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

2. 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).

3. Wing, R. A., Purugganan, M. D. & Zhang, Q. The rice genome revolution: from an ancient grain to green super rice. Nat. Rev. Genet. 19, 505–517 (2018).

4. Kingsolver, J. G. et al. The strength of phenotypic selection in natural populations. Am. Nat. 157, 245–261 (2001).

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

6. Anderson, J. T., Lee, C. R., Rushworth, C. A., Colautti, R. I. & Mitchell-Olds, T. Genetic trade-offs and conditional neutrality contribute to local adaptation. Mol. Ecol. 22, 699–708 (2013).

7. Lemos, B., Bettencourt, B. R., Meiklejohn, C. D. & Hartl, D. L. Evolution of proteins and gene expression levels are coupled in Drosophila and are independently associated with mRNA abundance, protein length, and number of protein–protein interactions. Mol. Biol. Evol. 22, 1345–1354 (2005).

8. Lehner, B. Selection to minimise noise in living systems and its implications for the evolution of gene expression. Mol. Syst. Biol. 4, 170 (2008).

9. MacNeil, L. T. & Walhout, A. J. Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. Genome Res. 21, 645–657 (2011).

10. Conner, J. & Via, S. Natural selection on body size in Tribolium: possible genetic constraints on adaptive evolution. Heredity 69, 73–83 (1992).

11. Franks, S. J. Plasticity and evolution in drought avoidance and escape in the annual plant Brassica rapa. New Phytol. 190, 249–257 (2011).

12. Kumar, A. et al. Breeding high-yielding drought-tolerant rice: genetic variations and conventional and molecular approaches. J. Exp. Bot. 65, 6265–6278 (2014).

13. Fornara, F. et al. Functional characterization of OsMADS18, a member of the AP1/SQUA subfamily of MADS box genes. Plant Physiol. 135, 2207–2219 (2004).

14. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

15. Ayroles, J. F. et al. Systems genetics of complex traits in Drosophila melanogaster. Nat. Genet. 41, 299–307 (2009).

16. Conner, J. Field measurements of natural and sexual selection in the fungus beetle, Bolitotherus cornutus. Evolution 42, 736–749 (1988).

17. Hoekstra, H. E. et al. Strength and tempo of directional selection in the wild. Proc. Natl Acad. Sci. USA 98, 9157–9160 (2001).

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

19. Ghalambor, C. K. et al. Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature. Nature 525, 372–375 (2015).

20. Kenkel, C. D. & Matz, M. V. Gene expression plasticity as a mechanism of coral adaptation to a variable environment. Nat. Ecol. Evol. 1, 0014 (2016).

21. Zhang, L. & Li, W. H. Mammalian housekeeping genes evolve more slowly than tissue-specific genes. Mol. Biol. Evol. 21, 236–239 (2004).

22. Hendry, A. P. & Kinnison, M. T. The pace of modern life: measuring rates of contemporary microevolution. Evolution 53, 1637–1653 (1999).

23. Duveau, F. et al. Fitness effects of altering gene expression noise in Saccharomyces cerevisiae. eLife 7, e37272 (2018).

24. Jimenez-Gomez, J. M., Corwin, J. A., Joseph, B., Maloof, J. N. & Kliebenstein, D. J. Genomic analysis of QTLs and genes altering natural variation in stochastic noise. PLoS Genet. 7, e1002295 (2011).

25. Plessis, A. et al. Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions. eLife 4, e08411 (2015).

26. Wilkins, O. et al. EGRINs (environmental gene regulatory influence networks) in rice that function in the response to water deficit, high temperature, and agricultural environments. Plant Cell 28, 2365–2384 (2016).

27. Huang, X. et al. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat. Genet. 44, 32–39 (2011).

28. Wang, Y. et al. Background-independent quantitative trait loci for drought tolerance identified using advanced backcross introgression lines in rice. Crop Sci. 53, 430–441 (2013).

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

30. Zaidem, M. L., Groen, S. C. & Purugganan, M. D. Evolutionary and ecological functional genomics, from lab to the wild. Plant J. 97, 40–55 (2019).

31. Keurentjes, J. J. et al. Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci. Proc. Natl Acad. Sci. USA 104, 1708–1713 (2007).

32. Caicedo, A. L. et al. Genome-wide patterns of nucleotide polymorphism in domesticated rice. PLoS Genet. 3, e163 (2007).

33. Garris, A. J., Tai, T. H., Coburn, J., Kresovich, S. & McCouch, S. Genetic structure and diversity in Oryza sativa L. Genetics 169, 1631–1638 (2005).

34. Gutaker, R. M. et al. Genomic history and ecology of the geographic spread of rice. Preprint at bioRxiv https://doi.org/10.1101/748178 (2019).

35. McCouch, S. R. et al. Open access resources for genome-wide association mapping in rice. Nat. Commun. 7, 10532 (2016).

36. McNally, K. L. et al. Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proc. Natl Acad. Sci. USA 106, 12273–12278 (2009).

37. Torres, R. O., McNally, K. L., Cruz, C. V., Serraj, R. & Henry, A. Screening of rice genebank germplasm for yield and selection of new drought tolerance donors. Field Crops Res. 147, 12–22 (2013).

38. Wang, W. et al. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557, 43–49 (2018).

39. Abramoff, M. D., Magalhaes, P. J. & Ram, S. J. Image processing with ImageJ. Biophoton. Int. 11, 36–42 (2004).

40. Bracken, B. Barcoded plate-based single cell RNA-seq. https://www.protocols.io/view/barcoded-plate-based-single-cell-rna-seq-nkgdctw (2018).

41. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

42. Soumillon, M., Cacchiarelli, D., Semrau, S., van Oudenaarden, A. & Mikkelsen, T. S. Characterization of directed differentiation by high-throughput single-cell RNA-seq. Preprint at bioRxiv https://doi.org/10.1101/003236 (2014).

43. Li, C. & Wong, W. H. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Natl Acad. Sci. USA 98, 31–36 (2001).

44. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

45. R Core Team. R: a language and environment for statistical computing. http://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, 2016).

46. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

47. Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

48. Yi, X., Du, Z. & Su, Z. PlantGSEA: a gene set enrichment analysis toolkit for plant community. Nucleic Acids Res. 41, W98–W103 (2013).

49. Brodie, E. D. III, Moore, A. J. & Janzen, F. J. Visualizing and quantifying natural selection. Trends Ecol. Evol. 10, 313–318 (1995).

50. Janzen, F. J. & Stern, H. S. Logistic regression for empirical studies of multivariate selection. Evolution 52, 1564–1571 (1998).

51. Koenig, W. D., Albano, S. S. & Dickinson, J. L. A comparison of methods to partition selection acting via components of fitness: do larger male bullfrogs have greater hatching success? J. Evol. Biol. 4, 309–320 (1991).

52. Kassambara, A. Practical Guide to Principal Component Methods in R: PCA, M (CA), FAMD, MFA, HCPC, factoextra (STHDA, 2017).

53. Davidson, R. M. et al. Comparative transcriptomics of three Poaceae species reveals patterns of gene expression evolution. Plant J. 71, 492–502 (2012).

54. Yanai, I. et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 21, 650–659 (2005).

55. Schäfer, J. & Strimmer, K. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat. Appl. Genet. Mol. Biol. 4, Article32 (2005).

56. Larracuente, A. M. et al. Evolution of protein-coding genes in Drosophila. Trends Genet. 24, 114–123 (2008).

57. Keren, L. et al. Noise in gene expression is coupled to growth rate. Genome Res. 25, 1893–1902 (2015)

58. Hieno, A. et al. ppdb: plant promoter database version 3.0. Nucleic Acids Res. 42, D1188–D1192 (2014).

59. Yamamoto, Y. Y. et al. Identification of plant promoter constituents by analysis of local distribution of short sequences. BMC Genomics 8, 67 (2007).

60. Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

61. Proost, S. et al. PLAZA: a comparative genomics resource to study gene and genome evolution in plants. Plant Cell 21, 3718–3731 (2009).

62. Van Bel, M. et al. PLAZA 4.0: an integrative resource for functional, evolutionary and comparative plant genomics. Nucleic Acids Res. 46, D1190–D1196 (2018).

63. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

64. Van der Auwera, G. A. et al. From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).

65. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

66. Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).

67. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

68. Tropf, F. C. et al. Human fertility, molecular genetics, and natural selection in modern societies. PLoS ONE 10, e0126821 (2015).

69. Lipka, A. E. et al. GAPIT: genome association and prediction integrated tool. Bioinformatics 28, 2397–2399 (2012).

70. VanRaden, P. M. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414–4423 (2008).

71. Kang, H. M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).

72. Kang, H. M. et al. Efficient control of population structure in model organism association mapping. Genetics 178, 1709–1723 (2008).

73. Segura, V. et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat. Genet. 44, 825–830 (2012).

74. Bland, J. M. & Altman, D. G. Multiple significance tests: the Bonferroni method. Br. Med. J. 310, 170 (1995).

75. Fournier-Level, A. et al. A map of local adaptation in Arabidopsis thaliana. Science 334, 86–89 (2011).

76. Huang, X. et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 42, 961–967 (2010).

77. Mather, K. A. et al. The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics 177, 2223–2232 (2007).