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Ancient hybridization and strong adaptation to viruses across African vervet monkey populations

Nature Genetics volume 49, pages 1705–1713 (2017)Cite this article

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A Publisher Correction to this article was published on 16 October 2018

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Abstract

Vervet monkeys are among the most widely distributed nonhuman primates, show considerable phenotypic diversity, and have long been an important biomedical model for a variety of human diseases and in vaccine research. Using whole-genome sequencing data from 163 vervets sampled from across Africa and the Caribbean, we find high diversity within and between taxa and clear evidence that taxonomic divergence was reticulate rather than following a simple branching pattern. A scan for diversifying selection across taxa identifies strong and highly polygenic selection signals affecting viral processes. Furthermore, selection scores are elevated in genes whose human orthologs interact with HIV and in genes that show a response to experimental simian immunodeficiency virus (SIV) infection in vervet monkeys but not in rhesus macaques, suggesting that part of the signal reflects taxon-specific adaptation to SIV.

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Figure 1: Sample information and genetic relatedness.
Figure 2: Evidence for gene flow across taxa.
Figure 3: Enrichment map network of GO categories enriched for high average gene selection scores.
Figure 4: Gene coexpression modules with differential expression before and after SIV infection that are also significantly enriched for high selection scores.
Figure 5: Selection scores across the genome and candidate genes with strong selection signals.

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  • 16 October 2018

    In the version of this article published, in the Online Methods eight citations to supplementary material refer to the wrong supplementary items. See the correction notice for full details.

References

  1. Cheney, D.L. & Seyfarth, R.M. The recognition of social alliances by vervet monkeys. Anim. Behav. 34, 1722–1731 (1986).

    Article  Google Scholar 

  2. Seyfarth, R.M., Cheney, D.L. & Marler, P. Vervet monkey alarm calls: semantic communication in a free-ranging primate. Anim. Behav. 28, 1070–1094 (1980).

    Article  Google Scholar 

  3. Jasinska, A.J. et al. Systems biology of the vervet monkey. ILAR J. 54, 122–143 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Briggs, C.M. et al. Live attenuated tetravalent dengue virus host range vaccine is immunogenic in African green monkeys following a single vaccination. J. Virol. 88, 6729–6742 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Matsuoka, Y. et al. African green monkeys recapitulate the clinical experience with replication of live attenuated pandemic influenza virus vaccine candidates. J. Virol. 88, 8139–8152 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Pripuzova, N.S. et al. Exploring of primate models of tick-borne flaviviruses infection for evaluation of vaccines and drugs efficacy. PLoS One 8, e61094 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Huang, Y.S. et al. Sequencing strategies and characterization of 721 vervet monkey genomes for future genetic analyses of medically relevant traits. BMC Biol. 13, 41 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Jasinska, A.J. et al. Genetic variation and gene expression across multiple tissues and developmental stages in a nonhuman primate. Nat. Genet. http://dx.doi.org/10.1038/ng.3959 (2017).

  9. Ma, D. et al. SIVagm infection in wild African green monkeys from South Africa: epidemiology, natural history, and evolutionary considerations. PLoS Pathog. 9, e1003011 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ma, D. et al. Factors associated with simian immunodeficiency virus transmission in a natural African nonhuman primate host in the wild. J. Virol. 88, 5687–5705 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Warren, W.C. et al. The genome of the vervet (Chlorocebusaethiopssabaeus). Genome Res. 25, 1921–1933 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Enstam, K.L. & Isbell, L.A. in Primates in Perspective (ed. Campbell, C.J.) 252–274 (Oxford University Press, 2007).

