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A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data
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A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data
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Journal Article
A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data
2019
Overview
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.Wang et al. present a computational framework integrating multi-omics data to infer key schizophrenia risk genes from GWAS data. These genes are predominantly expressed in the developing brain and are enriched for targets of approved drugs.
Publisher
Nature Publishing Group
Subject
