论文标题
在高维加速失效时间模型下，强大的鉴定基因环境相互作用
Robust Identification of Gene-Environment Interactions under High-Dimensional Accelerated Failure Time Models
论文作者
论文摘要
对于复杂疾病，除了遗传（G）和环境（E）因素的主要影响之外，基因环境（G-E）相互作用也起着重要作用。许多现有的G-E相互作用方法进行边际分析，这可能无法适当地描述疾病生物学。已经开发了联合分析方法，其中大多数现有的损失函数根据可能性构建。实际上，数据污染并不少见。开发可容纳数据污染的互动分析方法非常有限。在这项研究中，我们考虑了审查的生存数据并采用加速失败时间（AFT）模型。采用指数平方损失来实现鲁棒性。一种稀疏的群体惩罚方法，该方法尊重“主要效果，相互作用”层次结构，用于估计和识别。一致性属性是严格建立的。模拟表明，所提出的方法的表现优于直接竞争对手。在数据分析中，提出的方法使生物学上明智的发现。
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.
