分子定量性状座位（xQTLs）常被用于优先考虑起因于全基因组关联研究（GWASs）所鉴定的变异性状关联的基因或功能元素。在这里，我们介绍了OPERA，这是一种联合分析GWAS和多组学xQTL摘要统计的方法，通过共享的因果变异体增强了与复杂性状相关的分子表型的识别。将OPERA应用于50种复杂性状（n=20,833-766,345）的摘要级别GWAS数据和来自七个组学层面（n=100-31,684）的xQTL数据显示，50%的GWAS信号与至少一个分子表型共享。与多个分子表型共享的GWAS信号，如前列腺癌的MSMB座位的信号，对于理解复杂性状的遗传调控机制特别有信息价值。将更多分子表型与更大样本中的时空效应相结合的未来研究，可以获得将分子中间物连接到GWAS信号的更饱和图。© 2023 The Author(s).

Molecular quantitative trait loci (xQTLs) are often harnessed to prioritize genes or functional elements underpinning variant-trait associations identified from genome-wide association studies (GWASs). Here, we introduce OPERA, a method that jointly analyzes GWAS and multi-omics xQTL summary statistics to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants. Applying OPERA to summary-level GWAS data for 50 complex traits (n = 20,833-766,345) and xQTL data from seven omics layers (n = 100-31,684) reveals that 50% of the GWAS signals are shared with at least one molecular phenotype. GWAS signals shared with multiple molecular phenotypes, such as those at the MSMB locus for prostate cancer, are particularly informative for understanding the genetic regulatory mechanisms underlying complex traits. Future studies with more molecular phenotypes, measured considering spatiotemporal effects in larger samples, are required to obtain a more saturated map linking molecular intermediates to GWAS signals.© 2023 The Author(s).