Bayesian variable selection with a pleiotropic loss function in Mendelian randomization.

ABSTRACT: Mendelian randomization is the use of genetic variants as instruments to assess the existence of a causal relationship between a risk factor and an outcome. A Mendelian randomization analysis requires a set of genetic variants that are strongly associated with the risk factor and only associated with the outcome through their effect on the risk factor. We describe a novel variable selection algorithm for Mendelian randomization that can identify sets of genetic variants which are suitable in both these respects. Our algorithm is applicable in the context of two-sample summary-data Mendelian randomization and employs a recently proposed theoretical extension of the traditional Bayesian statistics framework, including a loss function to penalize genetic variants that exhibit pleiotropic effe

SUBMITTER: Gkatzionis A 

PROVIDER: S-EPMC8446304 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature