the BrainSTORM Consortium, Anttila, V., Bulik-Sullivan, B., Finucane,
H. K., Walters, R. K., Bras, J., Duncan, L., Escott-Price, V., Falcone,
G. J., Gormley, P., Malik, R., Patsopoulos, N. A., Ripke, S., Wei, Z.,
Yu, D., Lee, P. H., Turley, P., Grenier-Boley, B., Chouraki, V., ...
Dunn, E. (2018). Analysis of shared heritability in common disorders
of the brain. Science, 360(6395), [eaap8757].
https://doi.org/10.1126/science.aap8757
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10.1126/science.aap8757
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Supplementary Materials for
Analysis of Shared Heritability in Common Disorders of the Brain
Verneri Anttila…, Aiden Corvin, Benjamin M Neale, on behalf of the Brainstorm
consortium
correspondence to:
verneri.anttila@gmail.com
acorvin@tcd.ie
bneale@broadinstitute.org
This PDF file includes:
Materials and Methods
Supplementary Text
Figs. S1-S10
Tables S1-6, S8-11
Other Supplementary Materials for this manuscript includes the following:
Tables S7, S12, S13 (separate files)
1
Materials and Methods
Data processing
We obtained GWAS meta-analysis summary statistics for 25 brain disorders and 17
phenotypes. Wherever non-European cohorts formed a part of those meta-analyses, we
generated non-sex-stratified European-cohorts-only version of the meta-analysis of each
disorder together with the primary analysts for each disorder to avoid bias stemming from
ancestry differences. Prior to heritability analysis, each dataset underwent additional
filtering: markers were excluded for not being present among the HapMap Project Phase
3 SNPs(84), having an allele mismatch to 1000 Genomes alleles, ambiguous strand
information, INFO score <0.9 (where available), MAF<1%, and if considerable
missingness in the meta-analysis was observed (where available; defined as effective perSNP sample size less than two thirds of the 90th percentile of total sample size). To
remove a potential source of bias, the major histocompatibility complex region (all SNPs
on chromosome 6 between 25 and 35 Mb) was removed from all datasets, as was the
region surrounding the APOE locus (all SNPs on chromosome 19 between 44 and 47
Mb) from the Alzheimer’s disease summary data.
Simulations
To evaluate the robustness of these results under various scenarios, we performed
various simulations using data from the UK Biobank(85). Details about the UK Biobank
project are available at http://www.ukbiobank.ac.uk. Data for the current analyses were
obtained under an approved data request (application number #18597).
We used data from the interim release of 152,376 samples, originally genotyped on
the UK BiLEVE Axiom array and the UK Biobank Axiom array. We filtered individuals
for Caucasian ancestry and recommended removals to arrive at a final dataset of 120,267
individuals. Simulated datasets were generated to evaluate the behavior of correlation
estimates 1) under different degrees of misclassification; 2) under different heritability
estimates for the two traits and 3) under different liability thresholds (232 simulation
conditions, 100 replicates per condition, for a total of 2.95 billion simulated individuals).
In each simulation replicate, two sets of simulated quantitative phenotypes with
heritability ranging from 5-50% and prevalence ranging from 1-10% (relevant to the
study phenotypes) were generated by assigning 5% of total SNPs to have simulated effect
&
sizes drawn from 𝑁 "0, %(.(*
-, where h2 is the heritability and M is the total number of
,
'
markers in the genome, standardized for minor allele frequency (p) by .2 ∗ 𝑝 ∗ (1 − 𝑝).
Individual phenotypes were simulated by calculating the sum of mean centered betas
multiplied by the individual’s risk alleles with the –score option in PLINK
v1.90b3.38(86) and adding noise term e, drawn from 𝑁60, √1 − ℎ9 :, to achieve phenotypes
which sum to 𝑁(0, ℎ9 ). Dichotomous phenotypes were generated by assigning top 1%, 5%
and 10% of each heritability simulation to be cases, and misclassification scenarios by
mixing the simulated betas with those from a second, independently simulated phenotype
in proportions ranging between 0-100%. Association statistics were created using an
additive test in PLINK v1.90b3.38, and LDSC was used to calculate correlation
estimates.
2
Simulation results were summarized to evaluate three specific scenarios considered
relevant to the challenges (particularly for the psychiatric disorders, due to their
spectrum-like behavior) in brain disorder co-morbidity:
1. Effect of misclassification on phenotype heritability. Given that we generally
observe slightly lower heritability estimates in this study than reported in the literature
with previous studies (which generally have used smaller, possibly less heterogeneous
datasets), we generated 100 replicates each of simulated phenotypes at several prevalence
and heritability values, and with varying degrees of misclassification of cases from a
second, independent phenotype (Fig. S9A). These results demonstrate that while largescale misclassification will impact the estimated heritability, very large misclassification
proportions are required to by themselves give rise to large-scale changes in the observed
heritability to the degree shown in Table S3.
2. Effect of co-morbidity on genetic correlation. Given the overlapping
epidemiology of some phenotypes and the potential to observe false positive correlations
due to non-trivial case misclassification, we created a range of phenotypes with varying
mixing portions of correctly diagnosed cases (λ) and incorrectly diagnosed cases (1-λ)
from an independent second phenotype and evaluated the genetic correlation between the
hybrid phenotype and the second phenotype. This simulates the real-world scenario
where e.g. (1-λ) proportion of bipolar cases would in fact be misclassified cases of
schizophrenia free of bipolar disorder (Fig. S9B). We also derived a formula (see “Effect
of co-morbidity and phenotypic misclassification on correlation estimates” below) to
estimate the degree of misclassification required to produce the observed correlations in
the absence of true genetic correlation (Table S6).
3. Effect of bidirectional comorbidity on genetic correlation. We expanded the
simulation from the previous scenario given misclassification in both directions, i.e.
where a proportion (1-λ) of bipolar disorder cases are misclassified schizophrenia cases
and the same proportion of schizophrenia cases are misclassified bipolar disorder cases
(Fig. S9C).
Power calculations
Using the same methodology as described for the simulations, we created 100
replicated pairs of datasets, each with varying sample sizes (10,000, 20,000 and 40,000
individuals with a 50/50 case/control split, randomly selected from the UK Biobank data;
see section above), heritabilities (1%, 5%, 10% and 20%) and polygenicity (simulating
0.5%-100% of markers contributing to the heritability). In the second set of similarly
created replicates, phenotypes were additionally created to be 10%, 20%, 30%, or 40%
correlated to their pair in the first set. LDSC was used to calculate the correlation
between the pair (Figure S10).
Heritability analysis
For a given trait, the total additive common SNP heritability in a set of GWAS
summary statistics (h2g) is estimated by regressing the association χ2 statistic of a SNP
against the total amount of common genetic variation tagged by that SNP (i.e., the sum of
r2 between that SNP and all surrounding SNPs within a 1 Mb window, termed the LD
score). The LD scores themselves, for each SNP with MAF 5-50% in the Hapmap3 data,
were obtained from previously published data(87) (https://github.com/bulik/ldsc) but
3
edited by removing the at the time erroneously included HLA region markers [chr6, 2036 Mb]. Genetic correlations, rg, (i.e., the genome-wide average shared genetic risk) for a
pair of phenotypes was similarly estimated by regressing the product of Z-score for each
phenotype for each SNP, instead of the χ2 statistic. The LD score referenced above is
estimated from a common reference panel (for this work, the European subset of the 1000
Genomes Project reference). In this framework, including LD in the regression allows us
to distinguish and account for LD-independent error sources (such as sample sharing and
population stratification) from LD-dependent sources, like polygenic signal). It is
essential to use an approach which is not biased by sample overlaps when analyzing
summary statistics, given the large amount of control sharing between the GWAS metaanalyses in the study. P-values and effect directions for each phenotype were used to
create a set of directional χ2 statistics, which were then regressed against the SNP LD
scores (as the χ2 statistic is dependent on the amount of variation tagged by the SNP).
A univariate regression of these statistics against the LD statistic of each SNP was
used to estimate the heritability for each phenotype using LDSC v1.0.0(88). When
converting the results to liability scale, we assumed that all controls were unselected for
all brain disorders as well as coronary artery disease and Crohn’s disease from the
additional phenotypes (u = 1 for the formula presented in (89)). Phenotypes with a
univariate heritability Z-score < 2 were excluded from further analysis (cardioembolic,
large-, and small-vessel stroke and agreeableness personality measure), leaving 21 brain
disorder phenotypes and 16 traits of interest. In the genetic correlation analysis, the
product of χ2 statistics from the two phenotypes was similarly regressed.
Significance was assessed by Bonferroni multiple testing correction by estimating
the number of independent brain disorder phenotypes by matrix decomposition of the
genetic correlation results using matSpD (see Links)(90, 91). The number of independent
disorder phenotypes was estimated to be 17.7943 (from 22 initial disorders, after
exclusions), yielding a Bonferroni-corrected threshold of p < 3.35 x 10-4 for disorderdisorder pairs; 12.1925 independent phenotypes (from 16 initial phenotypes, after
exclusions) for a threshold of p < 7.33 x 10-4 for phenotype-phenotype pairs and a total of
216.96 disorder-phenotype pairs for a threshold of p < 2.30 x 10-4.
Functional enrichment and partitioning analysis
Partitioning analysis was conducted using LDSC v1.0.0(88), using stratified LD
score regression to identify enriched cell type groups, expanding on the work described in
Finucane et al(92). First, we obtained genome annotations for each of ten cell type
groups, created by taking a union of regions with any of four histone modifications
(H3K4me1, H3K4me3, H3K27ac, H3K9ac) in any cell type belonging to the cell type
group. We then added each of these ten annotations to the full baseline model one at a
time and performed LD score regression for each of the resulting ten models. For each of
these ten analyses, we computed a Z-score for the regression coefficient corresponding to
the cell type group, and we used this to test the hypothesis that the cell type group
contributes positively to SNP heritability after controlling for the 53 categories in the full
baseline model. Significance threshold was estimated from the number of independent
phenotypes across 10 tissue categories and 53 functional categories (latter evaluated as 24
independent categories due to overlapping category structure) for Bonferroni thresholds
4
of p < 2.81 x 10-4 and 1.17 x 10-4, respectively. For the behavioral-cognitive traits and
additional traits, the corresponding thresholds were p < 4.10 x 10-4 and p < 1.71 x 10-4.
Correlation between heritability and dataset-specific factors
A weighted-least squares analysis was conducted among the brain disorder
phenotypes in R, version 3.2, to determine what, if any, phenotype and dataset descriptive
factors correlate with univariate heritability estimates. Weights were estimated using the
squares of the standard errors of the univariate heritability estimates from the LD score
regression analysis.
Supplementary text
Effect of co-morbidity and phenotypic misclassification on correlation estimates
We derived a formula to quantify the effect of case misclassification on the
estimated genetic correlation between two traits, given the degree of misclassification,
the observed heritability and the true genetic correlation. We assume both traits have
similar sample and population prevalence.
Let λ be the fraction of correctly classified cases of phenotype 1, with the remainder
being cases of phenotype 2 misclassified as cases of phenotype 1, β1 and β2 be true
effects for phenotypes 1 and 2 on an arbitrary SNP. Therefore, the effect of the SNP on
the misspecified phenotype 1 is α:
𝛼 ≡ 𝜆𝛽? + (1 − 𝜆)𝛽9
Before considering the impact on the estimated genetic correlation, we note that this
change in SNP effects means the heritability of the observed (potentially misclassified)
phenotype may differ from the heritability of the true phenotype 1. Noting that the
observed heritability for each phenotype is proportional to the variance of their effect
sizes, we first calculate
Var(𝛼) = Var (𝜆𝛽? + (1 − 𝜆)𝛽9 )
= 𝜆9 Var (𝛽? ) + 2𝜆(1 − 𝜆)Cov(𝛽? , 𝛽9 ) + (1 − 𝜆)9 Var(𝛽9 )
= 𝜆9 Var (𝛽? ) + 2𝜆(1 − 𝜆)𝑟I .Var(𝛽? ) Var(𝛽9 ) + (1 − 𝜆)9 Var(𝛽9 )
Then assuming standardized regression coefficients (e.g. following the LD score
regression model), this can be written in terms of observed (obs) and true heritabilities for
the two phenotypes and the number of genome-wide variants M as
9
ℎ?,JKL
ℎ?9
ℎ?9 ℎ99
ℎ99
= 𝜆9 + 2𝜆 (1 − 𝜆) 𝑟I N N + (1 − 𝜆)9
𝑀
𝑀
𝑀 𝑀
𝑀
9
ℎ?,JKL
= 𝜆9 ℎ?9 + 2𝜆 (1 − 𝜆) 𝑟I %ℎ?9 ℎ99 + (1 − 𝜆)9 ℎ99
This allows solving for .ℎ?9 using a quadratic equation,
5
9
0 = 𝜆9 ℎ?9 + 2𝜆 (1 − 𝜆) 𝑟I %ℎ?9 ℎ99 + (1 − 𝜆)9 ℎ99 − ℎ?,JKL
%ℎ?9
?
