Genetic diversity of the African malaria vector Anopheles gambiae (original) (raw)

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Acknowledgements

The authors would like to thank the staff of the Wellcome Trust Sanger Institute Sample Logistics, Sequencing and Informatics facilities for their contributions. This work was supported by the Wellcome Trust (090770/Z/09/Z; 090532/Z/09/Z; 098051) and Medical Research Council UK and the Department for International Development (DFID) (MR/M006212/1). M.K.N.L. was supported by MRC grant G1100339. S.O.’L. and A.B. were supported by a grant from the Foundation for the National Institutes of Health through the Vector-Based Control of Transmission: Discovery Research (VCTR) program of the Grand Challenges in Global Health initiative of the Bill & Melinda Gates Foundation. D.W., C.S.W., H.D.M. and M.J.D. were supported by Award Numbers U19AI089674 and R01AI082734 from the National Institute of Allergy and Infectious Diseases (NIAID). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAID or NIH. T.A. was supported by a Sir Henry Wellcome Postdoctoral Fellowship.

Author information

Authors and Affiliations

  1. Malaria Programme, Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
    Alistair Miles, Chris S. Clarkson, Krzysztof Kozak, Richard D. Pearson, Martin J. Donnelly, Mara K. N. Lawniczak, Dominic P. Kwiatkowski, Martin J. Donnelly, Dominic P. Kwiatkowski, Mara K. N. Lawniczak, Martin J. Donnelly, Martin J. Donnelly, Jim Stalker, Eleanor Drury, Daniel Mead, Dushyanth Jyothi, Cinzia Malangone, Rachel Giacomantonio & Dominic P. Kwiatkowski
  2. MRC Centre for Genomics and Global Health, University of Oxford, Oxford, OX3 7BN, UK
    Alistair Miles, Nicholas J. Harding, Giordano Bottà, Tiago Antão, Richard D. Pearson, Dominic P. Kwiatkowski, Dominic P. Kwiatkowski, Kirk Rockett, Anna Jeffreys, Christina Hubbart, Kate Rowlands, Paul Vauterin, Ben Jeffery, Ian Wright, Lee Hart, Krzysztof Kluczyński, Victoria Cornelius, Christa Henrichs & Dominic P. Kwiatkowski
  3. Dipartimento di Sanita Pubblica e Malattie Infettive, Istituto Pasteur Italia – Fondazione Cenci Bolognetti, Università di Roma SAPIENZA, Rome, Italy
    Giordano Bottà, Beniamino Caputo & Alessandra della Torre
  4. Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
    Chris S. Clarkson, Tiago Antão, Martin J. Donnelly, Martin J. Donnelly, David Weetman, Craig S. Wilding, Craig S. Wilding, David Weetman, Martin J. Donnelly, David Weetman, Craig S. Wilding, Martin J. Donnelly & Alison T. Isaacs
  5. University of Montana, Missoula, Montana, 59812, USA
    Tiago Antão
  6. Department of Genetics, Rutgers University, 604 Alison Road, Piscataway, 08854, New Jersey, USA
    Daniel R. Schrider, Andrew D. Kern & Andrew D. Kern
  7. Genome Sequencing and Analysis Program, Broad Institute, 415 Main Street, Cambridge, 02142, Maryland, USA
    Seth Redmond & Daniel E. Neafsey
  8. Department of Entomology, Virginia Tech, Blacksburg, 24061, Virginia, USA
    Igor Sharakhov & Igor Sharakhov
  9. Laboratory of Ecology, Genetics and Environmental Protection, Tomsk State University, Tomsk, 634050, Russia
    Igor Sharakhov & Igor Sharakhov
  10. Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Indiana, 46556, USA
    Christina Bergey, Nora J. Besansky, Kyanne R. Rohatgi & Nora J. Besansky
  11. Groningen Institute for Evolutionary Life Sciences (GELIFES), Nijenborgh 7, Groningen, 9747 AG, The Netherlands
    Michael C. Fontaine & Michael C. Fontaine
  12. Unité d’Ecologie des Systèmes Vectoriels, Centre International de Recherches Médicales de Franceville, Franceville, Gabon
    Diego Ayala & Nohal Elissa
  13. Institut de Recherche pour le Développement (IRD), UMR MIVEGEC (UM1, UM2, Montpellier, CNRS 5290, IRD 224, France
    Diego Ayala & Carlo Costantini
  14. Department of Life Sciences, Imperial College, Silwood Park, Ascot, Berkshire, SL5 7PY, UK
    Austin Burt, Samantha O’Loughlin, Samantha O’Loughlin & Austin Burt
  15. Department of Zoology, University of Oxford, The Tinbergen Building, South Parks Road, Oxford, OX1 3PS, UK
    H. Charles J. Godfray
  16. Department of Biology and School of Informatics and Computing, Indiana University, Bloomington, Indiana, 47405, USA
    Matthew W. Hahn
  17. KEMRI-Wellcome Trust Research Programme, PO Box 230, Bofa Road, Kilifi, Kenya
    Janet Midega, Janet Midega, Charles Mbogo & Philip Bejon
  18. Global Health and Tropical Medicine, GHTM, Instituto de Higiene e Medicina Tropical, IHMT, Universidade Nova de Lisboa, UNL, Rua da Junqueira 100, Lisbon, 1349-008, Portugal
    João Pinto, João Pinto, João Pinto & João Pinto
  19. Department of Microbiology and Immunology, Microbial and Plant Genomics Institute, University of Minnesota, St Paul, 55108, Minnesota, USA
    Michelle M. Riehle & Michelle M. Riehle
  20. Unit for Genetics and Genomics of Insect Vectors, Institut Pasteur, Paris, France
    Kenneth D. Vernick & Kenneth D. Vernick
  21. School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, L3 3AF, UK
    Craig S. Wilding, Craig S. Wilding & Craig S. Wilding
  22. Department of Entomology, University of California, Riverside, California, USA
    Bradley J. White
  23. Programa Nacional de Controle da Malária, Direcção Nacional de Saúde Pública, Ministério da Saúde, Luanda, Angola
    Arlete D. Troco
  24. Institut de Recherche en Sciences de la Santé (IRSS), Bobo Dioulasso, Burkina Faso
    Abdoulaye Diabaté
  25. Laboratoire de Recherche sur le Paludisme, Organisation de Coordination pour la lutte contre les Endémies en Afrique Centrale (OCEAC), Yaoundé, Cameroon
    Carlo Costantini
  26. Malaria Research and Training Centre (MRTC), University of Bamako, Bamako, Mali
    Boubacar Coulibaly
  27. Instituto Nacional de Saúde Pública, Ministério da Saúde Pública, Bissau, Guiné-Bissau
    João Dinis
  28. Infectious Diseases Research Collaboration, 2C Nakasero Hill Road, PO Box, Kampala, 7475, Uganda
    Henry D. Mawejje
  29. The Broad Institute of Massachusetts Institute of Technology and Harvard, 415 Main Street, Cambridge, 02142, Massachusetts, USA
    Bronwyn MacInnis

