将生物学和临床见解纳入孟德尔随机化的变体选择:实例和原则

S. Burgess, H. T. Cronjé
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引用次数: 0

摘要

孟德尔随机法是一种简便易行且极具价值的流行病学方法,可帮助我们深入了解风险因素暴露与疾病结果之间的因果关系。然而,如果不加批判性思考,我们可能只是用另一组难以置信的假设(无褶效或其他工具无效)取代了一组难以置信的假设(无未测量混杂或反向因果关系)。避免多效性的最关键决定因素是使用哪些遗传变异作为工具变量。工具选择的两大策略分别是生物动机策略和全基因组策略;一般来说,生物动机策略更受青睐。在这篇综述中,我们将讨论实施生物动机选择策略的各种方法:使用暴露的编码基因区变异或编码暴露水平调节因子的基因区变异、使用阳性对照变量和使用生物标志物作为暴露而不是其行为替代物。在某些情况下,全基因组分析可以提供重要的补充证据,即使其可靠性值得怀疑。在另一些情况下,可能无法进行生物学分析。基因变异的选择必须尽可能考虑生物学和功能学因素,这就需要合作,将生物学和临床见解与适当的统计方法结合起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incorporating biological and clinical insights into variant choice for Mendelian randomisation: examples and principles
Mendelian randomisation is an accessible and valuable epidemiological approach to provide insight into the causal nature of relationships between risk factor exposures and disease outcomes. However, if performed without critical thought, we may simply have replaced one set of implausible assumptions (no unmeasured confounding or reverse causation) with another set of implausible assumptions (no pleiotropy or other instrument invalidity). The most critical decision to avoid pleiotropy is which genetic variants to use as instrumental variables. Two broad strategies for instrument selection are a biologically motivated strategy and a genome-wide strategy; in general, a biologically motivated strategy is preferred. In this review, we discuss various ways of implementing a biologically motivated selection strategy: using variants in a coding gene region for the exposure or a gene region that encodes a regulator of exposure levels, using a positive control variable and using a biomarker as the exposure rather than its behavioural proxy. In some cases, a genome-wide analysis can provide important complementary evidence, even when its reliability is questionable. In other cases, a biologically-motivated analysis may not be possible. The choice of genetic variants must be informed by biological and functional considerations where possible, requiring collaboration to combine biological and clinical insights with appropriate statistical methodology.
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