The common animal species have a narrow genetic pool due to domestication. In contrast, theirs wild relatives as a result of genetic history and selection pressure are becoming in reservoirs of natural genetic variation.

Genes associated with desired productive traits such as higher yield or disease resistance that could be lost in the breeding process can be restored using these wild species. The problem for breeder is to find the genes and find an efficient way to trace the genes and to incorporate them in breeding populations.

A survey of genetic relationship using molecular markers provides polymorphism information about a germplasm pool that is useful for developing mapping and breeding populations. If quantitative traits have also been evaluated for the same accessions, then statistical associations can be sought between markers and quantitative traits.

Such associations can be used to select a subset of candidate probes with enhanced potential for use in subsequent mapping experiments.

Quantitative Trait

A quantitative trait is one that has measurable phenotypic variation within a population owing to underlying variability in genetic and/or environmental influences. A QTL is a genetic locus in which allelic variation affects variation in the observed phenotype.

Generally, quantitative traits are multifactorial, meaning they are influenced by several polymorphic genes and environmental conditions. To map a QTL, its influence on a trait must be detected amid considerable “noise” from other QTLs and non-genetic sources of individual variation.

This has been made feasible through the implementation of technologies to identify genetic polymorphisms throughout the genome and the development of statistical methods to map QTLs from specific genetic marker and phenotypic (i.e., trait) data.

The identification of the chromosomal regions where marker allelic and phenotype variation co vary implicates the presence of a QTL.

Each QTL identifies the genomic location of a gene or genes (referred to as quantitative trait genes or QTGs) affecting the trait of interest.

The power of this approach was demonstrated first in plants and later in rodents, and has been used widely to identify genetic contributions to a wide variety of complex phenotypes.

The observed distributions of quantitative traits can arise because the traits are influenced by many genes, which result in many possible genotypes, and also by environments.

Thus the difference between the means of genotypes are unobservable because of the variability among the environments in which individuals with any particular genotype live.

Quantitative Trait Locus Mapping

A quantitative trait is a measurable phenotype emerging from genetic and environmental factors that is distributed in magnitude in a population rather than all or none.

A quantitative trait locus (QTL) is a specific chromosomal region or genetic locus in which particular sequences of bases in DNA markers are statistically associated with variation in the trait.

Several polymorphic genes and environmental conditions often influence these quantitative traits and one or many QTL(s) can influence a phenotypic trait.

Inbred strains, selected lines, and other genetically specified populations have been used in studies analogous to the human population association and linkage studies described above.

The goals are first to locate a QTL harboring a gene or genes affecting the trait to be mapped, and then refine that genomic map until a single gene or genes can be implicated in the effect on the trait.

Currently, QTL fine mapping usually involves the development of congenic strains. In a congenic strain, a very small sequence of DNA on a chromosome is moved from one genetic background to another inbred strain background.

An excellent discussion of QTL mapping methods discusses, in depth, the trait of alcohol withdrawal severity. Of course, each QTL generally accounts for only a small proportion of the variability in a complex behavioral trait like addiction, so this is a difficult task and cautious interpretation is warranted. The probability of success in QTL mapping depends on:

The heritability of the trait;

Whether the underlying quantitative trait gene (QTG) is dominant, recessive or additive;

The number of genes that affect the trait;

Whether or not their effects are interactive; and

Most importantly, the number of subjects that can be tested (i.e., the statistical power of the mapping effort).

Many addiction-related traits have been targeted for QTL mapping studies, although very few of these QTLs have been reduced to QTGs or quantitative trait nucleotides (QTNs). The recent discovery of the addiction-relevant QTG, Mpdz, which possesses pleiotropic effects on the predisposition to severe alcohol and barbiturate withdrawal, demonstrates the power of this approach.

Further studies have shown that variation in the human MPDZ gene is related to alcohol drinking. Unfortunately, QTL studies have yet to resolve to a QTG for drinking, in part, due to problems discussed above.

However, three candidate genes, neuropeptide Y, α-synuclein, and CRFR2 have been associated with ethanol-seeking. Encouraging evidence shows some consistencies for alcohol and other substance-dependence phenotypes in humans and mice.

The more long-term goals of QTL mapping projects are then to move to human populations for studies of the homologous or orthologous gene, and use information about the biological effects of the gene’s product to help design therapeutic agents or other therapies.

Determining Associations between Genetic Markers and Quantitative TraitLocus (QTL)

When breeders work with a particular trait in a species, they start to work with the genetics of the trait. Many agricultural characteristics are controlled by polygenes and are greatly dependent of genetic x environment interactions. In an aim to work with the patterns of segregation and inheritance for breeding those traits, we think about the positions of the traits in a genetic map.

Currently, when the position of a gene controlling traits is inferred we work with tools of genetic or physical mapping, depending on the information available for the species and the trait.

Traditionally it has been a challenge for breeders to work with quantitative trait loci (QTL), with the development of molecular markers technology, it has been possible to follow QTL segregation detecting markers linked to traits of interest and assessing effects, number and location of QTL in chromosomes.

An alternative to QTL mapping is association mapping also called association genetics, association studies and linkage disequilibrium mapping. These two methodologies have been advocated as the method of choice for identifying loci involved in the inheritance of complex traits.

Association mapping seeks to identify specific functional variants (loci, alleles) linked to phenotypic differences in a trait to facilitate detection of trait causing DNA sequence polymorphisms and selection of genotypes that closely resemble the phenotype (Oraguzie et al., 2007).

In order to identify these functional variants it requires high throughput markers like single nucleotide polymorphisms (SNPs).

Molecular markers are used not just to generate genetic maps but also to locate the places of interest in those maps with its incidence in the expression of the trait. That is because they are used in marker assisted selection programs.

To improve the breeding methods efficiency, breeders are using markers assisted selection techniques that show great advantages compared with traditional selection methods based on phenotypic traits evaluation. Molecular techniques allow accurate selection in early stages focusing directly in its genetic base.

In order to locate QTL in a genetic map relatively few techniques have been developed, one of those is linkage mapping. Linkage mapping is the traditional method for QTL mapping, it implies to generate simple crosses derived populations and to estimate marker-gene recombination frequencies.

Population mapping is frequently developed from diploid parental that are originated partially or completely from wild species. Such populations show only a small proportion of all the possible alleles.

In contrast, another method is association mappingbased on linkage disequilibrium (LD) concept; it is a method that exploits the diversity observed in existent cultivars and in breeding lines, without developing new populations.

Most of the important limitations for linkage mapping can be overcome using association genetics. Association genetics does not require building segregating populations and it can employ larger germplasm exploiting the natural variation that exists in the available germplasm and resolution for association could be of at least of 5 cM depending on LD decay of the species.

Principles of Genetic Mapping Population

Genetic mapping is mainly employed with two aims: to identify genetic factors or loci that influence phenotypic traits and to determine recombination distance among loci.

As a condition for mapping the traits to be studied must be polymorphic. One way for detecting those polymorphisms is using molecular markers.

Genetic mapping by linkage is supported in genetic recombination, as condition for mapping a particular trait. This trait should be polymorphic, displaying preferably a wide variation among the individuals under study.

When applying molecular markers in staid of a phenotypic trait these markers should be polymorphic as well, showing allelic variation. The selection of polymorphic markers required for QTL and single trait mapping depends on the existing knowledge regarding the species to study.

In species without detailed information of its sequence the candidate gene approach may be used. This approach is based on the production of markers from gene sequences that they have been observed to take place or they are suspected that have a functional role in the selected trait.

QTL mapping begins with the gathering of genotypic and phenotypic data from a segregating population, and it is followed for a statistical analysis where all the major loci responsible of the trait variation are located.

This analysis usually referred as primary QTL mapping could locate a QTL in an interval of approximately 10 to 30 cM, which may include several hundred of genes. Therefore, the genetic resolution has to be improved by assigning a QTL to the shortest chromosome segment including ideally one single gene.

The final goal is the identification of DNA coding or not coding sequences responsible for QTL (QTL cloning).

Determining Associations between Genetic Markers and Quantitative Trait Locus (QTL)

Two methods have been employed for verifying the association between the shortest possible regions of a chromosome tagged using molecular markers and the value of the studied trait: positional cloning and association mapping.

QTL cloning is difficult because of the resolution limitations, even though many QTL had been cloned since 2001 when the first QTL was cloned in but also in that year one QTL from rice was cloned as well, since this at least 20 QTL were cloned.

Positional cloning allows QTL resolution but it is necessary to produce a second and larger mapping population of 2000 or more F2 plants derived from a cross between two parental nearly isogenic lines with alleles functionally different in the targeted QTL.

These parental lines are called QTL-NILs (quantitative trait loci-nearly isogenic lines). The generation of these lines can be archived doing marker assisted backcrosses or iteratively identifying and selfing individuals that are heterozygous at the QTL region.

The production of such NILs can last several years depending on the plant material. Other important aspects to consider are the genetic limited variability as a result of the use of only two parental.

The generated population could segregate for just a fraction of many QTL that may affect the same trait in other populations. For primary QTL mapping, Monte Carlo simulations have shown that at least 200 individuals from the segregated population are required.

For higher resolution, as required for positional cloning, progenies of several thousand plants are needed. For example, in the Alpert and Tanksley´s work in 1996 more than 3,400 individuals were analyzed to obtain a detailed map around a fruit weight locus in tomato.

As an alternative to positional cloning, QTL may be determined using association mapping. This method allows identifying a statistic association between markers or candidates loci and the overall of an analyzed phenotype within a set of genotypes (natural populations, germplasm accessions and cultivars).

It is important that the plant collection contains a wide spectrum for the trait to evaluate, and in particular it is an advantage for the analysis if the collection shows up extreme phenotypes.

Five main step sexist for the association studies:

Selection of the population’s samples,

Determination of the level and influence of the structure population on the sample,

Phenotypic characterization of the population for the interest trait,

Population genotyping for regions/candidate genes candidates or as a whole genome scan,

Assessment of the association between genotypes and phenotypes. The selection of the association test is the last step and it depends on the population’s characteristics. Association mapping uses ancestral recombination and genetic natural diversity within a population to analyze quantitative traits and it is built on the base of the LD concept.

It is used to think that the terms linkage and linkage disequilibrium have similar meanings. However, although they are related, genetic linkage makes reference to the correlated inheritance of two loci through several generations because the two loci is at a sufficiently short physical distance that recombination meiotic events do not show up, and selection acts in the same way over the two loci, whereas LD refers to the identical frequency in the presence of two alleles of different loci inside a population, and this non-random association can be caused by other factors than linkage.

Contrary to linkage mapping, where the genetic maps are created using generations of well characterized pedigrees generated from simple or multiple crossings, the LD based association studies can rely on the variation generated by the segregation in natural populations of non-related individuals.

It is expected that the period of time until the most recent common ancestor between two non-related individuals of a population is bigger than the time presented by a population generated by a crossing, for this cause the samples used in LD mapping present more informative meiosis, generated through history, than the meiosis showed up in a traditional population mapping.

Meiosis is considered informative when effective recombinations are generated, sending information from one genetic pool to other genetic pool. In this way ancestral recombination’s can capture mixing between different populations and within this when LD is present this is important for the association assessment.

Factors That Affect LD

LD is affected by biological factors, as the recombination and the allelic frequencies, and for historical factors that affect population size, like the selection, and bottlenecks with extreme genetic drift, selection for or against a phenotype controlled by two non-linked loci (epitasis). Mating patterns and gene flow between individuals of genetically distinct populations followed by intermating can strongly influence LD.

LD decreases faster in out crossing species than selfing species, this is due to less effective recombination in selfing species where the individuals are more likely to be homozygous than in out crossing species.

In presence of a high LD a low density of markers is required in a target region. With low LD, many markers are required but the diagnostic markers resolution is higher, potentially until the level of the gene or of QTN (i.e. the quantitative trait nucleotide polymorphism responsible for the QTL effect).

It is expected high variable levels of LD through the genome due to variations in recombination rates, presence of hot spots and selection, variation in recombination rate is a key factor that contributes to the variance observed in LD patterns.

Possible complications to measure LD and therefore to carry out the association mapping, can show up due to structure population in the studied sample. The influence of structure population depends on the relationships among sampled individuals.

So, populations to be employed in an association study should be classified according to the sample individual relationship. Structure population can generate statistically significant but invalid biologically associations.

Low LD levels are expected when the population is diverse and the common ancestor within the individual population is too far in time, also low LD is not distributed uniformly along all the genome and it is located in short distances around specific loci, which produce only significant cooccurrences among physically near loci, increasing mapping resolution.

Breeding, domestication and a limited genetic flow in many wild species have generated erosion processes and genetic drift that have produced structured populations (i.e. populations with allelic frequencies differences among sub-populations).

These populations generate not functional significant associations among loci or between a marker and a phenotype, even without marker physically binding to the responsible locus for phenotypic variation.

However, different methods have already been generated; these methods make it possible to interpret results of association tests, controlling statistically the effects of stratified populations, because association studies that do not keep in mind the effects of structure population must be viewed with skepticism.

All these methods are based on the use of independent marker loci to detect and correct stratified populations.