Recent advances in genotyping technology have delivered a wealth of genetic data, which is rapidly advancing our understanding of the underlying genetic architecture of complex diseases. Mendelian Randomization (MR) leverages such genetic data to estimate the causal effect of an exposure factor on an outcome from observational studies. In this paper, we utilize genetic correlations to summarize information on a large set of genetic variants associated with the exposure factor. Our proposed approach is a generalization of the MR-inverse variance weighting (IVW) approach where we can accommodate many weak and pleiotropic effects. Our approach quantifies the variation explained by all valid instrumental variables (IVs) instead of estimating the individual effects and thus could accommodate weak IVs. This is particularly useful for performing MR estimation in small studies, or minority populations where the selection of valid IVs is unreliable and thus has a large influence on the MR estimation. Through simulation and real data analysis, we demonstrate that our approach provides a robust alternative to the existing MR methods. We illustrate the robustness of our proposed approach under the violation of MR assumptions and compare the performance with several existing approaches.

翻译：基因分型技术的最新进展提供了丰富的遗传数据，这极大地加深了我们对复杂疾病潜在遗传结构的理解。门德尔随机化（MR）利用此类遗传数据，通过观察性研究估计暴露因素对结局的因果效应。本文利用遗传相关性来概括与暴露因素相关的大规模遗传变异集的信息。我们提出的方法是MR逆方差加权（IVW）方法的推广，能够容纳许多弱工具变量和多效性效应。该方法量化所有有效工具变量（IV）解释的变异，而非估计个体效应，因此能处理弱IV问题。这对小型研究或少数群体中的MR估计尤为有用，因为在这些场景下选择有效IV的可靠性较低，从而对MR估计产生重大影响。通过模拟和真实数据分析，我们证明了该方法为现有MR方法提供了稳健的替代方案。我们展示了在违反MR假设下所提方法的稳健性，并与几种现有方法进行了性能比较。