Finite adaptation and multistep moves in the metropolis-hastings algorithm for variable selection in genome-wide association analysis.

ABSTRACT: High-dimensional datasets with large amounts of redundant information are nowadays available for hypothesis-free exploration of scientific questions. A particular case is genome-wide association analysis, where variations in the genome are searched for effects on disease or other traits. Bayesian variable selection has been demonstrated as a possible analysis approach, which can account for the multifactorial nature of the genetic effects in a linear regression model.Yet, the computation presents a challenge and application to large-scale data is not routine. Here, we study aspects of the computation using the Metropolis-Hastings algorithm for the variable selection: finite adaptation of the proposal distributions, multistep moves for changing the inclusion state of multiple variables in a

SUBMITTER: Peltola T 

PROVIDER: S-EPMC3499564 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature