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Quantitative Traits

Quantitative Traits

Quantitative traits are those that vary continuously. This is in contrast to qualitative traits, in which the phenotype is discrete and can take on one of only a few different values. Examples of quantitative traits include height, weight, and blood pressure. There is no single gene for any of these traits, instead it is generally believed that continuous variation in a trait such as blood pressure is partly due to DNA sequence variations at multiple genes, or loci . Such loci are referred to as quantitative trait loci (QTL). Much of how we study and characterize quantitative traits can be attributed to the work of Ronald Fisher and Sewall Wright, accomplished during the first half of the twentieth century.

The Genetic Architecture of Quantitative Traits

An important goal of genetic studies is to characterize the genetic architecture of quantitative traits. Genetic architecture can been defined in one of four ways. First, it refers to the number of QTLs that influence a quantitative trait. Second, it can mean the number of alleles that each QTL has. Third, it reflects the frequencies of the alleles in the population. And fourth, it refers to the influence of each QTL and its alleles on the quantitative trait. Imagine, for instance, a quantitative trait influenced by 6 loci, each of which has 3 alleles. This gives a total of 18 possible allele combinations. Some alleles may be very rare in a population, so that the phenotypes it contributes to may be rare as well. Some alleles have disproportionate effects on the phenotype (for instance, an allele that causes dwarfism), which may mask the more subtle effects of other alleles. The trait may also be influenced by the environment, giving an even wider range of phenotypic possibilities.

Understanding the genetic architecture of quantitative traits is important in a number of disciplines, including animal and plant breeding, medicine, and evolution. For example, a quantitative trait of interest to animal breeders might be meat quality in pigs. The identification and characterization of QTLs for meat quality might provide a basis for selecting and breeding pigs with certain desirable features. In medicine, an important goal is to identify genetic risk factors for various common diseases. Many genetic studies of common disease focus on the presence or absence of disease as the trait of interest. In some cases, however, quantitative traits may provide more information for identifying genes than qualitative traits. For example, identifying genetic risk factors for cardiovascular disease might be facilitated by studying the genetic architecture of cholesterol metabolism or blood pressure rather that the presence or absence of cardiovascular disease itself. Cholesterol metabolism is an example of an intermediate trait or endophenotype for cardiovascular disease. That is, it is related to the disease and may be useful as a "proxy measure" of the disease.

QTLs and Complex Effects on Phenotype

It is important to note that QTLs can influence quantitative traits in a number of different ways. First, variation at a QTL can impact quantitative trait levels. That is, the average or mean of the observed phenotypes for the trait may be different among different genotypes (for example, some genotypes will produce taller organisms than others). This is important because much of the basic theory underlying statistical methods for studying quantitative traits is based on genotypic means. For this reason, most genetic studies focus on quantitative trait means. However, there are a variety of other ways QTLs can influence quantitative traits. For example, it is possible that the trait means are the same among different genotypes but that the variances (the spread on either side of the mean) are not. In other words, variation in phenotypic values may be greater for some genotypes than for otherssome genotypes, for example, may give a wider range of heights than others. This is believed to be due to gene-gene and gene-environment interactions such that the magnitude of the effects of a particular environmental or genetic factor may differ across genotypes.

It is also possible for QTLs to influence the relationship or correlation among quantitative traits. For example, the rate at which two proteins bind might be due to variation in the QTLs that code for those proteins. As a final example, QTLs can also impact the dynamics of a trait. That is, change in a phenotype over time might be due to variation at a QTL, such as when blood pressure varies with the age of the individual. Thus, QTLs can affect quantitative trait levels, variability, co-variability, and dynamics.

In addition, each type of QTL effect may depend on a particular genetic or environmental context. Thus, the influence of a particular QTL on quantitative trait levels, variability, covariability, or dynamics may depend on one or more other QTLs (an effect called epistasis or gene-gene interaction) and/or one or more environmental factors. Although such context-dependent effects may be very common, and may play an important role in genetic architecture, they are typically very difficult to detect and characterize. This is partly due to limits of available statistical methods and the availability of large sample sizes.

Analysis of Quantitative Traits

Characterization of the genetic architecture of quantitative traits is typically carried out using one of two different study designs. The first approach starts with the quantitative trait of interest (such as height or blood pressure) and attempts to draw inferences about the underlying genetics from looking at the degree of trait resemblance among related subjects. This approach is sometimes referred to as a top-down or unmeasured genotype strategy because the inheritance pattern of the trait is the focus and no genetic variations are actually measured. The top-down approach is often the first step taken to determine whether there is evidence for a genetic component.

Heritability (the likelihood that the trait will be passed on to offspring) and segregation analysis are examples of statistical analyses that use a top-down approach. With the bottom-up or measured genotype approach, candidate QTLs are measured and then used to draw inferences about which genes might play a role in the genetic architecture of a quantitative trait. Prior to the availability of technologies for measuring QTLs, the top-down approach was very common. However, it is now inexpensive and efficient to measure many QTLs, making the bottom-up strategy a common study design. Linkage analysis and association analysis are two general statistical approaches that utilize the bottom-up study design.

The definition and characterization of quantitative traits is changing very rapidly. New technologies such as DNA microarrays and protein mass spectrometry are making it possible to quantitatively measure the expression levels of thousands of genes simultaneously. These new measures make it possible to study gene expression at both the RNA level and the protein level as a quantitative trait. These new quantitative traits open the door for understanding the hierarchy of the relationship between QTL variation and variation in quantitative traits at both the biochemical and physiological level.

see also Complex Traits; DNA Microarrays; Gene Discovery; Linkage and Recombination.

Jason H. Moore

Bibliography

Griffiths, A. J., et al. An Introduction to Genetic Analysis. New York: W. H. Freeman, 2000.

Hartl, D. L., and A. G. Clark. Principle of Population Genetics. Sunderland, MA: Sinauer Associates, 1997.

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Quantitative Trait Loci

Quantitative Trait Loci

Quantitative traits are characteristics such as plant height or seed size, which can vary over a large range of possible values. The chromosomal regions controlling variation in a quantitative trait are known as quantitative trait loci.

The set of hereditary material transmitted from parent to offspring is known as the genome. It consists of molecules of deoxyribonucleic acid (DNA) arranged on chromosomes. Genetic markers are neutral DNA sequences that have no effect on an individual's physical appearance but are identifiable in the laboratory. Using statistical methods, the location of markers within an organism's genome can be estimated. The linear ordering of markers literally acts as a road map across the organism's genetic composition. This information allows a plant breeder to associate (link) an inherited observable characteristic such as seed size with a marker. This makes it possible to identify progeny possessing that characteristic (even before it shows) by determining whether the individual has that particular genetic marker. In plant breeding applications, the location of specific regions of a genome responsible for controlling quantitative variation of a trait, such as seed size, is of increasing concern to those interested in crop performance.

Quantitative traits may be affected by many loci. A statistical representation (mathematical equation) of the quantitative trait describes the genetic variation in each region of the genome. Quantitative trait loci (QTL) analysis provides information for selectively manipulating genetic components of a trait. The basis of QTL detection, regardless of the crop to which it is applied, is the identification of associations between genetically determined phenotypes (physical characteristics) and genetic markers (genetic characteristics).

The emergence of high-resolution molecular marker technologies is likely to facilitate large-scale QTL analyses. QTL studies provide a first step in understanding the genetics that underlie the expression of quantitative traits. The hope for future research is that the foundation and knowledge gained from QTL research will aid our understanding of the biological function of genes, thus continuing the long history between the fields of genetics and statistics.

see also Breeder; Breeding; Genetic Engineer; Molecular Plant Genetics.

R. W. Doerge

Bibliography

Doerge, R. W., Zhao Bang Zeng, and Bruce S. Weir. "Statistical Issues in the Search for Genes Affecting Quantitative Traits in Experimental Populations." Statistical Science 12, no. 3 (1997): 195-219.

Thoday, J. M. "Location of Polygenes." Nature 191 (1961): 368-70.

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