10.5 QUANTITATIVE TRAIT LOCUS ANALYSIS

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Most of the phenotypic traits commonly used in introductory genetics are qualitative, meaning that the phenotype exists in only two (or possibly a few more) alternative forms, such as either purple or white flowers, or red or white eyes.  These qualitative traits are therefore said to exhibit discrete variation.  On the other hand, many interesting and important traits exhibit continuous variation; these exhibit a continuous range of phenotypes that are usually measured quantitatively, such as intelligence, body mass, blood pressure in animals including humans, and yield, water use, or vitamin content in crops.   Traits with continuous variation are often complex, and do not show the simple Mendelian segregation ratios (e.g. 3:1) observed with some qualitative traits.  Many complex traits are also influenced heavily by the environment.  Nevertheless, complex traits can often be shown to have a component that is heritable, and which must therefore involve one or more genes.

How can genes, which are inherited (in the case of a diploid) as at most two variants each, explain the wide range of continuous variation observed for many traits?  The lack of an immediately obvious explanation to this question was one of the early objections to Mendel's explanation of the mechanisms of heredity.   However, upon further consideration, it becomes clear that the more loci that contribute to trait, the  more phenotypic classes may be observed for that trait (Figure 10.6).   If the number of phenotypic classes is sufficiently large, individual classes may become indistinguishable from each other (particularly when environmental effects are included), and the result is continuous variation (Figure 10.7).   Thus, quantitative traits are sometimes called polygenic traits, because it is assumed that their phenotypes are controlled by the combined activity of many genes.   Note that this does not imply that each of the individual genes has an equal influence on a polygenic trait.  Furthermore, any give gene may influence more than one trait, whether these traits are quantitative or qualitative traits.

Figure 10.6: Punnett Squares for one, two, or three loci.  We are using a simplified example of up to three semi-dominant genes, and in each case the effect on the phenotype is additive, meaning the more “upper case” alleles present, the stronger the phenotype.  Comparison of the Punnett Squares and the associated phenotypes shows that under these conditions, the larger the number of genes that affect a trait, the more intermediate phenotypic classes that will be expected.

Figure 10.7: The more loci that affect a trait, the larger the number of phenotypic classes that can be expected.  For some traits, the number of contributing loci is so large that the phenotypic classes blend together in apparently continuous variation.

We can use molecular markers to identify at least some of the genes that affect a given quantitative trait.  This is essentially an extension of the mapping techniques we have already considered for discrete traits.  A QTL mapping experiment will ideally start with two pure-breeding lines that differ greatly from each other in respect to one or more quantitative traits (Figure 10.8).  The parents and all of their progeny should be raised in under similar environmental conditions, to ensure that observed variation is due to genetic rather than external factors. These parental lines must also be polymorphic for a large number of molecular loci, meaning that they must have different alleles from each other at hundreds of loci.  The parental lines are crossed, and then this F1 individual, in which recombination between parental chromosomes has occurred is self-fertilized (or back-crossed).   Because of recombination, each of the F2 individuals will contain a different combination of  molecular markers, and also a different combination of alleles for the genes that control the quantitative trait of interest (Table 10.1).  By comparing the molecular marker genotypes of several hundred F2 individuals with their quantitative phenotypes, a researcher can identify molecular markers for which the presence of particular alleles is always associated with extreme values of the trait.  In this way, regions of chromosomes that contain genes that contribute to quantitative traits can be identified. (Figure 10.9)  It then takes much more work (further mapping and other experimentation) to identify the individual genes in each of the regions that control the quantitative trait.

Figure 10.8: Strategy for a typical QTL mapping experiment.  Two parents that differ in a quantitative trait (e.g. fruit mass) are crossed, and the F1 is self-fertilized (as shown by the cross-in-circle symbol).  The  F2 progeny will show a range of quantitative values for the trait.  The task is then to identify alleles of markers  from one parent that are strongly correlated with the quantitative trait.  For example, markers from the large-fruit parent that are always present in large-fruit F2 individuals (but never in small-fruit individuals) are likely linked to loci that control fruit mass.

Table 10.1 Genotypes and quantitative data for some individuals from the crosses shown in Figure 10.8

 

genotype

fruit mass

P

A1A1B1B1C1C1D1D1E1E1F1F1G1G1H1H1J1J1K1K1

10g

P

A2A2B2B2C2C2D2D2E2E2F2F2G2G2H2H2J2J2K2K2

90g

F1

A1A2B1B2C1C2D1D2E1E2F1F2G1G2H1H2J1J2K1K2

50g

F2 #001

A1A1B1B2C1C1D2D2E1E2F1F2G1G2H1H1J1J2K1K2

80g

F2 #002

A1A2B1B2C1C2D1D1E1E2F1F2G2G2H1H2J2J2K1K1

10g

F2 #003

A2A2B1B2C2C2D1D2E1E2F1F2G1G1H1H2J1J2K1K2

50g

F2 #004

A1A2B1B2C1C2D1D2E1E2F1F2G1G2H1H2J1J2K2K2

60g

F2 #005

A1A2B1B1C1C2D2D2E1E2F1F2G1G2H1H2J1J1K2K2

90g

F2 #006

A1A2B2B2C1C2D1D2E1E2F1F2G2G2H1H2J1J2K1K2

60g

F2 #007

A2A2B1B1C1C2D2D2E1E2F1F2G1G1H1H2J1J1K1K2

80g

F2 #008

A1A1B1B2C1C2D1D2E1E2F1F2G1G2H1H2J1J2K1K2

50g

F2 #009

A1A2B1B2C2C2D1D2E1E2F1F2G1G2H1H2J1J2K1K2

50g

F2 #010

A1A2B1B2C1C2D1D2E1E2F1F2G1G2H1H2J1J2K2K2

30g

F2 #011

A1A2B1B2C1C2D2D2E1E1F1F2G1G2H1H2J1J2K1K2

80g

F2 #012

A1A1B1B2C1C2D1D1E1E2F2F2G1G2H1H2J1J2K2K2

30g

F2 #013

A2A2B1B1C1C2D1D1E1E2F1F1G1G2H2H2J1J1K1K1

10g

F2 #014

A2A2B1B1C1C1D2D2E1E2F1F2G1G2H1H2J1J1K1K1

70g

F2 #015

A2A2B2B2C1C2D1D2E1E2F2F2G1G2H1H1J2J2K1K2

40g

F2 #016

A1A2B2B2C1C2D1D1E1E2F1F1G2G2H1H1J1J2K1K1

10g

F2 #017

A1A2B2B2C1C2D2D2E2E2F1F1G2G2H1H2J1J2K2K2

90g

F2 #018

A1A2B2B2C1C2D1D2E1E2F1F1G2G2H1H2J1J2K1K1

40g

F2 #019

A1A1B1B2C1C2D1D1E1E2F2F2G1G1H1H1J1J2K1K2

20g

F2 #100

A1A1B1B2C1C2D2D2E1E2F1F2G2G2H1H2J2J2K1K2

80g

 

Figure 10.9: Plots of fruit mass and genotype for selected loci from Table 10.1.  For most loci (e.g. H), the genotype shows no significant correlation with fruit weight.  However, for some molecular markers, the genotype will be highly correlated with fruit weight.  Both D and K influence fruit weight, but the effect of genotype at locus D is larger than at locus K.