解释孟德尔随机化研究中的挑战,将疾病作为暴露:使用COVID-19责任研究作为示例
Siyu Chen1, Ying Liang1, Jacky Man Yuen Mo1
1School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China.
European journal of human genetics : EJHG
|March 31, 2025
概括
调查COVID-19后果的门德尔随机化 (MR) 研究面临挑战. 许多研究使用了大流行前的数据,由于潜在的类型和不正确的相关性假设,使因果解释变得困难.
科学领域:
- 流行病学 流行病学
- 遗传流行病学遗传流行病学
- 生物统计学 生物统计学
背景情况:
- 门德尔随机化 (MR) 研究越来越多地用于调查疾病影响.
- 然而,使用疾病作为暴露,如COVID-19,在建立因果关系方面提出了挑战.
- 观察到的关联并不意味着疾病的直接影响.
研究的目的:
- 系统地审查检查COVID-19后果的MR研究.
- 评估MR研究中的相关性假设的有效性,使用疾病责任作为暴露.
- 为了突出解释MR发现的挑战,当结果数据在暴露之前.
主要方法:
- 针对MR研究的PubMed,EMBASE和MEDLINE的系统文献搜索 (2019年1月 - 2023年5月).
- 纳入标准:使用COVID-19作为暴露评估健康结果的MR研究.
- 数据提取的重点是结果解释和相关性假设评估.
主要成果:
- 包括57项MR研究;45项使用了2019年之前发布的GWAS结果数据.
- 35项研究报告了COVID-19责任和健康结果之间的关联.
- 大多数研究将研究结果解释为COVID-19后果的证据,尽管有流行前的数据.
结论:
- 疾病暴露MR研究中的相关性假设需要仔细考虑GWAS结果数据中的疾病患病率.
- 在相关性评估中过度依赖p值或F统计是不够的.
- 类型可能会混MR发现,使疾病影响的解释变得复杂.
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