Pervasive population genomic consequences of genome duplication in Arabidopsis arenosa

Abstract

Ploidy-variable species allow direct inference of the effects of chromosome copy number on fundamental evolutionary processes. While an abundance of theoretical work suggests polyploidy should leave distinct population genomic signatures, empirical data remains sparse. We sequenced ~300 individuals from 39 populations of Arabidopsis arenosa, a naturally diploid-autotetraploid species. We find that the impacts of polyploidy on population genomic processes are subtle yet pervasive, such as reduced efficiency of purifying selection, differences in linked selection and rampant gene flow from diploids. Initial masking of deleterious mutations, faster rates of nucleotide substitution and interploidy introgression likely conspire to shape the evolutionary potential of polyploids.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Code availability

Custom scripts used to generate genome scan metrics are available at https://github.com/pmonnahan/ScanTools. Other analysis scripts are available at https://github.com/pmonnahan/ArenosaPloidy.

Data availability

Sequence data that support the findings of this study have been deposited in the Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra) with the primary accession code PRJNA484107 (available at http://www.ncbi.nlm.nih.gov/bioproject/484107) and PRJNA472485 for RNAseq data.

References

  1. 1.

    Wood, T. E. et al. The frequency of polyploid speciation in vascular plants. Proc. Natl Acad. Sci. USA 106, 13875–13879 (2009).

    CAS  Google Scholar 

  2. 2.

    Van de Peer, Y., Mizrachi, E. & Marchal, K. The evolutionary significance of polyploidy. Nat. Rev. Genet. 18, 411 (2017).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Salman-Minkov, A., Sabath, N. & Mayrose, I. Whole-genome duplication as a key factor in crop domestication. Nat. Plants 2, 16115 (2016).

    CAS  PubMed  Google Scholar 

  4. 4.

    Storchova, Z. & Pellman, D. From polyploidy to aneuploidy, genome instability and cancer. Nat. Rev. Mol. Cell Biol. 5, 45–54 (2004).

    CAS  PubMed  Google Scholar 

  5. 5.

    Yant, L. & Bomblies, K. Genome management and mismanagement—cell-level opportunities and challenges of whole-genome duplication. Genes Dev. 29, 2405–2419 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Levin D. A. The Role of Chromosomal Change in Plant Evolution (Oxford Univ. Press, Oxford, 2002).

  7. 7.

    Parisod, C., Holderegger, R. & Brochmann, C. Evolutionary consequences of autopolyploidy. New Phytol. 186, 5–17 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    te Beest, M. et al. The more the better? The role of polyploidy in facilitating plant invasions. Ann. Bot. 109, 19–45 (2011).

    Google Scholar 

  9. 9.

    Segraves, K. A. The effects of genome duplications in a community context. New Phytol. 215, 57–69 (2017).

    CAS  PubMed  Google Scholar 

  10. 10.

    Haldane J. B. S. The Causes of Evolution (Princeton Univ. Press, Princeton, 1932).

  11. 11.

    Wright, S. The distribution of gene frequencies in populations of polyploids. Proc. Natl Acad. Sci. USA 24, 372–377 (1938).

    CAS  PubMed  Google Scholar 

  12. 12.

    Fisher, R. The theoretical consequences of polyploid inheritance for the mid style form of Lythrum salicaria. Ann. Hum. Genet. 11, 31–38 (1941).

    Google Scholar 

  13. 13.

    Stebbins G. L. Chromosomal Evolution in Higher Plants (Edward Arnold, London, 1971).

  14. 14.

    Haldane, J. B. Theoretical genetics of autopolyploids. J. Genet. 22, 359–372 (1930).

    Google Scholar 

  15. 15.

    Bever, J. D. & Felber, F. The theoretical population genetics of autopolyploidy. Oxford Surv. Evol. Biol. 8, 185 (1992).

    Google Scholar 

  16. 16.

    Otto, S. P. & Whitton, J. Polyploid incidence and evolution. Annu. Rev. Genet. 34, 401–437 (2000).

    CAS  Google Scholar 

  17. 17.

    Ronfort, J., Jenczewski, E., Bataillon, T. & Rousset, F. Analysis of population structure in autotetraploid species. Genetics 150, 921–930 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Grant, V. Plant Speciation 2nd edn (Columbia Univ. Press, New York, 1981).

  19. 19.

    Coyne, J. A. & Orr, H. A. Speciation (Sinauer, Sunderland, MA, 2004).

  20. 20.

    Mallet, J. Hybrid speciation. Nature 446, 279 (2007).

    CAS  Google Scholar 

  21. 21.

    Slotte, T., Huang, H., Lascoux, M. & Ceplitis, A. Polyploid speciation did not confer instant reproductive isolation in Capsella (Brassicaceae). Mol. Biol. Evol. 25, 1472–1481 (2008).

    CAS  PubMed  Google Scholar 

  22. 22.

    Zohren, J. et al. Unidirectional diploid–tetraploid introgression among British birch trees with shifting ranges shown by restriction site‐associated markers. Mol. Ecol. 25, 2413–2426 (2016).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Lafon-Placette, C. et al. Endosperm-based hybridization barriers explain the pattern of gene flow between Arabidopsis lyrata and Arabidopsis arenosa in Central Europe. Proc. Natl Acad. Sci. USA 114, e1027–e1035 (2017).

    CAS  PubMed  Google Scholar 

  24. 24.

    Ronfort, J. The mutation load under tetrasomic inheritance and its consequences for the evolution of the selfing rate in autotetraploid species. Genet. Res. 74, 31–42 (1999).

    Google Scholar 

  25. 25.

    Hill, R. Selection in autotetraploids. Theoret. Appl. Genet. 41, 181–186 (1971).

    Google Scholar 

  26. 26.

    Selmecki, A. M. et al. Polyploidy can drive rapid adaptation in yeast. Nature 519, 349–352 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Schmickl, R., Marburger, S., Bray, S. & Yant, L. Hybrids and horizontal transfer: introgression allows adaptive allele discovery. J. Exp. Bot. 68, 5453–5470 (2017).

    CAS  PubMed  Google Scholar 

  28. 28.

    Arnold, M. L. & Kunte, K. Adaptive genetic exchange: a tangled history of admixture and evolutionary innovation. Trends Ecol. Evol. 32, 601–611 (2017).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Bomblies, K. & Madlung, A. Polyploidy in the Arabidopsis genus. Chromosome Res. 22, 117–134 (2014).

    CAS  PubMed  Google Scholar 

  30. 30.

    Yant, L. & Bomblies, K. Genomic studies of adaptive evolution in outcrossing Arabidopsis species. Curr. Opin. Plant. Biol. 36, 9–14 (2017).

    CAS  PubMed  Google Scholar 

  31. 31.

    Arnold, B., Kim, S.-T. & Bomblies, K. Single geographic origin of a widespread autotetraploid Arabidopsis arenosa lineage followed by interploidy admixture. Mol. Biol. Evol. 32, 1382–1395 (2015).

    CAS  PubMed  Google Scholar 

  32. 32.

    Hollister, J. D. et al. Genetic adaptation associated with genome-doubling in autotetraploid Arabidopsis arenosa. PLoS Genet. 8, e1003093 (2012).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Kolář, F. et al. Ecological segregation does not drive the intricate parapatric distribution of diploid and tetraploid cytotypes of the Arabidopsis arenosa group (Brassicaceae). Biol. J. Linnean Soc. 119, 673–688 (2016).

    Google Scholar 

  34. 34.

    Kolář, F. et al. Northern glacial refugia and altitudinal niche divergence shape genome‐wide differentiation in the emerging plant model Arabidopsis arenosa. Mol. Ecol. 25, 3929–3949 (2016).

    PubMed  Google Scholar 

  35. 35.

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

  36. 36.

    Ingvarsson, P. K. Gene expression and protein length influence codon usage and rates of sequence evolution in Populus tremula. Mol. Biol. Evol. 24, 836–844 (2007).

    CAS  PubMed  Google Scholar 

  37. 37.

    Wright, S. I., Yau, C. K., Looseley, M. & Meyers, B. C. Effects of gene expression on molecular evolution in Arabidopsis thaliana and Arabidopsis lyrata. Mol. Biol. Evol. 21, 1719–1726 (2004).

    CAS  PubMed  Google Scholar 

  38. 38.

    Popescu, C. E., Borza, T., Bielawski, J. P. & Lee, R. W. Evolutionary rates and expression level in Chlamydomonas. Genetics 172, 1567–1576 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Keightley, P. D. & Eyre-Walker, A. Joint inference of the distribution of fitness effects of deleterious mutations and population demography based on nucleotide polymorphism frequencies. Genetics 177, 2251–2261 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Eyre-Walker, A. & Keightley, P. D. Estimating the rate of adaptive molecular evolution in the presence of slightly deleterious mutations and population size change. Mol. Biol. Evol. 26, 2097–2108 (2009).

    CAS  PubMed  Google Scholar 

  41. 41.

    Rousselle, M., Mollion, M., Nabholz, B., Bataillon, T. & Galtier, N. Overestimation of the adaptive substitution rate in fluctuating populations. Biol. Lett. 14, 5 (2018).

    Google Scholar 

  42. 42.

    Venkat, A., Hahn, M. W. & Thornton, J. W. Multinucleotide mutations cause false inferences of lineage-specific positive selection. Nat. Ecol. Evol. 2, 1280–1288 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Yant, L. et al. Meiotic adaptation to genome duplication in Arabidopsis arenosa. Curr. Biol. 23, 2151–2156 (2013).

    CAS  PubMed  Google Scholar 

  44. 44.

    Baduel, P., Arnold, B., Weisman, C. M., Hunter, B. & Bomblies, K. Habitat-associated life history and stress-tolerance variation in Arabidopsis arenosa. Plant Physiol. 171, 437–451 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Schmickl, R. & Koch, M. A. Arabidopsis hybrid speciation processes. Proc. Natl Acad. Sci. USA 108, 14192–14197 (2011).

    PubMed  Google Scholar 

  46. 46.

    Gerstein, A. C. & Otto, S. P. Ploidy and the causes of genomic evolution. J. Hered. 100, 571–581 (2009).

    CAS  PubMed  Google Scholar 

  47. 47.

    Favarger, C. in Plant Biosystematics (ed. Grant, W. F.) 453–476 (Elsevier, 1984).

  48. 48.

    Brochmann, C. et al. Polyploidy in arctic plants. Biol. J. Linnean Soc. 82, 521–536 (2004).

    Google Scholar 

  49. 49.

    Butruille, D. V. & Boiteux, L. S. Selection–mutation balance in polysomic tetraploids: impact of double reduction and gametophytic selection on the frequency and subchromosomal localization of deleterious mutations. Proc. Natl Acad. Sci. USA 97, 6608–6613 (2000).

    CAS  PubMed  Google Scholar 

  50. 50.

    Willis, J. H. Inbreeding load, average dominance and the mutation rate for mildly deleterious alleles in Mimulus guttatus. Genetics 153, 1885 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Schmickl, R., Marburger, S., Bray, S. & Yant, L. Hybrids and horizontal transfer: introgression allows adaptive allele discovery. J. Exp. Bot. 68, 5453–5470 (2017).

    CAS  PubMed  Google Scholar 

  52. 52.

    Lowe, W. H., Muhlfeld, C. C. & Allendorf, F. W. Spatial sorting promotes the spread of maladaptive hybridization. Trends Ecol. Evol. 30, 456–462 (2015).

    PubMed  Google Scholar 

  53. 53.

    Yukilevich, R. Asymmetrical patterns of speciation uniquely support reinforcement in Drosophila. Evolution 66, 1430–1446 (2012).

    PubMed  Google Scholar 

  54. 54.

    Hylander, N. Cardaminopsis suecica (Fr.) Hiit., a northern amphidiploid species. Bulletin du Jardin botanique de l’Etat 27, 591–604 (1957).

    Google Scholar 

  55. 55.

    Baduel, P., Hunter, B., Yeola, S. & Bomblies, K. Genetic basis and evolution of rapid cycling in railway populations of tetraploid Arabidopsis arenosa. PLoS Genet. 14, e1007510 (2018).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Husband, B. C. & Sabara, H. A. Reproductive isolation between autotetraploids and their diploid progenitors in fireweed, Chamerion angustifolium (Onagraceae). New Phytol. 161, 703–713 (2004).

    Google Scholar 

  57. 57.

    Kolář, F., Čertner, M., Suda, J., Schönswetter, P. & Husband, B. C. Mixed-ploidy species: progress and opportunities in polyploid research. Trends Plant Sci. 22, 1041–1055 (2017).

    PubMed  Google Scholar 

  58. 58.

    Soltis, D. E. & Soltis, P. S. Polyploidy: recurrent formation and genome evolution. Trends Ecol. Evol. 14, 348–352 (1999).

    CAS  PubMed  Google Scholar 

  59. 59.

    Arnold, B. J. et al. Borrowed alleles and convergence in serpentine adaptation. Proc. Natl Acad. Sci. USA 113, 8320–8325 (2016).

    CAS  PubMed  Google Scholar 

  60. 60.

    Doyle, J. J. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull. 19, 11–15 (1987).

    Google Scholar 

  61. 61.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  62. 62.

    Hu, T. T. et al. The Arabidopsis lyrata genome sequence and the basis of rapid genome size change. Nat. Genet. 43, 476–481 (2011).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Wright, S. I., Lauga, B. & Charlesworth, D. Rates and patterns of molecular evolution in inbred and outbred Arabidopsis. Mol. Biol. Evol. 19, 1407–1420 (2002).

    CAS  PubMed  Google Scholar 

  67. 67.

    Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).

    CAS  PubMed  Google Scholar 

  68. 68.

    Nei, M. Genetic distance between populations. Am. Nat. 106, 283–292 (1972).

    Google Scholar 

  69. 69.

    Pembleton, L. W., Cogan, N. O. & Forster, J. W. StAMPP: an R package for calculation of genetic differentiation and structure of mixed‐ploidy level populations. Mol. Ecol. Res. 13, 946–952 (2013).

    CAS  Google Scholar 

  70. 70.

    Huson, D. H. SplitsTree: analyzing and visualizing evolutionary data. Bioinformatics 14, 68–73 (1998).

    CAS  PubMed  Google Scholar 

  71. 71.

    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Raj, A., Stephens, M. & Pritchard, J. K. fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics 197, 573–589 (2014).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Nordborg, M. et al. The pattern of polymorphism in Arabidopsis thaliana. PLoS Biol. 3, e196 (2005).

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Novikova, P. Y. et al. Sequencing of the genus Arabidopsis identifies a complex history of nonbifurcating speciation and abundant trans-specific polymorphism. Nat. Genet. 48, 1077–1082 (2016).

    CAS  PubMed  Google Scholar 

  77. 77.

    Paradis, E. pegas: an R package for population genetics with an integrated–modular approach. Bioinformatics 26, 419–420 (2010).

    CAS  PubMed  Google Scholar 

  78. 78.

    Dray, S. & Dufour, A.-B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).

    Google Scholar 

  79. 79.

    Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Nadachowska‐Brzyska, K., Burri, R., Smeds, L. & Ellegren, H. PSMC analysis of effective population sizes in molecular ecology and its application to black‐and‐white Ficedula flycatchers. Mol. Ecol. 25, 1058–1072 (2016).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Zeng, K., Fu, Y.-X., Shi, S. & Wu, C.-I. Statistical tests for detecting positive selection by utilizing high-frequency variants. Genetics 174, 1431–1439 (1996).

    Google Scholar 

  82. 82.

    Weir, B. S. & Cockerham, C. C. Estimating F‐statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).

    CAS  PubMed  Google Scholar 

  83. 83.

    Cruickshank, T. E. & Hahn, M. W. Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow. Mol. Ecol. 23, 3133–3157 (2014).

    PubMed  Google Scholar 

  84. 84.

    Hardy, O. J. & Vekemans, X. SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Res. 2, 618–620 (2002).

    Google Scholar 

  85. 85.

    Martin, S. H. & Van Belleghem, S. M. Exploring evolutionary relationships across the genome using topology weighting.Genetics 206, 429–438 (2017).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Duret, L. & Mouchiroud, D. Determinants of substitution rates in mammalian genes: expression pattern affects selection intensity but not mutation rate. Mol. Biol. Evol. 17, 68–070 (2000).

    CAS  PubMed  Google Scholar 

  87. 87.

    Rocha, E. P. & Danchin, A. An analysis of determinants of amino acids substitution rates in bacterial proteins. Mol. Biol. Evol. 21, 108–116 (2004).

    CAS  PubMed  Google Scholar 

  88. 88.

    Slotte, T. et al. Genomic determinants of protein evolution and polymorphism in Arabidopsis. Genome Biol. Evol. 3, 1210–1219 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome. Biol. 14, R36 (2013).

    PubMed  PubMed Central  Google Scholar 

  90. 90.

    Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936–939 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  PubMed  Google Scholar 

  92. 92.

    Love, M., Anders, S. & Huber, W. Differential analysis of count data–the DESeq2 package. Genome. Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  93. 93.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  94. 94.

    Gossmann, T. I. et al. Genome wide analyses reveal little evidence for adaptive evolution in many plant species. Mol. Biol. Evol. 27, 1822–1832 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Martin, S. H. et al. Natural selection and genetic diversity in the butterfly Heliconius melpomene. Genetics 203, 525–541 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Bates, D., Martin, M., Ben, B. & Walker S. lme4: linear mixed effects models using Eigen and S4 (R package v.1.0–6, 2014); http://CRAN.R-project.org/package=lme4

  97. 97.

    Kelleher, J., Etheridge, A. M. & McVean, G. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS Comput. Biol. 12, e1004842 (2016).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank E. Záveská, M. Lučanová and S. Španiel for help with fieldwork and J. Brookfield and S. Martin for helpful comments on versions of the manuscript. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme Projects of Large Research, Development, and Innovations Infrastructures, and by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project SNIC 2017/7–174. L.Y. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 679056) and the UK Biological and Biotechnology Research Council (BBSRC) via grant BB/P013511/1 to the John Innes Centre. K.B. acknowledges European Research Council Consolidator grant CoG EVO-MEIO 681946 and US National Science Foundation IOS-1146465. Additional support was provided by Czech Science Foundation (project 16–10809S to K.M. and 17–20357Y to F.K.), Charles University (project Primus/SCI/35 to F.K.), and a SNSF Early Postdoc Mobility fellowship (P2ZHP3_158773 to C.S.).

Author information

Affiliations

Authors

Contributions

L.Y., K.B., F.K., P.B. and P.M. conceived the study. P.M., F.K., P.B., B.L., C.S., J.K., R.H., R.S. and P.P. performed analyses with input from L.Y., K.B., R.H. and T.S. C.S., P.B., G.F., M.B. and C.M.W. performed laboratory experiments. P.M., F.K. and P.B. wrote the manuscript with primary input from K.B., L.Y., B.A., C.S. and T.S. All authors edited and approved of the final manuscript.

Corresponding author

Correspondence to Levi Yant.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1–24, Supplementary Tables 1–14 and Supplementary Note

Reporting Summary

Supplementary Data 1

Measures of genome-wide diversity within the 36 populations of A. arenosa with ≥5 individuals sequenced and details on inclusion of the populations in the downstream analyses.

Supplementary Data 2

Steps used for processing, mapping, and variant calling.

Supplementary Data 3

Sequence processing quality assessment of each sequenced individual.

Supplementary Data 4

Unfolded allele frequency spectra of the 36 A. arenosa populations with ≥5 individuals.

Supplementary Data 5

Fasta of 291 plastome sequences.

Supplementary Data 6

Maximum likelihood phylogeny of Arabidopsis plastomes from our study and of Novikova et al. (2016).

Supplementary Data 7

Example parameter files used in fastsimcoal2.

Supplementary Data 8

Data used to generate File_S4_AFS.pdf.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Monnahan, P., Kolář, F., Baduel, P. et al. Pervasive population genomic consequences of genome duplication in Arabidopsis arenosa. Nat Ecol Evol 3, 457–468 (2019). https://doi.org/10.1038/s41559-019-0807-4

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing