Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Int J Epidemiol. 2020 Aug 1;49(4):1147-1158. doi: 10.1093/ije/dyz161.
Factorial Mendelian randomization is the use of genetic variants to answer questions about interactions. Although the approach has been used in applied investigations, little methodological advice is available on how to design or perform a factorial Mendelian randomization analysis. Previous analyses have employed a 2 × 2 approach, using dichotomized genetic scores to divide the population into four subgroups as in a factorial randomized trial.
We describe two distinct contexts for factorial Mendelian randomization: investigating interactions between risk factors, and investigating interactions between pharmacological interventions on risk factors. We propose two-stage least squares methods using all available genetic variants and their interactions as instrumental variables, and using continuous genetic scores as instrumental variables rather than dichotomized scores. We illustrate our methods using data from UK Biobank to investigate the interaction between body mass index and alcohol consumption on systolic blood pressure.
Simulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between 4- and 10-fold improvement in efficiency is demonstrated over the 2 × 2 approach. Analyses using continuous genetic scores are more efficient than those using dichotomized scores. Efficiency is improved by finding genetic variants that divide the population at a natural break in the distribution of the risk factor, or else divide the population into more equal-sized groups.
Previous factorial Mendelian randomization analyses may have been underpowered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 × 2 approach.
析因 Mendelian 随机化是利用遗传变异来回答关于相互作用的问题。尽管该方法已在应用研究中使用，但关于如何设计或进行析因 Mendelian 随机化分析，几乎没有提供方法学建议。以前的分析采用了 2×2 方法，使用二分遗传评分将人群分为四个亚组，类似于析因随机试验。
我们描述了析因 Mendelian 随机化的两种不同情况：研究危险因素之间的相互作用，以及研究危险因素的药物干预之间的相互作用。我们提出了两阶段最小二乘法方法，使用所有可用的遗传变异及其相互作用作为工具变量，并使用连续遗传评分作为工具变量，而不是二分评分。我们使用 UK Biobank 中的数据来阐明我们的方法，以研究体质指数和饮酒对收缩压的相互作用。
模拟和真实数据表明，使用遗传变异之间的完整相互作用集作为工具可以最大限度地提高效率。在应用示例中，与 2×2 方法相比，效率提高了 4 到 10 倍。使用连续遗传评分的分析比使用二分评分的分析更有效。通过找到将人群分为风险因素分布的自然断点的遗传变异，或者将人群分为更均等大小的组，可以提高效率。
以前的析因 Mendelian 随机化分析可能效率低下。通过使用所有遗传变异及其相互作用作为工具变量，而不是 2×2 方法，可以提高效率。
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