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Detecting signatures of selection on gene expression

Nature Ecology & Evolution volume 6, pages 1035–1045 (2022)Cite this article

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Matters Arising to this article was published on 07 November 2022

Abstract

A substantial amount of phenotypic diversity results from changes in gene expression levels and patterns. Understanding how the transcriptome evolves is therefore a key priority in identifying mechanisms of adaptive change. However, in contrast to powerful models of sequence evolution, we lack a consensus model of gene expression evolution. Furthermore, recent work has shown that many of the comparative approaches used to study gene expression are subject to biases that can lead to false signatures of selection. Here we first outline the main approaches for describing expression evolution and their inherent biases. Next, we bridge the gap between the fields of phylogenetic comparative methods and transcriptomics to reinforce the main pitfalls of inferring selection on expression patterns and use simulation studies to show that shifts in tissue composition can heavily bias inferences of selection. We close by highlighting the multi-dimensional nature of transcriptional variation and identifying major unanswered questions in disentangling how selection acts on the transcriptome.

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Fig. 1: Approaches to detect selection on gene expression.
Fig. 2: Variation in tissue composition can lead to the perception of differential expression.
Fig. 3: Inferring selection when expression level is measured from a heterogeneous tissue.
Fig. 4: The magnitude of allometric shift and covariance of expression level biases the inference of selection.

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Data availability

All data have been previously published71.

Code availability

All code is publicly available at https://github.com/Wright-lab-2021-Transcriptome-Evo/Inferring_expression_evolution_review.

References

  1. Mank, J. E. The transcriptional architecture of phenotypic dimorphism. Nat. Ecol. Evol. 1, 6 (2017).

    Article  PubMed  Google Scholar 

  2. Parsch, J. & Ellegren, H. The evolutionary causes and consequences of sex-biased gene expression. Nat. Rev. Genet. 14, 83–87 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Carroll, S. B. Evo-devo and an expanding evolutionary synthesis: a genetic theory of morphological evolution. Cell 134, 25–36 (2008).

    Article  CAS  PubMed  Google Scholar 

  4. Wray, G. A. The evolutionary significance of cis-regulatory mutations. Nat. Rev. Genet. 8, 206–216 (2007).

    Article  CAS  PubMed  Google Scholar 

  5. Gilad, Y., Oshlack, A. & Rifkin, S. A. Natural selection on gene expression. Trends Genet. 22, 456–461 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. Necsulea, A. & Kaessmann, H. Evolutionary dynamics of coding and non-coding transcriptomes. Nat. Rev. Genet. 15, 734–748 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Hill, M. S., Vande Zande, P. & Wittkopp, P. J. Molecular and evolutionary processes generating variation in gene expression. Nat. Rev. Genet. 22, 203–215 (2021).

    Article  CAS  PubMed  Google Scholar 

  8. Romero, I. G., Ruvinsky, I. & Gilad, Y. Comparative studies of gene expression and the evolution of gene regulation. Nat. Rev. Genet. 13, 505–516 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Signor, S. A. & Nuzhdin, S. V. The evolution of gene expression in cis and trans. Trends Genet. 34, 532–544 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Fay, J. C. & Wittkopp, P. J. Evaluating the role of natural selection in the evolution of gene regulation. Heredity 100, 191–199 (2008).

    Article  CAS  PubMed  Google Scholar 

  11. Khaitovich, P., Enard, W., Lachmann, M. & Pääbo, S. Evolution of primate gene expression. Nat. Rev. Genet. 7, 693–702 (2006).

    Article  CAS  PubMed  Google Scholar 

  12. Bedford, T. & Hartl, D. L. Optimization of gene expression by natural selection. Proc. Natl Acad. Sci. USA 106, 1133–1138 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Whitehead, A. & Crawford, D. L. Neutral and adaptive variation in gene expression. Proc. Natl Acad. Sci. USA 103, 5425–5430 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hansen, T. F. Stabilizing selection and the comparative analysis of adaptation. Evolution 51, 1341–1351 (1997).

