Elucidation of disease etiology by trans-layer omics analysis
Open Access
- 8 February 2021
- journal article
- review article
- Published by Springer Science and Business Media LLC in Inflammation and Regeneration
- Vol. 41 (1), 1-7
- https://doi.org/10.1186/s41232-021-00155-w
Abstract
To date, genome-wide association studies (GWASs) have successfully identified thousands of associations between genetic polymorphisms and human traits. However, the pathways between the associated genotype and phenotype are often poorly understood. The transcriptome, proteome, and metabolome, the omics, are positioned along the pathway and can provide useful information to translate from genotype to phenotype. This review shows useful data resources for connecting each omics and describes how they are combined into a cohesive analysis. Quantitative trait loci (QTL) are useful information for connecting the genome and other omics. QTL represent how much genetic variants have effects on other omics and give us clues to how GWAS risk SNPs affect biological mechanisms. Integration of each omics provides a robust analytical framework for estimating disease causality, discovering drug targets, and identifying disease-associated tissues. Technological advances and the rise of consortia and biobanks have facilitated the analyses of unprecedented data, improving both the quality and quantity of research. Proficient management of these valuable datasets allows discovering novel insights into the genetic background and etiology of complex human diseases and contributing to personalized medicine.Keywords
Funding Information
- KAKENHI (19H01021, 20K21834)
- Japan Agency for Medical Research and Development (JP20km0405211, JP20ek0109413, JP20ek0410075, JP20gm4010006, and JP20km0405217)
- Takeda Science Foundation
- Bioinformatics Initiative of Osaka University Graduate School of Medicine, Osaka University
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