=
=
=
9
R
−2𝜆(1 − 𝜆) 𝑟I .ℎ99 ± %4𝜆9 (1 − 𝜆)9 𝑟I9 ℎ99 − 4𝜆9 Q(1 − 𝜆)9 ℎ99 − ℎ?,JKL
2𝜆9
9
−2𝜆(1 − 𝜆) 𝑟I .ℎ99 ± 2𝜆%(1 − 𝜆)9 6𝑟I9 − 1: ℎ99 + ℎ?,JKL
2𝜆9
9
−(1 − 𝜆) 𝑟I .ℎ99 ± %(1 − 𝜆)9 6𝑟I9 − 1: ℎ99 + ℎ?,JKL
𝜆
Note that the sign of the first term in the numerator will be opposite of the sign of 𝑟I .
Therefore if we select the sign of the phenotype so that 𝑟I > 0, then we must add the
second term to ensure .ℎ?9 > 0. This gives us
%ℎ?9
=
9
−(1 − 𝜆) 𝑟I .ℎ99 + %(1 − 𝜆)9 6𝑟I9 − 1: ℎ99 + ℎ?,JKL
𝜆
Note that this will not be bounded above by one when 𝜆 is small. This is not
surprising since a small 𝜆 implies that most of the cases reported for phenotype 1 are in
9
fact cases for phenotype 2, making particular combinations of ℎ99, ℎ?,JKL
and 𝑟I infeasible
for certain values of 𝜆. From the above, the determinant of the quadratic form must be
positive, thus
9
ℎ?,JKL
≥ (1 − 𝜆)9 61 − 𝑟I9 : ℎ99
Similarly, the determinant of the quadratic formula, solving for ℎ99, implies
ℎ?9 ≤
9
ℎ?,JKL
𝜆961 − 𝑟I9 :
This is the case unless no misclassification is present (𝜆 = 0) or the phenotypes are
functionally equivalent (𝑟I9 = 1).
We can now return to the original question regarding the relationship between 𝑟I and
𝑟I,JKL in the presence of phenotype misclassification. We derive for the SNP effects of α
and β2:
𝑟I,JKL ≡ Corr(𝛼, 𝛽9 )
Cov (𝛼, 𝛽9 )
=
.Var(𝛼)Var(𝛽9 )
Cov [𝜆𝛽? + (1 − 𝜆)𝛽9 , 𝛽9 ]
=
.Var(𝛼)Var(𝛽9 )
6
=
=
𝜆 Cov (𝛽? , 𝛽9 ) + (1 − 𝜆) Var(𝛽9 )
.Var(𝛼)Var(𝛽9 )
𝜆 𝑟I .Var (𝛽? ) Var (𝛽9 ) + (1 − 𝜆) Var(𝛽9 )
.Var(𝛼)Var(𝛽9 )
.Var(𝛽? )
.Var(𝛽9 )
= 𝜆 𝑟I
+ (1 − 𝜆)
.Var(𝛼)
.Var(𝛼)
Again assuming standardized regression coefficients, the variances can be written in
terms of heritability as
𝑟I,JKL =
𝜆 𝑟I .ℎ?9 + (1 − 𝜆).ℎ99
9
%ℎ?,JKL
Rearranging and substituting the expression for .ℎ?9 from above, assuming 𝑟I > 0,
gives
𝑟I =
=
=
9
𝑟I,JKL %ℎ?,JKL
− (1 − 𝜆).ℎ99
𝜆 .ℎ?9
9
𝑟I,JKL %ℎ?,JKL
− (1 − 𝜆).ℎ99
𝜆
𝜆
9
−(1 − 𝜆) 𝑟I .ℎ99 + %(1 − 𝜆)9 6𝑟I9 − 1: ℎ99 + ℎ?,JKL
9
𝑟I,JKL %ℎ?,JKL
− (1 − 𝜆).ℎ99
9
%(1 − 𝜆)9 6𝑟I9 − 1: ℎ99 + ℎ?,JKL
− (1 − 𝜆) 𝑟I .ℎ99
Note that in the case with no misclassification, i.e. 𝜆 = 1,
𝑟I =
9
𝑟I,JKL %ℎ?,JKL
%
9
ℎ?,JKL
= 𝑟I,JKL
To examine the effects of co-morbidity has on the estimates, Table S5 shows
numerical solutions for the estimated true correlation of some selected disorder pairs
based on literature estimates of co-morbidity, assuming unidirectional misclassification
9
and that ℎ?,JKL
is equal to true ℎ?9 (ie. that both disorders are roughly as heritable). For this
table, we substituted the lambda values (see table for reference) and used the formula
above to estimate what the true 𝑟I would be, based on the observed 𝑟I in this study. Figure
S8 shows how the true genetic correlation estimates for those pairs behave across a range
of λ values, given the observed 𝑟I in this study, under the same assumptions as Table S5.
7
We further estimate what degree of unidirectional misclassification would be
required to produce the significant rg values we observe in the paper, in the absence of
any true correlation. Given 𝑟I = 0,
𝑟I,JKL =
(1 − 𝜆).ℎ99
9
ℎ?,JKL
𝜆 = 1 − 𝑟I,JKL
9
ℎ?,JKL
.ℎ99
Table S6 lists the implied values of misclassification (1-𝜆) required to produce the
observed significant 𝑟I values between brain disorders in the study, if no true correlation
between the phenotypes exists.
Study-specific acknowledgments
IGAP (Alzheimer’s disease)
International Genomics of Alzheimer’s Project (IGAP) is a large two-stage study
based upon genome-wide association studies (GWAS) on individuals of European
ancestry. In stage 1, IGAP used genotyped and imputed data on 7,055,881 single
nucleotide polymorphisms (SNPs) to meta-analyze four previously-published GWAS
datasets consisting of 17,008 Alzheimer’s disease cases and 37,154 controls (The
European Alzheimer’s Disease Initiative, EADI [LabEx DISTALZ - Laboratory of
Excellence for Development of Innovative Strategies for a Transdisciplinary approach to
ALZheimer’s disease]; the Alzheimer Disease Genetics Consortium, ADGC
[UO1AG032984]; The Cohorts for Heart and Aging Research in Genomic Epidemiology
consortium, CHARGE [R01AG033193]; The Genetic and Environmental Risk in AD
consortium, GERAD [G0902227, MR/K013031/1, MR/L501517/1, ARUK-PG2014-1,
MR/L023784/1, MR/M009076/1, SGR544:CADR]). This work was supported by the
Framingham Heart Study’s National Heart, Lung, and Blood Institute contract (N01-HC25195; HHSN268201500001I), by grants (R01-AG016495, R01-AG008122, R01AG033040) from the National Institute on Aging, grant (R01-NS017950) from the
National Institute of Neurological Disorders and Stroke, as well as grants , P30AG10161,
R01AG15819, R01AG17917, 1R01HL128914, 2R01HL092577, and 3R01HL09257706S1. UK sample contribution was supported by the NIHR Queen Square Dementia
Biomedical Research Unit. Funding of some samples was supported by the NIHR
Biomedical Research Unit (Dementia) at University College London Hospitals NHS
Foundation Trust.
IHGC (Migraine)
We would like to thank the numerous individuals who contributed to sample
collection, storage, handling, phenotyping and genotyping within each of the individual
cohorts. We also thank the important contribution to research made by the study
participants. This work was supported by the National Human Genome Research Institute
of the National Institutes of Health (grant number R44HG006981) for 23andme and the
UK Medical Research Council and the Wellcome Trust (Grant ref: 102215/2/13/2) and
the University of Bristol for ALSPAC. The Australian cohort was supported by National
8
Institutes of Health (NIH) Grants AA07535, AA07728, AA13320, AA13321, AA14041,
AA11998, AA17688, DA012854, DA019951, AA010249, AA013320, AA013321,
AA011998, AA017688, and DA027995; by Grants from the Australian National Health
and Medical Research Council (NHMRC) (241944, 339462, 389927, 389875, 389891,
389892, 389938, 442915, 442981, 496739, 552485, 552498 and 1075175); by Grants
from the Australian Research Council (ARC) (A7960034, A79906588, A79801419,
DP0770096, DP0212016, and DP0343921); by the EU-funded GenomEUtwin (FP5QLG2-CT-2002-01254) and ENGAGE (FP7-HEALTH-201413) projects; and by the
European Union’s Seventh Framework program (2007–2013) under grant agreement no.
602633 (EUROHEADPAIN). DRN (FT0991022, 613674) and GWM (619667) were
supported by the ARC Future Fellowship and NHMRC Fellowship Schemes. We thank P
Visscher, D Duffy, A Henders, B Usher, E Souzeau, A Kuot, A McMellon, MJ Wright,
MJ Campbell, A Caracella, L Bowdler, S Smith, B Haddon, A Conciatore, D Smyth, H
Beeby, O Zheng and B Chapman for their input into project management, databases,
phenotype collection, and sample collection, processing and genotyping. We
acknowledge use of phenotype and genotype data from the British 1958 Birth Cohort
DNA collection, funded by the Medical Research Council grant G0000934 and the
Wellcome Trust grant 068545/Z/02. Genotyping for the B58C-WTCCC subset was
funded by the Wellcome Trust grant 076113/B/04/Z. The B58C-T1DGC genotyping
utilized resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative
clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney
Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID),
National Human Genome Research Institute (NHGRI), National Institute of Child Health
and Human Development (NICHD), and Juvenile Diabetes Research Foundation
International (JDRF) and supported by U01 DK062418. B58C-T1DGC GWAS data were
deposited by the Diabetes and Inflammation Laboratory, Cambridge Institute for Medical
Research (CIMR), University of Cambridge, which is funded by Juvenile Diabetes
Research Foundation International, the Wellcome Trust and the National Institute for
Health Research Cambridge Biomedical Research Centre; the CIMR is in receipt of a
Wellcome Trust Strategic Award (079895). The B58C-GABRIEL genotyping was
supported by a contract from the European Commission Framework Programme 6
(018996) and grants from the French Ministry of Research. The Danish HC study is
funded by grants from The Advanced Technology Foundation (001-2009-2), The
Lundbeck Foundation (R34-A3243), and The Lundbeck Foundation Initiative for
Integrative Psychiatric Research (R102-A9118). The deCODE migraine study was
funded in part by the European Commission (HEALTH-FP7-2013-Innovation-602891 to
the NeuroPain Consortium) and the National Institute of Dental and Craniofacial
Research (NIH R01DE022905). EGCUT received targeted financing from Estonian
Research Council grant IUT20-60, Center of Excellence in Genomics (EXCEGEN) and
University of Tartu (SP1GVARENG). We acknowledge EGCUT technical personnel,
especially Mr V. Soo and S. Smit. Data analyses were carried out in part in the High
Performance Computing Center of University of Tartu. For the Finnish MA cohort, the
Wellcome Trust (grants WT089062 and 098051 to A.P. ), the Academy of Finland
(grants 200923, 251704, and 286500 to A.P., and 139795 to M.W.); the Academy of
Finland Center of Excellence for Complex Disease Genetics; the EuroHead project
(LSM-CT-2004-504837); the Academy of Finland, Center of Excellence in Complex
9
Disease Genetics, (grant numbers 213506 and 129680 to A.P. and J.K.); FP7EUROHEADPAIN-no.602633, ENGAGE Consortium (grant agreement HEALTH-F42007-201413); EU/SYNSYS-Synaptic Systems (grant number 242167 to A.P.); the
Sigrid Juselius Foundation, Finland (to A.P.); the Folkhälsan Research Foundation,
Finland (to M.W.); Medicinska Understödsföreningen Liv & Hälsa (to M.W.), and the
Helsinki University Central Hospital (to M.K., V.A.). German MA/MO (Munich): This
study was supported by the Corona Foundation (S199/10052/2011). Health 2000: The
Health 2000 survey was funded by the Finnish National Institute for Health and Welfare
(THL) in collaboration with the National Social Insurance Institution and the five
university hospital districts. VS was supported by the Academy of Finland, grant
#139635, and the Finnish Foundation for Cardiovascular Research. The NFBC1966
sample received financial support from the Academy of Finland (project grants 104781,
120315, 129269, 1114194, 24300796, Center of Excellence in Complex Disease Genetics
and SALVE), University Hospital Oulu, Biocenter, University of Oulu, Finland (75617),
NHLBI grant 5R01HL087679-02 through the STAMPEED program (1RL1MH08326801), NIH/NIMH (5R01MH63706:02), ENGAGE project and grant agreement HEALTHF4-2007-201413, EU FP7 EurHEALTHAgeing -277849, the Medical Research Council,
UK (G0500539, G0600705, G1002319, PrevMetSyn/SALVE) and the MRC, Centenary
Early Career Award. The program is currently being funded by the H2020-633595
DynaHEALTH action and academy of Finland EGEA-project. For NTR/NESDA, we
acknowledge the Netherlands Organisation for Scientific Research (NWO), the
Netherlands Organisation for Health Research and Development (ZonMW), the EMGO+
Institute for Health and Care Research, the Neuroscience Campus Amsterdam, BBMRI–
NL (184.021.007: Biobanking and Biomolecular Resources Research Infrastructure), the
Avera Institute, Sioux Falls, South Dakota (USA), Rutgers University Cell and DNA
Repository cooperative agreement [National Institute of Mental Health U24 MH06845706], and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, Grand
Opportunity grants 1RC2 MH089951 and 1RC2 MH089995). Part of the genotyping and
analyses were funded by the Genetic Association Information Network (GAIN) of the
Foundation for the National Institutes of Health. Genotyping was funded in part by grants
from the National Institutes of Health (4R37DA018673-06, RC2 MH089951). The
statistical analyses were carried out on the Genetic Cluster Computer
(http://www.geneticcluster.org) which is supported by the Netherlands Scientific
Organization (NWO 480-05-003), the Dutch Brain Foundation and the Department of
Psychology and Education of the VU University Amsterdam. The Netherlands Twin
register was also supported by ZonMW Addiction 31160008; ZonMW 940-37-024;
NWO/SPI 56-464-14192; NWO-400-05-717; NWO-MW 904-61-19; NWO-MagW 48004-004; NWO-Veni 016-115-035 and the European Research Council (ERC 230374,
284167). The Netherlands Study of Depression and Anxiety (NESDA) was also funded
by ZonMW (Geestkracht program grant 10-000-1002). The generation and management
of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) was executed by
the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal
Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported
by the Netherlands Organisation of Scientific Research NWO Investments (nr.
175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal
Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015;
10
RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for
Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project
nr. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera
and Marjolein Peters, MSc, and Carolina Medina-Gomez, MSc, for their help in creating
the GWAS database, and Karol Estrada, PhD, Yurii Aulchenko, PhD, and Carolina
Medina-Gomez, MSc, for the creation and analysis of imputed data. The Rotterdam
Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam,
Netherlands Organization for the Health Research and Development (ZonMw), the
Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture
and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG
XII), and the Municipality of Rotterdam. The authors are grateful to the study
participants, the staff from the Rotterdam Study and the participating general
practitioners and pharmacists. The Swedish Twin Registry acknowledges funding support
from the Swedish Research Council, Swedish Brain Foundation, Karolinska Instutet
Resarch Funds, and the Åke Wibergs Stiftelse. The WGHS is supported by HL043851
and HL080467 from the National Heart, Lung, and Blood Institute and CA047988 from
the National Cancer Institute with collaborative scientific support and funding for
genotyping provided by Amgen. Young Finns: The Young Finns Study has been
financially supported by the Academy of Finland: grants 286284 (T.L.), 134309 (Eye),
126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social
Insurance Institution of Finland; Kuopio, Tampere and Turku University Hospital
Medical Funds (grant X51001 for T.L.); Juho Vainio Foundation; Paavo Nurmi
Foundation; Finnish Foundation of Cardiovascular Research (T.L.); Finnish Cultural
Foundation; Tampere Tuberculosis Foundation (T.L.); Emil Aaltonen Foundation (T.L.);
and Yrjö Jahnsson Foundation (T.L.). We gratefully acknowledge the THL DNA
laboratory for its skilful work to produce the DNA samples used in this study, and Ville
Aalto and Irina Lisinen for the expert technical assistance in the statistical analyses. Ester
Cuenca-Leon is supported by a 'Beatriu de Pinós' grant (2010 BP-A and BP-A2) funded
by AGAUR, Generalitat de Catalunya. Phil H. Lee is supported by NIMH K99
MH101367. Tune H. Pers is supported by the Alfred Benzon Foundation.
IPDGC (Parkinson’s disease)
We would like to thank all of the subjects who donated their time and biological
samples to be a part of this study. This work was supported in part by the Intramural
Research Programs of the National Institute of Neurological Disorders and Stroke
(NINDS), the National Institute on Aging (NIA), and the National Institute of
Environmental Health Sciences both part of the National Institutes of Health, Department
of Health and Human Services; project numbers Z01-AG000949-02 and Z01-ES101986.
In addition this work was supported by the Department of Defense (award W81XWH-092-0128), and The Michael J Fox Foundation for Parkinson’s Research. This work was
supported by National Institutes of Health grants R01NS037167, R01CA141668,
P50NS071674, American Parkinson Disease Association (APDA); Barnes Jewish
Hospital Foundation; Greater St Louis Chapter of the APDA; Hersenstichting Nederland;
Neuroscience Campus Amsterdam; and the section of medical genomics, the Prinses
Beatrix Fonds. The KORA (Cooperative Research in the Region of Augsburg) research
platform was started and financed by the Forschungszentrum für Umwelt und
11
Gesundheit, which is funded by the German Federal Ministry of Education, Science,
Research, and Technology and by the State of Bavaria. This study was also funded by the
German National Genome Network (NGFNplus number 01GS08134, German Ministry
for Education and Research); by the German Federal Ministry of Education and Research
(NGFN 01GR0468, PopGen); and 01EW0908 in the frame of ERA-NET NEURON and
Helmholtz Alliance Mental Health in an Ageing Society (HA-215), which was funded by
the Initiative and Networking Fund of the Helmholtz Association. The French GWAS
work was supported by the French National Agency of Research (ANR-08-MNP-012).
This study was also funded by France-Parkinson Association, the French program
“Investissements d’avenir” funding (ANR-10-IAIHU-06) and a grant from Assistance
Publique-Hôpitaux de Paris (PHRC, AOR-08010) for the French clinical data. This study
was also sponsored by the Landspitali University Hospital Research Fund (grant to SSv);
Icelandic Research Council (grant to SSv); and European Community Framework
Programme 7, People Programme, and IAPP on novel genetic and phenotypic markers of
Parkinson’s disease and Essential Tremor (MarkMD), contract number PIAP-GA-2008230596 MarkMD (to HP and JHu). This study utilized the high-performance
computational capabilities of the Biowulf Linux cluster at the National Institutes of
Health, Bethesda, Md. (http://biowulf.nih.gov), and DNA panels, samples, and clinical
data from the National Institute of Neurological Disorders and Stroke Human Genetics
Resource Center DNA and Cell Line Repository. People who contributed samples are
acknowledged in descriptions of every panel on the repository website. We thank the
French Parkinson’s Disease Genetics Study Group and the Drug Interaction with genes
(DIGPD) study group: Y Agid, M Anheim, A-M Bonnet, M Borg, A Brice, E Broussolle,
J-C Corvol, P Damier, A Destée, A Dürr, F Durif, A Elbaz, D Grabil, S Klebe, P. Krack,
E Lohmann, L. Lacomblez, M Martinez, V Mesnage, P Pollak, O Rascol, F Tison, C
Tranchant, M Vérin, F Viallet, and M Vidailhet. We also thank the members of the
French 3C Consortium: C Berr, C Tzourio, and P Amouyel for allowing us to use part of
the 3C cohort, and D Zelenika for support in generating the genome-wide molecular data.
We thank P Tienari (Molecular Neurology Programme, Biomedicum, University of
Helsinki), T Peuralinna (Department of Neurology, Helsinki University Central
Hospital), L Myllykangas (Folkhalsan Institute of Genetics and Department of Pathology,
University of Helsinki), and R Sulkava (Department of Public Health and General
Practice Division of Geriatrics, University of Eastern Finland) for the Finnish controls
(Vantaa85+ GWAS data). We used genome-wide association data generated by the
Wellcome Trust Case-Control Consortium 2 (WTCCC2) from UK patients with
Parkinson’s disease and UK control individuals from the 1958 Birth Cohort and National
Blood Service. Genotyping of UK replication cases on ImmunoChip was part of the
WTCCC2 project, which was funded by the Wellcome Trust (083948/Z/07/Z). UK
population control data was made available through WTCCC1. This study was supported
by the Medical Research Council and Wellcome Trust disease centre (grant
WT089698/Z/09/Z to NW, JHa, and ASc). As with previous IPDGC efforts, this study
makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list
of the investigators who contributed to the generation of the data is available from
www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under
award 076113, 085475 and 090355. This study was also supported by Parkinson’s UK
(grants 8047 and J-0804) and the Medical Research Council (G0700943). We thank
12
Jeffrey Barrett for assistance with the design of the ImmunoChip. DNA extraction work
that was done in the UK was undertaken at University College London Hospitals,
University College London, who received a proportion of funding from the Department
of Health’s National Institute for Health Research Biomedical Research Centres funding.
This study was supported in part by the Wellcome Trust/Medical Research Council Joint
Call in Neurodegeneration award (WT089698) to the Parkinson’s Disease Consortium
(UKPDC), whose members are from the UCL Institute of Neurology, University of
Sheffield, and the Medical Research Council Protein Phosphorylation Unit at the
University of Dundee. The MRC (JPND), Parkinsons UK and the NIHR UCLH
Biomedical Research centre are gratefully acknowledged (NWW)
METASTROKE consortium of the International Stroke Genetics Consortium
(Ischemic stroke)
This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreements No 666881, SVDs@target (to M
Dichgans) and No 667375, CoSTREAM (to M Dichgans); the DFG as part of the Munich
Cluster for Systems Neurology (EXC 1010 SyNergy) and the CRC 1123 (B3)(to M
Dichgans); the Corona Foundation (to M Dichgans); the Fondation Leducq (Transatlantic
Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain)(to M
Dichgans); the e:Med program (e:AtheroSysMed) (to M Dichgans) and the FP7/20072103 European Union project CVgenes@target (grant agreement number Health-F22013-601456) (to M Dichgans).
Eating Disorders Working Group of the Psychiatric Genomics Consortium
(Anorexia nervosa)
Data on anorexia nervosa were made possible by funding for the Genetic
Consortium on Anorexia Nervosa by the Wellcome Trust Case Control Consortium 3
project titled “A Genome-Wide Association Study of Anorexia Nervosa”
(WT088827/Z/09) and the Foundation of Hope, Raleigh, North Carolina, USA. Funding
for the Children’s Hospital of Pennsylvania and Price Foundation study was from the
Price Foundation, the Klarman Family Foundation, Scripps Translational Sciences
Institute Clinical Translational Science Award [Grant Number U54 RR0252204-01],
Institute Development Award to the Center for Applied Genomics from the CHOP; 2011
- 2014 Davis Foundation Postdoctoral Fellowship Program in Eating Disorders Research
Award, Yiran Guo; 2012 - 2015 Davis Foundation Postdoctoral Fellowship Program in
Eating Disorders Research Award, Dong Li.
Major Depressive Disorder Working Group of the Psychiatric Genomics
Consortium
This work was funded by the German Research Foundation (DFG, grant FOR2107
DA1151/5-1 to UD; SFB-TRR58, Project C09 to UD) and the Interdisciplinary Center
for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to
UD).
13
Tourette Syndrome and Obsessive Compulsive Disorder Working Group of the
Psychiatric Genomics Consortium (Tourette Syndrome and Obsessive Compulsive
Disorder)
This study was supported by Grants from the National Institute of Mental Health
[R01MH092290; R01MH092291; R01MH092292; R01MH092293; R01MH092513;
R01MH092516; R01MH092520; R01MH092289; U24MH068457] and the New Jersey
Center for Tourette Syndrome and Associated Disorders (NJCTS).
Consortium memberships
IGAP (Alzheimer’s disease)
Benjamin Grenier-Boley, Vincent Chouraki, Yoichiro Kamatani, Marie-Thérèse
Bihoreau, Florence Pasquier, Olivier Hanon, Dominique Campion, Claudine Berr, Luc
Letenneur, Nathalie Fievet, Alexis Brice, Didier Hannequin, Karen Ritchie, JeanFrancois Dartigues, Christophe Tzourio, Philippe Amouyel, Anne Boland, Jean-François
Deleuze, Jacques Epelbaum, Emmanuelle Duron, Bruno Dubois, Adam C. Naj, Gyungah
Jun, Gary W. Beecham, Badri N. Vardarajan, Brian Kunkle, Christiane Reitz, Joseph D.
Buxbaum, Paul K. Crane, Philip L. De Jager, Alison M. Goate, Matthew J. Huentelman,
M. Ilyas Kamboh, Eric B. Larson, Oscar L. Lopez, Amanda J. Myers, Ekaterina Rogaeva,
Peter St George-Hyslop, Debby W. Tsuang, John R. Gilbert, Hakon Hakonarson, Duane
Beekly, Kara L. Hamilton-Nelson, Kelley M. Faber, Laura B. Cantwell, Deborah
Blacker, David A. Bennett, Tatiana M. Foroud, Walter A. Kukull, Kathryn L. Lunetta,
Eden R. Martin, Li-San Wang, Richard Mayeux, Jonathan L. Haines, Margaret A.
Pericak-Vance, Lindsay A. Farrer, Gerard D. Schellenberg, Deborah C. Mash, Roger L.
Albin, Liana G. Apostolova, Steven E. Arnold, Clinton T. Baldwin, Lisa L. Barnes,
Thomas G. Beach, Thomas D. Bird, Bradley F. Boeve, James D. Bowen, James R. Burke,
Minerva M. Carrasquillo, Steven L. Carroll, Lori B. Chibnik, David G. Clark, Carlos
Cruchaga, Charles DeCarli, F. Yesim Demirci, Malcolm Dick, Kenneth B. Fallon, Martin
R. Farlow, Steven Ferris, Douglas R. Galasko, Piyush Gampawar, Yasaman Saba, Marla
Gearing, Daniel H. Geschwind, Neill R. Graff-Radford, Ronald L. Hamilton, John Hardy,
Elizabeth Head, Lawrence S. Honig, Christine M. Hulette, Bradley T. Hyman, Gail P.
Jarvik, Gregory A. Jicha, Lee-Way Jin, Anna Karydas, John S.K. Kauwe, Jeffrey A.
Kaye, Ronald Kim, Edward H. Koo, Neil W Kowall, Frank M. LaFerla, James B.
Leverenz, Allan I. Levey, Ge Li, Andrew P. Lieberman, Constantine G. Lyketsos, Wayne
C. McCormick, Susan M. McCurry, Carol A. Miller, Jill R. Murrell, John M. Olichney,
Vernon S. Pankratz, Aimee Pierce, Wayne W. Poon, Joseph F. Quinn, Murray Raskind,
Barry Reisberg, Erik D. Roberson, Mary Sano, Andrew J. Saykin, Julie A. Schneider,
Lon S. Schneider, William W. Seeley, Helena Schmidt, Albert V. Smith, Amanda G.
Smith, Joshua A. Sonnen, Salvatore Spina, Robert A. Stern, Rudolph E. Tanzi, John Q.
Trojanowski, Vivianna M. Van Deerlin, Linda J. Van Eldik, Sandra Weintraub, Kathleen
A. Welsh-Bohmer, Randall L. Woltjer, Chang-En Yu, Robert Barber, Denise Harold,
Rebecca Sims, Giancarlo Russo, Nicola Jones, Melanie L Dunstan, Alfredo Ramirez,
Janet A Johnston, David C Rubinsztein, Michael Gill, John Gallacher, Anthony Bayer,
Magda Tsolaki, Petra Proitsi, John Collinge, Nick C Fox, John Hardy, Robert Clarke,
Carol Brayne, Susanne Moebus, Wolfgang Maier, Harald Hampel, Sabrina Pichler,
Alison Goate, Minerva M Carrasquillo, Steven G Younkin, Michael J Owen, Michael C
O'Donovan, Simon Mead, Peter Passmore, Kevin Morgan, John F Powell, John S.K.
14
Kauwe, Carlos Cruchaga, Matthias Riemenschneider, Markus M Nöthen, Lesley Jones,
Peter A Holmans, Valentina Escott-Price, Julie Williams, Patrick G Kehoe, Jason D
Warren, Jonathan M Schott, Natalie Ryan, Martin Rossor, Yoav Ben-Shlomo, Michelle
Lupton, Steffi Riedel-Heller, Martin Dichgans, Reiner Heun, Per Hoffmann, Johannes
Kornhuber, Hendrik van den Bussche, Brian Lawlor, Aoibhinn Lynch, David Mann,
Isabella Heuser, Lutz Frölich, John C Morris, Andrew McQuillin, Ammar Al-Chalabi,
Christopher E Shaw, Andrew B Singleton, Rita Guerreiro, Karl-Heinz Jöckel, H-Erich
Wichmann, Dennis W Dickson, Ronald C Petersen, Carla A Ibrahim-Verbaas, Anita L
DeStefano, Joshua C Bis, M. Arfan Ikram, Agustin Ruiz, Seung-Hoan Choi, Vilmundur
Gudnason, Najaf Amin, Oscar L Lopez, Gudny Eiriksdottir, Thomas H Mosley, Jr.,
Tamara B Harris, Jerome I Rotter, Michael A Nalls, Palmi V Jonsson, Mercè Boada,
Albert Hofman, Lenore J Launer, Cornelia M van Duijn, Sudha Seshadri, Alexa Beiser,
Stephanie Debette, Qiong Yang, Galit Weinstein, Jing Wang, Andre G Uiterlinden, Peter
J Koudstaal, William T Longstreth, Jr, James T Becker, Thomas Lumley, Kenneth Rice,
Peter Dal-Bianco, Gerhard Ransmayr, Thomas Benke, Sven van der Lee, Jan Bressler,
Myriam Fornage, Isabel Hernández, Ana Mauleón and Montserrat Alegret
IHGC (Migraine)
Padhraig Gormley, Verneri Anttila, Bendik S Winsvold, Priit Palta, Tonu Esko, KaiHow Farh, Ester Cuenca-Leon, Mikko Muona, Nicholas A Furlotte, Tobias Kurth,
George McMahon, Lannie Ligthart, Gisela M Terwindt, Mikko Kallela, Rainer Malik,
Caroline Ran, Scott G Gordon, Stacy Steinberg, Guntram Borck, Markku Koiranen,
Hieab Adams, Terho Lehtimäki, Antti-Pekka Sarin, Juho Wedenoja, Lydia Quaye, David
A Hinds, Julie E Buring, Markus Schürks, Paul M Ridker, Maria Gudlaug Hrafnsdottir,
Jouke-Jan Hottenga, Brenda WJH Penninx, Markus Färkkilä, Ville Artto, Mari Kaunisto,
Salli Vepsäläinen, Tobias M Freilinger, Pamela A F Madden, Nicholas G Martin, Grant
W Montgomery, Eija Hamalainen, Hailiang Huang, Lude Franke, Jie Huang, Evie
Stergiakouli, Phil H Lee, Cynthia Sandor, Caleb Webber, Zameel Cader, Bertram MullerMyhsok, Stefan Schreiber, Thomas Meitinger, Johan Eriksson, Veikko Salomaa, Kauko
Heikkilä, Elizabeth Loehrer, Andre G Uitterlinden, Albert Hofman, Cornelia M van
Duijn, Lynn Cherkas, Audun Stubhaug, Christopher S Nielsen, Minna Männikko, Evelin
Mihailov, Lili Milani, Hartmut Göbel, Ann-Louise Esserlind, Anne Francke Christensen,
Thomas Folkmann Hansen, Thomas Werge, Bru Cormand, Lyn Griffiths, Marjo
Hiekkala, Alfons Macaya, Patricia Pozo-Rosich, Jaakko Kaprio, Arpo J Aromaa, Olli
Raitakari, M Arfan Ikram, Tim Spector, Marjo-Riitta Järvelin, Andres Metspalu,
Christian Kubisch, David P Strachan, Michel D Ferrari, Andrea C Belin, Martin
Dichgans, Maija Wessman, Arn MJM van den Maagdenberg, John-Anker Zwart, Dorret I
Boomsma, George Davey Smith, Nicholas Eriksson, Mark J Daly, Benjamin M Neale,
Jes Olesen, Daniel I. Chasman, Dale R Nyholt and Aarno Palotie
ILAE Consortium on Complex Epilepsies (Epilepsy)
Richard Anney, Andreja Avbersek, Larry Baum, Felicitas Becker, Samuel Berkovic,
Jonathan Bradfield, Russell Buono, Claudia B. Catarino, Gianpiero Cavalleri, Stacey
Cherny, Alison Coffey, Patrick Cossette, Gerrit-Jan Haan, Peter Jonghe, Carolien Kovel,
Chantal Depondt, Dennis Dlugos, Colin Doherty, Thomas Ferraro, Martha Feucht, Andre
Franke, Jacqueline French, Verena Gaus, Hongsheng Gui, Youling Guo, Hakon
15
Hakonarson, Erin Heinzen, Ingo Helbig, Helle Hjalgrim, Jennifer Jamnadas-Khoda,
Michael Johnson, Reetta Kälviäinen, Anne-Mari Kantanen, Dalia Kasperaviciute,
Dorothee Trenite, Bobby Koeleman, Wolfram Kunz, Patrick Kwan, Yu Lau, Holger
Lerche, Costin Leu, Wolfgang Lieb, Dick Lindhout, Warren Lo, Daniel Lowenstein,
Alberto Malovini, Anthony Marson, Mark McCormack, James Mills, Martina
Moerzinger, Rikke Møller, Anne Molloy, Hiltrud Muhle, Ping-Wing Ng, Markus M.
Nöthen, Terence O'Brien, Karen Oliver, Aarno Palotie, Michael Privitera, Rodney
Radtke, Philipp Reif, Ann-Kathrin Ruppert, Thomas Sander, Theresa Scattergood, Steven
Schachter, Christoph Schankin, Ingrid Scheffer, Bettina Schmitz, Susanne Schoch, Pak
Sham, Sanjay Sisodiya, David Smith, Philip Smith, Michael Sperling, Michael Steffens,
Pasquale Striano, Hans Stroink, Rainer Surges, Meng Tan, Neil G. Thomas, Marian
Todaro, Holger Trucks, Frank Visscher, Nicole Walley, Zhi Wei, Christopher D. Whelan,
Wanling Yang, Federico Zara and Fritz Zimprich
IMSGC (Multiple sclerosis)
Chris Cotsapas
IPDGC (Parkinson’s disease)
Jose Bras, Alexis Brice, Boniface Mok, Valentina Escott-Price, Thomas Foltynie,
Thomas Gasser, Raphael Gibbs, Rita Guerreiro, John Hardy, Dena Hernandez, Peter
Heutink, Peter Holmans, Suzanne Lesage, Steven Lubbe, Maria Martinez, Niccolo
Mencacci, Huw Morris, Michael Nalls, Claudia Schulte, Manu Sharma, Mina Ryten,
Joshua Shulman, Javier Simón-Sánchez, Andrew Singleton, Nigel Williams and Nick
Wood
METASTROKE consortium of the International Stroke Genetics Consortium
(Ischemic stroke)
Rainer Malik, Matthew Traylor, Sara Pulit, Steve Bevan, Jemma Hopewell,
Elizabeth Holliday, Wei Zhao, Philippe Amouyel, John Attia, Thomas Battey, Klaus
Berger, Giorgio Boncoraglio, Ganesh Chauhan, Wei-Min Chen, Ioana Cotlarciuc,
Stephanie Debette, Guido Falcone, Andreea Ilinca, Steven Kittner, Christina Kourkoulis,
Robin Lemmens, Arne Lindgren, James Meschia, Braxton Mitchell, Joana Pera, Peter
Rothwell, Pankaj Sharma, Cathie Sudlow, Turgut Tatlisumak, Vincent Thijs, Astrid
Vicente, Daniel Woo, Sudha Seshadri, Jonathan Rosand, Hugh Markus, Bradford Worrall
and Martin Dichgans
International Stroke Genetics Consortium Study of Intracerebral Hemorrhage
(Intracerebral Hemorrhage)
Miriam Raffeld, Guido Falcone, David Tirschwell, Bradford Worrall, Agnieszka
Slowik, Magdy Selim, Jordi Jimenez-Conde, Daniel Woo, Scott Silliman, Arne Lindgren,
James Meschia, Rainer Malik, Martin Dichgans and Jonathan Rosand
Attention-Deficit Hyperactivity Disorder Working Group of the Psychiatric
Genomics Consortium (Attention-deficit hyperactivity disorder)
Stephen V. Faraone, Bru Cormand, Josep Antoni Ramos-Quiroga, Cristina SánchezMora, Marta Ribasés, Miguel Casas, Amaia Hervas, Maria Jesús Arranz, Yufeng Wang,
16
Jan Haavik, Tetyana Zayats, Stefan Johansson, Carol Mathews, Nigel Williams, Peter
Holmans, Joanna Martin, Hakon Hakonarson, Josephine Elia, Tobias J Renner, Benno G
Schimmelmann, Anke Hinney, Özgür Albayrak, Johannes Hebebrand, André Scherag,
Astrid Dempfle, Beate Herpertz-Dahlmann, Judith Sinzig, Gerd Lehmkuhl, Aribert
Rothenberger, Edmund J S Sonuga-Barke, Hans-Christoph Steinhausen, Jonna Kuntsi,
Philip Asherson, Robert D. Oades, Tobias Banaschewski, Herbert Roeyers, Joseph
Sergeant, Richard P. Ebstein, Sarah Hohmann, Barbara Franke, Jan K. Buitelaar, Lindsey
Kent, Alejandro Arias Vasquez, Michael Gill, Nanda Lambregts-Rommelse, Richard JL
Anney, Aisling Mulligan, Joseph Biederman, Yanli Zhang-James, Alysa E. Doyle,
Andreas Reif, Klaus-Peter Lesch, Andreas Warnke, Christine M. Freitag, Susanne
Walitza, Olga Rivero, Haukur Palmason, Jobst Meyer, Marcel Romanos, Anita Thapar,
Kate Langley, Michael C. O’Donovan, Michael J. Owen, Marcella Rietschel, Stephanie
H Witt, Soeren Dalsgaard, Preben Bo Mortensen, Anders Børglum, Ditte Demontis, Joel
Gelernter, Irwin Waldman, Joel Nigg, Beth Wilmot, Nikolas Molly, Luis Rohde, Claiton
Bau, Eugenio Grevet, Mara Hutz, Nina Roth Mota, Eric Mick, Sarah E. Medland,
Benjamin M Neale, Raymond Walters, Mark J. Daly, Stephan Ripke, Alice Charach,
Russell Schachar, Jennifer Crosbie, Sandra K. Loo, Susan L. Smalley and James J.
