuals [124]. Tracks running from the anterior cingulum and uncinate fasciculus connect the
amygdala to the medial and lateral prefrontal cortex, the anterior cingulate and the orbitofrontal
cortex [124]. The amygdala is an important structure for emotional regulation, self-regulation and
self-referential processes. It is interconnected to the anterior cingulate cortex and many areas in
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the prefrontal cortex [125]. It is often the target structure in studies about anxiety, as dysfunction
is related to unregulated emotional states.
Neuroticism is well known for its connection to amygdala activation [110,118,126]. Ormel
et al. have summarized the findings of the positive association between neuroticism and
amygdala activity, describing a negative coupling between the left amygdala and the anterior
cingulate cortex and a positive coupling between the right amygdala and the medial prefrontal
cortex [125]. They hypothesized that it causes reduced control over evaluating negative stimuli
but increased self-referential evaluation, such that there is a hyper arousal state in the amygdala
that is not regulated by the anterior cortex or the prefrontal cortex [125]. This leads to an amygdala
that is on high alert, constantly looking for potential threats, or sustained vigilant monitoring
behaviour [125].
Many other studies support the hypothesis of increased anxiety. High neuroticism has also
been linked to higher activity in the insular cortex, which is important for homeostasis as it
regulates blood pressure, heart rate, respiration and gastrointestinal activity [102]. Feinstein et al.
looked at the anterior insular cortex function between neurotic and non-neurotic subjects, finding
that there was increased activation for the neurotic individuals in the action selection phase of a
decision-making task where the outcome was certain [127]. This could mean there is increased
anxiety around everyday low risk decisions in individuals suffering from anxiety. The anterior
insula may also play a role in the development and maintenance of anxiety and its activation
during a task where the outcome may reveal the increased anxiety felt by subjects who score
highly on neuroticism scales. Deckersbach et al. also investigated this region, looking at resting
regional cerebral glucose metabolism rates in the insular cortex and found there was a negative
correlation to neuroticism [102]. They hypothesized that this lower baseline metabolism caused
subjects to be more sensitive to signal changes in this region causing the increased activation
and ultimately anxiety [102].
The anterior cingulate cortex is another structure connected to neuroticism. It can be
divided into two sections, dorsal and ventral. The dorsal section is cognitively specialized for
recognizing errors, problem-solving and adapting to changing conditions [111]. It is connected to
the prefrontal cortex and monitors performance and rewards. The ventral section is specialized
towards emotional processes, and receives projections from the amygdala [111].The anterior
cingulate and amygdala function as a circuit that is regulated by serotonin, with the ventral anterior
cingulate showing positive coupling and the dorsal anterior cingulate showing negative coupling
20
with the amygdala [128]. This relationship could explain why there is sometimes difficultly problem
solving as anxiety builds.
There is also evidence of stronger coupling between the amygdala and ventral anterior
cingulate when neurotic subjects processed an emotional conflict [110]. The authors noted that
this was primarily driven by anxiety related facets of neuroticism, such that as situations become
more stressful there could be more processing through emotional processes, resulting in
increased anxiety, anger or depression [110].
Conscientiousness
Conscientiousness has been positively linked to a variety of structural changes in the
orbitofrontal prefrontal cortex [100,120], dorsomedial prefrontal cortex [100], premotor cortex
[100], putamen [100], middle frontal gyrus [101], frontopolar cortex [100], and lateral prefrontal
cortex [101] and negatively linked to the superior temporal gyrus and the supramarginal gyrus
areas [108]. Given the definition of individuals high in conscientiousness, it is not surprising to find
that these areas have a function in control. Where there could be heightened function in decision
making [105,129], moral reasoning and nonsocial semantic processing [130], learning [106,131],
planning and voluntary movement [100,132], cognitive and behavioural regulation [133], hand
manipulation [131], and assigning movement to specific environmental cues [131], there is a
potential for lessened capability in the processing of auditory stimuli and social cognition [134],
and lower ability to make social judgments due to poor regulation of the self-other distinction [135].
Higher conscientiousness is good for planning and learning but seems to decrease certain social
processes.
Agreeableness
Agreeableness has been positively linked to the volume of brain structures including the
lateral orbitofrontal cortex [100], cingulate cortex [101], and the fusiform gyrus [101]. Intriguingly,
Rankin et al. also found a positive relationship between agreeableness and the orbitofrontal
cortex; however, they found there was a difference between the right and left volumes, where the
right half was positively associated and the left was negatively associated [136]. They have
suggested that this split is caused by the right half playing a larger role in social functioning and
the left half looking after self-interests [136].
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Negative associations have also been found with the volumes of dorsomedial prefrontal
cortex [100], superior temporal sulcus [101] and the superior temporal gyrus [101]. These
structures may have functions in social cognition [130], interpreting the gaze and emotionally body
language of another person [137], and auditory processing and processing social cognition [134].
Openness
Openness is a difficult domain to study due to its association with intelligence. Often
measurements are for IQ or other tests of intelligence that are not specifically targeted towards
the personality domain. For example, when investigating the integrity of white matter tracks, there
is an association between higher openness and better integrity of the track near the dorsolateral
prefrontal cortex [124]. There is a general global increase in integrity, however, for subjects high
in openness that could be due to increased intelligence which would universally raise the integrity
of the white matter [124]. Further, specific facets have been associated with certain types of
intelligence and greater activation in the prefrontal cortex. Ideas and values were found to be
positively associated with fluid general cognitive ability, which is a measure of raw cognitive ability
[138]. Crystallized general cognitive ability, a measure of acquired knowledge, was found to be
positively associated with all facets of openness except for action [138]. DeYoung argues that
openness is related to dopamine through its activation of the prefrontal cortex [138].
Other areas that have shown a positive association with openness include the frontopolar
cortex [100], vs. negative associations with the lateral parietal cortex [122], fronto insular cortex
[100] and the medial orbitofrontal cortex [100,122]. The frontopolar cortex is involved with decision
making but functions to suspend certain tasks while other, more immediate plans, are
implemented [129]. This could function to protect long term plans from being overwhelmed by day
to day tasks, an important skill for intelligent animals to have. Some studies suggest this area has
a function in creativity [139] which would further highlight its connection to the openness domain.
The lateral parietal cortex, fronto-insular cortex and the medial orbitofrontal cortex studied are
potentially involved in episodic memory [140], homeostasis [102], and cautionary or inhibitory
responses which activate to social, aversive physical and fear of loss [100].
1.3.3. Neurotransmitters and Personality
Early research into the biology of affective disorders (now commonly referred to as mood
disorders such as bipolar disorder or depression) shed light on the connection between
22
personality and neurotransmitters, particularly how the catecholamine group of neurotransmitters
are linked to depression through pharmacological inhibition or activation [141]. Monoamines that
have received the most attention in such studies are the catecholamines, dopamine and
norepinephrine, and the indol amine, serotonin [142].
Dopamine has a strong connection to promotion of positive and negative incentives [143].
These connections to positive and negative incentives are linked to the extraversion facet of the
NEO-FFI through the reward system already discussed in the physiology section. Dopamine is
generally found in the corpus striatum with lower concentrations throughout the brain [142]. The
corpus striatum relays information from the cerebral cortex, thalamus, and substantia nigra to the
basal ganglia, which controls movement. The system is referred to as the meso-cortical pathway
[144]. The striatum helps regulate the motivations of the higher functions of the cerebral cortex
and the lower functions from the mid-brain [145].
Dopamine concentrations in the brain experience age related changes. Increasing age
shows a lower activation of dopamine pathways in many parts of the brain, in addition to a more
reduced activation of the meso-cortical pathway [144]. Low dopamine levels have been strongly
linked to the development of Parkinson’s disease [143].
Norepinephrine is associated with attentional processes, in particular selective attention,
and acts as serotonin's antagonist [143]. It is a critical neurotransmitter for inhibiting distracting
stimuli. Due to its relationship with attention, norepinephrine has been linked in some studies with
the extraversion and agreeableness factors [146]. Impaired norepinephrine receptors cause
symptoms similar to attention deficient disorder and increased receptor function improves the
prefrontal cortex regulation of behaviour and enhances attention [147]. Norepinephrine is found
throughout the brain with particularly high concentrations in the hypothalamus, which is primarily
involved in managing homeostasis [142].
Like norepinephrine, serotonin is found throughout the brain with the highest
concentrations in the hypothalamus and throughout the rest of the limbic system [142]. The limbic
system is involved in memory, emotion, learning and motivation. Serotonin is an important
mediator of the sleep/wake/arousal cycle, circadian variation, and sensory stimulation [148].
Introduction of serotonin causes a reduction of motivated and emotional behaviours such as
feeding, play and sexual behaviours, but a promotion of sleep [143]. Serotonin has been studied
extensively for its connection with impulsivity and aggression, which are facets of neuroticism in
23
the NEO-FFI. An inverse relationship has been found, such that low serotonin results in increased
aggression and high levels of serotonin can produce deceased aggression and increased
cooperativeness in individuals [149]. Low serotonin levels have also been linked to increased
impulsivity [149]. At the other end of the spectrum, high serotonin levels have been associated
with obsessive compulsive disorder, often characterized by a low level of impulsivity [149].
In connection with aging, dopamine and serotonin are two neurotransmitters that have
been implicated in cognitive decline in aging brains [150]. Monoamine oxidase function is another
chemical suspect in cognitive decline. Its presence increases with age, causing more free radicals
to be liberated from its catabolic activity. Anti-oxidation reserves become shallow in late life,
leaving free radicals to potentially harm synaptic pathways [150].
Personality has physiological origins, through neurological systems such as the dopamine
reward system, processes that regulate anxiety and impose potential structural changes in the
prefrontal cortex. These origins can be further reduced to look at their genetic basis.
1.4. The Genome and Genetic Variation
1.4.1. Basic Structures
The genome is an all encompassing term for the heritable genetic information found in a
cell. Human genetic information is organized into 46 chromosomes, a set of 23 from each parent.
At their simplest, chromosomes are two complimentary strands of deoxyribonucleic acid (DNA)
strings, with each nucleic acid representing a base pair (bp). There are approximately 3 billion
base pairs in the human genome [151], so the strands must be condensed into nucleosomes (a
histone and bound DNA), then into another helical structure called a solenoid, to fit within the
nucleus [152]. This packaging is dynamic and can become decondensed to expose the double
strand structure and the genes that are encoded on it.
A gene is a portion of a DNA strand that codes for a transcript of ribonucleic acid (RNA)
[152]. There are approximately 20-25,000 genes in the human genome, which is sparse
compared to the total size [153]. The RNA transcripts have a variety of functions and can also
experience post-transcriptional changes to further enhance their function. Messenger RNA
(mRNA), goes on to be translated into a shorter polypeptide chain, which can undergo further
24
alterations to become a functional protein [152]. The transcript is translated through a redundant
coding system, such that every three nucleic acids specify a particular amino acid [152].
Genes are further organized into functional regions (Figure 1). The promoter is located at
the 5 prime end and promotes the initiation of transcription through the recruitment of
transcriptional factors. The gene also contains start and stop signals for transcription, and
untranslated sequences [152]. The 5 prime untranslated region contains a signal to start
transcription [152]. The 3 prime untranslated region contains a signal to stop transcription and
add multiple adenosine residues to the end of the transcript, creating a poly(A) tail [152]. The
transcript contains both coding and non-coding sequences. The coding sequences are referred
to as exons and the non-coding as introns [152]. Introns are removed post-transcription through
a process known as splicing [152].
Figure 1.1. General Gene Structure Promoter region (in purple) is of variable length and distance from the 5 prime untranslated region, depending on the gene.
Genes differ slightly between individuals, through small variations in the DNA sequence,
referred to as alleles [152]. These alleles can have a variety of functional effects in a gene and
can contribute to the organism’s phenotype. So while genetic material is nearly identical between
two people, these small variations are capable of coding an amazing diversity of phenotypes in
the human species.
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1.4.2. Microsatellites and Minisatellites
Microsatellites and minisatellites are referred to as repeating elements. They both consist
of 2-200 tandem repeats of a number of nucleotides [152]. Because of this variation in the repeat
number, these satellites are multi-allelic due to the different lengths of alleles. Microsatellites are
characterized as smaller repeats, between 2 to 4 nucleotides and are sometimes referred to as
simple sequence repeats (SSRs) [152]. Minisatellites are often referred to as variable number
tandem repeats (VNTR), and consist of repeats of 10 to 100 nucleotides [152].
These strings of repeats are common in the genome [154] and have a nonrandom
distribution [155]. They are theorized to arise from slippage and unequal cross over events (where
the chromosome pairs break and recombine) during replication [154]. The resulting expansion or
shortening of areas in the genome has an important evolutionary role in adding to the genetic
complexity of the genome. Slippage events favour growth where point mutations can break down
the repeating segment [155]. Fondon et al. explain that slippage mutations are readily reversible
unlike point mutations [156], and can have an evolutionary advantage.
Repeating strings are important structures for creating genetic variation and are influential
mechanisms for adaptive evolution. Variations in the length of the base unit, and the purity of the
repeats help with specific site adjustments to affect the mutation rate or the effect of the mutation
itself [157]. King has famously named this phenomenon the “genetic tuning knob” (analogous to
those of a stringed instrument), since they are able to influence gene activity and aid in quick and
efficient evolutionary adaptation [158]. Microsatellites and Minisatellites can have an impact gene
activity, through evident mechanisms such as when they are inserted in the coding region, and
through less apparent means.
VNTRs in the exonic, coding region, could result in the loss or gain of function, a frameshift
mutation or an expanded mRNA [155]. Fortunately frameshift is not a common mutation in VNTR
expansion/reduction. Wren et al. used a human complimentary DNA (DNA synthesized from
mRNA) database to predict the occurrence of potential frameshifting repeating elements in coding
regions. They found that 92% of the repeating elements were in multiples of three, which would
protect against frameshift mutations [159]. Interestingly, some detrimental exonic expansions
have neurological phenotypes [155], such as spinobulbar muscular atrophy and Huntington’s
disease. Li et al. proposed this was due to instability in the protein product of the affected gene
[155].
26
VNTRs are more common in the untranslated regions of genes [159]. The 3 prime
untranslated region can cause transcriptional slippage, reducing expression, or result in an
expanded mRNA, while the 5 prime region can affect recruitment of both transcriptional and
translational machinery, affecting expression [155].
Impressively, intronic (non-coding region) VNTRs display a wide variety of effects on
genes. VNTRs can affect the efficiency of gene expression through two mechanisms: the binding
of transcriptional factors and alteration of splicing sites. A VNTR in intron 2 of the SLC6A4 gene
regulates transcription by binding transcriptional factor YB-1 [160]. YB-1 is a transcriptional
regulator that has been shown to have both repression and activation properties [161], but shows
activation in the SLC6A4 gene [160]. VNTRs can alter the spliceosome configuration or efficiency
of splicing. SSRs with a GGG sequence ( three guanines in tandem) have been implicated as
enhancers on the 5 prime end of an intron, as a place for splicing factors to bind, affecting
spliceosome assembly [162].
Splicing alterations caused by mini and micro-satellites can also influence which type of
mRNA is produced from a gene. Li et al. summarized the role of an SSR acting as an intronic
enhancer in CFTR. They show that shorter repeats significantly increased the skipping of exon 9,
and proposed that SSRs have functional significance in tissue specific exon inclusion [155].
Intronic VNTRs can also function as small regulatory RNAs. The eNOS gene has an
intronic VNTR that produces a small intronic repeat RNA (sir-RNA) that regulates expression of
the gene through negative feedback [163]. The greater the expression of the gene the more
sirRNA is produced, which in turn decreases expression of the gene. sirRNA is thought to affect
transcription by modifying methylation status, histone acetylation or by affecting splicing of the
mRNA [163,164].
VNTRs in imprinted regions have been implicated in affecting phenotype. The insulin
gene, INS, is within an imprinted region and has a VNTR upstream of it [164]. There are three
common alleles for the INS VNTR, alleles I, II, III [164]. Allele III is associated with an increased
risk of diabetes, with the risk being equivalent for the I/III, II/III, III/III genotypes. It was found,
however, that if the I allele of an individual with the I/III genotype came from the father, it conferred
a protective effective against diabetes [164]. Further, allele III inherited from the father was shown
to have increased risk of obesity and worse diabetic outcomes [164].
27
Microsatellites and minisatellites can exert effects by many different mechanisms, from
direct insertion into the coding region, affecting transcriptional machinery, recruitment of
transcription factors, functioning as feedback mechanisms, alterations to the splice sites and even
amplifying the affects of parental origin imprints. Due to the wealth of potentially functional
capabilities of VNTRs and SSRs, any VNTRs found in candidate genes will be genotyped and
analyzed.
Microsatellites and minisatellites are currently not often used in looking at genetic
variations. More popular is the use of SNPs to genotype genes of interest; SNPs will also be used
in this project.
1.4.3. Single Nucleotide Polymorphisms (SNPs)
SNPs are single nucleotide differences that are found throughout the genome and are
fairly uniformly distributed [152]. The ubiquity of SNPs has allowed creation of dense genetic
maps across the genome. Correlations between the genotypes of different SNPs have been
established in the form of a linkage disequilibrium map [152].
Linkage disequilibrium occurs when there is preferential association between two markers,
such that certain alleles are seen together more often than expected by chance [152]. If crossover
events (where chromosome pairs break and recombine their DNA strands) occurred completely
at random, we would expect to see to see a random distribution of crossing over events across
the genome, and alleles would be assorted randomly from each other. This is not the case. There
are stretches of DNA that show regions of preserved allele combinations across the genome
[152]. When two markers are in high linkage disequilibrium, for example if SNP #1’s ‘A’ allele is
usually found with SNP #2’s ‘C’ allele, then SNP #2 can be ‘tagged’ by genotyping SNP #1. SNP
tagging allows researchers to use fewer SNPs than the total number, to represent the genetic
variation across a gene.
This project uses SNPs and VNTRs as genetic polymorphisms for study. SNPs will be
used to tag across each candidate gene, and VNTRs will be genotyped when appropriate.
Variations in genes related to personality phenotypes are summarized in the next section.
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1.5. Candidate Genes
Candidate genes were selected based on a literature review described in the methods
section. Five genes were chosen based on their prevalence in the literature: COMT, DRD4,
MAOA, SLC6A4, and TH. Each gene is briefly overviewed for functional importance to personality
and notable polymorphisms.
1.5.1. Catechol-O-Methyltransferase (COMT)
Figure 1.2. Genomic structure of COMT Showing selected SNPs, including the missense rs4680 SNP located in exon 3.
The COMT gene is located on chromosome 22q11.21 [165]. The gene contains 6 exons,
with exon 1 and 2 as non-coding exons in a transcript variant [166]; see figure 1.2. It encodes an
enzyme that inactivates catechols, which include dopamine, norepinephrine (formally called
noradrenaline), epinephrine, and L-dopa (a precursor of dopamine) [167]. The COMT protein
catalyzes the inactivation by transferring a methyl group of S-adenosylmethionine to the hydroxyl
group on the catechols [167].
Tenhunen et al. found that exon 3 distinguishes the production of two variant transcripts,
a short 1.3kb and a longer 1.5kb. There are two promoters and two transcription initiating codons.
The proximal promoter is located between the two translational initiation codons, approximately
200bp up stream of the 1.5kb transcript’s ATG start codon [166]. The proximal promoter produces
the 1.3kb transcript and the more distal promoter produces the 1.5kb transcript [166]. The two
transcripts code for two variations of the COMT protein, a soluble form and a membrane-bound
form [168]. The 1.3kb codes for the soluble or S-COMT protein and the 1.5kb codes for the
29
membrane-bound MB-COMT protein. The variants are tissue specific and differentiated by the
presence of a hydrophobic amino acid extension in the MB-COMT protein [166]. MB-COMT was
also shown to have a lower Vmax, a measure of the rate of a reaction, with the same substrates
when compared to S-COMT [169]. However MB-COMT has a higher affinity for dopamine
compared to S-COMT [170].
The COMT enzyme competes with the Monoamine oxidase (MAOA) enzyme to inactivate
catechols. Rivett et al. studied the location and substrate affinity of both COMT and MAOA
enzymes [171]. They found that dopamine is preferentially inactivated by MAOA and
norepinephrine is metabolized by both COMT and MAOA [171]. In addition they showed that
COMT consistently had a lower affinity for the same substrates as MAOA and that the presence
of either COMT or MAOA was tissue dependent [171].
Matsumoto et al. determined that the highest levels of COMT mRNA are in the prefrontal
cortex, and that 70% of that COMT mRNA coded for MB-COMT protein [172], suggestive of
function in that location. They also found levels of COMT mRNA in structures important for the
dopamine reward system, including the striatum and the midbrain’s substantia nigra and ventral
tegmental area, although at much lower levels [172].
Matsumoto el al hypothesized that COMT may function as the main dopamine regulator
in the prefrontal cortex, since there is little recycling of dopamine there [170]. This is supported
by studies with Comt knockout mice, which show an increase in prefrontal cortex dopamine [172].
COMT encodes a protein that has a clear physiological effect and variations in this gene could
plausibly alter an organism’s phenotype and, in particular, alter personality through affecting the
prefrontal cortex.
One polymorphism that has undergone extensive research is the rs4680 SNP, which
corresponds to a methionine or valine amino acid in the protein. Biochemically the valine variant
increases protein activity by 38% [173], possibly due to valine being a more hydrophobic amino
acid than methionine, which helps to stabilize the surface of the protein [173]. The valine variant
has been found to be more thermostable compared to the methionine variant [169], supporting
the claim that the valine variant is more stable generally. Homozygous methionine individuals
were associated with improved performance on a sorting task used to measure prefrontal
cognitive function [174,175], indicating that lower activity of the COMT enzyme may help with
higher cognitive function resulting from more available dopamine.
30
Estrogen is a regulator of COMT and females are known to have consistently lower COMT
protein blood levels. Chen et al. also found their female participants on average had lower levels
of COMT activity [173]. As such, it is particularly important to conduct sex specific analysis in this
gene.
1.5.2. Dopamine Receptor D4 Gene (DRD4)
Figure 1.3. Genomic structure of DRD4 Showing a functional VNTR located in exon 3.
DRD4 is located on chromosome 11p15.5 [176]. The gene contains four exons and codes
for a 387 amino acid protein that has 7 transmembrane domains, an n-linked glycoslytion site and
several phosporylation sites [177], see figure 1.3.
The DRD4 receptor belongs to a family of D2-like receptors [178]. The receptor is activated
by dopamine and inhibits the activity of adenylyl cyclase, which catalyzes the conversion of
adenosine triphosphate to 3 prime, 5 prime cyclic AMP [178]. This receptor works to inhibit
dopamine signaling [178]. The receptor is located throughout the central nervous system, and is
highly concentrated in the prefrontal cortex, hippocampus, amygdala, hypothalamus, and meso-
limbic pathways (midbrain to striatum) [179], structures implicated in the dopamine reward
system.
DRD4 has a highly researched VNTR in exon 3. The tandem repeat is 48bp and codes
for the third cytoplasmic loop of the protein [180]. There can be 2-11 repeats of the 48bp repeat
but the majority of alleles are the 2 (2R), 4 (4R) and 7 (7R) repeats [180]. The 4R allele is most
frequent, at a 64.3% global allele frequency [181]. The 7R allele has a frequency of 20.6%,
31
although this is higher in an American population at 48.3%; the 2R allele has the lowest frequency
at 8.2%, but reaches 18.1% in an Asian population [181]. Other alleles were found to vary widely
between populations [181]. The 4R allele is considered the oldest allele as it is present in 36
studied populations [181]. It is thought that the 7R allele is relatively new as evidenced by to its
strong flanking linkage disequilibrium [182].
The 7R allele produces a functionally diminished receptor for adenylyl cyclase binding
[178]. The 2R and 4R alleles have receptor activity twice that of 7R [178]. Though not statistically
significant, there were possible small differences between the 2R and 4R alleles’ protein activity
[178]. Schoots et al. have suggested that there is also reduced expression of the gene with the
7R allele, potentially caused by an unstable RNA or translational inefficiencies [183]. The bulk of
the research however, has been on the functionality of the encoded receptor.
Adding to the complexity, dopamine can function as a chaperone to improperly folding
DRD4 receptors [184]. Van Craenenbroeck et al. illustrated that the 2R, 4R and 7R alleles have
different sensitivities to this chaperone effect [184]. A continuous relationship was found such that
the 7R experienced twice the up-regulating effect of the chaperone when compared to the 2R
allele [184]. The 4R allele was not significantly different from either and fell between the two in
terms of increased cellular response of the DRD4 receptor produced [184]. The researchers
hypothesized that the 2R allele experienced less up-regulation because the protein efficiently
folds on its own or it is too rigid and doesn’t allow for chaperone assistance [184]. This study
supports the notion that the decreased function of the 7R allele is the result of a mis-folded protein.
1.5.3. Monoamine Oxidase A (MAOA)
Figure 1.4. Genomic structure of MAOA Showing a functional VNTR 1.2kb upstream from the coding region.
32
Located on chromosome Xp11.23, MAOA has 15 exons and spans 60kb [185]; see figure
1.4. There is a promoter with two 90bp repeats [186]. Whether the gene escapes x-inactivation
or not has been a topic of debate, however, recent consensus is that x-inactivation does affect
this gene [187–189].
MAOA catalyzes oxidative deamination, which is the removal of an amine group, from
various biogenic amines, with the production of hydrogen peroxide [190]. There are two forms of
the monoamine oxidase, A and B, and each has different affinities for different substrates [190].
MAOA prefers serotonin but has an affinity to norepinephrine, dopamine and an inhibitor named
clorgyline [191]. The B form mainly catalyzes phenylethylamine, benzylamine and deprenyl [190].
While both forms are found throughout the brain, MAOA is mostly found in the dopamine and
norepinephrine pathways and in a specialized nucleus, called locus coeruleus, in the pons region
[190]. Schildkraut summarized that when MAO inhibitors are used there is a global increase of
norepinephrine in the brain [141], lending support that MAO is important in norepinephrine
pathways.
Studies by Buckholtz et al. found that MAOA dysregulation is linked to a particular
personality profile, such that individuals showed enhanced reactivity to threat cues (harm
avoidance), increased tendency to experience anger, frustration, and bitterness (anger hostility),
and reduced sensitivity to cues that elicit and maintain prosocial behaviour (reward dependence)
[192]. Harm avoidance is noted to have associations with serotonin functions and reward
dependence with norepinephrine [192]. Given the traits found in this study, there is evidence for
neuroticism being influenced by the MAOA gene and physiologically linked to serotonin and
norepinephrine.
MAOA has a functional VNTR about 1.2kb upstream from the coding region that affects
transcriptional activity. The tandem repeat is 30bp and repeats 2, 3, 3.5, 4, or 5 times [191]. There
are two categories that the repeats are typically used; Sabol et al. found in vitro that the 3.5R and
4R are transcribed approximately 2-10 more times than the 3R or 5R depending on the
combination of alleles [191]. The 3.5R and 4R are grouped as high functioning and the 3R and
5R are grouped as low functioning [191]. This was supported by Denney et al. who found similar
transcription rates [193]. In further support of this categorization, Wu et al. tested if MAOA affected
serotonin concentration in the pineal glands of Alzheimer’s patients (serotonin is a precursor of
melatonin, which is reduced in Alzheimer’s patients) [194]. While they did not find a difference
between the melatonin between their two groups they did find that 3.5R and 4R alleles ultimately
33
showed higher MAOA expression in the cells [194]. The VNTR alleles can therefore be reduced
to two categories, high (MAOAH) and low (MAOAL) functioning.
The MAOAH alleles have been linked to depression [195–197]. Dannolowski et al. looked
at MAOA alleles and the coupling of the amygdala with the prefrontal cortex in depressive and
non-depressive brains (the amygdala generates the affect and the prefrontal cortex regulates the
emotion [125]). Dannolowski et al. found that 3.5R and 4R alleles were associated with a reduced
connectivity and that if a carrier was depressed the symptoms were amplified [197]. In connection
to this, Meyer et al. found that MAOA expression was increased by approximately 34% through
the brains of depressed individuals when compared to healthy matched controls [196]. The high
functioning allele does seem to play a role in major depression, by possibly reducing the MAOA
substrate serotonin too drastically.
The MAOAL allele is highly associated with aggression in males [192,195,198]. Early
studies in Maoa knockout mice found that the knockout males became hyper-aggressive [199].
Huang et al. went on to show that MAOAL men had a higher risk of impulsivity if there was a
history of childhood abuse [198], hinting at the possibility of epigenetic effects. Buckholtz et al.
were able to deduce a cognitive consequence by looking at the structural and functional effects
of the MAOAL allele. They found that MAOAL males had decreased anterior cingulate activation
and ventral prefrontal engagement [192]. In particular the perigenual anterior cingulate was
affected which regulates negative amygdala function. They further suggested that MAOAL males
compensate by engaging the ventro-medial prefrontal cortex [192]. Where the MAOAH allele
reduces substrate levels too much, it seems as though the MAOAL allele does not reduce them
enough resulting in an over activated state.
Nishioka summarized in a meta-analysis the various associations for each allele, finding
hypoactive behaviours for MAOAH, such as depression, anxiety and neuroticism [195]. For
MAOAL, they found hyperactive behaviours like impulsivity, aggression, personality conduct
disorder and ADHD [195].
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1.5.4. Sodium Chloride Dependent Transporter (SLC6A4)
Figure 1.5. Genomic structure of SLC6A4 Showing two VNTRs, HTTPLR in the promoter region and STin2 in intron 2.
The sodium chloride dependent transporter gene is located on chromosome 17q11.2,
contains 14 exons and is approximately 31kb in size [200]; see figure 1.5. The gene codes for 5-
hydroxytryptamine transporter (5-HTT), or the serotonin transporter [201]. The sodium/chloride
dependent transporter clears serotonin from the synaptic gap after an action potential in neurons
of the central and peripheral nervous systems [201]. This reabsorbed serotonin is either recycled
or degraded [202]. Changes to 5-HTT can therefore result in prolonged or shortened serotonin
presence in the gap.
There can be a variety of consequences depending on what type of serotonin receptor is
present on the post-synaptic neuron. There are 7 families of serotonin receptors (5-HT1, 5-HT2
…, 5-HT7), with 15 subfamilies [202]. The first 3 families are expressed in the brain [203], and are
therefore of interest to this project. The 5-HT1 family is an inhibitor of adenylyl cyclase, with both
subfamilies usually found on post-synaptic neurons [202]. The 5-HT2 family binds ligands that
increase the hydrolysis of inositol phosphates which in turn increases the cytosolic calcium
concentration [203]. 5-HT3 family receptors bind ligands that control cation channels [202]. Low
5-HTT activity can cause reduced receptor binding in 5-HT1 but increased binding in 5-HT2 and
5-HT3 receptors [202], causing an overall excitatory effect.
In a review conducted by Lesch et al., functional importance of the serotonin transporter
was highlighted in its role as a drug target for serotonin-reuptake-inhibitors [204]. SLC6A4
35
regulates serotonin and deviation in its ability to regulate serotonin in the gap could have
implications for neuronal development [204], since serotonin plays a key role in the differentiation
of serotonergic and glutamatergic neurons [202]. Lesch et al. highlighted research areas of
depression, anxiety, stress response and aggression in connection to SLC6A4 dysregulation
[204].
