Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis

Andrew D. Grotzinger(University of Colorado Boulder), Travis T. Mallard(The University of Texas at Austin), Wonuola A. Akingbuwa(Amsterdam University Medical Centers), Hill F. Ip(Vrije Universiteit Amsterdam), Mark J. Adams(University of Edinburgh), Cathryn M. Lewis(King's College London), Andrew M. McIntosh(University of Edinburgh), Jakob Grove(Aarhus University), Søren Dalsgaard(Aarhus University), Klaus‐Peter Lesch(Sechenov University), Nora I. Strom(Karolinska Institutet), Sandra Meier(Dalhousie University), Manuel Mattheisen(Dalhousie University), Anders D. Børglum(Aarhus University), Ole Mors(Aarhus University Hospital), Gerome Breen(King's College London), iPSYCH(Aarhus University), Manuel Mattheisen(Dalhousie University), Ole Mors(Aarhus University), Sandra Meier(Dalhousie University), Phil H. Lee(Broad Institute), Kenneth S. Kendler(Virginia Commonwealth University), Jordan W. Smoller(Broad Institute), Elliot M. Tucker–Drob(The University of Texas at Austin), Michel G. Nivard(Vrije Universiteit Amsterdam)
Nature Genetics
May 1, 2022
Cited by 319Open Access
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Abstract

We interrogate the joint genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. We identify four broad factors (neurodevelopmental, compulsive, psychotic and internalizing) that underlie genetic correlations among the disorders and test whether these factors adequately explain their genetic correlations with biobehavioral traits. We introduce stratified genomic structural equation modeling, which we use to identify gene sets that disproportionately contribute to genetic risk sharing. This includes protein-truncating variant-intolerant genes expressed in excitatory and GABAergic brain cells that are enriched for genetic overlap across disorders with psychotic features. Multivariate association analyses detect 152 (20 new) independent loci that act on the individual factors and identify nine loci that act heterogeneously across disorders within a factor. Despite moderate-to-high genetic correlations across all 11 disorders, we find little utility of a single dimension of genetic risk across psychiatric disorders either at the level of biobehavioral correlates or at the level of individual variants. Joint analysis of 11 major psychiatric disorders identifies four broad factor underlying genetic correlations among the disorders. Association analyses detect 152 loci acting on these factors and identify 9 loci that act heterogeneously across disorders.


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