A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data

• Authors: Quan Wang, Vanderbilt University; Rui Chen, Vanderbilt University; Feixiong Cheng, Cleveland Clinic; Qiang Wei, Vanderbilt University; Ying Ji, Vanderbilt University; Hai Yang, Vanderbilt University; Xue Zhong, Vanderbilt University; Ran Tao, Vanderbilt University; Zhexing Wen, Emory University; James S. Sutcliffe, Vanderbilt University; Chunyu Liu, Upstate Medical University; Edwin H. Cook, University of Illinois; Nancy J. Cox, Vanderbilt University; Bingshan Li, Vanderbilt University
• Journal Title: Nature Neuroscience
• Volume: Volume 22, Number 5
• Publisher: Nature Research (part of Springer Nature) | 2019-05-01, Pages 691-+
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1038/s41593-019-0382-7
• Subjects: Health Sciences, Mental Health; Biology, Genetics
• Keywords: Science & Technology; Life Sciences & Biomedicine; Neurosciences; Neurosciences & Neurology; DE-NOVO MUTATIONS; MESSENGER-RNA; AUTISM; ASSOCIATION; EXPRESSION; GLUTAMATE; RESOURCE; VARIANTS; GRM3; KNOWLEDGEBASE

Abstract

Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.