Editing Quantitative trait locus

{{short description|DNA locus associated with variation in a quantitative trait}}
{{Use dmy dates|date=February 2020}}
A '''quantitative trait locus''' ('''QTL''') is a [[Locus (genetics)|locus]] (section of [[DNA]]) that correlates with variation of a [[quantitative trait]] in the [[phenotype]] of a [[Population genetics|population]] of [[organism]]s.<ref>{{Cite journal|url = http://www.nature.com/scitable/topicpage/quantitative-trait-locus-qtl-analysis-53904|title = Quantitative trait locus (QTL) analysis|last1 = Miles|first1 = C|date = 2008|journal = Nature Education|volume = 1|first2 = M|last2 = Wayne|issue = 1}}</ref> QTLs are mapped by identifying which molecular markers (such as [[single-nucleotide polymorphism|SNP]]s or [[amplified fragment length polymorphism|AFLPs]]) correlate with an observed trait. This is often an early step in identifying the actual [[gene]]s that cause the trait variation.

==Definition==
A '''quantitative trait locus''' ('''QTL''') is a region of [[DNA]] which is associated with a particular [[Phenotype|phenotypic]] [[Trait (biological)|trait]], which varies in degree and which can be attributed to [[polygene|polygenic]] effects, i.e., the product of two or more [[gene]]s, and their environment.<ref name="ComplTraitCons">{{cite journal |author1=Oduola Abiola |display-authors=et al |title=The nature and identification of quantitative trait loci: a community's view |journal=[[Nature Reviews Genetics]] |publisher=[[Nature Portfolio]] |date=2003 |volume=4 |issue=11 |pages=911–916 |doi=10.1038/nrg1206 |pmid=14634638 |pmc=2063446 |s2cid=27285742}} {{S2CID|195367115}}.</ref> These QTLs are often found on different [[chromosomes]]. The number of QTLs which explain variation in the phenotypic trait indicates the [[genetic architecture]] of a trait. It may indicate that plant height is controlled by many genes of small effect, or by a few genes of large effect.{{cn|date=July 2024}}

Typically, QTLs underlie continuous [[Trait (biological)|trait]]s (those traits which vary continuously, e.g. height) as opposed to discrete traits (traits that have two or several character values, e.g. red hair in humans, a recessive trait, or smooth vs. wrinkled peas used by [[Gregor Mendel|Mendel]] in his experiments).

Moreover, a single [[Phenotype|phenotypic]] trait is usually determined by many genes. Consequently, many QTLs are associated with a single trait.
Another use of QTLs is to identify [[candidate gene]]s underlying a trait. The DNA sequence of any genes in this region can then be compared to a database of DNA for genes whose function is already known, this task being fundamental for marker-assisted crop improvement.<ref>{{cite journal |last1=Watanabe |first1=Satoshi |last2=Hideshima |first2=Rumiko |last3=Xia |first3=Zhengjun  |display-authors=etal|title=Map-Based Cloning of the Gene Associated With the Soybean Maturity Locus E3 |journal=Genetics |date=2009 |volume=182 |issue=4 |pages=1251–1262 |doi=10.1534/genetics.108.098772|pmid=19474204 |pmc=2728863 }}</ref>

==History==
[[Mendelian inheritance]] was rediscovered at the beginning of the 20th century. As [[Gregor Mendel|Mendel]]'s ideas spread, geneticists began to connect Mendel's rules of inheritance of single factors to [[Darwinian evolution]]. For early geneticists, it was not immediately clear that the smooth variation in traits like body size (i.e., [[Dominance (genetics)#Incomplete dominance|incomplete dominance]]) was caused by the inheritance of single genetic factors. Although [[Charles Darwin|Darwin]] himself observed that inbred features of fancy pigeons were inherited in accordance with Mendel's laws (although Darwin did not actually know about Mendel's ideas when he made the observation), it was not obvious that these features selected by fancy pigeon breeders can similarly explain quantitative variation in nature.<ref name="literature.org">{{cite web |url=http://www.literature.org/authors/darwin-charles/the-origin-of-species/ |title=Origin of Species |access-date=24 September 2013 |url-status=dead |archive-url=https://web.archive.org/web/20131003145126/http://www.literature.org/authors/darwin-charles/the-origin-of-species/ |archive-date=3 October 2013  }}</ref>

An early attempt by [[William Ernest Castle]] to unify the laws of Mendelian inheritance with Darwin's theory of speciation invoked the idea that species become distinct from one another as one species or the other acquires a novel Mendelian factor.<ref name="ncbi.nlm.nih.gov">{{cite journal |pmid=17752783 | doi=10.1126/science.18.456.396 | volume=18 | issue=456 | title=Mendel's Law of Heredity | year=1903 | journal=Science | pages=396–406 | author = Castle WE| bibcode=1903Sci....18..396C | s2cid=11670642 | url=https://zenodo.org/record/1447900 }}</ref> Castle's conclusion was based on the observation that novel traits that could be studied in the lab and that show Mendelian inheritance patterns reflect a large deviation from the wild type, and Castle believed that acquisition of such features is the basis of "discontinuous variation" that characterizes speciation.<ref name="ncbi.nlm.nih.gov"/> Darwin discussed the inheritance of similar mutant features but did not invoke them as a requirement of speciation.<ref name="literature.org"/> Instead Darwin used the emergence of such features in breeding populations as evidence that mutation can occur at random within breeding populations, which is a central premise of his model of selection in nature.<ref name="literature.org"/> Later in his career, Castle would refine his model for speciation to allow for small variation to contribute to speciation over time. He also was able to demonstrate this point by selectively breeding laboratory populations of rats to obtain a hooded phenotype over several generations.<ref>{{cite journal|url=http://www.genetics.org/content/36/3/254|title=Variation in the Hooded Pattern of Rats, and a New Allele of Hooded|first=W. E.|last=Castle|date=1 May 1951|journal=Genetics|volume=36|issue=3|pages=254–266|doi=10.1093/genetics/36.3.254|via=www.genetics.org|pmid=14840647|pmc=1209518}}</ref>

Castle's was perhaps the first attempt made in the scientific literature to direct evolution by artificial selection of a trait with continuous underlying variation, however the practice had previously been widely employed in the development of [[agriculture]] to obtain livestock or plants with favorable features from populations that show quantitative variation in traits like body size or grain yield.{{cn|date=July 2024}}

Castle's work was among the first to attempt to unify the recently rediscovered laws of Mendelian inheritance with Darwin's theory of evolution. Still, it would be almost thirty years until the theoretical framework for evolution of [[complex traits]] would be widely formalized.<ref name="genetics.org">{{cite journal|url=http://www.genetics.org/content/16/2/97|title=Evolution in Mendelian Populations|first=Sewall|last=Wright|date=1 March 1931|journal=Genetics|volume=16|issue=2|pages=97–159|doi=10.1093/genetics/16.2.97|via=www.genetics.org|pmid=17246615|pmc=1201091}}</ref> In an early summary of the theory of evolution of continuous variation, [[Sewall Wright]], a graduate student who trained under Castle, summarized contemporary thinking about the genetic basis of quantitative natural variation: "As genetic studies continued, ever smaller differences were found to mendelize, and any character, sufficiently investigated, turned out to be affected by many factors."<ref name="genetics.org"/> Wright and others formalized population genetics theory that had been worked out over the preceding 30 years explaining how such traits can be inherited and create stably breeding populations with unique characteristics. Quantitative trait genetics today leverages Wright's observations about the statistical relationship between genotype and phenotype in families and populations to understand how certain genetic features can affect variation in natural and derived populations.{{cn|date=July 2024}}

==Quantitative traits==
{{See also|Monogenic inheritance|Oligogenic inheritance}}
'''Polygenic inheritance''' refers to inheritance of a [[phenotype|phenotypic]] characteristic (trait) that is attributable to two or more [[genes]] and can be measured quantitatively. '''Multifactorial inheritance''' refers to polygenic inheritance that also includes interactions with the environment. Unlike [[monogenic trait]]s, polygenic traits do not follow patterns of [[Mendelian inheritance]] (discrete categories). Instead, their phenotypes typically vary along a continuous gradient depicted by a [[Normal distribution|bell curve]].<ref>{{
citation
|author=Ricki Lewis
|title=Multifactorial Traits
|url=http://highered.mcgraw-hill.com/sites/007246268x/student_view0/chapter7/
|publisher=McGraw-Hill Higher Education
|date=2003
}}.</ref>

An example of a polygenic trait is [[human skin color]] variation. Several genes factor into determining a person's natural skin color, so modifying only one of those genes can change skin color slightly or in some cases, such as for [[SLC24A5]], moderately. Many disorders with [[genetic disorder|genetic components]] are polygenic, including [[autism]], [[cancer]], [[diabetes]] and numerous others. Most phenotypic characteristics are the result of the interaction of multiple genes.{{cn|date=July 2024}}


Multifactorially inherited diseases are said to constitute the majority of genetic disorders affecting humans which will result in hospitalization or special care of some kind.<ref name="Tissot">
{{cite web  | last = Tissot  | first = Robert  | title = Human Genetics for 1st Year Students: Multifactorial Inheritance  | url = http://www.uic.edu/classes/bms/bms655/lesson11.html  | access-date = 6 January 2007 }}
</ref><ref name="Clinical Genetics">
{{cite web  |author=Birth Defects Genetics Centre, University of South Dakota School of Medicine  | title = Multifactorial Inheritance  | work = Clinical Genetics: A Self-Study Guide for Health Care Providers  | publisher = University of South Dakota School of Medicine  | url = http://www.usd.edu/med/som/genetics/curriculum/1GMULTI5.htm  | access-date = 6 January 2007 |archive-url = https://web.archive.org/web/20061230084542/http://www.usd.edu/med/som/genetics/curriculum/1GMULTI5.htm |archive-date = 30 December 2006}}
</ref>

===Multifactorial traits in general===
Traits controlled both by the environment and by genetic factors are called multifactorial.
Usually, multifactorial traits outside of illness result in what we see as '''continuous characteristics''' in organisms, especially human organisms such as: height,<ref name="Tissot" /> skin color, and body mass.<ref name="MedicineNet">{{cite web  | title = Definition of Multifactorial inheritance  | work = MedicineNet.com MedTerms Dictionary  | publisher = MedicineNet.com  | url = http://www.medterms.com/script/main/art.asp?articlekey=4453  | access-date = 6 January 2007  | archive-date = 17 December 2013  | archive-url = https://web.archive.org/web/20131217193339/http://www.medterms.com/script/main/art.asp?articlekey=4453  | url-status = dead  }}</ref> All of these phenotypes are complicated by a great deal of give-and-take between genes and environmental effects.<ref name="Tissot" /> The continuous distribution of traits such as height and skin color described above, reflects the action of genes that do not manifest typical patterns of dominance and recessiveness. Instead the contributions of each involved locus are thought to be additive. Writers have distinguished this kind of inheritance as ''polygenic'', or ''quantitative inheritance''.<ref name="Turnpenny">
{{cite book  | last = Turnpenny  | first = Peter  | title = Emery's Elements of Medical Genetics  | edition = 12th  | chapter = Chapter 9  | publisher = Elsevier  | date = 2004  | chapter-url = http://www.fleshandbones.com/readingroom/viewchapter.cfm?ID=1041  | chapter-format = PDF  | access-date = 6 January 2007 }}
</ref>

Thus, due to the nature of polygenic traits, inheritance will not follow the same pattern as a simple [[monohybrid cross|monohybrid]] or [[dihybrid cross]].<ref name="Clinical Genetics" /> Polygenic inheritance can be explained as Mendelian inheritance at many loci,<ref name="Tissot" /> resulting in a trait which is [[normal distribution|normally-distributed]]. If ''n'' is the number of involved loci, then the coefficients of the [[binomial distribution|binomial expansion]] of (''a'' + ''b'')<sup>''2n''</sup> will give the frequency of distribution of all ''n'' allele [[combination]]s. For sufficiently high values of ''n'', this binomial distribution will begin to resemble a normal distribution. From this viewpoint, a disease state will become apparent at one of the tails of the distribution, past some threshold value. Disease states of increasing severity will be expected the further one goes past the threshold and away from the [[statistical mean|mean]].<ref name="Turnpenny" />