SLC6A4 is another gene that can be regulated by estrogen levels. McEwn et al. found that
estrogen treatments in women caused a decrease in SLC6A4 mRNA expression [205].
Stratification of sexes in analysis is of importance for this gene.
There are 2 VNTRs located in SLC6A4, the 5HTTLPR VNTR, located approximately 1Kb
upstream from the start codon in the promoter region [206], and the STin2 VNTR located in the
second intron [200]. Lesch et al. characterized the function of the 22bp 5HTTLPR VNTR in a
predominantly male population [206] . The VNTR has two common alleles, the 14 repeat and the
16 repeat, called the short ‘S’ and long ‘L’ alleles, respectively [206]. Lesch et al. found that the L
allele had a frequency of 57% and the S of 43% (Lesch et al., 1996). When the gene was inserted
into lymphoblast cell line, there was steady expression of the L allele, which produced mRNA
amounts 1.4 times higher than the S allele (Lesch et al., 1996). Cells expressing the L allele bound
30-40% more serotonin than the S allele expressing cells (Lesch et al., 1996).
They concluded that the L allele is more highly transcribed, such that it behaves in a
dominant fashion (Lesch et al., 1996). They confirmed the higher transcriptional activity in the L/L
genotypes in a repeated experiment but found S/L and S/S were equivalent [204], which was
confirmed by the Paaver et al. lab [207] This led them to hypothesize the S allele functioned in a
dominant manner. Bradley et al., however, found that there were differences in transcriptional
rates between the three genotypes, hypothesized that the pattern could occasionally appear
dominant due to the influence of another transcriptional regulatory element, although they did not
name any specific ones [208].
The S allele has been implicated in the activation of the amygdala [209,210], the structure
responsible for processing emotions [125]. The right amygdala shows stronger activation in
individuals carrying the S allele [209]; these individuals had greater positive coupling between the
amygdala and the ventromedial prefrontal cortex (which processes risk and fear and inhibition of
emotion) [210]. S-bearing individuals also showed lower grey matter in the anterior cingulate
cortex (emotional processing) and amygdala, which resulted in reduced coupling between the
36
areas [210]. The amygdala is regulated by the anterior cingulate cortex via a negative feedback
loop [210]. Reduced coupling could result in over-activation of the amygdala [210].
Given the relationship of the HTTLPR VNTR to amygdala, neuroticism has been
investigated for association with this polymorphism. Pezawas et al. found that 30% of the variance
in harm avoidance scores could be predicted by how well the amygdala and anterior cingulate
cortex were connected, notably the effect was more pronounced in males [210]. Harm avoidance,
a facet of neuroticism, is positively associated with the S allele. This association with neuroticism
was also found in other studies [211], with anxiety, angry hostility, depression and impulsiveness
as other prominent facets [206].
Conscientiousness has been associated with the S allele, but only in women with no men
showing the affect [211]. The S association with neuroticism is not consistently found but may be
due to mixed sex populations, as positive associations are often in all male or predominantly male
study populations [206,211].
The second VNTR, STin2, has a 16 or 17bp repeating unit. There are three common
alleles, the 9 repeat (9R), the 10 repeat (10R) and the 12 repeat (12R) [212]. The 9R is rare in
the European population with a frequency of 1-3% [213]. The VNTR is a transcriptional enhancer
and binds the YB-1 transcriptional factor [160]. The YB-1 protein has many functions which
include DNA replication and repair, transcription, pre-mRNA splicing and mRNA translation [214].
Higher expression of SLC6A4 has been found with the 12R when compared to the 10R
allele [204,215]. The 10R allele has been found to have lower expression, and one study has
found genotypes, 10R/10R and 10R/12R to be functionally equivalent, hypothesizing that the 10R
alleles acts in a dominant manner [216]. The 9R STin2 allele is not as well researched due to its
low frequency, and therefore has not been functionally characterized, although it may be
associated with unipolar disorder [217].
The 12R allele is notably associated with schizophrenia; Fan et al. conducted a meta-
analysis on SLC6A4 and found a positive association with the presence of the 12R allele and
schizophrenia [218]. While STin2 itself has not been associated with personality, the combined
effect of both HTTLPR and STin2 has. Kazantseva et al. found that S and 12R carriers were
associated with lower sociability related traits, such as extraversion and novelty seeking [219].
This S12R group was additionally associated with increased harm avoidance (Cloninger’s
37
neuroticism [41]) [219]. Upon sex stratification, they found that females who were carriers for both
L and 10R showed lower neuroticism [219].
Ali et al. looked at the contribution of both HTTLPR and STin2 VNTRs to the expression
of SLC6A4. They found no difference in the activity of STin2 12R alone, but an 8 fold increase in
expression with the HTTLPR S allele alone, and reported an impressive 17 fold increase when
both 12R and S allele were present [220]. This relationship could help explain inconsistent results
from studies looking at single VNTRs in the SLC6A4 gene.
1.5.5. Tyrosine Hydroxylase (TH)
Figure 1.6. Genomic structure of TH There is a VNTR located in intron 1 and a missense SNP, rs6356, in exon 2.
The tyrosine hydroxylase gene is located on chromosome 11p15.5 [221], has 14 exons
and spans approximately 8.5 kb [222], see figure 1.6. TH produces the tyrosine hydroxylase
protein that catalyzes the rate limiting conversion of L-tyrosine to 3,4-dihydroxy-L-phenylalanine,
or DOPA, which is a precursor in the synthesis of dopamine and norepinephrine [223]. TH activity
is found in the adrenal glands and brain stem [223].
TH expresses 4 different kinds of mRNA, which differ by the inclusion or exclusion of exon
1 and 2 [222]. TH expressed in the brain have exon 2 deleted and are active in the brain stem
[222,223]. The deletion of exon 2 occurs through a hairpin mechanism, where intron 1 and intron
2 have complementary sequences and interact to leave exon 2 at the top of the hairpin and
excluded from transcription [222].
38
A microsatellite located in intron 1, with a four bp (TCAT) tandem that repeats usually six
to ten times [224], has been researched for potential functional effects. Uniquely, the 10R allele
of this microsatellite has two versions; a perfect (10R) and an imperfect (10iR) repeat [225]. The
10iR allele is missing an adenine in the seventh copy and this variation is more common in the
Caucasian population; the 10R allele is rare [225]. Many studies do not distinguish between the
two alleles, although there seems to be little difference in their overall functional effects [226,227].
The 6R and 7R alleles are thought to be the ancestral alleles, with other longer alleles arising
later [228].
The repeat region’s sequence is very similar to the TRE sequence which recruits
transcriptional factors like the Fos-Jun complex [226], ZNF191 [227,229] and HBP1 [229]. Meloni
et al. found that when the 10R and 10iR alleles were placed upstream from a promoter in vitro,
the expression levels were approximately 9 times greater than when the alleles were absent [226].
Albanese et al. showed that, in vitro, the ZNF191 and HBP1 transcriptional factors exerted
silencing effects on the TH gene, and silencing increased with increasing repeat length [229].
Meloni et al. further refined this relationship to show that ZNF191 can only bind at one site on the
shorter repeats but can bind at two sites in repeats greater than 8R [227].
The extra inhibition could be due to longer repeats causing increased transcription. Wei
et al. found that longer repeats caused increases in serum norepinephrine and serum
homovanillic acid, the end product of dopamine degradation [230]. Specifically, they found the
9R/9R genotype produced the highest norepinephrine serum levels and 10R/10R produced the
highest homovanillic acid serum levels [230]. Zhang et al. also found that longer repeats were
associated with higher levels of norepinephrine [228]. The 10iR allele showed increased basal
and stress-induced heart rates and increased norepinephrine renal excretion when compared to
the 6R allele [228].
Numerous studies have linked alleles of this microsatellite to disorders. The general trend
is that shorter alleles offer protective effects and longer alleles are implicated in disorders. The
8R-10R have been associated with schizophrenia [227,231], suicide attempt [232] and bipolar
disorder [227]. The 7R allele has been associated with protective effects against smoking [233].
The microsatellite has also been associated with aging and personality. De Benedicts et
al. found that male centenarians were less likely to carry a longer allele, although they classified
8R as a short allele [234]. Persson et al. found that the 8R allele was associated with higher
39
scores on the neuroticism scale, and had significant associations in the angry hostility and
vulnerability facets [235]. The 8R-10R alleles for this VNTR may generate increased
norepinephrine levels in the brain from over active TH production, which may have potential
detrimental effects.
1.6. Thesis Objective
Healthy aging is a complex phenotype with many contributing factors. Here we
concentrate on the genetic contributions to the role of personality in achieving a long and healthy
life. Personality is a predictive pattern of behaviour, attitude and emotional responses that can be
used as a predictor of health. Personality is a heritable trait that is governed by neuro-chemical
and physiological processes that ultimately affects behaviour and decision styles in individuals.
Genes that encode neurotransmitters, their receptors or affect processes in their metabolism are
candidates for study on the effects of personality influencing behaviour and lifestyle. The objective
of this project is to determine if genetic variation in personality-related genes is associated with a
healthy aging phenotype, specifically whether genetic variants in genes involved in
neurotransmission or that underlie personality disorders influence healthy aging.
40
Chapter 2. Methods
2.1. Study Participants
The joint Clinical Research Ethics Board of the British Columbia Cancer Agency and the
University of British Columbia approved this study and all subjects were enrolled through signed
informed consent.
The Super-Seniors study recruited cases and controls between January 2004 and August
2007 in the Greater Vancouver Lower Mainland, British Columbia (BC). Inclusion criteria for
Super-Senior participants were that they be 85 years of age or older at the time of enrollment and
self-report that they have never been diagnosed with Alzheimer disease, diabetes, cardiovascular
disease, cancer or major pulmonary disease. Subjects were identified through the use of Ministry
of Health lists, Insurance Corporation of British Columbia (if they renewed their driver’s license in
the past three years) and as volunteers after press coverage. Controls are a population-based
group from the same geographic area, ranging from 41 to 54 years of age. They were identified
solely from Ministry of Health lists, and were not selected for health or disease status. The
response rate in both groups was approximately 60%.
DNA was purified from subjects’ blood samples using the PureGene DNA isolation kit,
following the manufacturer's instructions. An interviewer visited each Super-Senior at his or her
home and administered questionnaires regarding overall wellness (health, mental, physical and
occupational). A total of 462 (67.3% female) European-ancestry Super-Seniors and 418 (60.0%
females) European-ancestry controls have been genotyped for this study (880 samples, 63.9%
female). European ancestry was defined as having four grandparents of European ethnicity
though self-reporting. Individuals who were unsure of one or more of their grandparent’s ethnicity
have been excluded from this analysis.
2.2. Literature Search and Gene Selection
A literature search was conducted for gene selection. Categories for the literature search
included, researching a longevity personality profile, genes involved in personality, genes involved
personality disorders, genes involved in major psychiatric disorders, and genes identified in
41
genome wide association studies (GWAS) of personality traits. Web of Science was used to
search for the following criteria. The ‘very old and very healthy’ personality profile was researched
by searching for search terms “personality”, “aging”, “healthy aging”, “oldest-old” and “longevity”.
Searches were then conducted for genes related to personality traits associated with very old
healthy individuals. Search terms included “association”, “polymorphism”, “conscientiousness”,
“openness”, “emotional stability”, “neuroticism”, “extraversion”, “extroversion” and “gene*”.
Potential candidate genes of interest could also be associated with maladaptive
personality processes; therefore personality disorders were identified through the American
Psychiatric Association’s Diagnostic and Statistical Manual, 5th edition (DSM-V). Distinguishing
characteristics of personality disorders are “enduring pattern[s] of inner experience and behaviour
that deviates markedly from the expectations of the individual’s character” [36] and are only
classified when the traits are “inflexible, maladaptive, persist[ent], and cause significant functional
impairment or subjective distress” [36]. Search terms used in Web of Science to identify genes
were “paranoid personality disorder”, “schizoid personality disorder”, “schizotypal personality
disorder”, “antisocial disorder”, “borderline personality disorder”, “histrionic personality disorder”.
“narcisisti*”, “avoidant personality disorder”, “dependent personality disorder”, “obsessive
compulsive personality disorder”, “association”, “polymorphism”, and “gene*”.
Major psychiatric illnesses including depression, schizophrenia, obsessive compulsive
disorder, and anxiety disorder were consequently searched, as the DSM-V notes that these can
be difficult to distinguish when making a diagnosis of a personality disorder. Many personality
disorders can be thought of as on a spectrum with major mental illness, such as avoidant
personality disorder and general anxiety [36]. Searches for major mental health illnesses, such
as schizophrenia or depression, produce an enormous amount of results (over 1000), so searches
were limited by the search term “personality”. A Web of Science search was conducted for the
following terms “depression”, ‘schizophrenia”, “obsessive compulsive disorder”, “anxiety
disorder”, “bipolar disorder”, “association”, “polymorphism”, and “gene*”.
GWAS studies were looked at for potential genes of interest. Search terms in Web of
Science used were “genome wide association studies”, “GWAS”, “personality”, and “personality
disorder”. The candidate list was then refined based on biological plausibility and frequency of
the gene in relation to the search criteria. Searches in Web of Science were conducted to expand
association studies and identify SNPs for each candidate gene. Genes with low search yields and
42
no associations found were dropped from the list, such that the genes with a higher frequency of
published associations were included in the candidates selected.
2.3. SNP Selection
SNP Selection was completed using three methods. First, HapMap’s Tagger program
version 3, release 2, Northern European Utah population (CEU) was used to choose tagSNPs for
autosomal genes. For X-linked genes, version 2, release 21, CEU data was used. Selection
criteria for the tagSNPs were a minor allele frequency of at least 10%, an r2 of 0.8, and a minimum
distance of 300 bp between SNPs.
In addition to tagSNPs, SNPs that were found to be associated with a personality trait in
more than one paper during the literature search were considered. Inclusion criteria consisted of
the SNP being positively associated with a personality trait in more than one paper and having a
minor allele frequency of at least10%. Lastly, SNPs that reached genome wide significance and
had a minor allele frequency of at least 10% in the GWAS studies were also included.
2.4. SNP Genotyping
SNP genotyping was done at the McGill University and Génome Québec Innovation
Centre (Montreal) using the Sequenom MassArray Method. The Sequenom MassArray is being
utilized for its multiplexing ability and minimal assay setup costs. The technology measures the
mass of extended primers to genotype each single nucleotide allele [236]. Plates were prepared
in accordance to the Innovation Centre’s protocol of 30uL of 40 ng/uL of template submitted for
880 samples. Ten plates, each containing 96 samples, control samples, or blanks were shipped
on dry ice to the Innovation Centre. The SNPs were randomly split into two sets as the submission
of 44 SNPs exceeds the number of markers that can be genotyped in one set (34). Genotyping
results were downloaded through the Centre’s online system.
2.5. SNP Analysis
SNP filtering and analysis was conducted in Golden Helix SVS suite 8 (SVS8), with
planned sex stratification. The following filtering criteria were applied to the genotyped SNPs.
43
First, SNPs that failed genotyping were removed from the data set. Second, samples were filtered
for those with low genotype call rates (below 90%), outliers in the autosomal heterozygosity rate,
and presumed duplicate samples with identical genotypes. Lastly, SNPs were filtered for low call
rates (below 95%), low minor allele frequency (below 1%), and Hardy Weinberg Equilibrium
(HWE) failure in the control group.
Uncorrected Pearson Chi-squares were conducted in SVS8 for autosomal candidate
genes and for the female subgroup for X-linked makers. Male subjects were considered
hemizygous at X-linked genes, contributing only one allele to non-autosomal allele counts.
Hemizygote data cannot be analyzed in SVS8, therefore X-linked markers for the total study
sample and the male subgroup were analyzed in R Studio v 0.98.1103 with R v 3.2.2. Table 2.1
summarizes which programs were used for analysis. The false rate of discovery procedure (FDR),
Benjamini-Hochberg [237], was used for the multiple testing corrections in R. The p-value cutoff
was set to 0.05.
Table 2.1. Summary of program choice for genes and markers
Marker Group Autosomal genes Non-Autosomal genes
SNP Total Sample SVS8 R
Female SVS8 SVS8
Male SVS8 R
VNTR Total Sample R R
Female R R
Male R R
2.6. VNTR Genotyping
Polymerase Chain Reaction (PCR) amplification was conducted at the Michael Smith
Genome Sciences Centre (MSGSC). PCR Primer pairs flanking VNTRs of interest were chosen
from the literature and are summarized in Table 2.2. Primers were optimized initially without dyes
(for reasons related to cost), and Mendelian segregation checked using reference families from
Centre d’Etude du Polymorphisme Humain (CEPH). PCR products were run on a 2.5% agarose
gel for separation of the fragment sizes and visualization. Primer pairs showing the expected
sized PCR products were ordered with dye attached and checked again. Four different fluorescent
44
dyes are used to differentiate fragments produced using different primer pairs; this allowed
pooling of all PCR products from all VNTRs of each subject for fragment size analysis. Table 2.2
lists the optimized PCR conditions and dyes used. Each of the 5 VNTRs was PCR amplified
separately in the 880 samples, in ten 96-well plates (50 plates total). PCR products were checked
on an agarose gel to confirm successful amplification. Plates were then pooled by sample, and
the 10 plates of pooled samples were transported to the Centre for Molecular Medicine and
Therapeutics (CMMT), Vancouver for fragment size analysis.
Table 2.2. Primers and PCR conditions used for amplification of VNTRs
Gene Dye PCR Protocol Forward Reverse Reference
MAOA NED 94°C 2 m denaturation, followed by 30 cycles of 94°C 30s, 58°C 5s, 68°C 1 m and 68°C for 5m extension
5’- ACA GCC TGA CCG TGG AGA AG -3’
5’- GAA CGG ACG CTC CAT TCG GA -3’
[238]
DRD4 6-FAM
94°C 2 m denaturation, followed by 35 cycles of 94°C 30s, 62°C 5s, 68°C 1 m and 68°C for 5m extension
5′-GCG ACT ACG TGG TCT ACT CG-3′
5′-AGG ACC CTC ATG GCC TTG-3′ [239]
HTTLPR NED 94°C 2 m denaturation, followed by 30 cycles of 94°C 30s, 60°C 5s, 68°C 1 m and 68°C for 5m extension
5’-GGC GTT GCC GCT CTG AAT GC-3’
5’-GAG GGA CTG AGC TGG ACA ACC AC-3’
[240]
SIN2 HEX 94°C 2 m denaturation, followed by 35 cycles of 94°C 30s, 52°C 15s, 68°C 1 m and 68°C for 5m extension
5’-GGT CAG TAT CAC AGG CTG CGA GTA G-3’
5’-TGT TCC TAG TCT TAC GCC AGT GAA-3’
[241]
TH 6-FAM
94°C 2 m denaturation, followed by 35 cycles of 94°C 30s, 61°C1 5s, 68°C 1 m and 68°C for 5m extension
5’-CAG CTG CCC TAG TCA GCA C-3’
5’-GCT TCC GAG TGC AGG TCA CA-3’
[224]
An Applied Biosystems 3130 Genetic Analyzer was used to detect peak sizes of the PCR
products via fluorescence-based capillary electrophoresis. One µL of pooled PCR product was
loaded into the analyzer, which automates the processes of polymer loading, sample injection,
separation, detection and fragment size analysis.
Results from the ten plates were loaded into GeneMapper V5 and alleles were called
based on the following criteria: peak heights greater or equal to 150 for the NED and HEX dyes
and 50 for the 6-FAM dyes, peaks twice as high as the average baseline noise, and peaks in the
45
expected size ranges (as specified by the user). GeneMapper’s standard settings for peak quality
were used.
2.7. VNTR Analysis
No samples were excluded based on call rates, as VNTR data was genotyped until
completion. Additional sample quality checks were not performed on VNTR data, as there were
too few markers to test for identical genotypes or to test for excess heterozygosity. Markers were
checked for low call rates and HWE. HWE was checked using the full Bayesian significance test
(FBST), which is adapted for HWE by Lauretto et al for multiallelic data [242]. HWE was tested
only in controls and for X-linked genes only in female controls. Before HWE testing in the X-linked
genes, allele frequencies were compared between males and females with a Fisher’s Chi-square
test.
Rare alleles were of interest and were not excluded. Instead, rare alleles with frequency
less than 10% were grouped into a “rare alleles” category, for analysis of each VNTR.
Allele frequency data was used for Chi Squared analyses. Males were assigned a status
of hemizygous for their X-linked VNTR allele. Analysis was run in R Studio v 0.98.1103 with R v
3.2.2. As the data is multiallelic, Galta et al’s score test was used [243]. The score test functions
much like a Pearson’s chi-square, however, it gives more weight to common makers. The score
test was used as it has greater power in moderate sample sizes to detect true associations when
the effect of the association does not strongly affect the phenotype. The p-value cutoff was set to
0.05.
46
Chapter 3. Results
3.1. Literature Search and Gene Selection Results
The literature search was conducted in five parts: 1) personality profile of the very old and
very healthy, 2) genes involved in personality, 3) genes involved in personality disorders as
defined by the DSM-V, 4) genes involved in major psychiatric disorders, 5) genes identified as
associated with personality in GWAS studies.
1) Search results revealed specific personality traits for the very old [79,81,84,244–247]
and very healthy [248], where longevity is associated with individuals high in conscientiousness,
extraversion and openness, and low in neuroticism. 2) Searches for genes involved with
personality resulted in a total of 42 candidate genes. 3) Search criteria for genes involved in
personality disorders found eight additional genes. 4) No additional genes were found by looking
at major psychiatric disorders with the previously described criteria. 5) No genes were identified
in the GWAS literature searches; however, three SNPs associated with personality were located
in close proximity to known genes, RAS1A and KATNAL2.
The candidate list was refined to 32 genes based on biological plausibility and frequency
of the gene in prior searches (see Table 3.1). Twenty one candidate genes with low literature
yields were dropped and six more were dropped for having largely negative association results.
This resulted in five genes, DRD4, TH, COMT, MAOA, and SLC6A4.
47
Table 3.1. Summary of potential candidate genes found through literature search, including polymorphisms and results of association studies
Number of Papers that show (Yes) or do not show (No) association
Gene Polymorphism Yes No Reference
1 5HT2A rs6313 3 1 [249–252]
rs6311 2 1 [241,253,254]
rs4941573 1 0 [249]
2 ADRA2 rs1800544 3 2 [255–259]
rs1800545 1 1 [256,259]
rs7682295 1 0 [260]
rs521674 1 0 [261]
rs602618 1 0 [261]
rs583668 1 0 [256]
rs553668 1 0 [256,259]
3 BDNF rs6265 8 2 [262–271]
rs11030102 1 0 [272]
4 COMT rs4680 13 15 [146,241,255,262,273–296]
rs4818 1 2 [274,284,297]
rs4633 1 1 [283,284]
rs737866 1 1 [276,298]
rs737865 1 0 [283]
rs9332377 1 0 [283]
rs6269 1 0 [284]
rs165599 0 4 [262,276,283,287]
rs5993883 0 2 [276,283]
rs4646312 0 2 [276,299]
5 DAT/
SLC6A3
VNTR (3 prime end) 12 4 [146,239,241,250,255,277,300–309]
rs27072 2 1 [304,305,310]
rs6347 1 3 [255,299,310,311]
rs403636 0 2 [301,310]
VNTR (intron 8) 0 1 [304]
6 DRD2 rs1800497 16 7 [239,273,296,305,308,312–328]
rs6277 4 2 [280,296,300,314,320,329]
rs1799732 4 2 [280,314,320,330–332]
48
Number of Papers that show (Yes) or do not show (No) association
Gene Polymorphism Yes No Reference
rs1800479 1 1 [277,333]
rs1079597 1 0 [334]
rs6276 1 0 [320]
rs1076560 1 0 [335]
7 DRD4 rs1800955 9 7 [179,278,279,296,332,334,336–345]
VNTR 8 8 [179,239,275,277,301,302,328,337,341,342,345–350]
rs936461 2 0 [328,336]
rs747302 1 6 [179,334,336–338,343,345]
rs3758653 1 4 [276,299,311,336,351]
rs916457 1 1 [336,351]
rs7124601 1 0 [351]
rs916455 0 3 [299,336,340]
rs752306 0 2 [351,352]
8 MAOA VNTR 13 8 [146,195,238,241,275,277,289,318,323,353–364]
rs6323 1 2 [354,365,366]
rs979606 1 0 [367]
9 NET1 rs998424 2 3 [255,274,368–370]
rs2242447 2 2 [274,297,368,369]
rs3785157 2 1 [255,276,369]
rs3785143 1 5 [261,276,368,369,371,372]
rs36009 1 2 [260,276,368]
rs11568324 1 1 [368,372]
rs5558 1 1 [369,373]
rs36020 1 0 [261]
rs36029 1 0 [261]
rs28386840 1 0 [368]
10 SERT/
SLC6A4
5-HTTLPR VNTR 26 18 [146,240,252,267–269,282,289,304,321,325,374–406]
STin2 VNTR 7 10 [240,241,277,304,370,378,381,382,387,389,394,395,397,399,400,407,408]
rs25531 2 2 [240,280,313,385]
rs140700 1 5 [276,391,395,409–411]
49
Number of Papers that show (Yes) or do not show (No) association
Gene Polymorphism Yes No Reference
rs6354 1 4 [240,276,391,395,409]
rs2020942 1 3 [240,382,395,410]
rs140701 1 3 [240,382,395,397]
rs1042173 1 3 [240,276,382,410]
rs2020936 1 2 [276,391,397]
rs4583306 1 2 [276,382,410]
rs3794808 1 3 [240,276,382,397]
rs16965628 1 1 [240,410]
rs6355 1 1 [240,391]
rs4325622 1 1 [240,382]
rs25532 1 0 [240]
11 TH TCAT VNTR 3 3 [235,348,412–415]
rs6356 2 3 [387,416–419]
rs10770141 1 0 [420]
12 5HT1A rs6295 1 2 [275,421,422]
rs1800044 1 0 [373]
13 5HT1B rs6296 2 3 [383,422–425]
14 5HTR2C rs6318 2 0 [407,426]
rs3813928 1 0 [407]
rs3813929 1 0 [407]
rs518147 1 0 [407]
15 ALDH2 rs671 4 0 [317,356,358,427]
16 ANK3 rs10994336 1 0 [428]
17 CACNAC1 rs1006737 1 0 [428]
18 CRHR1 rs110402 1 0 [429]
19 DARPP-32 rs907094 1 0 [430]
20 DRD3 Ba1I 4 1 [404,431–434]
rs6280 2 1 [264,292,435]
21 GABRA2 rs279871 1 0 [436]
rs79867 1 0 [436]
22 GABRA6 rs3219151 1 0 [267]
23 NOS1 VNTR 1 0 [437]
rs7298903 1 0 [438]
50
Number of Papers that show (Yes) or do not show (No) association
Gene Polymorphism Yes No Reference
24 NRG1 rs3924999 1 0 [439]
rs10503929 0 1 [439]
25 NTRK2 NR 84515449 1 0 [440]
rs993315 1 0 [440]
rs10780691 1 0 [440]
rs7170215 1 0 [272]
rs11073742 1 0 [272]
26 OPRK1 36 G>T 1 0 [441]
27 OPRM1 Asn40Asp 0 1 [442]
28 p250GAP rs2298599 1 0 [443]
29 SNAP-25 rs1051312 1 0 [444]
rs3746544 1 0 [444]
30 TPH1 rs1800532 0 2 [381,445]
31 TPH2 rs4570625 1 1 [426,446]
rs10784941 1 0 [426]
rs2171363 1 0 [426]
32 ZNF804A rs1344706 2 0 [447,448]
rs7597593 1 0 [448]
3.2. SNP Selection Results
Table 2.2 shows a list of the tagSNPs selected. From HapMap’s Tagger program, 29 SNPs
were chosen to represent the five final candidate genes. From the literature, two more SNPs were
added and two others were already included among the tagSNPs. Three SNPs were found in the
GWAS searches and were included in the list. In total, 34 SNPs were chosen.
51
Table 3.2. SNPs Selected to Represent the Five Final Candidate Genes
Gene (CHR) SNP MAF (%) Method of Selection SNP Changes and Genotyping Outcome
RAS1A (5) rs1477268 22 GWAS
rs2032794 16 GWAS
KATNAL2 (18) rs2576037 43 GWAS
COMT (22) rs2020917 23 Tagger
rs740601 38 Tagger
rs5748489 33 Tagger
rs933271 39 Tagger
rs4680 39 Tagger/Literature search
rs9332377 16 Tagger
rs5993883 48 Tagger
rs4646316 25 Tagger
rs174696 44 Tagger
rs165815 34 Tagger
DRD4 (11) rs11246226 48 Tagger
rs11246228 40 Tagger
rs3758653 24 Tagger
rs1800955 37 Literature search Failed validation
MAOA (X) rs3027456 26 Tagger
rs3027450 11 Tagger
rs5905512 49 Tagger Failed call rate
rs1799836 43 Tagger
SERT (17) rs6354 21 Tagger
rs9303628 36 Tagger
SERT (17) rs140701 49 Tagger
rs7214248 29 Tagger
rs6505165 48 Tagger
rs4251417 11 Tagger
rs25531 11 Literature search Failed design
TH (11) rs7483056 48 Tagger
rs10840491 15 Tagger
rs6356 42 Tagger/Literature search
52
Gene (CHR) SNP MAF (%) Method of Selection SNP Changes and Genotyping Outcome
rs10840489 15 Tagger Failed design, replaced with rs1544325
rs10743152 30 Tagger
rs11042978 49 Tagger
3.3. SNP Genotyping Results
At the Innovation Centre, the 34 SNPs were tested in silico for their ability to support SNP
assay development. They were done in two batches, starting with an initial set of 30 SNPs. In
this set, SLC6A4 rs25531 and COMT rs5748489 failed design and were removed from the list.
COMT rs5748489 was replaced with a SNP in high linkage disequilibrium (LD) with it, rs1544325.
No SNP in high LD with SLC6A4 rs25531 was available. This set of 29 SNPs was then physically
validated, through testing of the SNP panel on one plate of study samples. Two markers were
identified as problematic; DRD4 rs1800955 failed testing, and COMT rs4680 was judged as ‘might
fail in production’. Since no SNPs were in linkage disequilibrium with these, they were left
unchanged in the hope that the assays would perform better than predicted. A higher
concentration of oligonucletides was added into the pool to assist reaction with rs4680. After
genotyping of these SNPs in study samples, four SNPs did not have a call rate of at least 95%
(TH rs11042978, 94%; KATNAL2 rs2576037, 93%; COMT rs933271, 94%; COMT rs4680, 91%)
and DRD4 rs1800955 failed to genotype as predicted. The four SNPs were submitted again in
the second set. No SNPs in the second set failed design, validation or production, or failed to
meet the call rate cutoff.
3.4. SNP Quality Control Results
The two datasets received from the Innovation Centre were merged into one list of 32
successfully genotyped SNPs, then filtered for quality. Figure 3.1 displays a flowchart of data
quality checks. Blank and control samples were checked for no genotype and identical
genotypes, respectively, before removal from the dataset. Twenty-two samples that did not meet
a genotype call rate of greater than 90% were excluded (12 Super-Senior and 10 control
samples). No samples were removed for excess heterozygosity or for having unexpected identical
53
genotypes. One SNP, MAOA rs5905512, did not meet a minimum SNP call rate of 95% (94%)
and was excluded. A total of 858 samples (62.6% female) and 31 SNPs remained after data
quality control.
Figure 3.1. Quality control for SNPs and VNTRs
54
* No samples were dropped from VNTR for low call rates as any failed samples were repeated.
3.5. VNTR Genotyping Results
Upon PCR amplification, the fragment sizes that define VNTR alleles showed Mendelian
segregation in CEPH family samples, as exemplified in figure 3.2. Mendelian segregation was
tested and confirmed in one family for each VNTR; in each case allele segregation was
Mendelian.
Figure 3.2. CEPH Family #1341 showing Mendelian Segregation of the DRD4 VNTR
GeneMapper software converts fragment sizes for each VNTR allele in each sample to
genotypes based on size ranges (bins) input by the user. An example of the allele bins for DRD4
is shown in figure 3.3. GeneMapper was run iteratively three times, the first time with initial bin
estimates, the second involved refinement of the bins, manual selection of the alleles and
identifying missing data, and the third included repeated samples and resulted in a complete
VNTR genotypes dataset.
55
Figure 3.3. DRD4 VNTR allele bins This individual is homozygous for the 4R allele. Bins are represented as grey columns; their widths reflect expected peak size ranges. Approximate peak sizes and bin names are shown. Peaks that fall in these size ranges, and meet minimum peak height and noise to signal ratios are called as VNTR alleles.
The first GeneMapper run showed one sample failing to size and two samples were
missing. There were 1999 results requiring visual check or for which the allele required manual
calling. The first run only included two bins for the two most frequent alleles of each VNTR;
additional allele bins were calculated and manually added into the program. The alleles of each
VNTR marker were named according to the number of repeats of each VNTR, in a manner
consistent with the literature.
GeneMapper was run a second time, after which 551 alleles were uncalled and 882 alleles
required a visual check. The majority of these non-calls and checks were due to peaks that did
56
not meet the calling criteria. Examples of reasons for failed calling and manual selection of peaks
are described below.
Most failed calls resulted from peaks failing the peak to noise ratio cutoffs, peaks too small
to detect, or too many apparent peaks due to spectral pull-up. Spectral pull-up occurs when the
signal from one dye is very strong and causes the other dyes in the same lane to show additional,
false, peaks the same size as the peaks of the first dye (Applied Biosystems 2011). The majority
of alleles needing a visual check were due to samples with uneven concentrations of the
fragments in the pooled sample. If one or two of the fragments in the pooled sample was at a
higher concentration than the rest, its peak was much stronger compared to the other fragment
peaks. GeneMapper flags such samples for a visual confirmation, as the smaller peaks meet the
minimum height requirement but fail signal to noise ratio. Alternatively these strong signals
occasionally caused spectral pull-up. Spectral pull-up was easily identified, however, as it
coincided with expected intervals of the size marker dye, or with other very strong fragments. See
figure 3.4, A and C for examples of common GeneMapper genotype calling issues.
When having to manually select a peak, the following set of rules was implemented: 1) for
a genotype to be considered bi-allelic, both peaks needed to be of the same relative strength. In
figure 3.4 A there are two peaks of equal strength at 7R and 9R so there are two alleles. In figure
3.4 B the apparent allele at 9R is much smaller than the one called at 12R; it is not a real allele
as the peaks are not of similar strengths. 2) Clusters of peaks, caused by excessive noise or
spectral pull-up, would occasionally be greater than the set minimum peak height and cause an
allele to be falsely called at that site. These were flagged for visual inspection, after which the
largest peaks were left and smaller ones deleted (figure 3.4 B). 3) Peaks caused by spectral pull-
up were also flagged for inspection and deleted (figure 3.4 C). 4) Weak peaks not meeting the
minimum height were called only if there were one or two peaks at least twice the height of the
noise (figure 3.4 D). 5) If there were more than two peaks at the same height and no visible
spectral pull-up, all peaks were deleted and the sample was considered failed.
57
Figure 3.4. Examples of common calling problems in GeneMapper v5 The x axis indicates peak size in bp; the y axis indicates the strength of the peak signal. A) Strong signal requiring a visual check due to high concentration of sample. Here TH peaks at 7R and 9R are very strong causing spectral pull-up. B) Deletion of a false peak based on strength of signal. The double 9R peak for SLC64A’s STin2 VNTR is much smaller that the peak at the 12R position. Because true peaks are of a comparable size, the apparent 9R peak was not called as an allele. C) A false peak due to spectral pull-up at a nearby marker site. MAOA shows a real peak at 4R but the strong red signal to the left is caused by marker dye pull up at the nearby 300bp site. D) A weak peak that did not meet the minimum height. This DRD4 peak almost meets the criteria at the 4R site and requires a visual check, the height of the peak is double that of the noise; manual calling of the peak was needed.
After manual selection, 171 fragments could not be called and were identified as missing
data. There were 6 samples that failed in all VNTRs (166, 469, 470, 473, 481, C541). Inter-plate
variability for allele calls was calculated for quality control, using the mean of the allele sizes and
identifying any alleles outside 1 standard deviation. Allele averages were checked to ensure the
alleles had the size differences observed in the literature.
58
Of note, samples on plates 5 and 6 displayed consistently 1-3 base pairs larger allele sizes
when compared to allele sizes determined from other plates, frequently showing alleles greater
than 1 standard deviation from the average. Samples showing these increased sizes were
checked visually again and showed a consistent pattern across all VNTR alleles. Figure 3.5 best
illustrates this for HTTLPR, showing some fragments to the right side of the bins. Other plates did
not show alleles deviating more than 1. Plates 5 and 6 were the first two plates to be sent to
CMMT, and it is likely that this shift is caused by a calibration problem. Upon visual check of the
gels, the fragment sizes for these plates were within the expected ranges; therefore the alleles
called in GeneMapper are correct. Alleles for plates 5 and 6 were left as they were originally
called.
A total of 239 failed samples were re-plated and run again on the PCR. These samples
were sent, unpooled, to CMMT for fragment analysis. Data from these samples were added to
the dataset in GeneMapper upon completion for the final allele calling run. All samples had two
alleles called for each of the five VNTRs, with the exception of two DRD4 samples, consistent
with the high heterozygosity generally seen in VNTRs. The final bin sets used in GeneMapper are
displayed in figure 3.5; average allele sizes with standard deviation given in table 3.3.
59
Figure 3.5. Bin sets used in GeneMapper v5 to call each VNTR
Bins are shown as grey columns, with fragment sizes (bp) along the x axis and peak strength along the y axis. See table 3.3 for average peak size.
60
Table 3.3. Average VNTR allele sizes and standard deviations
VNTR DRD4 HTTLPR MAOA STin2 TH
Allele Avg bp
Size (SD)
Allele Avg bp
Size (SD)
Allele Avg bp
Size (SD)
Allele Avg bp
Size (SD)
Allele Avg bp
Size (SD)
2R 369.77 (0.46)
S 483.09 (0.73)
2R 287.47 (0.18)
9R 250.98 (0.16)
5R 237.33 (0.12)
3R 416.67 (0.64)
L 525.77 (0.90)
3R 316.95 (0.27)
10R 268.33 (0.18)
6R 241.31 (0.15)
4R 464.58 (0.64)
17R 546.52 (-)
3.5R 334.83 (0.39)
12R 301.72 (0.15)
7R 245.31 (0.13)
5R 512.66 (0.52)
4R 346.70 (0.29)
13R 319.29 (-)
8R 249.33 (0.13)
6R 560.24 (0.39)
5R 376.48 (0.13)
9R 253.36 (0.14)
7R 607.96 (0.46)
10R 256.38 (0.24)
8R 655.80 (0.44)
9R 703.45 (-)
10R 749.31 (-)
11R 796.58 (-)
3.6. VNTR Quality Control Results
Results of quality control checks can be seen in figure 3.1. All 880 samples (63.9% female)
were genotyped with the exception of two DRD4 VNTR samples. Male and female frequencies
did not differ in MAOA (p-value 0.91), and no deviation from HWE was detected in the female
control group (p-values: DRD4 1.00; HTTLPR 0.80; MAOA 0.59; STin2 0.99; TH 0.62)
Rare alleles were identified in the data but at very low counts. Analyzing low-count alleles
would not be meaningful, so low count alleles were grouped together until the ‘rare alleles’
category was sufficiently large enough to support analysis. Grouping and analysis was performed
61
through a statistical code in R, created by Andy Leung. Groups required a minimum of five
samples, starting with the smallest counts, adding additional allele groups until a count of at least
five was achieved for both Super-Senior group and the controls.
3.7. Association Results
SNP data included 858 samples, of 450 Super-Seniors and 408 controls. Stratification
produced a female group with 305 Super-Senior and 246 controls, and a male group with 145
Super-Seniors and 162 controls. The VNTR data maintained the original sample size of 880, with
462 Super-Seniors and 418 controls. Once stratified, there were 311 Super-Senior females, 251
female controls, 151 male Super-Seniors and 167 male controls.
Table 3.4 shows the results of the uncorrected association tests. The following rare allele
groups were created for the VNTR data. The DRD4 VNTR had low counts for the 6R, 8R, 9R,
10R and 11R alleles, they were combined into one rare allele group for the combined and female
analysis. The males had fewer counts and did not show alleles for 10R or 11R. The rare allele
DRD4 VNTR group for the male-only analysis contained 3R, 5R, 6R and 8R.
MAOA’s rare allele group contained 2R and 5R for the combined and female groups, and
3R, 3.5R, 5R with no 2Rs available for the males. SLC6A4’s HTTLPR did not require a rare allele
group but STin2 did. Rare alleles for Stin2 for combined and females included 9R and 13R and
the males did not require one although they had zero counts for 13R. Lastly TH’s VNTR’s 5R and
8R alleles were pooled into the rare allele group for all three analyses.
Prior to multiple testing corrections there are three SNPs of interest, showing p-values
around the 0.05 threshold. COMT rs174696 in the combined sample shows a weak association
with an odds ratio of 1.30, CI 1.03-1.65. Upon stratification by sex, males show two very weak
associations; COMT rs933271 (OR 1.46, CI 0.98-2.19) and to the DRD4 VNTR rare allele group
(OR 1.96, CI 1.00-3.98); although these do not meet significance cut offs.
Once the FDR correction is applied these associations are not statistically significant (table
3.5). No associations were found between variants in these genes between the Super-Seniors
and the control group.
62
Table 3.4. Results from Pearson Chi-Square tests of SNP and Score Tests of VNTRs, for the Combined Sample and when Stratified by Sex
Combined Female Male
Gene Marker Allele Super-Senior Control Allele Super-Senior Control Allele Super-Senior Control
COMT rs1544325 T 406 (45.1) 367 (45) T 271 (44.4) 225 (45.7) T 135 (46.6) 142 (43.8)
C 494 (54.9) 449 (55) C 339 (55.6) 267 (54.3) C 155 (53.4) 182 (56.2)
p-value 0.96 0.66 0.50
COMT rs165815 G 130 (14.4) 112 (13.7) G 85 (13.9) 67 (13.6) G 45 (15.5) 45 (13.9)
A 770 (85.6) 704 (86.3) A 525 (86.1) 425 (86.4) A 245 (84.5) 279 (86.1)
p-value 0.67 0.88 0.57
COMT rs174696 C 211 (23.5) 156 (19.1) C 140 (23.1) 93 (18.9) C 71 (24.5) 63 (19.4)
T 685 (76.5) 660 (80.9) T 466 (76.9) 399 (81.1) T 219 (75.5) 261 (80.6)
p-value 0.03* 0.09 0.13
COMT rs2020917 T 243 (27) 240 (29.4) T 156 (25.6) 141 (28.7) T 87 (30) 99 (30.6)
C 657 (73) 576 (70.6) C 454 (74.4) 351 (71.3) C 203 (70) 225 (69.4)
p-value 0.27 0.25 0.88
COMT rs4646316 T 225 (25) 210 (25.7) T 160 (26.2) 130 (26.4) T 65 (22.4) 80 (24.7)
C 675 (75) 606 (74.3) C 450 (73.8) 362 (73.6) C 225 (77.6) 244 (75.3)
p-value 0.73 0.94 0.51
COMT rs4680 C 442 (49.2) 399 (48.9) T 301 (49.5) 248 (50.4) C 135 (46.6) 155 (47.8)
T 456 (50.8) 417 (51.1) C 307 (50.5) 244 (49.6) T 155 (53.4) 169 (52.2)
p-value 0.89 0.77 0.75
COMT rs5993883 T 436 (48.7) 396 (48.9) T 291 (48) 241 (49.4) T 145 (50) 155 (48.1)
G 460 (51.3) 414 (51.1) G 315 (52) 247 (50.6) G 145 (50) 167 (51.9)
p-value 0.92 0.65 0.65
COMT rs740601 C 360 (40) 343 (42) C 250 (41) 214 (43.5) C 110 (37.9) 129 (39.8)
A 540 (60) 473 (58) A 360 (59) 278 (56.5) A 180 (62.1) 195 (60.2)
63
Combined Female Male
Gene Marker Allele Super-Senior Control Allele Super-Senior Control Allele Super-Senior Control
COMT rs740601 p-value 0.39 0.40 0.63
COMT rs9332377 A 129 (14.4) 139 (17) A 88 (14.5) 83 (16.9) A 41 (14.2) 56 (17.3)
G 765 (85.6) 677 (83) G 518 (85.5) 409 (83.1) G 247 (85.8) 268 (82.7)
p-value 0.14 0.29 0.30
COMT rs933271 C 210 (25) 194 (24.3) C 162 (27.9) 115 (23.9) C 48 (18.5) 79 (24.8)
T 630 (75) 606 (75.8) T 418 (72.1) 367 (76.1) T 212 (81.5) 239 (75.2)
p-value 0.72 0.13 0.07**
DRD4 rs11246226 C 410 (45.9) 382 (47.4) C 288 (47.7) 235 (48.4) C 122 (42.1) 147 (45.9)
A 484 (54.1) 424 (52.6) A 316 (52.3) 251 (51.6) A 168 (57.9) 173 (54.1)
p-value 0.53 0.83 0.34
DRD4 rs11246228 C 412 (45.8) 365 (44.7) C 270 (44.3) 212 (43.1) C 142 (49) 153 (47.2)
T 488 (54.2) 451 (55.3) T 340 (55.7) 280 (56.9) T 148 (51) 171 (52.8)
p-value 0.66 0.70 0.67
DRD4 rs3758653 G 168 (18.9) 146 (18.1) G 110 (18.3) 88 (18) G 58 (20.1) 58 (18.1)
A 722 (81.1) 662 (81.9) A 492 (81.7) 400 (82) A 230 (79.9) 262 (81.9)
p-value 0.67 0.92 0.53
DRD4 VNTR rare 11 (1.2) 12 (1.4) rare 10 (1.6) 8 (1.6) rare 24 (7.9) 14 (4.2)
2R 78 (8.5) 58 (7) 2R 54 (8.7) 31 (6.2) 2R 24 (7.9) 27 (8.1)
3R 45 (4.9) 33 (4) 3R 24 (3.9) 24 (4.8) 4R 206 (68.2) 222 (66.5)
4R 618 (67) 560 (67.1) 4R 412 (66.5) 338 (67.6) 7R 48 (15.9) 71 (21.3)
5R 9 (1) 10 (1.2) 5R 7 (1.1) 9 (1.8)
7R 161 (17.5) 161 (19.3) 7R 113 (18.2) 90 (18)
p-value 0.63 0.52 0.06**
KATNAL2 rs2576037 T 377 (42.9) 351 (43.1) T 248 (41.9) 216 (44.1) T 129 (45.1) 135 (41.7)
C 501 (57.1) 463 (56.9) C 344 (58.1) 274 (55.9) C 157 (54.9) 189 (58.3)
64
Combined Female Male
Gene Marker Allele Super-Senior Control Allele Super-Senior Control Allele Super-Senior Control
KATNAL2 rs2576037 p-value 0.94 0.47 0.39
MAOA rs3027456 C 219 (28.9) 199 (30.9) C 180 (29.8) 150 (31) C 37 (25) 48 (30.4)
T 539 (71.1) 445 (69.1) T 424 (70.2) 334 (69) T 111 (75) 110 (69.6)
p-value 0.41 0.67 0.29
MAOA rs3027450 C 186 (24.1) 174 (26) C 149 (24.4) 124 (25.2) C 34 (22.5) 46 (27.4)
T 585 (75.9) 496 (74) T 461 (75.6) 368 (74.8) T 117 (77.5) 122 (72.6)
p-value 0.42 0.77 0.32
MAOA rs1799836 C 359 (46.7) 315 (47.2) C 289 (47.5) 234 (47.8) C 64 (42.7) 74 (44.3)
T 409 (53.3) 352 (52.8) T 319 (52.5) 256 (52.2) T 86 (57.3) 93 (55.7)
p-value 0.86 0.94 0.77
MAOA VNTR rare 10 (1.3) 14 (2.1) rare 9 (1.4) 12 (2.4) rare 61 (40.4) 63 (37.7)
3R 288 (37.3) 223 (33.3) 3R 231 (37.1) 164 (32.7) 4R 90 (59.6) 104 (62.3)
3.5R 14 (1.8) 13 (1.9) 3.5R 11 (1.8) 11 (2.2)
4R 461 (59.6) 419 (62.6) 4R 371 (59.6) 315 (62.7)
p-value 0.31 0.31 0.67
RAS1A rs2032794 C 200 (22.3) 177 (21.8) C 131 (21.6) 101 (20.6) C 69 (23.8) 76 (23.6)
T 696 (77.7) 635 (78.2) T 475 (78.4) 389 (79.4) T 221 (76.2) 246 (76.4)
p-value 0.79 0.69 0.96
RAS1A rs1477268 C 202 (22.5) 180 (22.1) C 131 (21.6) 103 (20.9) C 71 (24.5) 77 (23.8)
T 694 (77.5) 636 (77.9) T 475 (78.4) 389 (79.1) T 219 (75.5) 247 (76.2)
p-value 0.81 0.78 0.84
SLC6A4 HTTLPR L 564 (61.1) 512 (61.2) L 389 (62.5) 309 (61.6) L 175 (58.1) 203 (60.8)
S 359 (38.9) 324 (38.8) S 233 (37.5) 193 (38.4) S 126 (41.9) 131 (39.2)
p-value 0.96 0.76 0.53
SLC6A4 rs140701 T 348 (38.7) 304 (37.3) T 227 (37.2) 186 (37.8) T 121 (41.7) 118 (36.4)
65
Combined Female Male
Gene Marker Allele Super-Senior Control Allele Super-Senior Control Allele Super-Senior Control
SLC6A4 rs140701 C 552 (61.3) 512 (62.7) C 383 (62.8) 306 (62.2) C 169 (58.3) 206 (63.6)
p-value 0.55 0.84 0.18
SLC6A4 rs4251417 A 93 (10.3) 87 (10.7) A 71 (11.6) 52 (10.6) A 22 (7.6) 35 (10.8)
G 807 (89.7) 729 (89.3) G 539 (88.4) 440 (89.4) G 268 (92.4) 289 (89.2)
p-value 0.82 0.57 0.17
SLC6A4 rs6354 C 190 (21.1) 176 (21.6) C 131 (21.5) 105 (21.3) C 59 (20.3) 71 (21.9)
A 710 (78.9) 640 (78.4) A 479 (78.5) 387 (78.7) A 231 (79.7) 253 (78.1)
p-value 0.82 0.96 0.63
SLC6A4 rs6505165 G 422 (47) 392 (48.2) G 278 (45.7) 241 (49) G 144 (49.7) 151 (46.9)
A 476 (53) 422 (51.8) A 330 (54.3) 251 (51) A 146 (50.3) 171 (53.1)
p-value 0.63 0.28 0.49
SLC6A4 rs7214248 A 325 (36.2) 309 (38) A 217 (35.7) 195 (39.6) A 108 (37.2) 114 (35.4)
G 573 (63.8) 505 (62) G 391 (64.3) 297 (60.4) G 182 (62.8) 208 (64.6)
p-value 0.45 0.18 0.64
SLC6A4 rs9303628 G 399 (44.3) 361 (44.2) G 263 (43.1) 217 (44.1) G 136 (46.9) 144 (44.4)
A 501 (55.7) 455 (55.8) A 347 (56.9) 275 (55.9) A 154 (53.1) 180 (55.6)
p-value 0.97 0.74 0.54
SLC6A4 STin2 rare 13 (1.4) 15 (1.8) rare 7 (1.1) 5 (1) 9R 6 (2) 10 (3)
10R 346 (37.4) 326 (39) 10R 239 (38.4) 199 (39.6) 10R 107 (35.4) 127 (38)
12R 565 (61.1) 495 (59.2) 12R 376 (60.5) 298 (59.4) 12R 189 (62.6) 197 (59)
p-value 0.61 0.93 0.53
TH rs10743152 T 320 (35.6) 300 (36.9) T 214 (35.2) 176 (35.9) T 106 (36.6) 124 (38.5)
C 578 (64.4) 512 (63.1) C 394 (64.8) 314 (64.1) C 184 (63.4) 198 (61.5)
p-value 0.57 0.80 0.62
TH rs10840489 T 124 (13.8) 121 (14.8) T 87 (14.3) 84 (17.1) T 37 (12.8) 37 (11.4)
66
Combined Female Male
Gene Marker Allele Super-Senior Control Allele Super-Senior Control Allele Super-Senior Control
TH rs10840489 C 776 (86.2) 695 (85.2) C 523 (85.7) 408 (82.9) C 253 (87.2) 287 (88.6)
p-value 0.53 0.20 0.61
TH rs10840491 A 116 (12.9) 118 (14.5) A 83 (13.6) 81 (16.5) A 33 (11.4) 37 (11.5)
G 784 (87.1) 696 (85.5) G 527 (86.4) 411 (83.5) G 257 (88.6) 285 (88.5)
p-value 0.33 0.19 0.97
TH rs11042978 A 463 (51.4) 392 (48) A 313 (51.3) 232 (47.2) C 140 (48.3) 164 (50.6)
C 437 (48.6) 424 (52) C 297 (48.7) 260 (52.8) A 150 (51.7) 160 (49.4)
p-value 0.16 0.17 0.56
TH rs6356 T 326 (36.7) 289 (35.6) T 216 (35.8) 175 (35.7) T 110 (38.7) 114 (35.4)
C 562 (63.3) 523 (64.4) C 388 (64.2) 315 (64.3) C 174 (61.3) 208 (64.6)
p-value 0.63 0.99 0.40
TH rs7483056 C 424 (47.1) 378 (46.3) C 291 (47.7) 234 (47.6) C 133 (45.9) 144 (44.4)
T 476 (52.9) 438 (53.7) T 319 (52.3) 258 (52.4) T 157 (54.1) 180 (55.6)
p-value 0.74 0.96 0.72
TH VNTR rare 86 (9.3) 85 (10.2) rare 57 (9.2) 48 (9.6) rare 29 (9.6) 37 (11.1)
6R 201 (21.8) 207 (24.8) 6R 138 (22.2) 133 (26.5) 6R 63 (20.9) 74 (22.2)
7R 183 (19.8) 145 (17.3) 7R 125 (20.1) 84 (16.7) 7R 58 (19.2) 61 (18.3)
9R 134 (14.5) 126 (15.1) 9R 83 (13.3) 82 (16.3) 9R 51 (16.9) 44 (13.2)
10R 320 (34.6) 273 (32.7) 10R 219 (35.2) 155 (30.9) 10R 101 (33.4) 118 (35.3)
p-value 0.41 0.13 0.67
* Uncorrected p-values below 0.05
** Uncorrected p-values above 0.05 but below 0.10
67
Table 3.5. Adjusted p-values Using False Discovery Rate (FDR)*
Combined Female Male
Gene Marker Origina
l Adjuste
d
Original
Adjusted
Origina
l Adjuste
d
COMT rs1544325 0.955 0.969 0.665 0.987 0.498 0.837
rs165815 0.669 0.969 0.880 0.987 0.569 0.837
rs174696 0.026 0.923 0.091 0.987 0.131 0.837
rs2020917 0.267 0.969 0.251 0.987 0.881 0.933
rs4646316 0.727 0.969 0.942 0.987 0.507 0.837
rs4680 0.894 0.969 0.767 0.987 0.750 0.864
rs5993883 0.925 0.969 0.653 0.987 0.645 0.837
rs740601 0.392 0.969 0.401 0.987 0.633 0.837
rs9332377 0.139 0.969 0.286 0.987 0.303 0.837
rs933271 0.725 0.969 0.132 0.987 0.065 0.837
DRD4 rs11246226
0.527 0.969 0.825 0.987 0.337 0.837
rs11246228
0.663 0.969 0.696 0.987 0.666 0.837
rs3758653 0.669 0.969 0.919 0.987 0.528 0.837
VNTR 0.626 0.969 0.523 0.987 0.062 0.837
KATNAL2
rs2576037 0.940 0.969 0.469 0.987 0.392 0.837
MAOA rs3027456 0.413 0.969 0.671 0.987 0.294 0.837
rs3027450 0.420 0.969 0.766 0.987 0.317 0.837
rs1799836 0.855 0.969 0.942 0.987 0.768 0.864
VNTR 0.306 0.969 0.307 0.987 0.674 0.837
RAS1A rs2032794 0.795 0.969 0.686 0.987 0.956 0.966
rs1477268 0.809 0.969 0.784 0.987 0.836 0.912
SLC6A4 HTTLPR 0.959 0.969 0.762 0.987 0.529 0.837
rs140701 0.547 0.969 0.840 0.987 0.178 0.837
rs4251417 0.825 0.969 0.575 0.987 0.170 0.837
rs6354 0.817 0.969 0.957 0.987 0.635 0.837
rs6505165 0.630 0.969 0.281 0.987 0.495 0.837
rs7214248 0.449 0.969 0.179 0.987 0.637 0.837
rs9303628 0.969 0.969 0.742 0.987 0.543 0.837
STin2 0.607 0.969 0.932 0.987 0.531 0.837
TH rs10743152
0.573 0.969 0.804 0.987 0.618 0.837
68
rs10840489
0.534 0.969 0.200 0.987 0.611 0.837
rs10840491
0.333 0.969 0.185 0.987 0.966 0.966
TH rs11042978
0.159 0.969 0.170 0.987 0.562 0.837
rs6356 0.631 0.969 0.987 0.987 0.397 0.837
rs7483056 0.744 0.969 0.962 0.987 0.725 0.864
VNTR 0.414 0.969 0.134 0.987 0.673 0.837
* Corrected for 36 tests
69
Chapter 4. Discussion
4.1. Interpretation of Results
After multiple test corrections, no associations were found between allele
frequencies in five personality related genes (COMT, DRD4, MAOA, SLC6A4, TH) for the
Super-Seniors versus the middle-aged control group. There was one significant
association before corrections in the COMT SNP rs174696. Additionally, there were two
associations in the male group, COMT SNP rs933271 and the DRD4 VNTR rare alleles,
with p-values less than 0.10 but did not meet significance.
Associations with the COMT SNP rs174696 have been reported before. The C/C
genotype was associated with increased novelty seeking scores using Cloninger’s TCI in
a heroin dependent Chinese study group [449]. Novelty seeking is comparable to the big
five’s excitement-seeking. The Super-Seniors showed increased minor allele frequency
(C) at this marker before correction, possibly connecting novelty seeking to dopamine
systems as previously discussed in the gene’s background. The SNP is located mid intron
5 but there could be potential functional effects through the modification of a splicing site
or recruitment of transcriptional factors; however, this has not been reported in the
literature.
The COMT SNP rs933271 observed in the male group has not been well studied.
It has been associated with impulsivity in a haplotype report [450], but there is little other
research for association studies with personality. The DRD4 VNTR alleles that were
grouped together as rare alleles for analysis (3R, 5R, 6R and 8R) are understudied alleles.
There are very few publications that focus on alleles outside of the common 2R, 4R and
7R, most likely due to small sample sizes for the rare alleles. Recently, however, Hasler
et al. found the 6R allele to be associated with an adult ADHD phenotype [451]. The 6R
allele has also been associated in intellectual disabilities of unknown etiology [452]. As
discussed previously, the DRD4 VNTR causes functional changes to the dopamine
receptor, with the 7R allele showing diminished function [178], but these rare alleles have
not been investigated for their specific functionalities.
70
It was not surprising to see COMT as an association; COMT was a very good
candidate for potential difference between Super-Seniors and controls given the MB-
COMT’s regulation of dopamine [170] in the prefrontal cortex [170,172]. As dopamine is
integral to the reward-system [113,114,118,119], COMT should be an excellent candidate
for studies looking at associations with extraversion.
The combined sample was well powered (0.8) to detect an OR of 1.5. The p-value
threshold was set at 0.05, meaning there is one expected false positive result for every 20
tests performed. For the 36 tests conducted, one association was found before
corrections. The COMT SNP rs174696 was likely a false positive and was appropriately
corrected in the FDR calculation. The stratified male group consisted of 145 Super-
Seniors and 162 controls for a total power of 0.39 to detect an OR of 1.5 [453]. There is
only a 40% chance of finding a real effect in this sub-group. There were two notable weak
associations that did not meet the significance cutoff and were corrected for by the FDR
calculation.
One possibility is that there is truly an association between the Super-Senior
phenotype and the candidate genes, but we have failed to detect it due to power or multiple
testing adjustments. This is a valid concern for the female and male subgroups given their
respective powers are 0.62 and 0.39, however, the combined sample was sufficiently
powered to detect an effect of modest magnitude (OR 1.5). It is also possible that the
effect size, or contribution, of these candidate genes is less than the moderate OR of 1.5.
GWAS studies have reported associations to range from small [454] to modest [65,455].
If the candidate genes only have a small effect size the current design is not powered to
detect it. Alternative, the FDR adjustment could have eliminated true associations.
While these results are in agreement with other healthy aging and personality
studies (DRD4 [456], SLC6A4 [457,458]), other studies have been published supporting
associations (COMT [132], SLC64A [398], TH [234], RAS1A and KATNAL2 [454]). To our
knowledge this is the first study looking at MAOA associations within the scope of healthy
aging and personality.
71
4.1.1. Relevant Association Studies
COMT’s SNP rs4680 G allele was recently found to be associated with lower
neuroticism scores using 616 unrelated healthy young-seniors [132]. The sample was
Caucasian, 56% female, with an average age of 69.26 (SD 9.7) [132]. Subjects were
labeled as healthy if they passed dementia related criteria looking for depression and
cognitive impairments [132]. The study tested SNP rs4680 and scores from the NEO-FFI
[132]. It was well powered for their sample size at the domain level, and shows good
evidence for a true association in this sample.
