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Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems

Key Points

  • Epistasis has been used to describe a number of phenomena, including the functional interaction between genes, the genetic outcome of mutations acting within the same genetic pathway, and the statistical deviation from additive gene action. Converging interests across genetics suggests that it is now time to develop a more unified view of epistasis and gene interactions.

  • One of the traditional uses of epistasis analysis has been to order genes within developmental and metabolic pathways. These approaches have recently become much more systematic through the use of high-throughput genetic screens, especially in yeast. These studies show that gene interactions are ubiquitous and can be used to help understand the structure of complex genetic networks.

  • The major limitation of comprehensive analyses of gene interactions is the total number of interactions that must ultimately be tested, which grows at approximately the square of the number of genes (for example, >18 million interactions in the Saccharomyces cerevisiae genome). Future work in this area will need to focus on particular subsets of this interaction space using information from other sources, such as functional genomics.

  • Epistasis can be a major barrier to inferring the genetic basis of complex traits within natural populations. The effects of many QTLs might be obscured by interactions with other loci, which can make mapping difficult.

  • Human genetic disease is one area in which epistasis seems to be fairly common, although we have few examples in which the functional basis of a particular interaction has been demonstrated. Epistasis is one possible explanation for why human mapping results can be difficult to replicate.

  • Epistasis arises as a natural by-product of the evolutionary process, as all subsequent evolutionary change is built upon genetic changes that have occurred previously.

  • There is clear evidence that epistasis helps to structure the possible pathways that evolution can follow but, even after nearly a century of debate on the topic, we still do not know if epistasis creates a major barrier to evolutionary change.

  • An increased focus on the quantitative effects of gene interactions will provide the basis for a unified approach to studies of gene interaction, while providing a point of articulation between genetics, functional genomics, evolutionary genetics and systems biology.

Abstract

Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.

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Figure 1: Different viewpoints of epistasis.
Figure 2: Reconstructing genetic pathways using epistasis analysis.
Figure 3: Epistasis in complex traits.
Figure 4: Three different views of the generation of epistasis under natural selection.

References

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

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Phillips, P. C., Otto, S. P. & Whitlock, M. C. in Epistasis and the Evolutionary Process (eds Wolf, J. D., Brodie, E. D., III & Wade, M. J.) 20–38 (Oxford Univ. Press, Oxford, 2000).

    Google Scholar 

  3. Malmberg, R. L. & Mauricio, R. QTL-based evidence for the role of epistasis in evolution. Genet. Res. 86, 89–95 (2005).

    CAS  PubMed  Google Scholar 

  4. Otto, S. P. & Gerstein, A. C. Why have sex? The population genetics of sex and recombination. Biochem. Soc. Trans. 34, 519–522 (2006).

    CAS  PubMed  Google Scholar 

  5. Weinreich, D. M., Watson, R. A. & Chao, L. Perspective: sign epistasis and genetic constraint on evolutionary trajectories. Evolution 59, 1165–1174 (2005).

    CAS  PubMed  Google Scholar 

  6. Carlborg, O. & Haley, C. S. Epistasis: too often neglected in complex trait studies? Nature Rev. Genet. 5, 618–625 (2004).

    CAS  PubMed  Google Scholar 

  7. Holland, J. B. Genetic architecture of complex traits in plants. Curr. Opin. Plant Biol. 10, 156–161 (2007).

    CAS  PubMed  Google Scholar 

  8. Wade, M. J. Epistasis, complex traits, and mapping genes. Genetica 112–113, 59–69 (2001).

    PubMed  Google Scholar 

  9. Azevedo, L., Suriano, G., van Asch, B., Harding, R. M. & Amorim, A. Epistatic interactions: how strong in disease and evolution? Trends Genet. 22, 581–585 (2006).

    CAS  PubMed  Google Scholar 

  10. Nadeau, J. H. Modifier genes in mice and humans. Nature Rev. Genet. 2, 165–174 (2001).

    CAS  PubMed  Google Scholar 

  11. Moore, J. H. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum. Hered. 56, 73–82 (2003).

    PubMed  Google Scholar 

  12. Cordell, H. J. Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum. Mol. Genet. 11, 2463–2468 (2002). A clear review of the limitations in moving from statistical estimates of epistatic effects to understanding genetic causation.

    CAS  PubMed  Google Scholar 

  13. Demuth, J. P. & Wade, M. J. Experimental methods for measuring gene interactions. Ann. Rev. Ecol. Evol. Systematics 37, 289–316 (2006).

    Google Scholar 

  14. Musani, S. K. et al. Detection of gene × gene interactions in genome-wide association studies of human population data. Hum. Hered. 63, 67–84 (2007).

    CAS  PubMed  Google Scholar 

  15. McKinney, B. A., Reif, D. M., Ritchie, M. D. & Moore, J. H. Machine learning for detecting gene–gene interactions: a review. Appl. Bioinformatics 5, 77–88 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Marchini, J., Donnelly, P. & Cardon, L. R. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nature Genet. 37, 413–417 (2005).

    CAS  PubMed  Google Scholar 

  17. Alvarez-Castro, J. M., Le Rouzic, A. & Carlborg, O. How to perform meaningful estimates of genetic effects. PLoS Genet. 4, e1000062 (2008).

    PubMed  PubMed Central  Google Scholar 

  18. Boone, C., Bussey, H. & Andrews, B. J. Exploring genetic interactions and networks with yeast. Nature Rev. Genet. 8, 437–449 (2007). A comprehensive review of existing work on using high-throughput approaches in yeast to dissect complex gene interaction networks. Includes a good discussion of the overall conceptual framework.

    CAS  PubMed  Google Scholar 

  19. Costanzo, M., Giaever, G., Nislow, C. & Andrews, B. Experimental approaches to identify genetic networks. Curr. Opin. Biotechnol. 17, 472–480 (2006).

    CAS  PubMed  Google Scholar 

  20. Hansen, T. F. & Wagner, G. P. Modeling genetic architecture: a multilinear theory of gene interaction. Theoretical Popul. Biol. 59, 61–86 (2001).

    CAS  Google Scholar 

  21. Elena, S. F. & Lenski, R. E. Test of synergistic interactions among deleterious mutations in bacteria. Nature 390, 395–398 (1997). Uses randomly generated mutations in Escherichia coli to demonstrate that epistatic effects between loci can be highly variable and frequently cancel one another out.

    CAS  PubMed  Google Scholar 

  22. Routman, E. J. & Cheverud, J. M. Gene effects on a quantitative trait: two-locus epistatic effects measured at microsatellite markers and at estimated QTL. Evolution 51, 1654–1662 (1995).

    Google Scholar 

  23. Bateson, W., Saunders, E. R., Punnett, R. C. & Hurst, C. C. Reports to the Evolution Committee of the Royal Society, Report II (Harrison and Sons, London, 1905).

    Google Scholar 

  24. Beadle, G. W. Genetics and metabolism in Neurospora. Physiol. Rev. 25, 643–663 (1945).

    CAS  PubMed  Google Scholar 

  25. Avery, L. & Wasserman, S. Ordering gene function: the interpretation of epistasis in regulatory hierarchies. Trends Genet. 8, 312–316 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Huang, L. S. & Sternberg, P. W. Genetic dissection of developmental pathways. (doi: 10.1895/wormbook.1.88.2) WormBook [online], (2005). A comprehensive treatment of how to use classical epistasis analysis to reconstruct genetic pathways.

    Google Scholar 

  27. Goodwin, E. B. & Ellis, R. E. Turning clustering loops: sex determination in Caenorhabditis elegans. Curr. Biol. 12, R111–R120 (2002).

    CAS  PubMed  Google Scholar 

  28. Sternberg, P. W. & Horvitz, H. R. The combined action of two intercellular signaling pathways specifies three cell fates during vulval induction in C. elegans. Cell 58, 679–693 (1989).

    CAS  PubMed  Google Scholar 

  29. Thomas, J. H., Birnby, D. A. & Vowels, J. J. Evidence for parallel processing of sensory information controlling dauer formation in Caenorhabditis elegans. Genetics 134, 1105–1117 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Tong, A. H. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364–2368 (2001). A landmark paper that established the high-throughput double-deletion approach to detecting epistatic interactions.

    CAS  PubMed  Google Scholar 

  31. Tong, A. H. et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004).

    CAS  PubMed  Google Scholar 

  32. Hartman, J. L., Garvik, B. & Hartwell, L. Principles for the buffering of genetic variation. Science 291, 1001–1004 (2001).

    CAS  PubMed  Google Scholar 

  33. Segrè, D., Deluna, A., Church, G. M. & Kishony, R. Modular epistasis in yeast metabolism. Nature Genet. 37, 77–83 (2005).

    PubMed  Google Scholar 

  34. St. Onge, R. P. et al. Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions. Nature Genet. 39, 199–206 (2007). References 33 and 34 show how quantitative information can be incorporated into high-throughput interaction studies to yield deeper insights into the nature of genetic networks.

    CAS  PubMed  Google Scholar 

  35. Kroll, E. S., Hyland, K. M., Hieter, P. & Li, J. J. Establishing genetic interactions by a synthetic dosage lethality phenotype. Genetics 143, 95–102 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Sopko, R. et al. Mapping pathways and phenotypes by systematic gene overexpression. Mol. Cell 21, 319–330 (2006).

    CAS  PubMed  Google Scholar 

  37. Greenspan, R. J. The flexible genome. Nature Rev. Genet. 2, 383–387 (2001).

    CAS  PubMed  Google Scholar 

  38. Lehner, B., Crombie, C., Tischler, J., Fortunato, A. & Fraser, A. G. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nature Genet. 38, 896–903 (2006).

    CAS  PubMed  Google Scholar 

  39. Davierwala, A. P. et al. The synthetic genetic interaction spectrum of essential genes. Nature Genet. 37, 1147–1152 (2005).

    CAS  PubMed  Google Scholar 

  40. Tischler, J., Lehner, B. & Fraser, A. G. Evolutionary plasticity of genetic interaction networks. Nature Genet. 40, 390–391 (2008).

    CAS  PubMed  Google Scholar 

  41. Wong, S. L. et al. Combining biological networks to predict genetic interactions. Proc. Natl Acad. Sci. USA 101, 15682–15687 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Beyer, A., Bandyopadhyay, S. & Ideker, T. Integrating physical and genetic maps: from genomes to interaction networks. Nature Rev. Genet. 8, 699–710 (2007).

    CAS  PubMed  Google Scholar 

  43. Pattin, K. A. & Moore, J. H. Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases. Hum. Genet. 124, 19–29 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhu, J. et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genet. 40, 854–861 (2008). Shows how interaction information from many sources can be combined to provide a more comprehensive picture of interaction networks.

    CAS  PubMed  Google Scholar 

  45. Collins, S. R. et al. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446, 806–810 (2007).

    CAS  PubMed  Google Scholar 

  46. Carlborg, O., Jacobsson, L., Ahgren, P., Siegel, P. & Andersson, L. Epistasis and the release of genetic variation during long-term selection. Nature Genet. 38, 418–420 (2006).

    CAS  PubMed  Google Scholar 

  47. Stylianou, I. M. et al. Quantitative trait locus analysis for obesity reveals multiple networks of interacting loci. Mamm. Genome 17, 22–36 (2006).

    PubMed  Google Scholar 

  48. Ehrenreich, I. M., Stafford, P. A. & Purugganan, M. D. The genetic architecture of shoot branching in Arabidopsis thaliana: a comparative assessment of candidate gene associations vs. quantitative trait locus mapping. Genetics 176, 1223–1236 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Alvarez-Castro, J. M. & Carlborg, O. A unified model for functional and statistical epistasis and its application in quantitative trait loci analysis. Genetics 176, 1151–1167 (2007).

    PubMed  PubMed Central  Google Scholar 

  50. Cheverud, J. M. in Epistasis and the Evolutionary Process (eds Wolf, J., Brodie, E. D., III & Wade, M. J.) 58–81 (Oxford Univ. Press, Oxford, 2000).

    Google Scholar 

  51. Sambandan, D., Yamamoto, A., Fanara, J. J., Mackay, T. F. & Anholt, R. R. Dynamic genetic interactions determine odor-guided behavior in Drosophila melanogaster. Genetics 174, 1349–1363 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Causse, M., Chaïb, J., Lecomte, L., Buret, M. & Hospital, F. Both additivity and epistasis control the genetic variation for fruit quality traits in tomato. Theor. Appl. Genet. 115, 429–442 (2007).

    CAS  PubMed  Google Scholar 

  53. Rowe, H. C., Hansen, B. G., Halkier, B. A. & Kliebenstein, D. J. Biochemical networks and epistasis shape the Arabidopsis thaliana metabolome. Plant Cell 20, 1199–1216 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Wolf, J. B., Leamy, L. J., Routman, E. J. & Cheverud, J. M. Epistatic pleiotropy and the genetic architecture of covariation within early and late-developing skull trait complexes in mice. Genetics 171, 683–694 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Sinha, H., Nicholson, B. P., Steinmetz, L. M. & McCusker, J. H. Complex genetic interactions in a quantitative trait locus. PLoS Genet. 2, e13 (2006).

    PubMed  PubMed Central  Google Scholar 

  56. Nogami, S., Ohya, Y. & Yvert, G. Genetic complexity and quantitative trait loci mapping of yeast morphological traits. PLoS Genet. 3, e31 (2007).

    PubMed  PubMed Central  Google Scholar 

  57. Storey, J. D., Akey, J. M. & Kruglyak, L. Multiple locus linkage analysis of genomewide expression in yeast. PLoS Biol. 3, e267 (2005).

    PubMed  PubMed Central  Google Scholar 

  58. Brem, R. B., Storey, J. D., Whittle, J. & Kruglyak, L. Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436, 701–703 (2005). Shows how genetical genomics can be used to infer patterns of gene interaction.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Hill, W. G., Goddard, M. E. & Visscher, P. M. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 4, e1000008 (2008).

    PubMed  PubMed Central  Google Scholar 

  60. Cheverud, J. M. & Routman, E. J. Epistasis and its contribution to genetic variance components. Genetics 139, 1455–1461 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Phillips, P. C. & Johnson, N. A. The population genetics of synthetic lethals. Genetics 150, 449–458 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  63. Tsai, C. T. et al. Renin-angiotensin system gene polymorphisms and coronary artery disease in a large angiographic cohort: detection of high order gene–gene interaction. Atherosclerosis 195, 172–180 (2007).

    CAS  PubMed  Google Scholar 

  64. Wiltshire, S. et al. Epistasis between type 2 diabetes susceptibility loci on chromosomes 1q21–25 and 10q23–26 in northern Europeans. Ann. Hum. Genet. 70, 726–737 (2006).

    CAS  PubMed  Google Scholar 

  65. Abou Jamra, R. et al. The first genomewide interaction and locus-heterogeneity linkage scan in bipolar affective disorder: strong evidence of epistatic effects between loci on chromosomes 2q and 6q. Am. J. Hum. Genet. 81, 974–986 (2007).

    PubMed  PubMed Central  Google Scholar 

  66. Coutinho, A. M. et al. Evidence for epistasis between SLC6A4 and ITGB3 in autism etiology and in the determination of platelet serotonin levels. Hum. Genet. 121, 243–256 (2007).

    CAS  PubMed  Google Scholar 

  67. Gregersen, J. W. et al. Functional epistasis on a common MHC haplotype associated with multiple sclerosis. Nature 443, 574–577 (2006). Illustrates how functional hypotheses regarding gene interaction within human populations can be tested using model systems.

    CAS  PubMed  Google Scholar 

  68. Trowsdale, J. Multiple sclerosis: putting two and two together. Nature Med. 12, 1119–1121 (2006).

    CAS  PubMed  Google Scholar 

  69. Svejgaard, A. The immunogenetics of multiple sclerosis. Immunogenetics 60, 275–286 (2008).

    CAS  PubMed  Google Scholar 

  70. Gauderman, W. J. Sample size requirements for association studies of gene–gene interaction. Am. J. Epidemiol. 155, 478–484 (2002).

    PubMed  Google Scholar 

  71. Carlson, C. S., Eberle, M. A., Kruglyak, L. & Nickerson, D. A. Mapping complex disease loci in whole-genome association studies. Nature 429, 446–452 (2004).

    CAS  PubMed  Google Scholar 

  72. Xu, S. & Jia, Z. Genomewide analysis of epistatic effects for quantitative traits in barley. Genetics 175, 1955–1963 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Demant, P. Cancer susceptibility in the mouse: genetics, biology and implications for human cancer. Nature Rev. Genet. 4, 721–734 (2003).

    CAS  PubMed  Google Scholar 

  74. Jacob, F. Evolution and tinkering. Science 196, 1161–1166 (1977).

    CAS  PubMed  Google Scholar 

  75. Lynch, M. The frailty of adaptive hypotheses for the origins of organismal complexity. Proc. Natl Acad. Sci. USA 104 (Suppl. 1), 8597–8604 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Crow, J. F. How important is detecting interaction? Behav. Brain. Sci. 13, 126–127 (1990).

    Google Scholar 

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

    Google Scholar 

  78. Kauffman, S. A. The Origins of Order: Self-Organisation and Selection in Evolution (Oxford Univ. Press, New York, 1993).

    Google Scholar 

  79. Wu, C.-I. & Palopoli, M. F. Genetics of postmating reproductive isolation in animals. Annu. Rev. Genet. 27, 283–208 (1994).

    Google Scholar 

  80. de Visser, J. A. et al. Perspective: evolution and detection of genetic robustness. Evolution 57, 1959–1972 (2003).

    PubMed  Google Scholar 

  81. Bridgham, J. T., Carroll, S. M. & Thornton, J. W. Evolution of hormone-receptor complexity by molecular exploitation. Science 312, 97–101 (2006).

    CAS  PubMed  Google Scholar 

  82. Ortlund, E. A., Bridgham, J. T., Redinbo, M. R. & Thornton, J. W. Crystal structure of an ancient protein: evolution by conformational epistasis. Science 317, 1544–1548 (2007). A good example of moving between detailed functional analysis and long-term evolutionary inference.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Miller, S. P., Lunzer, M. & Dean, A. M. Direct demonstration of an adaptive constraint. Science 314, 458–461 (2006).

    CAS  PubMed  Google Scholar 

  84. Weinreich, D. M., Delaney, N. F., Depristo, M. A. & Hartl, D. L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006).

    CAS  PubMed  Google Scholar 

  85. Poelwijk, F. J., Kiviet, D. J., Weinreich, D. M. & Tans, S. J. Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383–386 (2007).

    CAS  PubMed  Google Scholar 

  86. Karlin, S. General two locus selection models: some objectives, results and interpretations. Theoret. Popul. Biol. 7, 364–398 (1975).

    CAS  Google Scholar 

  87. Encode Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).

  88. Moore, J. H. & Williams, S. M. Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. Bioessays 27, 637–646 (2005).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  90. Gjuvsland, A. B., Hayes, B. J., Omholt, S. W. & Carlborg, O. Statistical epistasis is a generic feature of gene regulatory networks. Genetics 175, 411–420 (2007).

    PubMed  PubMed Central  Google Scholar 

  91. Deutscher, D., Meilijson, I., Kupiec, M. & Ruppin, E. Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nature Genet. 38, 993–998 (2006).

    CAS  PubMed  Google Scholar 

  92. Jansen, R. C. Studying complex biological systems using multifactorial perturbation. Nature Rev. Genet. 4, 145–151 (2003). A perspective on how complex genetic systems can be best interrogated using multiple, rather than single, perturbations.

    CAS  PubMed  Google Scholar 

  93. Carter, G. W. et al. Prediction of phenotype and gene expression for combinations of mutations. Mol. Syst. Biol. 3, 96 (2007).

    PubMed  PubMed Central  Google Scholar 

  94. Bateson, W. Mendel's Principles of Heredity (Cambridge Univ. Press, Cambridge, 1909).

    Google Scholar 

  95. Fisher, R. A. The correlations between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb. 52, 399–433 (1918).

    Google Scholar 

  96. Tachida, H. & Cockerham, C. C. A building block model for quantitative genetics. Genetics 121, 839–844 (1989). A greatly underappreciated paper that provides a quantitative framework for moving between different perspectives for how phenotypes are built and how genetic effects can be estimated.

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Karlin, S. & Feldman, M. W. Simultaneous stability of D=0 and D≠0 for multiplicative viabilities at two loci. Genetics 90, 813–825 (1978).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Mani, R., St. Onge, R. P., Hartman, J. L. IV, Giaever, G. & Roth, F. P. Defining genetic interaction. Proc. Natl Acad. Sci. USA 105, 3461–3466 (2008). Shows how dependent the inference of epistasis is upon the scale of measurement.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Aylor, D. L. & Zeng, Z. B. From classical genetics to quantitative genetics to systems biology: modeling epistasis. PLoS Genet. 4, e1000029 (2008).

    PubMed  PubMed Central  Google Scholar 

  100. Feldman, M. W., Otto, S. P. & Christiansen, F. B. Population genetic perspectives on the evolution of recombination. Annu. Rev. Genet. 30, 261–295 (1997).

    Google Scholar 

  101. Bennett, D. C. & Lamoreux, M. L. The color loci of mice — a genetic century. Pigment Cell Res. 16, 333–344 (2003).

    CAS  PubMed  Google Scholar 

  102. Steiner, C. C., Weber, J. N. & Hoekstra, H. E. Adaptive variation in beach mice produced by two interacting pigmentation genes. PLoS Biol. 5, e219 (2007).

    PubMed  PubMed Central  Google Scholar 

  103. Silvers, W. The Coat Colors of Mice: A model for mammalian gene action and interaction (Springer, Berlin, 1979).

    Google Scholar 

  104. Hoekstra, H. E. Genetics, development and evolution of adaptive pigmentation in vertebrates. Heredity 97, 222–234 (2006).

    CAS  PubMed  Google Scholar 

  105. Wright, S. The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proc. 6th Int. Cong. Genet. 1, 356–366 (1932).

    Google Scholar 

  106. Gavrilets, S. Fitness landscapes and the Origin of Species (Princeton Univ. Press, Princeton, 2004).

    Google Scholar 

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Acknowledgements

This work was initiated while the author was a sabbatical visitor at the Gulbenkian Institute of Science. I gratefully acknowledge their support. I also deeply appreciate input from H. Hoekstra, S. Otto, J. Thornton and three anonymous reviewers, as well as a long-term synergistic interaction with M. Whitlock. This work was supported by a fellowship from the Guggenheim Foundation, and by grants from the National Institutes of Health and the National Science Foundation.

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Glossary

Admixture

The pattern of genetic variation that occurs when a population is derived from founders that originated from more than one ancestral population.

Punnett square

A method of calculating the outcomes of a genetic cross by multiplying the expected frequency of gametes from a mother by the expected frequency of gametes from the father.

Hardy–Weinberg equilibrium

A theoretical description of the relationship between genotype and allele frequencies that is based on expectation in a stable population undergoing random mating in the absence of selection, new mutations and gene flow; under these conditions (and in the absence of linkage disequilibrium) the genotype frequencies are equal to the product of the allele frequencies.

Dauer larva

A developmentally arrested, immature, long-lived and non-feeding form of Caenorhabditis elegans that forms under conditions of food scarcity and high population density, and that resumes development if food levels increase.

Synthetic-lethal mutations

Two mutations are considered to be synthetically lethal if they result in death when both are present, whereas an individual with either mutation alone is viable.

Chromatin immunoprecipitation

A technique used to identify potential regulatory sequences by isolating soluble DNA chromatin extracts (complexes of DNA and protein) using antibodies that recognize specific DNA-binding proteins.

Linkage disequilibrium

A measure of whether alleles at two loci coexist in a population in a non-random fashion. Alleles that are in linkage disequilibrium are found together on the same haplotype more often than would be expected under a random combination of alleles.

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Phillips, P. Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 9, 855–867 (2008). https://doi.org/10.1038/nrg2452

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