Taste of quals: understanding evolution through quantitative trait loci

This is the second post with examples of questions and answers from qualifying exams given in the graduate program of Ecology, Evolution and Systematics at UMSL. Here is another sample of my own quals, in this case, a question for a minor in Evolution. If you’d like to catch up with this discussion, read the initial post “The ultimate grad student guide to survive (and pass) qualifying exams“, and the first question/answer example on incomplete lineage sorting and species delimitations. I’ll post two more examples in the near future, from the quals of our dear peer, Robbie Hart. Stay tuned!

I remember that during my oral exam, my committee asked me to define epigenetics, which I do define in my text as you’ll see as you read, but I don’t coin the definition. So, here is another advice, make sure you give short and straightforward definitions for all concepts you use.

What are Quantitative Trait Loci and how are they relevant to the study of evolution?

The basic strategy behind mapping quantitative trait loci (QTL) is illustrated here for a | the density of hairs (trichomes) that occur on a plant leaf. Inbred parents that differ in the density of trichomes are crossed to form an F1 population with intermediate trichome density. b | An F1 individual is selfed to form a population of F 2 individuals. c | Each F2 is selfed for six additional generations, ultimately forming several recombinant inbred lines (RILs). Each RIL is homozygous for a section of a parental chromosome. The RILs are scored for several genetic markers, as well as for the trichome density phenotype. In c, the arrow marks a section of chromosome that derives from the parent with low trichome density. The leaves of all individuals that have inherited that section of chromosome from the parent with low trichome density also have low trichome density, indicating that this chromosomal region probably contains a QTL for this trait. Figure and legend taken from Mauricion 2001, Nature Genetics

The basic strategy behind mapping quantitative trait loci (QTL) is illustrated here for a | the density of hairs (trichomes) that occur on a plant leaf. Inbred parents that differ in the density of trichomes are crossed to form an F1 population with intermediate trichome density. b | An F1 individual is selfed to form a population of F 2 individuals. c | Each F2 is selfed for six additional generations, ultimately forming several recombinant inbred lines (RILs). Each RIL is homozygous for a section of a parental chromosome. The RILs are scored for several genetic markers, as well as for the trichome density phenotype. In c, the arrow marks a section of chromosome that derives from the parent with low trichome density. The leaves of all individuals that have inherited that section of chromosome from the parent with low trichome density also have low trichome density, indicating that this chromosomal region probably contains a QTL for this trait. Figure and legend taken from Mauricion 2001, Nature Genetics

Phenotype is the assemblage of observable characteristics, or traits, manifested by one individual as a result of the interaction between genes and the environment. Quantitative traits are phenotypic characteristics mediated by more than one gene (i.e. present polygenic control) (Erickson et al. 2004). Quantitative trait loci (QTL) are the several gene loci determining the expression of quantitative traits (Avise 2004). For instance, five QTLs determine morphological variation of male genitalia in Drosophila montana (Schafer et al. 2011), more than 800 QTLs are responsible the variation of 35 distinct traits in tomato, Solanum lycopersicum (Semel 2006), and few QTLs were described regulating the foraging choices in honey bees (Rüppell et al. 2004). The genetic base of ecologically and evolutionarily relevant traits has been described with QTL analysis. Evolution operates through heritable phenotypic variation, driving adaptation and diversity (Mauricio 2001). Describing QTLs supports the genetic background for understanding what determines phenotypic variation of quantitative traits, and how such variation is selected and fixed in populations (Erickson et al. 2004).

QTLs provide insights on the genetic mechanisms regulating phenotypic patterns, such as dominance, pleiotropism, epistasis or environmental interactions (Erickson et al. 2004, Avise 2004). Hybrids of S. lycopersicum with elevated reproductive fitness presented more overdominant (ODO) QTLs (Semel 2006). It seems that ODO QTLs (i.e. loci presenting heterozygous alleles with dominant expression over all homozygous alleles) were the genetic mechanism causing hybrids of Solanum sp. to present heterosis, a phenomenon in which hybrids outperform the most fit inbred parental lineage (Semel 2006). QTLs are also involved in pleiotropism, when a locus mediates the expression of multiple traits, and epistasis, when one locus suppresses the expression of alleles in a different locus (in an analogous way of dominance) (Phillips 1998). More than 60% of the phenotypic variation of body weight and fat accumulation in mice can be explained by QTLs in pleiotropy or epistasis (Brockmann et al. 2000). Moreover, the interaction of QTLs with environmental conditions explains phenotypic plasticity (i.e. habitat-dependent adaptive phenotype) in both barley and aphid populations (Tétard-Jones et al. 2011).

The basic procedure for QTL mapping in plants and animals is: 1) selection of two parental lineages that differ in the allele affecting a common trait; 2) generation of an F1 population by mating parents; 3) parental alleles are shuffled by creating a mapping population (F2); 4) traits are quantified and multilocus genotypes are identified (Mauricio 2001). Erickson et al. (2004) define three difficulties in identifying QTLs: 1) the genetic markers employed; 2) how the crosses of lineages are designed and 3) the magnitude of the QTL effect. For instance, if genetic markers are dominant, it will be harder to tell apart the effects of homozygotes dominants and heterozygotes. Random crossing of parental lineages might bias QTL identification towards alleles with large effects, but rare in natural populations (Pérez-Pérez et al. 2010). QTL identification is also biased towards the magnitude of its effect (i.e. genetic variance explained by the QTL) (Erickson et al. 2004); which is an issue in the presence of confounding factors, such as genotype-environment interactions, low heritability and imprecise estimation of genotypes and phenotypes (Erickson et al. 2004). One can overcome these problems by applying large sample sizes, adequate type and number of genetic markers, and carefully designed crosses. However, the current genomic era, with increasing number of whole sequenced genomes, overwhelms such problems by providing more markers, refining genetic maps and improving crosses due to reduction in genotyping costs (Mauricio 2001). QTL analysis detects and describes the regions of the genome responsible for the phenotypic variation under selection, shedding light on the mechanisms of evolution of complex traits.

References

Avise, J. 2004. Molecular markers, natural history, and evolution. 2nd edition. Sinauer Associates, Sunderland. 684 pp.

Brockmann, G. A., J. Kratzsch, C. S. Haley, U. Renne, M. Schwerin, and S. Karle. 2000. Single QTL Effects, Epistasis, and Pleiotropy Account for Two-thirds of the Phenotypic F2 Variance of Growth and Obesity in DU6i x DBA/2 Mice. Genome Research:1941–1957.

Erickson, D. L., C. B. Fenster, H. K. Stenøien, and D. Price. 2004. Quantitative trait locus analyses and the study of evolutionary process. Molecular Ecology 13:2505–2522.

Mauricio, R. 2001. Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology. Nature Reviews Genetics 2:370–381.

Pérez-Pérez, J. M., D. Esteve-Bruna, and J. L. Micol. 2010. QTL analysis of leaf architecture. Journal of Plant Research 123:15–23.

Phillips, P. C. 1998. The language of gene interaction. Genetics 149:1167–1171.

Rüppell, O., T. Pankiw, and R. E. Page. 2004. Pleiotropy, epistasis and new QTL: the genetic architecture of honey bee foraging behavior. The Journal of Heredity 95:481–491.

Schäfer, M. A., J. Routtu, J. Vieira, A. Hoikkala, M. G. Ritchie, And C. Schlötterer. 2011. Multiple quantitative trait loci influence intra-specific variation in genital morphology between phylogenetically distinct lines of Drosophila montana. Journal of Evolutionary Biology 24:1879–1886.

Semel, Y. 2006. Overdominant quantitative trait loci for yield and fitness in tomato. Proceedings of the National Academy of Sciences 103:12981–12986.

Tétard-Jones, C., M. A. Kertesz, and R. F. Preziosi. 2011. Quantitative trait loci mapping of phenotypic plasticity and genotype-environment interactions in plant and insect performance. Philosophical transactions of the Royal Society of London. Series B, Biological Sciences 366:1368–1379.

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