Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new schizophrenia-associated genes

Abstract Most genetic variants associated with complex heritability phenotypes lie in non-coding regions and are thought to influence disease risk by regulating gene expression. However, most transcriptome-wide association approaches primarily model local ( cis ) genetic effects, leaving much of gene regulation unexplained. Here, we show that incorporating distal ( trans ) regulatory effects improves the prediction of gene expression and the identification of disease-associated genes. Using RNA sequencing data from six human post-mortem brain regions, we developed INGENE and MODULE, two models capturing the combined influence of candidate trans- acting variants within gene coexpression networks. Integrating these models with conventional cis -based predictors improved gene expression imputation (maximum likelihood estimation, α = 0.05) for 18,744 genes across regions. Applying this framework to Psychiatric Genomics Consortium wave 3 genotypes identified 766 genes associated with schizophrenia ( P FDR < 0.01), including 641 not previously reported by transcriptome-wide analyses. These findings highlight the contribution of distal regulatory mechanisms and gene network interactions to schizophrenia risk.