Thermo ebook on eQTL - 4

be used to analyze the variants present in the genomic DNA (gDNA). For transcriptomic analysis, scientists can process the extracted RNA with Applied
Biosystems™ Clariom D Assays and microarrays.

approach to drawing connections between eQTLs
and phenotypes.

To gather even more transcript-related data for discovery research, scientists can explore whole transcriptome analysis with the Ion Torrent™ Ion AmpliSeq Transcriptome Human Gene Expression Kit.
Here a researcher can measure the expression levels
of more than 20,000 human genes-all in one assay.
Scientists can use this kit to discover disease-related
variants that can be studied for the development of
new medicines or other therapies.

The study of eQTLs is not entirely new, but it is new
enough that the term isn't always used. As an example, biotech writer Matthew Wygant discusses how
genomics spawns new genetic research on diseases, which is through eQTL analysis (see "Genomics
Is Driving New Opportunities in Genetic Disease Research;" page 14).

Finding eQTLs in more phenotypes

For instance, Wygant describes work on cystic fibrosis.
This disease is caused by a mutation in one gene that
creates sticky mucus that clogs the lungs, interferes
with the pancreas releasing digestive enzymes, can
block the liver's bile duct, and more. This study found
connections between GWAS and transcriptomics for
pathways involved with inflammation and other molecular processes. Beyond developing a better understanding of the etiology of cystic fibrosis, work like
this can reveal potential therapeutic targets.

For combining genotypic and transcriptomics results for eQTL analysis, scientists can turn to opensource software, such as Matrix EQTL, a free R-based
package. Then, the results can be confirmed with
Applied Biosystems™ TaqMan® SNP Genotyping Assays and TaqMan® Gene Expression Assays for genotyping and gene expression analysis, respectively.
Tommaso Dragani-the former director of the genetic epidemiology and pharmacogenomics research unit at the National Cancer Institute of Milan,
Italy-used a Thermo Fisher Scientific workflow to
explore the relationship between eQTLs and lung
adenocarcinoma, a common form of lung cancer
that scientists believe includes a significant heritable factor. As a result, his team verified 15 eQTLs and
believe that the analysis of more samples could reveal more links. (For more information, please view
the webinar listed in the Resource section, page 32.)

That's just one of many gene-based diseases that
eQTL analysis can help scientists understand and
possibly treat.

Dealing with the data
As an increasing number of scientists want to use
eQTLs, and as the available methods and tools make
it easier to dive into these techniques, scientists
will need a place to share the data. One of the most
popular places for that is the GTEx database.2 As described in "Navigating the GTEx eQTL Database and
Using TaqMan Assays to Verify eQTL Links in Disease
Research" (page 18), this database is useful for disease research by making it easier to link eQTLs to
GWAS phenotypes.

This workflow on eQTLs, though, can be used for
more than adenocarcinoma. In fact, as Dragani
points out, this method could be used to find eQTLs
associated with any phenotype or disease. Most importantly, his method of eQTL analysis simplifies the

Thermo ebook on eQTL

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