A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine. Issue 5 (May 2020)

• Record Type: Journal Article
• Authors: Ye, Youqiong; Zhang, Zhao; Liu, Yaoming; Diao, Lixia; Han, Leng
• Is Part Of: Trends in genetics. Volume 36:Issue 5(2020)
• Page Start: 318
• Page End: 336
• Publication Date: 2020-05
• Subjects: Genetics -- Periodicals; 576.5
• DOI: 10.1016/j.tig.2020.01.009
• Languages: English
• ISSNs: 0168-9525

Abstract

Abstract : Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine. Highlights: The enormous volume of genotyping data provides opportunities to discover novel types of QTLs. Various types of QTLs have emerged through advances in high-throughput technologies, including those for the transcriptome, epigenome, proteome, metabolome, and microbiome. Advanced algorithms enable the identification and inference of the causal effects of molQTLs. Integrative analysis of multi-omics data advances our understanding of the functional significance of genetic variants.