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miércoles, 5 de octubre de 2016

A genomic history of Aboriginal Australia



Nature
 
 
doi:10.1038/nature18299
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Received
 
Accepted
 
Published online
 

Abstract

The population history of Aboriginal Australians remains largely uncharacterized. Here we generate high-coverage genomes for 83 Aboriginal Australians (speakers of Pama–Nyungan languages) and 25 Papuans from the New Guinea Highlands. We find that Papuan and Aboriginal 
Australian ancestors diversified 25–40 thousand years ago (kya), suggesting pre-Holocene population structure in the ancient continent of Sahul (Australia, New Guinea and Tasmania). However, all of the studied Aboriginal Australians descend from a single founding population that differentiated ~10–32 kya. We infer a population expansion in northeast Australia during the Holocene epoch (past 10,000 years) associated with limited gene flow from this region to the rest of Australia, consistent with the spread of the Pama–Nyungan languages. We estimate that Aboriginal Australians and Papuans diverged from Eurasians 51–72 kya, following a single out-of-Africa dispersal, and subsequently admixed with archaic populations. Finally, we report evidence of selection in Aboriginal Australians potentially associated with living in the desert.
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Author information

Author footnotes

  1. These authors contributed equally to this work.

    • Anna-Sapfo Malaspinas, 
    • Michael C. Westaway, 
    • Craig Muller, 
    • Vitor C. Sousa, 
    • Oscar Lao, 
    • Isabel Alves & 
    • Anders Bergström

Affiliations

  1. Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Øster Voldgade 5–7, 1350 Copenhagen, Denmark

    • Anna-Sapfo Malaspinas,
      •  
    • Craig Muller,
      •  
    • Ashot Margaryan,
      •  
    • Thorfinn S. Korneliussen,
      •  
    • J. Víctor Moreno-Mayar,
      •  
    • Martin Sikora,
      •  
    • Paula F. Campos,
      •  
    • Robert A. Foley,
      •  
    • Marta Mirazón Lahr,
      •  
    • Rasmus Nielsen &
      •  
    • Eske Willerslev

  2. Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6, 3012 Bern, Switzerland

    • Anna-Sapfo Malaspinas,
      •  
    • Vitor C. Sousa,
      •  
    • Isabel Alves,
      •  
    • Isabelle Dupanloup &
      •  
    • Laurent Excoffier

  3. Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland

    • Anna-Sapfo Malaspinas,
      •  
    • Vitor C. Sousa,
      •  
    • Isabel Alves,
      •  
    • Stephan Peischl,
      •  
    • Isabelle Dupanloup &
    • Laurent Excoffier

  4. Research Centre for Human Evolution, Environmental Futures Research Institute, Griffith University, Nathan, Queensland 4111, Australia

    • Michael C. Westaway,
      •  
    • Tim H. Heupink,
      •  
    • Sankar Subramanian,
      •  
    • Joanne L. Wright &
      •  
    • David M. Lambert

  5. CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain

    • Oscar Lao

  6. Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain

    • Oscar Lao

  7. Population and Conservation Genetics Group, Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal

    • Isabel Alves

  8. Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK

    • Anders Bergström,
      •  
    • Yali Xue,
      •  
    • Chris Tyler-Smith,
      •  
    • Richard Durbin,
      •  
    • Manjinder S. Sandhu &
      •  
    • Eske Willerslev

  9. Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, Denmark

    • Georgios Athanasiadis,
      •  
    • Jade Y. Cheng,
      •  
    • Mikkel H. Schierup &
      •  
    • Thomas Mailund

  10. Department of Integrative Biology, University of California, Berkeley, California 94720, USA

    • Jade Y. Cheng,
      •  
    • Jacob E. Crawford &
      •  
    • Fernando Racimo

  11. Verily Life Sciences, 2425 Garcia Ave, Mountain View, California 94043, USA

    • Jacob E. Crawford

  12. Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany

    • Enrico Macholdt,
      •  
    • Chiara Barbieri,
      •  
    • Irina Pugach,
      •  
    • Shengyu Ni &
      •  
    • Mark Stoneking

  13. Interfaculty Bioinformatics Unit University of Bern, Baltzerstrasse 6, CH-3012 Bern, Switzerland

    • Stephan Peischl

  14. Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Building 208, 2800 Kongens Lyngby, Denmark

    • Simon Rasmussen

  15. Department for Archaeogenetics, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, D-07745 Jena, Germany

    • Stephan Schiffels

  16. The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark

    • Anders Albrechtsen &
      •  
    • Ida Moltke

  17. Department of Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, D-07745 Jena, Germany

    • Chiara Barbieri

  18. Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK

    • Anders Eriksson,
      •  
    • Andrea Manica &
      •  
    • Eske Willerslev

  19. Integrative Systems Biology Laboratory, Division of Biological and Environmental Sciences & Engineering, King Abdullah University of Science and Technology, 23955-6900 Thuwal, Saudi Arabia

    • Anders Eriksson

  20. Institute for Theoretical Physics, ETH Zürich, Wolfgang-Pauli-Str. 27, 8093 Zürich, Switzerland

    • Ivan P. Levkivskyi

  21. Jeffrey Cheah School of Medicine & Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, Sunway City, 46150 Selangor, Malaysia

    • Farhang A. Aghakhanian &
      •  
    • Maude E. Phipps

  22. Evolutionary Medicine Group, Laboratoire d’Anthropologie Moléculaire et Imagerie de Synthèse, UMR 5288, Centre National de la Recherche Scientifique, Université de Toulouse 3, 31073 Toulouse, France

    • Nicolas Brucato &
      •  
    • Francois-Xavier Ricaut

  23. Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark

    • Søren Brunak

  24. CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Rua dos Bragas 289, 4050-123 Porto, Portugal

    • Paula F. Campos

  25. National Parks and Wildlife, Sturt Highway, Buronga, New South Wales 2739, Australia

    • Warren Clark

  26. Explico Foundation, Vågavegen 16, 6900 Florø, Norway

    • Sturla Ellingvåg

  27. Giriwandi, Gimuy Yidinji Country, Queensland 4868, Australia

    • Gudjugudju Fourmile

  28. Research Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK

    • Pascale Gerbault &
      •  
    • Mark G. Thomas

  29. UCL Department of Anthropology, 14 Taviton Street, London WC1H 0BW, UK

    • Pascale Gerbault &
      •  
    • Andrea B. Migliano

  30. Yinhawangka elder, Perth, Western Australia 6062, Australia

    • Darren Injie

  31. Papua New Guinea Institute of Medical Research, PO Box 60, Goroka, Papua New Guinea

    • George Koki,
      •  
    • William Pomat &
      •  
    • Peter Siba

  32. Archaeology, School of Humanities & Social Sciences, University PO Box 320, University of Papua New Guinea & College of Arts, Society & Education, James Cook University, Cairns, Queensland 4811, Australia

    • Matthew Leavesley

  33. Ngadju elder, Coolgardie, Western Australia 6429, Australia

    • Betty Logan

  34. Wongatha elder, Kurrawang, Western Australia 6430, Australia

    • Aubrey Lynch

  35. Department of Anatomy, University of Otago, Dunedin 9054, New Zealand

    • Elizabeth A. Matisoo-Smith

  36. 2209 Springbrook Road, Springbrook, Queensland 4213, Australia

    • Peter J. McAllister

  37. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK

    • Alexander J. Mentzer

  38. Estonian Biocentre, Riia 23b, Tartu 51010, Estonia

    • Mait Metspalu

  39. 86 Workshop Road, Yarrabah, Queensland 4871, Australia

    • Les Murgha

  40. Esperance Nyungar elder, Esperance, Western Australia 6450, Australia

    • Doc Reynolds

  41. Atakani Street, Napranum, Queensland 4874, Australia

    • Thomas Wales

  42. 2 Wynnum North Road, Wynnum, Queensland 4178, Australia

    • Colleen Ma’run Wall

  43. School of Anthropology and Museum Ethnography, Oxford University, Oxford OX2 6PE, UK

    • Stephen J. Oppenheimer

  44. Centre for Rock Art Research and Management, M257, University of Western Australia, Perth, Western Australia 6009, Australia

