Multi-omics study for interpretation of genome-wide association study

Abstract

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.

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Correspondence to Masato Akiyama.

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MA is the endowed chair faculty member supported by NIDEK Company Ltd.

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Akiyama, M. Multi-omics study for interpretation of genome-wide association study. J Hum Genet (2020). https://doi.org/10.1038/s10038-020-00842-5

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