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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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  • 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.


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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

    • Hannes Svardal
    •  & Richard K Wilson

    Present addresses: Department of Genetics, University of Cambridge, Cambridge, UK (H.S.) and Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio, USA (R.K.W.).


  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, 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, 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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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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Magnus Nordborg.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Note, Supplementary Figures 1–36 and Supplementary Tables 1–4

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Data 1

    Table of sample IDs, taxonomic group attribution (c.f., Fig. 1a), taxonomic classification from the Integrated Taxonomic Information System (; 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.

  2. 2.

    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.

  3. 3.

    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.

  4. 4.

    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.

  5. 5.

    Supplementary Data 5

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

  6. 6.

    Supplementary Data 6

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

  7. 7.

    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.

  8. 8.

    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.

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