Student Number: 1035697
MSc Dissertation
Title: Polygenic association in ADHD for the prediction of cognitive
performance
Word Count: 10,659
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Contents
Abstract .................................................................................................................................... 5
Introduction .............................................................................................................................. 6
Epidemiology and Impairment .............................................................................................. 6
Diagnostic Classification ........................................................................................................ 7
Dimensional Trait .................................................................................................................. 8
Comorbidities ........................................................................................................................ 8
Aetiology and Heritability ...................................................................................................... 9
Molecular Genetic Investigation in ADHD ............................................................................. 9
Genome-wide Association Studies (GWA) .......................................................................... 10
Candidate Gene Approaches ............................................................................................... 11
Rare Variants ....................................................................................................................... 12
Challenges in Detecting Associations with Common Genetic Variants............................... 13
Candidate Cognitive Endophenotypes in ADHD.................................................................. 15
Polygenic Association .......................................................................................................... 17
Aims ..................................................................................................................................... 18
Method .................................................................................................................................... 19
Sample Selection ................................................................................................................. 19
Clinical Measures................................................................................................................. 20
Medication .......................................................................................................................... 20
Cognitive Measures ............................................................................................................. 20
Genotyping & QC process ................................................................................................... 22
TDT Testing and Covariates ................................................................................................. 23
Pseudo-controls and Imputation......................................................................................... 24
Polygenic Signal ................................................................................................................... 24
Major Allele Frequency ....................................................................................................... 26
Results.................................................................................................................................... 27
Discussion.............................................................................................................................. 28
Power................................................................................................................................... 29
Limitations ........................................................................................................................... 30
Use of ADHD Symptom Counts to Generate Polygenic Signals........................................... 30
Major Allele Bias .................................................................................................................. 31
Case/Pseudo-control ........................................................................................................... 32
Future Directions ................................................................................................................. 32
Conclusions.......................................................................................................................... 33
References............................................................................................................................. 35
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Appendix ................................................................................................................................ 44
Appendix 1 – List of Full Gene Names ................................................................................. 44
Appendix 2 – List of Abbreviations ...................................................................................... 45
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Polygenic association in ADHD for the prediction of cognitive performance
Abstract
Attention-deficit hyperactivity disorder (ADHD) is associated with multiple
cognitive performance deficits. Among these Kuntsi et al (2010, Archives of General
Psychiatry) selected the most promising indicators for a multivariate familial factor
analysis. Two familial cognitive impairment factors were identified, which accounted
for 85% and 13% of the familial variance on ADHD. The first, large factor captured
speed and variability of reaction times (RT) and the second factor captured
commission (Ce) and omission (Oe) errors on a go/no-go task. Using the same
sample we now evaluate whether polygenic signals for ADHD generated from
genome-wide SNP data are associated with the most promising cognitive variables.
Analysis used data from the International Multi-Centre ADHD Gene (IMAGE) project.
Transmission disequilibrium test (TDT) was conducted and the data used to generate
polygenic scores in a training sample at a range of different inclusion thresholds.
The best performing threshold (10% of SNP associations) was selected using crossvalidation and used produce a polygenic signal which was then apply to predict
cognitive performance using regression analysis in an independent test set. This
study was not able to detect a positive polygenic signal for ADHD, or able to predict
cognitive performance using this polygenic signal. Power calculations indicated that
the training sample was insufficient in this pilot study, but demonstrated that the
generation of a positive polygenic signal for ADHD should be achievable using
currently available ADHD GWA samples. The study also demonstrates the
effectiveness of case/pseudo-control imputation for dealing with major allele overtransmission bias, as well as suggesting a number of methodological improvements
and directions for future polygenic investigations.
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Introduction
Epidemiology and Impairment
Attention-deficit
hyperactivity
disorder
(ADHD)
is
a
common
neurodevelopmental disorder with a reported average childhood prevalence of 5%,
although reports of prevalence range from 1%-20% (Polanczyk et al., 2007).
Diagnosis persists into adulthood in approximately 15% of childhood cases, with 50%
who no longer meet diagnostic criteria continuing to express residual symptoms and
impairments into adult life (Faraone et al., 2006). Estimates for adult prevalence
ranges from 1-6% (Weiss et al., 1985, Murphy and Barkley, 1996), averaging around
3% (Faraone and Biederman, 2005). Significant variability exists within the reported
prevalence for ADHD, but is attributed to methodological differences rather than
geographical factors. No significant differences are observed in average rates
between North America, Europe, South America, Asia or Oceania (Polanczyk et al.,
2007), or in children with different ethnic backgrounds (Lahey et al., 1994).
Prevalence is highly sensitive to the diagnostic criteria used in defining the diagnosis,
with DSM-III or ICD-10 definitions producing lower prevalence rates against DSM-IV
criteria. Differing research methodologies, such as ignoring the requirement for
impairment, or the use of a single informant opposed to multiple informants can also
greatly influence reported prevalence rates. However, differences in prevalence are
consistently reported to vary by gender and age, being elevated in males over
females and more common in children than adolescents (Polanczyk et al., 2007).
Although generally, ADHD prevalence declines with age, persistence of symptoms is
equal in girls and boys (Biederman et al., 2004), with the ratio of male-females
diagnosed with adult ADHD being around 1.6; comparable to that of child and
adolescent ADHD (Scahill and Schwab-Stone, 2000).
ADHD is characterised by age-inappropriate hyperactivity, inattention and
impulsive behaviours, and is associated with a broad range of negative social,
cognitive and functional outcomes (National Institutes of Health, 2000). Impairments
may appear later than symptom onset, particularly in relation to inattention
behaviours and scholastic achievement (Applegate et al., 1997). In adults ADHD has
been associated with financial difficulties (National Institutes of Health, 2000), and
modestly with unemployment and divorce (Biederman, 2004, Barkley et al., 2002).
ADHD impairment in adults is represented differently to children, and may be
expressed by poor nutrition, increased rates of smoking, aggressive driving
behaviours, increased rates of accidents, legal problems, and workplace challenges
(Faraone et al., 2006, Kessler et al., 2006). Many adult-specific impairments are not
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well captured by current diagnostic criteria, and can also be prone to poor
measurement on account of the lack of insight and limitations of the self-report
methodology used for much adult ADHD research (Kessler et al., 2006). The
negative
functional
consequences
of
persistent
ADHD
may
therefore
be
underestimated, and may represent a significant public health burden.
Diagnostic Classification
DSM-IV (APA, 2000) classification of ADHD consists of 9 inattention items, 6
hyperactivity items and 3 impulsivity items, grouped by inattention and hyperactivityimpulsivity subtypes. A clinical diagnosis of ADHD requires the presence of six or
more symptoms on at least one subscale, which are maladaptive and inconsistent
with developmental level, for a period of at least six months. Symptoms must have
caused some impairment before age 7, in at least two or more settings such as
school and home, and must not occur exclusively as part of another disorder. Clear
evidence of significant implement in social, academic or occupational functioning
must also be present. If scores of six symptoms are present on both subscales then
a diagnosis of combined type ADHD is made, otherwise predominantly inattentive or
predominantly hyperactive-impulsive diagnosis may be applied depending on
symptom distribution. However, these subtypes have been shown to be unstable in
longitudinal follow-up, with 50% changing between subtypes (Valo and Tannock,
2010), and those with the hyperactive subtype being particularly likely to shift to a
combined type diagnosis (Lahey et al., 2005). This may therefore either represent a
tendency for symptom range to broadening with age or more likely, an inaccurate
representation of the underlying disorder by existing diagnostic criteria.
Proposed revisions to ADHD classifications for DSM-5 recognise subtypes as
unstable by redefining them as ‘current presentations’ (APA, 2010). However, the
existing structure has been retained, with the addition of a restrictive inattentive
presentation, requiring six symptoms on the inattentive scale but two or less on the
hyperactive-impulsive scale, for purely inattentive children. DSM-IV criteria were not
designed for the diagnosis of adult ADHD and so have been argued to be suboptimal for this purpose due to a child focused range of symptoms the requirement
for severe impairment and the requirement for a high symptom count.
Further,
childhood level criteria may not account for the adoption of coping mechanisms and
reduction of symptom counts in adult life. DSM-5 proposals directly address these
arguments through increasing the requirement for age of symptom onset to 12,
reducing symptom requirements for older adolescents and adults (ages 17 and older)
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to four, adding adult specific examples to symptom items, and reducing the
impairment criteria from “clinically significant” to “interfere with or reduce quality
of…functioning” (APA, 2000, APA, 2010). With the demonstrated sensitivity of ADHD
prevalence to methodological changes, these proposals are likely to increase
prevalence rates across the whole age spectrum, but especially in adult ADHD, as
well as increasing the heterogeneity of ADHD presentation. However, these
proposed criteria may arguably better represent the underlying phenotype.
Dimensional Trait
ADHD-type symptoms appear throughout the population at subclinical levels,
so ADHD should be considered a dimensional trait (Chen et al., 2008). This
dimensional construct of ADHD would be far better at capturing heterogeneity in
ADHD symptom presentation, but is not represented within current or proposed
DSM-IV and DSM-5 categorical definitions. Further, it has been argued that the
existing
subtype structure
over-represents inattention and under-represents
impulsivity symptoms (Bell, 2011). This structure can also lead to diagnostic
artefacts, such as where scores of five inattention symptoms and five hyperactiveimpulsive symptoms do not meet ADHD diagnostic thresholds. Quantitative genetic
investigations support the concept of ADHD diagnostic criteria as representing the
extreme end of a continuous dimensional trait (Levy et al., 1997) at which meaningful
levels of impairment are likely to be present. Thus, although the epidemiology of
ADHD is better explained as continuous trait, use of a categorical definition is
justified when interested in clinically significant expressions of the syndrome (Haslam
et al., 2006).
Comorbidities
ADHD is highly comorbid with other psychiatric disorders notably; with an
estimated 60-100% reported to display one or more comorbid disorder (Gillberg et
al., 2004). Comorbidities include both externalising and internalising disorders, with
conduct disorder, oppositional defiant disorder, anxiety, depression, bipolar disorder
and substance abuse being most common (Bauermeister et al., 2007, Biederman et
al., 1991, Biederman, 2004, Jensen et al., 1997, Gillberg et al., 2004, Faraone et al.,
2001, Faraone et al., 1997).
increased
functional
Typically those with comorbid problems, show
impairments
and
have
poorer
long-term
prognosis
(Bauermeister et al., 2007). However, currently it is unclear to what extent comorbid
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problems are related on a phenotypic level, or if they predominantly arise through
common
underling
genetic
liabilities
as
a
consequence
of
an
atypical
neurodevelopmental trajectory (Merwood and Asherson, in press). Evidence for
familial associations (Biederman et al., 1991, Faraone et al., 1997, Faraone et al.,
2001), and studies of co-occurrence of cognitive endophenotypes (Rommelse et al.,
2009) indicate possible shared genetic aetiology for some comorbidities. The close
relationship between ADHD and its common comorbidities is further reinforced by
indications that stimulant medication treatment of childhood ADHD may reduce the
number of comorbidities experienced in childhood (Biederman, 2003). However, a
distinction may exist between those with aggressive/conduct type comorbidities and
anxiety type comorbidities (Jensen et al., 1997). This distinction has been further
supported by evidence that late onset ADHD may be associated with less severe
symptoms overall, more anxiety symptoms and more internalising behaviours, but
less with conduct problems related to authority and discipline (Karam et al., 2009).
Further work including studies using polygenic methods is required to further
understand the possible genetic relationships between ADHD and different comorbid
disorders.
Aetiology and Heritability
The aetiology of ADHD is currently unknown but is likely to be a complex
interaction between genetic and environmental factors, with a substantial genetic
component. Familial investigations of ADHD show 1st degree relatives of children
with ADHD have a 4-10 fold elevated risk (Chen et al., 2008), and twin studies
estimate heritability rates of around 62-76% (Faraone et al., 2005, Wood and Neale,
2010). The two symptom dimensions of hyperactivity-impulsivity and inattention also
show substantial overlap in genetic liability (McLoughlin et al., 2007). ADHD has
been found to be highly heritable as either a categorical definition or dimensional
trait, although no specific susceptibility genes have been unequivocally identified.
Molecular Genetic Investigation in ADHD
Early ADHD linkage (Smalley et al., 2002) and GWA studies (Neale et al.,
2008) did not detect common genetic variations with large effect sizes associated
with ADHD. This implies that genetic liability for ADHD is likely to be conferred from
many thousands of genes of very small effect and may be due to additive and
interaction effects (Kuntsi et al., 2006a). This is in line with findings from genetic
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investigations of most other psychiatric disorders, such as bipolar disorder (Sklar et
al., 2008). Consequently, ADHD genetic research has tended to favour either
candidate gene investigations, focused on genes with roles in neural systems
impaired in ADHD (Brookes et al., 2006), or GWA meta-analyses with increasingly
large samples to improve power to detect associations with small effect sizes.
Recently, investigations looking for associations with rare structural variants
of comparatively large effect size have begun to be published, indicating ADHD may
be enriched for rare-variants. As a consequence, this research may indicate that the
role of common genetic variants for genetic liability in ADHD may be smaller than
has usually been assumed. An overview of current literature from the GWA,
candidate gene, and rare variant research in ADHD is presented here, followed by a
discussion of some problems facing genetic research in ADHD which make the
detection of associations difficult. This review will then outline research on cognitive
endophenotypes in ADHD, and the use of these markers in genetically sensitive
designs. Finally this section will outline a method for directly testing the common
variant model in ADHD: polygenic analysis; along with its advantages and potential
applications.
Genome-wide Association Studies (GWA)
GWA studies have as yet been unable to detect associations between
common genetic variations and ADHD in children (Neale et al., 2008, Mick et al.,
2010), or adults (Lesch et al., 2008), at the accepted genome-wide significant
threshold of 5 x 10^-8 (Dudbridge and Gusnanto, 2008). However, two GWA studies
(Lesch et al., 2008, Franke et al., 2009) have reported suggestive associations with
the CDH13 gene (see appendix for full gene names), which has been shown to code
for a neural adhesion protein and has been linked with methamphetamine
dependence (Uhl et al., 2008). CDH13 has also been implicated in ADHD in results
from meta-analysis of genome-wide linkage studies which reported significant
associations with the 16q21–16q24 region (Zhou et al., 2008). The largest GWA to
date, combined four large ADHD samples (2,960 cases, 2,455 controls) and reported
several associations on two regions of chromosome 7 and 8 at the <5x10^-6 level
(Neale et al., 2010). Multiple other top-50 hits were also reported within these same
two regions. The chromosome 7 SNPs fall within a “gene-poor” area of the genome
but the closest gene, SHFM1, is expressed within the brain and has been linked to
proteolysis within cells and congenital physical abnormalities. These functional roles
imply that this gene is particularly important during embryonic development. The
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SNPs on chromosome 8 fall within the coding region for CHMP7, which has been
shown to be expressed in the brain and has a role in protein sorting/recycling. Within
100kb of this region, other genes linked with apoptosis and cell adhesion are also
located
(TNFRSF10D,
TNFRSF10A,
LOXL2).
Although
these
suggestive
associations await replication, and do not meet genome-wide significance thresholds,
the consistency of the top hits falling within these two regions offers promising new
targets for candidate gene investigations and represents evidence for the role of
common genetic variants in the genetic liability of ADHD. Furthermore, the
chromosome 7 region reported in this study has also shown suggestive associations
with major depressive disorder and bipolar disorder (Moskvina et al., 2009) providing
further evidence for a close genetic link between ADHD and its common
comorbidities.
Some GWAs approaches have attempted to examine the genetic
associations with ADHD related quantitative traits. Lasky-Su et al. (2008) and
Faraone et al. (2007), explored associations with age-of-onset. No genome-wide
significant hits were reported but a risk variant for DRD5 was linked to earlier age of
onset, while nominal associations were reported for HTR2A and SLC9A9, which
have been identified as ADHD candidate genes in other studies.
Candidate Gene Approaches
Candidate gene approaches have a reduced requirement for the highly
conservative significance thresholds use in GWAs, but are limited by the need to
specify target genes prior to carrying out the investigation. This prevents this method
detecting novel associations outside the genes of interest, and restricts the method’s
usefulness to providing converging evidence following GWA investigations or for the
study of genes chosen though systemic pathway approaches. Brookes et al. (2006)
identified 51 genes of interest in ADHD predominantly focusing dopaminergic (DA),
noradrenergic (NA) and serotoninergic (5HT) pathways. Many were obvious targets
for candidate gene investigations as stimulant medication used to treat ADHD has a
known action on synaptic DA levels (Swanson et al., 2007), and 5-HT has been
previously linked with poor impulse regulation (Lucki, 1998).
A comprehensive meta-analysis of candidate gene studies by Gizer et al
(2009) concluded there was sufficient evidence to indicate reliable associations with
the dopamine transporter gene (DAT1; SLC6A3), two dopamine receptor genes
(DRD4, DRD5), the serotonin transporter gene (HTT; SLC6A4), one serotonin
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receptor gene (5HT1B; HTR1B) and SNAP25, a gene which codes for proteins
involved in synaptic plasticity and axonal growth. Yet, reports of associations with
these genes have been inconsistent across studies; a likely consequence of sample
heterogeneity and methodological differences in the criteria used to define ADHD.
Further, there are indications that some associations may be stronger across
genders or that the expression of some risk genes is developmentally variable. For
example, it has been suggested a risk variant of the COMT gene which codes for
Catechol-O-methyltransferase, an enzyme which degrades post-synaptic dopamine,
adrenaline and noradrenaline, may be expressed differently across genders
(Dempster et al., 2006). Although a general association with ADHD was not found by
Gizer and colleagues during meta-analysis, in a study with a predominantly male
sample (84%) the risk allele was associated with increased severity of ADHD
symptoms (Palmason et al., 2010). This might indicate differential risk conferred by
the variant across genders (Biederman et al., 2008), offering a potential mechanisms
to explain the observed gender difference in ADHD. However, this might indicate
further heterogeneity of genetic effects making reliable detection of associations in
mixed samples more difficult.
Rare Variants
Recently evidence for the influence of rare (<1% population frequency) copy
number variants (CNVs) on ADHD risk have been published. CNVs are a significant
component of genetic variation and genome-wide analysis of CNVs can be
accomplished in the same way as SNP based GWA studies with use of gene-chips
containing CNV probes. Of particular interest for ADHD genetic research are large
(>500 kb) CNVs that have been shown to have robust associations with other
psychiatric disorders such as schizophrenia and autism (International Schizophrenia
Consortium, 2008, Glessner et al., 2009). Larger CNVs also have the greatest callrate accuracy and show good concordance across platforms (Itsara et al., 2009),
making good initial targets for this approach.
The first analysis of this type did not report significant associations with
ADHD (Elia et al., 2010), yet subsequent investigations have indicated ADHD
samples to be significantly enriched for large CNVs against control samples (14% vs
7%; Williams et al., 2010). A subset of children with ADHD and severe intellectual
disabilities, showed particularly high penetrance for large, rare CNVs (36%), yet rates
of CNVs remained significantly elevated in children with ADHD but without an
intellectual disability. This suggests some CNVs may be associated with ADHD
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independent of cognitive ability, while others are likely to contribute to both. These
CNVs were shown to be both deletions and duplications, and to be a mixture of both
inherited and de novo mutations (11:4), supporting arguments for a functional role in
the development of the ADHD phenotype, and ADHD linked cognitive impairments.
Of the CNVs reported in Williams et al. (2010), seventeen also show associations
with either autism (Glessner et al., 2009, Daly et al., 2008, Roohi et al., 2009,
Kalscheuer et al., 2007) or schizophrenia (Stefansson et al., 2008, International
Schizophrenia Consortium, 2008, Ingason et al., 2011, McCarthy et al., 2009) in
other studies, with CNVs associated with both ADHD and schizophrenia being
predominantly located within the 16p13.11 region. One gene failing within this region
is NDE1 which has previously been linked to neurodevelopmental processes and is
known to interact with the DISC1 gene (Bradshaw et al., 2009).
Investigation of rare CNVs is yielding promising results. However, while
authors of CNV studies are keen to stress that research on common genetic variants
should not be abandoned; these findings present the possibility that the genetic
liability for ADHD could be explained predominantly through many thousands of rarevariants as opposed to the assumed common genetic variation model. If so this
would make these associations very difficult to detect with existing GWA methods,
and make the collection of increasingly large samples for common genetic variance
studies fruitless. In practice, both models are likely to be viable routes to developing
the phenotype, and evidence from other psychiatric disorders with greater depth of
CNV research implies symptoms are likely to arise from large numbers of common
variants of subtle effect in some cases and few larger effect rare variants in others.
However, with the exact contribution of rare variants and common variants to the
genetic liability of ADHD still underdetermined, there is value in directly testing the
common variant model in ADHD using polygenic analysis. Further, issues with
detecting genetic associations in ADHD and the advantages of using a polygenic
analysis are described in the following sections.
Challenges in Detecting Associations with Common Genetic Variants
GWAs for ADHD have thus far failed to detect a genome-wide significant
association with a common variant in ADHD, which is assumed to be due to a lack of
power. Currently, samples for ADHD are substantially smaller than those of other
psychiatric disorders, such as bipolar disorder and schizophrenia, which began to
yield the first genome-wide significant associations at around 2-3 times the sample
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currently available in ADHD.
Evidence from existing candidate gene studies
suggests that effect sizes for individual common variants in ADHD may be around
the 1.12-1.33 range. If common genetic variants do confer risk in ADHD, and effect
sizes are equivalent to those in other disorders, it is estimated around 4000 cases
may be required to have 80% power to detect a genome-wide significant association
with a minor allele frequency of >25% (Corvin et al., 2010). Post-hoc power analysis
from the largest sample to date with nearly 3000 cases, demonstrated effect sizes in
ADHD must be very small as the study reported 98% power to detect effects
contributing more than 0.5% of the variance in the phenotype (Neale et al., 2010).
However, this fell to only 2% power for effect sizes of 0.1%, which is likely to be
closer to the true effect size for many common variants in ADHD. Therefore, current
assumptions are that stronger associations for ADHD will emerge with time as GWA
samples sizes improve, but this has not yet ben conclusively proven. In light of the
evidence that rare variants may also play a role in the genetic liability for ADHD, it
remains justified to attempt to test these assumptions with existing available data
before undertaking the hugely demanding task of collecting additional large GWA
samples.
A second issue which is likely to be making detection of genetic associations
difficult is the heterogeneity of the ADHD phenotype. Symptom expression can vary
widely, as can prevalence based upon the inconsistent application of diagnostic
criteria across research investigations. Consequently, this can create difficulties in
the replication of results between studies and make resolution of the genetic liability
in ADHD challenging. The effects of population stratification in large samples, and
the possibility of differing effect sizes for some variants across genders (Biederman
et al., 2008) add further complexity. Equally, the expression of risk genes may show
age dependent developmental effects, altering production rates of gene products and
increasing phenotypic heterogeneity within the syndrome at different ages. Evidence
of this effect has been presented for DAT1, a gene coding for the dopamine
transporter protein, where the risk variant has stronger influence in adolescence
(Barkley et al., 2006, Elia and Devoto, 2007). COMT activity has also been reported
to correlate with age in animal models (Venero et al., 1991). Therefore,
developmental differences in gene expression and the interaction with risk variants
may account for the tendency of symptoms to decline into adulthood, and explain
why ADHD is typically associated with childhood (Faraone et al., 2000).
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Candidate Cognitive Endophenotypes in ADHD
The variable definitions of ADHD, combined with developmental variability
and gene-environment interactions effects (Kuntsi et al., 2006a) increase
phonotypical heterogeneity, reducing power to detect robust genetic associations.
However, the study of neurobiological processes thought to underpin deficits in
ADHD may help to counter phenotypic variability in the study of genetic associations
(Tye et al., 2011). In this analysis, genetic risk markers for ADHD have been used to
predicted cognitive performance on indices of established ADHD-linked cognitive
endophenotypes. According to Gottesman and Gould (2003) endophenotypes must
be associated with the clinical disorder, share overlapping genetic influences and be
present in non-affected family members at a higher rate than the general population.
Broadly, many impairments in ADHD relate to general categories of
attentional-arousal problems or executive function deficits. Specifically, cognitive
impairments include deficits in working memory, planning and organisation, set
shifting, processing speed, attention regulation, variability in reaction times,
impulsivity and response inhibition (Willcutt et al., 2005). A general IQ deficit of 7-12
points is also commonly associated with ADHD (Kuntsi et al., 2004), and measures
of reaction times, response inhibition and sustained attention (indexed by
commission errors and omission errors respectively), consistently show impaired
performance in ADHD (Willcutt et al., 2005, Klein et al., 2006, Johnson et al., 2009,
Kuntsi et al., 2009, Wood et al., 2010). Incentive factors have been shown to play a
role in performance on reaction time tasks, as performance can be significantly
improved under incentive conditions (Kuntsi et al., 2009, Uebel et al., 2010, Andreou
et al., 2007). However, incentives have not been shown to influence performance on
measures of omission or commission errors, suggesting different underling
processes (Uebel et al., 2010)
Variability in reaction time (RTV), demonstrates strong associations with
ADHD in children and adolescents (Klein et al., 2006, Rommelse, 2008, Wood et al.,
2010, Kuntsi et al., 2010) as well as adults (McLoughlin et al., 2010). This
endophenotype has been shown to have a heritability of around 50-60%, and has an
estimated familial correlation with ADHD of around 0.74 (Kuntsi et al., 2010).
Measures of attention and executive function, including inhibition, have been
demonstrated to be moderately heritable, and to share a genetic association with
ADHD (Doyle et al., 2005). As there is little evidence of shared environmental effects
in either ADHD or related cognitive variables, familial effects can be assumed to be
largely genetic in origin (Andreou et al., 2007). Similarly, IQ deficits which are also
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moderately strongly heritable have been linked with ADHD due to shared familial
genetic influences (Kuntsi et al., 2004, Polderman et al., 2006, Wood et al., 2010,
Wood et al., 2011).
In molecular genetic investigations of cognitive endophenotypes for ADHD,
most research has been focused on risk variants of DAT1, DRD4 and COMT, which
are among a number of key candidate genes for ADHD (Brookes et al., 2006).
DRD4 has been associated with increased RTV, and to a lesser extent
impulsiveness, but not response inhibition on the Go/NoGo task. DAT1 variants have
also been associated with increased RTV in a number of investigations, and in some
reports have been found to be associated with rates of omission and commission
errors on the continuous performance task. In contrast, no associations have been
found between COMT and RTV on constant performance or Go/NoGo tasks, but
associations have been reported with choice impulsivity in delay discounting tasks.
(See Kebir et al., 2009 for a detailed review of current literature). Therefore, although
evidence for molecular genetic associations with cognitive endophenotypes is still
limited, in combination with familial evidence, it provides initial indications that these
cognitive endophenotypes are genetically linked to ADHD.
Using multivariate familial factor analysis, Kuntsi and colleagues (2010)
demonstrated that familial factors for cognitive performance indicators of mean
reaction time (MRT), reaction time variability (RTV), omission errors and commission
errors separated into two separate factors. Both of these factors were associated
with ADHD. The larger factor accounted for the familial variance for MRT and RTV
and shared 85% of its familial variance with ADHD; the second factor captured
variance for omission and commission errors and shared 13% of its familial variance
with ADHD. This study indicates that two separate processes may underlie cognitive
impairments in ADHD. One factor captures speed and consistency of reaction time
performance, and may relate to arousal regulation; while the second factor captures
omission and commission errors and may relate to sustained attention and inhibition
or executive function. This division of processes is supported by previous findings
that incentives appear to normalise performance on indices of arousal-regulation, but
not those of executive function (Kuntsi et al., 2009, Uebel et al., 2010). In further
work it was demonstrated that genetic co-variance between reaction time variability
or errors, and ADHD was largely independent of IQ (Wood et al., 2010, Wood et al.,
2011).
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It is therefore warranted to make use of markers of brain function, such as
cognitive performance deficits, as endophenotypes for the investigation of ADHD to
understand the relationship between underlying endophenotypes and expression of
the ADHD phenotype. Evidence that cognitive impairments continue into adulthood,
even following remission of childhood diagnosis of ADHD (Faraone et al., 2006)
suggests some neurobiological correlates may represent more stable phenotypes
than the broad ADHD phenotype itself. Furthermore, it might be assumed that
genotypes underlying cognitive performance deficits or specific neurobiological
pathways would be less genetically diverse than the sum of all genotypic
associations with the ADHD phenotype; theoretically increasing power to detect
genetic associations (Gottesman and Gould, 2003).
However, although this
approach may reduce heterogeneity within samples, available GWA samples sizes
are still insufficient to reliability detect novel genetic associations with ADHD or
cognitive endophenotypes. To overcome some of these issues, this study adopted a
method of using polygenic association using common genetic variants in ADHD to
examine the genetic relationship with ADHD-linked endophenotypes of reaction time
variability, omission errors, commission errors and IQ. In examining the genetic
relationship between these underlying cognitive functions and the broad ADHD
phenotype, this type of analysis may help to understand the pathways from genetic
risk variants to cognitive impairments and expressed ADHD behaviours and identify
broad loci for novel candidate gene investigations based these identified systemic
pathways.
Polygenic Association
The use of polygenic association approaches using existing GWA data, can
overcome some of the challenges associated with looking for genetic associations in
ADHD, and offers a novel technique for the understanding of shared genetic liability
between ADHD and its comorbidities or endophenotypes. Polygenic association
utilises the suggestive genetic associations detected in GWA studies on “en masse”
to produce a risk score, which is able to predict the phenotype or related quantitative
trait in an independent test sample. From the many thousands of associations
detected in a typical GWA study very few will reach genome-wide significance.
However, many will be genuine associations with the phenotype but due to small
effect sizes of individual variants they are not able to be differentiated from the many
false associations present in the analysis. This necessitates the adoption of stringent
significance thresholds in GWA studies, and forces most of these genuine
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associations to be ignored. Yet, through combining a large number of these small
associations a useful polygenic ‘signal’ can emerge. This approach was successfully
used by the International Schizophrenia Consortium, to discriminate cases from
controls in an independent sample using a polygenic signal derived from
schizophrenia GWA data (Purcell et al., 2009). Critically, this polygenic score had
predictive power even though the signal explained only 3% of the underlying
variance and no genome-wide significant associations were reported in the dataset.
Given that the power of the polygenic signal comes from the combination of many
associations, polygenic analysis lacks the fidelity of GWAs to provide evidence of
specific associations with individual genetic markers. Rather, polygenic analysis can
demonstrate that the overall common genetic variation plays a role in the heritability
of disorder, and with large samples, may be able to estimate the relative
contributions of common variants against rare variants to the observed heritability. It
follows that if the contribution of the common genetic variant model in a disorder can
be demonstrated, then assumptions regarding the potential of GWA studies to detect
specific associations with expanding sample sizes are justified.
More broadly this technique may be applied to investigate the genetic overlap
between two different disorders, comorbid disorders, other traits or endophenotypes.
In their polygenic investigation of schizophrenia The International Schizophrenia
Consortium demonstrated they were able to use a schizophrenia polygenic signal to
predict bipolar disorder status in an independent sample, suggesting a shared
genetic liability between the two disorders and indicating a pleiotropic effect for
common variants. The polygenic signal was also found not to be predictive of six
other
non-psychiatric diseases
(coronary artery
disease,
Crohn’s disease,
hypertension, rheumatoid arthritis, type I and type II diabetes), demonstrating a
valuable degree of specificity in the investigation of genetically-related phenotypes.
However, at the time of writing polygenic analysis methods have not been
applied to psychiatric disorders other than schizophrenia, despite the substantial
quantity of available GWA data for a range of phenotypes. This investigation now
progresses the application of the polygenic analysis method in the investigation of
cognitive performance indicators in ADHD.
Aims
This pilot study aimed to test the feasibility of conducting polygenic analysis in
ADHD and to test methods for overcoming potential problems arising from the use of
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family based GWA data. An a priori hypothesis was made that risk markers for
ADHD will be associated with impaired cognitive performance, and thus a polygenic
signal for ADHD will be predictive of impaired cognitive performance on four indexes
(reaction time variability, omission errors, commission errors, IQ) which reliability
detect deficits in ADHD.
Method
Sample Selection
Data from ADHD parent-proband trios was available from the International
Multicentre ADHD Genetics (IMAGE) project. The IMAGE consortium consisted of
12 sites across eight countries (Belgium, Germany, Ireland, Israel, the Netherlands,
Spain, Switzerland, and the United Kingdom), which collected phenotypic and
genotype data on trios using standardised procedures. Eight of these 12 sites set up
a further collaboration to collect additional cognitive performance data. Standard
IMAGE exclusion criteria and QC procedures were applied to sample. Probands
were aged between 6 and 17 years. Cases we excluded if they reported autism,
epilepsy, a neurological disorder, a disorder with externalizing behaviours which
could be mistaken for ADHD, or IQ <70. This study focused on probands with a
diagnosis of ADHD combined type only, as these were the most common subgroup
within the sample.
Initially 862 QC’ed parent-proband trios were available before polygenic
analysis exclusion criteria were applied. 43 cases were excluded as they met criteria
for the inattentive or hyperactive subtypes (11 inattentive, 32 hyperactive).
This
sample of 819 was then divided into a training set for the generation of the polygenic
signal, and an independent test set. All cases with cognitive performance data
available were included in the test set (n=338). The remaining cases (n=454)
comprised the training set, which was further subdivided into five subsets that were
stratified by site so that an equal number of participants from each site was
represented in each of the five subsets. Where the majority of the cases from a site
were included with the cognitive test set, leaving a small number of cases (<10)
without cognitive data, those remaining cases were excluded from the sample rather
than populate the training subsets with individual cases from single site. This process
resulted in 27 cases across four sites being removed. The final number of cases
surviving all stages of QC and selection was 792.
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Clinical Measures
Under standard IMAGE protocols all probands were assessed by a
paediatrician or child psychiatrist using the Parental Account of Childhood Symptom
(PACS; Taylor et al., 1986). The PACS is semi-structured and designed to be
objective measure of child behaviour. The measure obtains detailed descriptions of
the child’s typical behaviour in a range of situations, both in the past week and the
past year. Situations are defined by external events or behaviours, with interviewers
rating severity and frequency on a four point scale for each situation. The measure
was administered by trained interviewers and had an inter-rater reliability ranging
between 0.79 to 0.96.
PACS scores were processed using a standardized algorithm to generate
DSM-IV symptom counts based upon the 18 DSM-IV ADHD items. Information from
the PACS was supplemented by items scoring in the mid to high range on the
teacher rated Conners’ ADHD subscale to provide robustness through use of another
rater of child behaviour in a second environment. Cases with potential comorbid
autism spectrum disorders, which might confound the analysis, were excluded on the
basis of atypical scores on both the social communication questionnaire, and the prosocial scale of the strengths and difficulties questionnaire (SDQ).
Medication
As stimulant medication can alter the expression of ADHD symptoms and
behaviours, probands were asked to not take stimulant medications for minimum of
48 hours prior to undertaking research assessments. Where this was not possible,
interviewers endeavoured to rate the child’s typical behaviours during past
medication free periods. Any proband without a medication free period in the past
two years was excluded from the sample. Cognitive data was not used unless the
participant was medication free during assessments.
Cognitive Measures
All participants completed four subtests (vocabulary, similarities, picture
completion, block design) of the Wechsler Intelligence Scales for Children, Third
Edition (WISC), or Wechsler Adult Intelligence Scale, fourth edition (WAIS) for
probands aged 16 or over, for an estimate of IQ.
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Eight sites conducted additional cognitive performance assessments in
addition to genotype and behavioural assessments. Probands were assessed on a
visual Go/No-Go task and a reaction time task: “The Fast Task”.
The Go/No-Go task (Kuntsi et al., 2005) required participants to respond as
quickly as possible to “go” stimuli, but not to “no-go” stimuli, whilst retaining accuracy.
The rate of “go” to “no-go” stimuli was 4:1, with stimuli being presented for 300
milliseconds. Data was collected on number of errors and reaction time (RT). Three
conditions matched on duration were run: slow (72 trials, 8-second inter-stimulus
interval), fast (462 trials, 1 second inter-stimulus interval) and incentive. Only
performance on fast and slow conditions was utilised for this analysis, as these
conditions showed the strongest phenotypic and familial association with ADHD
(Uebel et al., 2010). Presentation of fast and slow conditions was counterbalanced
by participant.
The Fast Task (Kuntsi et al., 2006b, Andreou et al., 2007), involved a
baseline condition consisting of 72 trials with an 8-second inter-stimulus interval, and
a fast-incentive condition with a 1-second inter-stimulus interval. Only reaction time
data from the baseline condition was used in this analysis. Participants saw four
empty circles on the screen at the beginning of the inter-stimulus interval and after 8
seconds one of these circles became coloured. Participants responded to this target
signal by pressing a corresponding response key, after which the stimuli disappeared
from the screen and a new trial started after a 2.5 second blank-screen period. At
the start of the paradigm participants were informed both speed and accuracy of
response was equally important.
High familial correlations (0.69–0.83), have been reported across the fasttask and Go/NoGo task and between the fast and slow conditions of the Go/NoGo
task, indicating performance on these cognitive indices measure the same two
underlying factors (Wood et al., 2011). This is useful as it allow combination of data
from similar, but not identical paradigms to increase power to detect associations
with these unitary constructs. The use of composite variables is also beneficial to
reduce the total number of variables in the analysis, limiting the potential burden of
multiple testing corrections. On this basis standard deviation of reaction time, or
‘reaction time variability’ (RTV), was selected as the sole reaction time variable over
mean reaction time (MRT), as RTV has consistently stronger and more robust
associations with ADHD.
Performance data was combined into three composite variables of omission
errors (Oe), commission errors (Ce), and RTV. Missing data existed for some
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cognitive variables due to two teams not using the go/no-go task, two not using the
fast-task, and due to occasional technical issues with data collection. Due to the
limited number of cases with cognitive data available, efforts were taken to utilise the
largest amount of data possible. Where data from both conditions was present a
composite score using means from both conditions was used, however where a
score from only one condition was available this score was used in place of a
composite score to conserve the amount of data available for analysis. A total of 338
probands had at least one cognitive performance index available (Table 1).
Table 1. Composition of cognitive performance variables and number of cases
available for each index.
Composite Variable
Condition
contributing
to composite
variable
Number of cases
Number of cases
with data available with data available
by condition
in final composite
score
Omission errors (Oe)
Go/NoGo fast
Go/NoGo slow
249
244
249
Commission errors (Ce)
Go/NoGo fast
Go/NoGo slow
249
244
249
Std. deviation of reaction
time (RTV)
Go/NoGo slow
fast-task baseline
244
147
277
Total number of cases with
at least one cognitive
performance indicator
available
338
Genotyping & QC process
Standard IMAGE genotyping and quality control (QC) procedures were
applied to the data prior to conducting the polygenic analysis. These are fully
documented in Neale et al. (2008), but a summary is provided here for convenience.
Perlegen 600K genotype platform was used to genotype DNA samples extracted
from blood. Samples were stored at the Rutgers depository and QC was undertaken
by The National Centre for Biotechnology Information (NCBI) using the GAIN QA/QC
Software Package (Version 0.7.4). Cases were excluded where call rate was <87%
(this threshold was chosen based on the distribution of missingness), gender
discrepancy existed between genotype and phenotype data, sample heterozygosity
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<32% or a per-family Mendelian error of >2%.
SNP QC exclusion criteria was
Mendel errors >4, duplicate sample discordance (>1/15), Hardy–Weinberg
disequilibrium (P<0.000001), and call rate conditional on minor allele frequency
(MAF).
In family-based association analysis the major allele can appear over-
transmitted, due the errors in calling minor alleles. During QC extra care was taken
to examine the relationship between minor allele frequency (MAF) and call rate in
association testing. As a result, three criteria were adopted for QC call-rate
thresholds: 0.01≤MAF<0.05 with a call rate of ≥99%; 0.05≤MAF<0.10 with a call rate
of ≥97%; and 010≥MAF with a call rate of ≥95%. For a complete account of this
process see Neale et al. (2008). After this procedure a mild inflation of the major
allele remained (222, 089 to 208, 838 SNPs), thus a number of post-QC procedures,
described in detail later, was undertaken to correct for this bias.
TDT Testing and Covariates
Family based associations were tested using a transmission/disequilibrium
test (TDT) (Spielman et al., 1993) implemented in PLINK (Purcell et al., 2007). TDT
is a method of directly testing associations between disorders and genetic marker in
family data through the comparison of transmitted alleles. The test requires at least
one parent to be heterozygous for a risk allele, and that the marker allele is in linkage
disequilibrium (LD) in the sample population. Generation of the test statistic is based
on the frequency of transmission of the risk allele to the affected probands from
parents, against transmission of another allele. This technique can be applied to
parent-proband trio data and does not require data from other unaffected/affected
siblings, making this method well suited to applications involving disorders which
may have low-penetrance within families.
Unlike other methods for association testing, TDT tests does not suffer
artifactual associations as a product of population stratification. The use of principle
components to control for population stratification was therefore not required in this
analysis. However, some phenotypic differences did exist between collection sites,
the largest of these differences being a significantly higher mean age at some sites
over others. To control for these differences, participants in the training sample were
randomised to subsets stratified by site, so that each subset consisted of an equal
numbers of cases from each site. There were no significant differences in mean age
or gender between the subsets. As an additional step, age, gender and dummy
coded site variables were included as covariates in the TDT analysis.
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Pseudo-controls and Imputation
Where trio data is available and transmitted/untransmitted alleles are known,
up to three potential ‘pseudo-controls’ can be generated at each locus derived from
the alternative combinations of parental alleles, based upon the conditional likelihood
output from TDT. Although multiple pseudo-controls can be generated, the use of a
single pseudo-control derived from both untransmitted alleles have been shown to be
most efficient (Cordell and Clayton, 2002), and was the only iteration generated for
this analysis. The advantage of a case/pseudo-control design is that it allows parentproband trio data to be to be analysed in a similar way to case-control data, and
provides the necessary format to conduct the polygenic analysis. The limitation of
this method over a case-control samples is a small reduction in power (Cordell et al.,
2004), however the convenience of not requiring a sample of screened control trios
makes this approach justified for this initial pilot investigation.
Imputation was carried out for ungenotyped SNPs and missing data. Due to
limitations of conditional logistic regression this step was necessary to allow standard
statistical packages to be used for the analysis. Generation of this data can be
performed accurately as SNPs often have high levels of LD. In this study, imputation
of missing data in parent-proband trios and generation of the pseudo-controls was
conducted prior to the polygenic analysis as part of the ADHD GWA meta-analysis
conducted by Neale and colleagues (2010).
In summary, HapMap Phase III
European CEU and TSI samples were used as reference sets (Thorisson et al.,
2005, The International HapMap Comsortium, 2003). Trios were phased in Beagle,
then pseudo-controls generated from resulting likelihood estimates of transmitted
and non-transmitted alleles, using a haplotype relative risk test (Knapp et al., 1993).
Imputation was then conducted for both case and pseudo-controls in Beagle
(Browning and Browning, 2009) and tested for accuracy using a logistic regression
model in MACH2DAT (Moskvina et al., 2009). Poorly imputed SNPS were excluded
from the dataset before analysis.
Polygenic Signal
Associations of genetic markers with ADHD symptom scores on the PACS
were created in the training sample. SNP associations with high symptom scores
were split by major and minor alleles and ranked by odds ratio, creating two SNP
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lists sorted by association strength. As a polygenic signal comprises of many nominal
associations, it will by nature contain many false associations unrelated to the
phonotypical trait, on account of the huge number of association tests that have been
carried out. As the effect of common genetic variants on risk is very small, as with
GWA studies the detection of robust associations is dependent on the power
provided by large sample sizes. As sample size increases, more true associations
“rise to the top” showing stronger associations with the phenotype, making it
increasing likely that they will contribute to the polygenic signal. Therefore, the size of
the training sample is key in creating a positive polygenic signal.
Following this, within any given data set, the quality of associations included
in the polygenic signal can be managed through adoption of inclusion thresholds. At
increasingly lenient thresholds a greater number of associations will be included in
the polygenic signal but these associations have a greater likelihood of being
statistical artefact unrelated to the trait. As a consequence of inclusion these
associations will have a net effect of reducing the predictive power of the polygenic
signal. Conversely, adopting too stringent a threshold may also reduce predictive
power by excluding too many true associations.
Therefore, to optimise the number of associations used to generate the
polygenic signal, a range of thresholds was tested in the training set using a crossvalidation procedure. Arbitrary thresholds of 0.1%, 1%, 10% and 100% of
associations were tested, using equal numbers of major and minor alleles from the
ranked SNP lists. For example, associations included in the 1% threshold were
comprised of the top 1% of SNPs from the major allele list and the top 1% from the
minor allele list. As a consequence of stratifying associations by major and minor
alleles, the total number of SNPs included in the 100% threshold was twice the total
number of minor alleles, rather than every association detected during TDT.
Cross-validation was conducted by dividing the training set into five equal
subgroups. Output from TDT from four training subsets were combined to create a
polygenic signal based in ADHD trait scores. A linear regression model was then
used to test if this four-subset polygenic signal was able to predict ADHD trait scores
in the fifth unused training subset.
Further iterations were run with different
combinations of four subsets being tested on the fifth, until all subgroups had been
tested. The mean effect size across all five iterations provides an index of the
thresholds’ predictive power for ADHD traits. This cross-validation was conducted for
each association threshold (0.1%, 1%, 10% and 100%), following which the best
performing threshold was selected for the generation of a polygenic signal using the
entire training sample. A linear regression model was then used to apply this
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polygenic signal for ADHD to the test set for the prediction of performance on the
cognitive variables of RTV, Ce, Oe, and IQ.
Major Allele Frequency
Family-based association studies show a systematic basis for major allele
transmission in genotype samples, due to allele calling bias. Heterozygous alleles
are harder to call accurately resulting in more missing data for minor alleles,
particularly those with low minor allele frequency (MAF).
The resultant over-
transmission of major alleles causes artefact in family-based association studies
(Hirschhorn and Daly, 2005, Cutler et al., 2001, Gordon et al., 2002), but may be
mitigated in case-control samples if the effect on the association statistic is the equal
in both groups (Clayton et al., 2005). However, under a case/pseudo-control design,
the pseudo-control data is imputed free of calling error, and thus the major-allele
over-transmission bias appears in case samples only. If uncorrected, any polygenic
signal generated from this data will be dominated by the major allele overtransmission in cases rather than being based on ADHD traits scores.
This analysis attempts to address this problem three-fold. Firstly, the IMAGE
QC procedures described previously sorted SNPs by MAF and set higher call-rate
thresholds for SNPs with the lower MAF (e.g. 0.01≤MAF<0.05, call rate of ≥99%).
Adoption of more stringent thresholds for low MAF SNPs should have reduced the
proportions of inaccurately called SNPs in the case genotype data. Secondly, the
conditional likelihood imputation step carried out for case data, and the generation of
pseudo-controls will replace any missing data, including that from poorly called minor
alleles, balancing up a proportion of the major allele bias. Thirdly, a conservative
approach was adopted during SNP selection for the polygenic score. Top
associations in the TDT analysis were ranked and stratified by major and minor
alleles, and equal numbers of minor and major alleles then selected for each
threshold. This method ensured that associations with major and minor allele
contributed equally to the polygenic signal, preventing any remained major allele
over-transmission effects from dominating the polygenic signal.
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Results
Results for the cross-validation in the training set indicated no threshold
produced a polygenic signal predictive of ADHD trait scores in the training sample at
a p<0.05 level (Figure 1). Variance explained by the signal was less than 0.5% in all
cases. The 0.1% threshold was the worst preforming, while the 1%, 10% and 100%
thresholds performed similarly. The 10% threshold was marginally more significant
than the 1% and 100% thresholds (p=0.17, against p=0.18), and despite the overall
non-significance was carried forward to generate the polygenic signal for testing
against the training set. However, this signal did not significantly predict cognitive
performance for the indexes of Oe, Ce, RTV or IQ (Figure 2). Variance explained by
the signal in each case was negligible.
1
0.9
0.8
0.7
0.6
r^2
0.5
p=0.18
p=0.17
1%
10%
0.4
p=0.18
0.3
0.2
0.1
0
p=0.77
0.1%
100%
Figure 1. Variance of ADHD symptom scores explained by a polygenic signal
derived from four percentage thresholds of major and minor SNP associations
included in the signal. No threshold significantly predicted ADHD symptom scores
during cross-validation.
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1
0.9
0.8
0.7
0.6
r^2
0.5
0.4
0.3
0.2
p=0.62
0.1
p=0.83
0
Oe
Ce
p=0.96
p=0.93
RTV
IQ
Figure 2. Variance for cognitive variables explained by a polygenic signal for ADHD
symptom scores using the best performing threshold of 10% of associated major and
minor SNPs. The polygenic signal did not significantly predict scores on any
cognitive performance index.
Discussion
This study was not able to generate a positive polygenic signal for ADHD
after testing four different association thresholds in the training set (0.1%, 1%, 10%
and 100% of all associations). The best performing threshold of 10% was unable to
predict cognitive performance in the test set. The non-significant polygenic predictor,
explaining less than 0.5% of the variance for ADHD trait scores in the training set,
indicates a lack of power in the training set. The aim of this investigation was to
predict cognitive performance using a polygenic signal for ADHD. In doing so, this
investigation also aimed to directly test the common genetic variant model for ADHD.
As it was not possible to generate a positive polygenic signal in this analysis, this
investigation was unable to directly address these aims. It is important to highlight
that the failure to detect a polygenic signal for ADHD in this study does not provide
evidence for a rare-variant model over the common genetic variant model in ADHD.
Nor is it possible to make comment regarding the relationships between those
genetic variants conferring risk of ADHD and indexes of cognitive performance.
Rather, it implies that methodological limitations is the most likely candidate for the
lack of a polygenic signal, and only following improvements to the design might the
method of polygenic analysis be in a position to provide in evidence in support of
either model. Nevertheless, this pilot study provides a valuable first test of polygenic
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analysis methods in ADHD, which will aid evaluation of specific polygenic
methodologies for use in subsequent investigations.
Power
The predictive power of a polygenic score is based upon the strength of the
association detected with TDT. Like GWA studies, sample size is critical to produce
strong associations given the very small effect size of each individual variant on
disease risk. The failure to produce a positive polygenic signal in this study is
probably due to a lack of power in the training sample. For contrast, the International
Schizophrenia Consortium (Purcell et al., 2009) used a case-control sample of nearly
7000 to produce a polygenic signal explaining around 3% of the variance in
schizophrenia. Although the phenotypes are not comparable, as ADHD and
schizophrenia may have substantially different genetic aetiologies, it does imply that
training sample size may be the primary limitation in this pilot investigation.
However, the data from the cross-validation threshold testing procedure
remains valuable as it represents the first example of effect sizes for a polygenic
signal in ADHD. Using the effect size from the best performing threshold (10%),
power calculations were carried out (Figure 3). These indicated that this pilot study
had 65% power to generate a positive polygenic predictor of ADHD at a p<0.05 level,
with a training set of 464 trios (equivalent to a 928 case-control sample).
Extrapolating from this, these calculations predict that 95% power can be achieved at
a sample size of 2500, equivalent to 1250 trios using the pseudo-control design. This
demonstrates that a polygenic approach in ADHD is feasible using existing ADHD
GWA samples and that an immediate future goal should be to repeat this analysis
with a larger training sample to attempt to generate a positive polygenic predictor for
ADHD. Furthermore, it is known that the pseudo-control design provides less power
to detect associations over a case-control design (Cordell et al., 2004). While
adoption of this method was justified for this initial pilot investigation, use of casecontrol sample in future could yield increased predictive power in the polygenic
signal. Hence, predictions of sample sizes presented here from case/pseudo-control
data may be overestimated.
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95% power: n=2473 predicted
80% power: n=1415 predicted
Pilot: effective n=928 (464 trios)
Figure 3. Power calculation using the effect size from the best performing polygenic
signal for ADHD trait scores in the training sample. This indicates that this pilot study
was underpowered, and gives estimates of the training sample required to give 80%
and 95% power to generate a predictive polygenic signal for ADHD at the p=0.05
level, assuming case-control design. Under the pseudo-control design, the number of
trios required would be half these estimates for case-control designs.
Limitations
This pilot study suffered some methodological limitations due to the
characteristics of the data used in the investigation. However, the outcomes remain
valuable in providing evidence for the efficacy of the methodology used and enable
this investigation to offer recommendations for methodological improvements for
future polygenic follow-up studies.
Use of ADHD Symptom Counts to Generate Polygenic Signals
Using ADHD symptom counts to generate the polygenic signal may have
been a limiting factor in this investigation. The ADHD probands in this sample had a
limited range of ADHD trait scores. Possible ADHD trait scores on the PACS range
from 0 to 90, although the proband sample had a range of 42-90 (Q1=71; Q3=84).
Pseudo-controls were assumed to have a score of 0. It is logical that future studies
may wish to examine the predictive power of an ADHD polygenic signal on
quantitative trait scores for ADHD symptoms. Such questions may shed light upon
the relationships between ADHD risk genes and symptom counts, and may imply
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general additive or more precise systemic models of action by the cumulative effect
of ADHD risk genes. However, these questions would not have been possible to
address with this analysis due to the limited range of trait scores available, and
because the pseudo-controls were prescribed an arbitrary trait score of 0. In studies
wishing to address questions related to quantitative trait scores, use of a screened
case-control sample with a representative range of ADHD symptom scores would be
the optimum design.
Furthermore, use of ADHD symptom scores to generate the polygenic signal
in this analysis created a confound as the strength of the genetic variants associated
with ADHD would have been weighted by this score, which displayed both a ceiling
effect and a negative skew on the distribution. Additionally, these trait scores are
unlikely to be a product of just genetic risk factors; as environmental factors will also
meditate the quantity of expressed symptoms. Therefore, a better approach in view
of this limitation would have been to use ADHD status as a categorical case-control
definition for the TDT. Using ADHD status would have been more robust as it would
not influence the association scores by weighting, and is free from confounds not
related to genetic liability which may influence trait scores. This method is therefore
recommended for subsequent analysis for the generation of the polygenic signal in
the training set. For the study of ADHD quantitative trait scores, a viable approach
would be to generate the polygenic signal using ADHD status in the training set but
then apply this signal to predict ADHD trait scores in the test set, providing a
sufficient range of scores is available.
Major Allele Bias
The IMAGE sample has been shown to have an over-transmission bias for
major-alleles, common in family based analysis due to inaccurate minor allele calling.
Without correction, this would have resulted in a systematic bias in the polygenic
signal for the major allele. To mitigate this potential risk, this study incorporated
three procedures to counter the major allele bias: strict QC, imputation and use of
equal numbers of major and minor allele within each polygenic signal. However, the
frequencies of the major and minor alleles in the sample were similar following
imputation (588999 major alleles to 586100 minor alleles). This suggests that the
imputation step used as part of the case/pseudo-control design was successful in
removing much of the bias, and that the stratification of SNPs by major and minor
alleles was unnecessary. Although this study lacked power, the outcomes would
recommend imputation through the case/pseudo-control procedure as an effective
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counter to major-allele bias. Further, the bias is argued to be less of a problem in
case-control samples, providing it is equally present for both cases and controls
(Clayton et al., 2005). Therefore, combining imputation with case-control samples
should make subsequent polygenic analysis studies resistant to major-allele overtransmission bias without the need to adopt additional correction procedures.
Case/Pseudo-control
As discussed, screened case-control samples have an advantage over
case/pseudo-control samples for polygenic analysis, particularly for addressing
research questions regarding the prediction of ADHD trait scores from genetic
liability. However, despite some limitations imposed by using pseudo-controls in this
data set, power calculations undertaken as part of this investigation imply that this
method may be successful at generating a positive polygenic signal with a relatively
modest expansion of the training sample (700-1250 trios required). Therefore use of
pseudo-controls should still be considered a viable method to utilise existing trio
samples for polygenic analysis where screened case-control data is unavailable.
Future Directions
The first goal of a follow-up investigation is to increase the size of the training
set, using screened case-control data, which may help mitigate some of the primary
limitations of this pilot study. Analyses are planned using part of the multi-consortium
sample used for the largest ADHD GWA to data (Neale et al., 2010). Around 3000
cases and 2500 independent controls are available (of which around 900 cases
come from the IMAGE sample). From the power estimates presented here, this
should provide an ample training sample for the generation of a positive polygenic
score, significantly improving the amount of variance explained and the predictive
power of the ADHD signal. If successful in producing a polygenic signal predictive for
ADHD in the training sample, this study may then be able to demonstrate that
common genetic variants play a role in conferring some ADHD risk. Following
successful generation of a polygenic signal for ADHD, the next goal of this extension
will be to rerun the analysis to test of the prediction of cognitive performance
variables in ADHD.
Successful predictions may indicate that some part of the
genetic risk for ADHD operates through the genetic liability underlying cognitive
abilities. However, it would be necessary to demonstrate that the genetic liability for
ADHD operates through the endophenotype, as opposed to it being an independent
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MSc. Dissertation
epiphenomenon arising from the same common genetic variants (Kendler and Neale,
2010). In light of this, follow up work could look at the mediation of ADHD risk by the
variance for cognitive factors, which may in turn offer evidence to validate or falsify
various models of potential relationships between the endophenotypes of ADHD and
common comorbidities (Rommelse et al., 2011). Examining the relationships
between ADHD, comorbidities and cognitive endophenotypes in this way will
specifically help researchers to understand the pathways from genetic risk variants to
cognitive impairments and other behaviours.
An alternative progression may be to explore if different parts of the polygenic
signal for ADHD are predictive of cognitive outcomes. For example, as familial
influence of IQ and RTV in ADHD has been shown to be largely independent (Wood
et al., 2011), it might be predicted that different parts of the signal (i.e. a different
profile of risk associations) would be predictive of different cognitive outcomes.
These developments may be valuable to identify regions of interest or novel
candidates for candidate gene studies in relation to genes underlying the ADHD
linked cognitive endophenotypes, without the requirement to obtain larger GWA
samples with associated cognitive performance data.
Conclusions
In conclusion, this pilot investigation was unsuccessful in generating a
significant polygenic predictor of ADHD using a training set of 464 parent-proband
trios and matched pseudo-controls. However, this study yielded useful effect sizes,
allowing power calculations to be carried out which indicated that the generation of a
polygenic signal for ADHD appears to be feasible using currently available ADHD
GWA data. Further, this study trialled a number of methodologies for use in polygenic
analysis, allowing recommendations for future study design to be made. Firstly, strict
QC in conjunction with imputation for missing case data was found to be successful
as correcting for the major-allele over transmission bias commonly found in family
based association testing. Secondly, use of an ADHD status score, over ADHD trait
scores, would have been the better technique to adopt during TDT to generate the
polygenic signal, as it would have been more robust in relation to non-genetic
confounds influencing the expression of symptoms. Thirdly, case-control data might
improve power and may mitigate problems with major allele over-transmission, as
well as offering the potential to examine relationships between genetic risk and
ADHD trait scores, if the range of scores is representative. However, it has been
demonstrated here that for specific investigations not related to ADHD trait scores,
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with methodological improvements, the case/pseudo-control design may upscale
effectively and would remain useful where case-control data in unavailable. The goal
of planned future investigations is to produce a polygenic signal for ADHD using a
large case-control sample, which can then be applied to predict cognitive
performance. If successful, progressions of this design may look at mediation of
ADHD risk by the variance associated with cognitive variables or isolation of specific
parts of the polygenic signal that are associated with difference cognitive
performance indicators.
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Appendix
Appendix 1 – List of Full Gene Names
CDH13 - cadherin 13
CHMP7 - CHMP family member 7
COMT - catechol-O-methyltransferase
DAT1/SLC6A3 - solute carrier family 6 (neurotransmitter transporter, dopamine),
member 3
DISC1 - disrupted in schizophrenia 1
DRD4 - dopamine receptor D4
DRD5 - dopamine receptor D5
HTR2A - 5-hydroxytryptamine (serotonin) receptor 2A
HTT/SLC6A4 - solute carrier family 6 (neurotransmitter transporter, serotonin),
member 4
LOXL2 – lysyl oxidase-like 2
NDE1 - nudE nuclear distribution gene E homolog 1 (A. nidulans
SHFM1 - split hand/foot malformation type 1
SLC9A9 - solute carrier family 9 (sodium/hydrogen exchanger), member 9
SNAP25 - synaptosomal-associated protein, 25kDa
TNFRSF10A - tumor necrosis factor receptor superfamily, member 10a
TNFRSF10D – tumor necrosis factor receptor superfamily, member 10d, decoy with
truncated death domain
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Appendix 2 – List of Abbreviations
5HT – serotonin
ADHD - Attention deficit hyperactivity disorder
APA - American Psychiatric Association
Ce - commission errors
CNV - copy number variants
DA – dopamine
DSM-5 - Diagnostic and Statistical Manual of Mental Disorders, fifth edition.
DSM-III - Diagnostic and Statistical Manual of Mental Disorders, third edition.
DSM-IV - Diagnostic and Statistical Manual of Mental Disorders, fourth edition.
GWA – genome-wide association
ICD-10 - International Statistical Classification of Diseases and Related Health
Problems, 10th Revision.
IMAGE - International Multi-Centre ADHD Gene project
IQ – intelligence quotient
LD - linkage disequilibrium
MAF - minor allele frequency
MRT – mean reaction time
NA – noradrenaline
Oe - omission errors
PACS - Parental Account of Childhood Symptom
QC – quality control
RT – reaction time
RTV – reaction time variability i.e. standard deviation of reaction time
SDQ - Strengths and Difficulties Questionnaire
SNP - single-nucleotide polymorphism
TDT - transmission disequilibrium test
WAIS - Wechsler Adult Intelligence Scale, fourth edition
WISC - Wechsler Intelligence Scales for Children, Third Edition
45