DRD4 has been tested for personality associations in a healthy young-senior
population [456]. Vandenberg et al. looked at novelty seeking, measured by the NEO-PI-
R, and the 7R allele in DRD4’s VNTR [456]. Using the Baltimore Longitudinal Study of
Aging (BLSA) cohort, they compared 100 subjects with the highest and 100 subjects with
the lowest novelty seeking scores [456], finding no associations. The average age of the
subject was 61.3 years and the sample was 56% male, no other characteristics were given
in the paper [456]. Given they were only testing for one allele and used extreme
phenotypes, their design was sufficient to detect an association if there was one.
SLC6A4’s HTTLPR VNTR has varying associations even within studies.
Terracciano et al. looked for associations between the VNTR and eight SNPs around that
region with personality in two sample groups [457]. The first group consisted of 3913
Italians from the SardiNIA cohort who completed the NEO-PI-R [457]. This first group had
an average age of 42.5 (SD 16.7) with 57% of the subjects being female [457]. There were
no associations found between personality and the nine markers [457]. Additionally, they
also used 548 mostly Caucasian individuals from the BLSA cohort, which averaged 52.9
years of age (SD 12.5), 51% being female [457]. In this sample they saw an association
between the VNTR S/L genotype and lower neuroticism scores [457]. The association
seen in the BLSA sample is likely a false positive given the lower sample size and inability
to establish an allelic affect.
Brummet et al. had previously looked at 103 depressed and 99 non-depressed
geriatric, mostly Caucasian, individuals for associations between depression, HTTLPR
alleles, and personality [458]. The sample was 63.9% female, personality was measured
72
with the NEO-PI-R, and criteria for consideration of geriatric status were not defined in the
paper [458]. No associations between depression status and personality were found [458],
but they did find that the S allele was associated with lower neuroticism in males [458].
Results are questionable given the low sample size and the division of the study sample
to look for alternative associations, likely there is no association for HTTLPR and
personality.
Gondo et al. published a study looking for associations between HTTLPR alleles
and personality in a centenarian sample [398]. The study contained 265 Japanese
centenarians who were measured for personality with the NEO-FFI and 1377 younger
controls with no personality measures [398]. The centenarian’s average age was 101.2
(SD 1.8) and 78% were female [398]. The younger cohort was derived through published
HTTLPR allele frequencies in other studies with Japanese samples of varying
characteristics [398]. They did not find an association between HTTLPR and personality
scores in the centenarians but did find that their centenarians more frequently carried the
L allele compared to the younger control group [398]. This study supports SLC6A4’s
association with longevity but fails to make a connection to personality. The population is
also not comparable to our Caucasian sample making similar associations unlikely.
TH has been associated to aging in a study conducted by De Benedict et al. [234].
The study looked at genotypic frequencies in a group of 196 Italian centenarians (73%
female) and 358 Italian controls (55% female) aged 10 to 85 year [234]. The TH VNTR
alleles were reduced to a long (9, 10 and 10i repeats) and short (6, 7 and 8 repeats)
version for analysis [234]. The group found that male centenarians carried the L/L
genotype less frequently than the control group [234], supporting the genes involvement
with longevity. The study, however, is not well powered to find associations in the male
group as there were only 53 male centenarians in the study.
RASA1 SNPs rs1477268 and rs2032794 and KATNAL2 SNP rs2576037 were
markers identified in de Moor et al.’s 2012 personality GWAS [454]. Personality was
measured with a variety of NEO questionnaires in the pooled study sample and the design
used 17375 middle aged samples in the discovery phase and 3294 middle aged samples
in the replication phase [454]. The number of SNPs genotyped varied depending on
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sample population but ranged from 6K-1M SNPs [454]. The RASA1 SNPs were found to
be associated with increased openness scores and the KATNAL2 SNP rs2576037 was
found to be associated to increased conscientiousness scores, although neither of these
associations were found in the replication sample [454]. This study was not concerned
with longevity, despite using longevity sample groups such as BLSA, and the results from
a middle aged sample may not apply to other age groups.
Amongst the candidate genes and SNPs selected for study, there is a lot of
variation in the published associations with personality and longevity. This variation makes
it difficult to determine if true relationships exist. Data in this study supports the conclusion
that there is no association between the candidate genes selected and the Super-Senior
phenotype but that does not exclude these genes from being associated in personality or
other healthy aging studies that study related but different phenotypes.
4.1.2. Potential Causes of Variation in Published Association Studies for Candidate Genes
Personality and aging are complex phenotypes, reasonably affected by many
small common contributing polymorphisms throughout the genome to produce the
phenotype. The expected effect size for any given marker could be quite small and difficult
to detect without a large sample size [459]. The study of psychiatric disorders, such as
schizophrenia and bi-polar disorder, represent related complex phenotypes. Identifying
genes in these disorders has been difficult due to imprecise clinical definitions of the
phenotype, allelic heterogeneity, epistasis (modifier genes) and interactions from non-
gene factors (i.e. environmental factors) [460]; issues that parallel problems within healthy
aging and personality research.
As previously discussed, healthy aging can vary considerably between studies in
its definition [5]. This study’s definition of aging is not exactly the same as that of previous
studies where significant associations were found [132,398,454], therefore making it
difficult to predict if previous associations would be observed in the Super-Senior sample.
Further, the unique features and regulatory elements of the candidate genes may
contribute to finding associations in some studies and none in others. SLC64A will be used
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as an example to illustrate mechanisms by which allelic heterogeneity and epistasis may
be at play in inconsistencies in establishing association.
The well studied SLC6A4 gene has many variants. The SLC6A4 VNTR, HTTLPR,
has two alleles, S and L, but these have been found to have many SNP variants
superimposed upon these size alleles. Nakamura et al. genotyped CEPH control and
Japanese samples finding there were 4 variants of the S allele and 6 of the L allele [376].
Investigations into allelic frequency differences between the Japanese and Caucasian
groups showed Caucasian samples displaying only two of the six L alleles and the
Japanese samples having a greater range of variants [376]. Variations of the SNPs on the
haplotypes of the S and L alleles could be contributing to association inconsistency
between personality studies.
The SLC6A4 has another polymorphism in the HTTLPR VNTR. Within the L allele,
an A/G SNP causes a functional change [461]. When G is present in the L allele, there is
a similar rate of transcription as the lower transcribed S allele [461]. An obsessive
compulsive disorder (OCD) study looked at allelic frequencies within different populations
and found that the L(A) allele is more frequent in Caucasian OCD [461]. Here the L(A)
allele is over expressing, causing more transporter proteins to be produced, clearing
serotonin from the synaptic gap too quickly. Very few studies test for this SNP, which could
add to inconsistencies between studies.
SLC6A4 has a well established epistasis effect with brain-derived neurotrophic
factor (BDNF) in emotional regulation. Individuals carrying the S or functional equivalent
L(G) allele and were homozygous for the Val allele in the BDNF gene are associated with
increased cognitive reactivity (a term for dysfunctional thinking where mood changes
rapidly from neutral or positive to negative) [462], and reduced volumes in the anterior
cortex (regulator of emotion) [463]. If the Met allele in BDNF was present, it showed a
protective effect, with subjects showing less cognitive reactivity [462] and smaller
reductions in the anterior cortex compared to the L allele [463]. BDNF interacts with
SLC6A4 to regulate emotional stability and could be adding to inconsistent associations
between SLC6A4 and observed personality.
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Epidemiological studies have reported associations between personality and
longevity [79,80,82,86]. Twin studies have shown personality and healthy aging to be
approximately 40% [40,65] and 25% [464] heritable, respectively. There is evidence of a
genetic factor but the effect may not be captured at an allelic level, which would explain
why this study could not find an association. Epigenetic factors are known to impact many
of the candidate genes and could explain why allelic associations were not seen.
Epigenetics is a dynamic modification of the DNA or histones to regulate expression [460],
and can result in short term or long term modifications [465]. Epigenetics has been
implicated in learning and memory [466,467] and four of the candidate genes have been
investigated for an epigenetic role in neurological functions.
For example, the COMT methylation site within the promoter region has shown
different methylation patterns in monozygotic twins [468]. The methylation of SLC6A4 is
implicated in the stress response, such that increased methylation results in increased
stress [469,470]. In human neural stem cells, methylation of TH was found to reduce its
expression [471]. Finally, bipolar patients had increased methylation on their MAOA and
COMT genes [472]. It is worth noting that there is regional tissue specificity for methylation
[473–475] and blood or buccal swab samples used in these experiments likely do not
represent the true methylation patterns in the brain. These epigenetic effects could be
causing false positives in personality association studies; where by chance, the genotype
of affected individuals at a gene appears associated, but the true effect is caused or
regulated by an epigenetic mechanism, complicating the search for true associations.
There are many scenarios that could explain the variation of reported associations
with the chosen candidate genes. Our failure to discover associations in personality-
related genes may be attributed to false positives in the literature [476], misguiding the
selection of candidate genes. The correct conclusion to draw is that there is likely no
association between the allelic frequencies of the Super-Seniors and the younger control
groups for these selected candidate genes. There are of course limitations to the design
of this study that limit its level of certainty.
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4.2. Limitations of Design
4.2.1. Candidate Gene Selection
Candidate gene designs are good to use in smaller sample sizes, as they require
fewer tests [477], however, they rely on current biological knowledge to select the
candidate genes [477]. Reliance on established knowledge can be difficult if the
phenotype is unique, and genetically heterogeneous.
Variability in associations between studies can be caused by differences in design,
particularly the definition of study populations and definition of the studied phenotype
[477,478]. This study’s population is uniquely defined compared to other healthy aging
studies in terms of age and health status [65,84,398]. The age classification begins at 85
and contains exclusion criteria for five specific chronic conditions (dementia, cancer,
diabetes, major pulmonary disease, and cardiovascular disease). This definition is
selected based on the top five chronic diseases and cutoffs for average life expectancy.
As this definition is unique, candidate gene selection relies on the use of results of studies
of similar phenotypes to ask related questions which may not have the same underlying
biological or genetic factors.
Genetic heterogeneity of complex traits could be a complication for determining
genetic contributions. The etiology of many psychiatric disorders, including personality
disorders is known to be variable [478]. It also stands to reason that personality could
develop through many different biological pathways, which would result in many genotypic
profiles producing a commonly observed phenotype. Limitations in our current knowledge
regarding the internal production of personality make this problem difficult to assess. Well
defined phenotypes and strict enrolment criteria for certain biological pathways can help
control for this heterogeneity [477], but such information is not currently available for aging
or personality. The uncertain biological origination of personality and aging make it difficult
to rely on associations seen in other studies for gene selection.
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4.2.2. Marker Selection
Many genetic study designs, in general, suffer from an over-reliance on linkage
disequilibrium. Linkage disequilibrium between two markers can vary across populations
[477] and the markers used can have different allele frequencies [479], although we try to
control for this by restricting the study population to one ethnicity. To illustrate, a marker
randomly drawn from a population of interest is in high linkage disequilibrium with the true
allele, the variant affecting the phenotype. In one study an association is found between
the marker and the phenotype of interest. When repeating the experiment, sampling from
the same study population, an effect was not found due to the sample by chance having
less linkage disequilibrium between the marker and the true variant. In this example the
difference in linkage disequilibrium between the marker and true allele can cause variation
in the observed association [476]. Differences in allele frequencies of the markers can
also affect the association [479].
When the frequency of the marker and true allele are similar there is greater power
to detect an association [479]. A marker with the same minor allelic frequency (30%) as
the true allele in high linkage disequilibrium, is more likely to show an association
compared to another marker, also in high linkage disequilibrium but with a different allele
frequency (60%) [479]. In complex traits, it is hypothesized that the underlying genetic
variants are very common (MAF >10%) [479], and this difference in allele frequencies
between sample population could account for variation in association findings. The current
design relies on using tagSNPs to represent areas of high linkage disequilibrium to detect
association within that area; however, this design has not accounted for the potential
variation in linkage disequilibrium or allelic frequencies other than selecting markers using
Caucasian HapMap data for the Caucasian study population.
The HapMap data has potential problems in admixed populations [480] but works
very well when the correct reference is matched to the study population. Studies looking
at the variance between linkage disequilibrium blocks in the CEU HapMap data and
European populations have found the blocks to be almost identical [481] and tagSNPs
were able to capture a high amount of variance in the regions tested [481,482]. Matching
the correct HapMap reference population to the study population is an important
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consideration to avoid problems in varying linkage disequilibrium blocks and allelic
frequencies.
VNTR data is not commonly used as it once had been in candidate studies. This
is due to the innovation in SNP genotyping technology that has made SNP markers a
more cost effective strategy and has allowed studies, such as GWAS designs, to look at
the whole genome for associations [459]. VNTR genotyping has remained a time
consuming process. Given the volume of samples and procedures needed, there are
many opportunities for human error. To reduce human errors during genotyping, plate
maps were checked twice, plates were prepared using two researchers, and pooling of
the VNTRs was checked by two researchers. Prior to performing wet laboratory
genotyping primers and primers with dye were optimized before use in samples. The
VNTR PCR fragments were verified for predicted size, and blank and repeat samples were
present on every plate sent for VNTR fragment analysis. Additionally, there is no database
or structured nomenclature to help researchers identify previous studies, alleles, imperfect
copies (such as the 10iR VNTR in TH [225]), or help score the repeats [459]. The
conducted literature search was done to ensure information regarding alternate names,
alleles, rare alleles and methods to score the alleles were known prior to study
commencement. While known beforehand, the current design was not able to differentiate
between TH’s 10R and 10iR repeats [225] and had to be combined during allele calling
with GeneMapper.
4.2.3. Power and Rejection of the Null Hypothesis
Candidate gene designs may ultimately have poor reproducibility because they
consistently fail to exclude chance as an explanation for weak associations [476]. Savitz
and Ramesar have summarized reasons behind the many false positives in candidate
gene studies, citing a failure to correct for multiple testing, arbitrary grouping of alleles,
ethnic stratification, lack of sex stratification, and poor sample size as factors behind
irreproducible results [483]. Calhoun et al. further criticized candidate studies for stratifying
or examining haplotypes only when there is failure to produce an association at the SNP
level to increase the likelihood of finding an association [476]. The current design attempts
to reconcile these concerns.
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Sample size in the combined group met power requirements and multiple
corrections were applied. Importantly, stratification was done by sex given many of the
candidate genes showed differences between females and males (COMT [173], MAOA
[192,199] and SLC6A4 [219]). The subgroups created were not well powered, however,
and the validity of the sex-specific results is limited. The current study design avoids using
potentially biased grouping of multi allelic data together and instead attempts to analyze
each allele. There were, however, many small counts of rare alleles that had to be binned
together for analysis purposes. The inclusion of subjects in the design requires all four
grandparents of the subject to be of European descent, so that there is ethnic stratification
at the design level, minimizing confounding due to population stratification.
Candidate study designs, like many other designs, suffer from publication bias
[484]. Studies showing associations are more likely to be found in subgroups (smaller
sample populations that have less power compared to the total group) and in markers of
interest found without a prior hypothesis as to which alleles are more likely to be
associated based on biological function [484]. The current design’s candidate genes were
chosen through a literature search and suffer from bias induced by possible unpublished
studies showing no associations. As this is an acknowledged risk, biological plausibility
for function of candidate genes in personality was also a criterion for selection. Prior
knowledge of problems with power in stratified groups ensures the potential association
is recorded and explored, but that the loss of power is acknowledged.
There is also loss of power from having no a prior hypothesis regarding which allele
is likely to associate to the Super-Senior status, as testing one allele against the rest
increases the allele count as opposed to testing all alleles against each other. The
literature reviewed for the candidate genes was heavily focused on alleles associated with
personality dysfunctions. For example, the 7R allele in DRD4 is well researched for its
association with impulsivity and aggression [195], and many study designs collapse alleles
to “7R” and “non-7R”. Difficulty arises in making predictions for functionality in the Super-
Seniors as there are no studies tailored to the action of these markers in the very old and
very healthy. This is primarily a problem in the multi-allelic VNTRs. As there are no
established alleles for the Super-Senior phenotype, an a priori hypothesis for functional
allele could not be established.