Consortia

The Anopheles gambiae 1000 Genomes Consortium

Contributions

Details of author contributions are given in the consortium author list.

Corresponding authors

Correspondence toAlistair Miles, Martin J. Donnelly, Mara K. N. Lawniczak or Dominic P. Kwiatkowski.

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Competing interests

The author declare no competing financial interests.

Additional information

Reviewer Information Nature thanks J. Pool and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Overview of population sampling.

Red circles show sampling locations for wild-caught mosquitoes. Colours in the map represent ecosystem classes; dark green represents forest ecosystems; see figure 9 in ref. 49 for a complete colour legend. The Congo Basin tropical rainforest is the large region of dark green in Central Africa. Sampling details for each site are shown in light grey boxes, including country (two-letter country code), location and year of collection, predominant ecosystem classification for the local region, and number and sex of individuals sequenced. For colony crosses, the direction of cross (colony of origin of mother and father) and number of offspring is shown. The inset map depicts geological fault lines in the East African rift system (http://pubs.usgs.gov/publications/text/East_Africa.html). Species assignment for Guinea-Bissau and Kenya specimens is uncertain, see main text. Sequencing depth per individual is shown as median (5th–95th percentile) for each population.

Extended Data Figure 2 Genome accessibility and haplotype validation.

a, Percentage of accessible bases in non-overlapping 400-kb windows. The schematic of chromosomes below shows chromatin state predictions from ref. 50. b, Haplotypes inferred in the crosses. Each panel shows either maternal or paternal haplotypes from a single cross. Each row within a panel represents a single progeny haplotype. Haplotypes are coloured by parental inheritance (blue denotes allele from parent’s first chromosome; red denotes allele from parent’s second chromosome). Switches between colours along a haplotype indicate recombination events. Regions that were within a run of homozygosity in the parent and thus not informative for haplotype validation are masked in grey. c, Error rate estimates for haplotypes inferred in wild-caught individuals. Top plots show estimates for the mean switch distance (red line), compared to the mean switch distance if heterozygotes were phased randomly (black line). Bottom plots show the switch error rate (probability of a switch error occurring between two adjacent heterozygous genotype calls).

Extended Data Figure 3 Variant discovery and nucleotide diversity.

a, Number of variant alleles discovered per individual mosquito. Only females are plotted. b, Genetic diversity within populations. Nucleotide diversity (π) and Tajima’s D were calculated in non-overlapping 20-kb genomic windows. SNP density depicts the distribution of allele frequencies (site frequency spectrum) for each population, scaled such that a population with constant size over time is expected to have a constant SNP density over all allele frequencies. c, Average nucleotide diversity (π) and ratio of diversity between sex-linked (X) and autosomal (A) chromosomes in relation to gene architecture. d, Relationship between number of individuals sampled and the cumulative number of variant sites discovered (left), availability of conserved Cas9 target sites within genes (centre), and number of genes containing at least 1 conserved Cas9 target site which could thus be ‘targetable’ for gene drive (right).

Extended Data Figure 4 ADMIXTURE analysis.

a, Ancestry proportions within individual mosquitoes for ADMIXTURE models from K = 2 to K = 10 ancestral populations. Each vertical bar represents the proportion of ancestry within a single individual, with colours corresponding to ancestral populations. These data are the average of the major _q_-matrix clusters derived by CLUMPAK analysis. b, Violin plot of cross-validation error for each of 100 replicates for each K value.

Extended Data Figure 5 Population structure and differentiation.

a, Principal components analysis of the 765 wild-caught mosquitoes. b, Average allele frequency differentiation (_F_ST) between pairs of populations. The bottom left triangle shows average _F_ST values between each population pair. The top right triangle shows the Z score for each _F_ST value estimated via a block-jackknife procedure39. CM* denotes Cameroon savannah sampling site only. c, Allele sharing in doubleton (_f_2) variants. The height of the coloured bars represent the probability of sharing a doubleton allele between two populations. Heights are normalized row-wise for each population.

Extended Data Figure 6 Ancestry informative markers.

Rows represent individual mosquitoes (grouped by population) and columns represent SNPs (grouped by chromosome arm). Colours represent species genotype. The column at the far left shows the species assignment according to the conventional molecular test based on a single marker on the X chromosome, which was performed for all individuals except Kenya (KE). The column at the far right shows the genotype for kdr variants in Vgsc codon 995. Lines at the lower edge show the physical locations of the AIM SNPs.

Extended Data Figure 7 Population size history.

a, Stairway plot of inferred histories for each population. The shaded area shows the 95% confidence interval from 199 bootstrap replicates. b, Inferred histories from three-epoch ∂a∂i models41. The thick line shows the history with the highest likelihood found by optimization; thin lines show 100 histories with the highest likelihoods from even sampling of the model parameter space. c, Inferred histories from ∂a∂i two-population models allowing for migration. For each population pair, solutions from 5 optimization runs with the highest likelihoods are shown, with the thick line showing the history with the highest likelihood. In all panels, time and _N_e are scaled assuming 11 generations per year and a mutation rate of μ = 3.5 × 10−9. Scaling of time and _N_e is proportional to 1/μ, for example, if the true mutation rate is twice as high then estimates of time and _N_e would be halved. ya, years ago.

Extended Data Figure 8 Identity by descent and recent effective population size history.

a, Patterns of IBD sharing within populations. Each marker represents a pair of individuals. b, The distribution of IBD tract lengths within populations. c, Recent population size history for the Kenyan population inferred by the IBDNe program45. d, Comparison of the IBD tract length distribution between Kenya and four simulated demographic scenarios. e, Population size histories inferred by IBDNe (red dashed lines) from data generated by simulations (black line shows the simulated population size history). f, Comparison of patterns of IBD sharing generated by simulations (black contour lines) with Kenyan data (filled blue contours). See Supplementary Information 8.4 for details of simulations. ga, generations ago.

Extended Data Figure 9 Genome scans for signatures of recent selection.

a, Haplotype diversity. Each track plots the H12 statistic in non-overlapping windows over the genome. A value of 1 indicates low haplotype diversity within a window, expected if one or two haplotypes have risen to high frequency owing to recent selection. A value of 0 indicates high haplotype diversity, expected in neutral regions. b, XP-EHH scans. For each population comparison (for example, BF gambiae versus BF coluzzii), positive scores indicate longer haplotypes and therefore recent selection in the first population (for example, BF gambiae), and negative scores indicate selection in the second population (for example, BF coluzzii).

Extended Data Figure 10 Haplotype structure at metabolic insecticide-resistance loci.

Plot components are as described for Fig. 4. For both loci, SNPs shown in the bottom panel are all either non-synonymous or splice site variants, and are associated with one or more haplotypes under selection. a, Haplotype clustering using 1,375 SNPs within the region 3R: 28,591,663–28,602,280 spanning 8 genes (Gste1_–_Gste8). b, Haplotype clustering using 1,844 SNPs within the region 2R: 28,491,415–28,502,910 spanning 5 genes (Cyp6p1_–_Cyp6p5).

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The Anopheles gambiae 1000 Genomes Consortium. Genetic diversity of the African malaria vector Anopheles gambiae.Nature 552, 96–100 (2017). https://doi.org/10.1038/nature24995

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