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Most biological traits have a multi factorial (or complex) inheritance, which indicates that they are influenced by numerous genes and environmental factors. A chromosomal region that contains one or more genes that influence a multi-factorial trait is known as a QTL. The QTLs for a particular trait are often found on different chromosomes Knowing the number of QTLs that explains variation in the phenotypic trait tells us about the genetic architecture of a trait. The principal challenge with multi-factorial traits lies not in detecting QTLs, but in unraveling the genes that underlie them.
Once a region of DNA is identified as contributing to a phenotype, it can be sequenced. 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. The identification of genes and mutations that underlie QTLs is problematic for several reasons.
For these reasons, QTLs are often mapped to chromosomal regions that are over 20 centi Morgan (cM) long (~20 megabase pairs (Mb)) and that might contain several hundred genes. Since the first experiment reported in 1995, several genomewide scans for QTL have resulted in identification of numerous QTLs affecting production, health or conformational traits in livestock and poultry. Depending on the assumed effective size of the population, between 50 and 100 segregating genes are expected to affect the variation of a given quantitative trait.
QTL mapping is the statistical study of the alleles that occur in a locus and the phenotypes (physical forms or traits) that they produce, because, most of the traits are governed by more than one gene. Defining and studying the entire locus of genes related to a trait helps in understanding the effect of the genotype of an individual on its phenotype.
Statistical analysis is required to demonstrate that different genes in a QTL interact with one another and to determine whether they produce a significant effect on the phenotype. QTLs for a particular trait are located in particular regions of the genome. In the mapping experiment the probability of association is plotted for each marker and shown as intervals across a chromosome.
To begin, a set of genetic markers must be developed for the species in question. The aim is to find a marker that is significantly more likely to co-occur with the trait than expected by chance, that is, a marker that has a statistical association with the trait. Ideally, they would be able to find the specific gene or genes in question. Instead, they can more readily find regions of DNA that are very close to the genes in question.
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, 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. 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.
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-statistic.
Lander and Botstein developed interval mapping, which is currently the most popular approach for QTL mapping in experimental crosses. In Composite Interval Mapping (CIM), one performs interval mapping using a subset of marker loci as covariates.
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