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Analysis of shared heritability in common disorders of the brain

June 2018
Science 360(6395):eaap8757

Authors:

Broad Institute of MIT and Harvard

Brainstorming diseases Consistent classification of neuropsychiatric diseases is problematic because it can lead to misunderstanding of etiology. The Brainstorm Consortium examined multiple genome-wide association studies drawn from more than 200,000 patients for 25 brain-associated disorders and 17 phenotypes. Broadly, it appears that psychiatric and neurologic disorders share relatively little common genetic risk. However, different and independent pathways can result in similar clinical manifestations (e.g., psychosis, which occurs in both schizophrenia and Alzheimer's disease). Schizophrenia correlated with many psychiatric disorders, whereas the immunopathological affliction Crohn's disease did not, and posttraumatic stress syndrome was also largely independent of underlying traits. Essentially, the earlier the onset of a disorder, the more inheritable it appeared to be. Science , this issue p. eaap8757

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RESEARCH ARTICLE SUMMARY

◥

PSYCHI ATRI C G ENO M ICS

Analysis of shared heritability in

common disorders of the brain

The Brainstorm Consortium†

INTRODUCTION: Brain disorders may exhibit

shared symptoms and substantial epidemio-

logical comorbidity, inciting debate about their

etiologic overlap. However, detailed study of

phenotypes with different ages of onset, sever-

ity, and presentation poses a considerable chal-

lenge. Recently developed heritability methods

allow us to accurately measure correlation of

genome-wide common variant risk between

two phenotypes from pools of different individ-

uals and assess how connected they, or at least

their genetic risks, are on the genomic level. We

used genome-wide association data for 265,218

patients and 784,643 control participants, as

well as 17 phenotypes from a total of 1,191,588

individuals, to quantify the degree of overlap

for genetic risk factors of 25 common brain

disorders.

RATIONALE: Over the past century, the clas-

sification of brain disorders has evolved to

reflect the medical and scientific communities’

assessments of the presumed root causes of

clinical phenomena such as behavioral change,

loss of motor function, or alterations of con-

sciousness. Directly observable phenomena (such

as the presence of emboli, protein tangles, or

unusual electrical activity patterns) generally

define and separate neurological disorders from

psychiatric disorders. Understanding the genetic

underpinnings and categorical distinctions

for brain disorders and related phenotypes

may inform the search for their biological

mechanisms.

RESULTS: Common variant risk for psychiatric

disorders was shown to correlate significant-

ly, especially among attention deficit hyper-

activity disorder (ADHD), bipolar disorder, major

depressive disorder (MDD), and schizophrenia.

By contrast, neurological disorders appear more

distinct from one another and from the psychi-

atric disorders, except for migraine, which was

significantly correlated to ADHD, MDD, and

Tourette syndrome. We demonstrate that, in

the general population, the personality trait

neuroticism is significantly correlated with

almost every psychiatric disorder and mi-

graine. We also identify significant genetic

sharing between disorders and early life cog-

nitive measures (e.g., years of education and

college attainment) in the general population,

demonstrating positive correlation with several

psychiatric disorders (e.g., anorexia nervosa and

bipolar disorder) and negative correlation with

several neurological phenotypes (e.g., Alzheimer’s

disease and ischemic stroke), even though the

latter are considered to result from specific

processes that occur later in life. Extensive sim-

ulations were also performed to inform how

statistical power, diagnostic misclassification,

and phenotypic heterogeneity influence genetic

correlations.

CONCLUSION: Thehighdegreeofgeneticcor-

relation among many of the psychiatric dis-

orders adds further evidence that their current

clinical boundaries do not reflect distinct un-

derlying pathogenic processes, at least on the

genetic level. This suggests a deeply intercon-

nected nature for psychi-

atric disorders, in contrast

to neurological disorders,

and underscores the need

to refine psychiatric diag-

nostics. Genetically informed

analyses may provide im-

portant “scaffolding”to support such restruc-

turing of psychiatric nosology, which likely

requires incorporating many levels of infor-

mation. By contrast, we find limited evidence

for widespread common genetic risk sharing

among neurological disorders or across neu-

rological and psychiatric disorders. We show

that both psychiatric and neurological disor-

ders have robust correlations with cognitive

and personality measures. Further study is

needed to evaluate whether overlapping ge-

netic contributions to psychiatric pathology

may influence treatment choices. Ultimately,

such developments may pave the way toward

reduced heterogeneity and improved diagno-

sis and treatment of psychiatric disorders. ▪

RESEARCH

The Brainstorm Consortium, Science 360, 1313 (2018) 22 June 2018 1of1

The list of author affiliations is available in the full article online.

†Corresponding authors: Verneri Anttila (verneri.anttila@gmail.

com); Aiden Corvin (acorvin@tcd.ie); Benjamin M. Neale

(bneale@broadinstitute.org)

Cite this article as The Brainstorm Consortium, Science 360,

eaap8757 (2018). DOI: 10.1126/science.aap8757

Psychiatric/quantitative Psychiatric/neurological

Psychiatric

Neurological/quantitative

Neurological

ADHD

MDD Epilepsy

Schizo-

phrenia Migraine

Alzheimer’s

disease

Neuroticism Never/ever

smoked

Years of

education

Neuroticism Never/ever

smoked

Years of

education

Neuroticism Never/ever

smoked

Years of

education

Correlation

+ – >50%

20 - 50%

10 - 20%

Quantitative

ADHD

MDD

Schizo-

phrenia

ADHD

MDD

Schizo-

phrenia

Epilepsy

Migraine

Alzheimer’s

disease

Epilepsy

Migraine

Alzheimer’s

disease

Subsection of genetic risk correlations among brain disorders and quantitative phenotypes. Heritability analysis of brain disorders points

to pervasive sharing of genetic risk among psychiatric disorders. These correlations are largely absent among neurological disorders but are

present for both groups in relation to neurocognitive quantitative phenotypes. Only significant correlations shown. Line color and solidity

indicate direction and magnitude of correlation, respectively.

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RESEARCH ARTICLE

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PSYCHI ATRI C G ENO M ICS

Analysis of shared heritability in

common disorders of the brain

The Brainstorm Consortium*†

Disorders of the brain can exhibit considerable epidemiological comorbidity and often

share symptoms, provoking debate about their etiologic overlap.We quantified the genetic

sharing of 25 brain disorders from genome-wide association studies of 265,218 patients

and 784,643 control participants and assessed their relationship to 17 phenotypes

from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas

neurological disorders appear more distinct from one another and from the psychiatric

disorders. We also identified significant sharing between disorders and a number of brain

phenotypes, including cognitive measures. Further, we conducted simulations to explore

how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect

genetic correlations. These results highlight the importance of common genetic variation

as a risk factor for brain disorders and the value of heritability-based methods in

understanding their etiology.

The classification of brain disorders has

evolved over the past century, reflecting

the medical and scientific communities’

assessments of the presumed root causes

of clinical phenomena such as behavioral

change, loss of motor function, spontaneous

movements, or alterations of consciousness. Di-

rectly observable phenomena (such as the presence

of emboli, protein tangles, or unusual electrical

activity patterns) generally define and separate

neurological disorders from psychiatric disorders

(1). Understanding the genetic underpinnings

and categorical distinctions between brain dis-

ordersmaybehelpfulininformingthesearchfor

the biological pathways underlying their patho-

physiology (2,3).

Studies of twins and families have indicated

that, in general, brain disorders (excepting those

caused by trauma, infection, or cancer) show sub-

stantial heritability (4). Epidemiological and twin

studies have explored patterns of phenotypic over-

laps (5–7), and comorbidity has been reported

for many pairs of disorders, including bipolar dis-

order and migraine (8), stroke and major de-

pressive disorder (MDD) (9), epilepsy and autism

spectrum disorder (ASD), and epilepsy and at-

tention deficit hyperactivity disorder (ADHD)

(10,11). Furthermore, direct etiological links may

also exist—e.g., mutations in the same ion chan-

nel genes confer pleiotropic risk for multiple

distinct brain phenotypes (12–14). Genome-wide

association studies (GWASs) have demonstra-

ted that individual common risk variants can

overlap across traditional diagnostic boundaries

(15,16) and that disorders such as schizo-

phrenia, MDD, and bipolar disorder can have

genetic correlations (17).

GWASs have also demonstrated that common

genetic variation contributes to the heritability

of brain disorders. Generally, this occurs via the

combination of many common variants—examples

include Alzheimer’sdisease(18), bipolar disorder

(19), migraine (20), Parkinson’s disease (21), and

schizophrenia (22)—each with a small individual

effect. In addition to locus discovery, the degree

of distinctiveness (23) across neurological and

psychiatric phenotypes can be evaluated with

the introduction of novel heritability-based meth-

ods (24) and sufficiently large sample sizes for

robust heritability analysis. These analyses can

shed light on the nature of these diagnostic bound-

aries and explore the extent of shared common

variant genetic influences.

Study design

The Brainstorm Consortium, a collaboration among

GWAS meta-analysis consortia for 25 disorders

(Table 1), was assembled to perform a compre-

hensive heritability and correlation analysis of

brain disorders. We included meta-analyses of

anycommonbraindisordersforwhichwecould

identify a GWAS meta-analysis consortium of

sufficient size for heritability analysis. The total

study sample consists of 265,218 cases of differ-

ent brain disorders and 784,643 controls (Table 1)

andincludesatleastonerepresentativeofmost

ICD-10 (10th revision of the International Statis-

tical Classification of Diseases and Related Health

Problems) blocks covering mental and behav-

ioral disorders and diseases of the central ner-

vous system (CNS). Also included are 1,191,588

samples for 13 behavioral-cognitive phenotypes

(n= 744,486 individuals) traditionally viewed as

brain-related, as well as 4 additional phenotypes

(n= 447,102 individuals) selected to represent

known, well-delineated etiological processes {im-

mune disorders (Crohn’s disease), vascular disease

(coronary artery disease), and anthropomorphic

measures [height and body mass index (BMI)]}

(Table 2).

GWAS summary statistics for the 42 disor-

ders and phenotypes were centralized and un-

derwent uniform quality control and processing

(25). To avoid potential bias arising from an-

cestry differences, we used European-only meta-

analyses for each disorder and generated new

meta-analyses for those datasets where the orig-

inal sample sets had diverse ancestries. Clin-

ically relevant subtypes from three disorders

(epilepsy, migraine, and ischemic stroke) were

also included; in these cases, the subtype data-

sets are parts of the top-level dataset (Table 1).

We have developed a heritability estimation

method, linkage disequilibrium score (LDSC)

regression (24), which was used to calculate her-

itability estimates and correlations, as well as

to estimate their statistical significance from

block jackknife–based standard errors. More for-

mally, we estimate the common variant heri-

tability (h

g) of each disorder, defined as the

proportion of phenotypic variance in the popu-

lation that could theoretically be explained by

an optimal linear predictor formed using the

additive effects of all common (minor allele fre-

quency >5%) autosomal single-nucleotide poly-

morphisms (SNPs). The genetic correlation for

a pair of phenotypes is then defined as the cor-

relation between their optimal genetic predictors.

Heritability for binary disorders and phenotypes

was transformed to the liability scale. We furthe r

performed a weighted least-squares regression

analysis to evaluate whether differences relating

to study makeup (such as sample size) were cor-

related with the magnitude of the correlation

estimates. Finally, we performed a heritability

partitioning analysis (25) by means of stratified

LD score regression to examine whether the ob-

served heritability for the disorders or pheno-

types was enriched into any of the tissue-specific

regulatory regions or functional category parti-

tions of the genome, using 10 top-level tissue-type

and 53 functional partitions from Finucane et al.

(26). Simulated phenotype data was then gen-

erated under different scenarios by permuting

120,267 genotyped individuals from the UK

Biobank (25) to evaluate statistical power and

aid in interpreting the results (25).

Heritability estimates and their

error sources

We observed a similar range of heritability es ti-

mates among the disorders and the behavioral-

cognitive phenotypes (fig. S1, A and B, and table

S1 and S2), roughly in line with previously re-

ported estimates from smaller datasets (table S3).

Threeischemicstrokesubtypes(cardioembolic,

large-vessel disease, and small-vessel disease) as

well as the “agreeableness”personality measure

from the NEO Five-Factor Inventory (27)hadin-

sufficient evidence of additive heritability for

robust analysis and thus were excluded from

further examination (25).Theonlyobservedcor-

relation between heritability estimates and fac-

tors relating to study makeup (table S4 and fig. S1,

RESEARCH

The Brainstorm Consortium, Science 360, eaap8757 (2018) 22 June 2018 1of12

*All authors with their affiliations are listed at the end of this paper.

†Collaborators and affiliations are listed in the supplementary

materials.

C to F) was a modest correlation between age

of disorder onset and heritability, suggesting

that early onset brain disorders tend to be more

heritable. Because some of our interpretation of

the results depends on lack of observed corre-

lation, we explored the behavior of observed cor-

relation versus power (fig. S2A), standard errors

(fig. S2B), and the individual results (fig. S2, C

andD)toidentifywherewecanbereasonably

robust in claiming lack of correlation.

The common variant heritability estimates

for the psychiatric and neurological disorders

were generally somewhat lower than previously

reported estimates from common variants (table

S5). When comparing estimates reported here

with those previously reported in studies with

smaller sample sizes (28), a similar pattern was

observed for the behavioral-cognitive traits, with

the exception of “openness,”“neuroticism,”and

“never/ever smoked”(defined as those who have

never smoked versus those who have smoked at

some point) suggesting that some attenuation in

heritability is observed when moving to larger

sample sizes. Measures related to cognitive abil-

ity, such as childhood cognitive performance

[heritability estimate of 0.19 (SE: 0.03)] and years

of education [heritability estimate of 0.30 (SE:

0.01)], yielded estimates that were more con-

sistent with previous estimates of the herita-

bility of intelligence (29,30), suggesting that the

cognitivemeasuresmaybelesspronetopheno–

typic measurement error and/or have a higher

heritability overall than the personality measures.

These heritability estimates should be inter-

preted somewhat cautiously, as they reflect the

phenotype ascertained in each study and will be

deflated in the presence of diagnostic heteroge-

neity, ascertainment errors, or unusual contribu-

tions of high-impact rare variants. To evaluate

potential sources of these differences, we explored

three approaches (25): evaluating the differences

in real data, simulation work (table S5), and quan-

tifying the magnitude of effect for potentially

implied misclassification (table S6).

In comparison with heritability estimates ob-

tained using twin and family data, the more

diverse selection and survival biases in the under-

lying data may attenuate the heritability estimates

and correlations, as may increased within-disorder

heterogeneity introduced by the larger meta-

analyses. A related explanation for the lower es-

timates of heritability may be that increasing sam-

ple sizes have led to expanded inclusion criteria,

meaning that less severely affected cases with a

lower overall burden of risk factors (both ge-

netic and environmental) might be included,

which in turn would attenuate estimates of her-

itability. However, the successful identification

of genome-wide significant loci suggests that

these larger samples are nevertheless very use-

ful for genetic studies, and the simulation results

suggest that this has, at most, a limited effect on

estimated genetic correlations (fig. S9). Even so,

some of the pairs of phenotypes included here

lack sufficient power for robust estimation of ge-

netic correlations. Moreover, our analyses examine

only the properties of common variant contribu-

tions; extending these analyses to include the ef-

fects of rare variants may further inform the extent

of genetic overlap. For example, epilepsy and ASD

show substantial overlap in genetic risk from de

novo loss-of-function mutations (31), in contrast to

the limited common variant sharing observed in

this study. This may suggest that the rare and com-

mon variant contributions to genetic overlap may

behave differently and that incorpor ati ng the two

variant classes into a single analysis may provide

further insight into brain disorder pathogenesis.

To address the possibility of methodological

differences contributing to the differences in the

estimates, and although LDSC and GREML have

previously been shown to yield similar estimates

from the same data (24), we performed our own

comparison in Alzheimer’sdiseasedata(32)(selec-

ted on the basis of data availability). In Alzheimer’s

disease, the previously published heritability esti-

mate [0.24 (SE: 0.03)] is significantly different

The Brainstorm Consortium, Science 360, eaap8757 (2018) 22 June 2018 2of12

Table 1. Brain disorder phenotypes used in the Brainstorm project.

Indented phenotypes are part of a larger whole (e.g., the epilepsy study

contains the samples from both focal epilepsy and generalized epilepsy).

“Anxiety disorders”refers to a meta-analysis of five subtypes (generalized

anxiety disorder, panic disorder, social phobia, agoraphobia, and specific

phobias). References are listed in table S1 and data availability in table S13.

PGC-ADD2, Psychiatric Genomics Consortium (PGC) Attention Deficit

Disorder Working Group; PGC-ED, PGC Eating Disorder Working Group;

ANGST, Anxiety Neuro Genetics STudy; PGC-AUT, PGC Autism Spectrum

Disorder Working Group; PGC-BIP2, PGC Bipolar Disorder Working Group;

PGC-MDD2, PGC Major Depressive Disorder Working Group; PGC-OCDTS,

PGC Obsessive Compulsive Disorder and Tourette Syndrome Working

Group; PGC-PTSD, PGC Posttraumatic Stress Disorder Working Group;

PGC-SCZ2, PGC Schizophrenia Working Group; IGAP, International

Genomics of Alzheimer’s Project; ILAE, International League Against

Epilepsy Consortium on Complex Epilepsies; ISGC, International Stroke

Genetics Consortium; METASTROKE, a consortium of the ISGC; IHGC,

International; Headache Genetics Consortium; IMSGC, International Multiple

Sclerosis Genetics Consortium; IPDGC, International Parkinson’s Disease

Genomics Consortium. Windicates same as above.

Psychiatric disorders Neurological disorders

Disorder Source Cases Controls Disorder Source Cases Controls

Attention deficit

hyperactivity disorder

PGC-ADD2 12,645 84,435 Alzheimer’s disease IGAP 17,008 37,154

.................................... .................................. ..................................... ..................................... ..................................... .................................... ................................... .................................... ..................................... .......

Anorexia nervosa PGC-ED 3495 10,982 Epilepsy ILAE 7779 20,439

Anxiety disorders ANGST 5761 11,765 Focal epilepsy W4601* 17,985*

Autism spectrum disorder PGC-AUT 6197 7377 Generalized epilepsy W2525* 16,244*

Bipolar disorder PGC-BIP2 20,352 31,358 Intracerebral hemorrhage ISGC 1545 1481

Major depressive disorder PGC-MDD2 66,358 153,234 Ischemic stroke METASTROKE 10,307 19,326

Obsessive-compulsive

disorder

PGC-OCDTS 2936 7279 Cardioembolic stroke W1859* 17,708*

Posttraumatic stress disorder PGC-PTSD 2424 7113 Early onset stroke W3274* 11,012*

Schizophrenia PGC-SCZ2 33,640 43,456 Large-vessel disease W1817* 17,708*

Tourette syndrome PGC-OCDTS 4220 8994 Small-vessel disease W1349* 17,708*

Migraine IHGC 59,673 316,078

Migraine with aura W6332* 142,817*

Migraine without aura W8348* 136,758*

Multiple sclerosis IMSGC 5545 12,153

Parkinson’s disease IPDGC 5333 12,019

Total psychiatric 158,028 365,993 Total neurologic 107,190 418,650

*Sample count for a phenotype that is part of a larger group.

RESEARCH |RESEARCH ARTICLE

fromtheestimateinthecurrentstudy[0.13(SE:

0.02)]. These differences may reflect implicit het-

erogeneity in a much larger case collection used

inthecurrentstudy(effectivesamplesize10,494

versus 46,669) and the potential reasons listed

above, but they could also be due to methodo-

logical variability (most of the previous approx-

imations listed in table S3 are estimated with

a different methodology). To evaluate this, we

applied the same analytical process used in this

paper to the summary statistics of the GERAD

(Genetic and Environmental Risk in Alzheimer’s

Disease) cohort (3941 cases and 7848 controls)

from the Alzheimer’s disease meta-analysis, where

the p rev ious her itability estimate was calculated.

There, we obtained a heritabi lit y estimat e of 0.25

(SE: 0.04), which agrees closely with the pub-

lished estimate of 0.24 (SE: 0.03), suggesting

that the different approximations may reflect dif-

ferences between datasets rather than method-

ological variability.

Correlations among brain disorders

We observed widespread sharing across psychi-

atric disorders (Fig. 1 and fig. S3) by expanding

the number of brain disorder pairs studied be-

yond those previously reported (17), but similar

sharing was not observed among neurological

disorders. Among the psychiatric disorders, schizo-

phrenia showed significant genetic correlation

with most of the psychiatric disorders, whereas

MDD was positively (though not necessarily sig-

nificantly) correlated with every other disorder

tested. Further, schizophrenia, bipolar disorder,

anxiety disorders, MDD, and ADHD each showed

a high degree of correlation to the four others

[average genetic correlation (r

) = 0.40] (table

S7A). Anorexia nervosa, obsessive-compulsive

disorder (OCD), and schizophrenia also demon-

strated significant sharing among themselves

(Fig. 1), as did Tourette syndrome (TS), OCD, and

MDD, as well as ASD and schizophrenia. Post-

traumatic stress disorder (PTSD) showed no sig-

nificant correlation with any of the other psychiatric

phenotypes (though some correlation to ADHD

and MDD was observed), and both ASD and TS

appear to potentially be more distinct from the

other psychiatric disorders. The modest power of

the ASD, PTSD, and TS meta-analyses, however,

limits the strength of this conc lusion (fig . S2C).

Neurological disorders showed a more lim-

ited extent of genetic correlation than that of the

psychiatric disorders (Fig. 2, fig. S4, and table

S7A), suggesting greater diagnostic specificity

and/or more distinct etiologies. Parkinson’sdis-

ease, Alzheimer’s disease, generalized epilepsy,

and multiple sclerosis (MS) showed little to no

correlation with other brain disorders. The highest

degree of genetic correlation among the neuro-

logical disorders was observed for focal epilepsy

(average r

=0.46,excludingtheotherepilepsy

datasets), though none of the correlations were

significant, reflecting the relatively modest power

of the current focal epilepsy meta-analysis (fig.

S2C). However, the modest heritability and the

broad pattern of sharing observed for focal epi-

lepsy may be consistent with heterogeneity and

The Brainstorm Consortium, Science 360, eaap8757 (2018) 22 June 2018 3of12

Table 2. Behavioral-cognitive and additional phenotypes used in the study. Indented

phenotypes are part of a larger whole (e.g., samples in the college attainment analysis are a subset

of those in the analysis for years of education). (d), dichotomous phenotype; (q), quantitative

phenotype. References and phenotype definitions are listed in table S2, and data availability in table

S13. SSGAC, Social Science Genetic Association Consortium; CTG, Complex Trait Genetics Lab;

GPC, Genetics of Personality Consortium; TAG, Tobacco and Genetics Consortium; GIANT, Genetic

Investigation of ANthropometric Traits consortium; Cardiogram, CARDIoGRAMplusC4D Consortium;

IIBDGC, International Inflammatory Bowel Disease Genetics Consortium.

Phenotype Source Samples

Behavioral-cognitive phenotypes

Cognitive

Years of education (q) SSGAC 293,723

College attainment (d) W120,917*

Cognitive performance (q) W17,989*

Intelligence (d) CTG 78,308

Personality measures

Subjective well-being SSGAC 298,420

Depressive symptoms W161,460*

Neuroticism (q) W170,911*

Extraversion (q) GPC 63,030*

Agreeableness (q) W17,375*

Conscientiousness (q) W17,375*

Openness (q) W17,375*

Smoking-related

Never/ever smoked (d) TAG 74,035

Cigarettes per day (q) TAG 38,617*

Additional phenotypes

BMI (q) GIANT 339,224

Height (q) W253,288*

Coronary artery disease (d) Cardiogram 86,995

Crohn’s disease IIBDGC 20,883

Total 1,124,048

*Sample counts represent overlap with preceding dataset.

Fig. 1. Genetic correlations across psychiatric phenotypes. The color of each box indicates the

magnitude of the correlation, and the size of the box indicates its significance (LDSC), with

significant correlations filling each square completely. Asterisks indicate genetic correlations that are

significantly different from zero after Bonferroni correction.

RESEARCH |RESEARCH ARTICLE

potentially even diagnostic misclassification across

a range of neurological conditions.

In the cross-category correlation analysis, the

observed pattern is consistent with limited sharing

across the included neurological and psychiatric

disorders (Fig. 3; average r

=0.03).Theonly

significant cross-category correlations were with

migraine, suggesting that this disorder may share

some of its genetic architecture with psychiatric

disorders: migraine and ADHD (r

=0.26,P=

8.81 × 10

−8

), migraine and TS (r

=0.19,P=1.80×

−5

), and migraine and MDD (r

=0.32,P=

1.42 × 10

−22

for all migraine; r

=0.23,P=5.23×

−5

for migraine without aura; r

=0.28,P=1.00×

−4

for migraine with aura).

We observed several significant genetic corre-

lations between the behavioral-cognitive or addi-

tional phenotypes and brain disorders (Fig. 4

and table S7B). Results for cognitive traits were

dichotomous among psychiatric phenotypes (fig.

S5A), with ADHD, anxiety disorders, MDD, and

TS showing negative correlations to the cognitive

measures and anorexia nervosa, ASD, bipolar

disorder, and OCD showing positive correlations.

Schizophrenia showed more mixed results, with

a significantly negative correlation to intelligence

but a positive correlation to years of education.

Among neurological phenotypes (fig. S5B), the

correlations were either negative or null, with

Alzheimer’s disease, epilepsy, intracerebral hem-

orrhage (ICH), ischemic stroke, early onset stroke,

and migraine showing significantly negative cor-

relations. Correlations between college attainment

and years of education with bipolar disorder (24),

Alzheimer’s disease, and schizophrenia have been

previously reported (33).

Among the personality and symptom measures,

significant positive correlations were observed

between neuroticism and anorexia nervosa, anx-

iety disorders, migraine, migraine without aura,

MDD, OCD, schizophrenia, and TS [fig. S6, A and

B; replicating previously reported correlations

with MDD and schizophrenia (34)]; between de-

pressive symptoms and ADHD, anxiety disorder,

bipolar disorder, MDD, and schizophrenia; and

between subjective well-being and anxiety dis-

order, bipolar disorder, and MDD. For smoking-

related measures, the only significant genetic

correlations were between never/ever smoked

and MDD (r

= 0.33, P=3.10×10

−11

)aswellas

ADHD (r

=0.37,P=3.15×10

−6

Among the additional phenotypes, the two

examples of disorders with well-defined etiolo-

gies had different results. Crohn’sdisease,repre-

senting immunological pathophysiology, showed

no correlation with any of the study phenotypes,

whereas the phenotype representing v ascular

pathophysiology (coronary artery disease) showed

significant correlation to MDD (r

=0.19,P=

8.71 × 10

−5

) as well as the two stroke-related

phenotypes (r

=0.69,P=2.47×10

−6

to ischemic

stroke and r

=0.86,P=2.26×10

−5

to early

onset stroke), suggesting shared genetic effects

across these phenotypes. Significant correlations

were also observed for BMI, which was positively

correlated with ADHD and MDD, and negative-

ly correlated with anorexia nervosa [as previous-

ly reported with a different dataset (24)] and

schizophrenia.

Our enrichment analysis (fig. S7 and tables S8

to S12) demonstrated significant heritability en-

richments between the CNS and generalized

epilepsy, MDD, TS, college attainment, intelli-

gence, neuroticism, and the never/ever smoked

trait; between depressive symptoms and adrenal/

pancreatic cells and tissues; as well as between

hematopoietic cells (including immune system

cells) and MS (fig. S7, A and B, and tables S8 and

S9). We replicated the reported (CNS) enrich-

ment for schizophrenia, bipolar disorder, and

years of education (tables S8 and S9) and

observed the reported enrichments for BMI (CNS),

years of education (CNS), height (connective tis-

sues and bone, cardiovascular system, and other),

and Crohn’s disease (hematopoietic cells) from

the same datasets (fig. S7, C and D) (26). The psy-

chiatric disorders with large numbers of identified

GWASloci(bipolardisorder,MDD,andschizo-

phrenia) and migraine, which was the only cross-

correlated neurological disorder, show enrichment

to conserved regions (tables S10 and S12), whereas

the other neurological disorders with similar

numbers of loci (MS, Alzheimer’s disease, and

Parkinson’s disease) do not (fig. S7, A and B).

Enrichment to conserved regions was also ob-

served for neuroticism, intelligence, and col-

lege attainment and to H3K9ac peaks for BMI

(tables S11 and S12).

Discussion

By integrating and analyzing the genome-wide

association summary statistic data from consor-

tia of 25 brain disorders, we find that psychiatric

disorders broadly share a considerable portion

of their common variant genetic risk, especially

across schizophrenia, MDD, bipolar disorder,

anxiety disorder, and ADHD, whereas neurolog-

ical disorders are more genetically distinct. Across

categories, psychiatric and neurologic disorders

share relatively little common genetic risk, sug-

gesting that multiple different and largely in-

dependently regulated etiological pathways may

give rise to similar clinical manifestations [e.g.,

psychosis, which manifests in both schizophrenia

(35)andAlzheimer’sdisease(36)]. Except for mi-

graine, which appears to share some genetic ar-

chitecture with psychiatric disorders, the existing

clinical delineation between neurology and psy-

chiatry is corroborated at the level of common

variant risk for the studied disorders.

On the basis of the observed results, we per-

formed some exploratory analyses to address

concerns about diagnostic overlap and misclas-

sification, which are particularly relevant to psy-

chiatric disorders, owing to their spectral nature.

Given that the broad and continuous nature of

psychiatric disorder spectra has long been clin-

ically recognized (37–39) and that patients can,

in small numbers, progress from one diagnosis

to another (40), we evaluated to what extent this

kind of diagnostic overlap could explain the ob-

served correlations. Genetic correlation could arise

if, for example, patients progress through multi-

ple diagnoses over their lifetime or if some spe-

cific diagnostic boundaries between phenotype

pairs are particularly porous to misclassification

(table S5). Although, for instance, migraine and

The Brainstorm Consortium, Science 360, eaap8757 (2018) 22 June 2018 4of12

Fig. 2. Genetic correlations across n eurological phenotypes. The color of each box indicates the

magnitude of the correlation, and the size of the box indicates its significance (LDSC), with

significant correlations filling each square completely. Asterisks indicate genetic correlations that are

significantly different from zero after Bonferroni correction. Some phenotypes have substantial

overlaps (Table 1)—for instance, all cases of generalized epilepsy are also cases of epilepsy. Asterisks

indicate significant genetic correlation after multiple testing correction.

RESEARCH |RESEARCH ARTICLE

schizophrenia are unlikely to be mistaken for one

another, there may be more substantial misclassi-

fication between particular psychiatric disorders,

consistent with the clinical controversies in classi-

fication. Previous work (41) suggests that sub-

stantial misclassification (on the order of 15 to

30%, depending on whether it is uni- or bidirec-

tional) is required to introduce false levels of

genetic correlation. We found that the observed

levels of correlation are unlikely to appear in the

absence of underlying genetic correlation (table

S6), as it is apparent that a very high degree of

misclassification (up to 79%) would be required

to produce the observed correlations in the ab-

sence of any true genetic correlation and that

reasonably expected misclassification would have

limited impact on the observed r

(fig. S8).

Therefore, these results suggest true sharing of

a substantial fraction of the common variant

genetic architecture among psychiatric disorders

as well as between behavioral-cognitive mea-

sures and brain disorders. We also performed

large-scale simulations to explore the effect of

sample size, polygenicity, and degree of correla-

tion on power to detect significant correlations.

First, we established that the observed herita-

bility of the simulated misclassified traits in the

UK Biobank data behaves as would be theoret-

ically expected (fig. S9A) and that the effects on

observed correlation (fig. S9, B and C) are in

line with the estimates from family data (41). Rea-

sonably low levels of misclassification or changes

to the exact level of heritability appear unlikely

to induce significant correlations, as observed in

the power analysis (fig. S10), though a lower ob-

served heritability caused by substantial misclas-

sification (fig. S9A) will decrease the power to

estimate true genetic overlap.

The high degree of genetic correlation among

the psychiatric disorders adds further evidence

that current clinical diagnostics do not reflect

specific genetic etiology for these disorders and

that genetic risk factors for psychiatric disorders

do not respect clinical diagnostic boundaries.

Rather, this finding suggests a more inter-

connected genetic etiology, in contrast to that

of neurological disorders, and underscores the

need to refine psychiatric diagnostics. This

study may provide important “scaffolding”to

support a framework for investigating mental

disorders, incorporating many levels of infor-

mation to understand basic dimensions of brain

function.

The observed positive genetic correlations are

consistent with a few hypothetical scenarios.

For example, this observation may reflect the ex-

istence of some portion of common genetic risk

factors conferring risks for multiple psychiatric

disorders and where other distinct additional

factors, both genetic and nongenetic, contribute

to the eventual clinical presentation. The pres-

ence of significant genetic correlation may also

reflect the phenotypic overlap between any two

disorders; for example, the sharing between

schizophrenia and ADHD might reflect underly-

ing difficulties in executive functioning, which

are well-established in both disorders (42), and

that the shared risk arises from a partial cap-

ture of its shared genetic component. Similarly,

we might speculate that a shared mechanism

underlying cognitive biases may extend from over-

valued ideas to delusions (ranging from anorexia

nervosa and OCD to schizophrenia), and that this

heritable intermediate trait confers pleiotropic

risk to multiple outcomes. This kind of latent

variable could give rise to the observed genetic

correlation between disorders, owing to the

shared portion of variation affecting that vari-

able. Though a combination of these is likely,

more genome-wide significant loci are needed to

evaluate these overlaps at the locus level.

Conversely, the low correlations observed

across neurological disorders suggest that the

current classification reflects relatively specific

genetic etiologies, although the limited sample

size for some of these disorders and the lack of

inclusion of disorders conceived as “circuit-based”

(e.g., restless legs syndrome, sleep disorders, and

possibly essential tremor) constrain the full gen-

eralizability of this conclusion. On the basis of

our observations, degenerative disorders (such as

Alzheimer’sandParkinson’s diseases) would there-

fore not be expected to share their polygenic risk

profiles with a neuroimmunological disorder (such

as MS) or neurovascular disorder (such as ische-

mic stroke). Similarly, we see limited evidence for

the reported comorbidity between migraine with

aura and ischemic stroke (43)(r

=0.29,P=0.099);

however, the standard errors of this comparison

are too high to draw strong conclusions. At the

disorder subtype level, migraine with and without

aura (r

=0.48,P=1.79×10

−5

) show substantial

genetic correlation, whereas focal and generalized

epilepsy (r

=0.16,P=0.388)showmuchless.

The few significant correlations across neurology

and psych iat ry —namely, between migraine and

ADHD, MDD, and TS—suggest modest shared eti-

ological overlap across the neurology-psychiatry dis-

tinction. The comorbidity of migraine with MDD,

TS, and ADHD has been previously reported in

epidemiological studies (44–47), whereas the pre-

viously reported comorbidity between migraine

and bipolar disorder seen in epidemiological

studies (48) was not reflected in our estimate of

genetic correlation (r

=−0.03, P=0.406).

Several phenotypes show only very low-level

correlations with any of the other disorders and

phenotypes that we studied, despite large sample

size and robust evidence for heritability, which sug-

gests that their common variant genetic risk may

largely be unique. Alzheimer’sdisease,Parkinson’s

disease, and MS show extremely limited sharing

with the other phenotypes and with each other.

Neuroinflammation has been implicated in the

pathophysiology of each of these conditions

(49–51), as it has for migraine (52) and many

psychiatric conditions, including schizophrenia

(53), but no considerable shared heritability was

observed with either of those conditions nor with

Crohn’s disease, nor did we observe enrichment

for immune-related tissues in the functional par-

titioning (fig. S7) as observed for Crohn’sdisease.

Although this does not preclude the sharing of

individual neuroinflammatory mechanisms in

these disorders, the large-scale lack of shared

common variant genetic influences supports the

distinctiveness of disorder etiology. Further, we

observed significant enrichment of heritability for

immunological cells and tissues in MS only, show-

ing that inflammation-specific regulatory marks

in the genome do not show overall enrichment

forcommonvariantriskforeitherAlzheimer’sor

Parkinson’sdiseases[thoughthisdoesnotpreclude

The Brainstorm Consortium, Science 360, eaap8757 (2018) 22 June 2018 5of12

Fig. 3. Genetic correlations across neurological and psychiatric phenotyp es. The color of each

box indicates the magnitude of the correlation, and the size of the box indicates its significance

(LDSC), with significant correlations filling each square completely. Asterisks indicate genetic

correlations that are significantly different from zero after Bonferroni correction.

RESEARCH |RESEARCH ARTICLE

the effects of specific, not particularly polygenic

neuroinflammatory mechanisms (54)]. Among

psychiatric disorders, ASD and TS showed a sim-

ilar absence of correlation with other disorders,

although this may reflect small sample sizes.

Analysis of the Big Five personality measures

suggestthatthecurrentsamplesizesmaybelarge

enough for correlation testing. Neuroticism, which

has by far the largest sample size, shows sev-

eral significant correlations. Most significant of

these was to MDD (r

= 0.737, P=5.04×10

−96

providing evidence for the link between these

phenotypes, as reported for polygenic risk scores

(55)andtwinstudies(56,57); as well as other

psychiatric disorders (Fig. 4 and table S7B). The

correlation between MDD and anxiety disorders,

with a similar pattern of correlation and the di-

mensional measures of depressive symptoms, sub-

jective well-being, and neuroticism suggests that

they all tag a similar underlying etiology. The sig-

nificant correlation between coronary artery dis-

ease and MDD supports the link between MDD

and CAD (58), and the observed correlation be-

tween ADHD and smoking initiation (r

=0.374,

P=3.15×10

−6

) is consistent with the epidemio-

logical evidence of overlap (59)andfindingsfrom

twin studies (60).

For the neurological disorders, five (Alzheimer’s

disease, intracerebral hemorrhage, ischemic and

early onset stroke, and migraine) showed signif-

icant negative genetic correlation to the cogni-

tive measures, whereas two (epilepsy and focal

epilepsy) showed moderate negative genetic cor-

relation (fig. S5). For Alzheimer’s disease, poor

cognitive performance in early life has been

linked to increased risk for developing the dis-

order (61),buttoourknowledgenosuchcon-

nection has been reported for other phenotypes.

Among the psychiatric disorders, ADHD, anxiety

disorders, and MDD show a significant negative

correlation to cognitive and education attain-

ment measures, whereas the remaining five of

the eight psychiatric disorders (anorexia nervosa,

ASD, bipolar disorder, OCD, and schizophrenia)

showed significant positive genetic correlation

with one or more cognitive measures. These results

suggest the existence of a link between cognitive

performance in early life and the genetic risk for

both psychiatric and neurological brain disorders.

The basis of the genetic correlations between edu-

cation, cognition, and brain disorders may have

a variety of root causes, including indexing per-

formance differences on the basis of behavioral

dysregulation (e.g., ADHD relating to attentional

problems during cognitive tests), or may reflect

ascertainment biases in certain disorders condi-

tional on impaired cognition (e.g., individuals with

lower cognitive reserve being more rapidly iden-

tified for Alzheimer’sdisease),buttheresultscould

also suggest a direct link between the underlying

etiologies.

BMI shows significant positive genetic corre-

lation to ADHD, consistent with a meta-analysis

linking ADHD to obesity (62), and negative ge-

netic correlation with anorexia nervosa, OCD, and

schizophrenia. This is consistent with evidence for

enrichment of BMI heritability in CNS tissues (26)

that suggest neuronal involvement (63); this

may also provide a partial genetic explanation

for lower BMI in anorexia nervosa patients even

after recovery (64). Given that no strong correla-

tions were observed between BMI and any of the

neurological phenotypes, BMI’sbrain-specificge-

netic architecture may be more closely related to

behavioral phenotypes. Ischemic stroke and BMI

show surprisingly little genetic correlation in this

analysis (r

=0.07,P= 0.26), suggesting that

although BMI is a risk factor for stroke (65), there

is little evidence for shared common genetic ef-

fects. These analyses also suggest that the re-

ported reduced rates of cardiovascular disease

in individuals with histories of anorexia nervosa

(66,67) are more likely due to BMI-related second-

aryeffects.Thelimitedevidence of genetic corre-

lation of anorexia nervosa with intracerebral

hemorrhage, ischemic stroke, early onset stroke,

and coronary artery disease suggests that any

lower cardiovascular mortality is more likely due

to direct BMI-related effects rather than to ge-

netic risk variants.

The genetic correlation results presented here

indicate that the clinical boundaries for the

studied psychiatric phenotypes do not reflect

distinct underlying pathogenic processes. This

suggests that genetically informed analyses may

provide a basis for restructuring of psychiatric

nosology, consistent with twin- and family-based

results. In contrast, neurological disorders show

greater genetic specificity, and although it is im-

portant to emphasize that while some brain dis-

orders are underrepresented here, our results

demonstratethelimitedevidenceforwidespread

common genetic risk sharing between psychiat-

ric and neurological disorders. However, we pro-

vide strong evidence that both psychiatric and

neurological disorders show robust correlations

with cognitive and personality measures, indicat-

ing avenues for follow-up studies. Further analysis

is needed to evaluate whether overlapping ge-

netic contributions to psychiatric pathology may

influence treatment choices. Ultimately, such de-

velopments are promising steps toward reducing

diagnostic heterogeneity and eventually improv-

ing the diagnostics and treatment of psychiatric

disorders.

Materials and methods summary

We collected GWAS meta-analysis summary statis-

tics for 25 brain disorders and 17 other phenotypes

from various consortia and, where necessary, gen -

erated new, non–sex-stratified European cohort–

only versions of the meta-analyses (25). All datasets

underwent uniform quality control (25).For each

trait, by using the LDSC framework (24), the total

additive common SNP heritability present in the

summary statistics (h

g) was estimated by regress-

ing the association c

statistic of a SNP against

the total amount of common genetic variation

tagged by that SNP, for all SNPs. Genetic correla-

tions (r

; i.e., the genome-wide average shared

genetic risk) for pairs of phenotypes were esti-

mated by regressing the product of z-scores, rather

than the c

statistic, for each phenotype and for

each SNP. Significance was assessed by Bonferroni

multiple testing correction via estimating the

number of independent brain disorder pheno-

types via matrix decomposition (25). Functional

and partitioning analyses for the GWAS data-

sets were also performed using LDSC regression.

Power analyses and simulation work to aid in

interpretation of the results were conducted

using genotype data from the UK Biobank re-

source (25).

The Brainstorm Consortium, Science 360, eaap8757 (2018) 22 June 2018 6of12

Fig. 4. Genetic correlations across brain disorders and behavioral-cognitive phenotypes. The

color of each box indicates the magnitude of the correlation, and the size of the box indicates its

significance (LDSC), with significant correlations filling each square completely. Asterisks indicate

genetic correlations that are significantly different from zero after Bonferroni correction.

RESEARCH |RESEARCH ARTICLE

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ACKNOWL EDGME NTS

We thank the members of the Neale and Daly laboratories for

helpful discussions; R. Hoskins, J. Wessman, and J. Martin for

comments on the manuscript; M. Whittall for inspiration; S. Knemeyer

for help with the summary figure; C. Hammond for organizational

assistance; and the patients and participants of the respective

consortia for their participation. Data on coronary artery disease

have been contributed by CARDIoGRAMplusC4D investigators

and have been downloaded from www.cardiogramplusc4d.org.

matSpD (Matrix Spectral Decomposition method) is available at

neurogenetics.qimrberghofer.edu.au/matSpD/. This research was

conducted using the UK Biobank resource (application 18597).

Funding: This work was supported by grants 1R01MH10764901

and 5U01MH09443203 from the National Institute of Mental

Health, as well as the Orion Farmos Research Foundation (V.A.)

and the Fannie and John Hertz Foundation (H.K.F.).

Consortium specific funding is detailed in the supplementary

materials (“Study-specific acknowledgments”). Author

contributions: V.A., A.C., and B.M.N. conceived of and coordinated

the study; V.A., B.B.S., H.K.F., R.W., and P.T. contributed

methodology; B.B.S. and H.K.F. contributed software; V.A.

and B.M.N. conducted the statistical analysis; B.M.N. obtained

funding and provided resources; R.W., J.B., L.D., V.E.-P., G.F.,

P.G., R.M., N.P., S.R., Z.W., and D.Y. were responsible for curation

of disorder-specific data; P.H.L. and C.C. helped with data

interpretation; V.A. and B.M.N. wrote the original draft, and all

authors contributed to review and editing; V.A. provided the

visualization; G.B., C.B., M.Daly, M.Dichgans, S.V.F., R.G., P.H.,

K.K., B.K., C.A.M., A.P., J.S., P.S., J.W., N.W., C.C., A.P., J.S.,

P.S., J.R., A.C., and B.M.N. provided supervision and project

administration; and the remaining authors contributed disorder-specific

sample collection and/or analysis. Consortium-specific personnel lists

can be found in the supplementary materials. Competing interests:

The authors declare no competing interests. Data and m aterials

availability: Data sources for the GWAS summary statistics used in the

study and their availability, as well as the study-specific

acknowledgments, are provided in the supplementary materials

(table S13 and supplementary text, respectively).