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4.2.4. Control Group
The control group used in this study is a younger cohort, which have not been
selected for disease status, to represent the population. They range in age at enrolment
from 41-54 and reside in the Greater Vancouver Lower Mainland, BC. This is not the ideal
control group for the Super-Seniors, whose age range is 85 and over; however, alternative
control groups for the Super-Seniors are not available. Ideal candidates are those born in
the same era, who have succumbed to disease and are already deceased. Long lived
individuals who have been diagnosed with one of the mentioned morbidities are not
appropriate either, as they have achieved longevity but not the comparable health status
as the Super-Seniors. If the purpose of the Super-Senior Study was to investigate the
genetics of a specific disease, this group would be appropriate. They would, however,
confound studies of longevity. The study design relies on using a known imperfect control
group that differs in environmental generational effects.
As the use of a younger cohort is common in the genetics of aging, Lewis et al.
conducted a meta-analysis of longevity studies looking at the validity of two assumptions
[485]. First, that the initial relative allelic frequencies between the cases and controls were
similar [485]. Secondly, that the risk of mortality and genotype did not depend the year of
birth [485]. The meta-analysis concluded that these two assumptions were generally
invalid for many studies but suggested some control measures [485]. Allelic differences
can arise from gene flow, or migration in and out of an area that alters the allelic frequency.
This can cause different allelic frequencies between decades and is a problem even after
ethnic stratification [485], as individuals of European ancestry can come from a variety of
regions and countries [485]. Choosing a stable population in terms of exposures and gene
flow would maintain the integrity of these assumptions [485] and can be controlled for by
classifying ethnicity by place of birth. The current design’s more stringent criteria require
European ancestry for all four grandparents to be classified as being of European descent.
Selection bias for the control groups is a possible issue in the study design.
Controls were not ascertained based on health status as they are to represent a random
sample from the population. Controls were contacted only through Ministry of Health lists
with a 60% response rate. There could be differences between the controls that responded
and those that did not, known as volunteer bias. Volunteers are known to be different from
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the population in terms of intelligence [486], wellbeing [487] and personality [488,489].
There is also evidence that volunteers have reduced mortality [490], which could be
confounding for this study if there are a greater number of controls that will reach Super-
Senior status than predicted. This would decrease the power of the current design by
lessening the magnitude of the effect size. Nevertheless, volunteer bias would be present
in both the Super-Seniors and controls which may help mitigate the overall bias.
Misclassification of exposure, the labeling of a non-Super-Senior as a Super-
Senior vice versa [476], is present in this study. Some members of the control group, on
the order of a few percent, could become Super-Seniors later in life, diminishing the allelic
frequency differences between the groups. Alternatively Super-Seniors are self-reported
and may not have meet study criteria at time of enrolment. Other classical means of
misclassification are through laboratory errors such as mislabelled samples and arranging
sample plate with all controls and all cases. These were controlled for with strict laboratory
procedures which included, double checking, two person plate preparations, and including
cases and controls on each plate prepared.
4.2.5. Sample Size
Sample size calculations were based on an OR value of 1.5, as this is a magnitude
of association that is plausible. Calhoun et al. noted initial associations reported are
inflated and in estimating power, a lower value should be used ([476]. The current design
has a power of 0.8 for an effect size of 1.5; however, if the effect size is reduced to 1.25
the study’s power drops to 0.3 [453]. As aging is a complex trait with potentially many
genes contributing small effects, it is plausible that effect sizes could be much lower than
1.5 and would require a much larger sample size to see an association.
4.2.6. Lack of Personality Testing
There was no formal personality testing done with the Super-Seniors or the control
group in the parent study. In a sub-study conducted by a directed studies student in our
laboratory, a group of 69 Super-Seniors were interviewed four to seven years after
recruitment. They displayed the typical pattern of high extraversion, conscientious,
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openness, agreeableness and low neuroticism found in the literature, when compared to
a younger and diseased burdened population (manuscript in preparation). Personality
testing was not part of the information captured at study enrollment, and testing requires
time consuming work through subject contact, coordinator time, and trained employees
for personality assessment. Four to seven years after recruitment, only 69 European-
ancestry Super-Seniors were available for the personality sub-study.
Based on previous research, in our lab and in the literature, it is inferred that the
Super-Seniors and controls have different personality profiles and therefore different allelic
frequencies in the selected candidate genes. Volunteer bias, however, could render that
untrue.
Personality is known to be a factor in volunteer bias, where volunteers who consent
to studies are typically more open and agreeable than non-volunteers [488,491], and those
than agree to long term follow up are more extraverted [488,491]. Subjects who
volunteered for the Super-Senior Study may be particularly high in extraversion, openness
and agreeableness, giving them similar personality profiles.
All study designs come with limitations. Candidate gene designs have better power
to detect smaller associations [477]; which serves the unique Super-Senior population
well, as recruitments of sufficient numbers for designs such as GWAS would be difficult.
The current design is able to detect moderate effects but still suffers from design
limitations, including phenotype definition, marker choices, misclassification of exposure,
sample size and undefined differences between cases and controls.
4.3. Recommendations
4.3.1. Candidate Gene Based Design
Longevity is a complex trait with many factors impacting the phenotype,
complicated further by the pleiotropic natures of longevity genes [484]. The candidate
gene method is a good design due to low costs, quick outcomes and directed design [492],
but there are some improvements that can be made to the current design. These include
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refined hypotheses for candidate gene selection, the use of modeling for analysis, and
added quality control SNPs.
Directed hypotheses for biological functions may also help illuminate true
associations in the Super-Seniors. Given the established personality profile [79,80,82,86]
and association of personality related genes to longevity (DRD4 [493], SLC6A4 [398], and
TH [494]), a hypothesis could be made as to which personality related biological systems
could be involved in longevity. Dopamine and serotonin are good candidate systems due
to their relationship to learning [111,113,114] and stress [111,149], respectively. Focusing
on one of these neurotransmitter systems could help identify true associations and
interrelated markers. Using the networking program GeneMANIA [495], related genes can
be used to from a candidate list to test for association. For example the original candidate
gene list was input into GeneMANIA (figure 4.1), showing a relatively sparse network with
few relationships between genes. When networks are mapped for serotonin receptors
(figure 4.2), however, the network becomes much more inter-related, making associations
potentially easier to identify by revealing other candidate genes to investigate, or building
models to explain phenotypes. The networks in figure 4.1 and 4.2 reveal new genes for
investigation that did not surface in the literature search.
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Figure 4.1. Candidate gene network
Black nodes represent input genes. Grey nodes are related genes, scored and ranked in decreasing size for how informative the gene is in the network of queried genes [495]. GeneMANIA uses large public datasets, such as e!Ensembl and NCBI, to find related genes [495].
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Figure 4.2. Serotonin receptors and associated BDNF, TPH1 and TPH2 network Black nodes represent input genes. TH is present as gene of interest in this network with other genes such as PAH, S1PR1 and GPR26 that did not show up in the literature search but may be of functional interest in the regulation of serotonin.
Derringer et al. used a candidate systems approach to identify aggregated SNPs
in the dopamine system. Associated SNPs were used in a regression model to show SNPs
across different genes accounted for variance in sensation-seeking behaviours [496].
Derringer et al. went further to develop a genetic risk score for sensation-seeking
behaviours [496], although there have been no replication studies to date. The serotonin
system could be used to look at stress scores or neuroticism scores in the Super-Seniors,
giving the design more focus and direction towards looking for genetic contributions to
aging phenomena such as inflammaging.
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Candidate systems could help with the discovery of related genes to study but this
may also increase the number of SNPs to test. Bayesian statistical approaches could help
mitigate the loss of power from multiple testing but require a prior assumption regarding
the effect size of each SNP being tested [497]. These effects can be difficult to predict,
especially when exploring new phenotype and polymorphism relationships. The use of
Bayesian methods can further be impeded by the lack of user friendly software, requiring
coding knowledge to execute. Programs such as SNPTEST, however, are becoming more
readily available and easier to use, making the methods more accessible to the genetic
community. Bayesian methods are likely to be more influential in large designs, such as
GWAS, where multiple corrections for hundreds of thousands of tests can mask
associations.
Alternatively, after initial association testing, model building could be conducted
with associated markers. Using logistical regression models could help control for known
environmental confounders [498], and build genetic scores for certain phenotypes, such
as stress response.
With a better system for selecting candidate genes, improvements should be made
on the markers selected. SNP selection can be done through a tagSNP approach, but
built in quality control checks should be selected as well. Adding extra SNPs that are
known to be in linkage disequilibrium with each other could serve as a quality control check
within the control group [477], but would also raise the cost of genotyping. By testing these
known SNPs for their level of linkage disequilibrium, the study would have greater control
for variation in linkage disequilibrium between SNPs. VNTRs should always be included if
available in candidate genes as they may represent missing heritability between what is
predicted in heredity studies and what is seen the GWAS data [459]. Before inclusion,
VNTRs should be assessed for the functional properties they could contribute, which
includes tissue specificity [459].
Changes to the candidate design should focus on investigating neurological
systems such as serotonin for gene selection, use a modeling approach and include SNPs
to confirm linkage disequilibrium. While these changes can be implemented in future
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candidate designs there are other recommendations for the Super-Senior Study that are
not as easily accomplished.
4.3.2. Super-Senior Study Design
Issues to improve upon in the parent study design include sample size and
phenotype testing in both populations.
An increased sample size would alleviate challenges with power, especially for
subgroup analysis. Recruitment has re-opened for the Super-Senior Study and collection
of additional participants is underway. With more participants enrolled, even larger
selections of candidate genes could take place. If the sample size doubled, there would
be sufficient power to detect an effect size of 1.35 [453]. Even if the sample size were
doubled, the design is limiting. As the effects of associations are predicted to be weak, it
is plausible that thousands of cases and controls would be needed for detection [484].
Sample pooling with other aging groups could increase the sample size enough to detect
these effects, and will be a consideration for our lab in the future.
It would be ideal to have the results of personality testing for all Super-Seniors and
controls, when looking for genes that could affect personality related physiological
functions. Such data would allow for a more conclusive analysis through logistical
regression with personality traits and markers. The genetics of personality is still a
developing field and suffers from technological constraints in linking genotype to
expression to physiological response and to phenotype. These processes happen in an
organ that is not easily sampled and produces a large volume of electrical data, making
visualization difficult. While there are promising candidates, there are no confirmed genes
that contribute to personality. Therefore, we cannot claim that differences in proposed
personality genes do indeed cause differences in personality phenotypes, which would be
expected to impact behavioural choices influencing longevity.
The recommend design would still maintain the current control group, increase the
sample size, and conduct either personality or stress testing in the study sample. Linking
longevity to neurological processes that govern personality is complex and many more
studies will be needed to understand how these two phenotypes could be connected. The
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personality and aging fields are both making headway to discover the complex and
nuanced ways that our genetics can affect our health, behaviours and perspectives.
Future expansion and culmination of smaller association studies could lead to
advancements of biological knowledge and lead to better heath care for an aging
population.
4.4. Future Developments
Association studies are important stepping stones to develop our understanding of
biological phenomena such as aging or discovering the biological mechanisms that dictate
our behavioural, emotional and thought processes. Association studies build upon one
another and future developments with better designs could help solve questions about
missing heritability in genetics and change our approaches to healthcare.
4.4.1. Towards Integrative Approaches
Missing heritability is the unaccountable difference between the heritability found
in twin studies and the phenotypic variation that can be explained through sequence
differences [460]. Personality is a moderately heritable complex trait, with twin studies
estimating it to be approximately 40% [40,65]; however, there are currently only
hypotheses regarding the genes involved in personality and the small effect sizes of
associated polymorphisms would not explain the observed 40% heritability. Extreme
neurological phenotypes, such as schizophrenia and bipolar disorder both which are about
80% heritable [460], have not fared much better in the search for associations, with only
a fraction of risk being attributed to rare mutations and common genetic factors [460].
Closing the gap on this missing heritability may not come from more association
studies of single SNPs, but from focusing on interactions between SNPs, other genes,
and environmental effects. Logistical regression will be an important tool to help
researchers accomplish this. Using candidate systems to model SNPs and develop risk
scores could be used to look for interactions across different genes and show how they
contribute to complex phenotypes. This kind of modeling can capture epistasis effects of
other genes, which may better help explain the heritability of traits.
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Environmental factors can confound genetics studies, particularly those of
heritability, as characteristics such as lifestyle habits, wealth, education and social status
can be inherited from family [464]. Further, environmental factors can induce epigenetic
regulation that can become heritable [460]. These effects may be causing a ‘phantom’
heritability [460], where the reported heritability is inflated from these interactions.
Logistical regression can also help with environmental confounders as known
environmental characteristics can be added as a covariate to models. Models like these
could be used to explain heritability and also to develop mortality risk scores including
personality related genes.
4.4.2. Personality and Personalized Medicine Interventions for Longevity
Personality has been shown to be an excellent predictor of health status [50,499]
and healthy behaviours [60,95]. Personality is already comparable to current standard
metrics such as SES [92] and out performs SES in predicting quality of life in old age [500].
Longevity itself is not a good predictor of healthy aging as it measures factors that
influence the length of life, not factors behind long term health [32].
Conscientiousness and neuroticism are the best predictors of mortality risk [93,94],
and would be good candidates for developing genetic scores of mortality risk with
candidate genes. Neuroticism is an exciting trait to study due to its association with the
stress response [501]. Risk scores could eventually be developed from genes controlling
neuroticism in the serotonin system and other stress related pathways that could predict
risk for inflammaging. This method of profiling could help medical professionals make
more informed decisions about treatment options in patients and could help modify
unhealthy behaviours.
Personalized medicine is a movement to make clinical decisions based on the
unique health profile of patients, which includes considering their genes, proteins and
environment when making a diagnosis and treatment plan [502]. Much of the focus of
personalized medicine has been on tailoring individual cancer treatments or using genetic
information as a clinical guide for drug dosage, optimizing efficacy and safety of treatments
90
[502]. Yet, there are a growing number of genetic scores being developed that can be
used as behavioural predictors.
A recently developed ‘smoking quit success genotype score’ has been developed
by Uhl et al. [503]. The score was shown to not only be predictive of smoking cessation
success in adults, but was used in a longitudinal study with Baltimore adolescents to show
it could be used to predict the rate and escalation of addictive substances use [503]. This
score could be used to predict smoking and addictive substance behaviours, allowing
healthcare providers an opportunity to administer preventative measures in at-risk youth.
Personalize medicine could be used to implement preventative measures in identified at-
risk groups and genetic personality scores could help identify individuals who need or do
not need extra resources.
By understanding and profiling the genetics of personality, treatment and therapy
plans could be built around a specific patient needs. Friedman et al. found that different
personality types used certain health services with varying frequencies [504]. The
scaffolding of treatments and therapies could be created based on genetic scores, and
fine tuned with patients to produce tailored plans; such as incorporating additional
assistance with coping strategies for individuals profiled to be high in neuroticism or
offering more social support to introverted patients.
Ultimately the goal of personalized medicine is best summarized by Benjamin
Chapman as a means to “identify those at risk for health problems before these problems
develop, so that preventive efforts can be successfully implemented” [39]. Personalized
medicine could help deliver more effective treatments and preventions for better patient
outcomes. With better outcomes and prevention of chronic disease there is an opportunity
for more individuals to reach Super-Senior status and to help current Super-Seniors
maintain their health.
91
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