Genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with various phenotypes, but together they explain only a fraction of heritability, suggesting many variants have yet to be discovered. Recently it has been recognized that incorporating functional information of genetic variants can improve power for identifying novel loci. For example, S-PrediXcan and TWAS tested the association of predicted gene expression with phenotypes based on GWAS summary statistics by leveraging the information on genetic regulation of gene expression and found many novel loci. However, as genetic variants may have effects on more than one gene and through different mechanisms, these methods likely only capture part of the total effects of these variants. In this paper, we propose a summary statistics-based mixed effects score test (sMiST) that tests for the total effect of both the effect of the mediator by imputing genetically predicted gene expression, like S-PrediXcan and TWAS, and the direct effects of individual variants. It allows for multiple functional annotations and multiple genetically predicted mediators. It can also perform conditional association analysis while adjusting for other genetic variants (e.g., known loci for the phenotype). Extensive simulation and real data analyses demonstrate that sMiST yields p-values that agree well with those obtained from individual level data but with substantively improved computational speed. Importantly, a broad application of sMiST to GWAS is possible, as only summary statistics of genetic variant associations are required. We apply sMiST to a large-scale GWAS of colorectal cancer using summary statistics from ∼120, 000 study participants and gene expression data from the Genotype-Tissue Expression (GTEx) project. We identify several novel and secondary independent genetic loci.

全基因组关联研究（GWAS）已成功识别出数以万计与各种表型相关的遗传变异，但它们加在一起仅解释了遗传力的一小部分，这表明许多变异尚未被发现。最近人们认识到，整合遗传变异的功能信息可以提高识别新基因座的能力。例如，S-PrediXcan和TWAS利用基因表达的遗传调控信息，基于GWAS汇总统计测试了预测的基因表达与表型的关联，发现了许多新的位点。然而，由于遗传变异可能通过不同的机制对多个基因产生影响，因此这些方法可能只能捕获这些变异总体影响的一部分。在本文中，我们提出了一种基于统计的混合效应评分测试（sMiST），通过估算遗传预测的基因表达（如 S-PrediXcan 和 TWAS）来测试中介效应的总效应，以及直接效应的直接效应。个别变体。它允许多种功能注释和多种基因预测介体。它还可以执行条件关联分析，同时调整其他遗传变异（例如，表型的已知基因座）。广泛的模拟和真实数据分析表明，sMiST 产生的 p 值与从个体水平数据获得的 p 值非常吻合，但计算速度显着提高。重要的是，sMiST 在 GWAS 中的广泛应用是可能的，因为只需要遗传变异关联的汇总统计。我们使用约 120, 000 名研究参与者的汇总统计数据和来自基因型组织表达 (GTEx) 项目的基因表达数据，将 sMiST 应用于结直肠癌的大规模 GWAS。我们确定了几个新的和次要的独立遗传位点。