We propose structural equation models (SEMs) as a general framework to infer causal networks for metabolites and other complex traits. Traditionally SEMs are used only for individual-level data under the assumption that all instrumental variables (IVs) are valid. To overcome these limitations, we propose both one- and two-sample approaches for causal network inference based on SEMs that can: (1) perform causal analysis and discover causal relationships among multiple traits; (2) account for the possible presence of some invalid IVs; (3) allow for data analysis using only genome-wide association studies (GWAS) summary statistics when individual-level data are not available; (4) consider the possibility of bidirectional relationships between traits. Our method employs a simple stepwise selection to identify invalid IVs, thus avoiding false positives while possibly increasing true discoveries based on two-stage least squares (2SLS). We use both real GWAS data and simulated data to demonstrate the superior performance of our method over the standard 2SLS/SEMs. For real data analysis, our proposed approach is applied to a human blood metabolite GWAS summary data set to uncover putative causal relationships among the metabolites; we also identify some metabolites (putative) causal to Alzheimer's disease (AD), which, along with the inferred causal metabolite network, suggest some possible pathways of metabolites involved in AD.

中文翻译：

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使用 GWAS 摘要数据在存在无效工具变量的情况下推断因果代谢物网络

我们提出结构方程模型（SEM）作为推断代谢物和其他复杂性状的因果网络的通用框架。传统上，SEM 仅在假设所有工具变量 (IV) 均有效的情况下用于个人层面的数据。为了克服这些限制，我们提出了基于 SEM 的因果网络推理的一样本和两样本方法，这些方法可以：（1）执行因果分析并发现多个特征之间的因果关系；(2) 考虑可能存在的一些无效 IV；(3) 当无法获得个体水平的数据时，允许仅使用全基因组关联研究（GWAS）汇总统计数据进行数据分析；(4)考虑性状之间存在双向关系的可能性。我们的方法采用简单的逐步选择来识别无效的 IV，从而避免误报，同时可能增加基于两阶段最小二乘法 (2SLS) 的真实发现。我们使用真实的 GWAS 数据和模拟数据来证明我们的方法比标准 2SLS/SEM 的优越性能。对于真实数据分析，我们提出的方法应用于人类血液代谢物 GWAS 摘要数据集，以揭示代谢物之间假定的因果关系；我们还确定了一些与阿尔茨海默病 (AD) 相关的代谢物（推定），这些代谢物与推断的因果代谢物网络一起表明了与 AD 相关的代谢物的一些可能途径。