===Heritable disease and multifactorial inheritance===
A mutation resulting in a disease state is often recessive, so both alleles must be mutant in order for the disease to be expressed phenotypically. A disease or syndrome may also be the result of the expression of mutant alleles at more than one locus. When more than one gene is involved, with or without the presence of environmental triggers, we say that the disease is the result of multifactorial inheritance.{{cn|date=July 2024}}

The more genes involved in the cross, the more the distribution of the [[genotype]]s will resemble a [[normal distribution|normal, or Gaussian]] distribution.<ref name="Tissot" /> This shows that multifactorial inheritance is polygenic, and genetic frequencies can be predicted by way of a polyhybrid [[Mendelian inheritance|Mendelian]] cross. Phenotypic frequencies are a different matter, especially if they are complicated by environmental factors.{{cn|date=July 2024}}

The paradigm of polygenic inheritance as being used to define multifactorial disease has encountered much disagreement. Turnpenny (2004) discusses how simple polygenic inheritance cannot explain some diseases such as the onset of Type I diabetes mellitus, and that in cases such as these, not all genes are thought to make an equal contribution.<ref name="Turnpenny" />

The assumption of polygenic inheritance is that all involved loci make an equal contribution to the symptoms of the disease. This should result in a normal (Gaussian) distribution of genotypes. When it does not, the idea of polygenetic inheritance cannot be supported for that illness.{{cn|date=July 2024}}

===Examples===
The above are well-known examples of diseases having both genetic and environmental components. Other examples involve atopic diseases such as [[atopic eczema|eczema]] or [[atopic dermatitis|dermatitis]],<ref name="Tissot"/> [[spina bifida]] (open spine), and [[anencephaly]] (open skull).<ref name="Proud">{{cite web  | author = Proud, Virginia  | author2 = Roberts, Helen  | name-list-style = amp  | title = Medical Genetics: Multifactorial Inheritance  | publisher = Children's Hospital of the King's Daughters  | date = 31 December 2005  | url = http://www.chkd.org/HealthLibrary/Content.aspx?pageid=P02134  | access-date = 6 January 2007  | archive-date = 15 October 2006  | archive-url = https://web.archive.org/web/20061015185017/http://www.chkd.org/HealthLibrary/Content.aspx?pageid=P02134  | url-status = dead  }}</ref>

While [[schizophrenia]] is widely believed to be multifactorially genetic by [[Biopsychiatry|biopsychiatrists]], no characteristic genetic markers have been determined with any certainty.{{cn|date=July 2024}}

If it is shown that the brothers and sisters of the patient have the disease, then there is a strong chance that the disease is genetic{{citation needed|date=March 2017}} and that the patient will also be a genetic carrier. This is not quite enough as it also needs to be proven that the pattern of inheritance is non-Mendelian. This would require studying dozens, even hundreds of different family pedigrees before a conclusion of multifactorial inheritance is drawn. This often takes several years.{{cn|date=July 2024}}

If multifactorial inheritance is indeed the case, then the chance of the patient contracting the disease is reduced only if cousins and more distant relatives have the disease.<ref name="Proud" /> While multifactorially-inherited diseases tend to run in families, inheritance will not follow the same pattern as a simple [[monohybrid cross|monohybrid]] or [[dihybrid cross]].<ref name="Clinical Genetics" />

If a genetic cause is suspected and little else is known about the illness, then it remains to be seen exactly how many genes are involved in the phenotypic expression of the disease. Once that is determined, the question must be answered: if two people have the required genes, why are there differences in expression between them? Generally, what makes the two individuals different are likely to be environmental factors. Due to the involved nature of genetic investigations needed to determine such inheritance patterns, this is not usually the first avenue of investigation one would choose to determine etiology.{{citation needed|date=August 2017}}

[[File:Example of QTL-Scan on a single Chromosom from PLoS Biology.jpg|thumb|right|A QTL for [[osteoporosis]] on the human chromosome 20]]

==QTL mapping==

[[File:Example of a Genome-wide QTL-Scan from PLoS Biology.jpg|thumb|right|Example of a genome-wide scan for QTL of [[osteoporosis]]]]

For organisms whose genomes are known, one might now try to exclude genes in the identified region whose function is known with some certainty not to be connected with the trait in question. If the genome is not available, it may be an option to sequence the identified region and determine the putative functions of genes by their similarity to genes with known function, usually in other genomes. This can be done using [[BLAST (biotechnology)|BLAST]], an online tool that allows users to enter a primary sequence and search for similar sequences within the BLAST database of genes from various organisms. It is often not the actual gene underlying the phenotypic trait, but rather a region of DNA that is closely linked with the gene<ref>{{Cite web|url=https://blast.ncbi.nlm.nih.gov/Blast.cgi|title=BLAST: Basic Local Alignment Search Tool|website=blast.ncbi.nlm.nih.gov|access-date=2018-02-18}}</ref>

Another interest of statistical geneticists using QTL mapping is to determine the complexity of the genetic architecture underlying a phenotypic trait. For example, they may be interested in knowing whether a phenotype is shaped by many independent loci, or by a few loci, and do those loci interact. This can provide information on how the phenotype may be evolving.<ref>{{Cite journal|last1=Grisel|first1=Judith E.|last2=Crabbe|first2=John C.|date=1995|title=Quantitative Trait Loci Mapping|journal=Alcohol Health and Research World|volume=19|issue=3|pages=220–227|issn=0090-838X|pmc=6875759|pmid=31798043}}</ref>

In a recent development, classical QTL analyses were combined with gene expression profiling i.e. by [[DNA microarray]]s. Such [[Expression quantitative trait loci|expression QTLs (eQTLs)]] describe [[Cis-acting|cis]]- and [[Trans-acting|trans]]-controlling elements for the expression of often disease-associated genes.<ref>{{cite journal  |vauthors=Westra HJ, etal | year = 2013 | title = Systematic identification of trans eQTLs as putative drivers of known disease associations | journal = Nat Genet | volume = 45 | issue = 10| pages = 1238–1243 | doi = 10.1038/ng.2756 | pmid=24013639 | pmc=3991562}}</ref> Observed [[epistasis|epistatic effects]] have been found beneficial to identify the gene responsible by a cross-validation of genes within the interacting loci with [[metabolic pathway]]- and [[scientific literature]] databases.{{cn|date=July 2024}}

===Analysis of variance===
The simplest method for QTL mapping is analysis of variance ([[ANOVA]], sometimes called "marker regression") at the marker loci. In this method, in a backcross, one may calculate a [[t-statistic]] to compare the averages of the two marker [[genotype]] groups. For other types of
crosses (such as the intercross), where there are more than two possible genotypes, one uses a more general form of ANOVA, which provides a so-called [[F-statistics|F-statistic]]. The ANOVA approach for QTL mapping has three important weaknesses. First, we do not receive separate estimates of QTL location and QTL effect. QTL location is indicated only by looking at which markers give the greatest differences between genotype group averages, and the apparent QTL effect at a marker will be smaller than the true QTL effect as a result of [[Genetic recombination|recombination]] between the marker and the QTL. Second, we must discard individuals whose genotypes are missing at the marker. Third, when the markers are widely spaced, the QTL may be quite far from all markers, and so the power for QTL detection will decrease.{{cn|date=July 2024}}

===Interval mapping===
Lander and Botstein developed interval mapping, which overcomes the three disadvantages of analysis of variance at marker loci.<ref>{{cite journal |last1=Lander |first1=E.S. |last2=Botstein |first2=D. |title=Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. |journal=Genetics |date=1989 |volume=121 |issue=1 |pages=185–199 |doi=10.1093/genetics/121.1.185 |pmid=2563713|pmc=1203601 }}</ref> Interval mapping is currently the most popular approach for QTL mapping in experimental crosses. The method makes use of a [[genetic map]] of the typed markers, and, like analysis of variance, assumes the presence of a single QTL. In interval mapping, each locus is considered one at a time and the logarithm of the [[odds ratio]] ([[LOD score]]) is calculated for the model that the given locus is a true QTL. The odds ratio is related to the [[Pearson correlation coefficient]] between the phenotype and the marker genotype for each individual in the experimental cross.<ref>Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits edn 1 (Sinauer Associates, 1998).</ref>

The term 'interval mapping' is used for estimating the position of a QTL within two markers (often indicated as 'marker-bracket'). Interval mapping is originally based on the maximum likelihood but there are also very good approximations possible with simple regression.{{cn|date=July 2024}}

The principle for QTL mapping is:
1) The likelihood can be calculated for a given set of parameters (particularly QTL effect and QTL position) given the observed data on phenotypes and marker genotypes.
2) The estimates for the parameters are those where the likelihood is highest.
3) A significance threshold can be established by permutation testing.<ref>{{cite journal |author1=Bloom J. S. |author2=Ehrenreich I. M. |author3=Loo W. T. |author4=Lite T.-L. V. |author5=Kruglyak L. | year = 2013 | title = Finding the sources of missing heritability in a yeast cross | journal = Nature | volume = 494 | issue = 7436| pages = 234–237 | doi = 10.1038/nature11867 | pmid=23376951 | pmc=4001867|arxiv=1208.2865 |bibcode=2013Natur.494..234B }}</ref>

Conventional methods for the detection of quantitative trait loci (QTLs) are based on a comparison of single QTL models with a model assuming no QTL. For instance in the "interval mapping" method<ref>Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. ES Lander and D Botstein. Genetics. 1989</ref> the likelihood for a single putative QTL is assessed at each location on the genome. However, QTLs located elsewhere on the genome can have an interfering effect. As a consequence, the power of detection may be compromised, and the estimates of locations and effects of QTLs may be biased (Lander and Botstein 1989; Knapp 1991). Even nonexisting so-called "ghost" QTLs may appear (Haley and Knott 1992; Martinez and Curnow 1992). Therefore, multiple QTLs could be mapped more efficiently and more accurately by using multiple QTL models.<ref>{{cite journal |last1=Jansen |first1=R C |title=Interval mapping of multiple quantitative trait loci. |journal=Genetics |date=1 September 1993 |volume=135 |issue=1 |pages=205–211 |doi=10.1093/genetics/135.1.205 |pmid=8224820 |pmc=1205619 |url=http://www.genetics.org/content/135/1/205.full.pdf |access-date=1 March 2023}}</ref> One popular approach to handle QTL mapping where multiple QTL contribute to a trait is to iteratively scan the genome and add known QTL to the regression model as QTLs are identified. This method, termed [[composite interval mapping]] determine both the location and effects size of QTL more accurately than single-QTL approaches, especially in small mapping populations where the effect of correlation between genotypes in the mapping population may be problematic.{{cn|date=July 2024}}
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===Composite interval mapping (CIM)===

In this method, one performs interval mapping using a subset of marker loci as covariates. These markers serve as proxies for other QTLs to increase the resolution of interval mapping, by accounting for linked QTLs and reducing the residual variation. The key problem with CIM concerns the choice of suitable marker loci to serve as covariates; once these have been chosen, CIM turns the model selection problem into a single-dimensional scan. The choice of marker covariates has not been solved, however. Not surprisingly, the appropriate markers are those closest to the true QTLs, and so if one could find these, the QTL mapping problem would be complete anyway.

[[Inclusive composite interval mapping]] (ICIM) has also been proposed as a potential method for QTL mapping.<ref>{{Cite journal |last1=Li |first1=Shanshan |last2=Wang |first2=Jiankang |last3=Zhang |first3=Luyan |date=2015-07-10 |title=Inclusive Composite Interval Mapping of QTL by Environment Interactions in Biparental Populations |journal=PLOS ONE |language=en |volume=10 |issue=7 |pages=e0132414 |doi=10.1371/journal.pone.0132414 |issn=1932-6203 |pmc=4498613 |pmid=26161656 |bibcode=2015PLoSO..1032414L |doi-access=free }}</ref>

===Family-pedigree based mapping===
[[Family-based QTL mapping]], or Family-pedigree based mapping (Linkage and [[association mapping]]), involves multiple families instead of a single family. Family-based QTL mapping has been the only way for mapping of genes where experimental crosses are difficult to make. However, due to some advantages, now plant geneticists are attempting to incorporate some of the methods pioneered in human genetics.<ref name=Jannink2001>{{cite journal |pmid=11495765 |date=Aug 2001 |author=Jannink, J |author2=Bink, Mc |author3=Jansen, Rc |title=Using complex plant pedigrees to map valuable genes |volume=6 |issue=8 |pages=337–42 |issn=1360-1385 |journal=Trends in Plant Science |doi=10.1016/S1360-1385(01)02017-9}}</ref> Using family-pedigree based approach has been discussed (Bink et al. 2008). Family-based linkage and association has been successfully implemented (Rosyara et al. 2009)<ref name=Rosyara2007>{{Cite journal | last1 = Rosyara | first1 = U. R. | last2 = Maxson-stein | first2 = K.L. | last3 = Glover | first3 = K.D. | last4 = Stein | first4 = J.M. | last5 = Gonzalez-hernandez | first5 = J.L. | date = 2007 | title = Family-based mapping of FHB resistance QTLs in hexaploid wheat | journal = Proceedings of National Fusarium Head Blight Forum }}</ref>

==See also==
{{col div|colwidth=30em}}
* [[Association mapping]]
* [[Family-based QTL mapping]]
* [[Epistasis]]
* [[Dominance (genetics)]]
* [[Expression quantitative trait loci|Expression quantitative trait loci (eQTL)]]
* [[Genetic predisposition]]
* [[Nested association mapping]]
* [[Oncogene]]
* [[Public health genomics#Genetic susceptibility to disease|Genetic susceptibility]]
*[[Protein Quantitative Trait Loci]]
{{colend}}

==References==
{{Reflist}}
* Bink MCAM, Boer MP, ter Braak CJF, Jansen J, Voorrips RE, van de Weg WE: Bayesian analysis of complex traits in pedigreed plant populations.
Euphytica 2008, 161:85–96.
* Rosyara U.R., J.L. Gonzalez-Hernandez, K.D. Glover, K.R. Gedye and J.M. Stein. 2009. Family-based mapping of quantitative trait loci in plant breeding populations with resistance to Fusarium head blight in wheat as an illustration [https://doi.org/10.1007%2Fs00122-009-1010-9 Theoretical Applied Genetics 118:1617–1631]
* Garnier, Sophie, Truong, Vinh, Genome-Wide Haplotype Analysis of Cis Expression Quantitative Trait Loci in Monocytes [http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003240]

==External links==
* [https://web.archive.org/web/20150918103054/http://www.extension.org/plant_breeding_genomics Plant Breeding and Genomics on eXtension.org]
* [http://intersnp.meb.uni-bonn.de/ INTERSNP] – a software for genome-wide interaction analysis (GWIA) of case-control SNP data and analysis of quantitative traits
* [http://www.genetics.org/cgi/content/abstract/136/4/1457 Precision Mapping of Quantitative Trait Loci]
* [http://statgen.ncsu.edu/qtlcart/ QTL Cartographer]
* [http://www.complextrait.org/ Complex Trait Consortium]
* [http://www.genetics.org/cgi/content/abstract/159/1/371 A Statistical Framework for Quantitative Trait Mapping]
* [http://www.genenetwork.org/ GeneNetwork]
* [http://www.gridqtl.org.uk/ GridQTL]
* [https://web.archive.org/web/20180805111331/http://qtl.com/ QTL discussion forum]
* [https://web.archive.org/web/20120826024732/http://www.nslij-genetics.org/soft/ A list of computer programs for genetic analysis including QTL analysis]
* [http://www.nature.com/scitable/topicpage/quantitative-trait-locus-qtl-analysis-53904 Quantitative Trait Locus (QTL) Analysis] @ [[Scitable]]
* [http://www.ndsu.edu/pubweb/~mcclean/plsc731/quant/quant1.htm Mapping Quantitative Trait Loci]
* [http://www2.warwick.ac.uk/fac/sci/lifesci/research/vegin/geneticimprovement/qtl/ What are Quantitative Trait Loci?] – [[University of Warwick]]

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{{DEFAULTSORT:Quantitative Trait Locus}}
[[Category:Classical genetics]]
[[Category:Statistical genetics]]
[[Category:Quantitative trait loci]]
[[Category:Genetic epidemiology]]
[[Category:Quantitative genetics]]