Evaluation of Bayesian Linear Regression derived gene set test methods.

ABSTRACT:

Background

Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations.

Results

Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations. Through a series of simulation studies, we demonstrated how the efficacy of BLR derived gene set tests is influenced by several factors, including the proportion of causal markers, the size of gene sets, the

SUBMITTER: Bai Z 

PROVIDER: S-EPMC11667926 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature