Natural selection contributed to immunological differences between hunter-gatherers and agriculturalists


The shift from a hunter-gatherer to an agricultural mode of subsistence is believed to have been associated with profound changes in the burden and diversity of pathogens across human populations. Yet, the extent to which the advent of agriculture affected the evolution of the human immune system remains unknown. Here we present a comparative study of variation in the transcriptional responses of peripheral blood mononuclear cells to bacterial and viral stimuli between Batwa rainforest hunter-gatherers and Bakiga agriculturalists from Uganda. We observed increased divergence between hunter-gatherers and agriculturalists in the early transcriptional response to viruses compared with that for bacterial stimuli. We demonstrate that a significant fraction of these transcriptional differences are under genetic control and we show that positive natural selection has helped to shape population differences in immune regulation. Across the set of genetic variants underlying inter-population immune-response differences, however, the signatures of positive selection were disproportionately observed in the rainforest hunter-gatherers. This result is counter to expectations on the basis of the popularized notion that shifts in pathogen exposure due to the advent of agriculture imposed radically heightened selective pressures in agriculturalist populations.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Transcriptional differences between Batwa-HG and Bakiga-AG populations.
Fig. 2: Differences in immune response between HG and AG populations.
Fig. 3: Analysis of the contribution of genetics to differences in immune response between the HG-Batwa and the AG-Bakiga.
Fig. 4: Evidence of selection driving population differences in immune response.

Data availability

The data that support the findings of this study are available at

Code availability

All scripts required to run the analyses described in the manuscript can be found at


  1. 1.

    Diamond, J. & Bellwood, P. Farmers and their languages: the first expansions. Science 300, 597–603 (2003).

    CAS  Article  Google Scholar 

  2. 2.

    Greger, M. The human/animal interface: emergence and resurgence of zoonotic infectious diseases. Crit. Rev. Microbiol. 33, 243–299 (2007).

    Article  Google Scholar 

  3. 3.

    Pearce-Duvet, J. M. The origin of human pathogens: evaluating the role of agriculture and domestic animals in the evolution of human disease. Biol. Rev. Camb. Phil. Soc. 81, 369–382 (2006).

    Article  Google Scholar 

  4. 4.

    Wolfe, N. D., Dunavan, C. P. & Diamond, J. Origins of major human infectious diseases. Nature 447, 279–283 (2007).

    CAS  Article  Google Scholar 

  5. 5.

    Gignoux, C. R., Henn, B. M. & Mountain, J. L. Rapid, global demographic expansions after the origins of agriculture. Proc. Natl Acad. Sci. USA 108, 6044–6049 (2011).

    CAS  Article  Google Scholar 

  6. 6.

    Page, A. E. et al. Reproductive trade-offs in extant hunter-gatherers suggest adaptive mechanism for the Neolithic expansion. Proc. Natl Acad. Sci. USA 113, 4694–4699 (2016).

    CAS  Article  Google Scholar 

  7. 7.

    Black, F. L. Measles endemicity in insular populations: critical community size and its evolutionary implication. J. Theor. Biol. 11, 207–211 (1966).

    CAS  Article  Google Scholar 

  8. 8.

    Anderson, R. M. & May, R. M. Infectious Diseases of Humans: Dynamics and Control (Oxford Univ. Press, 1992).

  9. 9.

    Furuse, Y., Suzuki, A. & Oshitani, H. Origin of measles virus: divergence from rinderpest virus between the 11th and 12th centuries. Virol. J. 7, 52 (2010).

    Article  Google Scholar 

  10. 10.

    Matthijnssens, J. et al. Full genome-based classification of rotaviruses reveals a common origin between human Wa-Like and porcine rotavirus strains and human DS-1-like and bovine rotavirus strains. J. Virol. 82, 3204–3219 (2008).

    CAS  Article  Google Scholar 

  11. 11.

    Suzuki, Y. & Nei, M. Origin and evolution of influenza virus hemagglutinin genes. Mol. Biol. Evol. 19, 501–509 (2002).

    Article  Google Scholar 

  12. 12.

    Sundararaman, S. A. et al. Genomes of cryptic chimpanzee Plasmodium species reveal key evolutionary events leading to human malaria. Nat. Commun. 7, 11078 (2016).

    CAS  Article  Google Scholar 

  13. 13.

    Otto, T. D. et al. Genomes of all known members of a Plasmodium subgenus reveal paths to virulent human malaria. Nat. Microbiol. 3, 687-697 (2018).

    Article  Google Scholar 

  14. 14.

    Dounias, E. & Froment, A. When forest-based hunter-gatherers become sedentary: consequences for diet and health. UNASYLVA-FAO 57, 26-33 (2006).

    Google Scholar 

  15. 15.

    Barreiro, L. B. & Quintana-Murci, L. From evolutionary genetics to human immunology: how selection shapes host defence genes. Nat. Rev. Genet. 11, 17-30 (2010).

    Article  Google Scholar 

  16. 16.

    Karlsson, E. K., Kwiatkowski, D. P. & Sabeti, P. C. Natural selection and infectious disease in human populations. Nat. Rev. Genet. 15, 379-393 (2014).

    Article  Google Scholar 

  17. 17.

    Perry, G. H. et al. Adaptive, convergent origins of the pygmy phenotype in African rainforest hunter-gatherers. Proc. Natl Acad. Sci. USA 111, E3596–E3603 (2014).

    CAS  Article  Google Scholar 

  18. 18.

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

    CAS  Article  Google Scholar 

  19. 19.

    Xu, G. J. et al. Comprehensive serological profiling of human populations using a synthetic human virome. Science 348, aaa0698 (2015).

    Article  Google Scholar 

  20. 20.

    McGeoch, D. & Davison, A. J. in Origin and Evolution of Viruses (eds Domingo, E., Webster, R. & Holland, J.) Ch. 17 (Academic Press, 1999).

  21. 21.

    McGeoch, D. J., Dolan, A. & Ralph, A. C. Toward a comprehensive phylogeny for mammalian and avian herpesviruses. J. Virol. 74, 10401–10406 (2000).

    CAS  Article  Google Scholar 

  22. 22.

    Van Blerkom, L. M. Role of viruses in human evolution. Am. J. Phys. Anthropol. 122, 14–46 (2003).

    Article  Google Scholar 

  23. 23.

    Barreiro, L. B. et al. Deciphering the genetic architecture of variation in the immune response to Mycobacterium tuberculosis infection. Proc. Natl Acad. Sci. USA 109, 1204–1209 (2012).

    CAS  Article  Google Scholar 

  24. 24.

    Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).

    Article  Google Scholar 

  25. 25.

    Nédélec, Y. et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell 167, 657–669 (2016).

    Article  Google Scholar 

  26. 26.

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

    CAS  Article  Google Scholar 

  27. 27.

    Yi, X. et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science 329, 75–78 (2010).

    CAS  Article  Google Scholar 

  28. 28.

    Voight, B. F., Kudaravalli, S., Wen, X. & Pritchard, J. K. A map of recent positive selection in the human genome. PLoS Biol. 4, e72 (2006).

    Article  Google Scholar 

  29. 29.

    Patin, E. et al. The impact of agricultural emergence on the genetic history of African rainforest hunter-gatherers and agriculturalists. Nat. Commun. 5, 3163 (2014).

    Article  Google Scholar 

  30. 30.

    Lopez, M. et al. The demographic history and mutational load of African hunter-gatherers and farmers. Nat. Ecol. Evol. 2, 721-730 (2018).

    Article  Google Scholar 

  31. 31.

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

    Article  Google Scholar 

  32. 32.

    Enard, D. & Petrov, D. A. RNA viruses drove adaptive introgressions between Neanderthals and modern humans. Preprint at bioRxiv (2017).

  33. 33.

    Gonzalez, J. P., Nakoune, E., Slenczka, W., Vidal, P. & Morvan, J. M. Ebola and Marburg virus antibody prevalence in selected populations of the Central African Republic. Microbes Infect. 2, 39–44 (2000).

    CAS  Article  Google Scholar 

  34. 34.

    Johnson, E., Gonzalez, J.-P. & Georges, A. Filovirus activity among selected ethnic groups inhabiting the tropical forest of equatorial Africa. Trans. R. Soc. Trop. Med. Hyg. 87, 536–538 (1993).

    CAS  Article  Google Scholar 

  35. 35.

    Prezeworski, M., Coop, G. & Wall, J. D. The signature of positive selection on standing genetic variation. Evolution 59, 2312–2323 (2005).

    Article  Google Scholar 

  36. 36.

    Mellars, P. Why did modern human populations disperse from Africa ca. 60,000 years ago? A new model. Proc. Natl Acad. Sci. USA 103, 9381–9386 (2006).

    CAS  Article  Google Scholar 

  37. 37.

    Verdu, P. et al. Origins and genetic diversity of pygmy hunter-gatherers from Western Central Africa. Curr. Biol. 19, 312–318 (2009).

    CAS  Article  Google Scholar 

  38. 38.

    Storey, J. D. & Tibshirani, R. in Functional Genomics (eds Brownstein, M. J. and Kohdursky, A. B.) 149–157 (Springer, 2003).

  39. 39.

    Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  Google Scholar 

  40. 40.

    Snyder-Mackler, N. et al. Social status alters immune regulation and response to infection in macaques. Science 354, 1041–1045 (2016).

    CAS  Article  Google Scholar 

  41. 41.

    Sams, A. J. et al. Adaptively introgressed Neandertal haplotype at the OAS locus functionally impacts innate immune responses in humans. Genome Biol. 17, 246–261 (2016).

    Article  Google Scholar 

  42. 42.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  43. 43.

    Anders, S. et al. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat. Protoc. 8, 1765–1786 (2013).

    Article  Google Scholar 

  44. 44.

    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).

    CAS  Article  Google Scholar 

  45. 45.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47–e47 (2015).

    Article  Google Scholar 

  46. 46.

    Piasecka, B. et al. Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges. Proc. Natl Acad. Sci. USA 115, E488–E497 (2018).

    CAS  Article  Google Scholar 

  47. 47.

    Guo, Y., Zhao, S., Li, C.-I., Sheng, Q. & Shyr, Y. RNAseqPS: a web tool for estimating sample size and power for RNAseq experiment. Cancer Inform. 13, 1–5 (2014).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009).

    CAS  Article  Google Scholar 

  49. 49.

    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).

    CAS  Article  Google Scholar 

  50. 50.

    Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    CAS  Article  Google Scholar 

  51. 51.

    Lindeman, R. H., Merenda, P. F. & Gold, R. Z. Introduction to Bivariate and Multivariate Analysis (Scott, Foresman and Co, 1980).

  52. 52.

    Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).

    Article  Google Scholar 

  53. 53.

    Jeffrey, C. Genome-wide association study and meta-analysis finds over 40 loci affect risk of type 1 diabetes. Nat. Genet. 41, 703–707 (2009).

    Article  Google Scholar 

  54. 54.

    Szpiech, Z. A. & Hernandez, R. D. selscan: an efficient multithreaded program to perform EHH-based scans for positive selection. Mol. Biol. Evol. 31, 2824–2827 (2014).

    CAS  Article  Google Scholar 

Download references


The authors thank the Batwa and Bakiga communities and all individuals who participated in this study; also the Batwa Development Program, J. Byaruhanga, M. Magambo, P. Byamugisha, S. Twesigomwe, J. Safari and L. Busingye for expert assistance during the sample collection process in Uganda. We thank S. Nanyunja for technical laboratory assistance. We thank J. Tung and L.B.B. laboratory members for critical reading of the manuscript. We thank Calcul Québec and Compute Canada for providing access to the supercomputer Briaree from the University of Montreal. This work was supported by NIH R01-GM115656 to G.H.P and L.B.B., a fellowship from the Réseau de Médecine Génétique Appliquée and the Fonds de Recherche du Québec−Santé to G.F.H, and 1 F32 GM125228-638 01A1 to C.M.B. RNA-seq data have been deposited in Gene Expression Omnibus (accession number GSE120502). The 1M SNP genotype data are available at the European Genome–Phenome archive, (accession numbers EGAS00001000605 and EGAS00001000908).

Author information




L.B.B. and G.H.P conceived and coordinated the study, and performed field work in Uganda. S.L.N facilitated samples collection. J.B., A.D. and V.Y. performed cell culture experiments. G.F.H. and J.S. conducted most data analysis, with support from F.C.G. and C.M.B and input from co-authors. M.J.M, Y.L. and S.J.E. generated VirScan data. E.S. and L.Q.M. contributed to data generation. G.F.H, L.B.B. and G.H.P. wrote the paper with input from all co-authors.

Corresponding author

Correspondence to Luis B. Barreiro.

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 Figures

Supplementary Figs. 1–11.

Reporting Summary

Supplementary Table 1

Metadata for samples used in the study.

Supplementary Table 2

Results for identifying PopDE and PopDR genes.

Supplementary Table 3

Results from GSEA for popDE and PopDR genes.

Supplementary Table 4

Results from VirScan analysis.

Supplementary Table 5

Results from mapping of cis-eQTL.

Supplementary Table 6

Delta-PVE among PopDE genes.

Supplementary Table 7

Selection statistics for SNPs that are mapped cis-eQTL.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Harrison, G.F., Sanz, J., Boulais, J. et al. Natural selection contributed to immunological differences between hunter-gatherers and agriculturalists. Nat Ecol Evol 3, 1253–1264 (2019).

Download citation

Further reading


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