McGough
Autism Spectrum Disorders Working Group of the Psychiatric Genomics
Consortium (Autism)
Richard Anney, Stephan Ripke, Verneri Anttila, Peter Holmans, Hailiang Huang,
Phil H Lee, Sarah Medland, Benjamin Neale, Elise Robinson, Lauren Weiss, Joana
Almeida, Evdokia Anagnostou, Elena Bacchelli, Joel Bader, Anthony Bailey, Vanessa
Bal, Agatino Battaglia, Raphael Bernier, Catalina Betancur, Sven Bölte, Patrick Bolton,
Sean Brennan, Rita Cantor, Patrícia Celestino-Soper, Andreas G. Chiocchetti, Michael
Cuccaro, Geraldine Dawson, Maretha De Jonge, Silvia De Rubeis, Richard Delorme,
Frederico Duque, Sean Ennis, A. Gulhan Ercan-Sencicek, Eric Fombonne, Christine M.
Freitag, Louise Gallagher, John Gilbert, Arthur Goldberg, Andrew Green, Dorothy Grice,
Stephen Guter, Jonathan Haines, Robert Hendren, Christina Hultman, Sabine mKlauck,
Alexander Kolevzon, Christine Ladd-Acosta, Marion Leboyer, David Ledbetter, Christa
Lese Martin, Pat Levitt, Jennifer Lowe, Elena Maestrini, Tiago Magalhaes, Shrikant
Mane, Donna Martin, William McMahon, Alison Merikangas, Nancy Minshew, Anthony
Monaco, Daniel Moreno-De-Luca, Eric Morrow, John Nurnberger, Guiomar Oliveira,
Jeremy Parr, Margaret Pericak-Vance, Dalila Pinto, Regina Regan, Karola Rehnström,
Abraham Reichenberg, Kathryn Roeder, Bernadette Rogé, Guy Rouleau, Sven Sandin,
Gerard Schellenberg, Stephen Scherer, Stacy Steinberg, Astrid Vicente, Jacob Vorstman,
Regina Waltes, Thomas Wassink, Ellen Wijsman, A. Jeremy Willsey, Lonnie
Zwaigenbaum, Timothy Yu, Joseph Buxbaum, Edwin Cook, Hilary Coon, Daniel
Geschwind, Michael Gill, Hakon Hakonarson, Joachim Hallmayer, Aarno Palotie, Susan
Santangelo, James Sutcliffe, Dan Arking and Mark Daly
Bipolar Disorders Working Group of the Psychiatric Genomics Consortium (Bipolar
disorder)
Marcella Rietschel, Thomas G Schulze, Jana Strohmaier, Robert C Thompson,
Stephanie H Witt, John Strauss, James L Kennedy, John B Vincent, Keith Matthews,
17
Shaun Purcell, Douglas Ruderfer, Pamela Sklar, Jordan Smoller, Laura J Scott, Margit
Burmeister, Devin Absher, William E Bunney, Huda Akil, Ole A Andreassen, Srdjan
Djurovic, Morten Mattingsdal, Ingrid Melle, Gunnar Morken, Aiden Corvin, Michael
Gill, Derek Morris, Adebayo Anjorin, Nick Bass, Andrew McQuillin, Douglas
Blackwood, Andrew McIntosh, Alan W McLean, Walter J Muir, Sarah E Bergen,
Vishwajit Nimgaonkar, Marian L Hamshere, Christina Hultman, Mikael Landén, Paul
Lichtenstein, Patrick Sullivan, Martin Schalling, Louise Frisén, Shaun Purcell, Eli Stahl,
Amanda Dobbyn, Laura Huckins, Stéphane Jamain, Marion Leboyer, Bruno Etain, Frank
Bellivier, Andreas J Forstner, Markus Leber, Stefan Herms, Per Hoffmann, Anna Maaser,
Sascha B Fischer, Céline S Reinbold, Sarah Kittel-Schneider, Jolanta Lissowska, Janice
M Fullerton, Joanna Hauser, Sarah E Medland, Scott D Gordon, Jens Treutlein, Josef
Frank, Fabian Streit, José Guzman-Parra, Fermin Mayoral, Piotr M Czerski, Neonila
Szeszenia-Dabrowska, Bertram Müller-Myhsok, Markus Schwarz, Peter R Schofield,
Nicholas G Martin, Grant W Montgomery, Mark Lathrop, Bernhard T Baune, Sven
Cichon, Thomas W Mühleisen, Franziska Degenhardt, Manuel Mattheisen, Johannes
Schumacher, Wolfgang Maier, Markus M Nöthen, Michael Bauer, Philip B Mitchell,
Andreas Reif, Tiffany A Greenwood, Caroline M Nievergelt, Paul D Shilling, Nicholas J
Schork, Erin N Smith, John I Nurnberger, Howard J Edenberg, Tatiana Foroud, Elliot S
Gershon, William B Lawson, Evaristus A Nwulia, Maria Hipolito, John Rice, William
Byerley, Francis J McMahon, Wade Berrettini, James B Potash, Peter P Zandi, Sebastian
Zöllner, Peng Zhang, Gerome Breen, Lisa Jones, Peter A Holmans, Ian R Jones, George
Kirov, Valentina Escott-Price, Ivan Nikolov, Michael C O'Donovan, Michael J Owen,
Nick Craddock, I Nicol Ferrier, Martin Alda, Pablo Cervantes, Cristiana Cruceanu, Guy
A. Rouleau, Gustavo Turecki, Qingqin Li, Roel Ophoff, Rolf Adolfsson, Carlos Pato and
Joanna M Biernacka
Eating Disorder Working Group of the Psychiatric Genomics Consortium (Anorexia
Nervosa)
Harry Brandt, Scott Crow, Manfred M. Fichter, Katherine A. Halmi, Maria La Via,
James Mitchell, Michael Strober, Alessandro Rotondo, D. Blake Woodside, Pamela Keel,
Lisa Lilenfeld, Andrew W. Bergen, Walter Kaye, Pierre Magistretti, Deborah Kaminska,
Vesna Boraska Perica, Christopher S. Franklin, James A. B. Floyd, Laura M. Thornton,
Laura M. Huckins, Lorraine Southam, N. William Rayner, Ioanna Tachmazidou, Kelly L.
Klump, Janet Treasure, Cathryn M. Lewis, Ulrike Schmidt, Federica Tozzi, Kirsty
Kiezebrink, Johannes Hebebrand, Philip Gorwood, Roger A. H. Adan, Martien J. H. Kas,
Angela Favaro, Paolo Santonastaso, Fernando Fernández-Aranda, Monica Gratacos, Filip
Rybakowski, Jaakko Kaprio, Anna Keski-Rahkonen, Anu Raevuori, Eric F. van Furth,
Margarita C. T. Slof-Op 't Landt, James I. Hudson, Ted Reichborn-Kjennerud, Gun
Peggy S. Knudsen, Palmiero Monteleone, Allan S. Kaplan, Andreas Karwautz, Hakon
Hakonarson, Wade H. Berrettini, Yiran Guo, Dong Li, Nicholas J. Schork, Tetsuya Ando,
Tõnu Esko, Krista Fischer, Katrin Mannik, Andres Metspalu, Jessica H. Baker, Roger D.
Cone, Janiece E. DeSocio, Christopher E. Hilliard, Julie K. O'Toole, Jacques Pantel, Jin
Peng Szatkiewicz, Chrysecolla Taico, Stephanie Zerwas, Sara E. Trace, Oliver S. P.
Davis, Sietske Helder, Roland Burghardt, Martina de Zwaan, Karin Egberts, Stefan
Ehrlich, Beate Herpertz-Dahlmann, Wolfgang Herzog, Hartmut Imgart, André Scherag,
Susann Scherag, Stephan Zipfel, Nicolas Ramoz, Unna N. Danner, Carolien de Kove,
18
Judith Hendriks, Bobby P.C. Koeleman, Roel A. Ophoff, Annemarie A. van Elburg,
Maurizio Clementi, Daniela Degortes, Monica Forzan, Elena Tenconi, Elisa Docampo,
Susana Jimenez-Murcia, Jolanta Lissowska, Neonilia Szeszenia-Dabrowska, Joanna
Hauser, Leila Karhunen, Ingrid Meulenbelt, P. Eline Slagboom, Steve Crawford, Alfonso
Tortorella, Mario Maj, George Dedoussis, Fragiskos Gonidakis, Konstantinos Tziouvas,
Artemis Tsitsika, Hana Papežová, Lenka Slachtova, Debora Martaskova, James L.
Kennedy, Robert D. Levitan, Zeynep Yilmaz, Julia Huemer, Gudrun Wagner, Paul
Lichtenstein, Gerome Breen, Sarah Cohen-Woods, Sven Cichon, Ina Giegling, Stefan
Herms, Dan Rujescu, Stefan Schreiber, H-Erich Wichmann, Christian Dina, Giovanni
Gambaro, Nicole Soranzo, Antonio Julia, Sara Marsal, Raquel Rabionet, Danielle M.
Dick, Aarno Palotie, Samuli Ripatti, Ole A. Andreassen, Thomas Espeseth, Astri
Lundervold, Ivar Reinvang, Vidar M. Steen, Stephanie Le Hellard, Morten Mattingsdal,
Ioanna Ntalla, Vladimir Bencko, Lenka Foretova, Marie Navratilova, Dalila Pinto,
Stephen W. Scherer, Harald Aschauer, Laura Carlberg, Alexandra Schosser, Lars
Alfredsson, Bo Ding, Leonid Padyukov, Gursharan Kalsi, Marion Roberts, Darren W.
