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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All data have been previously published71.
All code is publicly available at https://github.com/Wright-lab-2021-Transcriptome-Evo/Inferring_expression_evolution_review.
Mank, J. E. The transcriptional architecture of phenotypic dimorphism. Nat. Ecol. Evol. 1, 6 (2017).
Parsch, J. & Ellegren, H. The evolutionary causes and consequences of sex-biased gene expression. Nat. Rev. Genet. 14, 83–87 (2013).
Carroll, S. B. Evo-devo and an expanding evolutionary synthesis: a genetic theory of morphological evolution. Cell 134, 25–36 (2008).
Wray, G. A. The evolutionary significance of cis-regulatory mutations. Nat. Rev. Genet. 8, 206–216 (2007).
Gilad, Y., Oshlack, A. & Rifkin, S. A. Natural selection on gene expression. Trends Genet. 22, 456–461 (2006).
Necsulea, A. & Kaessmann, H. Evolutionary dynamics of coding and non-coding transcriptomes. Nat. Rev. Genet. 15, 734–748 (2014).
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).
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).
Signor, S. A. & Nuzhdin, S. V. The evolution of gene expression in cis and trans. Trends Genet. 34, 532–544 (2018).
Fay, J. C. & Wittkopp, P. J. Evaluating the role of natural selection in the evolution of gene regulation. Heredity 100, 191–199 (2008).
Khaitovich, P., Enard, W., Lachmann, M. & Pääbo, S. Evolution of primate gene expression. Nat. Rev. Genet. 7, 693–702 (2006).
Bedford, T. & Hartl, D. L. Optimization of gene expression by natural selection. Proc. Natl Acad. Sci. USA 106, 1133–1138 (2009).
Whitehead, A. & Crawford, D. L. Neutral and adaptive variation in gene expression. Proc. Natl Acad. Sci. USA 103, 5425–5430 (2006).
Hansen, T. F. Stabilizing selection and the comparative analysis of adaptation. Evolution 51, 1341–1351 (1997).
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).
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).
Ho, L. S. T. & Ané, C. Intrinsic inference difficulties for trait evolution with Ornstein-Uhlenbeck models. Methods Ecol. Evol. 5, 1133–1146 (2014).
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).
Rohlfs, R. V. & Nielsen, R. Phylogenetic ANOVA: the expression variance and evolution model for quantitative trait evolution. Syst. Biol. 64, 695–708 (2015).
Montgomery, S. H. & Mank, J. E. Inferring regulatory change from gene expression: the confounding effects of tissue scaling. Mol. Ecol. 25, 5114–5128 (2016).
Hunnicutt, K. E., Good, J. M. & Larson, E. L. Unraveling patterns of disrupted gene expression across a complex tissue. Evolution 76, 275–291 (2021).
Fair, B. J. et al. Gene expression variability in human and chimpanzee populations share common determinants. eLife 9, e59929 (2020).
Nourmohammad, A. et al. Adaptive evolution of gene expression in Drosophila. Cell Rep. 20, 1385–1395 (2017).
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).
Oleksiak, M. F., Churchill, G. A. & Crawford, D. L. Variation in gene expression within and among natural populations. Nat. Genet. 32, 261–266 (2002).
Khaitovich, P. et al. A neutral model of transcriptome evolution. PLoS Biol. 2, e132 (2004).
Rifkin, S. A., Kim, J. & White, K. P. Evolution of gene expression in the Drosophila melanogaster subgroup. Nat. Genet. 33, 138–144 (2003).
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).
Hudson, R. R., Kreitman, M. & Aguadé, M. A test of neutral molecular evolution based on nucleotide data. Genetics 116, 153–159 (1987).
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).
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).
Somel, M. et al. Transcriptional neoteny in the human brain. Proc. Natl Acad. Sci. USA 106, 5743–5748 (2009).
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).
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).
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).
Enard, W. Intra- and interspecific variation in primate gene expression patterns. Science 296, 340–343 (2002).
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).
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).
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).
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).
Denver, D. R. et al. The transcriptional consequences of mutation and natural selection in Caenorhabditis elegans. Nat. Genet. 37, 544–548 (2005).
Huang, W. et al. Spontaneous mutations and the origin and maintenance of quantitative genetic variation. eLife 5, e14625 (2016).
Fraser, H. B. Detecting selection with a genetic cross. Proc. Natl Acad. Sci. USA 117, 22323–22330 (2020).
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).
Mähler, N. et al. Gene co-expression network connectivity is an important determinant of selective constraint. PLoS Genet. 13, e1006402 (2017).
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).
Papakostas, S. et al. Gene pleiotropy constrains gene expression changes in fish adapted to different thermal conditions. Nat. Commun. 5, 4071 (2014).
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).
Blanc, J., Kremling, K. A. G., Buckler, E. & Josephs, E. B. Local adaptation contributes to gene expression divergence in maize. G3 11, jkab004 (2021).
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).
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).
Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).
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).
Felsenstein, J. Maximum-likelihood estimation of evolutionary trees from continuous characters. Am. J. Hum. Genet. 25, 471–492 (1973).
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).
Gu, X. Statistical framework for phylogenomic analysis of gene family expression profiles. Genetics 167, 531–542 (2004).
Butler, M. A. & King, A. A. Phylogenetic comparative analysis: a modeling approach for adaptive evolution. Am. Nat. 164, 683–695 (2004).
Brawand, D. et al. The evolution of gene expression levels in mammalian organs. Nature 478, 343–348 (2011).
Kalinka, A. T. et al. Gene expression divergence recapitulates the developmental hourglass model. Nature 468, 811–814 (2010).
El Taher, A. et al. Gene expression dynamics during rapid organismal diversification in African cichlid fishes. Nat. Ecol. Evol. 5, 243–250 (2021).
Chen, J. et al. A quantitative framework for characterizing the evolutionary history of mammalian gene expression. Genome Res. 29, 53–63 (2019).
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).
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).
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).
Gillard, G. B. et al. Comparative regulomics supports pervasive selection on gene dosage following whole genome duplication. Genome Biol. 22, 103 (2021).
Kopania, E. E. K., Larson, E. L., Callahan, C. & Keeble, S. Molecular evolution across mouse spermatogenesis. Mol. Biol. Evol. 39, msac023 (2022).
Groen, S. C. et al. The strength and pattern of natural selection on gene expression in rice. Nature 578, 572–576 (2020).
Ahmad, F. et al. The strength and form of natural selection on transcript abundance in the wild. Mol. Ecol. 30, 2724–2737 (2021).
Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).
Guschanski, K., Warnefors, M. & Kaessmann, H. The evolution of duplicate gene expression in mammalian organs. Genome Res. 27, 1461–1474 (2017).
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).
Estermann, M. A. et al. Insights into gonadal sex differentiation provided by single-cell transcriptomics in the chicken embryo. Cell Rep. 31, 107491 (2020).
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).
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).
Hermann, B. P. et al. The mammalian spermatogenesis single-cell transcriptome, from spermatogonial stem cells to spermatids. Cell Rep. 25, 1650–1667.e8 (2018).
Green, C. D. et al. A comprehensive roadmap of murine spermatogenesis defined by single-cell RNA-seq. Dev. Cell 46, 651–667.e10 (2018).
La Manno, G. et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167, 566–580.e19 (2016).
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).
Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).
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).
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).
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).
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).
Shafer, M. E. R. Cross-species analysis of single-cell transcriptomic data. Front. Cell Dev. Biol. 7, 175 (2019).
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).
Prud’homme, B. et al. Repeated morphological evolution through cis-regulatory changes in a pleiotropic gene. Nature 440, 1050–1053 (2006).
Liu, J., Mosti, F. & Silver, D. L. Human brain evolution: emerging roles for regulatory DNA and RNA. Curr. Opin. Neurobiol. 71, 170–177 (2021).
Sarropoulos, I. et al. Developmental and evolutionary dynamics of cis-regulatory elements in mouse cerebellar cells. Science 373, eabg4696 (2021).
Brown, J. B. et al. Diversity and dynamics of the Drosophila transcriptome. Nature 512, 393–399 (2014).
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).
Mazin, P. V., Khaitovich, P., Cardoso-Moreira, M. & Kaessmann, H. Alternative splicing during mammalian organ development. Nat. Genet. 53, 925–934 (2021).
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).
Singh, P. & Ahi, E. P. The importance of alternative splicing in adaptive evolution. Mol. Ecol. 31, 1928–1938 (2022).
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).
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).
Khan, Z. et al. Primate transcript and protein expression levels evolve under compensatory selection pressures. Science 342, 1100–1104 (2013).
Wang, Z.-Y. et al. Transcriptome and translatome co-evolution in mammals. Nature 588, 642–647 (2020).
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).
Vogel, C. & Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232 (2012).
Lopes-Ramos, C. M. et al. Sex differences in gene expression and regulatory networks across 29 human tissues. Cell Rep. 31, 107795 (2020).
Liu, X., Li, Y. I. & Pritchard, J. K. Trans effects on gene expression can drive omnigenic inheritance. Cell 177, 1022–1034.e6 (2019).
Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
Mathieson, I. The omnigenic model and polygenic prediction of complex traits. Am. J. Hum. Genet. 108, 1558–1563 (2021).
O’Connor, L. J. et al. Extreme polygenicity of complex traits is explained by negative selection. Am. J. Hum. Genet. 105, 456–476 (2019).
Pantalacci, S. & Sémon, M. Transcriptomics of developing embryos and organs: a raising tool for evo-devo. J. Exp. Zool. B 324, 363–371 (2015).
Liu, J. & Robinson-Rechavi, M. Developmental constraints on genome evolution in four bilaterian model species. Genome Biol. Evol. 10, 2266–2277 (2018).
Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019).
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).
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).
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).
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).
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).
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2011).
Ho, L., Si, T. & Ané, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).
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).
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).
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).
Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2, lqaa078 (2020).
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).
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).
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).
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).
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).
Höhna, S. et al. Probabilistic graphical model representation in phylogenetics. Syst. Biol. 63, 753–771 (2014).
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).
Barr, W. A. & Scott, R. S. Phylogenetic comparative methods complement discriminant function analysis in ecomorphology. Am. J. Phys. Anthropol. 153, 663–674 (2014).
Brzyski, D. et al. Controlling the rate of GWAS false discoveries. Genetics 205, 61–75 (2017).
Wang, X., Yu, L. & Wu, A. R. The effect of methanol fixation on single-cell RNA sequencing data. BMC Genomics 22, 420 (2021).
Jew, B. et al. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat. Commun. 11, 1971 (2020).
Aguirre-Gamboa, R. et al. Deconvolution of bulk blood eQTL effects into immune cell subpopulations. BMC Bioinformatics 21, 243 (2020).
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)
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).
Chakravarthy, A. et al. Pan-cancer deconvolution of tumour composition using DNA methylation. Nat. Commun. 9, 3220 (2018).
Shen-Orr, S. S. et al. Cell type-specific gene expression differences in complex tissues. Nat. Methods 7, 287–289 (2010).
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.
The authors declare no competing interests.
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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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