Gene-gene and gene-environment interactions are widely believed to play significant roles in explaining the variability of complex traits. While substantial research exists in this area, a comprehensive statistical framework that addresses multiple sources of uncertainty simultaneously remains lacking. In this article, we synthesize and propose extension of a novel class of Bayesian nonparametric approaches that account for interactions among genes, loci, and environmental factors while accommodating uncertainty about population substructure. Our contribution is threefold: (1) We provide a unified exposition of hierarchical Bayesian models driven by Dirichlet processes for genetic interactions, clarifying their conceptual advantages over traditional regression approaches; (2) We shed light on new computational strategies that combine transformation-based MCMC with parallel processing for scalable inference; and (3) We present enhanced hypothesis testing procedures for identifying disease-predisposing loci.Through applications to myocardial infarction data, we demonstrate how these methods offer biological insights not readily obtainable from standard approaches. Our synthesis highlights the advantages of Bayesian nonparametric thinking in genetic epidemiology while providing practical guidance for implementation.

翻译：基因-基因与基因-环境相互作用被广泛认为在解释复杂性状的变异性中发挥着重要作用。尽管该领域已有大量研究，但能够同时处理多重不确定性来源的综合性统计框架仍然缺乏。本文综合并扩展了一类新颖的贝叶斯非参数方法，这些方法在考虑基因、位点与环境因素间相互作用的同时，还能处理群体亚结构的不确定性。我们的贡献主要体现在三个方面：（1）系统阐述了基于狄利克雷过程的层次贝叶斯模型在遗传相互作用分析中的应用，阐明了其相较于传统回归方法的概念优势；（2）提出了结合变换MCMC与并行处理的可扩展计算新策略；（3）改进了识别疾病易感位点的假设检验流程。通过对心肌梗死数据的应用分析，我们证明了这些方法能够提供标准方法难以获得的生物学洞见。本综述不仅凸显了贝叶斯非参数思想在遗传流行病学中的优势，同时为实际应用提供了具体指导。