Our aim is to improve domesticated crop species by identifying useful genetic variation, and adapting this variation using conventional breeding techniques.

The beneficial variation can be derived from 'exotic' allelic variants that are present in the wider species genepool, or, new combinations of beneficial genetic variation can be uncovered in our existing modern crop genepool. This type of variation is more amenable to being incorporated into our modern crop types, since in many cases it is already present in a close relative.

Many of the characteristics that we wish to improve, such as, disease resistance, nitrogen use efficiency, post harvest quality, can be described as quantitative characteristics, since they display continuous variation and are relatively normally distributed in a population. The phenotype of a quantitative trait or characteristic is the cumulative result of many genes (polygenes) that may interact, are influenced to varying degrees by the environment, but together contribute towards the overall phenotype.

By contrast, qualitative characteristics tend to be the result of the action of variants for a major gene. Classic examples are the Mendelian traits observed for pea seed shape (wrinkled form versus smooth round) and blood grouping in humans; these traits tend to place measurements into distinct classes.

Since quantitative traits display continuous variation and polygenic inheritance, detecting such effects cannot be achieved using classical Mendelian methods. Sophisticated statistical techniques have been developed to estimate the most likely positions or places (the Latin for place: locus plural loci) in the DNA of members in a population (using the information provided in the marker genotypes) that contain the genes that contribute toward the variation observed for the particular trait/ characteristic or phenotype. A crude way of doing this would be to start with the first marker on linkage group 1, and to average the phenotype scores for all individuals with genotype A and then do the same for all individuals with genotype B, then to see if there is a significant difference between the two mean scores (we can use a t test for back cross lines and ANOVA for intercrosses). This is repeated for every marker. Using this method we could get an estimate of the markers that are most likely to be linked to a QTL. As methods have developed the more common method to test for linkage between a marker and a QTL is to use a logarithm of the odds (LOD) score (the log₁₀ likelihood ratio comparing the hypothesis that there is a QTL at the selected marker to the hypothesis that there is no QTL anywhere in the genome) the greater the LOD score the more evidence to support the presence of a QTL.

The places in the DNA that have a significant LOD score, and therefore an association with a significant difference in the trait score are called quantitative trait loci (QTL).

The techniques used to identify QTL make use of the fact that we can genotype or measure the difference in DNA at genetic markers between individuals within a population. Since the distribution of a trait will be continuous through the population, then individuals that have a particular QTL will collectively have a different average score for the characteristic compared to the individuals that do not have it. The genotype differences at the genetic markers linked to the QTL should allow us to identify the individuals that have the QTL.

Once the genetic markers that define the QTL have been identified we can use these to select those individuals that have the desired QTL genotype for future breeding.