Thermo ebook on eQTL - 18

Navigating the GTEx eQTL
Database and Using TaqMan
Assays to Verify eQTL Links in
Disease Research
It has been estimated that as many as 90% of SNPs identified in
GWAS map to noncoding regions.
Steve Jackson, Ph.D.
associated with over 2,700 traits or diseases.3 In spite
of this success, it has been estimated that as many
as 90% of single-nucleotide polymorphisms (SNPs)
identified in GWAS map to noncoding regions (for
example, see reference 4), complicating the mechanistic interpretation of the results. It is thought that
these noncoding SNPs fall into regulatory regions
and influence the expression of a gene or genes. The
study of eQTLs provides a method for understanding the link between genetic variants and altered
gene expression, and could potentially provide
new insights connecting GWAS results to molecular
mechanisms of various diseases.

In this section, we
*

describe the importance of expression
quantitative trait loci (eQTLs)

*

demonstrate ways to use the GenotypeTissue Expression (GTEx) database to find
eQTLs affecting the expression of various
genes in various tissues

*

illustrate how to find Applied Biosystems™
TaqMan® Assays that can be used to verify
and extend genome-wide association study
(GWAS) and eQTL analysis results

eQTLs are identified by linking variations in transcript
abundance with variations in genotypes (Figure 1A).
Typically, this involves collecting transcriptomic and
genomic variant data from samples in a large cohort, and correlating the difference in gene mRNA
levels with SNP genotypes in individuals. eQTLs can
fall into the following two categories: cis-eQTLs are
SNPs that are proximal to a target locus (Figure 1B).

Introduction
In the past 15 years, tremendous progress has been
made using GWAS to link genetic variation with phenotypes and pathologies.1,2 According to the most
recent data, over 76,000 genetic variants have been

18
Thermo ebook on eQTL

Table of Contents for the Digital Edition of Thermo ebook on eQTL
