The African continent is regarded as the cradle of modern humans and African genomes contain more genetic variation than those from any other continent, yet only a fraction of the genetic diversity among African individuals has been surveyed1. Here we performed whole-genome sequencing analyses of 426 individuals—comprising 50 ethnolinguistic groups, including previously unsampled populations—to explore the breadth of genomic diversity across Africa. We uncovered more than 3 million previously undescribed variants, most of which were found among individuals from newly sampled ethnolinguistic groups, as well as 62 previously unreported loci that are under strong selection, which were predominantly found in genes that are involved in viral immunity, DNA repair and metabolism. We observed complex patterns of ancestral admixture and putative-damaging and novel variation, both within and between populations, alongside evidence that population from Zambia were a likely intermediate site along the routes of expansion of Bantu-speaking populations. Pathogenic variants in genes that are currently characterized as medically relevant were uncommon—but in other genes, variants denoted as ‘likely pathogenic’ in the ClinVar database were commonly observed. Collectively, these findings refine our current understanding of continental migration, identify gene flow and the response to human disease as strong drivers of genome-level population variation, and underscore the scientific imperative for a broader characterization of the genomic diversity of African individuals to understand human ancestry and improve health.
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WGS data used in this paper are available through the European Genome-phenome Archive (EGA) under study accession number: EGAS00001002976. The data include genomic (BAMs and VCFs) and minimal phenotypic data from appropriately consented individuals. In compliance with current international standards to protect participant confidentiality, the H3Africa-generated data are available to bona fide researchers within the wider scientific community through a controlled access process. Some of the DNA samples are archived in H3Africa biorepositories as part of the H3Africa Consortium agreement. To gain access to data in the EGA or biospecimens in the biorepositories, requests must be submitted to firstname.lastname@example.org, or requested through the H3Africa Data and Biospecimen Catalogue (https://catalogue.h3africa.org). Requests are subject to approval by an independent H3Africa Data and Biospecimen Access Committee (DBAC). Novel SNVs identified and reported here will be deposited into dbSNP. The H3Africa Initiative is committed to providing research data generated by the H3Africa research projects to the entire research community. H3Africa research seeks to promote fair collaboration between scientists in Africa and those from elsewhere. The H3Africa Consortium Data Sharing, Access and Release Policy outlines a policy framework that places a firm focus on African leadership and capacity building as guiding principles for African genomics research. The policy and related documents are available here: https://h3africa.org/index.php/consortium/consortium-documents/.
Code for the implementation of PROCRUSTES is available at https://github.com/dshriner/Procrustes, licensed under the GNU General Public License v.3.0.
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We thank the members of the wider H3Africa Consortium (www.h3africa.org) for their support and input, particularly J. Troyer and A. Duncanson; S. Tishkoff, J. Lupski, J. Belmont and C. Tyler-Smith for comments and feedback on the manuscript; K. Garson, A. Gillum and K. Schulze for their help with figure visualizations and for giving permission for the use of these figures; M. Cherif Rahimy for their assistance with recruitment in Benin and L. Sergeevna Mainzer, G. Rendon and V. Jongeneel from the HPCBio team at the University of Illinois Urbana-Champaign for the initial processing and variant calling of the high depth H3A-Baylor dataset using the Blue Waters supercomputing centre. WGS in H3Africa cohorts was supported by a grant from the National Human Genome Research Institute, National Institutes of Health (NIH/NHGRI) U54HG003273. The African Collaborative Center for Microbiome and Genomics Research (ACCME) is funded by NIH/NHGRI grant U54HG006947. The AWI-Gen Collaborative Centre is funded by NIH grant U54HG006938. The Exploring Perspectives on Genomics and Sickle Cell Public Health Interventions was funded by NHGRI/NIH grant U01HG007459. The Clinical and Genetic Studies of Hereditary Neurological Disorders in Mali study was funded by the NHGRI/NIH grant U01HG007044. The Collaborative African Genomics Network (CAfGEN) is funded by the National Institute of Allergy and Infectious Diseases (NIAID) of NIH and the NHGRI of the NIH (U54AI110398). ‘TrypanoGEN: an integrated approach to the identification of genetic determinants of susceptibility to trypanosomiasis’, was funded by the Wellcome Trust (099310/Z/12/Z). L.R.B. was supported by the CERCA Programme/Generalitat de Catalunya and by the Spanish Ministry of Economy and Competitiveness, through the ‘Severo Ochoa Programme for Centres of Excellence in R&D’ 2016–2019 (SEV-2015-0533). N.M. (principal investigator), S.A., G.B., G.W., J.K., Y.J.F., T.O., O.F., E.A., S.H., G. Mazandu, M. Mbiyvanga, A.B., S.K.K., E.R.C. and A. Moussa are funded by the NIH H3ABioNet grant under award number U24HG006941. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the African Academy of Sciences, the National Institutes of Health or the Wellcome Trust.
The authors declare no competing interests.
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Extended data figures and tables
Existing African datasets from AGVP4, 1000 Genomes project2, SAHGP17 and previously published studies9,14 and a representative European population (CEU) from the 1000 Genomes Project are included as reference panels. K values from 2 to 10 are shown. See Supplementary Table 22 for definitions of abbreviations.
a, CLR score distributions in known selected genes (significant population-specific outlier scores (that is, with P < 0.01) for the window overlapping the gene are indicated by an asterisk). b, Summary of PBS comparisons. Genes with longer branch lengths in WGR compared to BOT and CAM are circled in blue; longer branch lengths in BOT and CAM in comparison to the other two populations are encircled in brown and dark green, respectively. c, Overlap between the proportion of KS ancestry (%) and CLR score across chromosome 6 in BOT.
a, EFO traits from the GWAS catalogue reflected by highly divergent SNVs within 50 kb of GWAS hits. From left to right, ribbons illustrate the relative representation of variants across pairwise population comparisons, GWAS ancestry, EFO top label, EFO trait or disease label, and disease or traits mapped to the EFO label. b, Distribution and sharing of common (MAF > 5%) putative LOF variants between two or more populations (coloured bars) and between all populations surveyed (red bars). c, Specific disease classes to which 5% or more genes with putative LOF variants shared between all populations were mapped. d, Correlation (Pearson) between WHO mortality rates for influenza and ratio of putative LOF variants in direct (n = 181) compared with indirect (n = 1842) influenza-associated genes (red solid line, all populations; red dotted line, west African populations). The blue dotted line represents the mean correlation for the same correlations generated using 1,000 permutations of random genes; the s.e.m. for all populations is shown in grey. e, Correlation statistics (adjusted R2) for the putative LOF ratio for genes related to hepatitis C (HCV, n = 190 direct genes, n = 1837 indirect genes), HIV(n = 724 direct genes, n = 1351 indirect genes), influenza in west African countries (CAM, MAL, FNB and BRN), and malaria (n = 484 direct genes, n = 1554 indirect genes) are shown as red dots against the box plot distributions of correlation statistics (adjusted R2) generated using 1,000 permutations of random genes (Supplementary Table 18). Box plots show the median value (centre line), whiskers indicate the limits of the highest (fourth) and lowest (first) quartiles of the data; distribution outliers are shown as dots.
Extended Data Fig. 4 Distribution of G6PD variants and ClinVar pathogenic variants across H3Africa populations.
a, Frequency distribution of pathogenic and likely pathogenic variants (n = 287) in H3Africa HC-WGS populations. Disease genes with variants that had an allele frequency > 5% across multiple populations (shown in Fig. 4c) are highlighted. Box plots show the median value (centre line), whiskers indicate the limits of the highest (fourth) and lowest (first) quartiles of the data; distribution outliers are shown as dots. b, Relative frequencies of 11 G6PD deficiency-associated alleles within each population separated by sex. G6PD A− 202A and 376G refer to the A-deficiency associated with either rs1050828 (c.202G>A) or rs1050829 (c.376A>G) (MIM 305900).
This file contains Supplementary Notes 1-5, Supplementary Figures 1-20, Supplementary Methods Figures 1–3 and Supplementary References.
This file contains Supplementary Methods Tables 1-2 and 23 Supplementary Tables (referred to in the main Supplementary Information file).
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Choudhury, A., Aron, S., Botigué, L.R. et al. High-depth African genomes inform human migration and health. Nature 586, 741–748 (2020). https://doi.org/10.1038/s41586-020-2859-7
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