Accommodating missingness in environmental measurements in gene ‐environment interaction analysis

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.
Source: Genetic Epidemiology - Category: Epidemiology Authors: Tags: RESEARCH ARTICLE Source Type: research