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

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 surv...

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Bibliographic Details
Published in:Genomics (San Diego, Calif.) Calif.), 2019-09, Vol.111 (5), p.1115-1123
Main Authors: Xu, Yaqing, Wu, Mengyun, Zhang, Qingzhao, Ma, Shuangge
Format: Article
Language:English
Subjects:
Censored quantile partial correlation
Computer Simulation
Gene-Environment Interaction
Humans
Lung Neoplasms - genetics
Lung Neoplasms - pathology
Melanoma - genetics
Melanoma - pathology
Models, Genetic
Prognosis
Robust G-E interaction
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Summary: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.
ISSN:0888-7543
1089-8646
DOI:10.1016/j.ygeno.2018.07.006