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

Genome-wide association studies (GWAS) have identified >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 (HRGs), most of which are not the nearest genes to the GWAS index variants. HRGs account for a significantly enriched heritability estimated by stratified LD-score regression. Moreover, HRGs 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 understanding of SCZ etiology and potential therapeutics.