Integrative Bayesian variable selection with gene-based informative priors for genome-wide association studies.

ABSTRACT:

Background

Genome-wide Association Studies (GWAS) are typically designed to identify phenotype-associated single nucleotide polymorphisms (SNPs) individually using univariate analysis methods. Though providing valuable insights into genetic risks of common diseases, the genetic variants identified by GWAS generally account for only a small proportion of the total heritability for complex diseases. To solve this "missing heritability" problem, we implemented a strategy called integrative Bayesian Variable Selection (iBVS), which is based on a hierarchical model that incorporates an informative prior by considering the gene interrelationship as a network. It was applied here to both simulated and real data sets.

Results

Simulation studies indicated that the iBVS method was adv

SUBMITTER: Zhang X 

PROVIDER: S-EPMC4275962 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature