Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with complex traits, including a wide variety of diseases. Despite the successful identification of associated loci, interpreting GWAS findings remains challenging and requires other biological resources. Omics, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, are increasingly used in a broad range of research fields. Integrative analyses applying GWAS with these omics data are expected to expand our knowledge of complex traits and provide insight into the pathogenesis of complex diseases and their causative factors. Recently, associations between genetic variants and omics data have been comprehensively evaluated, providing new information on the influence of genetic variants on omics. Furthermore, recent advances in analytic methods, including single-cell technologies, have revealed previously unknown disease mechanisms. To advance our understanding of complex traits, integrative analysis using GWAS with multi-omics data is needed. In this review, I describe successful examples of integrative analyses based on omics and GWAS, discuss the limitations of current multi-omics analyses, and provide a perspective on future integrative studies.

全基因组关联研究 (GWAS) 已经确定了数千个与复杂性状相关的基因位点，包括多种疾病。尽管成功识别了相关基因座，但解释 GWAS 发现仍然具有挑战性，并且需要其他生物资源。组学，包括基因组学、转录组学、蛋白质组学、代谢组学和表观基因组学，越来越多地用于广泛的研究领域。应用 GWAS 和这些组学数据的综合分析有望扩展我们对复杂性状的了解，并提供对复杂疾病发病机制及其致病因素的洞察。最近，对遗传变异和组学数据之间的关联进行了全面评估，为遗传变异对组学的影响提供了新的信息。此外，包括单细胞技术在内的分析方法的最新进展揭示了以前未知的疾病机制。为了促进我们对复杂性状的理解，需要使用 GWAS 与多组学数据进行综合分析。在这篇综述中，我描述了基于组学和 GWAS 的综合分析的成功例子，讨论了当前多组学分析的局限性，并为未来的综合研究提供了展望。