A scalable, knowledge-based analysis framework for genetic association studies.

ABSTRACT:

Background

Testing for marginal associations between numerous genetic variants and disease may miss complex relationships among variables (e.g., gene-gene interactions). Bayesian approaches can model multiple variables together and offer advantages over conventional model building strategies, including using existing biological evidence as modeling priors and acknowledging that many models may fit the data well. With many candidate variables, Bayesian approaches to variable selection rely on algorithms to approximate the posterior distribution of models, such as Markov-Chain Monte Carlo (MCMC). Unfortunately, MCMC is difficult to parallelize and requires many iterations to adequately sample the posterior. We introduce a scalable algorithm called PEAK that improves the efficiency of

SUBMITTER: Baurley JW 

PROVIDER: S-EPMC4015032 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature