BHCox:贝叶斯遗传受约束的Cox比例危险模型用于检测基因与环境相互作用
Na Sun1,2, Qiang Han1, Yu Wang1
1Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China.
BMC bioinformatics
|February 18, 2025
概括
我们开发了贝叶斯遗传受约束的考克斯比例危险 (BHCox) 模型,以检测生存数据中的基因环境相互作用. 我们的BHCox模型成功地确定了非小细胞肺癌预后中的基因吸烟相互作用.
科学领域:
- 遗传学和流行病学
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 基因与环境 (G×E) 相互作用对疾病病因和预后至关重要.
- 在被审查的生存数据中检测G×E相互作用会带来诸如高维度和复杂的环境影响等挑战.
- 效果遗传原理尚未应用于贝叶斯式考克斯模型,用于审查的生存结果相互作用.
研究的目的:
- 提出新的贝叶斯遗传受约束的考克斯比例危险 (BHCox) 模型.
- 将效应遗传纳入贝叶斯考克斯模型,用于检测在被审查的生存数据中的G×E相互作用.
- 用先进的贝叶斯先验来识别和估计主要和相互作用效应.
主要方法:
- 开发了BHCox模型,使用了尖和板和规则化的马先.
- 采用无转采样器 (NUTS) 算法,通过R套件车进行模型装配.
- 进行了广泛的模拟,以评估与替代方法相比BHCox模型的性能.
主要成果:
- 在模拟研究中,与替代模型相比,BHCox模型显示出更高的性能.
- 提出的方法成功地在非小细胞肺癌 (NSCLC) 患者数据中确定了生物学上可信的基因吸烟相互作用.
- 确定了影响NSCLC患者预后的显著G×E相互作用.
结论:
- BHCox模型有效地检测了高维度审查的生存数据中的主要效应和相互作用.
- 开发的方法对发现复杂的G×E相互作用具有重大意义.
- 为分析生存分析中的G×E相互作用提供了一个强大的框架.
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