A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies.

ABSTRACT:

Background

A central question for disease studies and crop improvements is how genetics variants drive phenotypes. Genome Wide Association Study (GWAS) provides a powerful tool for characterizing the genotype-phenotype relationships in complex traits and diseases. Epistasis (gene-gene interaction), including high-order interaction among more than two genes, often plays important roles in complex traits and diseases, but current GWAS analysis usually just focuses on additive effects of single nucleotide polymorphisms (SNPs). The lack of effective computational modelling of high-order functional interactions often leads to significant under-utilization of GWAS data.

Results

We have developed a novel Bayesian computational method with a Markov Chain Monte Carlo (MCMC) search, a

SUBMITTER: Wang J 

PROVIDER: S-EPMC4660815 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature