Identifying gene-gene interactions using penalized tensor regression.

ABSTRACT: Gene-gene (G×G) interactions have been shown to be critical for the fundamental mechanisms and development of complex diseases beyond main genetic effects. The commonly adopted marginal analysis is limited by considering only a small number of G factors at a time. With the "main effects, interactions" hierarchical constraint, many of the existing joint analysis methods suffer from prohibitively high computational cost. In this study, we propose a new method for identifying important G×G interactions under joint modeling. The proposed method adopts tensor regression to accommodate high data dimensionality and the penalization technique for selection. It naturally accommodates the strong hierarchical structure without imposing additional constraints, making optimization much simpler and fast

SUBMITTER: Wu M 

PROVIDER: S-EPMC5771864 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature