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Ecological factors influence balancing selection on leaf chemical profiles of a wildflower

Nature Ecology & Evolution volume 5, pages 1135–1144 (2021)Cite this article

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Abstract

Balancing selection is frequently invoked as a mechanism that maintains variation within and across populations. However, there are few examples of balancing selection operating on loci underpinning complex traits, which frequently display high levels of variation. We investigated mechanisms that may maintain variation in a focal polymorphism—leaf chemical profiles of a perennial wildflower (Boechera stricta, Brassicaceae)—explicitly interrogating multiple ecological and genetic processes including spatial variation in selection, antagonistic pleiotropy and frequency-dependent selection. A suite of common garden and greenhouse experiments showed that the alleles underlying variation in chemical profile have contrasting fitness effects across environments, implicating two ecological drivers of selection on chemical profile: herbivory and drought. Phenotype–environment associations and molecular genetic analyses revealed additional evidence of past selection by these drivers. Together, these data are consistent with balancing selection on chemical profile, probably caused by pleiotropic effects of secondary chemical biosynthesis genes on herbivore defence and drought response.

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Fig. 1: GS variation in B. stricta is highly polymorphic and widespread.
Fig. 2: Environmental variation in the effects of GS on fitness components.
Fig. 3: Drought influences selection on BCMA1/3.
Fig. 4: The BCMA1/3 CFR-NIL interval contains ten flanking loci, but they show little correlation (LD) in natural populations.
Fig. 5: Molecular evidence of balancing selection on BCMA3.

Data availability

The new reference genome assemblies and raw Nanopore reads for the SAD12 and LTM genotypes have been submitted to NCBI (BioProject number PRJNA609209). The short reads of the GBS data for the CFR-NIL families have been submitted to NCBI (BioProject number PRJNA659863). Previously published genomic data are archived with ref. 55. All other data reported in this manuscript are archived in the Dryad digital data repository (https://doi.org/10.5061/dryad.7h44j0zsr)56. All biological materials are available from the Arabidopsis Biological Resource Center (ABRC) or from the authors.

Code availability

The code used for this manuscript is archived in the Dryad digital repository (https://doi.org/10.5061/dryad.7h44j0zsr)56.

References

  1. Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics (Longman, 1996).

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

    Article  PubMed  Google Scholar 

  3. Kingsolver, J. G., Diamond, S. E., Siepielski, A. M. & Carlson, S. M. Synthetic analyses of phenotypic selection in natural populations: lessons, limitations and future directions. Evol. Ecol. 26, 1101–1118 (2012).

    Article  Google Scholar 

  4. Barrett, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).

    Article  PubMed  Google Scholar 

  5. Kulbaba, M. W., Sheth, S. N., Pain, R. E., Eckhart, V. M. & Shaw, R. G. Additive genetic variance for lifetime fitness and the capacity for adaptation in an annual plant. Evolution 73, 1746–1758 (2019).

    Article  PubMed  Google Scholar 

  6. Lande, R. & Shannon, S. The role of genetic variation in adaptation and population persistence in a changing environment. Evolution 50, 434–437 (1996).

    Article  PubMed  Google Scholar 

  7. Etterson, J. R. & Shaw, R. G. Constraint to adaptive evolution in response to global warming. Science 294, 151–154 (2001).

    Article  CAS  PubMed  Google Scholar 

  8. Anderson, J. T., Inouye, D. W., McKinney, A. M., Colautti, R. I. & Mitchell-Olds, T. Phenotypic plasticity and adaptive evolution contribute to advancing flowering phenology in response to climate change. Proc. R. Soc. B 279, 3843–3852 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Steffen, W., Crutzen, P. J. & McNeil, J. R. The Anthropocene: are humans now overwhelming the great forces of nature? Ambio 36, 614–621 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Zhang, X.-S. & Hill, W. G. Genetic variability under mutation selection balance. Trends Ecol. Evol. 20, 468–470 (2005).

    Article  PubMed  Google Scholar 

  11. McGuigan, K., Aguirre, J. D. & Blows, M. W. Simultaneous estimation of additive and mutational genetic variance in an outbred population of Drosophila serrata. Genetics 201, 1239–1251 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  12. 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 

  13. Mitchell-Olds, T., Willis, J. H. & Goldstein, D. B. Which evolutionary processes influence natural genetic variation for phenotypic traits? Nat. Rev. Genet. 8, 845–856 (2007).

    Article  CAS  PubMed  Google Scholar 

  14. Charlesworth, B. Causes of natural variation in fitness: evidence from studies of Drosophila populations. Proc. Natl Acad. Sci. USA 112, 1662–1669 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Subramaniam, B. & Rausher, M. D. Balancing selection on a floral polymorphism. Evolution 54, 691–695 (2000).

    CAS  PubMed  Google Scholar 

  16. Charlesworth, D. Balancing selection and its effects on sequences in nearby genome regions. PLoS Genet. 2, e64 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Hedrick, P. W. & Thomson, G. Evidence for balancing selection at HLA. Genetics 104, 449–456 (1983).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Troth, A., Puzey, J. R., Kim, R. S., Willis, J. H. & Kelly, J. K. Selective trade-offs maintain alleles underpinning complex trait variation in plants. Science 361, 475–478 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Delph, L. F. & Kelly, J. K. On the importance of balancing selection in plants. N. Phytol. 201, 45–56 (2014).

    Article  Google Scholar 

  20. Anderson, J. T., Wagner, M. R., Rushworth, C. A., Prasad, K. V. S. K. & Mitchell-Olds, T. The evolution of quantitative traits in complex environments. Heredity 112, 4–12 (2014).

    Article  CAS  PubMed  Google Scholar 

  21. Anderson, J. T. & Wadgymar, S. M. Climate change disrupts local adaptation and favours upslope migration. Ecol. Lett. 23, 181–192 (2020).

    Article  PubMed  Google Scholar 

  22. Agrawal, A. A. & Fishbein, M. Plant defense syndromes. Ecology 87, S132–S149 (2006).

    Article  PubMed  Google Scholar 

  23. Carmona, D., Lajeunesse, M. J. & Johnson, M. T. Plant traits that predict resistance to herbivores. Funct. Ecol. 25, 358–367 (2011).

    Article  Google Scholar 

  24. DeLucia, E. H., Nabity, P. D., Zavala, J. A. & Berenbaum, M. R. Climate change: resetting plant–insect interactions. Plant Physiol. 160, 1677–1685 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mithöfer, A. & Boland, W. Plant defense against herbivores: chemical aspects. Annu. Rev. Plant Biol. 63, 431–450 (2012).

    Article  PubMed  Google Scholar 

  26. Prasad, K. V. S. K. et al. A gain-of-function polymorphism controlling complex traits and fitness in nature. Science 337, 1081–1084 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Bergelson, J., Dwyer, G. & Emerson, J. J. Models and data on plant–enemy coevolution. Annu. Rev. Genet. 35, 469–499 (2001).

    Article  CAS  PubMed  Google Scholar 

  28. Hodgins, K. A. & Barrett, S. C. H. Female reproductive success and the evolution of mating-type frequencies in tristylous populations. N. Phytol. 171, 569–580 (2006).

    Article  Google Scholar 

  29. Trotter, M. V. & Spencer, H. G. Complex dynamics occur in a single-locus, multiallelic model of general frequency-dependent selection. Theor. Popul. Biol. 76, 292–298 (2009).

    Article  PubMed  Google Scholar 

  30. Tuinstra, M. R., Ejeta, G. & Goldsbrough, P. B. Heterogeneous inbred family (HIF) analysis: a method for developing near-isogenic loci that differ at quantitative traits. Theor. Appl. Genet. 95, 1005–1011 (1997).

    Article  CAS  Google Scholar 

  31. Salehin, M. et al. Auxin-sensitive Aux/IAA proteins mediate drought tolerance in Arabidopsis by regulating glucosinolate levels. Nat. Commun. 10, 4021 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hossain, M. S. et al. Glucosinolate degradation products, isothiocyanates, nitriles, and thiocyanates, induce stomatal closure accompanied by peroxidase-mediated reactive oxygen species production in Arabidopsis thaliana. Biosci. Biotechnol. Biochem. 77, 977–983 (2013).

    Article  CAS  PubMed  Google Scholar 

  33. Mitchell-Olds, T. & Schmitt, J. Genetic mechanisms and evolutionary significance of natural variation in Arabidopsis. Nature 441, 947–952 (2006).

    Article  CAS  PubMed  Google Scholar 

  34. Wang, B. et al. Ancient polymorphisms contribute to genome-wide variation by long-term balancing selection and divergent sorting in Boechera stricta. Genome Biol. 20, 126 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Bloom, T. C., Baskin, J. M. & Baskin, C. C. Ecological life history of the facultative woodland biennial Arabis laevigata variety laevigata (Brassicaceae): seed dispersal. J. Torrey Bot. Soc. 129, 21–28 (2002).

    Article  Google Scholar 

  36. Song, B.-H. et al. Multilocus patterns of nucleotide diversity, population structure, and linkage disequilibrium in Boechera stricta, a wild relative of Arabidopsis. Genetics 181, 1021–1033 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  38. Hedrick, P. W. Genetic polymorphism in heterogeneous environments: a decade later. Annu. Rev. Ecol. Syst. 17, 535–566 (1986).

    Article  Google Scholar 

  39. Hedrick, P. W. Antagonistic pleiotropy and genetic polymorphism: a perspective. Heredity 82, 126–133 (1999).

    Article  Google Scholar 

  40. Turelli, M. & Barton, N. H. Polygenic variation maintained by balancing selection: pleiotropy, sex-dependent allelic effects and G × E interactions. Genetics 166, 1053–1079 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Gillespie, J. H. & Langley, C. H. A general model to account for enzyme variation in natural populations. Genetics 76, 837–848 (1974).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Anderson, J. T., Willis, J. H. & Mitchell-Olds, T. Evolutionary genetics of plant adaptation. Trends Genet. 27, 258–266 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 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).

    Article  PubMed  Google Scholar 

  44. Oakley, C. G., Ågren, J., Atchison, R. A. & Schemske, D. W. QTL mapping of freezing tolerance: links to fitness and adaptive trade-offs. Mol. Ecol. 23, 4304–4315 (2014).

    Article  PubMed  Google Scholar 

  45. Price, N. et al. Combining population genomics and fitness QTLs to identify the genetics of local adaptation in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 115, 5028–5033 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat. Genet. 44, 269–276 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Abuelsoud, W., Hirschmann, F. & Papenbrock, J. in Drought Stress in Plants Vol. 1 (eds Hossain, M. A. et al.) 227–248 (Springer, 2016).

  48. Nguyen, D., Rieu, I., Mariani, C. & van Dam, N. M. How plants handle multiple stresses: hormonal interactions underlying responses to abiotic stress and insect herbivory. Plant Mol. Biol. 91, 727–740 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Shani, E. M. et al. Plant stress tolerance requires auxin-sensitive Aux/IAA transcriptional repressors. Curr. Biol. 27, 437–444 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hopkins, R. J., van Dam, N. M. & van Loon, J. J. A. Role of glucosinolates in insect–plant relationships and multitrophic interactions. Annu. Rev. Entomol. 54, 57–83 (2009).

    Article  CAS  PubMed  Google Scholar 

  51. Burow, M., Müller, R., Gershenzon, J. & Wittstock, U. Altered glucosinolate hydrolysis in genetically engineered Arabidopsis thaliana and its influence on the larval development of Spodoptera littoralis. J. Chem. Ecol. 32, 2333–2349 (2006).

    Article  CAS  PubMed  Google Scholar 

  52. Wagner, M. R. & Mitchell-Olds, T. Plasticity of plant defense and its evolutionary implications in wild populations of Boechera stricta. Evolution 72, 1034–1049 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Pagès, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: Efficient manipulation of biological strings. R package version 2.56.0 (2020).

  55. Wang et al. Correction to: Ancient polymorphisms contribute to genome-wide variation by long-term balancing selection and divergent sorting in Boechera stricta. Genome Biol. 20, 16 (2019).

    Google Scholar 

  56. Carley, L. et al. Data to accompany: Ecological factors influence balancing selection on leaf chemical profiles of a wildflower. Dryad Data https://doi.org/10.5061/dryad.7h44j0zsr (2021).

  57. Atkinson, N. J., Lilley, C. J. & Urwin, P. E. Identification of genes involved in the response of Arabidopsis to simultaneous biotic and abiotic stresses. Plant Physiol. 162, 2028–2041 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Sharma, A. et al. Comprehensive analysis of plant rapid alkalization factor (RALF) genes. Plant Physiol. Biochem. 106, 82–90 (2016).

    Article  CAS  PubMed  Google Scholar 

  59. Dutilleul, C., Jourdain, A., Bourguignon, J. & Hugouvieux, V. The Arabidopsis putative selenium-binding protein family: expression study and characterization of SBP1 as a potential new player in cadmium detoxification processes. Plant Physiol. 147, 239–251 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Jiang, S.-C. et al. Crucial roles of the pentatricopeptide repeat protein SOAR1 in Arabidopsis response to drought, salt and cold stresses. Plant Mol. Biol. 88, 369–385 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Wen, J., Vanek-Krebitz, M., Hoffmann-Sommergruber, K., Scheiner, O. & Breitender, H. The potential of Betv1 homologues, a nuclear multigene family, as phylogenetic markers in flowering plants. Mol. Phylogenet. Evol. 8, 317–333 (1997).

    Article  CAS  PubMed  Google Scholar 

  62. Koo, A. J., Fulda, M., Browse, J. & Ohlrogge, J. B. Identification of a plastid acyl‐acyl carrier protein synthetase in Arabidopsis and its role in the activation and elongation of exogenous fatty acids. Plant J. 44, 620–632 (2005).

    Article  CAS  PubMed  Google Scholar 

  63. Henrissat, B. et al. Conserved catalytic machinery and the prediction of a common fold for several families of glycosyl hydrolases. Proc. Natl Acad. Sci. USA 92, 7090–7094 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Duke University greenhouse staff, E. Hornstein, S. Mahanes, L. Schumm, R. Bingham, N. Niezgoda, C. Ried, A. Simha, W. de Vries, E. Cousins and K. Stinson for assistance with fieldwork in Colorado; E. Raskin, C. Strock, B. Guyton, W. Mitchell, J. Lessing, M. Olszack, A. Zemenick, M. McMunn, K. Stiff, S. Clemens, T. Park, S. Shriber-Olds and R. Colautti for assistance with fieldwork in Idaho; and J. Reithel, S. Sprott and C. Heald for logistical support that facilitated fieldwork. We thank the Rocky Mountain Biological Laboratory, the Crested Butte Land Trust, the US Forest Service, D. Finlayson, R. Capps, A. Mears and P. Lehr for permission to conduct field experiments. We thank V. Grant and A. Zhao for assistance with laboratory experiments, E. Iversen at the Duke University Statistical Consulting Center for guidance regarding some analyses, and K. Donohue, M. Rausher and W. Morris for feedback that greatly improved this manuscript. We thank the Computer and Information Networking Center at National Taiwan University for high-performance computing facilities and the Technology Commons in the College of Life Science at National Taiwan University for molecular biology equipment. This work was supported by the Ministry of Science and Technology of Taiwan (grant no. 108-2636-B-002-004 to C.-R.L.), the Guangdong Natural Science Funds for Distinguished Young Scholar (grant no. 2018B030306040 to B.W.), the National Institutes of Health (grant no. R01 GM086496 to T.M.-O.) and the Rocky Mountain Biological Laboratory (Snyder Endowment graduate fellowship to L.N.C.).

Author information

Authors and Affiliations

  1. Duke University Program in Ecology, Durham, NC, USA

    Lauren N. Carley & Chase L. Nuñez

  2. Biology Department, Duke University, Durham, NC, USA

    Lauren N. Carley, Julius P. Mojica, Baosheng Wang, Emily Chan, Rose Keith, Carrie F. Olson-Manning, Catherine A. Rushworth, Maggie R. Wagner, Jing Wang, Kathryn Ghattas & Thomas Mitchell-Olds

  3. Rocky Mountain Biological Laboratory, Gothic, CO, USA

    Lauren N. Carley & Thomas Mitchell-Olds

  4. Department of Plant and Microbial Biology, University of Minnesota Twin Cities, St Paul, MN, USA

    Lauren N. Carley & Catherine A. Rushworth

  5. Pairwise Plants, Durham, NC, USA

    Julius P. Mojica

  6. Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China

    Baosheng Wang & Jing Wang

  7. Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei, Taiwan

    Chia-Yu Chen, Ya-Ping Lin, Che-Wei Hsu, Pei-Min Yeh & Cheng-Ruei Lee

  8. Experimental and Clinical Research Center (ECRC) of the MDC and Charité Berlin, Berlin, Germany

    Chia-Yu Chen

  9. World Vegetable Center Headquarters, Tainan, Taiwan

    Ya-Ping Lin

  10. Department of Biology and Cell and Molecular Biology Program, Colorado State University, Fort Collins, CO, USA

    Kasavajhala V. S. K. Prasad

  11. Department of Biology, Humboldt Universität zu Berlin, Berlin, Germany

    Che-Wei Hsu

  12. The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany

    Che-Wei Hsu

  13. Biology Department, DePauw University, Greencastle, IN, USA

    Rose Keith

  14. Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Baden-Württemberg, Germany

    Chase L. Nuñez

  15. Augustana University, Sioux Falls, SD, USA

    Carrie F. Olson-Manning

  16. Evolution and Ecology Department, University of California Davis, Davis, CA, USA

    Catherine A. Rushworth

  17. University and Jepson Herbaria, University of California Berkeley, Berkeley, CA, USA

    Catherine A. Rushworth

  18. Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, USA

    Maggie R. Wagner

  19. Kansas Biological Survey, Lawrence, KS, USA

    Maggie R. Wagner

  20. Department of Biochemistry, Max Planck Institute for Chemical Ecology, Jena, Germany

    Michael Reichelt & Jonathan Gershenzon

  21. Institute of Plant Biology, National Taiwan University, Taipei, Taiwan

    Cheng-Ruei Lee

  22. Genome and Systems Biology Degree Program, National Taiwan University, Taipei, Taiwan

    Cheng-Ruei Lee

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  1. Lauren N. Carley
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Contributions

L.N.C., J.P.M., C.-R.L., J.W., C.L.N. and T.M.-O. designed the project. L.N.C., J.P.M., C.-Y.C., K.V.S.K.P., E.C., R.K., C.L.N., C.F.O.-M., C.A.R., M.R.W., J.W., P.-M.Y., K.G., C.-R.L. and T.M.-O. collected the data. L.N.C., J.P.M., B.W., C.-Y.C., Y.-P.L., M.R., J.G., C.-W.H., C.-R.L. and T.M.-O. analysed the data. L.N.C., C.-R.L. and T.M.-O. wrote the paper. All authors read and approved the paper.

Corresponding authors

Correspondence to Cheng-Ruei Lee or Thomas Mitchell-Olds.

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Peer review information Nature Ecology & Evolution thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Genotype frequency does not alter the effect of BCMA1/3 on herbivore resistance or survival.

In experimental arrays in which we manipulated the starting genotype frequency of the BCMA1/3 homozygotes, there was no effect of genotype frequency on herbivore damage or survival. In each panel, points represent least-squares means estimates of the response variable for each genotype in each BCMA1/3 frequency treatment, and error bars represent ± one standard error.

Extended Data Fig. 2 Genetic variation in norms of reaction to drought stress.

BCMA1/3 alleles in the CFR-NIL background confer contrasting response to drought by altering morphological traits such as leaf size and number as well as physiological traits such as growth. Both genotypes reduce leaf water content under drought, but genetic differences in this response were only marginally significant. In each panel, points represent least-squares means estimates of the response variable for each genotype in each BCMA1/3 frequency treatment, and error bars represent ± one standard error.

Extended Data Fig. 3 Drought-related climate variables are correlated with multivariate climatic predictors of BC-ratio.

a BC-ratio varies across climate space, with PC1 the strongest predictor (Supplementary Table 9). b Linear models and permutation tests reveal that low BC-ratio phenotypes are significantly correlated with drier environments of origin. Points in all panels represent phenotypic (LS mean BC-ratio) and environmental variation (WorldClim data from the location of origin) of a broad panel of accessions. Colors represent BC-ratio, ranging from 0% (blue) to 100% (red). Shapes denote genetic groups as described in Wang et al. (2019). COL: circles; NOR: triangles; UTA: squares; WES: crosses. Black lines represent lines of best fit estimated using linear models using discrete groups to control for population structure (‘approach A’) as described in Supplementary Methods.

Extended Data Fig. 4 Permuted vs. observed F-statistics relating BC-ratio to climate variables.

Panes correspond to linear models presented in Supplementary Table 11. In each pane, gray bars show the frequency distribution of the test statistic relating each climate variable to BC-ratio from 10,000 permutations shuffling BC-ratio values without replacement (Supplementary Methods), red arrows show the observed F-statistic from each true model (Supplementary Table 11), and dashed lines mark the location of the extreme 95% tail in the empirical cumulative distribution function of permuted F-statistics, using three different methods to control for population structure (columns A-C; Supplementary Methods).

Extended Data Fig. 5 Functional and copy number variation in BCMA evolved recently within B. stricta.

Maximum likelihood phylogenetic reconstruction of BCMA copy sequences (excluding severely truncated copies) elucidates the evolutionary history of BCMA duplications in Boechera. Colored boxes behind (pseudo)gene names categorize features as follows: blue boxes contain nonfunctional BCMA pseudogenes on chromosome 7, light yellow boxes contain functional copies of BCMA2 on chromosome 2, and dark yellow boxes contain functional copies of BCMA3 and BCMA1 on chromosome 7. Shaded boxes indicate paralogs in B. retrofracta, and green box indicates the A. thaliana ortholog (CYP79F1). Scale bar shows genetic distance in nucleotide differences per base pair.

Extended Data Fig. 6 BCMA1/3 CFR-NILs: Chromosome 7 Pedigree.

Chromosomal pedigree showing how closest flanking recombinant near-isogenic lines (CFR-NILs) were generated for use in laboratory and field experiments. See Methods and Supplementary Information for details. Within each step, diploid homologous pairs of Chromosome 7 are shown.

Extended Data Fig. 7 Long-read assemblies of the LTM and SAD12 parents reveal substantial variation in tandem repeats and BCMA copy number in a 200 kb region on chromosome 7.

Functional BCMA gene copies are indicated in yellow; red circles show severely truncated, non-functional BCMA copies; blue ellipses indicate close-to-full-length copies of BCMA containing frameshift deletions or transposon insertions. Blue and yellow elements match those shown in Extended Data Figures 5 and Supplementary Figure 2.

Supplementary information

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Supplementary Tables 1–18, Figs. 1–3, methods and results.

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Carley, L.N., Mojica, J.P., Wang, B. et al. Ecological factors influence balancing selection on leaf chemical profiles of a wildflower. Nat Ecol Evol 5, 1135–1144 (2021). https://doi.org/10.1038/s41559-021-01486-0

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