Selection and Complex Multigene Traits

Esteban R Hasson, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
Juan José Fanara, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
Nicolás Frankel, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina

Published online: May 2013

DOI: 10.1002/9780470015902.a0002295

Abstract

Phenotypic characters that display continuous variation are usually called ‘quantitative traits’ or ‘complex traits’. Alternatively, geneticists refer to them as ‘multigene traits’, because the underlying genetic architecture is assumed to be polygenic. Analyses of the genetic architecture of diverse quantitative traits suggest that the number of loci (quantitative trait loci, QTLs) affecting trait variation can be very different. Moreover, experimental studies report contrasting genetic architectures, where either large‐effect QTLs or small‐effect QTLs explain most of the phenotypic variation. In addition, recent reports highlight the pervasiveness of epistasis. Considerable evidence, obtained with the QST–FST methodology, supports the idea that natural selection plays a key role in the evolution of complex traits. Nevertheless, the identification of a representative number of genes underlying QTLs is necessary to determine the contribution of selection, drift and gene flow for the evolution of complex traits.

Key Concepts:

  • The phenotypic variation in complex traits is usually determined by multiple genes.

  • Understanding the genetic architecture of complex traits begins with the identification and characterisation of quantitative trait loci (QTLs).

  • The search for the genes that harbour naturally segregating variation affecting quantitative traits is commonly performed through linkage QTL mapping.

  • An analysis of the genetic architecture of different characters suggests that the number of QTLs contributing to a trait can be very different.

  • Researchers aim to discover the genes (QTGs) and nucleotides (QTNs) underlying QTL effects.

  • The comparison of the statistics of QST and FST is one of the most popular methods employed to search for the signature of natural selection on quantitative traits.

  • Considerable evidence supports the idea that natural selection is a key player in the evolution of complex traits.

  • The identification of a representative number of QTGs is necessary to determine the contributions of selection, drift and gene flow for the evolution of complex traits.

Keywords: selection; drift; complex traits; genetic architecture; QTL mapping; QST; FST

Figure 1.

Types of phenotypic characters. Some phenotypic characters can be grouped into discrete categories. For example, plumage colour in a population of birds. (a) The size of the bar represents the number of individuals that have that character state in the population. In contrast, other characters (such as height) exhibit continuous variation, and individual values have to be assigned to arbitrary bins in a histogram. (b) These characters usually follow a normal distribution.

Figure 2.

Quantitative trait locus mapping. (a) Two inbred parental lines (P1 and P2) are crossed to produce the F1 generation. Blue/red bars represent a pair of homologous chromosomes. Triangles indicate molecular markers specific for each parental line. F1 individuals can be crossed to P1 and/or P2 to generate a backcross (BC) mapping population or to each other to generate an F2 mapping population. Recombinant inbred lines (RILs) are generated by performing full‐sibling matings for many generations. (b) Identification of QTLs by linkage. Triangles on the x‐axis denote the locations of molecular markers. The likelihood ratio (y‐axis) is the quotient of the likelihood of two contrasting hypotheses: (H1) a QTL is linked to a specific marker and (H0) there is no QTL linked to that specific marker. The horizontal dotted line is the significance threshold for the likelihood ratio based on permutation tests. Genomic regions with markers above this line contain putative QTLs. The most likely location of a QTL is the position on the x‐axis associated with the highest likelihood value. For a detailed explanation of the likelihood ratio test and permutation tests see Lynch and Walsh .

Figure 3.

The workflow of the QST–FST method, illustrated with fly populations. The first step involves the capture of wild flies. The progenies of wild inseminated females are bred in controlled laboratory conditions. Wild caught females are genotyped to obtain measures of genetic differentiation within and between populations (FST) and the lab‐raised progeny is analysed for a particular trait (or set of traits). In this case, wing size is measured to obtain within and between population variances. These values represent within and between population additive genetic variances. QST is calculated with these values. Finally, the evolutionary forces acting on a trait (or suite of traits) may be inferred on the basis of the results of the comparison between QST and FST.

Figure 4.

Experimental studies show that QST is higher than FST for most cases. Data points (black diamonds) are QST–FST values obtained for single traits or averaged across several traits. The red line marks the neutral expectation (QST=FST). Empirical data from Rogell et al. , Chun et al. , Leinonen et al. , Richter‐Boix et al. , Santure et al. and Volis and Zhang were used to construct the graph.

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References

Anisimova M and Liberles DA (2007) The quest for natural selection in the age of comparative genomics. Heredity 99: 567–579.

Boyko AR, Quignon P, Li L et al. (2010) A simple genetic architecture underlies morphological variation in dogs. PLoS Biology 8: e1000451.

Buckler ES, Holland JB, Bradbury PJ et al. (2009) The genetic architecture of maize flowering time. Science 325: 714–718.

Carbone MA, Jordan KW, Lyman RF et al. (2006) Phenotypic variation and natural selection at Catsup, a pleiotropic quantitative trait gene in Drosophila. Current Biology 16: 912–919.

Chapuis E, Martin G and Goudet J (2008) Effects of selection and drift on G matrix evolution in a heterogeneous environment: a multivariate Qst‐Fst Test with the freshwater snail Galba truncatula. Genetics 180: 2151–2161.

Chun YJ, Nason JD and Moloney KA (2009) Comparison of quantitative and molecular genetic variation of native vs. invasive populations of purple loosestrife (Lythrum salicaria L., Lythraceae). Molecular Ecology 18: 3020–3035.

Cook RK, Christensen SJ, Deal JA et al. (2012) The generation of chromosomal deletions to provide extensive coverage and subdivision of the Drosophila melanogaster genome. Genome Biology 13: R21.

Crow JF (2007) Haldane, Bailey, Taylor and recombinant‐inbred lines. Genetics 176: 729–732.

Davis RH (2004) The age of model organisms. Nature Reviews Genetics 5(1): 69–76.

Ehrenreich IM, Bloom J, Torabi N et al. (2012) Genetic architecture of highly complex chemical resistance traits across four yeast strains. PLoS Genetics 8: e1002570.

Ehrenreich IM, Torabi N, Jia Y et al. (2010) Dissection of genetically complex traits with extremely large pools of yeast segregants. Nature 464: 1039–1042.

Falconer DS and Mackay TF (1996) Introduction to Quantitative Genetics. Essex: Addison Wesley Longman.

Fanara JJ, Robinson KO, Rollmann SM, Anholt RR and Mackay TF (2002) Vanaso is a candidate quantitative trait gene for Drosophila olfactory behavior. Genetics 162: 1321–1328.

Frankel N, Erezyilmaz DF, McGregor AP et al. (2011) Morphological evolution caused by many subtle‐effect substitutions in regulatory DNA. Nature 474: 598–603.

Fisher RA (1930) The Genetical Theory of Natural Selection. Oxford: Oxford University Press.

Gerke J, Lorenz K and Cohen B (2009) Genetic interactions between transcription factors cause natural variation in yeast. Science 323: 498–501.

Gibson G (2010) Hints of hidden heritability in GWAS. Nature Genetics 42: 558–560.

Gibson G (2011) Rare and common variants: twenty arguments. Nature Reviews Genetics 13: 135–145.

Goldschmidt R (1940) The Material Basis of Evolution. New Haven: Yale University Press.

Hill WG (2012) Quantitative genetics in the genomics era. Current Genomics 13: 196–206.

Hill WG, Goddard ME and Visscher PM (2008) Data and theory point to mainly additive genetic variance for complex traits. PLoS Genetics 4: e1000008.

Huang W, Richards S, Carbone MA et al. (2012) Epistasis dominates the genetic architecture of Drosophila quantitative traits. Proceedings of the National Academy of Sciences of the USA 109: 15553–15559.

Kimura M (1983) The Neutral Theory of Molecular Evolution. Cambridge: Cambridge University Press.

Lande R (1992) Neutral theory of quantitative genetic variance in an island model with local extinction and recolonization. Evolution 46: 381–389.

Lande R and Arnold SJ (1983) The measurement of selection on correlated characters. Evolution 37: 1210–1226.

Leinonen T, O'Hara RB, Cano JM et al. (2008) Comparative studies of quantitative trait and neutral marker divergence: a meta‐analysis. Journal of Evolutionary Biology 21: 1–17.

Li Y, Huang Y, Bergelson J, Nordborg M and Borevitz JO (2010) Association mapping of local climate‐sensitive quantitative trait loci in Arabidopsis thaliana. Proceedings of the National Academy of Sciences of the USA 107: 21199–21204.

Lorenz K and Cohen BA (2012) Small‐ and large‐effect quantitative trait locus interactions underlie variation in yeast sporulation efficiency. Genetics 192: 1123–1132.

Lynch M and Walsh JB (1998) Genetics and Analysis of Quantitative Traits. Sunderland: Sinauer Associates.

Mackay TF (2001) The genetic architecture of quantitative traits. Annual Reviews of Genetics 35: 303–339.

Mackay TF, Richards S, Stone EA et al. (2012) The Drosophila melanogaster genetic reference panel. Nature 482: 173–178.

McKay JK and Latta RG (2002) Adaptive population divergence: markers, QTL and traits. Trends in Ecology and Evolution 17: 285–291.

Merilä J and Bjorklund M (2004) Phenotypic integration as a constraint and adaptation. In: Pigliucci M and Preston K (eds) The Evolutionary Biology of Complex Phenotypes, pp. 107–129. Oxford: Oxford University Press.

Merilä J and Crnokrak P (2001) Comparison of genetic differentiation at marker loci and quantitative traits. Journal of Evolutionary Biology 14: 892–903.

Orr HA (2005) The genetic theory of adaptation: a brief history. Nature Reviews Genetics 6: 119–127.

Pelgas B, Bousquet J, Meirmans PG, Ritland K and Isabel N (2011) QTL mapping in white spruce: gene maps and genomic regions underlying adaptive traits across pedigrees, years and environments. BMC Genomics 12: 145.

Richter‐Boix A, Teplitsky C, Rogell B and Laurila A (2010) Local selection modifies phenotypic divergence among Rana temporaria populations in the presence of gene flow. Molecular Ecology 19: 716–731.

Rockman MV (2012) The QTN program and the alleles that matter for evolution: all that's gold does not glitter. Evolution 66: 1–17.

Rogell B, Eklund M, Thörngren H, Laurila A and Höglund J (2010) The effects of selection, drift and genetic variation on life‐history trait divergence among insular populations natterjack toad, Bufo calamita. Molecular Ecology 19: 2229–2240.

Rogers SM, Tamkee P, Summers B et al. (2012) Genetic signature of adaptive peak shift in threespine stickleback. Evolution 66: 2439–2450.

Santure AW, Ewen JG, Sicard D, Roff DA and Møller AP (2010) Population structure in the barn swallow, Hirundo rustica: a comparison between neutral DNA markers and quantitative traits. Biological Journal of the Linnean Society 99: 306–314.

Schluter D (1996) Adaptive radiation along genetic lines of least resistance. Evolution 50: 1766–1774.

Stapley J, Reger J, Feulner PG et al. (2010) Adaptation genomics: the next generation. Trends in Ecology and Evolution 25: 705–712.

Tian F, Bradbury PJ, Brown PJ et al. (2011) Genome‐wide association study of leaf architecture in the maize nested association mapping population. Nature Genetics 43: 159–162.

Visscher PM, Brown MA, McCarthy MI and Yang J (2012) Five years of GWAS discovery. American Journal of Human Genetics 90: 7–24.

Volis S and Zhang YH (2010) Separating effects of gene flow and natural selection along an environmental gradient. Evolutionary Biology 37: 187–199.

Whitlock MC (2008) Evolutionary inference from QST. Molecular Ecology 17: 1885–1896.

Wright S (1931) Evolution in Mendelian populations. Genetics 16: 97–159.

Wright S (1978) Evolution and the Genetics of Populations, Volume 4: Variabililty Within and Among Populations. London: The University of Chicago Press.

Zhen Y and Andolfatto P (2012) Methods to detect selection on noncoding DNA. Methods in Molecular Biology 856: 141–159.

Further Reading

Flint J and Mackay TF (2009) Genetic architecture of quantitative traits in mice, flies, and humans. Genome Research 19(5): 723–733.

Orr HA (2005) Theories of adaptation: what they do and don't say. Genetica 123: 3–13.

Pavlidis P, Metzler D and Stephan W (2012) Selective sweeps in multilocus models of quantitative traits. Genetics 192: 225–239.

Slate J (2005) Quantitative trait locus mapping in natural populations: progress, caveats and future directions. Molecular Ecology 14: 363–379.

Related Sub-Topics:

Evolutionary Processes | Quantitative Genetics | Molecular Evolution | Selection and Variation
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Article Title: Selection and Complex Multigene Traits
 
How to Cite close
Hasson, Esteban R, Fanara, Juan José, and Frankel, Nicolás(May 2013) Selection and Complex Multigene Traits. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0002295]