ANALYSES USING THE ADHD GWAS
CONSORTIUM DATA
Jan Haavik:
Parent of Origin Effects and ADHD
This project proposes the examination of imprinting/parent of origin effect (POE) in attention deficit hyperactivity
disorder (ADHD). Formerly, POE in ADHD has been examined using a traditional transmission disequilibrium and chisquare tests, the assumption of independent observations of which may be violated by statistical dependence
between maternal and paternal transmissions in case of triads with two heterozygous parents. In addition, imprinting
can often be masked by maternal effects, and the two cannot be clearly distinguished with those tests. We propose
the use of log-linear modelling, which is not only robust in detection of POE, but is also able to distinguish between
imprinting and maternal effects. We have already tested this method in IMAGE ADHD data and have found some
promising preliminary results. We are planning on replicating our initial findings in CHOP dataset and prepare a
manuscript by spring 2014.
Anke Hammerschlag and Danielle Posthuma
Functional gene-set analysis of ADHD
Proposing Investigators:
Name
Email
Workgroup
1. Danielle Posthuma
danielle.posthuma@vu.nl
PGC ADHD
2. Anke
Hammerschlag
a.r.hammerschlag@vu.nl
PGC ADHD
3. Steve Faraone
sfaraone@childpsychresearch.org
PGC ADHD
A. Research Question and Aims
Please provide a brief description (e.g. 1 paragraph) describing the aims of the proposed
analysis and the research questions that will be addressed.
The aim of our study is to detect functional gene sets that are important to the risk of ADHD. It is
likely that genes involved in brain functioning and brain development are involved in the etiology
of ADHD. We will apply functional gene-set analysis including expert-curated synaptic, glial and
lipid gene sets. This involves testing for the combined effect of multiple genetic variants which
individually may have a very small effect that does not reach significance. Moreover, testing genetic
variants within gene sets with similar cellular fuction will provide immediate functional meaning
which may lead to more biological insight into the etiology of ADHD.
B. Analytic Plan
Please provide a brief description of what analyses will be performed to address the research
question(s) described above. Include relevant details e.g. phenotype definition, QC methods,
analytic methods, plans to address population stratification and other confounders, power.
Analytic method: Associations between functional gene sets and ADHD will be tested with JAG
software (http://ctglab.nl/software/jag/). Two types of tests will be performed: self-contained
tests and competitive tests.
1) Self-contained tests evaluate if the gene sets are associated with ADHD. The ∑-log(P) method will
be used to calculate a test statistic of the combined SNP effects, and permutation will be used to
calculate an emperical p-value. Bonferroni correction will be used to account for multiple testing.
2) Competitive tests evaluate if the gene sets are more strongly associated with ADHD compared to
other matched, randomly drawn gene sets. These control gene sets will be matched with the
original gene set on number of genes. Because ADHD is a complex disorder which likely involves
many genes, it might be that any large gene set will be associated with ADHD by chance. The
competitive test tackles this potential problem. Competitive tests will be conducted for gene sets
significant on the self-contained test.. For every randomly drawn control gene set, a self-contained
test will be conducted. Competitive p-value will be calculated as the proportion of the control gene
sets with a self-contained p-value lower than that of the original gene set.
As a second negative control, gene-sets will be tested in a disorder for which we expect no
association (~ same sample size, e.g. GWAS results of the International Inflammatory Bowel Disease
Genetics Consortium [IIBDGC; http://www.ibdgenetics.org/]).
Summary statistics of GWA results can be used in JAG, by using a proxy dataset (instead of the
actual data that generated the p-values) to compute the sampling distribution. A suitable dataset is
the 1000 Genomes data. However, the ancestry of the original data needs to be the same as the
1000 Genomes panel.
Functional gene sets included in the analysis are based on cellular function and have been manually
curated by experts of our research center (Center for Neurogenomics and Cognitive Research;
CNCR), relying on empirical evidence. These gene sets have been successfully used in previous
studies1,2,3,4. Second, a gene set consisting of ADHD candidate genes proposed by the IMAGE
consortium5 will be added.
1. Ruano et al. (2010). Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in
cognitive ability. Am J Hum Genet, 86, 113-125.
2. Lips et al. (2012). Functional gene group analysis identifies synaptic gene groups as risk factors for schizophrenia.
Mol Psychiatr, 17, 996-1006.
3. Goudriaan et al. (2013). Specific glial functions contribute to schizophrenia susceptibility. Schizophrenia Bull,
Epub ahead of print.
4. Ripke et al. (2013). Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat Genet,
45, 1150-1159.
5. Brookes et al. (2006). The analysis of 51 genes in DSM-IV combined type attention deficit hyperactivity disorder:
association signals in DRD4, DAT1 and 16 other genes. Mol Psychiatr, 11, 934-953.
C. Analytic Personnel
Please indicate who will be responsible for performing the analyses
Anke Hammerschlag
D. Resources Needed
Please address what resources will be needed to achieve the aims of the analysis, including
variables needed, analytic support, and any other anticipated issues that may affect the
feasibility of the plan.
Summary statistics of GWA results. Ancestry of samples.
E. Timeline
Please estimate in months how much time you anticipate will be needed to complete the plan
(including data gathering, analysis, and drafting of results). Note, analyses and drafting of
results are expected to be completed within 6 months.
6 months
Novel approaches to the identification of genetic risk factors for ADHD
Marieke Klein1, 2, Anne van Rens1, Barbara Franke1, 2, 3
1
Department of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands
Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
3
Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
2
Authors from the PGC Working Group to be added
Research question
Can we identify novel genes underlying ADHD by using rare variant search strategies?
Background
Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder characterized by inattention
and/or impulsivity and hyperactivity (APA, 2000). It has a prevalence of 5 - 6 % in children and 2.5 - 5 %
in adults (Franke et al., 2012), and a heritability of 70 - 80 % (Franke et al., 2012). Gene-finding has been
difficult in ADHD, and has mainly focused on common genetics variants. However, genome-wide
association studies (GWAS) have not been able to identify genome-wide significant genetic variants
(Neale et al., 2010), probably because the effect of individual variants is very small.
We propose novel approaches for the identification of ADHD-related genes based on rare genetic
variants: (1) We combine linkage analysis and whole-exome sequencing (WES) to detect novel single
nucleotide variants (SNVs) segregating with disease in multi-generation pedigrees with multiple affected
individuals. (2) Given the known overlap in genetic risk factors between different psychiatric diseases, we
study large copy number variations (CNVs) (a) segregating in families from our genetics clinic with
severe psychiatric phenotypes including ADHD, and (b) observed in patients with intellectual disability, a
genetically less complex disorder in which ADHD symptoms often co-occur.
Downstream of these gene-identification strategies we plan to perform imaging genetics studies as well as
studies in animal models. A first step however, is a validation of the involvement of the candidate genes
in ADHD in the population. We thus want to perform association studies of our candidate genes in the
largest possible ADHD dataset by using the ADHD meta-analysis results of the Psychiatric Genomics
Consortium (PGC).
Methods
To examine the effect of our novel ADHD candidate genes in the PGC data, we plan to (1) look for
statistically significant association of ADHD with single nucleotide polymorphisms (SNPs) and CNVs
within/including our selected candidate genes and (2) to run gene-wide analyses in two different ways.
For the latter, we will perform gene-wide analyses in VEGAS by using the summary statistics of the PGC
ADHD meta-analysis (Liu, 2010). Secondly, we will run the gene-wide analyses in PLINK (Purcell,
2007), as described previously (Bralten, 2011), by using individual genotype data. The variable gender
will be used as a covariate.
Data request
(1) We would like to use the results of the PGC ADHD meta-analysis that was presented at the WCPG
2013 by Ben Neale, including the 23andMe data. Essential information we need to know for each SNP is
the chromosome, base pair position, SNP name, reference allele, odds ratio and p-value.
(2) In addition, we would like to access the genotype data, diagnostic status (case or control) and gender
information of all studies included in the latest meta-analysis, in order to run the gene-wide analyses in
PLINK. We are aware that this is not possible for the 23andMe data. We have all approvals from PGC,
dbGAP and NIH for use of these data.
(3) Finally, we would like access to the called CNV data for the largest PGC ADHD and control dataset.
References
 American Psychiatry Association (APA, 2000). "Diagnostic and statistical manual of mental
disorders." DSM-IV-TR. 4th revised edition, American Psyciatric Publishing, July 2000.
 Bralten, J., A. Arias-Vasquez, et al. (2011). "Association of the Alzheimer's gene SORL1 with
hippocampal volume in young, healthy adults." Am J Psychiatry 168(10): 1083-1089.
 Cross-Disorder Group of the Psychiatric Genomics Consortium, Genetic Risk Outcome of
Psychosis (GROUP) Consortium (2013). "Identification of risk loci with shared effects on five
major psychiatric disorders: a genome-wide analysis." The Lancet 381(9875): 1371-1379.
 Franke, B., S. V. Faraone, et al. (2012). "The genetics of attention deficit/hyperactivity disorder
in adults, a review." Mol Psychiatry 17(10): 960-987.
 Liu, J. Z., A. F. McRae, et al. (2010). "A versatile gene-based test for genome-wide association
studies." Am J Hum Genet 87(1): 139-145.
 Neale, B. M., S. E. Medland, et al. (2010). "Meta-analysis of genome-wide association studies of
attention-deficit/hyperactivity disorder." J Am Acad Child Adolesc Psychiatry 49(9): 884-897.
 Purcell, S., B. Neale, et al. (2007). "PLINK: a tool set for whole-genome association and
population-based linkage analyses." Am J Hum Genet 81(3): 559-575.
 23andMe (2013). https://www.23andme.com. Consulted on November 20, 2013.
ADHD Gene pathways and epistasis: New methods
Principal Investigator: Joel Nigg, PhD;
Co-Investigators: Shannon McWeeney, PhD; Beth Wilmot, PhD; Michael Mooney, PhD
(all at Oregon Health & Science University)
Steve Faraone, Ph.D., SUNY Buffalo
Benjamin Neale, Ph.D., BROAD/MIT
Jan Buitelaar, Ph.D., Radboud University Nijmegen
Barbara Franke, Ph.D., Radboud University Nijmegen
PGC Project Summary
We have two immediate aims and one long term aim. The two immediate aims are to (a) identify
novel gene metabolic pathways using unbiased discovery methods that to our knowledge have not yet
been tried with ADHD, (b) identify novel epistatic effects related to ADHD in the consortium data sets.
The long term goal is to link these results to neuroimaging data in our data set and those of
collaborators in the consortium.
As a proof of concept, we performed an analysis of data from the IMAGE II study to detect
associations between pathways (gene sets) and ADHD. Gene set ridge regression analysis was
performed [1]. Pathways with significant associations (P < 0.001) included Hedgehog, p-53, and signaling
mediated by c-Kit—all brain relevant. These findings are distinct, in part because the approach differs
from recent papers examining ADHD pathway-based analyses. The approach is not limited to candidate
genes and does not bias the gene selection toward genes that are hit by any of the top SNPs. This
approach can be used to discover more complex gene groups (pathways) that are related to ADHD
phenotypes and brain imaging metrics.
We also performed preliminary analyses to investigate the possibility of SNP interactions (epistasis)
[2] in the IMAGE II data set. The two most significant interactions detected (LPCAT2 with ITPR2; p =
1.18e-6; and TRUB1 with SMOC1, p = 5.91e-6) involve genes that were also implicated in the first IMAGE
study [3], including a quantitative trait analysis [4]. The first pair is an example of an interaction between
two genes involved in the same pathway: opioid signalling (G-protein mediated events). For the second
pair, however, neither gene was a member of any of the pathways we evaluated. Thus, not only does
this approach allow identification of novel interactions among members of a pathway, it also can
implicate new pathways or pathway interactions.
We will proceed with our epistasis detection and pathway-based analyses by including other
datasets available from the PGC. For both methods, we believe that statistical models will be improved
by incorporating relevant phenotypic variables (e.g., covariate adjustment, quantitative phenotypes and
multivariate analyses). Multivariate analyses that utilize methods such as Principle Component Analysis
and Seemingly Unrelated Regressions have been shown to increase the power to detect genetic
associations [5], and we will incorporated these methods in our work. We are also working to refine
pathway definitions to remove unnecessary complexity and noise from the models, and to implement
additional pathway scores (i.e., multi-variant collapsing methods).
Going forward, after completing this first set of studies on gene pathways and epistasis, we intend
to collect and analyze new data in Oregon, link pathways to neuroimaging, and then attempt to
replicate in existing data sets particularly the Dutch data set.
References
1.
Chen, L.S., et al. Insights into colon cancer etiology via a regularized approach to gene set analysis
of GWAS data. Am J Hum Genet, 86, 860-871 (2010).
2.
Mooney MA, Wilmot B, The Bipolar Genome Study, McWeeney SK. The GA and the GWAS: Using
Genetic Algorithms to Search for Multilocus Associations, IEEE/ACM Trans Comput Biol Bioinform,
9(3), 899-910, May-June 2012.
3.
Neale, B.M., et al. Genome-wide association scan of attention deficit hyperactivity disorder. Am J
Med Genet B Neuropsychiatr Genet, 147B, 1337-1344 (2008).
4.
Lasky-Su, J., et al. Genome-wide association scan of the time to onset of attention deficit
hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet, 147B, 1355-1358 (2008).
5.
Saint-Pierre A, et al. Bivariate association analysis in selected samples: application to a GWAS of
two bone mineral density phenotypes in males with high or low BMD. Eur J Hum Genet. 2011
Jun;19(6):710-6.
Title: Gene-based and SNP-based analyses of GWAS data on Conduct Disorder and
Oppositional Defiant Disorder and their overlap with ADHD
Date of Proposal: 11/25/13
Proposing Investigators:
Name
Email
Workgroup
1. Irwin Waldman
psyiw@emory.edu
ADHD
2. Phil Asherson
philip.asherson@kcl.ac.uk
“”
3. Richard Anney
anneyr@tcd.ie
“”
4. Jan Buitelaar
Jan.Buitelaar@radboudumc.nl
“”
5. Anita Thapar
Thapar@cardiff.ac.uk
“”
6. Marta Ribases
marta.ribases@gmail.com
“”
7. Bru Cormand
bcormand@gmail.com
“”
8. Tony Ramos
ramosquiroga@gmail.com
“”
9. Steve Faraone
sfaraone@childpsychresearch.org
ADHD, CDG, PGC steering committee
Has this proposal been approved by the Workgroups whose data will be involved?
No. approval pending.
A. Research Question and Aims
Please provide a brief description (e.g. 1 paragraph) describing the aims of the proposed
analysis and the research questions that will be addressed
The project aims to conduct a set of SNP-based and gene-based analyses of conduct disorder
(CD) and oppositional defiant disorder (ODD), and their overlap with ADHD. While we are
just beginning to uncover the specific genetic contributions to ADHD we know even less about
the contributions of specific genes to risk for CD and ODD. Gene-based analyses can play an
important role in this effort, given that allelic heterogeneity across samples might create
seemingly inconsistent signals. Gene-based tests may reveal stronger and more significant
associations than SNP-based tests, and examining associations of genes with these disorders
simultaneously might reveal stronger and more consistent effects than associations with each
disorder individually. We will first use Plink to conduct GWAS on each disorder and its
symptom dimensions and then use KGG to conduct gene-based tests. The specific issues we aim
to address include:
1. Conduct univariate and multivariate SNP-based and Gene-based analyses of GWAS data on
ODD and CD diagnoses and their overlap with ADHD diagnoses,
2. Conduct univariate and multivariate SNP-based and Gene-based analyses of GWAS data on
ODD and CD symptom dimensions and their overlap with ADHD symptom dimensions,
3. Conduct univariate and multivariate SNP-based and Gene-based analyses of GWAS data on
CD symptom sub-dimensions of Aggression, Property Violations, and Status Violations,
4. Analyze symptom-level data using Confirmatory Factor Analysis and Item Response Theory
methods to better characterize CD, ODD, and ADHD in SNP-based and Gene-based GWAS
analyses
B. Analytic Plan
Please provide a brief description of what analyses will be performed to address the research
question(s) described above. Include relevant details e.g. phenotype definition, QC methods,
analytic methods, plans to address population stratification and other confounders, power
-Individual level data: genotype / dosage files on the Dutch server and phenotypic data on
diagnoses and symptoms (to be gathered from Study PIs). Initial focus on CD, ODD, and
ADHD diagnoses with subsequent analyses of symptoms and symptom dimensions as those
data become available.
- Collaboration with interested members of the ADHD workgroup
- Several methods (Plink, KGG HYST and GATES tests, Canonical correlation analytic methods
for gene-base and multivariate tests in Plink and R, Mplus for analyses of symptom-level data)
- We will explore the functional effects of SNPs in gene-based associations by including eQTLs
for each gene as opposed to strictly geographical representation of genes. This follows a
similar analysis conducted by the PGC-NPA group
C. Analytic Personnel
Please indicate who will be responsible for performing the analyses
Irwin Waldman (Emory)
Others?
D. Resources Needed
Please address what resources will be needed to achieve the aims of the analysis, including
variables needed, analytic support, and any other anticipated issues that may affect the
feasibility of the plan
Individual level data: genotype / dosage files on the Dutch server and phenotypic data on
diagnoses and symptoms (the latter to be gathered from Study PIs).
E. Timeline
Please estimate in months how much time you anticipate will be needed to complete the plan
(including data gathering, analysis, and drafting of results). Note, analyses and drafting of
results are expected to be completed within 6 months
~1 year (to accommodate the time needed to obtain the symptom data from participating
sites)
Testing of regulatory polymorphisms in key candidate genes for
associations with clinical ADHD phenotypes
Wolfgang Sadee, for the XGEN Group*
Objective. We propose to test the clinical influence of frequent regulatory polymorphisms in
key candidate genes on clinical phenotypes in ADHD cohorts. The main hypothesis underlying
our studies posits that critical genes affecting CNS signaling and behavior have been under
evolutionary pressures yielding genetic variants encoding high and low expressed protein
activity, representing normal variation not directly involved in pathophysiology. Such variants
can therefore accumulate to high frequencies (positive selection or balancing selection), and
overwhelmingly are regulatory in nature (rather than nonsynonymous SNPs affecting protein
activity in each organ). Our systematic search for regulatory variants has now revealed their
existence in a number of key genes (eg, DRD2, DAT, HTR2A, CHRNA5, MAOA, TPH2, DBH,
CHRNA5, CYP3A4, CYP2D6, often containing more than one regulatory variant requiring
tailored genetic models), that have shown a spectrum of clinical associations in small diverse
cohorts. We wish to gain access to and collaboration with the PGC, and specifically
investigators of the ADHD consortium, to test these variants. We hypothesize that such ‘normal’
variants nevertheless will affect behavioral phenotypes in CNS disorders (targets of therapy),
and moreover, that epistatic interactions between them can lead to large effect size (example
(DRD2-DAT interactions). In addition, we have developed a functional genomics platform that
enables us to test novel ADHD candidate genes for regulatory variants of interest to PGC
collaborators.
Background. Evolution of the human lineage has taken a rapid path, in part owing to the
extreme expansion of population size and large environmental and cultural changes.
Evolutionary pressures likely proceed to cause genetic changes predominantly via regulation of
gene expression at multiple levels (transcription, mRNA processing/function (including noncoding RNAs), and translation). Building the OSU Program in Pharmacogenomics, I have
further hypothesized that genes encoding drug targets are likely candidates subject to
evolutionary pressures, because such genes often reside in the center of signaling pathways
with impact on the entire system. However, regulatory variants have been difficult to identify as
they can reside anywhere in a gene locus, such as large introns or >100 kb up- or downstream
of the transcribed region (we have just identified an enhancer SNP ~120kb downstream of
CYP2D6 that increases transcription 2-4 fold; submitted). To detect regulatory variants, we
have developed a suite of methods, in particular allelic mRNA expression analysis in human
brain autopsy tissues), revealing a considerable number of frequent regulatory variants in drug
targets and related genes (1-9). It has further proven critical for us to perform detailed
molecular genetics studies on the top target genes, to understand the nature and magnitude of
the effect. For those genes that also show signs of possible evolutionary selection, we often
discover more than one regulatory variant (eg, CHRNA5, DRD2, DAT, HTR2A) (5-8), one with
high (20-50%) minor allele frequency, and the other often at lower (5-20) MAF, suggesting
favorable variants have arisen separately on distinct haplotypes. Disregarding the linkage
disequilibrium and interactions between such variants in the same gene is likely to dilute or
mislead any clinical association analysis.
Molecular genetics studies. Each gene studied in detail required 2-4 years of experimentation.
For DRD2, we have identified a promoter SNP and two intronic SNPs that affect splicing of
exon6, shifting the expression of D2S (presynaptic) to D2L (largely considered postsynaptic with
distinct functions) (1). The splicing SNPs have been implicated in cognitive processing (1) and
an increasing number of behavioral disorders. Similarly, the dopamine transporter DAT was
found to harbor three regulatory variants, with one nominally associated with bipolar disorder (7)
and ADHD by others. We have further characterized variants in HTR2A, MAOA, and TPH2
(2,8,9), affecting serotonergic signaling, and an upstream enhancer variant in CHRNA5
reducing expression fourfold (5). Additional genes are under study, with candidate SNPs that
we can test in GWAS datasets.
Epistasis as an important contributor to clinical phenotype. We hypothesize and systematically
begin to search for cases where frequent regulatory variants in key genes interact with each
other dynamically, so that a normal variation can turn detrimental under specific conditions. As
an example, we have studied the interactions between DRD2 and DAT, in affecting risk of death
as a result of severe cocaine abuse (10). The DRD2 splicing SNPs alone conveyed a 3-4 fold
risk (6), whereas no DAT variant was significant. In a subsequent study testing statistical
genetics models, a specific combination of DRD2/DAT variants conveyed a ~8-fold risk (10). In
addition, a rare DAT haplotype conveyed high risk, but only in African Americans, owing to large
MAF differences between ethnic groups (10). Discovery of these interactions was made
possible only after comprehensive molecular genetics studies revealing the nature of all main
regulatory variants in these two genes.
Early results relevant to ADHD. We have yet to publish promising results that were obtained in
several separate small cohort, some with ADHD or ADHD-related symptoms in ASD children.
Several of our regulatory variants scored with surprisingly high effect sizes – typically with
respect to specific symptoms (such as phobias, hyperactivity, etc). We think that these genes
are not causative of disease per se but determine the course of the disease, at least at the
behavioral level. Also, the DRD2/DAT interaction is detectable with specific phenotypes. We
are now pursuing these findings in targeted clinical trials, for example, a prospective trial in
autistic children with ADHD like symptoms undergoing atomoxetine therapy (ongoing) – a
paradigm relating to gene-gene-environment interactions (where in this case the environmental
stimulus is the drug). We have preliminary data from another association study suggesting a
DAT-DRD2 epistatic interaction may play a role in obsessive compulsive disorder, certain
phobias, and panic disorder. In a separate study of ADHD symptoms in autism, preliminary
analyses have uncovered a significant association between a DAT variant, a polymorphism in
the gene encoding monoamine oxidase A (MAOA) and the presence of ADHD symptoms in
males with autism spectrum disorders. We plan to examine these phenotypes in GWAS
studies, testing epistatic interactions of DAT or DRD2 variants with functional variants in other
genes currently under study in our group, including: COMT, HTR2A, DBH, DRD4, MAOA,
TPH2, SERT, and ESR1. Our analyses will employ several interaction models to test genegene interactions with phenotype using statistical software such as STATA 11. One method
employs simple contingency tests comparing dual variant carriers in one phenotype-defined
group to controls. Logistic regression will be used to study the effect of each variant individually
with respect to phenotype, as well as gene-gene interactions.
Functional genomics studies guiding candidate gene selection. We have established a strong
next-generation sequencing laboratory (SOLiD5500, PGM, and Proton ion torrent technologies),
performing RNAseq on a large number of prefrontal cortex tissues (cocaine abusers, nicotine
abusers, autism subjects, Lewy body dementias, and controls). As part of the PGRN CNS
working group, we are also measuring the transcriptomes of 10 brain regions in 5 smokers and
5 controls, providing tissue specific differences (completion expected in the Fall 2013). Our
RNAseq process enables us to measure the entire transcriptome, including large and small
noncoding RNAs (such as lncRNAs and microRNAs). We have a manuscript submitted
addressing means to reveal allelic expression imbalance by RNAseq (fraught with potential
pitfalls unless properly controlled). The results are beginning to reveal novel candidates with
frequent variants, for example in the glutamate pathway. Any genes implicated in the ADHD
GWAS analyses can be interrogated against this functional genomics database, and we are
open to collaborations.
In summary, our studies have progressed to a point where interactions with larger consortia will
prove essential for further advances. We now wish to replicate these results and test other
conditions under which these genetic interaction occurs, particularly in psychiatric disorders and
in therapies with stimulant drugs or antipsychotics. We think that our approach targeting
frequent regulatory variants can be paired with polygenic models typically involving rare variants
– with the combination having an excellent chance of shedding light on the ‘missing heritability’
in complex disorders such as ADHD, and yielding new guides towards improved therapies.
References
1. Zhang Y, Bertolino A, Fazio L, Blasi G, Rampino A, Romano R, Lee M-L T, Xiao T , Papp
A, Wang D, and Sadee W.Novel polymorphisms in human dopamine D2 receptor gene
affect gene expression, splicing, and neuronal activity during working memory.
Proc.Natl.Acad.Sci.USA. 104: 20552-20557 (2007).