Logan, Xavier Estivill, Anke Hinney, Patrick F. Sullivan, David A. Collier, Eleftheria
Zeggini and Cynthia M. Bulik
Major Depressive Disorder Working Group of the Psychiatric Genomics
Consortium (Major depressive disorder)
Patrick Sullivan, Stephan Ripke, Danielle Posthuma, André G Uitterlinden, Albert
Hofman, Stefan Kloiber, Klaus Berger, Bertram Müller-Myhsok, Qingqin Li, Till
Andlauer, Marcella Rietschel, Andreas J Forstner, Fabian Streit, Jana Strohmaier, Josef
Frank, Stefan Herms, Stephanie Witt, Jens Treutlein, Markus M. Nöthen, Sven Cichon,
Franziska Degenhardt, Per Hoffmann, Thomas G. Schulze, Bernhard T Baune, Udo
Dannlowski, Tracy Air, Grant Sinnamon, Naomi Wray, Andrew McIntosh, douglas
blackwood, Toni-Kim Clarke, Donald MacIntyre, David Porteous, Caroline Hayward,
Tonu Esko, Evelin Mihailov, Lili Milani, Andres Metspalu, Hans J Grabe, Henry Völzke,
Alexander Teumer, Sandra Van der Auwera, Georg Homuth, Matthias Nauck, Cathryn
Lewis, Gerome Breen, Margarita Rivera, Michael Gill, Nick Craddock, John P Rice,
Michael Owen, Ole Mors, Anders Børglum, Jakob Grove, Daniel Umbricht, Carsten
Horn, Christel Middeldorp, Enda Byrne, Baptiste Couvy-Duchesne, Scott Gordon,
Andrew C Heath, Anjali Henders, Ian Hickie, Nicholas Martin, Sarah Medland, Grant
Montgomery, Dale Nyholt, Michele Pergadia, Divya Mehta, Martin Preisig, Zoltán
Kutalik, Rudolf Uher, Michael O'Donovan, Brenda WJH Penninx, Yuri Milaneschi,
Wouter Peyrot, Johannes H Smit, Rick Jansen, Aartjan TF Beekman, Robert A
Schoevers, Albert van Hemert, Gerard van Grootheest, Dorret Boomsma, Jouke- Jan
Hottenga, Eco de Geus, Erin Dunn, Jordan Smoller, Patrik Magnusson, Nancy Pedersen,
Alexander Viktorin, Thomas Werge, Thomas Folkmann Hansen, Sara Paciga, Hualin Xi,
Douglas Levinson, James Potash and James A. Knowles
Tourette Syndrome and Obsessive Compulsive Disorder Working Group of the
Psychiatric Genomics Consortium (Tourette Syndrome and Obsessive-compulsive
disorder)
John Alexander, Paul Arnold, Harald Aschauer, Cristina Barlassina, Cathy Barr,
Robert Batterson, Laura Bellodi, Cheston Berlin, Gabriel Bedoya-Berrio, O. Bienvenu,
19
Donald Black, Michael Bloch, Rianne Blom, Helena Brentani, Lawrence W. Brown,
Ruth Bruun, Cathy Budman, Christie Burton, Beatriz Camarena, Carolina Cappi, Julio
Cardona-Silgado, Danielle Cath, Maria Cavallini, Keun-Ah Cheon, Barbara J. Coffey,
David Conti, Edwin Cook, Nancy Cox, Bernadette Cullen, Daniele Cusi, Sabrina
Darrow, Lea Davis, Dieter Deforce, Richard Delorme, Damiaan Denys, Christel
Depienne, Eske Derks, Andrea Dietrich, Yves Dion, Shan Dong, Valsamma Eapen,
Valsama Eapen, Karin Egberts, Lonneke Elzerman, Patrick Evans, Peter Falkai, Thomas
V. Fernandez, Martijn Figee, Nelson Freimer, Odette Fründt, Abby Fyer, Blanca GarciaDelgar, Helena Garrido, Daniel Geller, Gloria Gerber, Donald L. Gilbert, Fernando Goes,
Hans-Jorgen Grabe, Marco Grados, Erica Greenberg, Benjamin Greenberg, Dorothy E.
Grice, Edna Grünblatt, Julie Hagstrøm, Gregory Hanna, Andreas Hartmann, Johannes
Hebebrand, Tammy Hedderly, Gary A. Heiman, Sian Hemmings, Luis Herrera, Peter
Heutink, Isobel Heyman, Matthew Hirschtritt, Pieter J. Hoekstra, Hyun Ju Hong, Ana
Hounie, Alden Huang, Chaim Huyser, Laura Ibanez-Gomez, Cornelia Illmann, Michael
Jenike, Clare Keenan, James L. Kennedy, Judith Kidd, Kenneth Kidd, Young Key Kim,
Young-Shin Kim, Robert A. King, James A. Knowles, Anuar Konkashbaev, Anastasios
Konstantinidis, Sodahm Kook, Samuel Kuperman, Nuria Lanzagorta, Marion Leboyer,
James Leckman, Phil H Lee, Leonhard Lennertz, Bennett L. Leventhal, Christine
Lochner, Thomas Lowe, Andrea G. Ludolph, Gholson Lyon, Fabio Macciardi, Marcos
Madruga-Garrido, Brion Maher, Wolfgang Maier, Irene A. Malaty, Paolo Manunta,
Athanasios Maras, Carol A. Mathews, Manuel Mattheisen, James McCracken, Lauren
McGrath, Nicole McLaughlin, William McMahon, Sandra Mesa Restrepo, Euripedes
Miguel, Pablo Mir, Rainald Moessner, Astrid Morer, Kirsten Müller-Vahl, Alexander
Münchau, Tara L. Murphy, Dennis Murphy, Benjamin Neale, Gerald Nestadt, Paul S.
Nestadt, Humberto Nicolini, Markus Noethen, Erika Nurmi, William Cornejo-Ochoa,
Michael S. Okun, Lisa Osiecki, Andrew Pakstis, Peristera Paschou, Michele Pato, Carlos
Pato, David Pauls, John Piacentini, Christopher Pittenger, Kerstin J. Plessen, Yehuda
Pollak, Danielle Posthuma, Vasily Ramensky, Eliana Mariana Ramos, Steven
Rasmussen, Tobias Renner, Victor Reus, Margaret Richter, Mark Riddle, Renata Rizzo,
Mary Robertson, Veit Roessner, Maria Rosário, David Rosenberg, Guy Rouleau, Stephan
Ruhrmann, Andres Ruiz-Linares, Aline Sampaio, Jack Samuels, Paul Sandor, Jeremiah
Scharf, Eun-Young Shin, Yin Shugart, Harvey Singer, Jan Smit, Jordan Smoller, Jungeun
Song, Dan Stein, S Evelyn Stewart, Nawei Sun, Jay A. Tischfield, Fotis Tsetsos, Jennifer
Tübing, Maurizio Turiel, Ana Valencia Duarte, Homero Vallada, Filip Van
Nieuwerburgh, Frank Visscher, Nienke Vulink, Michael Wagner, Susanne Walitza, Sina
Wanderer, Ying Wang, Sarah Weatherall, Jens Wendland, Tomasz Wolanczyk, Martin
Woods, Yulia Worbe, Jinchuan Xing, Dongmei Yu, Gwyneth Zai, Ivette Zelaya, Yeting
Zhang and Samuel H. Zinner
Schizophrenia Working Group of the Psychiatric Genomics Consortium
(Schizophrenia)
Rolf Adolfsson, Ingrid Agartz, Ole Andreassen, Martin Begemann, Sarah Bergen,
Donald Black, Douglas Blackwood, Anders Børglum, Elvira Bramon, Richard
Bruggeman, Nancy Buccola, Brendan Bulik-Sullivan, Joseph Buxbaum, William
Byerley, Wiepke Cahn, Murray Cairns, Dominique Campion, Rita Cantor, Vaughan Carr,
Raymond Chan, Ronald Chen, Wei Cheng, Eric Cheung, Siow Chong, Sven Cichon,
20
Robert Cloninger, David Cohen, David Collier, Paul Cormican, Aiden Corvin, Nick
Craddock, Benedicto Crespo-Facorro, James Crowley, David Curtis, Mark Daly, Michael
Davidson, Franziska Degenhardt, Jurgen Del Favero, Lynn DeLisi, Ditte Demontis,
Timothy Dinan, Srdjan Djurovic, Enrico Domenici, Gary Donohoe, Elodie Drapeau,
Jubao Duan, Hannelore Ehrenreich, Johan Eriksson, Valentina Escott-Price, Tõnu Esko,
Ayman Fanous, Kai-How Farh, Josef Frank, Lude Franke, Robert Freedman, Nelson
Freimer, Joseph Friedman, Menachem Fromer, Pablo Gejman, Elliot Gershon, Ina
Giegling, Michael Gill, Paola Giusti-Rodríguez, Stephanie Godard, Lieuwe Haan,
Christian Hammer, Marian Hamshere, Thomas Hansen, Vahram Haroutunian, Annette
Hartmann, Frans Henskens, Stefan Herms, Per Hoffmann, Andrea Hofman, Peter
Holmans, David Hougaard, Hailiang Huang, Christina Hultman, Assen Jablensky, Inge
Joa, Erik Jönsson, Antonio Julià, René Kahn, Luba Kalaydjieva, Sena KarachanakYankova, Brian Kelly, Kenneth Kendler, James Kennedy, Andrey Khrunin, George
Kirov, Janis Klovins, Jo Knight, James A. Knowles, Bettina Konte, Vaidutis Kucinskas,
Claudine Laurent, Phil H Lee, Hong Lee, Jimmy Lee Chee Keong, Bernard Lerer,
Douglas Levinson, Miaoxin Li, Tao Li, Qingqin Li, Svetlana Limborska, Jianjun Liu,
Carmel Loughland, Jouko Lönnqvist, Milan Macek, Patrik Magnusson, Brion Maher,
Wolfgang Maier, Sara Marsal, Manuel Mattheisen, Morten Mattingsdal, Colm
McDonald, Andrew McIntosh, Andrew McQuillin, Sandra Meier, Ingrid Melle, Andres
Metspalu, Lili Milani, Derek Morris, Ole Mors, Preben Mortensen, Bryan Mowry,
Bertram Müller-Myhsok, Kieran Murphy, Robin Murray, Benjamin Neale, Igor Nenadic,
Gerald Nestadt, Annelie Nordin, Markus M. Nöthen, Eadbhard O'Callaghan, Michael
O'Donovan, Sang-Yun Oh, Roel Ophoff, Jim Os, Michael Owen, Aarno Palotie, Christos
Pantelis, Sergi Papiol, Michele Pato, Carlos Pato, Psychosis Endophenotypes
International Consortium, Tracey Petryshen, Jonathan Pimm, Danielle Posthuma, Alkes
Price, Shaun Purcell, Digby Quested, Abraham Reichenberg, Alexander Richards,
Marcella Rietschel, Brien Riley, Stephan Ripke, Joshua Roffman, Panos Roussos,
Douglas Ruderfer, Dan Rujescu, Veikko Salomaa, Alan Sanders, Ulrich Schall, Sibylle
Schwab, Rodney Scott, Pak Sham, Jeremy Silverman, Kang Sim, Pamela Sklar, Jordan
Smoller, Hon-Cheong So, David Clair, Eli Stahl, Elisabeth Stögmann, Jana Strohmaier,
Scott Stroup, Mythily Subramaniam, Patrick Sullivan, Jaana Suvisaari, Jin Szatkiewicz,
Srinivas Thirumalai, Draga Toncheva, Sarah Tosato, John Waddington, James Walters,
Dai Wang, Qiang Wang, Bradley Webb, Mark Weiser, Thomas Werge, Dieter
Wildenauer, Nigel Williams, Stephanie Witt, Emily Wong, Naomi Wray, Wellcome
Trust Case-Control Consortium 2, Hualin Xi, Clement Zai and Fritz Zimprich
21
Fig. S1A. Heritability estimates for brain disorders
Red bars denote psychiatric disorders, while blue bars denote neurological disorders.
ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. Error bars show one
standard error.
22
Fig. S1B. Heritability estimates for quantitative and additional phenotypes
BMI – body-mass index. Heritabilities are reported on the observed scale for quantitative
phenotypes (q) and liability scale for dichotomous phenotypes (d). Error bars show one
standard error.
23
Fig. S1C. Heritability and effective sample size
ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. Shaded area shows 95%
confidence interval. Error bars show one standard error.
24
Fig. S1D. Heritability and case/control ratio
ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. Shaded area shows 95%
confidence interval. Error bars show one standard error.
25
Fig. S1E. Heritability and disorder prevalence
ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. Shaded area shows 95%
confidence interval. Error bars show one standard error.
26
Fig. S1F. Heritability and average age of onset for the disorder.
ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. Shaded area shows 95%
confidence interval. Error bars show one standard error.
27
Fig. S2A. Genetic correlations against power to detect heritability
Red points show significant correlations among all disorder-disorder pairs. Two outlier
values over 1 (see Table S7A) have been reduced to 1. The points close or equal to rg = 1
are pairs of a top-level disorder with a subtype of the same disorder, where high
correlation is expected, ie. all migraine and migraine with aura.
28
Fig. S2B. Inverses of standard errors against power to detect heritability
Red points show significant correlations among all disorder-disorder pairs. SE – standard
error.
29
Fig. S2C. Matrix of standard errors for the genetic correlations for disorderdisorder pairs.
Plotted values indicate 1/standard error * 1/25 (1/25 chosen for scaling convenience);
darker shades indicate tests with more power. Six outlier values over 1 (see Table S7A)
have been reduced to 1. Asterisks highlight results which are significant after Bonferroni
correction. ADHD - attention deficit hyperactivity disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder.
30
Fig. S2D. Matrix of standard errors for the genetic correlations for disorderphenotype pairs.
Plotted values indicate 1/standard error * 1/25 (1/25 chosen for scaling convenience);
darker shades indicate tests with more power. 39 outlier values over 1 (see Table S7B)
have been reduced to 1. Asterisks highlight results which are significant after Bonferroni
correction. ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum
disorder; BMI – body-mass index; ICH – intracerebral hemorrhage; MDD – major
depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress
disorder.
