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Pleiotropy, epistasis and the genetic architecture of quantitative traits

Abstract

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

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Fig. 1: Pleiotropy for quantitative traits.
Fig. 2: Pleiotropy of P-element insertions in Drosophila melanogaster.
Fig. 3: Systems genetic analysis of pleiotropy.
Fig. 4: Epistasis for Mendelian and quantitative trait loci.
Fig. 5: Epistatic effects and epistatic variance.
Fig. 6: Variance quantitative trait loci and epistasis.

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References

  1. Fisher, R. A. The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb. 52, 399–433 (1918). This landmark paper lays out the theoretical foundations of the genetics of quantitative traits, reconciling previous observations of Mendelian segregation and continuous variation for quantitative traits.

    Article  Google Scholar 

  2. Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics 4th edn (Longman, 1996).

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

  4. Feagan, B. G. et al. Risankizumab in patients with moderate to severe Crohn’s disease: an open-label extension study. Lancet Gastroenterol. Hepatol. 3, 671–680 (2018).

    Article  PubMed  Google Scholar 

  5. Hertz, D. L. & Rae, J. Pharmacogenetics of cancer drugs. Annu. Rev. Med. 66, 65–81 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Kullo, I. J. et al. Polygenic scores in biomedical research. Nat. Rev. Genet. 23, 524–532 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Goddard, M. E. & Hayes, B. J. Genomic selection. J. Anim. Breed. Genet. 124, 323–330 (2007).

    Article  CAS  PubMed  Google Scholar 

  8. Enbody, E. D. et al. Community-wide genome sequencing reveals 30 years of Darwin’s finch evolution. Science 381, eadf6218 (2023).

    Article  CAS  PubMed  Google Scholar 

  9. Abdellaoui, A., Yengo, L., Verweij, K. J. H. & Visscher, P. M. 15 years of GWAS discovery: realizing the promise. Am. J. Hum. Genet. 110, 179–194 (2023). This work presents an excellent review of the lessons learned from large GWAS in humans and future directions in human quantitative genetics.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. van Rheenen, W., Peyrot, W. J., Schork, A. J., Hong Lee, S. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20, 567–581 (2019). This work presents a review of the theory underlying genetic correlations and pleiotropy and methods for detecting pleiotropy in human populations.

    Article  PubMed  Google Scholar 

  12. Flatt, T. Life-history evolution and the genetics of fitness components in Drosophila melanogaster. Genetics 214, 3–48 (2020).

    Article  CAS  PubMed  Google Scholar 

  13. Sieberts, S. K. & Schadt, E. E. Moving toward a system genetics view of disease. Mamm. Genome 18, 389–401 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Greene, C. S., Penrod, N. M., Williams, S. M. & Moore, J. H. Failure to replicate a genetic association may provide important clues about genetic architecture. PLoS ONE 4, e5639 (2009). This analysis shows that the effect of a focal locus may differ in populations with different allele frequencies if it interacts with other loci; a phenomenon that can be used to detect epistatically interacting loci in replicated association studies in populations with different allele frequencies.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Gibson, G. & Dworkin, I. Uncovering cryptic genetic variation. Nat. Rev. Genet. 5, 681–690 (2004).

    Article  CAS  PubMed  Google Scholar 

  16. Ober, U. et al. Accounting for genetic architecture improves sequence based genomic prediction for a Drosophila fitness trait. PLoS ONE 10, e0126880 (2015). This study demonstrates that incorporating epistatic interactions into genomic prediction models can substantially improve predictive ability when traits exhibit epistasis.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Pate, L. in Festschrift zum sechzigsten Geburtstag Richard Hertwigs [German] 536–610 (Fischer, 1910).

  18. Stearns, F. W. One hundred years of pleiotropy: a retrospective. Genetics 186, 767–773 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M. & Smoller, J. W. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Reissmann, M. & Ludwig, A. Pleiotropic effects of coat colour-associated mutations in humans, mice and other mammals. Semin. Cell Dev. Biol. 24, 576–586 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Do, R. et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat. Genet. 45, 1345–1352 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Carbone, M. A. et al. Phenotypic variation and natural selection at Catsup, a pleiotropic quantitative trait gene in Drosophila. Curr. Biol. 2, 912–919 (2006).

    Article  Google Scholar 

  23. Falconer, D. S. The problem of environment and selection. Am. Nat. 86, 293–298 (1952).

    Article  Google Scholar 

  24. Mackay, T. F. C. & Huang, W. Charting the genotype–phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel. Wiley Interdiscip. Rev. Dev. Biol. 7, e289 (2018).

    Article  Google Scholar 

  25. Ho, P. W. et al. Massive QTL analysis identifies pleiotropic genetic determinants for stress resistance, aroma formation, and ethanol, glycerol and isobutanol production in Saccharomyces cerevisiae. Biotechnol. Biofuels 14, 211 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Giaever, G. & Nislow, C. The yeast deletion collection: a decade of functional genomics. Genetics 197, 451–465 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kamath, R. & Ahringer, J. Genome-wide RNAi screening in Caenorhabditis elegans. Methods 30, 313–321 (2003).

    Article  CAS  PubMed  Google Scholar 

  29. Alonso, J. M. et al. Genome-wide insertional mutagenesis of Arabidopsis thaliana. Science 301, 653–657 (2003).

    Article  PubMed  Google Scholar 

  30. Bellen, H. J. et al. The Drosophila gene disruption project: progress using transposons with distinctive site specificities. Genetics 188, 731–743 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Dietzl, G. et al. A genome-wide transgenic RNAi library for conditional gene activation in Drosophila. Nature 448, 151–156 (2007).

    Article  CAS  PubMed  Google Scholar 

  32. Zirin, J. et al. Large-scale transgenic Drosophila resource collections for loss- and gain-of-function studies. Genetics 214, 755–767 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Brown, S. D. M. Advances in mouse genetics for the study of human disease. Hum. Mol. Genet. 30, R274–R264 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ericson, E. et al. Genetic pleiotropy in Saccharomyces cerevisiae quantified by high-resolution phenotypic profiling. Mol. Genet. Genomics 275, 605–614 (2006).

    Article  CAS  PubMed  Google Scholar 

  35. Norga, K. K. et al. Quantitative analysis of bristle number in Drosophila mutants identifies genes involved in neural development. Curr. Biol. 13, 1388–1396 (2003).

    Article  CAS  PubMed  Google Scholar 

  36. Anholt, R. R. H., Lyman, R. F. & Mackay, T. F. C. Effects of single P-element insertions on olfactory behavior in Drosophila melanogaster. Genetics 143, 293–301 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yamamoto, A. et al. Neurogenetic networks for startle-induced locomotion in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 105, 12393–12398 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Harbison, S. T. & Seghal, A. Quantitative genetic analysis of sleep in Drosophila melanogaster. Genetics 178, 2341–23460 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Harbison, S. T. et al. Quantitative trait loci affecting starvation resistance in Drosophila melanogaster. Genetics 166, 1807–1823 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Morozova, T. V., Mackay, T. F. C. & Anholt, R. R. H. Transcriptional networks for alcohol sensitivity in Drosophila melanogaster. Genetics 187, 1193–1205 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nolan, P. M. et al. A systematic, genome-wide, phenotype-driven mutagenesis programme for gene function studies in the mouse. Nat. Genet. 25, 440–443 (2000).

    Article  CAS  PubMed  Google Scholar 

  42. De Angelis, M. H. et al. Genome-wide, large-scale production of mutant mice by ENU mutagenesis. Nat. Genet. 25, 444–447 (2000).

    Article  Google Scholar 

  43. De Angelis, M. H. et al. Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics. Nat. Genet. 47, 969–978 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Birling, M.-C. et al. A resource of targeted mutant mouse lines for 5,061 genes. Nat. Genet. 53, 416–419 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Groza, T. et al. The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease. Nucleic Acids Res. 51, D1038–D1045 (2023).

    Article  CAS  PubMed  Google Scholar 

  46. Paaby, A. B. & Rockman, M. V. The many faces of pleiotropy. Trends Genet. 29, 66–73 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. The Gene Ontology Consortium. The Gene Ontology resource: 20 years and still GOing strong [database issue]. Nucleic Acids Res. 47, D330–D338 (2019).

    Article  Google Scholar 

  48. Hughes, T. R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000).

    Article  CAS  PubMed  Google Scholar 

  49. Featherstone, D. E. & Broadie, K. Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network. Bioessays 24, 267–274 (2002).

    Article  CAS  PubMed  Google Scholar 

  50. Kemmeren, P. et al. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell 157, 740–752 (2014).

    Article  CAS  PubMed  Google Scholar 

  51. Vande Zande, P., Hill, M. S. & Wittkopp, P. J. Pleiotropic effects of trans-regulatory mutations on fitness and gene expression. Science 377, 105–109 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Vande Zande, P. & Wittkopp, P. J. Network topology can explain differences in pleiotropy between cis- and trans-regulatory mutations. Mol. Biol. Evol. 39, msac266 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Anholt, R. R. H. et al. The genetic architecture of odor-guided behavior in Drosophila: epistasis and the transcriptome. Nat. Genet. 35, 180–184 (2003).

    Article  CAS  PubMed  Google Scholar 

  54. Magwire, M. M. et al. Quantitative and molecular genetic analyses of mutations increasing Drosophila life span. PLoS Genet. 6, e1001037 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Amberger, J. S. & Hamosh, A. Searching Online mendelian Inheritance in Man (OMIM): a knowledgebase of human genes and genetic phenotypes. Curr. Protoc. Bioinformatics 58, 1.2.1–1.2.12 (2017).

    Article  PubMed  Google Scholar 

  56. Replogle, J. M. et al. Mapping information-rich genotype–phenotype landscapes with genome-scale Perturb-seq. Cell 185, 2559–2575 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Li, H. & Auwerx, J. Mouse systems genetics as a prelude to precision medicine. Trends Genet. 36, 259–272 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Aitman, T. J. et al. Progress and prospects in rat genetics: a community view. Nat. Genet. 40, 516–522 (2008).

    Article  CAS  PubMed  Google Scholar 

  59. Mulligan, M. K., Mozhui, K., Prins, P. & Williams, R. W. GeneNetwork: a toolbox for systems genetics. Methods Mol. Biol. 1488, 75–120 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Bouge, M. A. et al. Mouse phenome database: curated data repository with interactive multi-population and multi-trait analyses. Mamm. Genome 34, 509–519 (2023).

    Article  Google Scholar 

  61. Smith, J. R. et al. The year of the rat: the rat genome database at 20: a multi-species knowledgebase and analysis platform. Nucleic Acids Res. 48, D731–D742 (2020).

    CAS  PubMed  Google Scholar 

  62. Mackay, T. F. C. et al. The Drosophila melanogaster Genetic Reference Panel. Nature 482, 173–178 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Huang, W. et al. Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines. Genome Res. 24, 1193–1208 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. King, E. G. et al. Genetic dissection of a model complex trait using the Drosophila Synthetic Population Resource. Genome Res. 22, 1558–1566 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Evans, K. S., van Wijk, M. H., McGrath, P. T., Andersen, E. C. & Sterken, M. G. From QTL to gene: C. elegans facilitates discoveries of the genetic mechanisms underlying natural variation. Trends Genet. 37, 933–947 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. The 1001 Genomes Consortium. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell 166, 481–491 (2016).

    Article  Google Scholar 

  67. Kover, P. X. et al. A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet. 5, e1000551 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Evans, K. S. et al. Shared genomic regions underlie natural variation in diverse toxin responses. Genetics 210, 1509–1525 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Ba, A. N. N. et al. Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast. eLife 11, e73983 (2022).

    Article  CAS  Google Scholar 

  70. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. The All of Us Research Program Investigators. The “All of Us” research program. N. Eng. J. Med. 381, 6680676 (2019).

    Article  Google Scholar 

  72. Solis, E. et al. The NHGRI-EBI GWAS catalog: knowledgebase and deposition resource. Nucleic Acids Res. 51, D977–D985 (2023).

    Article  Google Scholar 

  73. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014). This article reports a statistical method for inferring whether two association signals, such as an organismal trait and an eQTL, map to the same molecular polymorphism.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015). This study is among the first to report substantial genetic correlation among human psychiatric disorders and quantitative traits.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Denny, J. C. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics 26, 1205–1210 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome–phenome relationship using phenome-wide association studies. Nat. Rev. Genet. 17, 129–145 (2016).

    Article  CAS  PubMed  Google Scholar 

  77. International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

    Article  PubMed Central  Google Scholar 

  78. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

    Article  PubMed Central  Google Scholar 

  79. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    Article  CAS  PubMed  Google Scholar 

  80. Mackay, T. F. C. The genetic architecture of quantitative traits. Annu. Rev. Genet. 35, 303–339 (2001).

    Article  CAS  PubMed  Google Scholar 

  81. Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017). This paper describes an excellent study fine-mapping GWAS associations at single-variant resolution.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Pickar-Oliver, A. & Gersbach, C. A. The next generation of CRISPR–Cas technologies and applications. Nat. Rev. Mol. Cell Biol. 20, 490–507 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Lamb, A. M., Walker, E. A. & Wittkopp, P. J. Tools and strategies for scarless allele replacement in Drosophila using CRISPR/Cas9. Fly 11, 53–64 (2017).

    Article  PubMed  Google Scholar 

  84. Hoedjes, K. M., Kostic, H., Flatt, T. & Keller, L. A single nucleotide variant in the PPARγ-homolog Eip75B affects fecundity in Drosophila. Mol. Biol. Evol. 40, msad018 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Bassett, A. R. Editing the genome of hiPSC with CRISPR/Cas9: disease models. Mamm. Genome 28, 348–364 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Morris, J. A. et al. Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens. Science 380, eadh7699 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Rockman, M. V. Reverse engineering the genotype–phenotype map with natural genetic variation. Nature 456, 738–744 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  89. MacKinnon, D. in Multivariate Applications in Substance Use Research: New Methods for New Questions (eds Rose, J. S., Chassin, L., Presson, C. C. & Sherman, S. J.) 141–160 (Lawrence Erlbaum, 2000).

  90. Zeng, P., Shao, Z. & Zhou, X. Statistical methods for mediation analysis in the era of high-throughput genomics: current successes and future challenges. Comput. Struct. Biotechnol. J. 19, 3209–3224 (2021). This work presents an excellent review on methods for mediation analysis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Davey Smith, G. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).

    Article  Google Scholar 

  92. Sanderson, E. et al. Mendelian randomization. Nat. Rev. Methods Primers 2, 6 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. O’Connor, L. J. & Price, A. L. Distinguishing genetic correlation from causation across 52 diseases and complex traits. Nat. Genet. 50, 1728–1734 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Jensen, R. C. & Nap, J.-P. Genetical genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001).

    Article  Google Scholar 

  96. Cheung, V. G. & Spielman, R. S. The genetics of variation in gene expression. Nat. Genet. 32, 522–525 (2002).

    Article  CAS  PubMed  Google Scholar 

  97. Jin, W. et al. The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat. Genet. 29, 389–395 (2001).

    Article  CAS  PubMed  Google Scholar 

  98. Sandberg, R. et al. Regional and strain-specific gene expression mapping in the adult mouse brain. Proc. Natl Acad. Sci. USA 97, 11038–11043 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Oleksiak, M. F., Churchill, G. A. & Crawford, D. L. Variation in gene expression within and among natural populations. Nat. Genet. 32, 261–266 (2002).

    Article  CAS  PubMed  Google Scholar 

  100. Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002). This paper describes the first study to map QTL association with variation in genome-wide gene expression.

    Article  CAS  PubMed  Google Scholar 

  101. Yvert, G. et al. trans-Acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nat. Genet. 35, 57–64 (2003).

    Article  CAS  PubMed  Google Scholar 

  102. Brem, R. B. & Kruglyak, L. The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc. Natl Acad. Sci. USA 102, 1572–1577 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Schadt, E. E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003).

    Article  CAS  PubMed  Google Scholar 

  104. Fu, Y. et al. Genetic dissection of intermated recombinant inbred lines using a new genetic map of maize. Genetics 174, 1671–1683 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. West, M. A. et al. Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis. Genetics 175, 1441–1450 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Ruden, D. M. et al. Genetical toxicogenomics in Drosophila identifies master-modulatory loci that are regulated by developmental exposure to lead. Neurotoxicology 30, 898–914 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Li, Y. et al. Mapping determinants of gene expression plasticity by genetical genomics in C. elegans. PLoS Genet. 2, e222 (2006).

    Article  PubMed  Google Scholar 

  108. Cheung, V. G. et al. Natural variation in human gene expression assessed in lymphoblastoid cells. Nat. Genet. 33, 422–425 (2003).

    Article  CAS  PubMed  Google Scholar 

  109. Monks, S. A. et al. Genetic inheritance of gene expression in human cell lines. Am. J. Hum. Genet. 75, 1094–1105 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Drake, T. A., Schadt, E. E. & Lusis, A. J. Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice. Mamm. Genome 17, 466–479 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Nicolae, D. L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article  CAS  PubMed  Google Scholar 

  113. Hormozdiari, F. et al. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat. Genet. 50, 1041–1047 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012). This study shows that non-coding variants associated with human complex traits and diseases predominantly map to regulatory DNA sequences.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  PubMed Central  Google Scholar 

  118. Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015). This study introduces PrediXcan, a popular method for predicting associations of quantitative traits with imputed transcript abundance genome-wide.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 300–1310 (2021).

    Article  Google Scholar 

  121. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017). This work on an omnigenic model of quantitative genetic variation proposes that the molecular basis of trait variation arises from regulatory genetic variation downstream of core genes affecting the traits in trait-relevant tissues.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Liu, X., Li, Y. I. & Pritchard, J. K. trans effects on gene expression can drive omnigenetic inheritance. Cell 177, 1022–1034 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Huang, W. et al. Genetic basis of transcriptome diversity in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 112, E6010–E6019 (2015). This study shows that variance eQTLs interact epistatically with cis-eQTLs affecting the same gene expression trait.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Everett, L. J. et al. Gene expression networks in the Drosophila Genetic Reference Panel. Genome Res. 30, 485–496 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Chun, S. et al. Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nat. Genet. 49, 600–605 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Mancuso, N. et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat. Genet 51, 675–682 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Connally, N. J. et al. The missing link between genetic association and regulatory function. eLife 11, e74970 (2022). This study challenges the view that variants affecting human quantitative traits are enriched for eQTLs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Yao, D. W., O’Connor, L. J., Price, A. L. & Gusev, A. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat. Genet 52, 626–633 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Nathan, A. et al. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature 606, 120–128 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Gazal, S. et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat. Genet 49, 1421–1427 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Zeng, J. et al. Signatures of negative selection in the genetic architecture of human complex traits. Nat. Genet 50, 746–753 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Hill, W. G. & Keightley, P. D. Quantitative genetic variability maintained by mutation-stabilizing selection balance in finite populations. Genet. Res 52, 33–43 (1988).

    Article  PubMed  Google Scholar 

  135. Simons, Y. B., Bullaughey, K., Hudson, R. R. & Sella, G. A population genetic interpretation of GWAS findings for human quantitative traits. PLoS Biol. 16, e2002985 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Williams, G. C. Pleiotropy, natural selection, and the evolution of senescence. Evolution 11, 398–411 (1957). This paper provides an exposition of the role of antagonistic pleiotropy in the evolution of life history traits.

    Article  Google Scholar 

  137. Maharjan, R., McKenzie, C., Yeung, A. & Ferenci, T. The basis of antagonistic pleiotropy in hfq mutations that have opposite effects on fitness at slow and fast growth rates. Heredity 110, 10–18 (2013).

    Article  CAS  PubMed  Google Scholar 

  138. Delaney, J. R., Murakami, C. J., Olsen, B., Kennedy, B. K. & Kaeberlein, M. Quantitative evidence for early life fitness defects from 32 longevity-associated alleles in yeast. Cell Cycle 10, 156–165 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Qian, W., Ma, D., Xiao, C., Wang, Z. & Zhang, J. The genomic landscape and evolutionary resolution of antagonistic pleiotropy in yeast. Cell Rep. 2, 1399–1410 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Maklakov, A. A. et al. Antagonistically pleiotropic allele increases lifespan and late-life reproduction at the cost of early-life reproduction and individual fitness. Proc. Biol. Sci. 284, 20170376 (2017).

    PubMed  PubMed Central  Google Scholar 

  141. Huang, W. et al. Context-dependent genetic architecture of Drosophila life span. PLoS Biol. 18, e3000645 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Rodríguez, J. A. et al. Antagonistic pleiotropy and mutation accumulation influence human senescence and disease. Nat. Ecol. Evol. 1, 55 (2017).

    Article  PubMed  Google Scholar 

  143. Byars, S. G. & Voskarides, K. Antagonistic pleiotropy in human disease. J. Mol. Evol. 88, 12–25 (2020).

    Article  CAS  PubMed  Google Scholar 

  144. Song, W. et al. Locus-level antagonistic selection shaped the polygenic architecture of human complex diseases. Hum. Genet. 141, 1935–1947 (2022).

    Article  CAS  PubMed  Google Scholar 

  145. Wright S. Evolution and the Genetics of Populations. Volume 1: Genetic and Biometric Foundations (Univ. Chicago Press, 1968).

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

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

    Article  CAS  PubMed  Google Scholar 

  148. Mendel, G. Versuche über Pflanzen-Hybriden [German]. Verh. Naturforsch. Ver. Brünn 4, 3–47 (1865).

    Google Scholar 

  149. Bateson, W. Facts limiting the theory of heredity. Science 26, 649–660 (1907).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  151. Hu, Z. et al. Genomic value prediction for quantitative traits under the epistatic model. BMC Genet. 12, 15 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  152. Wang, D. et al. Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations. Heredity 109, 313–319 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Martini, J. W. R., Wimmer, V., Erbe, M. & Simianer, H. Epistasis and covariance: how gene interaction translates into genomic relationship. Theor. Appl. Genet. 129, 963–976 (2016).

    Article  CAS  PubMed  Google Scholar 

  154. Vojgani, E. et al. Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments. Theor. Appl. Genet. 134, 2913–2930 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010). This paper describes a pioneering study mapping gene–gene interactions genome-wide using the yeast deletion collection.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Kuzmin, E. et al. Synthetic genetic array analysis for global mapping of genetic networks in yeast. Methods Mol. Biol. 1205, 143–168 (2014).

    Article  CAS  PubMed  Google Scholar 

  157. Dixon, S. J., Costanzo, M., Baryshnikova, A., Andrews, B. & Boone, C. Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601–625 (2009).

    Article  CAS  PubMed  Google Scholar 

  158. Kuzmin, E. et al. Systematic analysis of complex genetic interactions. Science 360, eaao1729 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  159. 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. Nat. Genet. 38, 896–903 (2006).

    Article  CAS  PubMed  Google Scholar 

  160. Byrne, A. B. et al. A global analysis of genetic interactions in Caenorhabditis elegans. J. Biol. 6, 8 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  161. Bakal, C. et al. Phosphorylation networks regulating JNK activity in diverse genetic backgrounds. Science 322, 453–456 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Zender, L. et al. An oncogenomics-based in vivo RNAi screen identifies tumor suppressors in liver cancer. Cell 135, 852–864 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Luo, B. et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl Acad. Sci. USA 105, 20380–20385 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Lee, H. M. T. et al. Epistatic, synthetic, and balancing interactions among tubulin missense mutations affecting neurite growth in Caenorhabditis elegans. Mol. Biol. Cell 32, 331–347 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Kwong, L. N. et al. Identification of Mom7, a novel modifier of ApcMin/+ on mouse chromosome 18. Genetics 176, 1237–1244 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Weatherly, S. M. et al. Identification of Arhgef12 and Prkci as genetic modifiers of retinal dysplasia in the Crb1rd8 mouse model. PLoS Genet. 18, e1009798 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Clark, A. G. & Wang, L. Epistasis in measured genotypes: Drosophila P-element insertions. Genetics 147, 157–163 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Zwarts, L. et al. Complex genetic architecture of Drosophila aggressive behavior. Proc. Natl Acad. Sci. USA 108, 17070–17075 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Li, B. et al. Epistatic transcription factor networks differentially modulate Arabidopsis growth and defense. Genetics 214, 529–541 (2020).

    Article  CAS  PubMed  Google Scholar 

  170. Li, C., Qian, W., Maclean, C. J. & Zhang, J. The fitness landscape of a tRNA gene. Science 352, 837–840 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Puchta, O. et al. Network of epistatic interactions within a yeast snoRNA. Science 352, 840–844 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. Gonzalez, C. E. & Ostermeier, M. Pervasive pairwise intragenic epistasis among sequential mutations in TEM-1 β-lactamase. J. Mol. Biol. 431, 1981–1992 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Huang, W. & Mackay, T. F. C. The genetic architecture of quantitative traits cannot be inferred from variance component analysis. PLoS Genet. 12, e1006421 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  174. 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). This paper introduces a theoretical treatment showing that epistatic effects, even if large, contribute more to additive genetic variance than epistatic variance, and hence can be ignored in analyses of human quantitative traits.

    Article  PubMed  PubMed Central  Google Scholar 

  175. Mäki-Tanila, A. & Hill, W. G. Influence of gene interaction on complex trait variation with multilocus models. Genetics 198, 355–367 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  176. Cheverud, J. M. & Routman, E. J. Epistasis and its contribution to genetic variance components. Genetics 139, 1455–1461 (1995). This analysis distinguishes between epistatic effects (physiological epistasis) and epistatic variance (statistical epistasis) and argues that the former can be large and needs to be accounted for to fully understand the genotype–phenotype map, although the latter is typically negligible.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Eshed, Y. & Zamir, D. Less-than-additive epistatic interactions of quantitative trait loci in tomato. Genetics 143, 1807–1817 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Torgeman, S. & Zamir, D. Epistatic QTLs for yield heterosis in tomato. Proc. Natl Acad. Sci. USA 120, e2205787119 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Long, A. D. et al. High resolution mapping of genetic factors affecting abdominal bristle number in Drosophila melanogaster. Genetics 139, 1273–1291 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Gurganus, M. C., Nuzhdin, S. V., Leips, J. W. & Mackay, T. F. C. High-resolution mapping of quantitative trait loci for sternopleural bristle number in Drosophila melanogaster. Genetics 152, 1585–1604 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Deutschbauer, A. M. & Davis, R. W. Quantitative trait loci mapped to single-nucleotide resolution in yeast. Nat. Genet. 37, 1333–1340 (2005). This study maps three QTLs affecting natural variation in yeast sporulation to three nucleotides that interact epistatically.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Kim, H. S. & Fay, J. C. A combined-cross analysis reveals genes with drug-specific and background-dependent effects on drug sensitivity in Saccharomyces cerevisiae. Genetics 183, 1141–1151 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Forsberg, S. K., Bloom, J. S., Sadhu, M. J., Kruglyak, L. & Carlborg, Ö. Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast. Nat. Genet. 49, 497–503 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  186. Matsui, T. et al. The interplay of additivity, dominance, and epistasis on fitness in a diploid yeast cross. Nat. Commun. 13, 1463 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. Gaertner, B. E., Parmenter, M. D., Rockman, M. V., Kruglyak, L. & Phillips, P. C. More than the sum of its parts: a complex epistatic network underlies natural variation in thermal preference behavior in Caenorhabditis elegans. Genetics 192, 1533–1542 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  188. Leips, J. & Mackay, T. F. C. Quantitative trait loci for life span in Drosophila melanogaster: interactions with genetic background and larval density. Genetics 155, 1773–1788 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Leips, J. & Mackay, T. F. C. The complex genetic architecture of Drosophila life span. Exp. Aging Res. 28, 361–390 (2002).

    Article  PubMed  Google Scholar 

  190. Shao, H. et al. Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc. Natl Acad. Sci. USA 105, 19910–19914 (2008). This study of chromosome substitution panels in rodents reveals substantial epistasis for all traits studied such that the sum of the individual chromosome effects greatly exceeds the total phenotypic difference between the parental strains.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  191. Carlborg, Ö. et al. A global search reveals epistatic interaction between QTL for early growth in the chicken. Genome Res. 13, 413–421 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Pettersson, M., Besnier, F., Siegel, P. B. & Carlborg, Ö. Replication and explorations of high-order epistasis using a large advanced intercross line pedigree. PLoS Genet. 7, e1002180 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  193. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  194. Wentzell, A. M., Boeye, I., Zhang, Z. & Kliebenstein, D. J. Genetic networks controlling structural outcome of glucosinolate activation across development. PLoS Genet. 4, e1000234 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  195. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  196. Cordell, H. J., Todd, J. A., Bennett, S. T., Kawaguchi, Y. & Farrall, M. Two-locus maximum LOD score analysis of a multifactorial trait: joint consideration of IDDM2 and IDDM4 with IDDM1 in type 1 diabetes. Am. J. Hum. Genet. 57, 920–934 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  197. Cox, N. J. et al. Loci on chromosomes 2 (NIDDM1) and 15 interact to increase susceptibility to diabetes in Mexican Americans. Nat. Genet. 21, 213–215 (1999).

    Article  CAS  PubMed  Google Scholar 

  198. Cho, J. H. et al. Identification of novel susceptibility loci for inflammatory bowel disease on chromosomes 1p, 3q, and 4q: evidence for epistasis between 1p and IBD1. Proc. Natl Acad. Sci. USA 95, 7502–7507 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  199. Lai, C., Lyman, R. F., Long, A. D., Langley, C. H. & Mackay, T. F. C. Naturally occurring variation in bristle number and DNA polymorphisms at the scabrous locus of Drosophila melanogaster. Science 266, 1697–1702 (1994).

    Article  CAS  PubMed  Google Scholar 

  200. Hivert, V. et al. Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals. Am. J. Hum. Genet. 108, 786–798 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Hivert, V., Wray, N. R. & Visscher, P. M. Gene action, genetic variation, and GWAS: a user-friendly web tool. PLoS Genet. 17, e1009548 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  202. Huang, W. et al. Epistasis dominates the genetic architecture of Drosophila quantitative traits. Proc. Natl Acad. Sci. USA 109, 15553–15559 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  203. Shorter, J. et al. Genetic architecture of natural variation in Drosophila melanogaster aggressive behavior. Proc. Natl Acad. Sci. USA 112, E3555–E3563 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  204. Rönnegård, L. & Valdar, W. Detecting major genetic loci controlling phenotypic variability in experimental crosses. Genetics 188, 435–447 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  205. Hulse, A. M. & Cai, J. J. Genetic variants contribute to gene expression variability in humans. Genetics 193, 95–108 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  206. Brown, A. A. et al. Genetic interactions affecting human gene expression identified by variance association mapping. eLife 3, e01381 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  207. Singhal, P. et al. Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets. Am. J. Hum. Genet. 110, 575–591 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  208. Ang, R. M. L., Chen, S. A., Kern, A. F., Xie, Y. & Fraser, H. B. Widespread epistasis among beneficial genetic variants revealed by high-throughput genome editing. Cell Genom. 3, 100260 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  209. Zhang, S. et al. Multiple genes in a single GWAS risk locus synergistically mediate aberrant synaptic development and function in human neurons. Cell Genom. 3, 100399 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  210. Bis-Brewer, D. M., Fazal, S. & Züchner, S. Genetic modifiers and non-Mendelian aspects of CMT. Brain Res. 1726, 146459 (2020).

    Article  CAS  PubMed  Google Scholar 

  211. Rahit, K. M. T. H. & Tarailo-Graovac, M. Genetic modifiers and rare Mendelian disease. Genes 11, 239 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  212. Chen, R. et al. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat. Biotechnol. 34, 531–538 (2016).

    Article  CAS  PubMed  Google Scholar 

  213. Tarailo-Graovac, M., Zhu, J. Y. A., Matthews, A., van Karnebeek, C. D. M. & Wasserman, W. W. Assessment of the ExAC data set for the presence of individuals with pathogenic genotypes implicated in severe Mendelian pediatric disorders. Genet. Med. 19, 1300–1308 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  214. Fahed, A. C. et al. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions. Nat. Commun. 11, 3635 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  215. Goodrich, J. K. et al. Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes. Nat. Commun. 12, 3505 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  216. Mullis, M. N., Matsui, T., Schell, R., Foree, R. & Ehrenreich, I. M. The complex underpinnings of genetic background effects. Nat. Commun. 9, 3548 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  217. Galardini, M. et al. The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae. Mol. Syst. Biol. 15, e8831 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  218. Dworkin, I. et al. Genomic consequences of background effects on scalloped mutant expressivity in the wing of Drosophila melanogaster. Genetics 181, 1065–1076 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  219. Yamamoto, A., Anholt, R. R. H. & Mackay, T. F. C. Epistatic interactions attenuate mutations affecting startle behaviour in Drosophila melanogaster. Genet. Res. 91, 373–382 (2009).

    Article  CAS  Google Scholar 

  220. Chandler, C. H. et al. How well do you know your mutation? Complex effects of genetic background on expressivity, complementation, and ordering of allelic effects. PLoS Genet. 13, e1007075 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  221. He, X., Zhou, S., St. Armour, G. E., Mackay, T. F. C. & Anholt, R. R. H. Epistatic partners of neurogenic genes modulate Drosophila olfactory behavior. Genes. Brain Behav. 15, 280–290 (2016).

    Article  CAS  PubMed  Google Scholar 

  222. Özsoy, E. D. et al. Epistasis for head morphology in Drosophila melanogaster. G3 11, jkab285 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  223. Dworkin, I., Palsson, A., Birdsall, K. & Gibson, G. Evidence that Egfr contributes to cryptic genetic variation for photoreceptor determination in natural populations of Drosophila melanogaster. Curr. Biol. 13, 1888–1893 (2003).

    Article  CAS  PubMed  Google Scholar 

  224. Chow, C. Y. Bringing genetic background into focus. Nat. Rev. Genet. 17, 63–64 (2016).

    Article  CAS  PubMed  Google Scholar 

  225. Palu, R. A. S. et al. Natural genetic variation screen in Drosophila identifies Wnt signaling, mitochondrial metabolism, and redox homeostasis genes as modifiers of apoptosis. G3 9, 3995–4005 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  226. Talsness, D. M. et al. A Drosophila screen identifies NKCC1 as a modifier of NGLY1 deficiency. eLife 9, e57831 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  227. Hansen, T. F. Why epistasis is important for selection and adaptation. Evolution 67, 3501–3511 (2013).

    Article  PubMed  Google Scholar 

  228. Sohail, M. et al. Negative selection in humans and fruit flies involves synergistic epistasis. Science 356, 539–542 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  229. Rutherford, S. L. & Lindquist, S. Hsp90 as a capacitor for morphological evolution. Nature 396, 336–342 (1998).

    Article  CAS  PubMed  Google Scholar 

  230. McGuigan, K. & Sgro, C. M. Evolutionary consequences of cryptic genetic variation. Trends Ecol. Evol. 24, 305–311 (2009).

    Article  PubMed  Google Scholar 

  231. Paaby, A. B. & Rockman, M. V. Cryptic genetic variation: evolution’s hidden substrate. Nat. Rev. Genet. 15, 247–258 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  232. Zheng, J., Payne, J. L. & Wagner, A. Cryptic genetic variation accelerates evolution by opening access to diverse adaptive peaks. Science 365, 347–353 (2019).

    Article  CAS  PubMed  Google Scholar 

  233. Barton, N. H. & Turelli, M. Effects of genetic drift on variance components under a general model of epistasis. Evolution 58, 2111–2132 (2004).

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  235. Barton, N. H. How does epistasis influence the response to selection? Heredity 118, 96–109 (2017).

    Article  CAS  PubMed  Google Scholar 

  236. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  238. Orr, H. A. & Turelli, M. The evolution of postzygotic isolation: accumulating Dobzhansky–Muller incompatibilities. Evolution 55, 1085–1094 (2001).

    CAS  PubMed  Google Scholar 

  239. Fierst, J. L. & Hansen, T. F. Genetic architecture and postzygotic reproductive isolation: evolution of Bateson–Dobzhansky–Muller incompatibilities in a polygenic model. Evolution 64, 675–693 (2010).

    Article  PubMed  Google Scholar 

  240. Rollmann, S. M. et al. Pleiotropic fitness effects of the Tre1–Gr5a region in Drosophila melanogaster. Nat. Genet. 38, 824–829 (2006).

    Article  CAS  PubMed  Google Scholar 

  241. Carter, G. W., Hays, M., Sherman, A. & Galitski, T. Use of pleiotropy to model genetic interactions in a population. PLoS Genet. 8, e1003010 (2012). This paper introduces a novel method to utilize co-expression pleiotropy to define epistatic networks.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  242. Carter, G. W. Inferring gene function and network organization in Drosophila signaling by combined analysis of pleiotropy and epistasis. G3 3, 807–814 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  243. Philip, V. M., Tyler, A. L. & Carter, G. W. Dissection of complex gene expression using the combined analysis of pleiotropy and epistasis. Pac. Symp. Biocomput. 2014, 200–211 (2014).

    Google Scholar 

  244. Tyler, A. L. et al. Epistatic networks jointly influence phenotypes related to metabolic disease and gene expression in diversity Outbred mice. Genetics 206, 621–639 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  245. 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 

  246. Pavlicev, M., Norgard, E. A., Fawcett, G. L. & Cheverud, J. M. Evolution of pleiotropy: epistatic interaction pattern supports a mechanistic model underlying variation in genotype–phenotype map. J. Exp. Zool. B Mol. Dev. Evol. 316, 371–385 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  247. Maxwell, T. J. et al. APOE modulates the correlation between triglycerides, cholesterol, and CHD through pleiotropy, and gene-by-gene interactions. Genetics 195, 1397–1405 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  248. Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nat. Rev. Genet. 11, 855–866 (2010).

    Article  CAS  PubMed  Google Scholar 

  249. Watt, M. et al. Phenotyping: new windows into the plant for breeding. Annu. Rev. Plant. Biol. 71, 689–712 (2020).

    Article  CAS  PubMed  Google Scholar 

  250. Pérez-Enciso, M. & Steibel, J. P. Phenomes: the current frontier in animal breeding. Genet. Sel. Evol. 53, 22 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  251. Yang, C., Li, C., Wang, Q., Chung, D. & Zhao, H. Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine. Front. Genet. 6, 229 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  252. Yengo, L. et al. A saturated map of common genetic variants associated with human height. Nature 610, 704–712 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  253. Morgante, F., Huang, W., Maltecca, C. & Mackay, T. F. C. Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals. Heredity 120, 500–514 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  254. Mackay, T. F. C. & Moore, J. H. Why epistasis is important for tackling complex human disease genetics. Genome Med. 6, 124 (2014).

    Article  PubMed  Google Scholar 

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Authors and Affiliations

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Contributions

Both authors wrote the article. T.F.C.M. researched data for the article and wrote the first draft of the manuscript based on discussion of the content and organization with R.R.H.A. R.R.H.A. reviewed and edited the manuscript before submission.

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Correspondence to Trudy F. C. Mackay or Robert R. H. Anholt.

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Nature Reviews Genetics thanks Luke O’Connor, Kristel van Steen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Additive effects

One half of the difference in mean phenotypes associated with homozygous genotypes at a biallelic locus affecting a quantitative trait.

Additive genetic variance

The variance of breeding values.

Deoxyribonuclease I (DNase I) hypersensitivity sites

Accessible nucleosome-free chromatin regions sensitive to cleavage by the DNase I enzyme; these regions are inferred to be involved in gene regulation.

Dominance effects

The difference between the mean phenotype of the heterozygous genotype and the mean phenotype of the two homozygous genotypes at a biallelic locus affecting a quantitative trait

Fitness

The contribution of offspring to the next generation, or reproductive success (viability and fertility) of an individual.

Genetic architecture

The loci, genomic locations and additive, dominance, epistatic and pleiotropic effects and allele frequencies of causal variants affecting a quantitative trait.

Haplotypes

Alleles at different loci on a chromosome that tend to be inherited together.

Heritability

The ratio of the variance of additive genetic variance (narrow sense heritability) or the total genetic variance (broad sense heritability) to the total phenotypic variance.

P-element

A Drosophila transposable element that can be mobilized to new genomic locations by simple genetic crosses.

Shifting balance theory of evolution

A model of evolution proposed by Sewell Wright that assumes widespread epistasis for fitness, natural selection, genetic drift and differentiation among subpopulations, and migration.

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Mackay, T.F.C., Anholt, R.R.H. Pleiotropy, epistasis and the genetic architecture of quantitative traits. Nat Rev Genet (2024). https://doi.org/10.1038/s41576-024-00711-3

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