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

Author(s): Andrew D. Grotzinger ¹ ^, ² ^, ^* , Travis T. Mallard ³ , Wonuola A. Akingbuwa ⁴ ^, ⁵ , Hill F. Ip ⁴ , Mark J. Adams ⁶ , Cathryn M. Lewis ⁷ ^, ⁸ , Andrew M. McIntosh ⁶ , Jakob Grove ⁹ ^, ¹⁰ ^, ¹¹ ^, ¹² , Søren Dalsgaard ¹³ , Klaus-Peter Lesch ¹⁴ ^, ¹⁵ ^, ¹⁶ , Nora Strom ¹⁷ ^, ¹⁸ ^, ¹⁹ , Sandra M. Meier ¹⁰ ^, ²⁰ , Manuel Mattheisen ¹⁰ ^, ¹⁷ ^, ¹⁹ ^, ²⁰ ^, ²¹ ^, ²² , Anders D. Børglum ⁹ ^, ¹⁰ ^, ¹¹ , Ole Mors ⁹ ^, ²³ , Gerome Breen ⁷ ^, ⁸ , iPSYCH, Tourette Syndrome and Obsessive Compulsive Disorder Working Group of the Psychiatric Genetics Consortium, Bipolar Disorder Working Group of the Psychiatric Genetics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genetics Consortium, Schizophrenia Working Group of the Psychiatric Genetics Consortium, Phil H. Lee ²⁴ ^, ²⁵ , Kenneth S. Kendler ²⁶ , Jordan W. Smoller ²⁴ ^, ²⁵ , Elliot M. Tucker-Drob ³ ^, ²⁷ ^, ²⁸ , Michel G. Nivard ⁴ ^, ²⁸

Publication date (Electronic): 05 May 2022

Journal: Nature genetics

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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 novel) 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.

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PLINK: a tool set for whole-genome association and population-based linkage analyses.

Shaun Purcell, Benjamin M. Neale, Kathe Todd-Brown … (2007)

Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

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The mutational constraint spectrum quantified from variation in 141,456 humans

Konrad J. Karczewski, Laurent C. Francioli, Grace Tiao … (2021)

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.

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GCTA: a tool for genome-wide complex trait analysis.

Jian Yang, S. Lee, Michael E Goddard … (2011)

For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9216904

Journal ID (pubmed-jr-id): 2419

Journal ID (nlm-ta): Nat Genet

Journal ID (iso-abbrev): Nat Genet

Title: Nature genetics

ISSN (Print): 1061-4036

ISSN (Electronic): 1546-1718

Publication date Nihms-submitted: 30 March 2022

Publication date (Print): May 2022

Publication date (Electronic): 05 May 2022

Publication date PMC-release: 05 November 2022

Volume: 54

Issue: 5

Pages: 548-559

Affiliations

[1 ]Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA.

[2 ]Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA.

[3 ]Department of Psychology, University of Texas at Austin, Austin, TX, USA.

[4 ]Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

[5 ]Amsterdam Public Health Research Institute, Amsterdam University Medical Centres, Amsterdam, the Netherlands.

[6 ]Division of Psychiatry, University of Edinburgh, Edinburgh, UK.

[7 ]Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK.

[8 ]NIHR Maudsley Biomedical Research Centre, King's College London, London, UK.

[9 ]iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark.

[10 ]Department of Biomedicine, Aarhus University, Aarhus, Denmark.

[11 ]Center for Genomics and Personalized Medicine, Aarhus, Denmark.

[12 ]Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.

[13 ]National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark.

[14 ]Section of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany.

[15 ]Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, Sechenov First Moscow State Medical University, Moscow, Russia.

[16 ]Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands.

[17 ]Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany.

[18 ]Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.

[19 ]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.

[20 ]Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.

[21 ]iSEQ Center, Aarhus University, Aarhus, Denmark.

[22 ]Department of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada.

[23 ]Psychosis Research Unit, Aarhus University Hospital, Aarhus, Denmark.

[24 ]Psychiatric and Neurodevelopmental Genetics Unit (PNGU) and the Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.

[25 ]Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

[26 ]Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.

[27 ]Population Research Center, University of Texas at Austin, Austin, TX, USA.

[28 ]These authors jointly supervised this work.

Author notes

[* ] Andrew.Grotzinger@ 123456colorado.edu

Author Contributions

Study design: A.D.G., M.G.N., E.M.T.-D.

Methods development: A.D.G., M.G.N., E.M.T.-D.

Software development: A.D.G., H.F.I., M.G.N., E.M.T.-D.

Simulation studies: A.D.G., M.G.N., E,M.T.-D.

Gene set and annotation creation: W.A.A., A.D.G., M.G.N.

Genetic factor modelling, multivariate GWAS, complex trait correlations, and multivariate enrichment analyses: A.D.G., T.T.M., M.G.N., E.M.T.-D.

Writing: A.D.G., M.G.N., E.M.T.-D.

Feedback and editing: A.D.G., T.T.M., W.A.A., H.F.I., M.J.A., C.M.L., A.M.M., J.G., S.D., K.-P.L., N.S., S.M.M., M.M., A.D.B., O.M., G.B., P.H.L., K.S.K., J.W.S., E.M.T.-D., M.G.N.

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Manuscript ID: NIHMS1791507

DOI: 10.1038/s41588-022-01057-4

PMC ID: 9117465

PubMed ID: 35513722

SO-VID: 26da7a0e-68e2-43c0-9e80-938179c7e08f

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

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Abstract

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Genome Integrity

Most cited references 64

PLINK: a tool set for whole-genome association and population-based linkage analyses.

The mutational constraint spectrum quantified from variation in 141,456 humans

GCTA: a tool for genome-wide complex trait analysis.

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