    Article  PubMed  Google Scholar 

  15. Cooper, N., Thomas, G. H., Venditti, C., Meade, A. & Freckleton, R. P. A cautionary note on the use of Ornstein Uhlenbeck models in macroevolutionary studies. Biol. J. Linn. Soc. Lond. 118, 64–77 (2016).

    Article  PubMed  Google Scholar 

  16. Silvestro, D., Kostikova, A., Litsios, G., Pearman, P. B. & Salamin, N. Measurement errors should always be incorporated in phylogenetic comparative analysis. Methods Ecol. Evol. 6, 340–346 (2015).

    Article  Google Scholar 

  17. Ho, L. S. T. & Ané, C. Intrinsic inference difficulties for trait evolution with Ornstein-Uhlenbeck models. Methods Ecol. Evol. 5, 1133–1146 (2014).

    Article  Google Scholar 

  18. Rohlfs, R. V., Harrigan, P. & Nielsen, R. Modeling gene expression evolution with an extended Ornstein–Uhlenbeck process accounting for within-species variation. Mol. Biol. 31, 201–211 (2014).

    Article  CAS  Google Scholar 

  19. Rohlfs, R. V. & Nielsen, R. Phylogenetic ANOVA: the expression variance and evolution model for quantitative trait evolution. Syst. Biol. 64, 695–708 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Montgomery, S. H. & Mank, J. E. Inferring regulatory change from gene expression: the confounding effects of tissue scaling. Mol. Ecol. 25, 5114–5128 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. Hunnicutt, K. E., Good, J. M. & Larson, E. L. Unraveling patterns of disrupted gene expression across a complex tissue. Evolution 76, 275–291 (2021).

    Article  Google Scholar 

  22. Fair, B. J. et al. Gene expression variability in human and chimpanzee populations share common determinants. eLife 9, e59929 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Nourmohammad, A. et al. Adaptive evolution of gene expression in Drosophila. Cell Rep. 20, 1385–1395 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. Catalán, A., Briscoe, A. D. & Höhna, S. Drift and directional selection are the evolutionary forces driving gene expression divergence in eye and brain tissue of Heliconius butterflies. Genetics 213, 581–594 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  25. 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 

  26. Khaitovich, P. et al. A neutral model of transcriptome evolution. PLoS Biol. 2, e132 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Rifkin, S. A., Kim, J. & White, K. P. Evolution of gene expression in the Drosophila melanogaster subgroup. Nat. Genet. 33, 138–144 (2003).

    Article  CAS  PubMed  Google Scholar 

  28. Lemos, B., Meiklejohn, C. D., Cáceres, M. & Hartl, D. L. Rates of divergence in gene expression profiles of primates, mice, and flies: stabilizing selection and variability among functional categories. Evolution 59, 126–137 (2005).

    Article  CAS  PubMed  Google Scholar 

  29. Hudson, R. R., Kreitman, M. & Aguadé, M. A test of neutral molecular evolution based on nucleotide data. Genetics 116, 153–159 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kimura, M. Genetic variability maintained in a finite population due to mutational production of neutral and nearly neutral isoalleles. Genet. Res 11, 247–270 (1968).

    Article  CAS  PubMed  Google Scholar 

  31. Staubach, F., Teschke, M., Voolstra, C. R., Wolf, J. B. W. & Tautz, D. A test of the neutral model of expression change in natural populations of house mouse subspecies. Evolution 64, 549–560 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Somel, M. et al. Transcriptional neoteny in the human brain. Proc. Natl Acad. Sci. USA 106, 5743–5748 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Blekhman, R., Marioni, J. C., Zumbo, P., Stephens, M. & Gilad, Y. Sex-specific and lineage-specific alternative splicing in primates. Genome Res. 20, 180–189 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Moghadam, H. K., Pointer, M. A., Wright, A. E., Berlin, S. & Mank, J. E. W chromosome expression responds to female-specific selection. Proc. Natl Acad. Sci. USA 109, 8207–8211 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gilad, Y., Oshlack, A., Smyth, G. K., Speed, T. P. & White, K. P. Expression profiling in primates reveals a rapid evolution of human transcription factors. Nature 440, 242–245 (2006).

    Article  CAS  PubMed  Google Scholar 

  36. Enard, W. Intra- and interspecific variation in primate gene expression patterns. Science 296, 340–343 (2002).

    Article  CAS  PubMed  Google Scholar 

  37. Blekhman, R., Oshlack, A., Chabot, A. E., Smyth, G. K. & Gilad, Y. Gene regulation in primates evolves under tissue-specific selection pressures. PLoS Genet. 4, e1000271 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Warnefors, M. & Eyre-Walker, A. A selection index for gene expression evolution and its application to the divergence between humans and chimpanzees. PLoS ONE 7, e34935 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Ometto, L., Shoemaker, D., Ross, K. G. & Keller, L. Evolution of gene expression in fire ants: the effects of developmental stage, caste, and species. Mol. Biol. Evol. 28, 1381–1392 (2011).

    Article  CAS  PubMed  Google Scholar 

  40. Rifkin, S. A., Houle, D., Kim, J. & White, K. P. A mutation accumulation assay reveals a broad capacity for rapid evolution of gene expression. Nature 438, 220–223 (2005).

    Article  CAS  PubMed  Google Scholar 

  41. Denver, D. R. et al. The transcriptional consequences of mutation and natural selection in Caenorhabditis elegans. Nat. Genet. 37, 544–548 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Huang, W. et al. Spontaneous mutations and the origin and maintenance of quantitative genetic variation. eLife 5, e14625 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Fraser, H. B. Detecting selection with a genetic cross. Proc. Natl Acad. Sci. USA 117, 22323–22330 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Leinonen, T., McCairns, R. J. S., O’Hara, R. B. & Merilä, J. Q(ST)-F(ST) comparisons: evolutionary and ecological insights from genomic heterogeneity. Nat. Rev. Genet. 14, 179–190 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Mähler, N. et al. Gene co-expression network connectivity is an important determinant of selective constraint. PLoS Genet. 13, e1006402 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Kohn, M. H., Shapiro, J. & Wu, C.-I. Decoupled differentiation of gene expression and coding sequence among Drosophila populations. Genes Genet. Syst. 83, 265–273 (2008).

    Article  CAS  PubMed  Google Scholar 

  47. Papakostas, S. et al. Gene pleiotropy constrains gene expression changes in fish adapted to different thermal conditions. Nat. Commun. 5, 4071 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Leder, E. H. et al. The evolution and adaptive potential of transcriptional variation in sticklebacks—signatures of selection and widespread heritability. Mol. Biol. Evol. 32, 674–689 (2015).

    Article  CAS  PubMed  Google Scholar 

  49. Blanc, J., Kremling, K. A. G., Buckler, E. & Josephs, E. B. Local adaptation contributes to gene expression divergence in maize. G3 11, jkab004 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Pujol, B., Wilson, A. J., Ross, R. I. C. & Pannell, J. R. Are QST-FST comparisons for natural populations meaningful? Mol. Ecol. 17, 4782–4785 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Dunn, C. W., Zapata, F., Munro, C., Siebert, S. & Hejnol, A. Pairwise comparisons across species are problematic when analyzing functional genomic data. Proc. Natl Acad. Sci. USA 115, e409–e417 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).

    Article  Google Scholar 

  53. Pennell, M. W. & Harmon, L. J. An integrative view of phylogenetic comparative methods: connections to population genetics, community ecology, and paleobiology. Ann. N. Y. Acad. Sci. 1289, 90–105 (2013).

    Article  PubMed  Google Scholar 

  54. Felsenstein, J. Maximum-likelihood estimation of evolutionary trees from continuous characters. Am. J. Hum. Genet. 25, 471–492 (1973).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Oakley, T. H., Gu, Z., Abouheif, E., Patel, N. H. & Li, W.-H. Comparative methods for the analysis of gene-expression evolution: an example using yeast functional genomic data. Mol. Biol. Evol. 22, 40–50 (2005).

    Article  CAS  PubMed  Google Scholar 

  56. Gu, X. Statistical framework for phylogenomic analysis of gene family expression profiles. Genetics 167, 531–542 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Butler, M. A. & King, A. A. Phylogenetic comparative analysis: a modeling approach for adaptive evolution. Am. Nat. 164, 683–695 (2004).

    Article  PubMed  Google Scholar 

  58. Brawand, D. et al. The evolution of gene expression levels in mammalian organs. Nature 478, 343–348 (2011).

    Article  CAS  PubMed  Google Scholar 

  59. Kalinka, A. T. et al. Gene expression divergence recapitulates the developmental hourglass model. Nature 468, 811–814 (2010).

    Article  CAS  PubMed  Google Scholar 

  60. El Taher, A. et al. Gene expression dynamics during rapid organismal diversification in African cichlid fishes. Nat. Ecol. Evol. 5, 243–250 (2021).

    Article  PubMed  Google Scholar 

  61. Chen, J. et al. A quantitative framework for characterizing the evolutionary history of mammalian gene expression. Genome Res. 29, 53–63 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Pal, S., Oliver, B. & Przytycka, T. M. Modeling gene expression evolution with EvoGeneX uncovers differences in evolution of species, organs and sexes. Preprint at bioRxiv https://doi.org/10.1101/2020.01.06.895615 (2021).

  63. Greenway, R. et al. Convergent evolution of conserved mitochondrial pathways underlies repeated adaptation to extreme environments. Proc. Natl Acad. Sci. USA 117, 16424–16430 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Vegesna, R. et al. Ampliconic genes on the great ape Y chromosomes: rapid evolution of copy number but conservation of expression levels. Genome Biol. Evol. 12, 842–859 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Gillard, G. B. et al. Comparative regulomics supports pervasive selection on gene dosage following whole genome duplication. Genome Biol. 22, 103 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kopania, E. E. K., Larson, E. L., Callahan, C. & Keeble, S. Molecular evolution across mouse spermatogenesis. Mol. Biol. Evol. 39, msac023 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Groen, S. C. et al. The strength and pattern of natural selection on gene expression in rice. Nature 578, 572–576 (2020).

    Article  CAS  PubMed  Google Scholar 

  68. Ahmad, F. et al. The strength and form of natural selection on transcript abundance in the wild. Mol. Ecol. 30, 2724–2737 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  70. Guschanski, K., Warnefors, M. & Kaessmann, H. The evolution of duplicate gene expression in mammalian organs. Genome Res. 27, 1461–1474 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Kim, D. W. et al. Single-cell analysis of early chick hypothalamic development reveals that hypothalamic cells are induced from prethalamic-like progenitors. Cell Rep. 38, 110251 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Estermann, M. A. et al. Insights into gonadal sex differentiation provided by single-cell transcriptomics in the chicken embryo. Cell Rep. 31, 107491 (2020).

    Article  CAS  PubMed  Google Scholar 

  73. Niu, W. & Spradling, A. C. Two distinct pathways of pregranulosa cell differentiation support follicle formation in the mouse ovary. Proc. Natl Acad. Sci. USA 117, 20015–20026 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Witt, E., Benjamin, S., Svetec, N. & Zhao, L. Testis single-cell RNA-seq reveals the dynamics of de novo gene transcription and germline mutational bias in Drosophila. eLife 8, e47138 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Hermann, B. P. et al. The mammalian spermatogenesis single-cell transcriptome, from spermatogonial stem cells to spermatids. Cell Rep. 25, 1650–1667.e8 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Green, C. D. et al. A comprehensive roadmap of murine spermatogenesis defined by single-cell RNA-seq. Dev. Cell 46, 651–667.e10 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. La Manno, G. et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167, 566–580.e19 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Tosches, M. A. et al. Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles. Science 360, 881–888 (2018).

    Article  CAS  PubMed  Google Scholar 

  79. Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Lüpold, S., Linz, G. M., Rivers, J. W., Westneat, D. F. & Birkhead, T. R. Sperm competition selects beyond relative testes size in birds. Evolution 63, 391–402 (2009).

    Article  PubMed  Google Scholar 

  81. Shami, A. N. et al. Single-cell RNA sequencing of human, macaque, and mouse testes uncovers conserved and divergent features of mammalian spermatogenesis. Dev. Cell 54, 529–547.e12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Harrison, P. W. et al. Sexual selection drives evolution and rapid turnover of male gene expression. Proc. Natl Acad. Sci. USA 112, 4393–4398 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Bauernfeind, A. L. et al. Tempo and mode of gene expression evolution in the brain across primates. Preprint at bioRxiv https://doi.org/10.1101/2021.04.21.440670 (2021).

  84. Shafer, M. E. R. Cross-species analysis of single-cell transcriptomic data. Front. Cell Dev. Biol. 7, 175 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Gompel, N., Prud’homme, B., Wittkopp, P. J., Kassner, V. A. & Carroll, S. B. Chance caught on the wing: cis-regulatory evolution and the origin of pigment patterns in Drosophila. Nature 433, 481–487 (2005).

    Article  CAS  PubMed  Google Scholar 

  86. Prud’homme, B. et al. Repeated morphological evolution through cis-regulatory changes in a pleiotropic gene. Nature 440, 1050–1053 (2006).

    Article  PubMed  Google Scholar 

  87. Liu, J., Mosti, F. & Silver, D. L. Human brain evolution: emerging roles for regulatory DNA and RNA. Curr. Opin. Neurobiol. 71, 170–177 (2021).

    Article  CAS  PubMed  Google Scholar 

  88. Sarropoulos, I. et al. Developmental and evolutionary dynamics of cis-regulatory elements in mouse cerebellar cells. Science 373, eabg4696 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Brown, J. B. et al. Diversity and dynamics of the Drosophila transcriptome. Nature 512, 393–399 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Gibilisco, L., Zhou, Q., Mahajan, S. & Bachtrog, D. Alternative splicing within and between Drosophila species, sexes, tissues, and developmental stages. PLoS Genet. 12, e1006464 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Mazin, P. V., Khaitovich, P., Cardoso-Moreira, M. & Kaessmann, H. Alternative splicing during mammalian organ development. Nat. Genet. 53, 925–934 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Gómez-Redondo, I., Planells, B., Navarrete, P. & Gutiérrez-Adán, A. Role of alternative splicing in sex determination in vertebrates. Sex. Dev. 15, 381–391 (2021).

    Article  PubMed  Google Scholar 

  93. Singh, P. & Ahi, E. P. The importance of alternative splicing in adaptive evolution. Mol. Ecol. 31, 1928–1938 (2022).

    Article  PubMed  Google Scholar 

  94. Rogers, T. F., Palmer, D. H. & Wright, A. E. Sex-specific selection drives the evolution of alternative splicing in birds. Mol. Biol. Evol. 38, 519–530 (2021).

    Article  CAS  PubMed  Google Scholar 

  95. Naftaly, A. S., Pau, S. & White, M. A. Long-read RNA sequencing reveals widespread sex-specific alternative splicing in threespine stickleback fish. Genome Res. 31, 1486–1497 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Khan, Z. et al. Primate transcript and protein expression levels evolve under compensatory selection pressures. Science 342, 1100–1104 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Wang, Z.-Y. et al. Transcriptome and translatome co-evolution in mammals. Nature 588, 642–647 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Koussounadis, A., Langdon, S. P., Um, I. H., Harrison, D. J. & Smith, V. A. Relationship between differentially expressed mRNA and mRNA-protein correlations in a xenograft model system. Sci. Rep. 5, 10775 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Vogel, C. & Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Lopes-Ramos, C. M. et al. Sex differences in gene expression and regulatory networks across 29 human tissues. Cell Rep. 31, 107795 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Liu, X., Li, Y. I. & Pritchard, J. K. Trans effects on gene expression can drive omnigenic inheritance. Cell 177, 1022–1034.e6 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Mathieson, I. The omnigenic model and polygenic prediction of complex traits. Am. J. Hum. Genet. 108, 1558–1563 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. O’Connor, L. J. et al. Extreme polygenicity of complex traits is explained by negative selection. Am. J. Hum. Genet. 105, 456–476 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Pantalacci, S. & Sémon, M. Transcriptomics of developing embryos and organs: a raising tool for evo-devo. J. Exp. Zool. B 324, 363–371 (2015).

    Article  CAS  Google Scholar 

  106. Liu, J. & Robinson-Rechavi, M. Developmental constraints on genome evolution in four bilaterian model species. Genome Biol. Evol. 10, 2266–2277 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Metzger, B. P. H., Yuan, D. C., Gruber, J. D., Duveau, F. & Wittkopp, P. J. Selection on noise constrains variation in a eukaryotic promoter. Nature 521, 344–347 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Metzger, B. P. H. et al. Contrasting frequencies and effects of cis- and trans-regulatory mutations affecting gene expression. Mol. Biol. Evol. 33, 1131–1146 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Hodgins-Davis, A., Duveau, F., Walker, E. & Wittkopp, P. J. Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae. Proc. Nat. Acad. Sci. USA 116, 21085–21093 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Vaishnav, E. D. et al. A comprehensive fitness landscape model reveals the evolutionary history and future evolvability of eukaryotic cis-regulatory DNA sequences. Preprint at bioRxiv https://doi.org/10.1101/2021.02.17.430503 (2021).

  112. Josephson, M. P. & Bull, J. K. Innovative mark–recapture experiment shows patterns of selection on transcript abundance in the wild. Mol. Ecol. 30, 2707–2709 (2021).

    Article  CAS  PubMed  Google Scholar 

  113. Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2011).

    Article  Google Scholar 

  114. Ho, L., Si, T. & Ané, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).

    Article  PubMed  Google Scholar 

  115. Beaulieu, J. M., Jhwueng, D.-C., Boettiger, C. & O’Meara, B. C. Modeling stabilizing selection: expanding the Ornstein-Uhlenbeck model of adaptive evolution. Evolution 66, 2369–2383 (2012).

    Article  PubMed  Google Scholar 

  116. Harrison, P. W., Wright, A. E. & Mank, J. E. The evolution of gene expression and the transcriptome–phenotype relationship. Semin. Cell Dev. Biol. 23, 222–229 (2012).

    Article  CAS  PubMed  Google Scholar 

  117. Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2, lqaa078 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  119. Jacob, L., Gagnon-Bartsch, J. A. & Speed, T. P. Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed. Biostatistics 17, 16–28 (2016).

    Article  PubMed  Google Scholar 

  120. Chira, A. M. & Thomas, G. H. The impact of rate heterogeneity on inference of phylogenetic models of trait evolution. J. Evol. Biol. 29, 2502–2518 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Allen, S. L., Bonduriansky, R. & Chenoweth, S. F. Genetic constraints on microevolutionary divergence of sex-biased gene expression. Phil. Trans. R. Soc. B 373, 20170427 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Dean, R. & Mank, J. E. Tissue specificity and sex-specific regulatory variation permit the evolution of sex-biased gene expression. Am. Nat. 188, e74–e84 (2016).

    Article  PubMed  Google Scholar 

  123. Pennell, M. W., FitzJohn, R. G., Cornwell, W. K. & Harmon, L. J. Model adequacy and the macroevolution of angiosperm functional traits. Am. Nat. 186, e33–e50 (2015).

    Article  PubMed  Google Scholar 

  124. Höhna, S. et al. Probabilistic graphical model representation in phylogenetics. Syst. Biol. 63, 753–771 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  125. Slater, G. J. & Pennell, M. W. Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution. Syst. Biol. 63, 293–308 (2014).

    Article  PubMed  Google Scholar 

  126. Barr, W. A. & Scott, R. S. Phylogenetic comparative methods complement discriminant function analysis in ecomorphology. Am. J. Phys. Anthropol. 153, 663–674 (2014).

    Article  PubMed  Google Scholar 

  127. Brzyski, D. et al. Controlling the rate of GWAS false discoveries. Genetics 205, 61–75 (2017).

    Article  PubMed  Google Scholar 

  128. Wang, X., Yu, L. & Wu, A. R. The effect of methanol fixation on single-cell RNA sequencing data. BMC Genomics 22, 420 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Jew, B. et al. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat. Commun. 11, 1971 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Aguirre-Gamboa, R. et al. Deconvolution of bulk blood eQTL effects into immune cell subpopulations. BMC Bioinformatics 21, 243 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Chu, T., Wang, Z., Pe’er, D. & Danko, C. G. Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions. Preprint at bioRxiv https://doi.org/10.1101/2020.01.07.897900 (2021)

  132. Monaco, G. et al. RNA-Seq signatures normalized by mRNA abundance allow absolute deconvolution of human immune cell types. Cell Rep. 26, 1627–1640.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Chakravarthy, A. et al. Pan-cancer deconvolution of tumour composition using DNA methylation. Nat. Commun. 9, 3220 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Shen-Orr, S. S. et al. Cell type-specific gene expression differences in complex tissues. Nat. Methods 7, 287–289 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was funded by an NERC Independent Research Fellowship to C.R.C. (NE/T01105X/1); an NERC Independent Research Fellowship to A.E.W. (NE/N013948/1); a grant from the European Research Council (grant agreement 680951) and a Canada 150 Research Chair to J.E.M.; an NERC ACCE DTP to P.D.P.; and an NSF Postdoctoral Research Fellowship and an MSU Presidential Postdoctoral Fellowship to D.H.P.D. We thank M. Placzek, P. E. Pifarré, E. Josephs, A. Platts, M. Roberts, R. Panko, M. Wilson Brown and S. Buysse for helpful comments and suggestions on the manuscript.

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Author notes

  1. These authors contributed equally: Christopher R. Cooney, Alison E. Wright.

Authors and Affiliations

  1. Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK

    Peter D. Price, Daniela H. Palmer Droguett, Jessica A. Taylor, Thea F. Rogers, Christopher R. Cooney & Alison E. Wright

  2. Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI, USA

    Daniela H. Palmer Droguett

  3. Department of Biosciences, Durham University, Durham, UK

    Jessica A. Taylor

  4. Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Dong Won Kim

  5. Development, Regeneration and Neurophysiology, School of Biosciences, University of Sheffield, Sheffield, UK

    Elsie S. Place

  6. Department of Zoology, University of British Columbia, Vancouver, British Columbia, Canada

    Judith E. Mank

  7. Beaty Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada

    Judith E. Mank

  8. Centre for Ecology and Conservation, University of Exeter, Penryn, UK

    Judith E. Mank

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Contributions

A.E.W., C.R.C., D.H.P.D., P.D.P. and J.E.M. designed the study. D.W.K., E.S.P., A.E.W., C.R.C. and P.D.P. analysed the data. All authors wrote and edited the manuscript.

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Correspondence to Peter D. Price or Alison E. Wright.

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Price, P.D., Palmer Droguett, D.H., Taylor, J.A. et al. Detecting signatures of selection on gene expression. Nat Ecol Evol 6, 1035–1045 (2022). https://doi.org/10.1038/s41559-022-01761-8

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