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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 adopts partial correlation to identify important interactions while properly controlling for the main genetic and environmental effects. In simulation, it outperforms multiple competitors with more accurate identification. In the analysis of TCGA data on lung cancer and melanoma, biologically sensible findings different from using the alternatives are made. • For prognosis data, this article delivers a robust gene-environment interaction identification approach. • A censored quantile partial correlation approach is proposed. • The proposed approach has satisfactory statistical and numerical properties. • In the analysis of TCGA data on lung cancer and melanoma, biologically sensible findings are made. [ABSTRACT FROM AUTHOR]

In biomedical and epidemiological studies, gene-environment (G-E) interactions have been shown to importantly contribute to the etiology and progression of many complex diseases. Most existing approaches for identifying G-E interactions are limited by the lack of robustness against outliers/contaminations in response and predictor spaces. In this study, we develop a novel robust G-E identification approach using the trimmed regression technique under joint modelling. A robust data-driven criterion and stability selection are adopted to determine the trimmed subset which is free from both vertical outliers and leverage points. An effective penalization approach is developed to identify important G-E interactions, respecting the ‘main effects, interactions’ hierarchical structure. Extensive simulations demonstrate the better performance of the proposed approach compared to multiple alternatives. Interesting findings with superior prediction accuracy and stability are observed in the analysis of The Cancer Genome Atlas data on cutaneous melanoma and breast invasive carcinoma. [ABSTRACT FROM AUTHOR]

For the survival of cancer and many other complex diseases, gene–environment (G-E) interactions have been established as having essential importance. G-E interaction analysis can be roughly classified as marginal and joint, depending on the number of G variables analyzed at a time. In this study, we focus on joint analysis, which can better reflect disease biology and is statistically more challenging. Many approaches have been developed for joint G-E interaction analysis for survival outcomes and led to important findings. However, without rigorous statistical development, quite a few methods have a weak theoretical ground. To fill this knowledge gap, in this article, we consider joint G-E interaction analysis under the Cox model. Sparse group penalization is adopted for regularizing estimation and selecting important main effects and interactions. The "main effects, interactions" variable selection hierarchy, which has been strongly advocated in recent literature, is satisfied. Significantly advancing from some published studies, we rigorously establish the consistency properties under high dimensionality. An effective computational algorithm is developed, simulation demonstrates competitive performance of the proposed approach, and analysis of The Cancer Genome Atlas (TCGA) data on stomach adenocarcinoma (STAD) further demonstrates its practical utility. [ABSTRACT FROM AUTHOR]