2. Lim J-E, Pinsonneault J, Sadee W, Saffen D. Tryptophan hydroxylase 2 (TPH2) haplotypes
predict levels of TPH2 mRNA expression in human pons. Molec.Psychiatry, 12: 491-501
(2007).
3. Zhang Y, Wang D, Johnson AD, Papp AC, Sadee W. Allelic expression imbalance of human mu
opioid receptor (OPRM1) caused by variant A118G. J Biol Chem 280: 32618-32624 (2005).
4. Wang D, Guo Y, Wrighton SA, Cooke GE, Sadee W. Intronic polymorphism in CYP3A4
affects hepatic expression and response to statin drugs. Pharmacogenomics J. 11: 274-286
(2011). PMID 20386561
5. Smith RM, Alachkar H, Papp AC, Wang D, Mash DC, Wang JC, Bierut LJ, Sadee W.
Nicotinic α5 rec eptor subunit mRNA expression is associated with distant 5’ upstream
polymorphisms. Europ.J.Hum.Gen. 19: 76-83 (2011). PMCID: PMC2995013
6. Moyer RA, Wang D, Papp AC, Smith RM, Duque L, Mash DC, and Sadee W. Intronic
polymorphisms affecting alternative splicing of human dopamine D2 receptor are associated
with cocaine abuse. Neuropsychopharm. 36: 753-762 (2011). PMID 21150907.
7. Dopamine transporter gene variant affecting expression in human brain is associated with
bipolar disorder. Pinsonneault JK, Han DD, Burdick KE, Kataki M, Bertolino A, Malhotra AK,
Gu HH, Sadee W. Neuropsychopharm. 8: 1644-1655 (2011). PMID:21525861
8. Smith RM, Papp AC, Webb A, Ruble CL, Munsie LM, Nisenbaum LK, Kleinman JE, Lipska
BK, Sadee W. Multiple regulatory variants modulate expression of 5-hydroxytryptamine 2A
receptors in human cortex. Biol. Psych. 73: 546-554 (2013).
9. Allelic mRNA expression of X-linked monoamine oxidase A (MAOA) in human brain:
Dissection of epigenetic and genetic factors. J.K. Pinsonneault*, A.C Papp, W. Sadee.
Hum.Molec.Genet. 15:2636-49 (2006).
10. Sullivan D, Pinsonneault JK, Papp AC, Zhu H, Lemeshow S, Mash DC, Sadee S. Dopamine
transporter DAT and receptor DRD2 variants affect risk of lethal cocaine abuse: A genegene-environment interaction. Translat.Psych. 3, e222 (2013). doi:10.1038/tp.2012.146
THRB and ADHD
Gunter Schumann
Attention-deficit/hyperactivity disorder (ADHD) is a common dimensional and heritable
neurodevelopmental disorder which is characterized by inattention, impulsiveness and
hyperactivity. ADHD patients show neuropsychological deficits of motor response and cognitive
inhibition, sustained attention, and timing functions, which are attributed to a delay in
maturation of fronto-striato-cerebellar and frontoparietal brain circuits. However, the etiology
of these neurodevelopmental abnormalities is unknown. In a brain-wide resting state analysis
of ADHD patients and controls we identified a link coupling anterior cingulate cortex (ACC) and
insula (INS). This link is associated with ADHD symptoms and with neuropsychological measures
of execution and attention. A subsequent independent genome-wide analysis revealed an
association of a functional common variant in the thyroxin-receptor B gene (THRB) with the
ACC-INS link, and also with ADHD symptoms. Furthermore, we found that ADHD symptoms and
the risk allele of the variant are both associated with increased free Thyroxin (T3) serum levels.
While thyroid hormones control neurodevelopmental processes, THRB has been implicated in
resistance to thyroid hormones (RTHS), a syndrome, which often presents with symptoms of
ADHD. These data suggest that a common genetic variant is linked to a mild form of RTHS
resulting in ADHD symptoms in the absence of gross endocrinological abnormalities. We would
now like to replicate our finding of an association of the THRB gene with ADHD symptoms in an
independent cohort, and would like to identify subgroups of individuals with ADHD where this
association is particularly pertinent.
Irwin Waldman
Gene-Based Tests of GWAS Data for ADHD
In this study I propose to conduct several gene-based tests of GWAS data for ADHD. Specifically, I
propose to conduct two sets of gene-based tests, one of which is based on the combination of p-values
and LD information for all of the SNPs within each of the genes in the genome and their flanking regions,
and the second which would conduct omnibus multivariate tests of the actual genotypes for all of the
SNPs within each of the genes in the genome and their flanking regions directly, rather than relying on
summary data such as p-values. The approach I will take in the first set of gene-based tests will use the
programs KGG and VEGAS, which in turn uses PLINK and a set of supporting R programs and routines
necessary to correctly implement the simulations it requires. The second set of gene-based tests will use
PLINK and PBAT to conduct omnibus multivariate tests of all the constituent SNPs within a gene and its
flanking regions for each of the genes in the genome. I will conduct these tests for a series of
phenotypes that include ADHD diagnosis, ADHD diagnostic subtypes, the inattentive and hyperactiveimpulsive symptom dimensions, and age-of-onset. The results from each of the gene-based analytic
methods will be compared to each other and to the SNP-based tests that already have been conducted.
For this project I will use data from the ADHD GWAS datasets that are in dbGAP (i.e., from the Children’s
Hospital of Philadelphia, IMAGE 1 and 2, and PUWMA studies), as well as the additional datasets that
have been provided to the Psychiatric Genetics Consortium.
Pathway/gene ontology analysis
Peter Holmans
So far, genome-wide association studies in ADHD have not delivered convincing new risk genes
for the disorder. Nevertheless, preliminary work of our group suggests that a number of
pathways are overrepresented in the top-findings of the GWAS on ADHD.
Using different software programs suitable for pathway analysis, this work will determine if
genes coding for proteins in canonical biological pathways are significantly enriched in the top
hits from the meta-analysis. Such research will provide important input for the clinical utility of
genetic data for ADHD. In addition, it might be used to develop new statistical methods for
GWAS, in which pathways instead of single variants are tested for association with disease.
Importantly, this has the potential of reducing the effects of genetic heterogeneity and
increasing the power of genetic association studies.
Haplotype Analyses
Lindsey Kent
Haplotype sharing analysis: cases will be identical by descent for longer distances than controls
around disease SNPs with recent ancestry, and therefore rare frequency. This can be estimated
using pairwise identity by state matrices which are then compared between cases and controls.
The approach can be viewed as a population based linkage analysis and may detect loci that are
too rare to be found by association analysis and too weakly penetrant to show up in pedigrees.
Emerging methods implemented in eg Plink and Beagle are promising but time consuming. We
are interested in applying recent advances by Allen and Satten that allow genomewide
haplotype sharing analysis to be performed relatively efficiently and then refined using the
former more sophisticated models.
Helmut Schäfer
Bias-reduced estimators of genetic effects in genome wide association studies
We would do an analysis that gives bias-reduced estimators of genetic effects in genome wide
association studies. Due to the very large number of markers tested and the selection of those
markers with the smallest p-values, naïve estimates of genetic effects (e.g. odds ratios) are
biased in genome wide association studies (e.g. Garner C. Upward bias in odds ratio estimates
from genome-wide association studies.Genet Epidemiol. 2007;31(4):288-295.). We have
constructed bias-reduced estimators not only for a single marker but for an arbitrary number of
markers on top of the sorted marker list, whether or not they reach formal genome-wide
significance. This is based on a special bootstrap re-sampling method (Pahl et al. Bias-Corrected
Effect Estimators for Genome-Wide Association Studies. Genet Epidemiol. 2008;32:676). We
have developed a fast algorithm that can handle marker sets of the size used in modern GWAS.
We also construct marker-wise and simultaneous confidence intervals adjusted for marker
selection. Finally, we want to apply statistical tests which are robust with respect to the
unknown mode of inheritance (genetic model). We are developing robust tests which are more
powerful than the usual maximum statistics (work in progress). We have developed a fast
algorithm for the calculation of efficient p-values adjusted for genome-wide significance testing
taking into consideration the marker LD and which therefore are not over-conservative like
Bonferroni (factor 50 compared to PLINK).
For the ADHD GWAS, we would like to apply these methods to the genotype data from all sites
and the phenotypes for a case-control design, i.e. ADHD affection status. This analysis would
thus be complementary to the primary analysis which provides the ranking of markers by pvalues. Alternatively, these novel methods could be applied to more specific research
questions, such as analysis of DSM IV subtypes, sex effects, comorbidities or copy number
variation.
The principal participants who form the manuscript working group for this analysis project
would be Prof. Dr. Helmut Schäfer, Dr. Astrid Dempfle, Thuy Trang Nguyen and Roman Pahl
from the Institute of Medical Biometry and Epidemiology, Marburg, and we would be happy to
cooperate with other members of the ADHD GWA Consortium.
Andrew Merwood
Assessment of polygenic inheritance for ADHD
Genome-wide association studies (GWAS) of complex psychiatric disorders have yielded only a handful of genomewide significant results, likely due to the small, additive risk conferred by common genetic variants. However
polygenic approaches have been successfully applied to explain some of the variance in psychiatric disorders,
including ADHD. ADHD is therefore likely associated with multiple common variants, however our understanding
of how much common variation contributes to the risk for ADHD, or indeed for risk of comorbidity remains
unclear. This document presents an update to our previously proposed analyses of the PGC ADHD data.
Specifically, we set out an amended proposal for refining and testing a polygenic signal associated with ADHD, with
the following aims:
1) To use the most up-to-date ADHD meta-data to generate and test a polygenic signal for association
with ADHD. To further test this signal for its ability to predict inattentive, hyperactive and emotional
lability symptoms of ADHD and to predict cognitive traits, within a subset of the PGC ADHD data
(IMAGE).
2) To use a machine-learning approach to optimise the ADHD polygenic signal, using the signal
generated under aim 1 as the benchmark against which to compare machine-learning efforts. It is
anticipated that other teams will conduct comparable machine learning analyses using the same data.
These can be compared to the polygenic signal we propose to generate in aim 1.
3) To apply the optimised ADHD polygenic signal to predict other disorders using data from the PGC.
This is different to existing cross-disorder studies in that the ADHD signal will have been optimised
using a machine-learning approach. This aim will involve the cross-disorder collaborators listed
above.
4) To apply the optimised ADHD signal to predict ADHD and other traits in a general population sample
(TEDS). This is a local project that will also include the ADHD working group listed here.
Philip Asherson
GWAS of reaction time variability (RTV) in ADHD
Our recent multivariate analyses showed that RTV has a high phenotypic and familial
correlation with ADHD; furthermore that RTV data can be combined across different tasks for
use in collaborative GWAS datasets. We therefore propose search for QTLs using RTV data
available across the collaborative datasets.
Philip Asherson
Analysis of candidate system associations, derived from animal gene expression models of home cage activity
and fital exposure to nicotine.
We are generating hippocampal gene expression profiles across 200 outbred heterogeneosu
stock (HS) mice with home cage activity measures. In another study we are generating
expression data from striatal tissue in rats exposed to nicotine in utero, compared to controls.
Using a gene system approach, we intend to delineate sets of genes that can be investigated for
polygenic association using score test method.
Gerome Breen
Hap-swap-CNV: Haplotype Swapping Copy Number Variant Detection
This is a very simple idea developed in response to our inability to call CNVs from Perlegen data
to call deletions events, both inherited and denovo. Essentially, you identify runs of
homozygosity in the parents and probands/children (after cleaning of bad individuals but
only of snps where they are weird in the parents and after careful consideration of missingness
issues). In essence a run of homozygosity in the child will then either be:
(a) Inheritance of the same halotype from both parents - mendelization correct.
(b) a denovo deletion, marked by a run of both homozygosity and non mendelization on an
otherwise okay background. The presence of a denovo deletion can be partially confirmed by
the use of Germline after phasing the haplotypes using Beagle. This uses a population
linkage type test looking for interruptions of runs of homozygosity on otherwise shared IBD
segments.
(c) if an inherited deletion, then the run of homozygosity will be physically the same but on a
different hemizygous background, i.e. the run of homozygosity in the same place but the
haplotypes switch or rather swap so that a haplotype from the parent without the run of
homozygosity is observed in the child over that run of homozygosity. The phasing of the
haplotypes will be of great use here.
We have r-scripts set up and running to call the runs of homozygosity as well as considerable
experience using Beagle and Germline on different datasets. There are two ways we could run
this analysis: (a) on our cluster here (b) by getting access to the data on a remote server with R,
Beagle and Germline installed.
Klaus-Peter Lesch
Comprehensive analysis of CDH13, Diras2, KCNIP4, Spock3, Kcnj6 and ADHD.
We are doing a comprehensive analysis of our current candidate genes and ADHD. The
candidate genes are CDH13, Diras2, KCNIP4, Spock3 and Kcnj6. We will calculate the combined
p-values using a weighted z-score approach. We will look at associations for both ADHD
affection status and quantitative phenotypes for these genes.
ADHD polygenic risk scores predict ADHD and aggression in childhood and adolescence, but not
anxiety and depression
Christel M. Middeldorp, Maria M. Groen-Blokhuis, Kees-Jan Kan, Eveline L de Zeeuw, Abdel Abdellaoui,
Catharina E.M. van Beijsterveldt, M Bartels, Erik A. Ehli, Gareth E. Davies, Paul A. Scheet, James J.
Hudziak, Jouke-Jan Hottenga, Psychiatric Genomics Consortium ADHD Subgroup, Ben M. Neale, Dorret I.
Boomsma
Background
With polygenic score analyses we recently demonstrated that genetic risk factors associated with an
ADHD diagnosis predict continuous ADHD scores in the general population in preschool and school-aged
children. ADHD symptoms can persist in adolescence and adulthood and are frequently co-morbid to
aggression, anxiety and depression in childhood and adolescence. We present the results of polygenic
risk scores analyses for these phenotypes.
Methods
In participants from the Netherlands Twin Register, polygenic risk scores were calculated based on the
latest results from the childhood ADHD mega-analysis performed in PGC. In a linear mixed model, taking
into account the relatedness between twins, the prediction of the polygenic risk scores was tested for
maternal ratings at age 3, 7, 10 and 12 for the Child Behavior Checklist Scales (www.aseba.org) of
aggression, anxious depression and withdrawn depressed. In adolescence, the prediction was tested for
the self reports for these scales and attention problems. For each age, data were available for between
1,000 and 2,000 individuals. We also analyzed the largest childhood and largest adolescent dataset
including 2263 and 3424 individuals respectively.
Results
Significant prediction was found for the aggression and for attention problem scores throughout
childhood and adolescence, for maternal ratings as well as self reports and for each age. There were no
significant predictions from ADHD polygenic scores towards anxious depression and withdrawn
depressed behavior, not even in the largest samples.
Conclusion
There is consistent overlap in genetic risk factors for a diagnosis of ADHD and continuous ADHD and
aggression scores in childhood. Moreover, this prediction extends into adolescence. Although the
phenotypic correlation between ADHD scores and anxious depression is as high as for ADHD scores and
aggression, no significant predictions were detected from genetic risk scores to a diagnosis to anxious
depression. Modeling of twin data and bivariate genome-wide association or GCTA analyses need to
shed light on the different mechanisms that underlie the phenotypic associations of childhood ADHD
and other behavioral and emotional problems.