Deciphering the relationships between genes and complex traits can enhance our understanding of phenotypic variations and disease mechanisms. However, determining the specific roles of individual genes and quantifying their direct and indirect causal effects on complex traits remains a significant challenge. Here we present a framework (called Bayesian network genome-wide association studies (BN-GWAS)) to decipher the total and direct causal effects of individual genes. BN-GWAS leverages imputed expression profiles from GWAS and raw expression data from a reference dataset to construct a directed gene–gene–phenotype causal network. It allows gene expression and disease traits to be evaluated in different samples, significantly improving the flexibility and applicability of the approach. It can be extended to decipher the joint causal network of two or more traits, and exhibits high specificity and precision (positive predictive value), making it particularly useful for selecting genes for follow-up studies. We verified the feasibility and validity of BN-GWAS by extensive simulations and applications to 52 traits across 14 tissues in the UK Biobank, revealing insights into their genetic architectures, including the relative contributions of direct, indirect and mediating causal genes. The identified (direct) causal genes were significantly enriched for genes highlighted in the Open Targets database. Overall, BN-GWAS provides a flexible and powerful framework for elucidating the genetic basis of complex traits through a systems-level, causal inference approach.

破译基因与复杂性状之间的关系可以增强我们对表型变异和疾病机制的理解。然而，确定单个基因的具体作用并量化它们对复杂性状的直接和间接因果影响仍然是一个重大挑战。在这里，我们提出了一个框架（称为贝叶斯网络全基因组关联研究 （BN-GWAS））来破译单个基因的总体和直接因果效应。BN-GWAS 利用来自 GWAS 的插补表达谱和来自参考数据集的原始表达数据来构建定向基因-基因-表型因果网络。它允许在不同样本中评估基因表达和疾病性状，显著提高了该方法的灵活性和适用性。它可以扩展为破译两个或多个性状的联合因果网络，并表现出高特异性和精确度 （阳性预测值），使其特别适用于选择用于后续研究的基因。我们通过对英国生物样本库中 14 个组织的 52 个性状进行广泛模拟和应用，验证了 BN-GWAS 的可行性和有效性，揭示了对其遗传结构的见解，包括直接、间接和介导致病基因的相对贡献。已鉴定的（直接）致病基因对 Open Targets 数据库中突出显示的基因进行了显著富集。总体而言，BN-GWAS 提供了一个灵活而强大的框架，用于通过系统级因果推理方法阐明复杂性状的遗传基础。