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

被引：40

作者：

Akiyama, Masato ^{[1
,2
]}

机构：

[1] Kyushu Univ, Grad Sch Med Sci, Dept Ocular Pathol & Imaging Sci, Fukuoka 8128582, Japan

[2] RIKEN Ctr Integrat Med Sci, Stat & Translat Genet, Yokohama, Kanagawa 2300045, Japan

来源：

JOURNAL OF HUMAN GENETICS | 2021年 / 66卷 / 01期

关键词：

MISSING HERITABILITY; CODING VARIANTS; ATLAS; GENE; CHALLENGES; LOCI; SUSCEPTIBILITY; EQTLS; RISK;

D O I：

10.1038/s10038-020-00842-5

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

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.

引用

页码：3 / 10

页数：8

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