Burgess Stephen, Thompson Deborah J, Rees Jessica M B, Day Felix R, Perry John R, Ong Ken K
MRC Biostatistics Unit, University of Cambridge, CB2 0SR Cambridgeshire, United Kingdom
Cardiovascular Epidemiology Unit, University of Cambridge, CB1 8RN Cambridgeshire, United Kingdom.
Genetics. 2017 Oct;207(2):481-487. doi: 10.1534/genetics.117.300191. Epub 2017 Aug 23.
Mendelian randomization is the use of genetic variants as instrumental variables to estimate causal effects of risk factors on outcomes. The total causal effect of a risk factor is the change in the outcome resulting from intervening on the risk factor. This total causal effect may potentially encompass multiple mediating mechanisms. For a proposed mediator, the direct effect of the risk factor is the change in the outcome resulting from a change in the risk factor, keeping the mediator constant. A difference between the total effect and the direct effect indicates that the causal pathway from the risk factor to the outcome acts at least in part via the mediator (an indirect effect). Here, we show that Mendelian randomization estimates of total and direct effects can be obtained using summarized data on genetic associations with the risk factor, mediator, and outcome, potentially from different data sources. We perform simulations to test the validity of this approach when there is unmeasured confounding and/or bidirectional effects between the risk factor and mediator. We illustrate this method using the relationship between age at menarche and risk of breast cancer, with body mass index (BMI) as a potential mediator. We show an inverse direct causal effect of age at menarche on risk of breast cancer (independent of BMI), and a positive indirect effect via BMI. In conclusion, multivariable Mendelian randomization using summarized genetic data provides a rapid and accessible analytic strategy that can be undertaken using publicly available data to better understand causal mechanisms.
孟德尔随机化是利用基因变异作为工具变量来估计风险因素对结局的因果效应。风险因素的总因果效应是指对风险因素进行干预所导致的结局变化。这种总因果效应可能潜在地包含多种中介机制。对于一个假定的中介因素，风险因素的直接效应是指在中介因素保持不变的情况下，风险因素的变化所导致的结局变化。总效应与直接效应之间的差异表明，从风险因素到结局的因果途径至少部分是通过中介因素起作用的（间接效应）。在此，我们表明，可以使用关于风险因素、中介因素和结局的基因关联汇总数据（可能来自不同数据源）来获得总效应和直接效应的孟德尔随机化估计值。我们进行模拟，以检验当风险因素与中介因素之间存在未测量的混杂因素和/或双向效应时这种方法的有效性。我们以初潮年龄与乳腺癌风险之间的关系为例进行说明，将体重指数（BMI）作为一个潜在的中介因素。我们发现初潮年龄对乳腺癌风险存在反向直接因果效应（独立于BMI），并且通过BMI存在正向间接效应。总之，使用汇总基因数据的多变量孟德尔随机化提供了一种快速且可及的分析策略，该策略可利用公开可用数据来更好地理解因果机制。
