Robust semiparametric gene-environment interaction analysis using sparse boosting.

ABSTRACT: For the pathogenesis of complex diseases, gene-environment (G-E) interactions have been shown to have important implications. G-E interaction analysis can be challenging with the need to jointly analyze a large number of main effects and interactions and to respect the "main effects, interactions" hierarchical constraint. Extensive methodological developments on G-E interaction analysis have been conducted in recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate long-tailed distributions/data contamination, make the restricted assumption of linear effects, and cannot effectively accommodate missingness in E variables. To directly tackle these problems, a semiparametric model is assumed to accommodate nonlinear effects,

SUBMITTER: Wu M 

PROVIDER: S-EPMC6736719 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature