The statistical methods we have just described can be used both to make predictions about the average phenotype expected in offspring and to estimate the overall contribution of genes to variation in a characteristic. These methods do not, however, allow us to identify and determine the influence of individual genes that affect quantitative characteristics. As stated in the introduction to this chapter, chromosome regions with genes that control polygenic characteristics are referred to as quantitative trait loci (QTLs). Although quantitative genetics has made important contributions to basic biology and to plant and animal breeding, our inability to identify QTLs and to measure their individual effects severely limited the application of quantitative genetic methods until recently.

MAPPING QTLs In recent years, numerous genetic markers have been identified and mapped with the use of molecular techniques, making it possible to identify QTLs by linkage analysis. The underlying idea is simple: if the inheritance of a genetic marker is associated consistently with the inheritance of a particular phenotype, then that marker must be linked to a QTL that affects that phenotype. Any correlation between the inheritance of a particular marker allele and a quantitative phenotype in a series of crosses indicates that a QTL is physically linked to that marker. If enough markers are used, the detection of all the QTLs affecting a characteristic is theoretically possible.

It is important to recognize that a QTL is not a gene; rather, a QTL is a map location for a chromosome region that is associated with a quantitative trait. After a QTL has been identified, it can be studied for the presence of one or more specific genes or other sequences that influence the trait. The introduction to this chapter considers how this approach was used to identify a major gene that affects the oil content of corn. QTL mapping has been used to detect genes affecting a variety of characteristics in plant and animal species (Figure 17.13 and Table 17.3).

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GENOME-WIDE ASSOCIATION STUDIES The traditional method of identifying QTLs is to carry out crosses between varieties that differ in a quantitative trait and then genotype numerous progeny for many markers. Although effective, this method is slow and labor-intensive.

An alternative technique for identifying genes that affect quantitative traits is to conduct genome-wide association studies (introduced in Chapter 5). Unlike traditional linkage analysis, which examines the association of a trait and gene markers among the progeny of a cross, genome-wide association studies look for associations between traits and genetic markers in a biological population, a group of interbreeding individuals. The presence of an association between genetic markers and a trait indicates that the genetic markers are closely linked to one or more genes that affect variation in the trait. Genome-wide association studies have been facilitated by the identification of single-nucleotide polymorphisms (SNPs), which are positions in the genome at which individual organisms vary in a single base pair (see Chapter 15). It is often possible to quickly and inexpensively genotype individual organisms for numerous SNPs, which provide the genetic markers necessary to conduct genome-wide association studies.

Genome-wide association studies have been widely used to locate genes that affect quantitative traits in humans, including disease susceptibility, obesity, intelligence, and height. A number of quantitative traits in plants have also been studied, including kernel composition, size, color and taste, disease resistance, and starch quality. Genome-wide association studies in domestic animals have identified chromosome segments affecting body weight, body composition, reproductive traits, hormone levels, hair characteristics, and behaviors.