Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic.
Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic.
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DOI：
10.1093/ije/dyw220
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发表时间：
2016-12-01
影响因子：
7.7
通讯作者：
Thompson JR
中科院分区：
文献类型：
--
作者：
Bowden J;Del Greco M F;Minelli C;Davey Smith G;Sheehan NA;Thompson JR
背景资料：MR-Egger回归最近被提出作为孟德尔随机化（MR）分析的方法，该方法结合了多个个体变量因果效应的汇总数据估计，对无效工具具有鲁棒性。它可用于测试方向多效性，并提供了一个估计的因果关系的影响调整其存在。MR-Egger回归为标准逆方差加权（IVW）方法提供了有用的额外敏感性分析，该方法假设所有变量都是有效工具。这两种方法都使用权重，认为单核苷酸多态性（SNP）-暴露关联是已知的，而不是估计的。我们称之为“无测量误差”（NOME）假设。当使用的遗传变异违反NOME假设时，IVW方法的因果效应估计值表现出较弱的仪器偏倚，这可以使用F统计量可靠地测量。NOME破坏对MR-Egger回归的影响还有待研究。 方法：提出了一种来自荟萃分析领域的统计量的适应性调整，以量化MR-Egger的NOME违反强度。它介于0和1之间，表示在双样本MR背景下MR-Egger因果估计的预期相对偏倚（或稀释）。我们称之为。同时还探索了模拟外推法来抵消稀释效应。他们的联合效用进行评估，使用模拟数据和应用到一个真实的MR的例子。 结果如下：在模拟的双样本MR分析中，我们发现，当因果效应存在时，因果效应的MR-Egger估计值在NOME被违反时偏向于零，并且违反越强（如较低的值所示），稀释越强。此外，当所有的遗传变异都是有效的工具时，用于多效性的MR-Egger检验的I型错误率被夸大，因果效应被低估。模拟外推显示，大大减轻这些不利影响。我们证明了我们提出的方法，用于双样本汇总数据MR分析，以估计低密度脂蛋白对心脏病风险的因果影响。接近1的高值表明稀释不会对这些数据的标准MR-Egger分析产生实质性影响。 结论：在双样本汇总数据背景下实施标准MR-Egger回归之前，必须小心通过统计数据评估NOME假设。如果足够低（小于90%），则应谨慎解释该方法的推论，并考虑调整方法。
Background: MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error' (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. Methods: An adaptation of the statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. Results: In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two-sample summary data MR analysis to estimate the causal effect of low-density lipoprotein on heart disease risk. A high value of close to 1 indicates that dilution does not materially affect the standard MR-Egger analyses for these data. Conclusions: Care must be taken to assess the NOME assumption via the statistic before implementing standard MR-Egger regression in the two-sample summary data context. If is sufficiently low (less than 90%), inferences from the method should be interpreted with caution and adjustment methods considered.