Motivation: Genome-wide association studies (GWAS) have achieved remarkable success in identifying SNP-trait associations in the last decade. However, it is challenging to identify the mechanisms that connect the genetic variants with complex traits as the majority of GWAS associations are in non-coding regions. Methods that integrate genomic and transcriptomic data allow us to investigate how genetic variants may affect a trait through their effect on gene expression. These include CoMM and CoMM-S², likelihood-ratio-based methods that integrate GWAS and eQTL studies to assess expression-trait association. However, their reliance on individual-level eQTL data render them inapplicable when only summary-level eQTL results, such as those from large-scale eQTL analyses, are available.

Result: We develop an efficient probabilistic model, CoMM-S⁴, to explore the expression-trait association using summary-level eQTL and GWAS datasets. Compared with CoMM-S², which uses individual-level eQTL data, CoMM-S⁴ requires only summary-level eQTL data. To test expression-trait association, an efficient variational Bayesian EM algorithm and a likelihood ratio test were constructed. We applied CoMM-S⁴ to both simulated and real data. The simulation results demonstrate that CoMM-S⁴ can perform as well as CoMM-S² and S-PrediXcan, and analyses using GWAS summary statistics from Biobank Japan and eQTL summary statistics from eQTLGen and GTEx suggest novel susceptibility loci for cardiovascular diseases and osteoporosis.

Availability and implementation: The developed R package is available at https://github.com/gordonliu810822/CoMM.

动机：在过去十年中，全基因组关联研究 (GWAS) 在识别 SNP 性状关联方面取得了显着的成功。然而，由于大多数 GWAS 关联位于非编码区域，因此很难确定将遗传变异与复杂性状联系起来的机制。整合基因组和转录组数据的方法使我们能够研究遗传变异如何通过对基因表达的影响来影响性状。这些包括 CoMM 和 CoMM-S ²，基于似然比的方法，整合了 GWAS 和 eQTL 研究以评估表达-性状关联。然而，当只有汇总级 eQTL 结果（例如来自大规模 eQTL 分析的结果）可用时，它们对个体级 eQTL 数据的依赖使其不适用。

结果：我们开发了一个有效的概率模型 CoMM-S ⁴，以使用摘要级 eQTL 和 GWAS 数据集探索表达-特征关联。与使用个体级 eQTL 数据的CoMM-S ²相比，CoMM-S ⁴只需要汇总级 eQTL 数据。为了测试表达-性状关联，构建了一种有效的变分贝叶斯 EM 算法和似然比测试。我们将 CoMM-S ⁴应用于模拟数据和真实数据。仿真结果表明 CoMM-S ^{4 的}性能与 CoMM-S ²一样好 和 S-PrediXcan，并使用 Biobank Japan 的 GWAS 汇总统计数据和 eQTLGen 和 GTEx 的 eQTL 汇总统计数据进行分析，表明心血管疾病和骨质疏松症的新易感位点。

可用性和实施​​： 开发的 R 包可在 https://github.com/gordonliu810822/CoMM.