Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Correlational selection in the age of genomics


Ecologists and evolutionary biologists are well aware that natural and sexual selection do not operate on traits in isolation, but instead act on combinations of traits. This long-recognized and pervasive phenomenon is known as multivariate selection, or—in the particular case where it favours correlations between interacting traits—correlational selection. Despite broad acknowledgement of correlational selection, the relevant theory has often been overlooked in genomic research. Here, we discuss theory and empirical findings from ecological, quantitative genetic and genomic research, linking key insights from different fields. Correlational selection can operate on both discrete trait combinations and quantitative characters, with profound implications for genomic architecture, linkage, pleiotropy, evolvability, modularity, phenotypic integration and phenotypic plasticity. We synthesize current knowledge and discuss promising research approaches that will enable us to understand how correlational selection shapes genomic architecture, thereby linking quantitative genetic approaches with emerging genomic methods. We suggest that research on correlational selection has great potential to integrate multiple fields in evolutionary biology, including developmental and functional biology, ecology, quantitative genetics, phenotypic polymorphisms, hybrid zones and speciation processes.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: The scope of correlational selection and its links to different fields in evolutionary biology.
Fig. 2: Phenotypic and quantitative genetics studies on organisms and traits in which correlational selection has experimentally been demonstrated or inferred, in the field or in laboratory studies.
Fig. 3: Illustration of correlational selection, with important parameters used to quantify it and determine how its effects are carried across generations.
Fig. 4: Examples of genomic trait architectures that might reflect past or ongoing correlational selection.


  1. 1.

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

    CAS  PubMed  Article  Google Scholar 

  2. 2.

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

    PubMed  Article  Google Scholar 

  3. 3.

    Draghi, J. A. & Whitlock, M. C. Phenotypic plasticity facilitates mutational variance, genetic variance, and evolvability along the major axis of environmental variation. Evolution 66, 2891–2902 (2012).

    PubMed  Article  Google Scholar 

  4. 4.

    Cheverud, J. M. Quantitative genetics and developmental constraints on evolution by selection. J. Theor. Biol. 110, 155–171 (1984).

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Phillips, P. C. & Arnold, S. J. Visualizing multivariate selection. Evolution 43, 1209–1266 (1989).

    PubMed  Article  Google Scholar 

  6. 6.

    Sinervo, B. & Svensson, E. Correlational selection and the evolution of genomic architecture. Heredity 16, 948–955 (2002).

    Google Scholar 

  7. 7.

    Blows, M. W. & Brooks, R. Measuring nonlinear selection. Am. Nat. 162, 815–820 (2003).

    PubMed  Article  Google Scholar 

  8. 8.

    Blows, M. W., Brooks, R. & Kraft, P. G. Exploring complex fitness surfaces: multiple ornamentation and polymorphism in male guppies. Evolution 57, 1622–1630 (2003).

    PubMed  Article  Google Scholar 

  9. 9.

    Jones, A. G., Arnold, S. J. & Bürger, R. Stability of the G-matrix in a population experiencing pleiotropic mutation, stabilizing selection, and genetic drift. Evolution 57, 1747–1760 (2003).

    PubMed  Article  Google Scholar 

  10. 10.

    Jones, A. G., Arnold, S. J. & Bürger, R. Evolution and stability of the G-matrix on a landscape with a moving optimum. Evolution 58, 1639–1654 (2004).

    PubMed  Article  Google Scholar 

  11. 11.

    Jones, A. G., Arnold, S. J. & Bürger, R. The mutation matrix and the evolution of evolvability. Evolution 61, 727–745 (2007).

    PubMed  Article  Google Scholar 

  12. 12.

    Jones, A. G., Bürger, R. & Arnold, S. J. Epistasis and natural selection shape the mutational architecture of complex traits. Nat. Commun. 5, 3709 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Lande, R. The genetic covariance between characters maintained by pleiotropic mutations. Genetics 94, 203–215 (1980).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Armbruster, W. S., Pélabon, C., Hansen, T. F. & Mulder, C. P. H. in Phenotypic Integration: Studying the Ecology and Evolution of Complex Phenotypes (eds Pigliucci, M. & Preston, K.) 23–49 (Oxford Univ. Press, 2004).

  15. 15.

    Bell, A. M. & Sih, A. Exposure to predation generates personality in threespined sticklebacks (Gasterosteus aculeatus). Ecol. Lett. 10, 828–834 (2007).

    PubMed  Article  Google Scholar 

  16. 16.

    Dingemanse, N. J., Barber, I. & Dochtermann, N. A. Non-consumptive effects of predation: does perceived risk strengthen the genetic integration of behaviour and morphology in stickleback? Ecol. Lett. 23, 107–118 (2020).

    PubMed  Article  Google Scholar 

  17. 17.

    Hansen Wheat, C., Fitzpatrick, J. L., Rogell, B. & Temrin, H. Behavioural correlations of the domestication syndrome are decoupled in modern dog breeds. Nat. Commun. 10, 2422 (2019).

  18. 18.

    Hurst, L. D., Pál, C. & Lercher, M. J. The evolutionary dynamics of eukaryotic gene order. Nat. Rev. Genet. 5, 299–310 (2004).

    PubMed  Article  CAS  Google Scholar 

  19. 19.

    Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).

    PubMed  Article  Google Scholar 

  20. 20.

    Schluter, D. & Nychka, D. Exploring fitness surfaces. Am. Nat. 143, 597–616 (1994).

    Article  Google Scholar 

  21. 21.

    Siepielski, A. M. et al. Precipitation drives global variation in natural selection. Science 355, 959–962 (2017).

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Roff, D. A. & Fairbairn, D. J. A test of the hypothesis that correlational selection generates genetic correlations. Evolution 66, 2953–2960 (2012).

    PubMed  Article  Google Scholar 

  23. 23.

    Svensson, E. I., McAdam, A. G. & Sinervo, B. Intralocus sexual conflict over immune defense, gender load, and sex-specific signaling in a natural lizard population. Evolution 63, 3124–3135 (2009).

    PubMed  Article  Google Scholar 

  24. 24.

    McGlothlin, J. W., Parker, P. G., Nolan, V. & Ketterson, E. D. Correlational selection leads to genetic integration of body size and an attractive plumage trait in dark-eyed juncos. Evolution 59, 658–671 (2005).

    PubMed  Article  Google Scholar 

  25. 25.

    Duckworth, R. A. & Kruuk, L. E. B. Evolution of genetic integration between dispersal and colonization ability in a bird. Evolution 63, 968–977 (2009).

    PubMed  Article  Google Scholar 

  26. 26.

    Brodie, E. D. III Correlational selection for color pattern and antipredator behavior in the garter snake Thamnophis ordinoides. Evolution 46, 1284–1298 (1992).

    PubMed  Article  Google Scholar 

  27. 27.

    Wise, M. J. & Rausher, M. D. Costs of resistance and correlational selection in the multiple-herbivore community of Solanum carolinense. Evolution 70, 2411–2420 (2016).

    PubMed  Article  Google Scholar 

  28. 28.

    Fenster, C. B., Reynolds, R. J., Williams, C. W., Makowsky, R. & Dudash, M. R. Quantifying hummingbird preference for floral trait combinations: the role of selection on trait interactions in the evolution of pollination syndromes. Evolution 69, 1113–1127 (2015).

    PubMed  Article  Google Scholar 

  29. 29.

    Arnegard, M. E. et al. Genetics of ecological divergence during speciation. Nature 511, 307–311 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Martin, C. H. & Wainwright, P. C. Multiple fitness peaks on the adaptive landscape drive adaptive radiation in the wild. Science 339, 208–211 (2013).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Phillips, P. C. Epistasis - the essential role of gene interactions in the structure and evolution of genetic systems. Nat. Rev. Genet. 9, 855–867 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Steppan, S. J., Phillips, P. C. & Houle, D. Comparative quantitative genetics: evolution of the G matrix. Trends Ecol. Evol. 17, 320–327 (2002).

    Article  Google Scholar 

  33. 33.

    Blows, M. W. & McGuigan, K. The distribution of genetic variance across phenotypic space and the response to selection. Mol. Ecol. 24, 2056–2072 (2015).

    PubMed  Article  Google Scholar 

  34. 34.

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

    PubMed  Article  Google Scholar 

  35. 35.

    Lande, R. The maintenance of genetic variability by mutation in a polygenic character with linked loci. Genet. Res. 26, 221–235 (1976).

    Article  Google Scholar 

  36. 36.

    Lande, R. The genetic correlation between characters maintained by selection, linkage and inbreeding. Genet. Res. 44, 309–320 (1984).

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Bulmer, M. G. The effect of selection on genetic variability: a simulation study. Genet. Res. 28, 101–117 (1976).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, 1998).

  39. 39.

    Guillaume, F. & Whitlock, M. C. Effects of migration on the genetic covariance matrix. Evolution 61, 2398–2409 (2007).

    PubMed  Article  Google Scholar 

  40. 40.

    Noble, D. W. A., Radersma, R. & Uller, T. Plastic responses to novel environments are biased towards phenotype dimensions with high additive genetic variation. Proc. Natl Acad. Sci. USA 116, 13452–13461 (2019).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Houle, D., Bolstad, G. H., van der Linde, K. & Hansen, T. F. Mutation predicts 40 million years of fly wing evolution. Nature 548, 447–450 (2017).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Svensson, E. I. & Berger, D. The role of mutation bias in adaptive evolution. Trends Ecol. Evol. 34, 422–434 (2019).

    PubMed  Article  Google Scholar 

  43. 43.

    Schweizer, G. & Wagner, A. Genotype networks of 80 quantitative Arabidopsis thaliana phenotypes reveal phenotypic evolvability despite pervasive epistasis. PLoS Comput. Biol. 16, e1008082 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Delph, L. F., Steven, J. C., Anderson, I. A., Herlihy, C. R. & Brodie, E. D. III Elimination of a genetic correlation between the sexes via artificial correlational selection. Evolution 65, 2872–2880 (2011).

    PubMed  Article  Google Scholar 

  45. 45.

    Conner, J. K. Genetic mechanisms of floral trait correlations in a natural population. Nature 420, 407–410 (2002).

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Wagner, G. P. & Zhang, J. The pleiotropic structure of the genotype–phenotype map: the evolvability of complex organisms. Nat. Rev. Genet. 12, 204–213 (2011).

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Barton, N. H., Etheridge, A. M. & Véber, A. The infinitesimal model: definition, derivation, and implications. Theor. Popul. Biol. 118, 50–73 (2017).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Orr, H. A. The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution 52, 935–948 (1998).

    PubMed  Article  Google Scholar 

  49. 49.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Stinchcombe, J. R., Weinig, C., Heath, K. D., Brock, M. T. & Schmitt, J. Polymorphic genes of major effect: consequences for variation, selection and evolution in Arabidopsis thaliana. Genetics 182, 911–922 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Orr, H. A. The genetics of species differences. Trends Ecol. Evol. 16, 343–350 (2001).

    Article  Google Scholar 

  52. 52.

    Nadeau, N. J. et al. The gene cortex controls mimicry and crypsis in butterflies and moths. Nature 534, 106–110 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Pitchers, W. et al. A multivariate genome-wide association study of wing shape in Drosophila melanogaster. Genetics 211, 1429–1447 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Mackay, T. F. C. Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat. Rev. Genet. 15, 22–33 (2014).

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Sailer, Z. R. & Harms, M. J. Detecting high-order epistasis in nonlinear genotype–phenotype maps. Genetics 205, 1079–1088 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Hill, W. G. “Conversion” of epistatic into additive genetic variance in finite populations and possible impact on long-term selection response. J. Anim. Breed. Genet. 134, 196–201 (2017).

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Gienapp, P. et al. Genomic quantitative genetics to study evolution in the wild. Trends Ecol. Evol. 32, 897–908 (2017).

    PubMed  Article  Google Scholar 

  59. 59.

    Nosil, P. et al. Ecology shapes epistasis in a genotype–phenotype–fitness map for stick insect colour. Nat. Ecol. Evol. 4, 1673–1684 (2020).

  60. 60.

    Blount, Z. D., Lenski, R. E. & Losos, J. B. Contingency and determinism in evolution: replaying life’s tape. Science 362, eaam5979 (2018).

    Article  CAS  PubMed  Google Scholar 

  61. 61.

    Bolnick, D. I., Barrett, R. D. H., Oke, K. B., Rennison, D. J. & Stuart, Y. E. (Non)parallel evolution. Annu. Rev. Ecol. Evol. Syst. 49, 303–330 (2018).

    Article  Google Scholar 

  62. 62.

    Therkildsen, N. O. et al. Contrasting genomic shifts underlie parallel phenotypic evolution in response to fishing. Science 365, 487–490 (2019).

    CAS  PubMed  Article  Google Scholar 

  63. 63.

    Stern, D. L. & Orgogozo, V. Is genetic evolution predictable? Science 323, 746–751 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Colosimo, P. F. et al. Widespread parallel evolution in sticklebacks by repeated fixation of Ectodysplasin alleles. Science 307, 1928–1933 (2005).

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Archambeault, S. L., Bärtschi, L. R., Merminod, A. D. & Peichel, C. L. Adaptation via pleiotropy and linkage: association mapping reveals a complex genetic architecture within the stickleback Eda locus. Evol. Lett. 4, 282–301 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20, 567–581 (2019).

    PubMed  Article  CAS  Google Scholar 

  67. 67.

    Stapley, J., Feulner, P. G. D., Johnston, S. E., Santure, A. W. & Smadja, C. M. Variation in recombination frequency and distribution across eukaryotes: patterns and processes. Phil. Trans. R. Soc. B 372, 20160455 (2017).

    PubMed  Article  Google Scholar 

  68. 68.

    Choudhury, R. R., Rogivue, A., Gugerli, F. & Parisod, C. Impact of polymorphic transposable elements on linkage disequilibrium along chromosomes. Mol. Ecol. 28, 1550–1562 (2019).

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Thompson, M. J. & Jiggins, C. D. Supergenes and their role in evolution. Heredity 113, 1–8 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    Yeaman, S. Genomic rearrangements and the evolution of clusters of locally adaptive loci. Proc. Natl Acad. Sci. USA 110, E1743–E1751 (2013).

    CAS  PubMed  Article  Google Scholar 

  71. 71.

    Faria, R., Johannesson, K., Butlin, R. K. & Westram, A. M. Evolving inversions. Trends Ecol. Evol. 34, 239–248 (2019).

    PubMed  Article  Google Scholar 

  72. 72.

    Tuttle, E. M. et al. Divergence and functional degradation of a sex chromosome-like supergene. Curr. Biol. 26, 344–350 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Kupper, C. et al. A supergene determines highly divergent male reproductive morphs in the ruff. Nat. Genet. 48, 79–83 (2016).

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Lamichhaney, S. et al. Structural genomic changes underlie alternative reproductive strategies in the ruff (Philomachus pugnax). Nat. Genet. 48, 84–88 (2016).

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Huu, C. N., Keller, B., Conti, E., Kappel, C. & Lenhard, M. Supergene evolution via stepwise duplications and neofunctionalization of a floral-organ identity gene. Proc. Natl Acad. Sci. USA 117, 23148–23157 (2020).

    CAS  PubMed  Article  Google Scholar 

  76. 76.

    Merrill, R. M. et al. Genetic dissection of assortative mating behavior. PLoS Biol. 17, e2005902 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  77. 77.

    Whitlock, M. C., Phillips, P. C., Moore, F. B.-G. & Tonsor, S. J. Multiple fitness peaks and epistasis. Annu. Rev. Ecol. Syst. 26, 601–629 (1995).

    Article  Google Scholar 

  78. 78.

    Dudley, S. A. The response to selection on plant physiological traits: evidence for local adaptation. Evolution 50, 103–110 (1996).

    PubMed  Article  Google Scholar 

  79. 79.

    Kirkpatrick, M. & Ravigné, V. Speciation by natural and sexual selection: models and experiments. Am. Nat. 159, S22–S35 (2002).

    PubMed  Article  Google Scholar 

  80. 80.

    Hohenlohe, P. A., Bassham, S., Currey, M. & Cresko, W. A. Extensive linkage disequilibrium and parallel adaptive divergence across threespine stickleback genomes. Phil. Trans. R. Soc. B 367, 395–408 (2012).

  81. 81.

    Hench, K., Vargas, M., Höppner, M. P., McMillan, W. O. & Puebla, O. Inter-chromosomal coupling between vision and pigmentation genes during genomic divergence. Nat. Ecol. Evol. 3, 657–667 (2019).

    PubMed  Article  Google Scholar 

  82. 82.

    Gienapp, P., Calus, M. P. L., Laine, V. N. & Visser, M. E. Genomic selection on breeding time in a wild bird population. Evol. Lett. 3, 142–151 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    McGuigan, K., Collet, J. M., Allen, S. L., Chenoweth, S. F. & Blows, M. W. Pleiotropic mutations are subject to strong stabilizing selection. Genetics 197, 1051–105 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  84. 84.

    McGuigan, K. et al. The nature and extent of mutational pleiotropy in gene expression of male Drosophila serrata. Genetics 196, 911–921 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Hine, E., Runcie, D. E., McGuigan, K. & Blows, M. W. Uneven distribution of mutational variance across the transcriptome of Drosophila serrata revealed by high-dimensional analysis of gene expression. Genetics (2018).

  86. 86.

    Estes, S., Ajie, B. C., Lynch, M. & Phillips, P. C. Spontaneous mutational correlations for life-history, morphological and behavioral characters in Caenorhabditis elegans. Genetics 170, 645–653 (2005).

    PubMed  PubMed Central  Article  Google Scholar 

  87. 87.

    Houle, D. & Fierst, J. Properties of spontaneous mutational variance and covariance for wing size and shape in Drosophila melanogaster. Evolution 67, 1116–1130 (2013).

    PubMed  Article  Google Scholar 

  88. 88.

    Ovaskainen, O., Karhunen, M., Zheng, C., Arias, J. M. C. & Merilä, J. A new method to uncover signatures of divergent and stabilizing selection in quantitative traits. Genetics 189, 621–632 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Csilléry, K. et al. Adaptation to local climate in multi-trait space: evidence from silver fir (Abies alba Mill.) populations across a heterogeneous environment. Heredity 124, 77–92 (2020).

    PubMed  Article  Google Scholar 

  90. 90.

    Berg, J. J. & Coop, G. A population genetic signal of polygenic adaptation. PLoS Genet. 10, e1004412 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  91. 91.

    Orr, H. A. Adaptation and the cost of complexity. Evolution 54, 13–20 (2000).

    CAS  PubMed  Article  Google Scholar 

  92. 92.

    Fisher, R. A. The Genetical Theory of Natural Selection (Clarendon, 1930).

  93. 93.

    Pavlicev, M. & Hansen, T. F. Genotype–phenotype maps maximizing evolvability: modularity revisited. Evol. Biol. 38, 371–389 (2011).

    Article  Google Scholar 

  94. 94.

    Hine, E., McGuigan, K. & Blows, M. W. Evolutionary constraints in high-dimensional trait sets. Am. Nat. 184, 119–131 (2014).

    PubMed  Article  Google Scholar 

  95. 95.

    Melo, D. & Marroig, G. Directional selection can drive the evolution of modularity in complex traits. Proc. Natl Acad. Sci. USA 112, 470–475 (2015).

    CAS  PubMed  Article  Google Scholar 

  96. 96.

    Kashtan, N. & Alon, U. Spontaneous evolution of modularity and network motifs. Proc. Natl Acad. Sci. USA 102, 13773–13778 (2005).

    CAS  PubMed  Article  Google Scholar 

  97. 97.

    Espinosa-Soto, C. & Wagner, A. Specialization can drive the evolution of modularity. PLoS Comput. Biol. 6, e1000719 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  98. 98.

    Ancel, L. W. & Fontana, W. in Modularity: Understanding the Development and Evolution of Natural Complex Systems (eds Callebaut, W. & Rasskin-Gutman, D.) 129–141 (MIT Press, 2009).

  99. 99.

    Wagner, G. P. & Mezey, J. G. in Modularity in Development and Evolution (eds Schlosser, G. & Wagner, G. P.) 338–358 (Univ. Chicago Press, 2004).

  100. 100.

    Fokkens, L. & Snel, B. Cohesive versus flexible evolution of functional modules in eukaryotes. PLoS Comput. Biol. 5, e1000276 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  101. 101.

    Huang, W. et al. Genetic basis of transcriptome diversity in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 112, E6010–E6019 (2015).

    CAS  PubMed  Article  Google Scholar 

  102. 102.

    Schweizer, R. M. et al. Physiological and genomic evidence that selection on the transcription factor Epas1 has altered cardiovascular function in high-altitude deer mice. PLoS Genet. 15, e1008420 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  103. 103.

    Hämälä, T. et al. Gene expression modularity reveals footprints of polygenic adaptation in Theobroma cacao. Mol. Biol. Evol. 37, 110–123 (2020).

    PubMed  Article  CAS  Google Scholar 

  104. 104.

    Collet, J. M., McGuigan, K., Allen, S. L., Chenoweth, S. F. & Blows, M. W. Mutational pleiotropy and the strength of stabilizing selection within and between functional modules of gene expression. Genetics 208, 1601–1616 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. 105.

    Jiménez, A., Cotterell, J., Munteanu, A. & Sharpe, J. A spectrum of modularity in multi‐functional gene circuits. Mol. Syst. Biol. 13, 925 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  106. 106.

    Verd, B., Monk, N. A. & Jaeger, J. Modularity, criticality, and evolvability of a developmental gene regulatory network. eLife 8, e42832 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  107. 107.

    Pallares, L. F. et al. Mapping of craniofacial traits in outbred mice identifies major developmental genes involved in shape determination. PLoS Genet. 11, e1005607 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  108. 108.

    Arnold, S. J., Pfrender, M. E. & Jones, A. G. The adaptive landscape as a conceptual bridge between micro- and macroevolution. Genetica 112113, 9–32 (2001).

    PubMed  Article  Google Scholar 

  109. 109.

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

    PubMed  Article  Google Scholar 

  110. 110.

    Shikov, A. E., Skitchenko, R. K., Predeus, A. V. & Barbitoff, Y. A. Phenome-wide functional dissection of pleiotropic effects highlights key molecular pathways for human complex traits. Sci. Rep. 10, 1037 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. 111.

    Sella, G. & Barton, N. H. Thinking about the evolution of complex traits in the era of genome-wide association studies. Annu. Rev. Genom. Hum. Genet. 20, 461–493 (2019).

    CAS  Article  Google Scholar 

  112. 112.

    Walsh, B. & Blows, M. W. Abundant genetic variation plus strong selection = multivariate genetic constraints: a geometric view of adaptation. Annu. Rev. Ecol. Evol. Syst. 40, 41–59 (2009).

    Article  Google Scholar 

  113. 113.

    Teplitsky, C. et al. Assessing multivariate constraints to evolution across ten long-term avian studies. PLoS ONE 9, e90444 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  114. 114.

    Pavlicev, M. & Cheverud, J. M. Constraints evolve: context dependency of gene effects allows evolution of pleiotropy. Annu. Rev. Ecol. Evol. Syst. 46, 413–434 (2015).

  115. 115.

    Wei, X. & Zhang, J. Environment-dependent pleiotropic effects of mutations on the maximum growth rate r and carrying capacity K of population growth. PLoS Biol. 17, e3000121 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  116. 116.

    Parter, M., Kashtan, N. & Alon, U. Facilitated variation: how evolution learns from past environments to generalize to new environments. PLoS Comput. Biol. 4, e1000206 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  117. 117.

    Lande, R. Quantitative genetic analysis of multivariate evolution, applied to brain:body size allometry. Evolution 33, 402–416 (1979).

    PubMed  Article  Google Scholar 

  118. 118.

    Bolstad, G. H. et al. Complex constraints on allometry revealed by artificial selection on the wing of Drosophila melanogaster. Proc. Natl Acad. Sci. USA 112, 13284–13289 (2015).

    CAS  PubMed  Article  Google Scholar 

  119. 119.

    Tsuboi, M. et al. Breakdown of brain–body allometry and the encephalization of birds and mammals. Nat. Ecol. Evol. 2, 1492–1500 (2018).

    PubMed  Article  Google Scholar 

  120. 120.

    White, C. R. et al. The origin and maintenance of metabolic allometry in animals. Nat. Ecol. Evol. 3, 598–603 (2019).

    PubMed  Article  Google Scholar 

  121. 121.

    Mullon, C., Keller, L. & Lehmann, L. Social polymorphism is favoured by the co-evolution of dispersal with social behaviour. Nat. Ecol. Evol. 2, 132–140 (2018).

    PubMed  Article  Google Scholar 

  122. 122.

    Schweizer, R. M. et al. Natural selection and origin of a melanistic allele in North American gray wolves. Mol. Biol. Evol. 35, 1190–1209 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  123. 123.

    Hämälä, T., Gorton, A. J., Moeller, D. A. & Tiffin, P. Pleiotropy facilitates local adaptation to distant optima in common ragweed (Ambrosia artemisiifolia). PLoS Genet. 16, e1008707 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  124. 124.

    Roda, F., Walter, G. M., Nipper, R. & Ortiz-Barrientos, D. Genomic clustering of adaptive loci during parallel evolution of an Australian wildflower. Mol. Ecol. 26, 3687–3699 (2017).

    CAS  PubMed  Article  Google Scholar 

  125. 125.

    Sinervo, B. & Lively, C. M. The rock–paper–scissors game and the evolution of alternative male strategies. Nature 380, 240–243 (1996).

    CAS  Article  Google Scholar 

  126. 126.

    Hughes, K. A., Houde, A. E., Price, A. C. & Rodd, F. H. Mating advantage for rare males in wild guppy populations. Nature 503, 108–110 (2013).

    CAS  PubMed  Article  Google Scholar 

  127. 127.

    Marques, D. A., Jones, F. C., Di Palma, F., Kingsley, D. M. & Reimchen, T. E. Experimental evidence for rapid genomic adaptation to a new niche in an adaptive radiation. Nat. Ecol. Evol. 2, 1128–1138 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  128. 128.

    Brodie, E. D. III Genetic correlations between morphology and antipredator behaviour in natural populations of the garter snake Thamnophis ordinoides. Nature 342, 542–543 (1989).

    PubMed  Article  Google Scholar 

  129. 129.

    Auinger, H.-J. et al. Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.). Theor. Appl. Genet. 129, 2043–2053 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  130. 130.

    Xie, K. T. et al. DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science 363, 81–84 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

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

    CAS  PubMed  Article  Google Scholar 

  132. 132.

    Brieuc, M. S. O., Waters, C. D., Drinan, D. P. & Naish, K. A. A practical introduction to random forest for genetic association studies in ecology and evolution. Mol. Ecol. Resour. 18, 755–766 (2018).

    PubMed  Article  Google Scholar 

  133. 133.

    Nielsen, R. Molecular signatures of natural selection. Annu. Rev. Genet. 39, 197–218 (2005).

    CAS  PubMed  Article  Google Scholar 

  134. 134.

    Barghi, N., Hermisson, J. & Schlötterer, C. Polygenic adaptation: a unifying framework to understand positive selection. Nat. Rev. Genet. 21, 769–781 (2020).

  135. 135.

    Lemos, B., Araripe, L. O. & Hartl, D. L. Polymorphic Y chromosomes harbor cryptic variation with manifold functional consequences. Science 319, 91–93 (2008).

    CAS  PubMed  Article  Google Scholar 

  136. 136.

    Haddad, R., Meter, B. & Ross, J. A. The genetic architecture of intra-species hybrid mito-nuclear epistasis. Front. Genet. 9, 481 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  137. 137.

    Long, A., Liti, G., Luptak, A. & Tenaillon, O. Elucidating the molecular architecture of adaptation via evolve and resequence experiments. Nat. Rev. Genet. 16, 567–582 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  138. 138.

    Bollback, J. P., York, T. L. & Nielsen, R. Estimation of 2Nes from temporal allele frequency data. Genetics 179, 497–502 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  139. 139.

    Svensson, E. I. & Calsbeek, R. The Adaptive Landscape in Evolutionary Biology (Oxford Univ. Press, 2012).

Download references


We are grateful to D. Goedert for comments on the first draft of this manuscript. E.I.S. and A.R. were funded by grants from the Swedish Research Council (VR; grant numbers 2016-03356 and 2018-04537, respectively). D.A.M. was supported by the Swiss National Science Foundation (grant no. 31003A_163338 to O. Seehausen, L. Excoffier and R. Bruggmann). J.D. acknowledges support by NSF 13-510 Systems & Synthetic Biology, award no. 1714550. J.M.H. is supported by the German Federal Ministry of Education and Research (BMBF). K.C. was supported by a Swiss National Science Foundation grant (CRSK-3_190288). K.M. was funded by the Australian Research Council (DP190101661). M.N.S. was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), projects 2015/19556-4 and 2016/22159-0. We also wish to thank B. Brodie for kindly providing us with the photograph of the garter snakes in Fig. 2a and the original figure of his classic fitness surface in Box 1.

Author information




E.I.S. and A.R. conceived the paper, organized the writing and put together the first draft, based on input and written material from the other authors. All authors contributed to written sections, figures, Supplementary information, and improving and finalizing the manuscript. All authors approved the final manuscript version prior to submission and after acceptance.

Corresponding author

Correspondence to Erik I. Svensson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Alison Bell, Frederic Guillaume and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information

1. Evolution of the G matrix by correlational selection. 2. The role of gmax in modularity and plasticity. 3. Supplemental material for Fig. 3.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Svensson, E.I., Arnold, S.J., Bürger, R. et al. Correlational selection in the age of genomics. Nat Ecol Evol 5, 562–573 (2021).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing