Expression quantitative trait loci

Expression quantitative trait loci (eQTLs) are genomic loci that explain variation in expression levels of mRNAs.[1][2]

Distant and local, trans- and cis-eQTLs, respectively

An expression quantitative trait is an amount of an mRNA transcript or a protein. These are usually the product of a single gene with a specific chromosomal location. This distinguishes expression quantitative traits from most complex traits, which are not the product of the expression of a single gene. Chromosomal loci that explain variance in expression traits are called eQTLs. eQTLs located near the gene-of-origin (gene which produces the transcript or protein) are referred to as local eQTLs or cis-eQTLs. By contrast, those located distant from their gene of origin, often on different chromosomes, are referred to as distant eQTLs or trans-eQTLs. [3] The first genome-wide study of gene expression was carried out in yeast and published in 2002.[4] The initial wave of eQTL studies employed microarrays to measure genome-wide gene expression; more recent studies have employed massively parallel RNA sequencing. Many expression QTL studies were performed in plants and animals, including humans[5], non-human primates[6][7] and mice[8].

Some cis eQTLs are detected in many tissue types but the majority of trans eQTLs are tissue-dependent (dynamic).[9] eQTLs may act in cis (locally) or trans (at a distance) to a gene.[10] The abundance of a gene transcript is directly modified by polymorphism in regulatory elements. Consequently, transcript abundance might be considered as a quantitative trait that can be mapped with considerable power. These have been named expression QTLs (eQTLs).[11] The combination of whole-genome genetic association studies and the measurement of global gene expression allows the systematic identification of eQTLs. By assaying gene expression and genetic variation simultaneously on a genome-wide basis in a large number of individuals, statistical genetic methods can be used to map the genetic factors that underpin individual differences in quantitative levels of expression of many thousands of transcripts.[12] Studies have shown that single nucleotide polymorphisms (SNPs) reproducibly associated with complex disorders [13] as well as certain pharmacologic phenotypes [14] are found to be significantly enriched for eQTLs, relative to frequency-matched control SNPs.

Detecting eQTLs

Mapping eQTLs is done using standard QTL mapping methods that test the linkage between variation in expression and genetic polymorphisms. The only considerable difference is that eQTL studies can involve a million or more expression microtraits. Standard gene mapping software packages can be used, although it is often faster to use custom code such as QTL Reaper or the web-based eQTL mapping system GeneNetwork. GeneNetwork hosts many large eQTL mapping data sets and provide access to fast algorithms to map single loci and epistatic interactions. As is true in all QTL mapping studies, the final steps in defining DNA variants that cause variation in traits are usually difficult and require a second round of experimentation. This is especially the case for trans eQTLs that do not benefit from the strong prior probability that relevant variants are in the immediate vicinity of the parent gene. Statistical, graphical, and bioinformatic methods are used to evaluate positional candidate genes and entire systems of interactions.[15][16]

gollark: It MIGHT be.
gollark: OIR:EM is currently playing the FTL soundtrack eternally on loop.
gollark: Yes.
gollark: It was mostly an afterthought to satisfy hæv.
gollark: I don't know. There's no web UI for the EM one.

See also

References
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  3. "Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles". Nat. Genet. 44 (5): 502–510. 2012. doi:10.1038/ng.2205. PMC 3437404. PMID 22446964.
  4. Brem RB, Yvert G, Clinton R, Kruglyak L (April 2002). "Genetic dissection of transcriptional regulation in budding yeast". Science. 296 (5568): 752–5. Bibcode:2002Sci...296..752B. doi:10.1126/science.1069516. PMID 11923494.
  5. Lonsdale, John; Thomas, Jeffrey; Salvatore, Mike; Phillips, Rebecca; Lo, Edmund; Shad, Saboor; Hasz, Richard; Walters, Gary; Garcia, Fernando; Young, Nancy; Foster, Barbara; Moser, Mike; Karasik, Ellen; Gillard, Bryan; Ramsey, Kimberley; Sullivan, Susan; Bridge, Jason; Magazine, Harold; Syron, John; Fleming, Johnelle; Siminoff, Laura; Traino, Heather; Mosavel, Maghboeba; Barker, Laura; Jewell, Scott; Rohrer, Dan; Maxim, Dan; Filkins, Dana; Harbach, Philip; et al. (June 2013). "The Genotype-Tissue Expression (GTEx) project". Nature Genetics. 45 (6): 580–5. doi:10.1038/ng.2653. PMC 4692118. PMID 23715323.
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  11. Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M (March 2009). "Mapping complex disease traits with global gene expression". Nature Reviews. Genetics. 10 (3): 184–94. doi:10.1038/nrg2537. PMC 4550035. PMID 19223927.
  12. Cookson et. al Nat Rev Genet. 2009 Mar;10(3):184-94
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