Integration of Multi-omics Data for Expression Quantitative Trait Loci (eQTL) Analysis and eQTL Epistasis

Authors

Mingon Kang, University of Nevada, Las VegasFollow
Jean Gao, University of Texas at Arlington

Editors

Xinghua Mindy Shi

Document Type

Book Section

Publication Date

12-18-2019

Publication Title

eQTL Analysis

Publisher

Humana

Publisher Location

New York, NY

Volume

2082

First page number:

151

Last page number:

171

Abstract

Expression quantitative trait loci (eQTL) mapping studies identify genetic loci that regulate gene expression. eQTL mapping studies can capture gene regulatory interactions and provide insight into the genetic mechanism of biological systems. Recently, the integration of multi-omics data, such as single-nucleotide polymorphisms (SNPs), copy number variations (CNVs), DNA methylation, and gene expression, plays an important role in elucidating complex biological systems, since biological systems involve a sequence of complex interactions between various biological processes. This chapter introduces multi-omics data that have been used in many eQTL studies and integrative methodologies that incorporate multi-omics data for eQTL studies. Furthermore, we describe a statistical approach that can detect nonlinear causal relationships between eQTLs, called eQTL epistasis, and its importance.

Keywords

eQTL mapping study; Integrative analysis; Multi-omics; eQTL epistasis; Single-nucleotide polymorphism; Gene expression; Copy number variation; DNA methylation

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Language

English

Repository Citation

Kang, M., Gao, J. (2019). Integration of Multi-omics Data for Expression Quantitative Trait Loci (eQTL) Analysis and eQTL Epistasis. In Xinghua Mindy Shi, eQTL Analysis, 2082 151-171. New York, NY: Humana.
http://dx.doi.org/10.1007/978-1-0716-0026-9_11