Robust identification of gene-environment interactions for prognosis using a quantile partial correlation approach.

ABSTRACT: Gene-environment (G-E) interactions have important implications for the etiology and progression of many complex diseases. Compared to continuous markers and categorical disease status, prognosis has been less investigated, with the additional challenges brought by the unique characteristics of survival outcomes. Most of the existing G-E interaction approaches for prognosis data share the limitation that they cannot accommodate long-tailed or contaminated outcomes. In this study, for prognosis data, we develop a robust G-E interaction identification approach using the censored quantile partial correlation (CQPCorr) technique. The proposed approach is built on the quantile regression technique (and hence has a solid statistical basis), uses weights to easily accommodate censoring, and adopt

SUBMITTER: Xu Y 

PROVIDER: S-EPMC6335188 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature