Expression quantitative trait loci

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.{{cite journal | title = Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. | journal = Nat. Genet. | volume = 44 | issue = 5 | pages = 502–510 | date = 2012 | pmid = 22446964 | doi = 10.1038/ng.2205 | pmc = 3437404 | last1 = Fairfax | first1 = Benjamin P. | last2 = Makino | first2 = Seiko | last3 = Radhakrishnan | first3 = Jayachandran | last4 = Plant | first4 = Katharine | last5 = Leslie | first5 = Stephen | last6 = Dilthey | first6 = Alexander | last7 = Ellis | first7 = Peter | last8 = Langford | first8 = Cordelia | last9 = Vannberg | first9 = Fredrik O. | last10 = Knight | first10 = Julian C. }} {{cite journal | vauthors = Liu S, Won H, Clarke D, Matoba N, Khullar S, Mu Y, Wang D, Gerstein M | title = Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex | journal = Genome Medicine | volume = 14 | issue = 1 | date = 2022 | page = 133 | pmid = 36424644 | doi = 10.1186/s13073-022-01133-8 | pmc = 9685876 | doi-access = free }} The first genome-wide study of gene expression was carried out in yeast and published in 2002.{{cite journal | vauthors = Brem RB, Yvert G, Clinton R, Kruglyak L | title = Genetic dissection of transcriptional regulation in budding yeast | journal = Science | volume = 296 | issue = 5568 | pages = 752–5 | date = April 2002 | pmid = 11923494 | doi = 10.1126/science.1069516 | bibcode = 2002Sci...296..752B | s2cid = 9569352 }} 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,{{cite journal | title = The Genotype-Tissue Expression (GTEx) project | language = En | journal = Nature Genetics | volume = 45 | issue = 6 | pages = 580–5 | date = June 2013 | pmid = 23715323 | doi = 10.1038/ng.2653 | pmc = 4692118 | last1 = Lonsdale | first1 = John | last2 = Thomas | first2 = Jeffrey | last3 = Salvatore | first3 = Mike | last4 = Phillips | first4 = Rebecca | last5 = Lo | first5 = Edmund | last6 = Shad | first6 = Saboor | last7 = Hasz | first7 = Richard | last8 = Walters | first8 = Gary | last9 = Garcia | first9 = Fernando | last10 = Young | first10 = Nancy | last11 = Foster | first11 = Barbara | last12 = Moser | first12 = Mike | last13 = Karasik | first13 = Ellen | last14 = Gillard | first14 = Bryan | last15 = Ramsey | first15 = Kimberley | last16 = Sullivan | first16 = Susan | last17 = Bridge | first17 = Jason | last18 = Magazine | first18 = Harold | last19 = Syron | first19 = John | last20 = Fleming | first20 = Johnelle | last21 = Siminoff | first21 = Laura | last22 = Traino | first22 = Heather | last23 = Mosavel | first23 = Maghboeba | last24 = Barker | first24 = Laura | last25 = Jewell | first25 = Scott | last26 = Rohrer | first26 = Dan | last27 = Maxim | first27 = Dan | last28 = Filkins | first28 = Dana | last29 = Harbach | first29 = Philip | last30 = Cortadillo | first30 = Eddie | display-authors = 29 }} non-human primates{{cite journal | vauthors = Tung J, Zhou X, Alberts SC, Stephens M, Gilad Y | title = The genetic architecture of gene expression levels in wild baboons | journal = eLife | volume = 4 | date = February 2015 | pmid = 25714927 | pmc = 4383332 | doi = 10.7554/eLife.04729 | doi-access = free }}{{cite journal | vauthors = Jasinska AJ, Zelaya I, Service SK, Peterson CB, Cantor RM, Choi OW, DeYoung J, Eskin E, Fairbanks LA, Fears S, Furterer AE, Huang YS, Ramensky V, Schmitt CA, Svardal H, Jorgensen MJ, Kaplan JR, Villar D, Aken BL, Flicek P, Nag R, Wong ES, Blangero J, Dyer TD, Bogomolov M, Benjamini Y, Weinstock GM, Dewar K, Sabatti C, Wilson RK, Jentsch JD, Warren W, Coppola G, Woods RP, Freimer NB | display-authors = 6 | title = Genetic variation and gene expression across multiple tissues and developmental stages in a nonhuman primate | journal = Nature Genetics | volume = 49 | issue = 12 | pages = 1714–1721 | date = December 2017 | pmid = 29083405 | pmc = 5714271 | doi = 10.1038/ng.3959 }} and mice.{{cite journal | vauthors = Doss S, Schadt EE, Drake TA, Lusis AJ | title = Cis-acting expression quantitative trait loci in mice | journal = Genome Research | volume = 15 | issue = 5 | pages = 681–91 | date = May 2005 | pmid = 15837804 | pmc = 1088296 | doi = 10.1101/gr.3216905 }}

Some cis eQTLs are detected in many tissue types but the majority of trans eQTLs are tissue-dependent (dynamic).{{cite journal | vauthors = Gerrits A, Li Y, Tesson BM, Bystrykh LV, Weersing E, Ausema A, Dontje B, Wang X, Breitling R, Jansen RC, de Haan G | title = Expression quantitative trait loci are highly sensitive to cellular differentiation state | journal = PLOS Genetics | volume = 5 | issue = 10 | pages = e1000692 | date = October 2009 | pmid = 19834560 | pmc = 2757904 | doi = 10.1371/journal.pgen.1000692 | editor1-last = Gibson | editor1-first = Greg | doi-access = free }} eQTLs may act in cis (locally) or trans (at a distance) to a gene.{{cite journal | vauthors = Michaelson JJ, Loguercio S, Beyer A | title = Detection and interpretation of expression quantitative trait loci (eQTL) | journal = Methods | volume = 48 | issue = 3 | pages = 265–76 | date = July 2009 | pmid = 19303049 | doi = 10.1016/j.ymeth.2009.03.004 | url = https://zenodo.org/record/3423504 }} 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).{{cite journal | vauthors = Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M | title = Mapping complex disease traits with global gene expression | journal = Nature Reviews. Genetics | volume = 10 | issue = 3 | pages = 184–94 | date = March 2009 | pmid = 19223927 | doi = 10.1038/nrg2537 | pmc = 4550035 }} 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.Cookson et al. Nat Rev Genet. 2009 Mar;10(3):184-94 Studies have shown that single nucleotide polymorphisms (SNPs) reproducibly associated with complex disorders {{cite journal | vauthors = Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ | title = Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS | journal = PLOS Genetics | volume = 6 | issue = 4 | pages = e1000888 | date = April 2010 | pmid = 20369019 | pmc = 2848547 | doi = 10.1371/journal.pgen.1000888 | editor1-last = Gibson | editor1-first = Greg | doi-access = free }} as well as certain pharmacologic phenotypes {{cite journal | vauthors = Gamazon ER, Huang RS, Cox NJ, Dolan ME | title = Chemotherapeutic drug susceptibility associated SNPs are enriched in expression quantitative trait loci | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 107 | issue = 20 | pages = 9287–92 | date = May 2010 | pmid = 20442332 | pmc = 2889115 | doi = 10.1073/pnas.1001827107 | bibcode = 2010PNAS..107.9287G | doi-access = free }} 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.{{cite journal | vauthors = Gamazon ER, Wheeler HE, Shah KP, et al | title = A gene-based association method for mapping traits using reference transcriptome data | journal = Nature Genetics | volume = 47 | issue = 9 | pages = 1091–1098 | date = September 2015 | pmid = 26258848 | pmc = 4552594 | doi = 10.1038/ng.3367 }}{{cite journal | vauthors = Gusev A, Ko A, Shi H, et al | title = Integrative approaches for large-scale transcriptome-wide association studies | journal = Nature Genetics | volume = 48 | issue = 3 | pages = 245–252 | date = March 2016 | pmid = 26854917 | pmc = 4767558 | doi = 10.1038/ng.3506 }}

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.{{cite journal | vauthors = Kulp DC, Jagalur M | title = Causal inference of regulator-target pairs by gene mapping of expression phenotypes | journal = BMC Genomics | volume = 7 | pages = 125 | year = 2006 | pmid = 16719927 | pmc = 1481560 | doi = 10.1186/1471-2164-7-125 | doi-access = free }}{{cite journal | vauthors = Lee SI, Dudley AM, Drubin D, Silver PA, Krogan NJ, Pe'er D, Koller D | title = Learning a prior on regulatory potential from eQTL data | journal = PLOS Genetics | volume = 5 | issue = 1 | pages = e1000358 | year = 2009 | pmid = 19180192 | pmc = 2627940 | doi = 10.1371/journal.pgen.1000358 | doi-access = free }} 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.{{cite journal |last1=van der Wijst |first1=M |last2=de Vries |first2=DH |last3=Groot |first3=HE |last4=Trynka |first4=G |last5=Hon |first5=CC |last6=Bonder |first6=MJ |last7=Stegle |first7=O |last8=Nawijn |first8=MC |last9=Idaghdour |first9=Y |last10=van der Harst |first10=P |last11=Ye |first11=CJ |last12=Powell |first12=J |last13=Theis |first13=FJ |last14=Mahfouz |first14=A |last15=Heinig |first15=M |last16=Franke |first16=L |title=The single-cell eQTLGen consortium. |journal=eLife |date=9 March 2020 |volume=9 |doi=10.7554/eLife.52155 |pmid=32149610|pmc=7077978 |doi-access=free }}{{cite journal |last1=Nathan |first1=A |last2=Asgari |first2=S |last3=Ishigaki |first3=K |last4=Valencia |first4=C |last5=Amariuta |first5=T |last6=Luo |first6=Y |last7=Beynor |first7=JI |last8=Baglaenko |first8=Y |last9=Suliman |first9=S |last10=Price |first10=AL |last11=Lecca |first11=L |last12=Murray |first12=MB |last13=Moody |first13=DB |last14=Raychaudhuri |first14=S |title=Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. |journal=Nature |date=June 2022 |volume=606 |issue=7912 |pages=120–128 |doi=10.1038/s41586-022-04713-1 |pmid=35545678|s2cid=248730439 |pmc=9842455 |bibcode=2022Natur.606..120N }}

• Epigenome-wide association study
• Quantitative trait locus (QTL)
• Transcriptome-wide association study

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{{DEFAULTSORT:Expression Quantitative Trait Loci}}

Category:Classical genetics

Category:Statistical genetics

Category:Quantitative trait loci