Amente Lamessa Dube, Mills Natalie T, Le Thuc Duy, Hyppönen Elina, Lee S Hong
Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
Hum Genet. 2025 May;144(5):559-574. doi: 10.1007/s00439-025-02739-9. Epub 2025 Apr 11.
Mendelian randomization (MR) is a widely used tool to uncover causal relationships between exposures and outcomes. However, existing MR methods can suffer from inflated type I error rates and biased causal effects in the presence of invalid instruments. Our proposed method enhances MR analysis by augmenting latent phenotypes of the outcome, explicitly disentangling horizontal and vertical pleiotropy effects. This allows for explicit assessment of the exclusion restriction assumption and iteratively refines causal estimates through the expectation-maximization algorithm. This approach offers a unique and potentially more precise framework compared to existing MR methods. We rigorously evaluate our method against established MR approaches across diverse simulation scenarios, including balanced and directional pleiotropy, as well as violations of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. Our findings consistently demonstrate superior performance of our method in terms of controlling type I error rates, bias, and robustness to genetic confounding, regardless of whether individual-level or summary data is used. Additionally, our method facilitates testing for directional horizontal pleiotropy and outperforms MR-Egger in this regard, while also effectively testing for violations of the InSIDE assumption. We apply our method to real data, demonstrating its effectiveness compared to traditional MR methods. This analysis reveals the causal effects of body mass index (BMI) on metabolic syndrome (MetS) and a composite MetS score calculated by the weighted sum of its component factors. While the causal relationship is consistent across most methods, our proposed method shows fewer violations of the exclusion restriction assumption, especially for MetS scores where horizontal pleiotropy persists and other methods suffer from inflation.
孟德尔随机化（MR）是一种广泛用于揭示暴露因素与结局之间因果关系的工具。然而，在存在无效工具变量的情况下，现有的MR方法可能会出现I型错误率膨胀和因果效应偏差的问题。我们提出的方法通过增强结局的潜在表型来改进MR分析，明确区分水平多效性和垂直多效性效应。这使得能够明确评估排他性约束假设，并通过期望最大化算法迭代地优化因果估计。与现有的MR方法相比，这种方法提供了一个独特且可能更精确的框架。我们在各种模拟场景下，包括平衡和定向多效性以及违反工具强度独立于直接效应（InSIDE）假设的情况下，针对已有的MR方法对我们的方法进行了严格评估。我们的研究结果一致表明，无论使用个体水平数据还是汇总数据，我们的方法在控制I型错误率、偏差以及对基因混杂的稳健性方面均表现出卓越的性能。此外，我们的方法便于检验定向水平多效性，在这方面优于MR-Egger，同时还能有效地检验InSIDE假设的违反情况。我们将我们的方法应用于实际数据，证明了其相较于传统MR方法的有效性。该分析揭示了体重指数（BMI）对代谢综合征（MetS）以及由其组成因素加权和计算得出的综合MetS评分的因果效应。虽然大多数方法得出的因果关系一致，但我们提出的方法在排他性约束假设的违反情况上较少，特别是对于存在水平多效性且其他方法出现膨胀的MetS评分。
