Leveraging gene co-regulation to identify gene sets enriched for disease heritability.

ABSTRACT: Identifying gene sets that are associated to disease can provide valuable biological knowledge, but a fundamental challenge of gene set analyses of GWAS data is linking disease-associated SNPs to genes. Transcriptome-wide association studies (TWASs) detect associations between the genetically predicted expression of a gene and disease risk, thus implicating candidate disease genes. However, causal disease genes at TWAS-associated loci generally remain unknown due to gene co-regulation, which leads to correlations across genes in predicted expression. We developed a method, gene co-regulation score (GCSC) regression, to identify gene sets that are enriched for disease heritability explained by predicted expression. GCSC regresses TWAS chi-square statistics on gene co-regulation scores refle

SUBMITTER: Siewert-Rocks KM 

PROVIDER: S-EPMC8948163 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature