Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.
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DOI:
10.1093/ije/dyv080
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发表时间:
2015-04
影响因子:
7.7
通讯作者:
Burgess S
Burgess S
中科院分区:
医学1区
文献类型:
--
作者:
Bowden J;Davey Smith G;Burgess S

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背景:包括大量遗传变异的孟德尔随机化分析的数量正在迅速增加。这是由于全基因组关联研究的激增,以及获得更精确的因果效应估计的愿望。然而,一些遗传变异可能不是有效的工具变量,特别是由于它们具有多于一个的近端表型相关(多效性)。研究方法:我们将多工具孟德尔随机化视为荟萃分析,并表明多效性引起的偏倚可以被视为类似于小的研究偏倚。使用每种仪器的因果估计可以通过漏斗图直观地显示,以评估潜在的不对称性。Egger回归是一种在荟萃分析中检测小的研究偏倚的工具,可以适用于多效性偏倚的检验,Egger回归的斜率系数提供了因果效应的估计值。在假设每个遗传变异与暴露的关联独立于变异的多效性效应(而不是通过暴露)的情况下,Egger检验给出了零因果假设的有效检验和一致的因果效应估计,即使所有遗传变异都是无效的工具变量。结果如下:我们通过重新分析两项已发表的孟德尔随机化研究来说明这种方法的使用,这些研究是关于身高对肺功能的因果影响,以及血压对冠状动脉疾病风险的因果影响。这些例子说明了这种方法的保守性。结论:Egger回归(我们称之为MR-Egger)的自适应可以检测到标准工具变量假设的一些违规行为,并提供不受这些违规行为影响的效应估计。该方法提供了一个敏感性分析的结果从孟德尔随机化调查的稳健性。
Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
DOI: 10.1038/nature10405
发表时间: 2011-09-11
期刊: NATURE
影响因子: 64.8
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