Mendelian randomization (MR) is a method of exploiting genetic variation to unbiasedly estimate a causal effect in presence of unmeasured confounding. MR is being widely used in epidemiology and other related areas of population science. In this paper, we study statistical inference in the increasingly popular two-sample summary-data MR. We show a linear model for the observed associations approximately holds in a wide variety of settings when all the genetic variants satisfy the exclusion restriction assumption, or in genetic terms, when there is no pleiotropy. In this scenario, we derive a maximum profile likelihood estimator with provable consistency and asymptotic normality. However, through analyzing real datasets, we find strong evidence of both systematic and idiosyncratic pleiotropy in MR, echoing some recent discoveries in statistical genetics. We model the systematic pleiotropy by a random effects model, where no genetic variant satisfies the exclusion restriction condition exactly. In this case propose a consistent and asymptotically normal estimator by adjusting the profile score. We then tackle the idiosyncratic pleiotropy by robustifying the adjusted profile score. We demonstrate the robustness and efficiency of the proposed methods using several simulated and real datasets.

中文翻译：

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使用稳健的调整配置文件分数在两样本摘要数据孟德尔随机化中的统计推断

孟德尔随机化 (MR) 是一种利用遗传变异在存在未测量混杂的情况下无偏见地估计因果效应的方法。MR 被广泛应用于流行病学和人口科学的其他相关领域。在本文中，我们研究了日益流行的双样本汇总数据 MR 中的统计推断。当所有遗传变异满足排除限制假设时，或在遗传方面，当没有多效性时，我们展示了观察到的关联的线性模型在各种设置中近似成立。在这种情况下，我们推导出具有可证明的一致性和渐近正态性的最大轮廓似然估计量。然而，通过分析真实数据集，我们发现了 MR 中系统性和特质性多效性的有力证据，与统计遗传学的一些最新发现相呼应。我们通过随机效应模型对系统多效性进行建模，其中没有遗传变异完全满足排除限制条件。在这种情况下，通过调整轮廓分数提出一致且渐近正态的估计量。然后，我们通过增强调整后的配置文件分数来解决特殊的多效性。我们使用几个模拟和真实数据集证明了所提出方法的稳健性和效率。