除了考虑主要影响外，了解基因-环境（G × E）相互作用对于确定疾病的病因和影响其预后的因素至关重要。在现有的审查生存结果的统计框架中，检测 G × E 相互作用存在一些挑战，例如处理高维组学数据、多样化的环境因素以及生存分析中的算法复杂性。效应遗传原理广泛应用于涉及相互作用识别的研究中，因为它结合了主效应和相互作用效应的依赖性。然而，包含这一原理假设的贝叶斯生存模型尚未开发出来。因此，我们提出贝叶斯遗传约束加速失效时间（BHAFT）模型，用于识别具有新颖尖峰和平板或正则化马蹄先验的主效应和相互作用（M-I）效应，以纳入效应遗传原理的假设。 R 包 rstan 用于拟合所提出的模型。大量的仿真表明，BHAFT 模型在信号识别、系数估计和预后预测方面优于其他现有模型。使用我们提出的模型确定了与肺腺癌预后相关的生物学上合理的 G × E 相互作用。值得注意的是，结合效应遗传原理的 BHAFT 模型可以识别主效应和交互效应，这对于探索高维生存分析中的 G × E 相互作用非常有用。我们论文中使用的代码和数据可在 https://github.com/SunNa-bayesian/BHAFT 获取。© 2024 John Wiley

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.© 2024 John Wiley & Sons Ltd.