Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits

Abstract

The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants1 located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression2,3,4. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.

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Fig. 1: PrediXcan and eQTL analysis of GWAS of psychiatric traits.
Fig. 2: Proposing causal variant and causal gene mechanism at known schizophrenia-associated loci (n = 145 loci; 179 index SNPs).
Fig. 3: Complexity of identifying the relevant gene mechanism in tissue of pathology.
Fig. 4: Genetically determined co-expression networks of disease-associated GReX.
Fig. 5: Tissue-specific GReX, joint-tissue eQTL mapping and tissue-specific or tissue-shared functional categories for schizophrenia.

Data availability

The protected data for the GTEx project (for example, genotype and RNA-sequence data) are available via access request to dbGaP accession number phs000424.v6.p1. Processed GTEx data (for example, gene expression and eQTLs) are available on the GTEx portal (https://gtexportal.org). The URLs of the summary statistics datasets of all the GWAS meta-analyses analyzed in the paper can be found in Supplementary Table 1.

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Acknowledgements

The GTEx Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health. Additional funds were provided by the National Cancer Institute (NCI), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), National Institute on Drug Abuse (NIDA), National Institute of Mental Health (NIMH) and National Institute of Neurological Disorders and Stroke (NINDS). Donors were enrolled at Biospecimen Source Sites funded by NCI/SAIC-Frederick, Inc. SAIC-F subcontracts to the National Disease Research Interchange (grant no. 10XS170), Roswell Park Cancer Institute (grant no. 10XS171) and Science Care, Inc. (grant no. X10S172). The Laboratory, Data Analysis and Coordinating Center (LDACC) was funded through a contract (grant no. HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations were funded through an SAIC-F subcontract to Van Andel Institute (grant no. 10ST1035). Additional data repository and project management were provided by SAIC-F (grant no. HHSN261200800001E). The Brain Bank was supported by supplements to University of Miami grants (nos. DA006227 and DA033684) and to contract no. N01MH000028. Statistical Methods development grants were made to the University of Geneva (nos. MH090941 and MH101814), the University of Chicago (nos. MH090951, MH090937, MH101820, MH101825), the University of North Carolina at Chapel Hill (nos. MH090936 and MH101819), Harvard University (no. MH090948), Stanford University (no. MH101782), Washington University St Louis (no. MH101810) and the University of Pennsylvania (no. MH101822). E.M.D. is supported by the Foundation Volksbond Rotterdam. E.R.G. is grateful to the President and Fellows of Clare Hall, University of Cambridge, for providing a stimulating intellectual home and benefited from a Fellowship in the College. E.R.G. also acknowledges support from the Academic Medical Center, University of Amsterdam, during the early stages of the study. E.R.G. and N.J.C. acknowledge support from grant no. NIMH R01MH113362. We acknowledge the Psychiatric Genomics Consortium groups for sharing the published meta-analysis data.

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E.R.G. and E.M.D. conceived of and designed the study, analysed the data and drafted the manuscript. E.R.G., A.H.Z. and E.M.D. interpreted the data. N.J.C. and D.D. revised the manuscript critically for important intellectual content. All authors approved of the version of the manuscript to be published.

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Correspondence to Eric R. Gamazon or Eske M. Derks.

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Gamazon, E.R., Zwinderman, A.H., Cox, N.J. et al. Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits. Nat Genet 51, 933–940 (2019). https://doi.org/10.1038/s41588-019-0409-8

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