Sparse group variable selection for gene-environment interactions in the longitudinal study.

ABSTRACT: Penalized variable selection for high-dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi-level gene-environment (G  ×$\times $  E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual levels. Simulation

SUBMITTER: Zhou F 

PROVIDER: S-EPMC9426288 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature