Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The demographic history and mutational load of African hunter-gatherers and farmers

Nature Ecology & Evolution volume 2, pages 721–730 (2018)Cite this article

Subjects

Abstract

Understanding how deleterious genetic variation is distributed across human populations is of key importance in evolutionary biology and medical genetics. However, the impact of population size changes and gene flow on the corresponding mutational load remains a controversial topic. Here, we report high-coverage exomes from 300 rainforest hunter-gatherers and farmers of central Africa, whose distinct subsistence strategies are expected to have impacted their demographic pasts. Detailed demographic inference indicates that hunter-gatherers and farmers recently experienced population collapses and expansions, respectively, accompanied by increased gene flow. We show that the distribution of deleterious alleles across these populations is compatible with a similar efficacy of selection to remove deleterious variants with additive effects, and predict with simulations that their present-day additive mutation load is almost identical. For recessive mutations, although an increased load is predicted for hunter-gatherers, this increase has probably been partially counteracted by strong gene flow from expanding farmers. Collectively, our predicted and empirical observations suggest that the impact of the recent population decline of African hunter-gatherers on their mutation load has been modest and more restrained than would be expected under a fully recessive model of dominance.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Genetic structure and diversity of African rainforest hunter-gatherers and farmers.
Fig. 2: Inferred demographic models of the studied populations.
Fig. 3: Population frequencies and predicted selective effects of non-synonymous mutations in African rainforest hunter-gatherers and farmers, compared with Europeans.
Fig. 4: DFE of new non-synonymous mutations.
Fig. 5: Trajectory of mutation load through time obtained with simulations.
Fig. 6: Comparison of observed Nalleles with simulated predictions between the studied populations.

Similar content being viewed by others

References

  1. Veeramah, K. R. & Hammer, M. F. The impact of whole-genome sequencing on the reconstruction of human population history. Nat. Rev. Genet. 15, 149–162 (2014).

    CAS  PubMed  Google Scholar 

  2. Nielsen, R. et al. Tracing the peopling of the world through genomics. Nature 541, 302–310 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Henn, B. M., Cavalli-Sforza, L. L. & Feldman, M. W. The great human expansion. Proc. Natl Acad. Sci. USA 109, 17758–17764 (2012).

    CAS  PubMed  Google Scholar 

  4. Charlesworth, B. Fundamental concepts in genetics: effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 10, 195–205 (2009).

    CAS  PubMed  Google Scholar 

  5. Lohmueller, K. E. et al. Proportionally more deleterious genetic variation in European than in African populations. Nature 451, 994–997 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Casals, F. et al. Whole-exome sequencing reveals a rapid change in the frequency of rare functional variants in a founding population of humans. PLoS Genet. 9, e1003815 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Henn, B. M. et al. Distance from sub-Saharan Africa predicts mutational load in diverse human genomes. Proc. Natl Acad. Sci. USA 113, E440–E449 (2016).

    CAS  PubMed  Google Scholar 

  8. Tennessen, J. A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Pedersen, C. T. et al. The effect of an extreme and prolonged population bottleneck on patterns of deleterious variation: insights from the Greenlandic Inuit. Genetics 205, 787–801 (2017).

    PubMed  Google Scholar 

  10. Lim, E. T. et al. Distribution and medical impact of loss-of-function variants in the Finnish founder population. PLoS Genet. 10, e1004494 (2014).

    PubMed  PubMed Central  Google Scholar 

  11. Fu, W., Gittelman, R. M., Bamshad, M. J. & Akey, J. M. Characteristics of neutral and deleterious protein-coding variation among individuals and populations. Am. J. Hum. Genet. 95, 421–436 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Xue, Y. et al. Enrichment of low-frequency functional variants revealed by whole-genome sequencing of multiple isolated European populations. Nat. Commun. 8, 15927 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Peischl, S. et al. Relaxed selection during a recent human expansion. Genetics 208, 763–777 (2018).

    CAS  PubMed  Google Scholar 

  14. Agarwala, V., Flannick, J., Sunyaev, S., Go, T. D. C. & Altshuler, D. Evaluating empirical bounds on complex disease genetic architecture. Nat. Genet. 45, 1418–1427 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Maher, M. C., Uricchio, L. H., Torgerson, D. G. & Hernandez, R. D. Population genetics of rare variants and complex diseases. Hum. Hered. 74, 118–128 (2012).

    PubMed  Google Scholar 

  16. Harris, K. & Nielsen, R. The genetic cost of Neanderthal introgression. Genetics 203, 881–891 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Muller, H. J. Our load of mutations. Am. J. Hum. Genet. 2, 111–176 (1950).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Wright, S. The distribution of gene frequencies in populations. Science 85, 504 (1937).

    CAS  PubMed  Google Scholar 

  19. Kimura, M., Maruyama, T. & Crow, J. F. The mutation load in small populations. Genetics 48, 1303–1312 (1963).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge Univ. Press, Cambridge, 1984).

  21. Simons, Y. B. & Sella, G. The impact of recent population history on the deleterious mutation load in humans and close evolutionary relatives. Curr. Opin. Genet. Dev. 41, 150–158 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Simons, Y. B., Turchin, M. C., Pritchard, J. K. & Sella, G. The deleterious mutation load is insensitive to recent population history. Nat. Genet. 46, 220–224 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Do, R. et al. No evidence that selection has been less effective at removing deleterious mutations in Europeans than in Africans. Nat. Genet. 47, 126–131 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  25. Keinan, A. & Clark, A. G. Recent explosive human population growth has resulted in an excess of rare genetic variants. Science 336, 740–743 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Bocquet-Appel, J. P. When the world’s population took off: the springboard of the Neolithic Demographic Transition. Science 333, 560–561 (2011).

    CAS  PubMed  Google Scholar 

  27. Cavalli-Sforza, L. L. African Pygmies (Academic, New York, 1986).

  28. Bostoen, K. et al. Middle to Late Holocene paleoclimatic change and the early Bantu expansion in the rain forests of western central Africa. Curr. Anthropol. 56, 354–384 (2015).

    Google Scholar 

  29. Patin, E. et al. Inferring the demographic history of African farmers and pygmy hunter-gatherers using a multilocus resequencing data set. PLoS Genet. 5, e1000448 (2009).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  31. Veeramah, K. R. et al. An early divergence of KhoeSan ancestors from those of other modern humans is supported by an ABC-based analysis of autosomal resequencing data. Mol. Biol. Evol. 29, 617–630 (2012).

    CAS  PubMed  Google Scholar 

  32. Hsieh, P. et al. Whole-genome sequence analyses of western central African pygmy hunter-gatherers reveal a complex demographic history and identify candidate genes under positive natural selection. Genome Res. 26, 279–290 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 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).

    PubMed  Google Scholar 

  34. Charlesworth, B. & Charlesworth, D. Elements of Evolutionary Genetics (Roberts & Company, Greenwood Village, 2010).

  35. Batini, C. et al. Insights into the demographic history of African pygmies from complete mitochondrial genomes. Mol. Biol. Evol. 28, 1099–1110 (2011).

    CAS  PubMed  Google Scholar 

  36. Quach, H. et al. Genetic adaptation and Neandertal admixture shaped the immune system of human populations. Cell 167, 643–656.e17 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Gravel, S. et al. Demographic history and rare allele sharing among human populations. Proc. Natl Acad. Sci. USA 108, 11983–11988 (2011).

    CAS  PubMed  Google Scholar 

  38. Pagani, L. et al. Ethiopian genetic diversity reveals linguistic stratification and complex influences on the Ethiopian gene pool. Am. J. Hum. Genet. 91, 83–96 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Hodgson, J. A., Mulligan, C. J., Al-Meeri, A. & Raaum, R. L. Early back-to-Africa migration into the Horn of Africa. PLoS Genet. 10, e1004393 (2014).

    PubMed  PubMed Central  Google Scholar 

  40. Davydov, E. V. et al. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol. 6, e1001025 (2010).

    PubMed  PubMed Central  Google Scholar 

  41. Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 5, e1000695 (2009).

    PubMed  PubMed Central  Google Scholar 

  42. Kim, B. Y., Huber, C. D. & Lohmueller, K. E. Inference of the distribution of selection coefficients for new nonsynonymous mutations using large samples. Genetics 206, 345–361 (2017).

    PubMed  PubMed Central  Google Scholar 

  43. 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 

  44. Balick, D. J., Do, R., Cassa, C. A., Reich, D. & Sunyaev, S. R. Dominance of deleterious alleles controls the response to a population bottleneck. PLoS Genet. 11, e1005436 (2015).

    PubMed  PubMed Central  Google Scholar 

  45. Gulko, B., Hubisz, M. J., Gronau, I. & Siepel, A. A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat. Genet. 47, 276–283 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Mallick, S. et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538, 201–206 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Scholz, C. A. et al. East African megadroughts between 135 and 75 thousand years ago and bearing on early-modern human origins. Proc. Natl Acad. Sci. USA 104, 16416–16421 (2007).

    CAS  PubMed  Google Scholar 

  48. Kim, H. L. et al. Khoisan hunter-gatherers have been the largest population throughout most of modern-human demographic history. Nat. Commun. 5, 5692 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Skoglund, P. et al. Reconstructing prehistoric African population structure. Cell 171, 59–71.e21 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Lohmueller, K. E. The impact of population demography and selection on the genetic architecture of complex traits. PLoS Genet. 10, e1004379 (2014).

    PubMed  PubMed Central  Google Scholar 

  51. Fagny, M. et al. The epigenomic landscape of African rainforest hunter-gatherers and farmers. Nat. Commun. 6, 10047 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Patin, E. et al. Dispersals and genetic adaptation of Bantu-speaking populations in Africa and North America. Science 356, 543–546 (2017).

    CAS  PubMed  Google Scholar 

  53. 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  PubMed  Google Scholar 

  54. 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 

  55. 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 

  56. 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 

  57. Hamosh, A., Scott, A. F., Amberger, J. S., Bocchini, C. A. & McKusick, V. A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D514–D517 (2005).

    CAS  PubMed  Google Scholar 

  58. Kousathanas, A., Oliver, F., Halligan, D. L. & Keightley, P. D. Positive and negative selection on noncoding DNA close to protein-coding genes in wild house mice. Mol. Biol. Evol. 28, 1183–1191 (2011).

    CAS  PubMed  Google Scholar 

  59. 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  PubMed  PubMed Central  Google Scholar 

  60. Lischer, H. E. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28, 298–299 (2012).

    CAS  PubMed  Google Scholar 

  61. Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).

    PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  63. Lipson, M. et al. Calibrating the human mutation rate via ancestral recombination density in diploid genomes. PLoS Genet. 11, e1005550 (2015).

    PubMed  PubMed Central  Google Scholar 

  64. Fenner, J. N. Cross-cultural estimation of the human generation interval for use in genetics-based population divergence studies. Am. J. Phys. Anthropol. 128, 415–423 (2005).

    PubMed  Google Scholar 

  65. Ewing, G. & Jensen, J. The consequences of not accounting for background selection in demographic inference. Mol. Ecol. 25, 135–141 (2016).

    PubMed  Google Scholar 

  66. Schrider, D. R., Shanku, A. G. & Kern, A. D. Effects of linked selective sweeps on demographic inference and model selection. Genetics 204, 1207–1223 (2016).

    PubMed  PubMed Central  Google Scholar 

  67. Eyre-Walker, A. & Keightley, P. D. The distribution of fitness effects of new mutations. Nat. Rev. Genet. 8, 610–618 (2007).

    CAS  PubMed  Google Scholar 

  68. Boyko, A. R. et al. Assessing the evolutionary impact of amino acid mutations in the human genome. PLoS Genet. 4, e1000083 (2008).

    PubMed  PubMed Central  Google Scholar 

  69. Hernandez, R. D. et al. Classic selective sweeps were rare in recent human evolution. Science 331, 920–924 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Kousathanas, A. & Keightley, P. D. A comparison of models to infer the distribution of fitness effects of new mutations. Genetics 193, 1197–1208 (2013).

    PubMed  PubMed Central  Google Scholar 

  71. Messer, P. W. & Petrov, D. A. Frequent adaptation and the McDonald–Kreitman test. Proc. Natl Acad. Sci. USA 110, 8615–8620 (2013).

    CAS  PubMed  Google Scholar 

  72. Haller, B. C. & Messer, P. W. SLiM 2: flexible, interactive forward genetic simulations. Mol. Biol. Evol. 34, 230–240 (2017).

    CAS  PubMed  Google Scholar 

  73. Gravel, S. When is selection effective? Genetics 203, 451–462 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Pemberton, T. J. et al. Genomic patterns of homozygosity in worldwide human populations. Am. J. Hum. Genet. 91, 275–292 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Hussin, J. G. et al. Recombination affects accumulation of damaging and disease-associated mutations in human populations. Nat. Genet. 47, 400–404 (2015).

    CAS  PubMed  Google Scholar 

  76. Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all the participants for providing the DNA samples used in this study. We thank the Paleogenomics and Molecular Genetics Platform of the Musée de l’Homme-Muséum National d'Histoire Naturelle for technical assistance with DNA sample preparation. We thank G. Laval, L. Excoffier, M. Rotival and S. Ait Kaci Azzou for helpful discussions, and N. Joly for help with computational resources. This work was supported by the Institut Pasteur, Centre National de la Recherche Scientifique and Agence Nationale de la Recherche grant 'AGRHUM' (ANR-14-CE02-0003-01). M.L. was supported by the Fondation pour la Recherche Médicale (FDT20170436932) and A.K. by a Pasteur-Roux fellowship.

Author information

Author notes

  1. These authors contributed equally: Marie Lopez and Athanasios Kousathanas.

Authors and Affiliations

  1. Unit of Human Evolutionary Genetics, Institut Pasteur, Paris, France

    Marie Lopez, Athanasios Kousathanas, Hélène Quach, Christine Harmant, Etienne Patin & Lluís Quintana-Murci

  2. Centre National de la Recherche Scientifique UMR 2000, Paris, France

    Marie Lopez, Athanasios Kousathanas, Hélène Quach, Christine Harmant, Etienne Patin & Lluís Quintana-Murci

  3. Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France

    Marie Lopez, Athanasios Kousathanas, Hélène Quach, Christine Harmant, Etienne Patin & Lluís Quintana-Murci

  4. Laboratoire de Langue, Culture et Cognition, Université Omar Bongo, Libreville, Gabon

    Patrick Mouguiama-Daouda

  5. Centre National de la Recherche Scientifique UMR 5596, Dynamique du Langage, Université Lumière-Lyon 2, Lyon, France

    Patrick Mouguiama-Daouda & Jean-Marie Hombert

  6. Institut de Recherche pour le Développement UMR 208, Muséum National d’Histoire Naturelle, Paris, France

    Alain Froment

  7. Departments of Anthropology and Biology, Pennsylvania State University, University Park, PA, USA

    George H. Perry

  8. Université de Montréal, Centre de Recherche du Centre Hospitalier Universitaire Sainte-Justine, Montréal, Canada

    Luis B. Barreiro

  9. Centre National de la Recherche Scientifique UMR 7206, Muséum National d’Histoire Naturelle, Université Paris Diderot, Sorbonne Paris Cité, Paris, France

    Paul Verdu

Authors
  1. Marie Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Athanasios Kousathanas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hélène Quach
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christine Harmant
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Patrick Mouguiama-Daouda
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jean-Marie Hombert
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alain Froment
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. George H. Perry
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Luis B. Barreiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Paul Verdu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Etienne Patin
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Lluís Quintana-Murci
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.K. and M.L. designed the analytical approach and performed the analyses with input from E.P. and L.Q.-M. H.Q. and C.H. performed the experiments. P.M.-D., J.-M.H., A.F., G.H.P., L.B.B. and P.V. collected the samples. L.Q.-M. conceived the study with input from E.P. and obtained funding. The manuscript was written by A.K., M.L., E.P. and L.Q.-M. with input from all authors.

Corresponding authors

Correspondence to Athanasios Kousathanas or Lluís Quintana-Murci.

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 notes, figures, tables (1–9) and references

Life Sciences Reporting Summary

Supplementary Table 10

Clinically relevant variants identified in the exome of African hunter-gathering and farming populations. List of variants annotated with the highest clinical significance in ClinVar database and their frequencies in each of the examined populations

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lopez, M., Kousathanas, A., Quach, H. et al. The demographic history and mutational load of African hunter-gatherers and farmers. Nat Ecol Evol 2, 721–730 (2018). https://doi.org/10.1038/s41559-018-0496-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-018-0496-4

This article is cited by

Search

Advanced 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