    • Joe Dortch

  45. Leverhulme Centre for Human Evolutionary Studies, Department of Archaeology and Anthropology, University of Cambridge, Fitzwilliam Street, Cambridge CB2 1QH, UK

    • Robert A. Foley &
      •  
    • Marta Mirazón Lahr

  46. Department of Linguistics, Yale University, 370 Temple Street, New Haven, Connecticut 06520, USA

    • Claire Bowern

  47. Institute for Human Genetics, University of California, San Francisco, California 94143, USA

    • Jeffrey D. Wall

  48. Departments of Integrative Biology and Statistics, University of California, Berkeley, California 94720, USA

    • Rasmus Nielsen

Contributions

G.A., J.Y.C., J.E.C., T.H.H., E.M., S.P., S.R., S.Sc., S.Su. and J.L.W. contributed equally and are listed alphabetically in the author list; A.A., C.Ba., I.D., A.E., A.Mar., I.M. and I.P. contributed equally and are listed alphabetically in the author list; T.S.K., I.P.L., J.V.M.-M., S.N., F.R., M.Si. and Y.X. contributed equally and are listed alphabetically in the author list. E.W. and D.M.L. initially conceived and headed the project. L.E. led the genetic load and the SFS-based demographic analyses. M.S.S. headed the research at the Wellcome Trust Sanger Institute. A.-S.M. planned and coordinated the genetic analyses and the sequencing of the Aboriginal Australian genomes. C.M., J.L.W., T.H.H., P.F.C., W.C., G.F., D.I., B.L., A.L., P.J.M., L.M., D.R., T.W., C.W., J.D., M.C.W. and E.W. collaborated with local groups to collect Aboriginal Australian samples. N.B., P.G., G.K., M.L., A.J.M., A.B.M., W.P., F.-X.R., P.S., M.G.T. and S.J.O. collaborated with local groups to collect Papuan samples. S.E. collaborated with local groups to collect the Rapanui sample. A.Mar. extracted DNA for the Aboriginal Australian genomes. M.S.S., A.B. and C.T.-S. coordinated the design and sequencing of the Papuan genomes. O.L., V.C.S., I.A., A.-S.M., A.B., G.A., J.Y.C., J.E.C., T.H.H., E.M., S.P., S.R., S.Sc., S.Su., J.L.W., A.A., C.Ba., I.D., A.E., A.Man., I.M., I.P., T.S.K., I.P.L., J.V.M.-M., S.N., F.R., M.Si., F.A., S.B., L.E., J.D.W. and T.M. analysed genetic data. C.Bo. collected and analysed linguistic data. L.E., E.W., D.M.L., Y.X., M.E.P., C.T.-S., R.D., M.S.S., A.Man., M.H.S., T.M., M.St. and R.N. supervised genetic analyses. M.C.W., C.M., W.C., G.F., D.I., B.L., A.L., P.J.M., L.M., D.R., T.W., C.W., E.A.M.-S., M.M., M.E.P., S.J.O., J.D., A.B.M., R.A.F. and M.M.L. provided archaeological, anthropological and historical context. A.-S.M., V.C.S., O.L., I.A., A.B., M.M.L., R.N., L.E., D.M.L. and E.W. wrote the manuscript with critical input from G.A., T.H.H., E.M., S.Sc., S.Su., J.L.W., C.Ba., A.E., I.P., E.A.M.-S., M.S.S., S.J.O., C.T.-S., R.D., M.G.T., J.D., A.Man., M.H.S., R.A.F., C.Bo., J.D.W., T.M., M.St. and all other coauthors. A.-S.M., V.C.S., O.L., I.A. and A.B. revised and compiled the Supplementary Information.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to: 
The Aboriginal Australian and Papuan whole genome sequence data generated in this study have been deposited at the European Genome-phenome Archive (EGA, http://www.ebi.ac.uk/ega/), which is hosted by the EBI, under the accession numbers EGAS00001001766 andEGAS00001001247, respectively. The Papuan SNP array data generated in this study can be found under http://geogenetics.ku.dk/latest-news/alle_nyheder/2016/data.

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Per-individual admixture proportions of K = 7 ancestral components including Aboriginal Australians, New Guineans, Europeans, Africans, Melanesians and Polynesians. (581 KB)
    The genome of each individual is depicted as a bar and is coloured according to the estimated genome-wide proportions of ancestry components. An unrooted tree showing the relationships between the identified ancestral components is also estimated by our method. Each ancestry has been labelled with the name of the population (see also map) showing the highest fraction of that ancestral component. The cross-validation error is minimized for this value of K for fivefold cross-validation. The rooted tree supports the shared genetic origin of Aboriginal Australians, Papuans and Bougainvilleans. Note that only individuals with more than 50% of Aboriginal Australian ancestry in their genomes (defined in Supplementary Information section S06) were included in the analyses. Refer to ref. 58 and Supplementary Information section S05 for details about the method and the analysis. Map data ©2016 Google, INEGI. Tree constructed with http://jade-cheng.com/trees/.
  2. Extended Data Figure 2: Genetic relationships of Aboriginal Australians and Papuans. (491 KB)
    a, Genetic affinities between a western central desert (WCD02) genome and Aboriginal Australians and Papuans. Outgroup f3 statistics between WCD02 and all other Aboriginal Australians and Highland Papuan individuals that were whole-genome sequenced for this study, using the genotypes called from the sequencing data. Because the widespread recent admixture in Aboriginal Australians has large confounding effects on the f3statistics, the values were adjusted using the slope coefficient from a simple linear regression model fitted to the relationship between f3 and the fraction of non-indigenous (that is, neither Aboriginal Australian nor Papuan) ancestry in each individual genome. The adjusted f3 statistics display a genetic gradient that separates western and eastern Aboriginal Australian populations. However, we find no differences between Papuan population samples in their level of Aboriginal Australian affinity (Kruskal–Wallis test, P = 0.083). Horizontal lines correspond to ±1 standard error. b, Genetic affinities between a Papuan highlander genome and Aboriginal Australians and Papuans. The Papuan highlander sample MAR01 from the Marawaka area was arbitrarily chosen as a reference point for this analysis. f3 values were adjusted for recent admixture as in a. All Aboriginal Australian groups display a similar level of Highland Papuan affinity (with the exception of three outlier individuals from the north-eastern WPA and CAI populations: WPA06, WPA05 and CAI10, the latter two of which are known to have at least one parent with origins in Papua New Guinea or the Torres Strait Islands). While some differences between groups are actually statistically significant (Kruskal–Wallis test, P = 0.0002, after removing the three outliers), which could be consistent with, for example, low levels of Papuan gene flow into some Aboriginal Australian groups (see Supplementary Information sections S06 and S07), we caution that some of these differences are probably due to imperfect adjustment for Eurasian admixture (the adjusted f3 is highest in the WCD population, which has the least Eurasian admixture). Horizontal lines correspond to ±1 standard error. c, MSMC analyses. Linear interpolation through the midpoints of the time intervals of the relative cross coalescence rate estimates from MSMC25 using pairs of individuals including one HGDP-Papuan and one other individual as indicated. We used CAI01, PIL06, WCD01, WON03 and an ECCAC sample for this analysis (see Supplementary Information section S08 for details). The MSMC results were scaled using a mutation rate of 1.25×10−8 per generation per site as suggested in ref. 41 and a generation time of 29 years, corresponding to the average hunter–gatherer generation interval for males and females42.
  3. Extended Data Figure 3: Introgressed archaic sites and putative Denisovan and Neanderthal haplotypes. (876 KB)
    a, Distribution of number of putative introgressed sites per individual from archaic humans. The number of Neanderthal-specific introgressed sites per individual increases from Europe to Australia, and then decreases in Amerindians, which is consistent with recurrent Neanderthal (or Neanderthal-related archaic) gene flow during the expansion into Eurasia. Our results are thus indicative of several pulses of Neanderthal gene flow into modern humans, as inferred previously48, 49, 50. We note, however, that the apparent high levels of Neanderthal-specific introgressed sites in Australo-Papuans can be explained by the expected number of misclassified Neanderthal introgressed sites resulting from the shared ancestry with Denisovans (see Supplementary Information section S11 for details). b–e, Putative Denisovan (PDH) and Neanderthal haplotypes (PNH). The putative haplotypes correspond to clusters (four or more SNPs spanning at least 4 kb) of heterozygous or homozygous genotypes in complete linkage disequilibrium (‘diplotypes’) that are potentially the result of Neanderthal or Denisovan admixture. Those diplotypes are homozygous ancestral in 10 Africans, homozygous derived in the Denisovan for the PDH (respectively Neanderthal for the PNH), homozygous ancestral in the Neanderthal for the PDH (respectively Denisovan for the PNH), and with the derived allele segregating in all other contemporary non-African humans (see Supplementary Information section S10 for details). We report the average number of PDHs and PNHs (b), the correlation between the estimated amount of Australo-Papuan ancestry (see Fig. 2a, Extended Data Fig. 1, Supplementary Information section S05) and the number of identified PDHs for each Australian sample (c), the sum of the lengths (d) and the average length (e) of the PDHs and PNHs per individual for worldwide populations included in our reference panel (see Supplementary Information section S04).
  4. Extended Data Figure 4: Out of Africa: admixture graphs based on D-statistics and MSMC analyses. (303 KB)
    a, Admixture graphs representing some of the topologies considered for the two waves and one wave Out of Africa models assuming Denisovan admixture. All topologies are identical except for the coloured lineages representing Australo-Papuans (green), Neanderthal (Nea, orange) and Denisovan (Den, blue). The graphs differ in (1) the number of OoA events, and (2) the number of Neanderthal admixture pulses. Png, HGDP-Papuan. b, Sum of squared errors between the observed D-statistics and the expectations for each quartet in the graph involving the chimpanzee as an outgroup for each of the admixture graphs shown in a and the corresponding four without Denisovan admixture. Each point is the result of the optimization procedure with a different starting point. See Supplementary Information section S09 for details. c, Relative cross coalescence rate (CCR) estimates from MSMC25 for pairs of individuals including one African sample (Yoruba, Dinka and San) and one other, as indicated in the legend. d, Simulation study to assess the effect of archaic admixture on the CCR rates. Relative CCR estimated for data simulated under a simple two-population divergence model where one of the populations admixed at different rates with an archaic population. SeeSupplementary Information section S08 for details.
  5. Extended Data Figure 5: Inferred deleterious mutations. (121 KB)
    a, Box plot of the number of derived homozygous sites per individual for worldwide populations that are predicted to be deleterious. Deleteriousness of SNPs was inferred using genomic evolutionary rate profiling (GERP) rejected substitution scores. Derived alleles with a rejected substitution score larger than 2 were considered to be deleterious, see Supplementary Information section S11. b, c, Average rejected substitution score per individual calculated across heterozygous sites (b), and derived homozygous sites (c). Each coloured symbol corresponds to estimates from a single individual. Homozygosity is calculated as the number of derived homozygous sites divided by the number of sites at which an individual carries at least one copy of the derived allele. Solid lines show the linear regression of homozygosity against average rejected substitution score per individual for non-African modern humans. Dashed lines indicate the 95% confidence interval for the linear regression. See Supplementary Information S11 for details.
  6. Extended Data Figure 6: Effective population size changes over time. (213 KB)
    a, Population size estimates from MSMC for pairs of individuals from several populations within and outside of Australia. For each run, we used two individuals from each population, that is, four haplotypes in each run. MSMC results were scaled as in Fig. 3. b, Bayesian skyline plots (BSP) calculated from the mtDNA genome sequences, showing the effective population size estimates over time when considering either groups from northeastern Australia (CAI, WPA) or groups from southwestern Australia (ENY, NGA, WCD, WON). Solid lines are the estimates, dashed lines are the corresponding 95% credible intervals (see Supplementary Information section S12).
  7. Extended Data Figure 7: Genetics mirrors geography and languages. (488 KB)
    a, b, Procrustes analyses of the first two dimensions of a classical multidimensional scaling (MDS) analysis of the Aboriginal Australian genome sequences (autosomes). We considered two cases: an analysis including all variants (a), or only the variants remaining after genomic regions of putative recent European and East Asian origin are ‘masked’ (b,Supplementary Information section S06). Both MDS plots have been rotated towards the best overlap with geographic sampling locations as defined by Procrustes analysis51. In each plot, the connecting lines indicate the error of the MDS coordinates towards the assigned population-sampling geographic coordinates. We find that the genetic relationships within Australia mirrors geography, with a significant correlation for both cases, that is, rGEN,GEO = 0.59, P < 0.0005 for all variants and even higher, rGEN,GEO = 0.77, P <0 .0005="" 65="" a="" aboriginal="" an="" angle="" approach="" at="" australian="" axis="" bearing="" compared="" correlogram="" data-saferedirecturl="https://www.google.com/url?hl=en&q=http://www.nature.com/nature/journal/vaop/ncurrent/full/nature18299.html%23supplementary-information&source=gmail&ust=1475765934850000&usg=AFQjCNEPahU7QRpnRRAgqfCbdabAJgn6Rw" data.="" differentiation="" direction="" equator="" find="" for="" genetic="" genomes="" href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature18299.html#supplementary-information" in="" is="" main="" masked="" northeast="" of="" southwest="" style="color: #5c7996; text-decoration: none;" target="_blank" that="" the="" to="" using="" we="">Supplementary Information section S13
). c, d, Correspondence between genetics and linguistics. c, Unrooted neighbour-joining FST-based genetic tree (cladogram). Weir and Cockerham FST distance was computed between the Aboriginal Australian populations after masking the Eurasian tracts. Statistical robustness of each branch was estimated by means of a bootstrap analysis (1,000 replicates, Supplementary Information section S05). d, Bayesian phylogenetic tree for the 28 different Pama–Nyungan languages represented in this sample (from ref. 13, see Supplementary Information section S15). Posterior probabilities are also indicated. Note that one language group can be shared by different Aboriginal Australian groups. The linguistic tree was built with BEAST52. e–g, Gene flow across the continent. e, Mantel non-parametric r (estimating the goodness of fit between genetic differentiation and connectivity) versus ratios of resistance of inland to coastal nodes, showing a peak at 1.7.f, Best fit of pairwise population genetic differentiation, FST (computed between the nine Aboriginal Australian groups after masking Eurasian tracts (Supplementary Information section S06)), versus pairwise connectivity based on the environment (estimated as resistance) when moving inland is 1.7 times harder than moving along coastal nodes. g, Gene flow across the Australian landscape, quantified as the cumulative current for pairwise connections among Aboriginal Australian groups (black circles), with larger current (warmer colours) representing greater gene flow.
  • Extended Data Figure 8: European, East Asian and Papuan genomic tracts in Aboriginal Australians. (137 KB)
    a, Distribution of the tracts assigned to Aboriginal Australian (WCD), Papuan, East Asian or European ancestry for 58 unrelated non-WCD Aboriginal Australian samples. Most of the shorter tracts were of Papuan origin, suggesting that a large fraction of the Papuan gene flow is much older than that from Europe and East Asia, consistent with a Papuan influence spreading slowly from northeastern to southwestern Australia by ancient migration. b, Corresponding scatter plot with fitted line of per-individual variance in Papuan tract length versus geographic distance from WCD, the latter calculated using the great-circle distance formula for pairs of individual GPS coordinates. Papuan tract distribution showed a strong and significant correlation with distance from WCD (r = 0.64;P < 1 × 10−5), with ‘younger tracts’ (that is, with a larger variance) closer to New Guinea and ‘older tracts’ (that is, with a smaller variance) closer to WCD. This is also consistent with continuous Papuan gene flow spreading from the northeast.

    • Extended Data Tables

    1. Extended Data Table 1: Whole genome sequence depth of coverage, haplogroup and language assignments for the Aboriginal Australian samples (413 KB)
    2. Extended Data Table 2: Selection scan in Aboriginal Australians (365 KB)

    Supplementary information

    PDF files

    1. Supplementary Information (18 MB)
      This file contains Supplementary Text and Data, Supplementary Figures, Supplementary Tables and additional references (see Contents for more details).
      FIGURES: Show only the first Figure. The rest must be consulted at the original site. 




















      SOURCE: NATURE

      Nature
       
       
      doi:10.1038/nature18299
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