  13. Daub, J.T. et al. Evidence for polygenic adaptation to pathogens in the human genome. Mol. Biol. Evol. 30, 1544–1558 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. Enard, D., Cai, L., Gwennap, C. & Petrov, D.A. Viruses are a dominant driver of protein adaptation in mammals. eLife 5, e12469 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Quach, H. et al. Genetic adaptation and Neandertal admixture shaped the immune system of human populations. Cell 167, 643–656 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Leffler, E.M. et al. Revisiting an old riddle: what determines genetic diversity levels within species? PLoS Biol. 10, e1001388 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Prado-Martinez, J. et al. Great ape genetic diversity and population history. Nature 499, 471–475 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Perry, G.H. et al. Comparative RNA sequencing reveals substantial genetic variation in endangered primates. Genome Res. 22, 602–610 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  20. Hernandez, R.D. et al. Demographic histories and patterns of linkage disequilibrium in Chinese and Indian rhesus macaques. Science 316, 240–243 (2007).

    Article  CAS  PubMed  Google Scholar 

  21. Ebersberger, I., Metzler, D., Schwarz, C. & Pääbo, S. Genomewide comparison of DNA sequences between humans and chimpanzees. Am. J. Hum. Genet. 70, 1490–1497 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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 

  23. Durand, E.Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Patterson, N. et al. Ancient admixture in human history. Genetics 192, 1065–1093 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Schiffels, S. & Durbin, R. Inferring human population size and separation history from multiple genome sequences. Nat. Genet. 46, 919–925 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chen, H., Patterson, N. & Reich, D. Population differentiation as a test for selective sweeps. Genome Res. 20, 393–402 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhou, C. & Rana, T.M. A bimolecular mechanism of HIV-1 Tat protein interaction with RNA polymerase II transcription elongation complexes. J. Mol. Biol. 320, 925–942 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zeng, K. & Corcoran, P. The effects of background and interference selection on patterns of genetic variation in subdivided populations. Genetics 201, 1539–1554 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ako-Adjei, D. et al. HIV-1, human interaction database: current status and new features. Nucleic Acids Res. 43, D566–D570 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Kapusinszky, B. et al. Local virus extinctions following a host population bottleneck. J. Virol. 89, 8152–8161 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Müller, M.C. et al. Simian immunodeficiency viruses from central and western Africa: evidence for a new species-specific lentivirus in tantalus monkeys. J. Virol. 67, 1227–1235 (1993).

    PubMed  PubMed Central  Google Scholar 

  33. Goldstein, S. et al. Plateau levels of viremia correlate with the degree of CD4+-T-cell loss in simian immunodeficiency virus SIVagm-infected pigtailed macaques: variable pathogenicity of natural SIVagm isolates. J. Virol. 79, 5153–5162 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Mandell, D.T. et al. Pathogenic features associated with increased virulence upon simian immunodeficiency virus cross-species transmission from natural hosts. J. Virol. 88, 6778–6792 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Jacquelin, B. et al. Nonpathogenic SIV infection of African green monkeys induces a strong but rapidly controlled type I IFN response. J. Clin. Invest. 119, 3544–3555 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Jacquelin, B. et al. Innate immune responses and rapid control of inflammation in African green monkeys treated or not with interferon-α during primary SIVagm infection. PLoS Pathog. 10, e1004241 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Miyauchi, K., Kim, Y., Latinovic, O., Morozov, V. & Melikyan, G.B. HIV enters cells via endocytosis and dynamin-dependent fusion with endosomes. Cell 137, 433–444 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Barouch, D.H. et al. Rapid inflammasome activation following mucosal SIV infection of rhesus monkeys. Cell 165, 656–667 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Pan, X.-Y. et al. Heat shock factor 1 mediates latent HIV reactivation. Sci. Rep. 6, 26294 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Klein, F. et al. Somatic mutations of the immunoglobulin framework are generally required for broad and potent HIV-1 neutralization. Cell 153, 126–138 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Pereira, L.E., Johnson, R.P. & Ansari, A.A. Sooty mangabeys and rhesus macaques exhibit significant divergent natural killer cell responses during both acute and chronic phases of SIV infection. Cell. Immunol. 254, 10–19 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. Meythaler, M. et al. Early induction of polyfunctional simian immunodeficiency virus (SIV)-specific T lymphocytes and rapid disappearance of SIV from lymph nodes of sooty mangabeys during primary infection. J. Immunol. 186, 5151–5161 (2011).

    Article  CAS  PubMed  Google Scholar 

  44. Brenchley, J.M. et al. Differential infection patterns of CD4+ T cells and lymphoid tissue viral burden distinguish progressive and nonprogressive lentiviral infections. Blood 120, 4172–4181 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zhang, R. et al. Envelope-specific B-cell populations in African green monkeys chronically infected with simian immunodeficiency virus. Nat. Commun. 7, 12131 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Predicala, R. & Zhou, Y. The role of Ran-binding protein 3 during influenza A virus replication. J. Gen. Virol. 94, 977–984 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Hakata, Y., Yamada, M. & Shida, H. A multifunctional domain in human CRM1 (exportin 1) mediates RanBP3 binding and multimerization of human T-cell leukemia virus type 1 Rex protein. Mol. Cell. Biol. 23, 8751–8761 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Langer, K., Dian, C., Rybin, V., Müller, C.W. & Petosa, C. Insights into the function of the CRM1 cofactor RanBP3 from the structure of its Ran-binding domain. PLoS One 6, e17011 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Shida, H. Role of nucleocytoplasmic RNA transport during the life cycle of retroviruses. Front. Microbiol. 3, 179 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Messam, C.A., Hou, J., Gronostajski, R.M. & Major, E.O. Lineage pathway of human brain progenitor cells identified by JC virus susceptibility. Ann. Neurol. 53, 636–646 (2003).

    Article  CAS  PubMed  Google Scholar 

  51. Traut, W. & Fanning, E. Sequence-specific interactions between a cellular DNA-binding protein and the simian virus 40 origin of DNA replication. Mol. Cell. Biol. 8, 903–911 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kaliyaperumal, S. et al. Frequent infection of neurons by SV40 virus in SIV-infected macaque monkeys with progressive multifocal leukoencephalopathy and meningoencephalitis. Am. J. Pathol. 183, 1910–1917 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Ferenczy, M.W. et al. Molecular biology, epidemiology, and pathogenesis of progressive multifocal leukoencephalopathy, the JC virus–induced demyelinating disease of the human brain. Clin. Microbiol. Rev. 25, 471–506 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Pfeifer, S.P. The demographic and adaptive history of the African green monkey. Mol. Biol. Evol. 34, 1055–1065 (2017).

    Article  CAS  PubMed  Google Scholar 

  55. Romiguier, J. et al. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 515, 261–263 (2014).

    Article  CAS  PubMed  Google Scholar 

  56. 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).

    Article  CAS  PubMed  Google Scholar 

  57. Mallet, J., Besansky, N. & Hahn, M.W. How reticulated are species? BioEssays 38, 140–149 (2016).

    Article  PubMed  Google Scholar 

  58. Matsubayashi, K., Hirai, M., Watanabe, T., Ohkura, Y. & Nozawa, K. A case of patas–vervet hybrid in captivity. Primates 19, 785–793 (1978).

    Article  Google Scholar 

  59. de Jong, Y.A. & Butynski, T.M. Three Sykes's monkey Cercopithecus mitis × vervet monkey Chlorocebus pygerythrus hybrids in Kenya. Primate Conserv. 25, 43–56 (2010).

    Article  Google Scholar 

  60. Danko, C.G. et al. Natural selection has shaped coding and non-coding transcription in primate CD4+ T-cells. Preprint at bioRxiv https://doi.org/10.1101/083212 (2016).

  61. Haus, T. et al. Mitochondrial diversity and distribution of African green monkeys (Chlorocebus gray, 1870). Am. J. Primatol. 75, 350–360 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Hill, W.C.O. in Primates, Comparative Anatomy and Taxonomy 533–581 (Edinburgh University Press, 1966).

  63. Demchak, B. et al. Cytoscape: the network visualization tool for GenomeSpace workflows. F1000Res 3, 151 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 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).

    Google Scholar 

  67. Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Duforet-Frebourg, N., Bazin, E. & Blum, M.G.B. Genome scans for detecting footprints of local adaptation using a Bayesian factor model. Mol. Biol. Evol. 31, 2483–2495 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Edgar, R.C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5, 113 (2004).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Hasegawa, M., Kishino, H. & Yano, T. Dating of the human–ape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evol. 22, 160–174 (1985).

    Article  CAS  PubMed  Google Scholar 

  73. Swofford, D.L. & Jack, S. in The Phylogenetic Handbook (eds. Lemey, P., Salemi, M. & Vandamme, A.M.) 267–312 (Cambridge University Press, 2009).

  74. Posada, D. & Crandall, K.A. Selecting the best-fit model of nucleotide substitution. Syst. Biol. 50, 580–601 (2001).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Delaneau, O., Howie, B., Cox, A.J., Zagury, J.-F. & Marchini, J. Haplotype estimation using sequencing reads. Am. J. Hum. Genet. 93, 687–696 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005).

    Article  PubMed  Google Scholar 

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Acknowledgements

Samples were collected through the UCLA Systems Biology Sample Repository funded by US National Institutes of Health grants R01RR016300 and R01OD010980 to N.F. For permits allowing us to collect samples, we thank the Gambia Department of Parks & Wildlife Management; the Botswana Ministry of Environment & Wildlife and Tourism; the Ghana Wildlife Division, Forestry Commission; the Zambia Wildlife Authority; the Ethiopian Wildlife Conservation Authority; the Ministry of Forestry & the Environment, Department of Environmental Affairs, South Africa; the Department of Economic Development and Environmental Affairs, Eastern Cape; the Department of Tourism, Environmental and Economic Affairs, Free State; Ezemvelo KZN Wildlife, KwaZulu-Natal; and the Department of Economic Development, Environment and Tourism, Limpopo. We also thank G. Redmond and the St. Kitts Biomedical Research Foundation for facilitating sample collection in St. Kitts and Nevis. We thank J. Brenchley, K. Reimann (R24OD010976), and J. Baulu and the Barbados Primate Research Center and Wildlife Reserve for providing samples of Tanzanian origin and Barbadian vervets. For help with sample collection and processing, we thank J. Danzy-Cramer, Y. Jung, O. Morton and J. Freimer. We thank J. Kamm for discussion, Ü. Seren, J. Wasserscheid and N. Juretic for IT support, and R. Halai for help with figure design. H.S. has been supported by a travel grant from the Austrian Ministry of Science and Research. C.A. is supported by RO1 AI119346 from the National Institute of Allergy and Infectious Diseases (NIAID). We acknowledge the support of the National Institute of Neurological Disorders and Stroke (NINDS) Informatics Center for Neurogenetics and Neurogenomics (P30 NS062691). We would like to thank F. Gao for assistance with microarray data analysis.

Author information

Author notes

  1. Hannes Svardal

    Present address: Department of Genetics, University of Cambridge, Cambridge, UK

  2. Richard K Wilson

    Present address: Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio, USA

Authors and Affiliations

  1. Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter (VBC), Vienna, Austria

    Hannes Svardal & Magnus Nordborg

  2. Center for Neurobehavioral Genetics, University of California, Los Angeles, California, USA

    Anna J Jasinska, Giovanni Coppola, Vasily Ramensky & Nelson B Freimer

  3. Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland

    Anna J Jasinska

  4. Center for Vaccine Research, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Cristian Apetrei

  5. Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Cristian Apetrei

  6. Department of Neurology, University of California, Los Angeles, California, USA

    Giovanni Coppola

  7. State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

    Yu Huang

  8. Department of Anthropology, Boston University, Boston, Massachusetts, USA

    Christopher A Schmitt

  9. Institut Pasteur, Unité HIPER, Paris, France

    Beatrice Jacquelin & Michaela Müller-Trutwin

  10. Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation

    Vasily Ramensky

  11. Medical Research Council (MRC), The Gambia Unit, The Gambia

    Martin Antonio

  12. Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA

    George Weinstock & Richard K Wilson

  13. Department of Genetics, University of the Free State, Bloemfontein, South Africa

    J Paul Grobler & Trudy R Turner

  14. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Ken Dewar

  15. Department of Anthropology, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin, USA

    Trudy R Turner

  16. McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri, USA

    Wesley C Warren

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Contributions

N.B.F., T.R.T., M.N., A.J.J., K.D., W.C.W. and R.K.W. conceived the study. M.N. and H.S. designed the analysis strategy. H.S. analyzed the data and prepared tables and figures. C.A. contributed the SIVagm sequence analysis. G.C. contributed the WGCNA analysis. B.J. and M.M.-T. provided expertise on the expression data analysis and SIV. Y.H. and V.R. provided bioinformatic support. C.A.S., J.P.G., M.A. and T.R.T. collected samples and obtained permits. N.B.F., G.W., R.K.W., K.D. and W.C.W. oversaw sequencing. M.N., H.S., N.F. and A.J.J. wrote the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Magnus Nordborg.

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Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Figures 1–36 and Supplementary Tables 1–4 (PDF 13048 kb)

Life Sciences Reporting Summary (PDF 129 kb)

Supplementary Data 1

Table of sample IDs, taxonomic group attribution (c.f., Fig. 1a), taxonomic classification from the Integrated Taxonomic Information System (http://www.itis.gov/; last accessed June 2016), collection site, country, coordinates, actual fold coverage, percentage of mapped reads (including all scaffolds), SRA Sample ID and BioProject accession number. (XLSX 22 kb)

Supplementary Data 2

Results for all D-statistic (ABBA-BABA test) comparisons that are consistent with the UPGMA clustering tree of pairwise differences. z scores were obtained through block jackknifing. Samples were grouped by country. Figure 2e and Supplementary Figures 14 and 36 show a subset of the data. See Supplementary Table 4 for IDs of the samples used in the single-sample analysis. (XLSX 19 kb)

Supplementary Data 3

Average and maximum of XP-CLR root-mean-square average selection scores for each gene. Details on how these scores were obtained are given in the Online Methods. (XLSX 1121 kb)

Supplementary Data 4

Significance P values for enrichment of selection scores in gene ontology categories using the R package TopGO with a Kolmogorov–Smirnov test and the weight01 algorithm for all categories with P < 0.1. (XLSX 67 kb)

Supplementary Data 5

Significance P values for sumstat enrichment of selection scores in NCBI HIV-1–human interaction gene categories (Online Methods). (XLSX 24 kb)

Supplementary Data 6

Significance P values for enrichment of selection scores in gene expression categories (Online Methods). (XLSX 19 kb)

Supplementary Data 7

Significant GO enrichments for WGCNA modules significantly enriched in high selection scores that only show a short-term response in vervet (mainly day 6 after infection), i.e., for genes from the green, blue and magenta modules with asterisks in Supplementary Figure 35. The R package TopGO with Fisher's exact test and the weight01 algorithm was used. (XLSX 13 kb)

Supplementary Data 8

Significant GO enrichments for WGCNA modules significantly enriched in high selection scores that show a long-term response in vervet (day 115 after infection), i.e., for genes from the yellow and tan modules with asterisks in Supplementary Figure 35. The R package TopGO with Fisher's exact test and the weight01 algorithm was used. (XLSX 11 kb)

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Svardal, H., Jasinska, A., Apetrei, C. et al. Ancient hybridization and strong adaptation to viruses across African vervet monkey populations. Nat Genet 49, 1705–1713 (2017). https://doi.org/10.1038/ng.3980

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