QTL analysis

QTL analysis is based on the principle of detecting an association between phenotype and the genotype of genomic markers. QTL analysis find its application in marker-assisted selection (MAS). DNA markers which are linked to agronomically important genes can be used as molecular tools for MAS.

QTLs, quantative trait loci, are genomic regions which contain genes associated with a particular quantative trait (including polygenic, multifactorial or complex traits). QTLs cannot be identified by conventrional phenotypic evaluation; molecular markers are required for QTL identification. QTL mapping is the result of the process of linkage maps construction combined with QTL analysis. DNA markers which are linked to agronomically important genes can be used as molecular tools for marker-assisted selection.

Markers are used to partition the mapping population into different genotypic groups, based on the presence or absence of a particular marker locus, and to determine whether significant differences exist between groups with respect to the trait being measured.
A significant difference between phenotypic means of the groups (either 2 or 3), depending on the marker system and type of population, indicates that the marker locus being used to partition the mapping population is linked to a QTL controlling the trait.
The closer a marker is to a QTL, the lower the chance of recombination occurring between marker and QTL. A linked QTL and marker combination will be usually be inherited together in the progeny. The mean of the group with the tightly-linked marker will be significantly different (P < 0.05) to the mean of the group without the marker.

Three widely-used methods for detecting QTLs are single-marker analysis, simple interval mapping and composite interval mapping.
Single-marker analysis (or single-point analysis) is the simplest method for detecting QTLs associated with single markers. The statistical methods used for single-marker analysis include t-tests, analysis of variance (ANOVA) and linear regression.
The simple interval mapping (SIM) method makes use of linkage maps and analyses intervals between adjacent pairs of linked markers along chromosomes simultaneously, instead of analyzing single markers.
The use of linked markers for analysis compensates for recombination between the markers and the QTL, and is considered statistically more powerful compared to single-point analysis.
Composite interval mapping (CIM) recently became popular for QTL mapping. This method combines interval mapping with linear regression and includes additional genetic markers in the statistical model in addition to an adjacent pair of linked markers for interval mapping. CIM is more precise and effective at mapping QTLs compared to single-point analysis and interval mapping, especially when linked QTLs are involved.

Within the GPC SP4 developed or adapted software for iMAS and QTL analyis (QTLxE).

• iMAS


• QTLxE ( the QTL software is under development)

Presentations and documents presented at GCP statistics courses can be found on the Germplasm Data Analysis (GDA) pages of Biometris, the WUR centre for mathematical and statistical expertise.
Subjects discussed are molecular and phenotypic data, germplasm sampling, linkage disequilibrium and association analysis.


Note: the information above is to a great extent derived from
Collard BCY, MZZ Jahufer, JB Brouwer and ECK Pang. 2005. An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142: 169–196.

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