4.8 Article

Complete Khoisan and Bantu genomes from southern Africa

Journal

NATURE
Volume 463, Issue 7283, Pages 943-947

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/nature08795

Keywords

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Funding

  1. Pennsylvania State University
  2. National Human Genome Research Institute, National Institutes of Health
  3. NSF [DEB-0733029]
  4. NIH [R01GM087472]
  5. Gordon and Betty Moore Foundation
  6. Direct For Computer & Info Scie & Enginr
  7. Office of Advanced Cyberinfrastructure (OAC) [821527] Funding Source: National Science Foundation
  8. Div Of Biological Infrastructure
  9. Direct For Biological Sciences [0850103] Funding Source: National Science Foundation

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The genetic structure of the indigenous hunter-gatherer peoples of southern Africa, the oldest known lineage of modern human, is important for understanding human diversity. Studies based on mitochondrial(1) and small sets of nuclear markers(2) have shown that these hunter-gatherers, known as Khoisan, San, or Bushmen, are genetically divergent from other humans(1,3). However, until now, fully sequenced human genomes have been limited to recently diverged populations(4-8). Here we present the complete genome sequences of an indigenous hunter-gatherer from the Kalahari Desert and a Bantu from southern Africa, as well as protein-coding regions from an additional three hunter-gatherers from disparate regions of the Kalahari. We characterize the extent of whole-genome and exome diversity among the five men, reporting 1.3 million novel DNA differences genome-wide, including 13,146 novel amino acid variants. In terms of nucleotide substitutions, the Bushmen seem to be, on average, more different from each other than, for example, a European and an Asian. Observed genomic differences between the hunter-gatherers and others may help to pinpoint genetic adaptations to an agricultural lifestyle. Adding the described variants to current databases will facilitate inclusion of southern Africans in medical research efforts, particularly when family and medical histories can be correlated with genome-wide data.

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