英国BILEVE和门德尔随机化:使用多变量仪表变量来解决"如果你该死的,如果你不该死的"调整问题
Benjamin Woolf1,2,3, Dipender Gill4,5, Hannah Sallis6,7,8
1School of Psychological Science, University of Bristol, Bristol, UK. benjamin.woolf@bristol.ac.uk.
BMC research notes
|July 25, 2023
概括
多变量仪表变量分析可以解决孟德尔随机化 (MR) 研究中的偏差. 在英国生物库数据中调整基因型识别芯片显示,吸烟影响估计的偏差很小.
科学领域:
- 遗传学 是一个遗传学.
- 流行病学 流行病学
- 统计分析 统计分析
背景情况:
- 门德尔随机化 (MR) 研究可能会面临"如果你做了该死的,如果你不做了该死的"调整问题.
- 当调整一个变量引入偏差时,这些问题就会出现,例如英国生物库 (UKB) 内的基因型芯片的差异,基于吸烟或肺功能表型.
研究的目的:
- 调查多变量仪表变量 (MVIV) 方法在MR分析中解决调整问题的实用性.
- 评估调整UKB基因型识别芯片对MR对吸烟对各种健康结果影响的估计的影响.
主要方法:
- 进行了模拟,以评估MVIV分析的减轻对撞机偏差的能力.
- MVIV模型应用于UKB数据,以检查吸烟与七种医疗结果之间的关联,并对基因型化芯片进行调整.
- 结果与传统的单变MR分析进行了比较,这些分析对基因型识别芯片进行了调整,并没有进行调整.
主要成果:
- MVIV分析表明,在对UKB基因定型芯片等共变量进行调整时,有可能减轻碰撞器偏差.
- 在多变量MR模型中对基因型识别芯片的调整表明,在分析吸烟对健康结果的影响时,只引入了少量的偏差.
结论:
- 在MR研究中,MVIV方法提供了一种有价值的方法来解决复杂的调整问题.
- 在UKB中不同的基因型识别芯片对与吸烟相关的MR估计的影响似乎很小,这表明这些发现是可靠的.
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