一个贝叶斯式的方法来进行门德尔式随机化,使用简要的统计数据,在与相关的类型相关的单变量和多变量设置中
Andrew J Grant1, Stephen Burgess2
1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Sydney School of Public Health, University of Sydney, Sydney, NSW, Australia.
American journal of human genetics
|January 5, 2024
概括
这项研究引入了孟德尔随机化 (MR) 的新贝叶斯框架,该框架使用遗传变异推断因果关系. 在MR-Horse和MVMR-Horse方法提供有效的因果推断,即使与相关的类和弱的仪器,只使用总结统计数据.
科学领域:
- 遗传流行病学遗传流行病学
- 统计遗传学 统计遗传学
- 因果推理因果推理
背景情况:
- 门德尔随机化 (MR) 使用遗传变异作为因果推理的工具变量.
- 全基因组关联研究 (GWAS) 提供了多基因MR的众多遗传变异,增加了统计能力,但也容易受到无效仪器的偏差影响.
- 相关的性质,违反了"仪器强度独立于直接效应"的假设,是MR的一个关键挑战.
研究的目的:
- 为孟德尔随机化提出一个灵活的贝叶斯框架,使得在一般情况下能够进行有效的因果推理.
- 引入MR-Horse和MVMR-Horse方法,可以处理相关的和非相关的质变异.
- 开发可使用全基因组关联研究 (GWAS) 总结统计数据的方法,而不需要个人级别的数据.
主要方法:
- 开发了一种用于孟德尔随机化 (MR) 的新贝叶斯框架.
- 建议使用MR-Horse和MVMR-Horse方法,使用GWAS的总结统计数据.
- 该框架适应了相关的和非相关的平流体.
主要成果:
- 模拟研究表明,拟议的方法保持了I型错误率低于名义水平,即使在具有高类型的场景中.
- 在单变量和多变量设置中应用的例子,包括那些具有非常弱的仪器的例子,展示了这些方法的实际实用性.
- 这些方法提供了有效的因果推断,而无需访问个人级数据.
结论:
- 建议的贝叶斯框架和相关的MR-Horse/MVMR-Horse方法为孟德尔随机化提供了一个强大的方法.
- 这些方法有效地解决了因关联质变异而产生的偏差,并且可以使用易于获得的GWAS总结统计数据来应用.
- 该框架在遗传流行病学中促进了更可靠的因果推断,即使在具有挑战性的条件下,如软弱的仪器和高性.
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