Quantitative traits

• Cleft palate
• Congenital heart defects
• Neural tube defects
• Pyloric stenosis
• Talipes Adult onset diseases
• Diabetes Mellitus
• Cancer .

  Multifactorial traits in general

  Generally, multifactorial traits outside of illness contribute to what we see as continuous characteristics in organisms, such as height. All of these phenotypes are complicated by a great deal of interplay between genes and environment.
     Thus, due to the nature of polygenic traits, inheritance won't follow the same pattern as a simple monohybrid or dihybrid cross More often than not, investigators will hypothesise that a disease is multifactorially heritable, along with a cluster of other hypotheses when it isn't known what causes the disease.

  Quantitative trait locus

  Typically, QTLs underlie continuous traits (those traits that vary continuously - the trait could have any value within a range - for example, height) as opposed to discrete traits (traits that have two or several character values - for example, eye colour or smooth vs. wrinkled peas used by Mendel in his experiments).
     Moreover, a single phenotypic trait is usually determined by many genes. Consequently, many QTLs are associated with a single trait.
     A quantitative trait locus (QTL) is a region of DNA that's associated with a particular phenotypic trait - these QTLs 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. It may tell us that plant height is controlled by many genes of small effect, or by a few genes of large effect.
     Another use of QTLs is to identify candidate genes underlying a trait. 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.
     In a recent development, classical QTL analyses are combined with gene expression profiling for example by DNA microarrays. Such expression QTLs (e-QTLs) describe cis- and trans-controlling elements for the expression of often disease-associated genes. Observed 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.

  QTL mapping

  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 traits of interest are governed by more than one gene, defining and studying the entire locus of genes related to a trait gives hope of understanding what effect the genotype of an individual might have in the real world.
     Statistical analysis is required to demonstrate that different genes interact with one another and to determine whether they produce a significant effect on the phenotype. QTLs identify a particular region of the genome as containing a gene that's associated with the trait being assayed or measured. They are shown as intervals across a chromosome, where the probability of association is plotted for each marker used in the mapping experiment.
     The QTL techniques were developed in the late 1980s and can be performed on inbred strains of any species.
     To begin, a set of genetic markers must be developed for the species in question. A marker is an identifiable region of variable DNA. Biologists are interested in understanding the genetic basis of phenotypes (physical traits). The aim is to find a marker that's 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'd be able to find the specific gene or genes in question, but this is a long and difficult undertaking. Instead, they can more readily find regions of DNA that are very close to the genes in question. When a QTL is found, it's often not the actual gene underlying the phenotypic trait, but rather a region of DNA that's closely linked with the gene.
     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 isn't 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.
     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.

  QTL mapping in plants

  The beauty of QTL mapping in plants is facility of experimental crosses in contrast to humans.

  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-statistic. The ANOVA approach for QTL mapping has three important weaknesses. First, we don't 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 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.

  Interval mapping

  Lander and Botstein developed interval mapping, which overcomes the three disadvantages of analysis of variance at marker loci. 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. Each location in the genome is posited, one at a time, as the location of the putative QTL.

  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 hasn't 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.

  Non-traditional methods: Family-pedigree based mapping

  The human genetic approach has taken attention to plant scientist to fit in to plant breeders families (for review- Jannik et al., 2001). There are some successful attempts to do so. One of quick method of QTL mapping is recently discussed (Rosyara et al., 2007).
     Jannink, J.; Bink, M. C.A.M.; and Jansen, R.C. 2001. Using complex plant pedigrees to map valuable genes. Trends in Plant Science 6: 337-342.
     Rosyara, U. R.; K.L. Maxson-Stein; K.D. Glover; J.M. Stein; J.L. Gonzalez-Hernandez. 2007. Family-based mapping of FHB resistance QTLs in hexaploid wheat. Proceedings of National Fusarium head blight forum, 2007, Dec 2-4, Kansas City, MO.
  

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