31
Fig. S3A and B. Genetic correlations for attention-deficit hyperactivity disorder
(top) and anorexia nervosa (bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
32
Fig. S3C and D. Genetic correlations for anxiety disorders (top) and autism
spectrum disorder (bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
33
Fig. S3E and F. Genetic correlations for bipolar disorder (top) and major depressive
disorder (bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
34
Fig. S3G and H. Genetic correlations for obsessive-compulsive disorder (top) and
post-traumatic stress disorder (bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
35
Fig. S3I and J. Genetic correlations for schizophrenia (top) and Tourette Syndrome
(bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
36
Fig. S4A and B. Genetic correlations for Alzheimer’s disease (top) and epilepsy
(bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
37
Fig. S4C and D. Genetic correlations for focal epilepsy (top) and generalized
epilepsy (bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
38
Fig. S4E and F. Genetic correlations for intracerebral hemorrhage (top) and
ischemic stroke (bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
39
Fig. S4G and H. Genetic correlations for early-onset stroke (top) and migraine
(bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
40
Fig. S4I and J. Genetic correlations for migraine without aura (top) and migraine
with aura (bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
41
Fig. S4K and L. Genetic correlations for multiple sclerosis (top) and Parkinson’s
disease (bottom).
ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH –
intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessivecompulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are
shown at the end of each bar. Error bars show one standard error.
42
Fig. S5A and B. Genetic correlations for psychiatric and neurological disorders
against cognitive measures.
Asterisks highlight results which are significant after Bonferroni correction. ADHD attention deficit hyperactivity disorder; ASD – autism spectrum disorder; MDD – major
depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress
disorder; ICH – intracerebral hemorrhage. Dotted line divides the psychiatric phenotypes
from the neurological phenotypes. Error bars show one standard error.
43
Fig. S6A. Genetic correlations for psychiatric disorders and four personality axes.
Grey sectors denote the extent of genetic correlation between each brain disorder and the
four personality axes. Red line denotes zero correlation, with positive correlations on the
outside and negative correlations on the inside. Error bars show one standard error.
Asterisks highlight results which are significant after Bonferroni correction. Consc. –
Conscientiousness; ADHD - attention deficit hyperactivity disorder; ASD – autism
spectrum disorder; MDD – major depressive disorder; OCD – obsessive-compulsive
disorder; PTSD – post-traumatic stress disorder.
44
Fig. S6B. Genetic correlations for neurological disorders and four personality axes.
Grey bars denote the extent of genetic correlation between each brain disorder and the
four personality axes. Red line denotes zero correlation, with positive correlations on the
outside and negative correlations on the inside. Error bars show one standard error.
Asterisks highlight results which are significant after Bonferroni correction. Consc. –
Conscientiousness; ICH – intracerebral hemorrhage.
45
Fig. S7A. Tissue category heritability enrichment analysis in psychiatric phenotypes
ADHD – attention deficit hyperactivity disorder; CNS – central nervous system; GI –
gastro-intestinal system; OCD – obsessive-compulsive disorder; PTSD – post-traumatic
stress disorder. Results for largely overlapping dataset in schizophrenia has been
previously reported in Finucane et al(92). Black line denotes significance threshold for
Bonferroni multiple testing correction, p=2.81 x 10-4. Only positive enrichment reported.
46
Fig. S7B. Tissue category heritability enrichment analysis in neurological
phenotypes
CNS – central nervous system; GI – gastro-intestinal system. Black line denotes
significance threshold for Bonferroni multiple testing correction, p=2.81 x 10-4. Only
positive enrichment reported.
47
Fig. S7C. Tissue category heritability enrichment analysis in quantitative and
additional phenotypes
BMI – body-mass index. CNS – central nervous system; GI – gastro-intestinal system.
Results for identical datasets in BMI, Crohn’s disease and height have been previously
reported in Finucane et al(92), and those for depressive symptoms in Okbay et al(93).
The black line denotes significance threshold for Bonferroni multiple testing correction,
p=4.10 x 10-4. Only positive enrichment reported.
48
Fig. S7D. Partitioned heritability analysis across 53 functional categories in study
disorders
ADHD - attention deficit hyperactivity disorder; ICH – intracerebral hemorrhage; MDD
– major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – posttraumatic stress disorder. Results for a largely overlapping dataset in schizophrenia have
been previously reported in Finucane et al(92). The black line denotes significance
threshold for Bonferroni multiple testing correction, p=1.17 x 10-4. Only positive
enrichment reported.
49
Fig. S8. Effect of case misclassification on true underlying genetic correlation given
the observed results.
ADHD - attention deficit hyperactivity disorder; BIP – bipolar disorder; OCD –
obsessive-compulsive disorder; SCZ – schizophrenia. Genetic correlations as a function
of misclassification based on derivation described in the section “Effect of co-morbidity
and phenotypic misclassification on correlation estimates”, for the same phenotype pairs
as reported in Table S5.
50
Fig. S9A. Effect of case misclassification on observed heritability
See Supplementary Text “Effect of co-morbidity and phenotypic misclassification on
correlation estimates” for details. Error bars show one standard error.
51
Fig. S9B. Effect of case misclassification on genetic correlation.
See Supplementary Text “Effect of co-morbidity and phenotypic misclassification on
correlation estimates” for details. Error bars show one standard error.
52
Fig. S9C. Effect of bidirectional case misclassification on genetic correlation
See Supplementary Text “Effect of co-morbidity and phenotypic misclassification on
correlation estimates” for details. Error bars show one standard error.
53
Fig. S10. Power analysis for detecting genetic correlations.
Shown at each combination of parameters is the fraction of simulations out of 100
replicates which detect the simulated correlation between the pair of phenotypes and are
within the 95% confidence interval from the true correlation.
54
Table S1. Dataset features for the brain disorder phenotypes.
ADHD – attention deficit hyperactivity disorder; OCD – obsessive-compulsive disorder;
PTSD – post-traumatic stress disorder; Pop. prev. – population prevalence; AOE –
average age of onset; GC – genomic control; Publ. – publication for genotype data; MUP
– manuscript under preparation; Preval. ref. – publication for prevalence estimate; PC –
personal communication. All age of onset estimates based on personal communication
and constitute rough estimates. Anxiety disorders refers to a meta-analysis of five
subtypes (generalized anxiety disorder, panic disorder, social phobia, agoraphobia and
specific phobias; see reference). Numbers in gray denote a dataset which is non-unique,
e.g. all cardioembolic stroke cases and controls are also part of ischemic stroke cases and
controls, respectively. For genomic control, - : no GC; + : study-level GC; ++ : metaanalysis GC. Note: genomic control will impact the univariate estimate of heritability, but
the genetic correlation estimation is robust to genomic control. References are: a(94),
b(95), c(96), d(97), e(98), f(99), g(100), h(101), i(102), j(103), k(104), l(105), m(106),
n(107), o(108), p(109), q(110), r(111), s(112), approximated from t(113), approximated
from u(114), v(115), w(116), and x(117).
Phenotype
Psychiatric disorders
ADHD
Anorexia nervosa
Anxiety disorders
Autism spectrum disorder
Bipolar disorder
Major depressive disorder
OCD
PTSD
Schizophrenia
Tourette's syndrome
Neurological disorders
Alzheimer's disease
Epilepsy
Focal epilepsy
Generalized epilepsy
Intracerebral hemorrhage
Ischemic stroke
Cardioembolic stroke
Early-onset stroke
Large-vessel disease
Small-vessel disease
Migraine
Migraine with aura
Migraine without aura
Multiple sclerosis
Parkinson's disease
2
Cases Controls Pop. prev. AOE Heritability (SE) GC Mean χ Lambda Intercept (SE) Publ. Preval. ref.
12,645
3,495
5,761
6,197
20,352
16,823
2,936
2,424
33,640
4,220
84,435
11,105
11,765
7,377
31,358
25,632
7,279
7,113
43,456
8,994
0.050
0.006
0.100
0.010
0.010
0.150
0.016
0.080
0.010
0.005
12
15
11
2
25
32
16
23
21
7
0.100 (0.011)
0.172 (0.027)
0.112 (0.045)
0.189 (0.025)
0.205 (0.010)
0.112 (0.006)
0.255 (0.037)
0.148 (0.065)
0.256 (0.010)
0.196 (0.025)
+
-
1.102
1.077
1.035
1.081
1.324
1.263
1.059
1.107
1.588
1.096
1.107 1.014 (0.007)
1.086 1.012 (0.008)
1.030 1.003 (0.008)
1.071 0.987 (0.009)
1.387 1.021 (0.010)
1.293 1.005 (0.010)
1.065 1.000 (0.007)
1.102 1.014 (0.007)
1.768 1.059 (0.012)
1.103 1.010 (0.007)
MUP
a
b
c
MUP
MUP
MUP
d
e
MUP
m
n
o
m
m
m
p
q
m
r
17,008 37,154
7,779 20,439
4,601 17,985
2,525 16,244
1,545
1,481
10,307 19,326
1,859 17,708
3,274 11,012
1,817 17,708
1,349 17,708
59,673 316,078
6,332 142,817
8,348 136,758
5,545 12,153
5,333 12,019
0.170
0.030
0.020
0.008
0.002
0.010
0.003
0.160
0.075
0.130
0.002
0.002
65
25
15
15
70
71
50
30
30
30
30
60
0.130 (0.023) 0.101 (0.022) +
0.053 (0.026) +
0.351 (0.039) +
0.156 (0.060) 0.038 (0.010) - 0.051 (0.020) - - 0.150 (0.007) 0.124 (0.024) 0.208 (0.025) 0.141 (0.016) ++
0.105 (0.017) +
1.093
1.047
1.023
1.065
1.038
1.065
1.061
1.029
1.061
1.048
1.293
1.077
1.080
1.050
1.026
1.104 1.038 (0.007)
1.057 0.993 (0.010)
1.013 0.988 (0.009)
1.081 0.960 (0.009)
1.037 1.012 (0.007)
1.066 1.032 (0.006)
1.047 1.049 (0.006)
1.033 1.009 (0.007)
1.053 1.052 (0.006)
1.047 1.052 (0.006)
1.375 1.036 (0.010)
1.087 1.003 (0.007)
1.085 1.033 (0.007)
1.078 0.975 (0.008)
1.044 0.965 (0.008)
f
g
g
g
h
i
i
i
i
i
j
j
j
k
l
PC
s
PC
PC
t
u
PC
v
w
w
x
x
55
Table S2. Dataset features for the behavioral-cognitive and additional phenotypes
Numbers in gray denote a sample set which is non-unique, e.g. all samples in the BMI
analysis are also part of the height analysis. SE – standard error; Ref. – reference; ISCE International Standard Classification of Education (1997); NEO-FFI - NeuroticismExtraversion-Openness Five-Factor Inventory; BMI – body-mass index; CAD – coronary
artery disease; MI – myocardial infarction; (d) – dichotomous phenotype; (q) –
quantitative phenotype. References are: a(118), b(119), c(120), d(121), e(93), f(122),
g(123), h(124), i(125), j(126), k(127) and l(128).
2
Phenotype
n
Heritability (SE) Mean χ Lambda Intercept (SE) Ref. Definition
Cognitive measures
Years of education (q)
293,723
0.302 (0.010) 1.645 1.475 0.938 (0.009)
a Years of schooling, measured with the ISCE scale
College attainment (d)
120,917
0.109 (0.008) 1.223 1.194 1.021 (0.009)
b College completion (ISCE scale value >=5)
Cognitive performance (q)
17,989
0.191 (0.031) 1.075 1.065 1.001 (0.009)
c General cognitive ability in childhood (ages 6-18)
Intelligence (q)
78,308
0.194 (0.010) 1.299 1.260 1.015 (0.008)
d Intelligence measures (fluid intelligence scores in
adults or general cognitive ability in children)
Personality measures
Subjective well-being (q)
298,420
0.062 (0.005) 1.152 1.130 1.001 (0.007)
e Self-assessed psychological well-being, based on
positive affect or life satisfaction questionnaires
Depressive symptoms (q)
161,460
0.063 (0.005) 1.153 1.133 1.000 (0.007)
e Score for depressive symptoms, based on positive
affect or life satisfaction questionnaires
Neuroticism (q)
170,911
0.125 (0.010) 1.307 1.237 0.994 (0.010)
e Personality score for neuroticism symptoms, based on
positive affect or life satisfaction questionnaires
Extraversion (q)
63,030
0.049 (0.008) 1.073 1.065 1.008 (0.007)
f Extraversion personality trait, as measured by several
different questionnaires
Agreeableness (q)
17,375
- 1.010 0.999 1.001 (0.010)
g NEO-FFI questionnaire for personality scores
Conscientiousness (q)
17,375
0.070 (0.033) 1.029 1.020 1.001 (0.009)
g NEO-FFI questionnaire for personality scores
Openness (q)
17,375
0.125 (0.030) 1.037 1.041 0.988 (0.009)
g NEO-FFI questionnaire for personality scores
Smoking-related measures
Never/ever smoked (d)
74,035
0.120 (0.010) 1.103 1.090 0.996 (0.006)
h Lifetime cigarette consumption >= 100
Cigarettes per day (q)
38,617
0.057 (0.013) 1.049 1.053 1.007 (0.006)
h Average or maximum number of cigarettes per day
Additional phenotypes
BMI (q)
339,224
0.109 (0.003) 1.158 1.038 0.672 (0.008)
i BMI, as measured
Height (q)
253,288
0.312 (0.014) 2.949 2.001 1.325 (0.019)
j Height, as measured
Coronary artery disease (d)
86,995
0.098 (0.013) 1.145 1.105 1.027 (0.009)
k Presence of CAD, MI, or both
Crohn's disease (d)
20,883
0.177 (0.021) 1.242 1.143 1.028 (0.012)
l Presence of Crohn’s disease
56
Table S3. Comparison of heritability estimates in this study with previously
reported estimates based on SNP data.
ADHD – attention deficit hyperactivity disorder; ESS – effective sample size; OCD –
obsessive-compulsive disorder; SE – standard error. References previous reports are:
a(106), b(95), c(129), d(97), e(130), f(131), g(132), h(133), and i(134). * - Previously
reported heritability for anxiety disorders is an LDSC analysis of the same dataset;
difference between the estimates is due to the current study estimating heritability under
unscreened controls.
Phenotype
Psychiatric disorders
ADHD
Anorexia nervosa
Anxiety disorders*
Autism spectrum disorder
Bipolar disorder
Major depressive disorder
OCD
PTSD*
Schizophrenia
Tourette's syndrome
Neurological disorders
Alzheimer's disease
Epilepsy
Focal epilepsy
Generalized epilepsy
Intracerebral hemorrhage
Ischemic stroke
Cardioembolic stroke
Early-onset stroke
Large-vessel disease
Small-vessel disease
Migraine
Migraine with aura
Migraine without aura
Multiple sclerosis
Parkinson's disease
Previously reported
Heritability (SE)
ESS
0.28 (0.023)
0.10 (0.037)
0.17 (0.025)
0.25 (0.012)
0.21 (0.021)
0.37 (0.070)
0.15(0.060)
0.23 (0.008)
0.58 (0.090)
12,374
15,469
6,729
15,391
18,416
3,394
7,232
20,811
2,146
0.24 (0.030) 7,095
0.32 (0.046) 4,041
0.23 (0.102) 3,229
0.36 (0.117) 1,134
0.29 (0.110) 1,663
0.38 (0.052) 8,025
0.33 (0.074) 2,592
0.40 (0.076) 2,698
0.16 (0.077) 1,993
0.30 (0.030) 3,523
0.27 (0.053) 20,798
Current study
Heritability (SE) ESS
0.100 (0.011)
0.172 (0.027)
0.112 (0.045)
0.189 (0.025)
0.205 (0.010)
0.112 (0.006)
0.255 (0.037)
0.148 (0.065)
0.256 (0.010)
0.196 (0.025)
Reference
43,992
10,633
15,469
10,610
49,367
40,627
8,369
7,232
75,846
11,489
a
b
a
a
a
c
d
a
c
0.130 (0.023)
0.101 (0.022) 46,669
0.053 (0.026) 22,538
0.351 (0.039) 14,655
0.156 (0.060)
8,741
0.038 (0.010)
3,025
- 26,888
0.051 (0.020)
6,730
- 10,095
6,592
0.150 (0.007)
5,014
0.124 (0.024) 200,785
0.208 (0.025) 24,253
0.141 (0.016) 31,471
0.105 (0.017) 15,231
0.105 (0.017) 14,776
e
f
f
f
g
h
h
h
h
e
i
57
Table S4. Heritability estimates and selected study variables in weighted-least
squares analysis.
P-values are uncorrected for multiple testing. Age of onset refers to the average age of
onset of the disorder. Asterisk indicates results which are significant after Bonferroni
correction for four tests.
Study feature
Case/control ratio
Effective sample size
Phenotype prevalence
Age of onset
2
F-statistic Adjusted R P-value
0.534
-0.023 0.474
1.039
0.002 0.320
0.341
-0.032 0.566
10.280
0.307 0.004*
Table S5. Implied true correlations between selected phenotypes, given co-morbidity
estimates from literature.
ADHD – attention deficit hyperactivity disorder; OCD – obsessive-compulsive disorder.
References used for λ values (proportion of cases correctly called cases) are a (135),
b(136), c(137). From reference a, λ was calculated by summing over all relevant disorder
progression paths. See Supplementary text (“Effect of co-morbidity and phenotypic
misclassification on correlation estimates”) for further details.
Phenotype 1
Schizophrenia
Schizophrenia
Bipolar disorder
Bipolar disorder
Phenotype 2
True rg Observed rg λ
h1,obs h2
Reference
Bipolar disorder 0.654
0.681 0.946 0.506 0.453 a
OCD
0.120
0.428 0.683 0.506 0.505 b
ADHD
0.043
0.261 0.686 0.453 0.316 c
Schizophrenia
0.514
0.681 0.808 0.453 0.506 a
58
Table S6. Proportions of unidirectional misclassification.
Listed are the proportions of unidirectional misclassification which would be required to
reach the observed genetic correlation under the assumption of no true genetic correlation
for the significantly correlated disorder-disorder pairs in this study, in order of decreasing
significance. ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum
disorder; MDD – major depressive disorder; OCD – obsessive-compulsive disorder.
Type-subtype pairs (e.g. epilepsy and focal epilepsy) have been excluded.
Phenotype 1
Bipolar disorder
MDD
Bipolar disorder
MDD
ADHD
OCD
ADHD
Anxiety disorders
ADHD
OCD
Bipolar disorder
ADHD
Anorexia nervosa
Migraine
MDD
Anorexia nervosa
MDD
MDD
MDD
ASD
Phenotype 2
Schizophrenia
Schizophrenia
MDD
Migraine
MDD
Schizophrenia
Migraine
MDD
Schizophrenia
Tourette Syndrome
OCD
Bipolar disorder
OCD
Tourette Syndrome
Migraine without aura
Schizophrenia
Migraine with aura
Tourette Syndrome
OCD
Schizophrenia
Observed rg Misclassification %
0.681
54.5%
0.338
14.8%
0.351
64.2%
0.323
24.8%
0.521
46.5%
0.327
32.6%
0.261
17.9%
0.794
79.4%
0.223
8.7%
0.428
55.7%
0.311
25.0%
0.261
12.7%
0.517
34.9%
0.192
14.3%
0.225
12.2%
0.219
14.7%
0.278
29.4%
0.213
12.2%
0.228
10.0%
0.208
15.5%
Table S7 (separate file). Disorder-disorder (A), disorder-phenotype (B) and
phenotype-phenotype (C) correlation results.
59
Table S8. Tissue enrichment analysis for brain disorders.
Results shown for phenotype-tissue pairs where P-value for enrichment coefficient pvalue below the Bonferroni threshold (p < 2.81 x 10-4; data for all pairs in Table S12A).
CNS – central nervous system; Coeff. – coefficient; SE – standard error. Results for
schizophrenia in a largely overlapping dataset have been previously reported in Finucane
et al (38).
Phenotype
Schizophrenia
Bipolar disorder
Major depressive disorder
Multiple sclerosis
Tourette Syndrome
Generalized epilepsy
Tissue
Enrichment SE Coeff. SE
Coeff. p-value
CNS
3.25 0.18 1.65E-07 1.92E-08
3.35E-18
CNS
3.81 0.32 1.43E-07 2.28E-08
1.69E-10
CNS
2.76 0.30 2.03E-08 3.58E-09
6.73E-09
Hematopoietic
4.90 0.54 2.85E-07 5.44E-08
8.03E-08
CNS
4.23 0.78 2.32E-07 5.29E-08
5.67E-06
CNS
2.79 0.60 1.68E-07 4.34E-08
5.44E-05
Table S9. Tissue enrichment analysis for behavioral-cognitive phenotypes and
additional traits
Results shown for phenotype-tissue pairs where P-value for enrichment coefficient pvalue below the Bonferroni threshold (p < 4.10 x 10-4; data for all pairs in Table S12A).
BMI – body-mass index; CNS – central nervous system; Coeff. – coefficient; SE –
standard error. Results for the same dataset in height, BMI and Crohn’s disease have been
previously reported in Finucane et al (38), and depressive symptoms in Okbay et al(93).
Phenotype
Years of education
Intelligence
Height
BMI
Crohn's disease
Neuroticism
College attainment
Height
Height
Depressive symptoms
Never/ever smoked
Tissue
Enrichment SE Coeff. SE
Coeff. p-value
CNS
2.87 0.19 9.51E-08 1.15E-08
5.66E-17
CNS
3.38 0.31 8.34E-08 1.20E-08
2.26E-12
Connective_Bone
5.32 0.38 2.24E-07 3.55E-08
1.31E-10
CNS
2.67 0.18 2.73E-08 4.46E-09
4.39E-10
Hematopoietic
4.19 0.43 3.60E-07 6.66E-08
3.15E-08
CNS
2.47 0.29 3.83E-08 8.45E-09
2.95E-06
CNS
3.31 0.45 2.71E-08 6.52E-09
1.59E-05
Cardiovascular
4.23 0.38 1.28E-07 3.08E-08
1.66E-05
Other
3.42 0.21 8.26E-08 2.19E-08
7.84E-05
Adrenal_Pancreas
5.15 0.94 3.77E-08 1.04E-08
1.47E-04
CNS
3.45 0.73 3.06E-08 9.13E-09
4.04E-04
Table S10. Functional category enrichment analysis for brain disorders
Results shown for phenotype-tissue pairs where P-value for enrichment coefficient pvalue below the Bonferroni threshold (p < 1.17 x 10-4; data for all pairs in Table S12B).
ADHD – attention deficit hyperactivity disorder; Coeff. – coefficient; SE – standard
error. Results for schizophrenia in a largely overlapping dataset have been previously
reported in Finucane et al (38).
Phenotype
Major depressive disorder
Migraine
Schizophrenia
ADHD
Migraine without aura
Bipolar disorder
Category
Enrichment SE Coeff. SE
Coeff. p-value
Conserved_LindbladToh
19.14 2.50 1.74E-07 2.52E-08
2.42E-12
Conserved_LindbladToh
16.88 2.08 1.09E-07 1.72E-08
1.15E-10
Conserved_LindbladToh
11.03 1.55 6.73E-07 1.21E-07
1.27E-08
Conserved_LindbladToh
27.15 6.24 2.00E-07 4.61E-08
7.46E-06
Conserved_LindbladToh
20.64 4.99 9.63E-08 2.61E-08
1.12E-04
Conserved_LindbladToh
9.95 1.98 3.80E-07 1.03E-07
1.17E-04
60
Table S11. Functional category enrichment analysis for behavioral-cognitive
phenotypes and additional traits
Results shown for phenotype-tissue pairs where P-value for enrichment coefficient pvalue below the Bonferroni threshold (p < 1.71 x 10-4; data for all pairs in Table S12B).
BMI – body-mass index; Coeff. – coefficient; SE – standard error. Results for the same
dataset in height and BMI have been previously reported in Finucane et al (38).
Phenotype
BMI
Years of education
Height
BMI
Neuroticism
Intelligence
College attainment
Category
Enrichment SE Coeff. SE
Coeff. p-value
Conserved_LindbladToh
16.68 1.69 2.76E-07 3.34E-08
7.28E-17
Conserved_LindbladToh
14.96 1.68 6.56E-07 8.80E-08
4.67E-14
Conserved_LindbladToh
11.07 1.59 5.15E-07 9.79E-08
7.20E-08
H3K9ac_peaks_Trynka
7.00 0.97 1.18E-07 2.41E-08
4.79E-07
Conserved_LindbladToh
11.96 2.95 2.26E-07 5.10E-08
4.50E-06
Conserved_LindbladToh
13.50 2.56 3.51E-07 8.24E-08
9.77E-06
Conserved_LindbladToh
16.32 3.31 1.61E-07 3.92E-08
1.98E-05
Table S12 (separate file). Tissue (A) and functional category (B) enrichment analysis
results for brain disorders, behavioral-cognitive phenotypes, and additional traits.
Table S13 (separate file). Data sources, responsible consortia, and data availability.
61