Robust Identification of Gene-Environment Interactions under High-Dimensional Accelerated Failure Time Models
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For complex diseases, beyond the main effects of genetic (G) and environmental (E) factors, gene-environment (G-E) interactions also play an important role. Many of the existing G-E interaction methods conduct marginal analysis, which may not appropriately describe disease biology. Joint analysis methods have been developed, with most of the existing loss functions constructed based on likelihood. In practice, data contamination is not uncommon. Development of robust methods for interaction analysis that can accommodate data contamination is very limited. In this study, we consider censored survival data and adopt an accelerated failure time (AFT) model. An exponential squared loss is adopted to achieve robustness. A sparse group penalization approach, which respects the "main effects, interactions" hierarchy, is adopted for estimation and identification. Consistency properties are rigorously established. Simulation shows that the proposed method outperforms direct competitors. In data analysis, the proposed method makes biologically sensible findings.
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