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

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, su

SUBMITTER: Wang Q 

PROVIDER: S-EPMC6646046 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature