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Expression quantitative trait loci

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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] [4] The first genome-wide study of gene expression was carried out in yeast and published in 2002.[5] 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,[6] non-human primates[7][8] and mice.[9]

Some cis eQTLs are detected in many tissue types but the majority of trans eQTLs are tissue-dependent (dynamic).[10] eQTLs may act in cis (locally) or trans (at a distance) to a gene.[11] 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).[12] 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.[13] Studies have shown that single nucleotide polymorphisms (SNPs) reproducibly associated with complex disorders [14] as well as certain pharmacologic phenotypes [15] are found to be significantly enriched for eQTLs, relative to frequency-matched control SNPs. The integration of eQTLs with GWAS has led to development of the transcriptome-wide association study (TWAS) methodology.[16][17]

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.[18][19] The development of single cell technologies, and parallel advances in statistical methods has made it possible to define even subtle changes in eQTLs as cell-states change.[20][21]

See also

References

  1. ^ Rockman MV, Kruglyak L (November 2006). "Genetics of global gene expression". Nature Reviews. Genetics. 7 (11): 862–72. doi:10.1038/nrg1964. PMID 17047685. S2CID 150368.
  2. ^ Nica, Alexandra C.; Dermitzakis, Emmanouil T. (2013). "Expression quantitative trait loci: Present and future". Philosophical Transactions of the Royal Society B: Biological Sciences. 368 (1620): 20120362. doi:10.1098/rstb.2012.0362. PMC 3682727. PMID 23650636.
  3. ^ Fairfax, Benjamin P.; Makino, Seiko; Radhakrishnan, Jayachandran; Plant, Katharine; Leslie, Stephen; Dilthey, Alexander; Ellis, Peter; Langford, Cordelia; Vannberg, Fredrik O.; Knight, Julian C. (2012). "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. doi:10.1038/ng.2205. PMC 3437404. PMID 22446964.
  4. ^ Liu S, Won H, Clarke D, Matoba N, Khullar S, Mu Y, Wang D, Gerstein M (2022). "Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex". Genome Medicine. 14 (1): 133. doi:10.1186/s13073-022-01133-8. PMC 9685876. PMID 36424644.
  5. ^ 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. S2CID 9569352.
  6. ^ 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.
  7. ^ Tung J, Zhou X, Alberts SC, Stephens M, Gilad Y (February 2015). "The genetic architecture of gene expression levels in wild baboons". eLife. 4. doi:10.7554/eLife.04729. PMC 4383332. PMID 25714927.
  8. ^ Jasinska AJ, Zelaya I, Service SK, Peterson CB, Cantor RM, Choi OW, et al. (December 2017). "Genetic variation and gene expression across multiple tissues and developmental stages in a nonhuman primate". Nature Genetics. 49 (12): 1714–1721. doi:10.1038/ng.3959. PMC 5714271. PMID 29083405.
  9. ^ Doss S, Schadt EE, Drake TA, Lusis AJ (May 2005). "Cis-acting expression quantitative trait loci in mice". Genome Research. 15 (5): 681–91. doi:10.1101/gr.3216905. PMC 1088296. PMID 15837804.
  10. ^ Gerrits A, Li Y, Tesson BM, Bystrykh LV, Weersing E, Ausema A, Dontje B, Wang X, Breitling R, Jansen RC, de Haan G (October 2009). Gibson G (ed.). "Expression quantitative trait loci are highly sensitive to cellular differentiation state". PLOS Genetics. 5 (10): e1000692. doi:10.1371/journal.pgen.1000692. PMC 2757904. PMID 19834560.
  11. ^ Michaelson JJ, Loguercio S, Beyer A (July 2009). "Detection and interpretation of expression quantitative trait loci (eQTL)". Methods. 48 (3): 265–76. doi:10.1016/j.ymeth.2009.03.004. PMID 19303049.
  12. ^ 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.
  13. ^ Cookson et al. Nat Rev Genet. 2009 Mar;10(3):184-94
  14. ^ Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ (April 2010). Gibson G (ed.). "Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS". PLOS Genetics. 6 (4): e1000888. doi:10.1371/journal.pgen.1000888. PMC 2848547. PMID 20369019.
  15. ^ Gamazon ER, Huang RS, Cox NJ, Dolan ME (May 2010). "Chemotherapeutic drug susceptibility associated SNPs are enriched in expression quantitative trait loci". Proceedings of the National Academy of Sciences of the United States of America. 107 (20): 9287–92. Bibcode:2010PNAS..107.9287G. doi:10.1073/pnas.1001827107. PMC 2889115. PMID 20442332.
  16. ^ Gamazon ER, Wheeler HE, Shah KP, et al. (September 2015). "A gene-based association method for mapping traits using reference transcriptome data". Nature Genetics. 47 (9): 1091–1098. doi:10.1038/ng.3367. PMC 4552594. PMID 26258848.
  17. ^ Gusev A, Ko A, Shi H, et al. (March 2016). "Integrative approaches for large-scale transcriptome-wide association studies". Nature Genetics. 48 (3): 245–252. doi:10.1038/ng.3506. PMC 4767558. PMID 26854917.
  18. ^ Kulp DC, Jagalur M (2006). "Causal inference of regulator-target pairs by gene mapping of expression phenotypes". BMC Genomics. 7: 125. doi:10.1186/1471-2164-7-125. PMC 1481560. PMID 16719927.
  19. ^ Lee SI, Dudley AM, Drubin D, Silver PA, Krogan NJ, Pe'er D, Koller D (2009). "Learning a prior on regulatory potential from eQTL data". PLOS Genetics. 5 (1): e1000358. doi:10.1371/journal.pgen.1000358. PMC 2627940. PMID 19180192.
  20. ^ van der Wijst, M; de Vries, DH; Groot, HE; Trynka, G; Hon, CC; Bonder, MJ; Stegle, O; Nawijn, MC; Idaghdour, Y; van der Harst, P; Ye, CJ; Powell, J; Theis, FJ; Mahfouz, A; Heinig, M; Franke, L (9 March 2020). "The single-cell eQTLGen consortium". eLife. 9. doi:10.7554/eLife.52155. PMC 7077978. PMID 32149610.
  21. ^ Nathan, A; Asgari, S; Ishigaki, K; Valencia, C; Amariuta, T; Luo, Y; Beynor, JI; Baglaenko, Y; Suliman, S; Price, AL; Lecca, L; Murray, MB; Moody, DB; Raychaudhuri, S (June 2022). "Single-cell eQTL models reveal dynamic T cell state dependence of disease loci". Nature. 606 (7912): 120–128. Bibcode:2022Natur.606..120N. doi:10.1038/s41586-022-04713-1. PMC 9842455. PMID 35545678. S2CID 248730439.
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