在案例控制研究中使用多基因风险评分对基因环境相关性和相互作用的联合建模
Ziqiao Wang1, Wen Shi2, Raymond J Carroll3
1Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, United States.
American journal of epidemiology
|May 28, 2024
概括
本研究引入了一种有效的方法,用于分析多基因风险评分 (PRS) 与环境因素一起在案例控制研究中. 这种新方法准确地模拟了基因环境相互作用和相关性,改善了疾病风险预测.
科学领域:
- 遗传学和生物信息学 遗传学和生物信息学
- 流行病学 流行病学
- 统计建模 统计建模
背景情况:
- 多基因风险评分 (PRS) 汇总多个遗传变异以评估疾病易感性.
- 了解基因环境 (GxE) 相互作用对于PRS在各种环境中的应用至关重要.
- 现有的方法往往难以同时建模GxE相关性和相互作用.
研究的目的:
- 开发一种高效的统计方法,在案例控制研究中使用PRS同时建模GxE相关性和相互作用.
- 为估计疾病风险和PRS分布提供灵活的框架.
- 为了能够从病例控制数据中共同推断.
主要方法:
- 利用物流-正常回归建模框架用于疾病风险和PRS分布.
- 建议使用病例控制数据的回顾性概率进行联合推断.
- 开发了一种计算效率高的方法,在R包中实现.
主要成果:
- 模拟研究证实了该方法在平衡参数估计偏差差异权衡方面的灵活性.
- 这种方法在GxE相关性和相互作用方面表现优于标准后勤回归和仅案例/仅对照分析.
- 通过使用英国生物银行数据,成功检测GxE相互作用 (口服避孕药使用和PRS) 对乳腺癌风险的前性队列结果.
结论:
- 开发的方法提供了一个高效和灵活的方法来分析PRS和环境因素在案例控制研究.
- 它准确地建模了GxE相关性和相互作用,增强了疾病风险预测.
- "R包"有助于在遗传流行病学研究中的实际应用.
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