Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci.

ABSTRACT:

Motivation

Genome-wide association studies (GWAS) have identified genetic variants, usually single-nucleotide polymorphisms (SNPs), associated with human traits, including disease and disease risk. These variants (or causal variants in linkage disequilibrium with them) usually affect the regulation or function of a nearby gene. A GWAS locus can span many genes, however, and prioritizing which gene or genes in a locus are most likely to be causal remains a challenge. Better prioritization and prediction of causal genes could reveal disease mechanisms and suggest interventions.

Results

We describe a new Bayesian method, termed SigNet for significance networks, that combines information both within and across loci to identify the most likely causal gene at each locus. The SigNe

SUBMITTER: Mousavi Z 

PROVIDER: S-EPMC11741684 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature