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The pleiotropic structure of the genotype–phenotype map: the evolvability of complex organisms

Nature Reviews Genetics volume 12, pages 204–213 (2011)Cite this article

Subjects

Key Points

  • Pleiotropy refers to the phenomenon of one gene or one mutation affecting multiple phenotypic traits. It has widespread implications in genetics, disease, development, ageing and evolution.

  • Although the definition of pleiotropy is straightforward, its measurement is not. This is due to many complicating factors, including the way in which traits are defined, interdependence among traits, the traits chosen for examination and the power of detecting an effect.

  • Despite the aforementioned difficulties, pleiotropy has been measured recently by mapping the QTLs underlying certain groups of traits and by phenotyping gene knockout or knockdown mutants of model organisms.

  • Contrary to the idea of universal pleiotropy, empirical data consistently suggest that the median degree of pleiotropy for QTLs or genes is only a few per cent of the total number of traits scored.

  • The pleiotropic structure of the genotype–phenotype map is strongly modular, meaning that sets of traits are co-controlled by sets of genes in a highly nonrandom manner.

  • In contrast to current theoretical models of pleiotropy, empirical data show that the average phenotypic effect of a mutation on a trait increases with the number of traits that are affected by the mutation.

  • Consideration of the empirical patterns of pleiotropy resolves the 'cost of complexity' conundrum, which asserts that complex organisms are less adaptable than simple ones as a result of the constraints that are imposed by pleiotropy.

  • The idea of the cost of complexity may be better phrased as the 'cost of pleiotropy', because the degree of pleiotropy per mutation is substantially lower than the number of traits (that is, complexity) in an organism.

  • Pleiotropy may be conferred by multiple molecular functions of a gene (type I pleiotropy) or multiple morphological and physiological consequences of a single molecular function (type II pleiotropy). Empirical data support the idea that pleiotropy is largely of type II.

  • The predominance of type II pleiotropy suggests that developing drugs that target only one particular phenotypic effect of a pleiotropic gene may be difficult.

Abstract

It was first noticed 100 years ago that mutations tend to affect more than one phenotypic characteristic, a phenomenon that was called 'pleiotropy'. Because pleiotropy was found so frequently, the notion arose that pleiotropy is 'universal'. However, quantitative estimates of pleiotropy have not been available until recently. These estimates show that pleiotropy is highly restricted and are more in line with the notion of variational modularity than with universal pleiotropy. This finding has major implications for the evolvability of complex organisms and the mapping of disease-causing mutations.

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Figure 1: Possible causes of co-inheritance of phenotypic traits.
Figure 2: The degree of pleiotropy depends on the definition of phenotypic traits.
Figure 3: Distribution of the degree of pleiotropy.

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References

  1. Stearns, F. W. One hundred years of pleiotropy: a retrospective. Genetics 186, 767–773 (2010). A historical overview of the study of pleiotropy.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. He, X., Qian, W., Wang, Z., Li, Y. & Zhang, J. Prevalent positive epistasis in Escherichia coli and Saccharomyces cerevisiae metabolic networks. Nature Genet. 42, 272–276 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Wolf, J. B., Pomp, D., Eisen, E. J., Cheverud, J. M. & Leamy, L. J. The contribution of epistatic pleiotropy to the genetic architecture of covariation among polygenic traits in mice. Evol. Dev. 8, 468–476 (2006).

    Article  CAS  PubMed  Google Scholar 

  4. Tyler, A. L., Asselbergs, F. W., Williams, S. M. & Moore, J. H. Shadows of complexity: what biological networks reveal about epistasis and pleiotropy. Bioessays 31, 220–227 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Plomin, R., Haworth, C. M. & Davis, O. S. Common disorders are quantitative traits. Nature Reviews Genetics 10, 872–878 (2009).

    Article  CAS  PubMed  Google Scholar 

  6. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cordell, H. J. Detecting gene-gene interactions that underlie human diseases. Nature Reviews Genetics 10, 392–404 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. McCarthy, M. I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Rev. Genet. 9, 356–369 (2008).

    Article  CAS  PubMed  Google Scholar 

  9. Flint, J. & Mackay, T. F. Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res. 19, 723–733 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Fisher, R. A. The Genetical Theory of Natural Selection (Clarendon, Oxford, 1930). A classic book that for the first time put the study of pleiotropy and its impact on evolution in a theoretical framework. It also initiated the discussion of the cost of complexity.

    Book  Google Scholar 

  11. Orr, H. A. Adaptation and the cost of complexity. Evolution 54, 13–20 (2000). An influential paper that quantitatively analses the impact of pleiotropy on the rate of adaptation, thus popularizing the idea of the cost of complexity.

    Article  CAS  PubMed  Google Scholar 

  12. Waxman, D. & Peck, J. R. Pleiotropy and the preservation of perfection. Science 279, 1210–1213 (1998).

    Article  CAS  PubMed  Google Scholar 

  13. Barton, N. H. Pleiotropic models of quantitative variation. Genetics 124, 773–782 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Otto, S. P. Two steps forward, one step back: the pleiotropic effects of favoured alleles. Proc. Biol. Sci. 271, 705–714 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hodgkin, J. Seven types of pleiotropy. Int. J. Dev. Biol. 42, 501–505 (1998).

    CAS  PubMed  Google Scholar 

  16. Flatt, T. The evolutionary genetics of canalization. Q. Rev. Biol. 80, 287–316 (2005).

    Article  PubMed  Google Scholar 

  17. Williams, G. C. Pleiotropy, natural selection, and the evolution of senescence. Evolution 11, 398–411 (1957). This classic paper proposes the hypothesis that antagonistic pleiotropy underlies ageing.

    Article  Google Scholar 

  18. Crespi, B. J. The origins and evolution of genetic disease risk in modern humans. Ann. N. Y. Acad. Sci. 1206, 80–109 (2010).

    Article  PubMed  Google Scholar 

  19. Brunner, H. G. & van Driel, M. A. From syndrome families to functional genomics. Nature Rev. Genet. 5, 545–551 (2004).

    Article  CAS  PubMed  Google Scholar 

  20. Slatkin, M. Pleiotropy and parapatric speciation. Evolition 36, 263–270 (1982).

    Article  Google Scholar 

  21. Foster, K. R., Shaulsky, G., Strassmann, J. E., Queller, D. C. & Thompson, C. R. Pleiotropy as a mechanism to stabilize cooperation. Nature 431, 693–696 (2004). An excellent example of pleiotropy-stabilizing cooperation.

    Article  CAS  PubMed  Google Scholar 

  22. Hodgkin, J. Seven types of pleiotropy. Int. J. Dev. Biol. 42, 501–505 (1998).

    CAS  PubMed  Google Scholar 

  23. Knott, S. A. & Haley, C. S. Multitrait least squares for quantitative trait loci detection. Genetics 156, 899–911 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Dudley, A., Janse, D., Tanay, A., Shamir, R. & Church, G. A global view of pleiotropy and phenotypically derived gene function in yeast. Mol. Syst. Biol. 1, doi:doi:10.1038/msb4100004 (2005). The first study of gene pleiotropy at the genomic scale.

  25. Stern, D. L. Evolutionary developmental biology and the problem of variation. Evolution 54, 1079–1091 (2000). This review introduces the distinction between the number of pleiotropic functions of a gene, as revealed by developmental analysis, and the pleiotropy of mutations at a locus. It shows that, in general, mutational pleiotropy tends to be smaller than gene pleiotropy (that is, the number of biological roles that a gene has).

    Article  CAS  PubMed  Google Scholar 

  26. Sönnichsen, B. et al. Full-genome RNAi profiling of early embryogenesis in Caenorhabditis elegans. Nature 434, 462–469 (2005).

    Article  PubMed  Google Scholar 

  27. Wagner, G. P. (ed.) The Character Concept in Evolutionary Biology. Vol. xxiii 622 (Academic, San Diego, 2001).

    Google Scholar 

  28. Wang, Z., Liao, B. Y. & Zhang, J. Genomic patterns of pleiotropy and the evolution of complexity. Proc. Natl Acad. Sci. USA 107, 18034–18039 (2010). This article reports on genome-wide patterns of gene pleiotropy in yeast, nematode worms and mice, and predicts that organisms with intermediate levels of complexity have the highest rate of adaptation.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Krantz, D. H., Luce, R. D., Suppes, P. & Tversky, A. Foundations of Measurement Vol I. (Academic Press, 1971).

    Google Scholar 

  30. Wagner, G. P. et al. Pleiotropic scaling of gene effects and the 'cost of complexity'. Nature 452, 470–472 (2008). A QTL study that for the first time reveals low degrees of mutational pleiotropy, and the scaling properties of the effect sizes — both of which potentially diminish the cost of complexity.

    Article  CAS  PubMed  Google Scholar 

  31. Pavlicev, M., Cheverud, J. M. & Wagner, G. P. Measuring morphological integration using eigenvalue variance. Evol. Biol. 36, 157–170 (2009).

    Article  Google Scholar 

  32. Monteiro, A., Prijs, J., Bax, M., Hakkaart, T. & Brakefield, P. M. Mutants highlight the modular control of butterfly eyespot patterns. Evol. Dev. 5, 180–187 (2003).

    Article  PubMed  Google Scholar 

  33. Albert, A. Y. K. et al. The genetics of adaptive shape shift in stickleback: pleiotropy and effect size. Evolution 62, 76–85 (2008).

    PubMed  Google Scholar 

  34. Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002).

    Article  CAS  PubMed  Google Scholar 

  35. Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Grüneberg, H. An analysis of the “pleiotropic” effects of a new lethal mutation in the rat (Mus. norvegicus). Proc. R. Soc. Lond. 125, 123–144 (1938). A classic analysis of pleiotropy, and among the first studies of its kind.

    Article  Google Scholar 

  37. Rafikova, O., Rafikov, R. & Nudler, E. Catalysis of S-nitrosothiols formation by serum albumin: the mechanism and implication in vascular control. Proc. Natl Acad. Sci. USA 99, 5913–5918 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sugio, S., A. Kashima, Mochizuki, S., Noda, M. & Kobayashi, K. Crystal structure of human serum albumin at 2.5 A resolution. Protein Eng. 12, 439–446 (1999).

    Article  CAS  PubMed  Google Scholar 

  39. He, X. & Zhang, J. Toward a molecular understanding of pleiotropy. Genetics 173, 1885–1891 (2006). This paper shows that most gene pleiotropy is of type II.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Valentine, J. W., Collins, A. G. & Meyer, C. P. Morphological complexity increase in metazoans. Paleobiology 20, 131–142 (1994).

    Article  Google Scholar 

  41. Lane, N. & Martin, W. The energetics of genome complexity. Nature 467, 929–934 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Goh, K. I. et al. The human disease network. Proc. Natl Acad. Sci. USA 104, 8685–8690 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Wright, S. Evolution and the Genetics of Populations (Univ. Chicago Press, Chicago, 1968). A classic book that proposes the universal pleiotropy concept, although the original meaning seems to be 'every gene affects more than one trait' rather than 'every gene affects every trait'.

    Google Scholar 

  44. Mackay, T. F., Stone, E. A. & Ayroles, J. F. The genetics of quantitative traits: challenges and prospects. Nature Rev. Genet. 10, 565–577 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Cheverud, J. M. et al. Pleiotropic effects on mandibular morphology II: differential epistasis and genetic variation in morphological integration. J. Exp. Zool. B Mol. Dev. Evol. 302B, 424–435 (2004).

    Article  CAS  Google Scholar 

  46. Pavlicev, M. et al. Genetic variation in pleiotropy: differential epistasis as a source of variation in the allometric relationship between long bone lengths and body weight. Evolution 62, 199–213 (2008).

    PubMed  Google Scholar 

  47. Wagner, G. P. & Altenberg, L. Complex adaptations and the evolution of evolvability. Evolution 50, 967–976 (1996).

    Article  PubMed  Google Scholar 

  48. Mitteroecker, P. The developmental basis of variational modularity: insights from quantitative genetics, morphometrics, and developmental biology. Evol. Biol. 36, 377–385 (2009).

    Article  Google Scholar 

  49. Welch, J. J. & Waxman, D. Modularity and the cost of complexity. Evolution 57, 1723+1734 (2003).

    Article  PubMed  Google Scholar 

  50. Orr, H. A. & Coyne, J. A. The genetics of adaptation revisited. Am. Nat. 140, 725–742 (1992).

    Article  CAS  PubMed  Google Scholar 

  51. Poon, A. & Otto, S. P. Compensating for our load of mutations: freezing the meltdown of small populations. Evolution 54, 1467–1479 (2000).

    Article  CAS  PubMed  Google Scholar 

  52. Waxman, D. & Welch, J. J. Fisher's microscope and Haldane's ellipse. Am. Nat. 166, 447–457 (2005).

    Article  CAS  PubMed  Google Scholar 

  53. Martin, G. & Lenormand, T. A general multivariate extension of Fisher's geometrical model and the distribution of mutation fitness effects across species. Evolution 60, 893–907 (2006). This paper represents a major step in generalizing the results of the classical FGM.

    Article  PubMed  Google Scholar 

  54. Chevin, L. M., Martin, G. & Lenormand, T. Fisher's model and the genomics of adaptation: restricted pleiotropy, heterogeneous mutation, and parallel evolution. Evolution 64, 3213–3231 (2010).

    Article  PubMed  Google Scholar 

  55. Whitlock, M. C., Griswold, C. K. & Peters, A. D. Compensating for the meltdown: The critical effective size of a population with deleterious and compensatory mutations. Annales Zoologici Fennici 40, 169–183 (2003).

    Google Scholar 

  56. Hartl, D. L. & Taubes, C. H. Towards a theory of evolutionary adaptation. Genetica 102/103, 525–533 (1998).

    Article  Google Scholar 

  57. Wagner, G. P. & Gabriel, W. Quantitative variation in finite parthenogenetic populations: What stops Muller's ratchet in the absence of recombination? Evolution 44, 715–731 (1990).

    Article  PubMed  Google Scholar 

  58. Wagner, G. P. The influence of variation and of developmental constraints on the rate of multivariate phenotypic evolution. J. Evol. Biol. 1, 45–66 (1988).

    Article  Google Scholar 

  59. Hansen, T. F. Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability. BioSystems 69, 83–94 (2003).

    Article  PubMed  Google Scholar 

  60. Hansen, T. F. & Houle, D. Measuring and comparing evolvability and constraint in multivariate characters. Journal of Evolutionary Biology 21, 1201–1219, (2008).

    Article  CAS  PubMed  Google Scholar 

  61. Wagner, G. P. in Evolution Since Darwin: The First 150 Years (eds Bell, M. A., Futuyma, D. J., Eanes, W. F. & Levinton, J. S.) 197–213 (Sinauer Associates, Sunderland, Massachusetts, 2010).

    Google Scholar 

  62. Tenaillon, O., Silander, O. K., Uzan, J.-P. & Chao. L. Quantifying organismal complexity using a population genetic approach. PLoS ONE 2, e217 (2007).

    Article  Google Scholar 

  63. Su, Z., Zeng, Y. & Gu, X. A preliminary analysis of gene pleiotropy estimated from protein sequences. J. Exp. Zool. B Mol. Dev. Evol. 314, 115–122 (2010).

    Article  PubMed  Google Scholar 

  64. Gu, X. Evolutionary framework for protein sequence evolution and gene pleiotropy. Genetics 175, 1813–1822 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Gu, X. Stabilizing selection of protein function and distribution of selection coefficient among sites. Genetica 130, 93–97 (2007).

    Article  CAS  PubMed  Google Scholar 

  66. Wagner, G. P., Pavlicev, M. & Cheverud, J. M. The road to modularity. Nature Rev. Genet. 8, 921–931 (2007).

    Article  CAS  PubMed  Google Scholar 

  67. Turelli, M. Effects of pleiotropy on predictions concerning mutation-selection balance for polygenic traits. Genetics 111, 165–195 (1985).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Hermisson, J. & McGregor, A. P. Pleiotropic scaling and QTL data. Nature 456, E3 (2008).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Research in the Wagner laboratory is supported by the John Templeton Foundation (grant number 12793). Pleiotropy research in the Zhang laboratory has been supported by the US National Institutes of Health.

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Authors and Affiliations

  1. and Department of Ecology and Evolutionary Biology, Yale Systems Biology Institute, Yale University, 165 Prospect Street, POB 208106, New Haven, 06405-8106, Connecticut, USA

    Günter P. Wagner

  2. Department of Ecology and Evolutionary Biology, University of Michigan, 830 North University, Ann Arbor, 48109-1048, Michigan, USA

    Jianzhi Zhang

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Glossary

Genetic load

The difference between the mean fitness of the population and the fitness of the fittest genotype in the population. The more deleterious the mutations in a population, the lower the mean fitness and the higher the genetic load.

Effective population size

(Ne). A measure of the strength of random genetic drift in a population. The lower the effective population size, the stronger the genetic drift. Ne is influenced by the census population size, the breeding system, the fitness differences among individuals, the sex ratio and other factors.

Eigenvalue

A characteristic parameter of a matrix. In the case of co-variance matrices, the eigenvalues are equal to the amount of variance that is associated with the principal components of the co-variance matrix.

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Wagner, G., Zhang, J. The pleiotropic structure of the genotype–phenotype map: the evolvability of complex organisms. Nat Rev Genet 12, 204–213 (2011). https://doi.org/10.1038/nrg2949

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