Mendelian randomization is the use of genetic variants to assess the effect of intervening on a risk factor using observational data. We consider the scenario in which there is a pharmacomimetic (i.e., treatment‐mimicking) genetic variant that can be used as a proxy for a particular pharmacological treatment that changes the level of the risk factor. If the association of the pharmacomimetic genetic variant with the risk factor is stronger in one subgroup of the population, then we may expect the effect of the treatment to be stronger in that subgroup. We test for gene–gene interactions in the associations of variants with a modifiable risk factor, where one genetic variant is treated as pharmacomimetic and the other as an effect modifier, to find genetic subgroups of the population with different predicted response to treatment. If individual genetic variants that are strong effect modifiers cannot be found, moderating variants can be combined using a random forest of interaction trees method into a polygenic response score, analogous to a polygenic risk score for risk prediction. We illustrate the application of the method to investigate effect heterogeneity in the effect of statins on low‐density lipoprotein cholesterol.

中文翻译：

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治疗效果异质性的多基因建模。

孟德尔随机化是使用遗传变异来评估使用观察数据干预风险因素的效果。我们考虑存在一种药物模拟（即治疗模拟）遗传变异的情况，该变异可用作改变风险因素水平的特定药物治疗的代理。如果药物模拟遗传变异与风险因素的关联在人群的一个亚组中更强，那么我们可以预期治疗的效果在该亚组中更强。我们测试了变异与可修改风险因素关联中的基因 - 基因相互作用，其中一种遗传变异被视为药物模拟物，另一种被视为效应修饰剂，以找到对治疗具有不同预测反应的人群遗传亚组。如果无法找到作为强效应修饰剂的个体遗传变异，则可以使用相互作用树的随机森林方法将调节变异组合成多基因响应评分，类似于用于风险预测的多基因风险评分。我们说明了该方法在研究他汀类药物对低密度脂蛋白胆固醇影响中的效应异质性的应用。