Editing Quantitative trait locus (section)

==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>