Methodological challenges in mendelian randomization.
Methodological challenges in mendelian randomization.
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DOI：
10.1097/ede.0000000000000081
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
Kraft P
中科院分区：
文献类型：
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作者：
VanderWeele TJ;Tchetgen Tchetgen EJ;Cornelis M;Kraft P
我们对孟德尔随机化分析的基本假设及其生物相容性给予了高度关注。违反孟德尔随机化假设的几种情况进行了描述，包括设置表型定义不足，设置随时间变化的曝光，存在的基因-环境相互作用，存在的测量误差，反向因果关系的可能性，和存在的连锁不平衡。数据分析的例子说明，不适当地使用工具变量技术时，孟德尔随机化假设被违反可能会导致巨大的偏差。为了帮助解决一些强有力的假设，提出了三种可能的方法。首先，卡坦（《柳叶刀》）的最初提议。一九八六年; 1：507-508）并不使用工具变量技术来获得估计值，而仅仅是检查基因型-结果关联以测试暴露对结果的影响的存在。我们表明，这种更温和的目标和方法可以规避许多，但不是所有的，所描述的潜在偏见。其次，我们讨论了敏感性分析在评估违反假设的后果，并试图纠正这些违规行为。第三，我们认为，关注消极的，而不是积极的，孟德尔随机化结果可能会更可靠。
We give critical attention to the assumptions underlying Mendelian randomization analysis and their biological plausibility. Several scenarios violating the Mendelian randomization assumptions are described, including settings with inadequate phenotype definition, the setting of time-varying exposures, the presence of gene-environment interaction, the existence of measurement error, the possibility of reverse causation, and the presence of linkage disequilibrium. Data analysis examples are given illustrating that the inappropriate use of instrumental variable techniques when the Mendelian randomization assumptions are violated can lead to biases of enormous magnitude. To help address some of the strong assumptions being made, three possible approaches are suggested. First, the original proposal of Katan (Lancet. 1986; 1:507-508) for Mendelian randomization was not to use instrumental variable techniques to obtain estimates, but merely to examine genotype-outcome associations to test for the presence of an effect of the exposure on the outcome. We show that this more modest goal and approach can circumvent many, though not all of, the potential biases described. Second, we discuss the use of sensitivity analysis in evaluating the consequences of violations in the assumptions and attempts to correct for those violations. Third, we suggest that a focus on negative, rather than positive, Mendelian randomization results may turn out to be more reliable.