Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.

ABSTRACT: Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights in

SUBMITTER: Gholipourshahraki T 

PROVIDER: S-EPMC11563439 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature