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Genome-wide analyses of introgression between two sympatric Asian oak species

Nature Ecology & Evolution volume 6, pages 924–935 (2022)Cite this article

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Abstract

Introgression can be an important source of new alleles for adaption under rapidly changing environments, perhaps even more important than standing variation. Though introgression has been extensively studied in many plants and animals, key questions on the underlying mechanisms of introgression still remain unanswered. In particular, we are yet to determine the genomic distribution of introgressed regions along the genome; whether the extent and patterns of introgression are influenced by ecological factors; and when and how introgression contributes to adaptation. Here, we generated high-quality genomic resources for two sympatric widespread Asian oak species, Quercus acutissima and Q. variabilis, sampled in multiple forests to study introgression between them. We show that introgressed regions are broadly distributed across the genome. Introgression was affected by genetic divergence between pairs of populations and by the similarity of the environments in which they live—populations occupying similar ecological sites tended to share the same introgressed regions. Introgressed genomic footprints of adaptation were preferentially located in regions with suppressed recombination rate. Introgression probably confers adaptation in these oak populations by introducing allelic variation in cis-regulatory elements, in particular through transposable element insertions, thereby altering the regulation of genes related to stress. Our results provide new avenues of research for uncovering mechanisms of adaptation due to hybridization in sympatric species.

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Fig. 1: The distribution ranges, phylogenetic and admixture analyses and demographic inference.
Fig. 2: The distributions of genome-wide introgression for two sympatric oak species and its correlation with geographic and environmental distances.
Fig. 3: The distribution of adaptive introgressed SNPs across the genome and its correlation with population recombination rate.
Fig. 4: The numbers of adaptive introgressed SNPs and genes harbouring them associated with different environmental factors and their functions in GO terms.

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Data availability

All sequencing data used in this study have been deposited in NCBI SRA database with the Bioproject number PRJNA763710 for the reference genome, PRJNA765790 for population resequencing libraries and PRJNA762601 for the RNA-seq libraries. The reference genome and gene annotations have also been deposited in the Genome Warehouse in National Genomics Data Center (https://ngdc.cncb.ac.cn/gwh) under the accession number GWHBGBO00000000. Figures 1–4 have associated source data at https://github.com/cjevol/protocols-for-introgression-in-oaks. There are no restrictions on data availability.

Code availability

All codes used for main analyses in this paper are available for download from https://github.com/cjevol/protocols-for-introgression-in-oaks and zenodo (https://doi.org/10.5281/zenodo.5726659).

References

  1. Suarez-Gonzalez, A. et al. Introgression from Populus balsamifera underlies adaptively significant variation and range boundaries in P. trichocarpa. New Phytol. 217, 416–427 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Mallet, J. Hybridization as an invasion of the genome. Trends Ecol. Evol. 20, 229–237 (2005).

    Article  PubMed  Google Scholar 

  3. Harrison, R. G. & Larson, E. L. Hybridization, introgression, and the nature of species boundaries. J. Hered. 105, 795–809 (2014).

    Article  PubMed  Google Scholar 

  4. Anderson, E. Introgressive Hybridization (John Wiley, 1949).

  5. Hedrick, P. W. Adaptive introgression in animals: examples and comparison to new mutation and standing variation as sources of adaptive variation. Mol. Ecol. 22, 4606–4618 (2013).

    Article  PubMed  Google Scholar 

  6. Edelman, N. B. et al. Genomic architecture and introgression shape a butterfly radiation. Science 366, 594–599 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Dasmahapatra, K. K. et al. Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature 487, 94–98 (2012).

    Article  CAS  PubMed Central  Google Scholar 

  8. Soltis, P. S. in Encyclopedia of Biodiversity 2nd edn (ed. Levin, S. A.)166–176 (Academic Press, 2013).

  9. Mallet, J. Hybrid speciation. Nature 446, 279–283 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Grant, V. Plant Speciation (Columbia Univ. Press, 1981).

  11. Burgarella, C. et al. Adaptive introgression: an untapped evolutionary mechanism for crop adaptation. Front. Plant Sci. 10, 4 (2019).

  12. Janzen, G. M., Wang, L. & Hufford, M. B. The extent of adaptive wild introgression in crops. New Phytol. 221, 1279–1288 (2019).

    Article  PubMed  Google Scholar 

  13. Calfee, E. et al. Selective sorting of ancestral introgression in maize and teosinte along an elevational cline. PLoS Genet. 17, e1009810 (2021).

  14. Janke, A. Divergence with Genetic Exchange.—M. L. Arnold. Syst. Biol. 65, 941–942 (2016).

    Article  Google Scholar 

  15. Edelman, N. B. & Mallet, J. Prevalence and adaptive impact of introgression. Annu. Rev. Genet. 55, 265–283 (2021).

    Article  PubMed  Google Scholar 

  16. Yeaman, S. & Whitlock, M. C. The genetic architecture of adaptation under migration–selection balance. Evolution 65, 1897–1911 (2011).

    Article  PubMed  Google Scholar 

  17. Hamala, T. & Savolainen, O. Genomic patterns of local adaptation under gene flow in Arabidopsis lyrata. Mol. Biol. Evol. 36, 2557–2571 (2019).

    Article  Google Scholar 

  18. Leroy, T. et al. Adaptive introgression as a driver of local adaptation to climate in European white oaks. New Phytol. 226, 1171–1182 (2020).

    Article  PubMed  Google Scholar 

  19. Gower, G. et al. Detecting adaptive introgression in human evolution using convolutional neural networks. eLife 10, e64669 (2021).

  20. Jones, M. R. et al. The origin and spread of locally adaptive seasonal camouflage in snowshoe hares. Am. Nat. 196, 316–332 (2020).

    Article  PubMed  Google Scholar 

  21. Kim, M. S. et al. The patterns of deleterious mutations during the domestication of soybean. Nat. Commun. 12, 97 (2021).

  22. Weiss, C. V. et al. The cis-regulatory effects of modern human-specific variants. eLife 10, e63713 (2021).

  23. Kim, M. et al. Regulatory genes control a key morphological and ecological trait transferred between species. Science 322, 1116–1119 (2008).

    Article  CAS  PubMed  Google Scholar 

  24. Plomion, C. et al. Oak genome reveals facets of long lifespan. Nat. Plants 4, 440–452 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kremer, A. & Hipp, A. L. Oaks: an evolutionary success story. New Phytol. 226, 987–1011 (2020).

    Article  PubMed  Google Scholar 

  26. Hipp, A. L. et al. Genomic identity of white oak species in an Eastern North American syngameon. Ann. Missouri Bot. Gard. 104, 455–477 (2019).

    Article  Google Scholar 

  27. Li, X. et al. Hybridization and introgression in sympatric and allopatric populations of four oak species. BMC Plant Biol. 21, 266 (2021).

  28. Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chin, C. S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 10, 563–569 (2013).

    Article  CAS  PubMed  Google Scholar 

  30. Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).

  31. Adey, A. et al. In vitro, long-range sequence information for de novo genome assembly via transposase contiguity. Genome Res. 24, 2041–2049 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol. 31, 1119–1125 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067 (2007).

    Article  CAS  PubMed  Google Scholar 

  34. Currat, M. et al. The hidden side of invasions: massive introgression by local genes. Evolution 62, 1908–1920 (2008).

    PubMed  Google Scholar 

  35. Excofffier, L. et al. fastsimcoal2: demographic inference under complex evolutionary scenarios. Bioinformatics 37, 4882–4885 (2021).

    Article  Google Scholar 

  36. Martin, S. H. et al. Genome-wide evidence for speciation with gene flow in Heliconius butterflies. Genome Res. 23, 1817–1828 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Torres-Ruiz, J. M. et al. Genetic differentiation in functional traits among European sessile oak populations. Tree Physiol. 39, 1736–1749 (2019).

    Article  PubMed  Google Scholar 

  38. Godbout, J., Yeh, F. C. & Bousquet, J. Large-scale asymmetric introgression of cytoplasmic DNA reveals Holocene range displacement in a North American boreal pine complex. Ecol. Evol. 2, 1853–1866 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Sella, G. et al. Pervasive natural selection in the Drosophila genome?. PLoS Genetics 5, e1000495 (2009).

  40. Murphy, D. et al. Broad-scale variation in human genetic diversity levels is predicted by purifying selection on coding and non-coding elements. Preprint at bioRxiv https://doi.org/10.1101/2021.07.02.450762 (2021).

  41. Chen, J. et al. What does the distribution of fitness effects of new mutations reflect? Insights from plants. New Phytol. 233, 1613–1619 (2022).

    Article  PubMed  Google Scholar 

  42. Martin, S. H. et al. Recombination rate variation shapes barriers to introgression across butterfly genomes. PLoS Biol. https://doi.org/10.1371/journal.pbio.2006288 (2019).

  43. Dreissig, S. et al. Natural variation in meiotic recombination rate shapes introgression patterns in intraspecific hybrids between wild and domesticated barley. New Phytol. 228, 1852–1863 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Finnegan, D. J. Eukaryotic transposable elements and genome evolution. Trends Genet. 5, 103–107 (1989).

    Article  CAS  PubMed  Google Scholar 

  45. Mcdonald, J. F. Evolution and consequences of transposable elements. Curr. Opin. Genet. Dev. 3, 855–864 (1993).

    Article  CAS  PubMed  Google Scholar 

  46. Klein, S. J. & O’Neill, R. J. Transposable elements: genome innovation, chromosome diversity, and centromere conflict. Chromosome Res. 26, 5–23 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kirkpatrick, M. How and why chromosome inversions evolve. PLoS Biol. 8, e1000501 (2010).

  48. Hirsch, C. D. & Springer, N. M. Transposable element influences on gene expression in plants. Biochim. Biophys.Acta Gene Regul. Mech. 1860, 157–165 (2017).

    Article  CAS  PubMed  Google Scholar 

  49. Negi, P., Rai, A. N. & Suprasanna, P. Moving through the stressed genome: emerging regulatory roles for transposons in plant stress response. Front. Plant Sci. 7, 1448 (2016).

  50. Lu, Z. F. et al. The prevalence, evolution and chromatin signatures of plant regulatory elements. Nat. Plants 5, 1250–1259 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Noshay, J. M. et al. Cis-regulatory elements within TEs can influence expression of nearby maize genes. Preprint at bioRxiv https://www.biorxiv.org/content/https://doi.org/10.1101/2020.05.20.107169 (2020).

  52. Benoit, M. et al. Environmental and epigenetic regulation of Rider retrotransposons in tomato. PLoS Genet. 15, e1008370 (2019).

  53. Vickrey, A. I. et al. Introgression of regulatory alleles and a missense coding mutation drive plumage pattern diversity in the rock pigeon. eLife 7, e34803 (2018).

  54. Dannemann, M., Prufer, K. & Kelso, J. Functional implications of Neandertal introgression in modern humans. Genome Biol. 18, 61 (2017).

  55. Silvert, M., Quintana-Murci, L. & Rotival, M. Impact and evolutionary determinants of Neanderthal introgression on transcriptional and post-transcriptional regulation. Am. J. Hum. Genet. 104, 1241–1250 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. King, M.-C. & Wilson, A. C. Evolution at two levels in humans and chimpanzees. Science 188, 107–116 (1975).

    Article  CAS  PubMed  Google Scholar 

  57. Fraser, H. B. Gene expression drives local adaptation in humans. Genome Res. 23, 1089–1096 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kita, R. & Fraser, H. B. Local adaptation of sun-exposure-dependent gene expression regulation in human skin. PLoS Genet. 12, e1006382 (2016).

  59. Gould, B. A., Chen, Y. & Lowry, D. B. Gene regulatory divergence between locally adapted ecotypes in their native habitats. Mol. Ecol. 27, 4174–4188 (2018).

    Article  PubMed  Google Scholar 

  60. Qiu, Y. C. & Kohler, C. Mobility connects: transposable elements wire new transcriptional networks by transferring transcription factor binding motifs. Biochem. Soc. Trans. 48, 1005–1017 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Makarevitch, I. et al. Transposable elements contribute to activation of maize genes in response to abiotic stress. PLoS Genet. 11, e1004915 (2015).

  62. Hiroki, S. & Kamiya, T. Discrimination of hybrids between Quercus variabilis and Q. acutissima by using stellate hairs, and analysis of the hybridization zone in the Chubu District of central Japan. J. Phytogeogr. Taxon. 53, 145–152 (2005).

    Google Scholar 

  63. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://doi.org/10.48550/arXiv.1303.3997 (2013).

  64. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Linck, E. & Battey, C. J. Minor allele frequency thresholds strongly affect population structure inference with genomic data sets. Mol. Ecol. Resour. 19, 639–647 (2019).

    Article  CAS  PubMed  Google Scholar 

  67. Browning, B. L., Zhou, Y. & Browning, S. R. A one-penny imputed genome from next-generation reference panels. Am. J. Hum. Genet. 103, 338–348 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Quick, C. et al. emeraLD: rapid linkage disequilibrium estimation with massive datasets. Bioinformatics 35, 164–166 (2019).

    Article  CAS  PubMed  Google Scholar 

  69. Gao, F. et al. New software for the fast estimation of population recombination rates (FastEPRR) in the genomic era. Genes Genomes Genet. 6, 1563–1571 (2016).

    CAS  Google Scholar 

  70. Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, I333–I339 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Nguyen, L.-T. et al. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Diaz-Papkovich, A. et al. UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts. PLoS Genet. 15, e1008432 (2019).

  73. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Keightley, P. D. & Jackson, B. C. Inferring the probability of the derived vs. the ancestral allelic state at a polymorphic site. Genetics 209, 897–906 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Durand, E. Y. et al. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Martin, S. H., Davey, J. W. & Jiggins, C. D. Evaluating the use of ABBA-BABA statistics to locate introgressed loci. Mol. Biol. Evol. 32, 244–257 (2015).

    Article  CAS  PubMed  Google Scholar 

  78. Reich, D. et al. Reconstructing Indian population history. Nature 461, 489–494 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Zhang, W. et al. Genome-wide introgression among distantly related Heliconius butterfly species. Genome Biol. 17, 25 (2016).

  80. Malinsky, M., Matschiner, M. & Svardal, H. Dsuite—Fast D-statistics and related admixture evidence from VCF files. Mol. Ecol. Resour. 21, 584–595 (2021).

    Article  PubMed  Google Scholar 

  81. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

    Article  Google Scholar 

  82. Gautier, M. Genome-wide scan for adaptive divergence and association with population-specific covariates. Genetics 201, 1555–1579 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Olazcuaga, L. et al. A whole-genome scan for association with invasion success in the fruit fly Drosophila suzukii using contrasts of allele frequencies corrected for population structure. Mol. Biol. Evol. 37, 2369–2385 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Higo, K. et al. Plant cis-acting regulatory DNA elements (PLACE) database: 1999. Nucleic Acids Res. 27, 297–300 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Wu, T. D. & Watanabe, C. K. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875 (2005).

    Article  CAS  PubMed  Google Scholar 

  86. 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).

    Article  CAS  PubMed  Google Scholar 

  87. Mi, H. Y. et al. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–1566 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This project was supported by National Natural Science Foundation of China (31972946) and the Fundamental Research Funds for the Central Universities 2-2050205-21-688 granted to J.C.

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Authors and Affiliations

  1. College of Life Sciences, Zhejiang University, Hangzhou, China

    Ruirui Fu, Yuxiang Zhu, Ying Liu, Yu Feng, Rui-Sen Lu, Pan Li & Jun Chen

  2. CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China

    Yu Feng

  3. Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, China

    Rui-Sen Lu

  4. Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Biology and the Environment, Nanjing Forestry University, Nanjing, China

    Yao Li

  5. UMR BIOGECO, INRAE, Université de Bordeaux, Cestas, France

    Antoine Kremer

  6. Program in Plant Ecology and Evolution, Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden

    Martin Lascoux

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Contributions

J.C. conceived the research. All analyses were performed by R.F., with contributions to chromosome inversion analysis and gene expression analysis from Y.Z. and Y. Liu, respectively. Y.Z., Y.F., R.-S.L., Y. Li. and P.L. contributed plant material collection. R.F., J.C., A.K. and M.L. wrote the manuscript.

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Correspondence to Jun Chen.

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Extended data

Extended Data Fig. 1

Admixture plots with K values from 2 to 4.

Extended Data Fig. 2 The correlation between environmental distance and geographic distance.

Shaded areas represent 95% confidence intervals. Each dot represents one population pairs. Statistical significance was determined by Mantel test based on Pearson’s product-moment correlation with two-sided and multiple comparisons was performed. Adjusted R2 and significance of the correlation (p-value) are shown for plot.

Extended Data Fig. 3 FST distribution for allopatric (red) and sympatric (blue) Q. acutissima -Q. variabilis pairs examined in ABBA analyses.

Populations were labelled in ((P1, P2), P3) order. Significance shows introgression has lowered the genetic divergence between sympatric species. Statistical significance was determined by T-test with two-sided and multiple comparisons was performed (p-values: * ≤ 0.05, **≤ 0.01, ***≤ 0.001, **** ≤ 0.0001). Specifically, Adjusted P-values = 8.9E-249, 1.9E-134, 4.6E-249, 1.1E-128, 0, 0.58, 1.1E-4, 0, 4.1E-5, 0 from BWL_KYS_KY to LFS_TBS_TB, respectively. The box plots were based on n=72,866 sliding windows for all Q. acutissima -Q. variabilis pairs (from BWL_KYS_KY to LFS_TBS_TB). The central lines, box limits, whiskers and the top and bottom ends show the median values, upper and lower quartiles, 1.5× the interquartile ranges, and the maximum and minimum values, respectively.

Extended Data Fig. 4

The distribution of population recombination rate (rho = 4Ner) for all chromosomes.

Extended Data Fig. 5

The cross-validation errors against K values in Admixture analysis.

Extended Data Fig. 6 The comparison of FST matrix and covariance matrix for Q. acutissima.

FST matrix of Q. acutissima populations (left) and the covariance matrix of Q. acutissima populations (right).

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Fu, R., Zhu, Y., Liu, Y. et al. Genome-wide analyses of introgression between two sympatric Asian oak species. Nat Ecol Evol 6, 924–935 (2022). https://doi.org/10.1038/s41559-022-01754-7

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