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Accommodating missingness in environmental measurements in gene-environment interaction analysis
Journal article   Peer reviewed

Accommodating missingness in environmental measurements in gene-environment interaction analysis

Mengyun Wu, Yangguang Zang, Sanguo Zhang, Jian Huang and Shuangge Ma
Genetic epidemiology, Vol.41(6), pp.523-554
09/2017
DOI: 10.1002/gepi.22055
PMCID: PMC5561007
PMID: 28657194

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Abstract

For the prognosis of complex diseases, beyond the main effects of genetic (G) and environmental (E) factors, gene-environment (G-E) interactions also play an important role. Many approaches have been developed for detecting important G-E interactions, most of which assume that measurements are complete. In practical data analysis, missingness in E measurements is not uncommon, and failing to properly accommodate such missingness leads to biased estimation and false marker identification. In this study, we conduct G-E interaction analysis with prognosis data under an accelerated failure time (AFT) model. To accommodate missingness in E measurements, we adopt a nonparametric kernel-based data augmentation approach. With a well-designed weighting scheme, a nice "byproduct" is that the proposed approach enjoys a certain robustness property. A penalization approach, which respects the "main effects, interactions" hierarchy, is adopted for selection (of important interactions and main effects) and regularized estimation. The proposed approach has sound interpretations and a solid statistical basis. It outperforms multiple alternatives in simulation. The analysis of TCGA data on lung cancer and melanoma leads to interesting findings and models with superior prediction.
 Adenocarcinoma - genetics Adenocarcinoma of Lung Computer Simulation Databases, Genetic Gene-Environment Interaction Humans Lung Neoplasms - genetics Melanoma - genetics Models, Genetic Skin Neoplasms - genetics

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