Web viewWord Count: 10,659. Contents. Abstract5. Introduction6. Epidemiology and Impairment6. Diagnostic Classification7. Dimensional Trait8. Comorbidities8

Student Number: 1035697

MSc Dissertation

Title: Polygenic association in ADHD for the prediction of cognitive performance

Word Count: 10,659

1035697MSc. Dissertation

ContentsAbstract...................................................................................................................................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

2


References...........................................................................................................................35

Appendix...............................................................................................................................44

Appendix 1 – List of Full Gene Names.................................................................................44

Appendix 2 – List of Abbreviations.....................................................................................45

3


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 cross-

validation 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 over-

transmission 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

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(Faraone et al., 2006, Kessler et al., 2006). Many adult-specific impairments are not

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 hyperactivity-

impulsivity 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 sub-

optimal 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,

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reducing symptom requirements for older adolescents and adults (ages 17 and older)

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 hyperactive-

impulsive 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). Typically those with comorbid problems, show

increased functional impairments and have poorer long-term prognosis

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(Bauermeister et al., 2007). However, currently it is unclear to what extent comorbid

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

8


interaction effects (Kuntsi et al., 2006a). This is in line with findings from genetic

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

9


imply that this gene is particularly important during embryonic development. The

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

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(DRD4, DRD5), the serotonin transporter gene (HTT; SLC6A4), one serotonin

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 call-

rate 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

11


disability. This suggests some CNVs may be associated with ADHD 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 rare-

variants 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

12


yield the first genome-wide significant associations at around 2-3 times the sample

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

14


largely genetic in origin (Andreou et al., 2007). Similarly, IQ deficits which are also

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).

15


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

16


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

17


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.

18


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 pro-

social 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.

19


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 fast-task

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

20


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 contributingto composite variable

Number of cases with data available by condition

Number of cases with data available in final composite score

Omission errors (Oe) Go/NoGo fastGo/NoGo slow

249244

249

Commission errors (Ce) Go/NoGo fastGo/NoGo slow

249244

249

Std. deviation of reaction time (RTV)

Go/NoGo slowfast-task baseline

244147

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

21


<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.

22


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 parent-

proband 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 lists

23


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 cross-

validation 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

24


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 over-

transmission 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.

25


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.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

p=0.77

p=0.18 p=0.17 p=0.18r^2

0.1% 1% 10% 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.

26


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

p=0.83p=0.62

p=0.96 p=0.93

r^2

Oe Ce 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

27


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 case-

control 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.

28


Power (1-β err prob)

Tota

l sam

ple

size

t tests - Correlation: Point biserial modelTail(s) = One, α err prob = 0.05, Effect size |r| = 0.066

500

1000

1500

2000

2500

0.3 0.4 0.5 0.6 0.7 0.8 0.9

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

29

Pilot: effective n=928 (464 trios)

80% power: n=1415 predicted

95% power: n=2473 predicted


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

30


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 over-

transmission 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

31


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,

32


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.

33


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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

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Documents

Web viewWord Count: 10,659. Contents. Abstract5. Introduction6. Epidemiology and Impairment6. Diagnostic Classification7. Dimensional Trait8. Comorbidities8