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

Abstract excerpt

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...

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