Quantitative Trait Locus, Essay Example

Quantititative trait locus is a biological term that refers to the inheritance of phenotypic characteristics which vary in interaction between two or more genes.  Essentially stretches of DNA that underpin the very traits of the genes.  Normally a single phenotypic trait contains the characteristics of many genes. An example of a complex genetic disorder is that of autism characterised by language deficiency, behavioural problems and repetition.  A highly qualified medical team conducted a genome scan using the social responsiveness scale to conduct a quantitative trait locus analysis over a sample base of over 100 families.  The study demonstrated the use of the social responsiveness scale in the detection of the autism genetic related loci; with quantitative behavioural analysis pointing to the genetic dispositions of the disease. (Jacqueline A. Duvall, 2007).

The statistical analysis techniques of QTL combine two basic types of information – phenotypic data (trait measurements) and genotypic data (usually molecular markers). In order to carry out our QTL analysis you require the following things:

1. Organisms, two or more samples that contain different traits;
2. Genetic markers that distinguish between the parental lines

Hence the analysis .. ” Then, to carry out the QTL analysis, the parental strains are crossed, resulting in heterozygous (F₁) individuals, and these individuals are then crossed using one of a number of different schemes (Darvasi, 1998). Finally, the phenotypes and genotypes of the derived (F₂) population are scored. Markers that are genetically linked to a QTL influencing the trait of interest will segregate more frequently with trait values (large or small egg size in our example), whereas unlinked markers will not show significant association with phenotype ”  (Cecelia M. Miles, 2008)

The main goal of QTL Analysis is the determination of whether phenotypic differences are derived from few loci with large effects or many loci with small effects.  Statistics seems to concur that most differences take place with few loci causing large effects. ” Historically, the availability of adequately dense markers (genotypes) has been the limiting step for QTL analysis. However, high-throughput technologies and genomics have begun to overcome this barrier. Thus, the remaining limitations in QTL analysis are now predominantly at the level of phenotyping, although the use of genomic and proteomic data as phenotypes circumvents this challenge to some extent.”  (Cecelia M. Miles, 2008).

A large number of phenotypic traits in disease susceptibility is researched in both animals and plants via QTL mapping.  QTL are essentially regions of genomes that are associated with specific traits.  Evolutionary biologists and geneticists use QTL analysis in order to obtain a broader view of molecular structures.  ” One goal of global eQTL analysis is to identify loci controlling the expression variation of gene networks associated with various biological functions. One approach is to generate a mapping population, assess global gene expression using microarrays, and identify eQTLs controlling the expression of each gene via individual statistical analyses. The eQTL locations from these individual analyses for all genes are then superimposed to identify common regions that control the expression of a large number of genes, i.e. contain ‘broad effect’ eQTLs.”  (Daniel J Kliebenstein, 2006)

eQTL and mQTL Mapping:  An interesting exercise was conducted in May 2006 by collecting sample tissues from 6 spruce pedigrees (trees).  There were 417 trees in the sample  with 6 variations.  At the time of sampling they recorded weevil attack in a range from very low to severe.  ” After scoring and collection, and removal of all extraneous samples (i.e. dead leader, other insect infestation other than weevil, aphids, physical damage, etc.), DNA extraction was performed.  Also, upper laterals that appear to be leader-like were taken from 263 individuals of the four crosses, fixed in FAA and stored in 70% ethanol. Those samples will be investigated for 4-5 attributes that can be rapidly detected through light-microscopy.”  (Treenomix, 2009).

Nickolai Alexandrov, considered that ” phenotypic variations can be explained by differential gene expression” and in addition the change in protein sequence. Further the actual change in gene expression is one of the most typical and common type of phenotypic variations. Further in the resulting experiments it was discovered that ” genes whose expression correlate most significantly with various traits and found that their location in the genome remarkably coincides with the independently discovered QTLs for these traits. In 10 out of 15 traits the best correlating genes are located within the main QTL. In 14 out of 15 cases, the main QTL peak includes one of the top three most significantly correlating genes.  QTL peaks were identified by composite interval mapping using QTL Cartographer software.”  (Nickolai Alexandrov, 2009).

Works Cited

Cecelia M. Miles, P. &. (2008). Quantitative Trait Locus (QTL) Analysis. Retrieved 11 14, 2009, from Genetics: http://www.nature.com/scitable/topicpage/Quantitative-Trait-Locus-QTL-Analysis-53904

Daniel J Kliebenstein, M. A. (2006, 7). Identification of QTLs controlling gene expression networks defined a priori. Retrieved 11 13, 2011, from BMC Bioinfomatics: http://www.biomedcentral.com/1471-2105/7/308

Jacqueline A. Duvall, B. A. (2007, 4). A Quantitative Trait Locus Analysis of Social Responsiveness in Multiplex Autism Families. Retrieved 11 14, 2011, from American Journal of Psychiatry: http://ajp.psychiatryonline.org/cgi/content/abstract/164/4/656

Nickolai Alexandrov, J. G. (2009, 11 3). Footrprints in the Landscape. Retrieved 11 14, 2011, from ASA-CSSA-SSSA: http://acs.confex.com/crops/2009am/webprogram/Paper54610.html

Treenomix. (2009). Treenomix. Retrieved 11 14, 2011, from QTL and Association Studies of Resistance and Mapping of Gene Expression Profiles : http://www.treenomix.ca/Home/ResearchActivities/ConiferComparativeGenomics/QTLandAssociationStudies.aspx