The Brainstorm Consortium

Verneri Anttila

1,2,3

*, Brendan Bulik-Sullivan

1,3

, Hilary K. Finucane

2,3,4,5

Raymond K. Walters

1,2,3

, Jose Bras

6,7

, Laramie Duncan

1,2,3,8

Valentina Escott-Price

9,10

,GuidoJ.Falcone

11,12,13

, Padhraig Gormley

1,2,3,11

Rainer Malik

,NikolaosA.Patsopoulos

3,15

, Stephan Ripke

1,2,3,16

Zhi Wei

,DongmeiYu

2,11

,PhilH.Lee

2,11

, Patrick Turley

1,3

Benjamin Grenier-Boley

18,19,20

, Vincent Chouraki

18,19,20,21

Yoichiro Kamatani

22,23

, Claudine Berr

24,25,26

, Luc Letenneur

27,28

Didier Hannequin

29,30

, Philippe Amouyel

18,19,20,21

, Anne Boland

Jean-François Deleuze

, Emmanuelle Duron

32,33

, Badri N. Vardarajan

Christiane Reitz

,AlisonM.Goate

, Matthew J. Huentelman

M. Ilyas Kamboh

, Eric B. Larson

39,40

, Ekaterina Rogaeva

Peter St George-Hyslop

41,42

,HakonHakonarson

43,44,45

,WalterA.Kukull

Lindsay A. Farrer

, Lisa L. Barnes

48,49,50

,ThomasG.Beach

F. Yesim Demirci

, Elizabeth Head

, Christine M. Hulette

Gregory A. Jicha

,JohnS.K.Kauwe

, Jeffrey A. Kaye

James B. Leverenz

, Allan I. Levey

, Andrew P. Lieberman

Vernon S. Pankratz

, Wayne W. Poon

,JosephF.Quinn

62,63

Andrew J. Saykin

, Lon S. Schneider

,AmandaG.Smith

Joshua A. Sonnen

67,68

,RobertA.Stern

, Vivianna M. Van Deerlin

Linda J. Van Eldik

, Denise Harold

, Giancarlo Russo

David C. Rubinsztein

73,74

, Anthony Bayer

,MagdaTsolaki

76,77

Petra Proitsi

,NickC.Fox

79,6

, Harald Hampel

80,81,82,83

, Michael J. Owen

84,85

Simon Mead

, Peter Passmore

, Kevin Morgan

Markus M. Nöthen

89,90

, Martin Rossor

, Michelle K. Lupton

92,93

Per Hoffmann

89,90,94,95

, Johannes Kornhuber

,BrianLawlor

Andrew McQuillin

, Ammar Al-Chalabi

99,100

,JoshuaC.Bis

101

Agustin Ruiz

102,103

,MercèBoada

102

,SudhaSeshadri

104,105,106

Alexa Beiser

107,108,106

,KennethRice

109

,SvenJ.vanderLee

110

Philip L. De Jager

111

, Daniel H. Geschwind

112,113,114

Matthias Riemenschneider

115

, Steffi Riedel-Heller

116

, Jerome I. Rotter

117

Gerhard Ransmayr

118

, Bradley T. Hyman

12,13

, Carlos Cruchaga

120

Montserrat Alegret

102

, Bendik Winsvold

121,122

, Priit Palta

123,124

Kai-How Farh

125,3

, Ester Cuenca-Leon

11,3,2

, Nicholas Furlotte

126

Tobias Kurth

127

, Lannie Ligthart

128

, Gisela M.Terwindt

129

Tobias Freilinger

130,131

, Caroline Ran

132

, Scott D.Gordon

Guntram Borck

133

, Hieab H.H. Adams

110,134

, Terho Lehtimäki

135

Juho Wedenoja

136,137

, Julie E. Buring

138

, Markus Schürks

139

Maria Hrafnsdottir

140

, Jouke-Jan Hottenga

128,141

,BrendaPenninx

142

Ville Artto

143

, Mari Kaunisto

123

,SalliVepsäläinen

143

Nicholas G. Martin

, Grant W. Montgomery

92,144

, Mitja I. Kurki

1,3,11,123

Eija Hämäläinen

123

, Hailiang Huang

1,2,145

, Jie Huang

146,147

Cynthia Sandor

148

, Caleb Webber

148,10

, Bertram Muller-Myhsok

149,150,151

Stefan Schreiber

152,153

, Veikko Salomaa

154

,ElizabethLoehrer

155

Hartmut Göbel

156

, Alfons Macaya

157

,PatriciaPozo-Rosich

158,159

Thomas Hansen

160,161

,ThomasWerge

161,162,163

, Jaakko Kaprio

123,137

Andres Metspalu

124

, Christian Kubisch

164

, Michel D. Ferrari

129

Andrea C. Belin

132

, Arn M.J.M. Maagdenberg

166,129

,John-AnkerZwart

167

Dorret Boomsma

168.128

, Nicholas Eriksson

126

, Jes Olesen

160

Daniel I. Chasman

169,13

,DaleR.Nyholt

170

,AndrejaAvbersek

171

Larry Baum

172

, Samuel Berkovic

173

,JonathanBradfield

174

Russell Buono

175,176,177

, Claudia B. Catarino

171,178

, Patrick Cossette

179

Peter De Jonghe

180,181,182

, Chantal Depondt

183

, Dennis Dlugos

184,185

Thomas N. Ferraro

186,187

, Jacqueline French

188

, Helle Hjalgrim

189

Jennifer Jamnadas-Khoda

171,190

, Reetta Kälviäinen

192,193

Wolfram S. Kunz

194,195

, Holger Lerche

131

,CostinLeu

196

Dick Lindhout

197,198

, Warren Lo

199,200

, Daniel Lowenstein

201

Mark McCormack

202,203

, Rikke S. Møller

204,205

,AnneMolloy

206

Ping-Wing Ng

207,208

,KarenOliver

209

, Michael Privitera

210,211

Rodney Radtke

212

, Ann-Kathrin Ruppert

213

, Thomas Sander

213

Steven Schachter

214,12,13

, Christoph Schankin

215,216

Ingrid Scheffer

217,218,219

, Susanne Schoch

220

, Sanjay M. Sisodiya

221,222

Philip Smith

223

, Michael Sperling

224

, Pasquale Striano

225

Rainer Surges

226,227

, G. Neil Thomas

228

, Frank Visscher

229

Christopher D. Whelan

202

, Federico Zara

Analysis of shared heritability in common disorders of the brain

Abstract