Improving Mendelian Randomization Analysis by Instrument Borrowing from Auxiliary Outcome Traits
从辅助结局性状借用工具变量以改进孟德尔随机化分析AI 解读 ↓
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从辅助结局性状借用工具变量以改进孟德尔随机化分析
- 研究问题
- 面对无效遗传工具变量造成的偏差与功效损失，能否从相关辅助结局中借用共享的有效工具，改善主要结局的因果推断？
- 方法与思路
- 作者提出共同异质性统计量及其渐近理论，用于筛选可能共享有效工具的辅助性状，并扩展 MR-Mode 与 MR-PRESSO。
- 材料陈述的贡献
- 摘要称，模拟中的改进随有效工具重叠程度增加而扩大，正负对照应用提示更好的第一类错误控制和更高功效。
- 阅读关注点
- 应核对工具有效性与跨性状共享的条件、辅助性状筛选的推断影响，以及模拟中无效工具结构和对照设计的合理性。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列工具有效性条件，相关结局并不必然共享足够的有效工具。
原文摘要
Mendelian randomization (MR) is a widely used approach for inferring causal effects of exposures on outcomes using genetic variants as instrumental variables; however, existing methods remain vulnerable to bias and/or loss of power in the presence of invalid instruments. We hypothesize that closely related outcome traits are likely to have a large overlap in underlying valid instruments in relation to a given exposure and that instrument borrowing (IB) across such traits can therefore yield more robust MR inference. To operationalize this idea, we first introduce a novel coheterogeneity statistic and its asymptotic theory under different paradigms which can be used to identify secondary outcome traits that are likely to share valid instruments with the primary trait. We then propose extensions of two popular methods, MR-Mode and MR-PRESSO, that improve MR inference for the primary trait, taking advantage of a secondary trait. Extensive simulation studies demonstrate that the proposed coheterogeneity statistic has expected finite-sample properties and IB-based methods consistently outperform their standard counterparts, with gains increasing as the degree of overlap in valid instruments across outcome traits grows. Applications to established positive and negative control hypotheses suggest that IB-based methods offer improved control of type I error and increased power. We further illustrate the practical utility of these methods by revisiting the long-debated hypothesis on the causal effect of vitamin D on cardiometabolic traits.