利用相关工具变量集合预测具有多效性基因调控效应的GWAS位点中的因果基因
Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables
AI总结：
本研究利用相关工具变量的多变量孟德尔随机化预测GWAS位点的因果基因，通过因果推断理论证明其有效性，并在冠状动脉疾病数据中识别出十二个位点的因果基因。
摘要：
多变量孟德尔随机化（MVMR）是一种统计技术，利用一组遗传工具来估计多个暴露因素对感兴趣结局的直接因果效应。在具有多效性基因调控效应的基因组位点，即同一遗传变异与多个邻近基因相关联的位点，MVMR可潜在地用于预测候选因果基因。然而，该领域的共识要求MVMR中的遗传工具必须相互独立，而在考虑来自同一位点的一组候选基因时，这通常无法实现。我们利用因果推断理论证明，具有相关工具的MVMR满足工具集条件。这是Brito和Pearl（2002）关于结构方程模型的经典结果，它保证了在多个暴露因素共同（而非单独）将一组工具变量与结局变量分离的情况下，因果效应的可识别性。大量模拟实验证实了这些理论结果的有效性和实用性，即使在样本量适中的情况下也是如此。重要的是，当工具高度相关时，因果效应估计仍保持无偏，且方差较小。我们利用STARNET研究的eQTL数据，将具有相关工具变量集的MVMR应用于冠状动脉疾病的全基因组关联研究（GWAS）风险位点。我们的方法预测了十二个位点的因果基因，每个位点与多个组织中的多个共定位基因相关。然而，跨组织的调控多效性程度较高，且每个位点的因果变异数量有限，仍要求MVMR按组织逐一运行，而在单个模型中测试给定位点的所有基因-组织对以预测因果基因-组织组合仍然不可行。
英文摘要：
Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent, which is usually not possible when considering a group of candidate genes from the same locus. We used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results even at modest sample sizes. Importantly, the causal effect estimates remain unbiased and their variance small when instruments are highly correlated. We applied MVMR with correlated instrumental variable sets at risk loci from genome-wide association studies (GWAS) for coronary artery disease using eQTL data from the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs at a given locus in a single model to predict causal gene-tissue combinations remains infeasible.
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