Abstract
The Pacific region is of major importance for addressing questions regarding human dispersals, interactions with archaic hominins and natural selection processes1. However, the demographic and adaptive history of Oceanian populations remains largely uncharacterized. Here we report high-coverage genomes of 317 individuals from 20 populations from the Pacific region. We find that the ancestors of Papuan-related (‘Near Oceanian’) groups underwent a strong bottleneck before the settlement of the region, and separated around 20,000–40,000 years ago. We infer that the East Asian ancestors of Pacific populations may have diverged from Taiwanese Indigenous peoples before the Neolithic expansion, which is thought to have started from Taiwan around 5,000 years ago2,3,4. Additionally, this dispersal was not followed by an immediate, single admixture event with Near Oceanian populations, but involved recurrent episodes of genetic interactions. Our analyses reveal marked differences in the proportion and nature of Denisovan heritage among Pacific groups, suggesting that independent interbreeding with highly structured archaic populations occurred. Furthermore, whereas introgression of Neanderthal genetic information facilitated the adaptation of modern humans related to multiple phenotypes (for example, metabolism, pigmentation and neuronal development), Denisovan introgression was primarily beneficial for immune-related functions. Finally, we report evidence of selective sweeps and polygenic adaptation associated with pathogen exposure and lipid metabolism in the Pacific region, increasing our understanding of the mechanisms of biological adaptation to island environments.
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Data availability
The whole-genome sequencing dataset generated and analysed in this study is available from the European Genome-Phenome Archive (EGA; https://www.ebi.ac.uk/ega/), under accession code EGAS00001004540. Data access and use is restricted to academic research in population genetics, including research on population origins, ancestry and history. The SGDP genome data were retrieved from the EBI European Nucleotide Archive (accession codes PRJEB9586 and ERP010710). The genome data from Malaspinas et al.18 were retrieved from the EGA (accession code EGAS00001001247). The genome data from Vernot et al.16 were retrieved from dbGAP (accession code phs001085.v1.p1).
Code availability
Neutrality statistics were computed with the optimized, window-based algorithms implemented in selink (https://github.com/h-e-g/selink). All other custom-generated computer codes or algorithms used in this study are available on GitHub (https://github.com/h-e-g/evoceania).
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Acknowledgements
We thank all volunteers and Indigenous communities participating in this research; S. Créno and the HPC Core Facility of Institut Pasteur (Paris) for the management of computational resources; F. Mendoza de Leon Jr, NCCA chairperson 2010–2016, for his support; C. Ebeo, O. Casel, K. Pullupul Hagada, D. Guilay, A. Manera and R. Quilang of Cagayan State University, Lahaina Sue Azarcon and Samuel Benigno of Quirino State University, and the regional and provincial offices of the National Commission for Indigenous Peoples (NCIP)–Cagayan Valley for their support and assistance. J.C. is supported by the INCEPTION programme ANR-16-CONV-0005 and the Ecole Doctorale FIRE-CRI-Programme Bettencourt and L.R.A. by a Pasteur-Roux-Cantarini fellowship. The CNRGH sequencing platform was supported by the France Génomique National infrastructure, funded as part of the « Investissements d’Avenir » programme managed by the Agence Nationale pour la Recherche (ANR-10-INBS-09). M.J. is supported by the Knut and Alice Wallenberg foundation. M.S. is supported by the Max Planck Society. The laboratory of L.Q.-M. is supported by the Institut Pasteur, the Collège de France, the CNRS, the Fondation Allianz-Institut de France and the French Government’s Investissement d’Avenir programme, Laboratoires d’Excellence ‘Integrative Biology of Emerging Infectious Diseases’ (ANR-10-LABX-62-IBEID) and ‘Milieu Intérieur’ (ANR-10-LABX-69-01).
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E.P. and L.Q.-M. conceived and supervised the project; J.C. led and performed the processing of the genetic data as well as the analyses of population structure and demographic inference; J.M.-R. led and performed the analyses of archaic and adaptive introgression; L.R.A. led and performed the analyses of genetic adaptation; S.C.-E., R.L. and P.V. performed the analyses of admixture models; E.P. coordinated all genetic analyses; O.C., M.L., A.M.-S.K., Y.-C.K., M.J., A.G. and M.S collaborated with local groups to collect population samples; C.H., A.B., R.O. and J.-F.D. coordinated and performed sample preparation and sequencing; F.V. provided the archaeological and anthropological context; G.L. and L.E. provided the theoretical and methodological context; J.C., J.M.-R., L.R.A., E.P. and L.Q.-M. wrote the manuscript, with critical input from all authors.
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Extended data figures and tables
Extended Data Fig. 1 Genetic structure of Pacific populations.
ADMIXTURE ancestry components are shown from K = 2 (top) to K = 10 (bottom) for the 462 unrelated individuals. The lowest cross-validation error was obtained at K = 6 (Supplementary Fig. 5). Populations are delimited by black borders. Population width is not proportional to population sample size, which is indicated in parentheses.
Extended Data Fig. 2 Demographic models for Pacific populations.
a, Maximum-likelihood demographic model for baseline populations. Point estimates of parameters and 95% confidence intervals are shown in Supplementary Table 2. b, Maximum-likelihood demographic models for western Remote Oceanian individuals (VAN). The likelihoods of the two models are not considered to be different. Point estimates of parameters and 95% confidence intervals are shown in Supplementary Table 5. The (VAN, PNG) model (left) assumes that the ni-Vanuatu diverged from Papua New Guinean Highlanders and then received gene flow from Solomon Islanders, Bismarck Islanders and Austronesian-speaking Taiwanese Indigenous peoples. The (VAN, SLI) (right) model assumes that the ni-Vanuatu diverged from the Solomon Islanders and then received gene flow from the other three groups. For the sake of clarity, only Taiwanese Indigenous, Near Oceanian and western Remote Oceanian populations are shown. c, Maximum-likelihood model for Austronesian-speaking populations, represented by Taiwanese Indigenous, Philippine Kankanaey and Tikopia Polynesian individuals. BKA, Bismarck Islanders; HAN, Han Chinese individuals (China); NOC GST, a meta-population of Near Oceanian individuals; OoA GST, an unsampled population to represent the Out-of-Africa exodus; PHP, Philippine individuals; PNG, Papua New Guinean Highlanders; POL, Polynesian individuals from the Solomon Islands; SAR, Sardinian individuals (Italy); SLI, Solomon Islanders; TWN, Taiwanese Indigenous peoples; VAN, ni-Vanuatu; YRB, Yoruba individuals (Nigeria). We assumed a mutation rate of 1.25 × 10−8 mutations per generation per site and a generation time of 29 years. Single-pulse introgression rates are reported as a percentage. The 95% confidence intervals are shown in square brackets. The larger the rectangle width, the larger the estimated effective population size (Ne), except for b. Bottlenecks are indicated by black rectangles. Grey and black arrows represent continuous and single pulse gene flow, respectively. One- and two-directional arrows indicate asymmetric and symmetric gene flow, respectively. We limited the number of parameter estimations by making simplifying assumptions regarding the recent demography of East-Asian-related and Near Oceanian populations in a and c, respectively (Supplementary Note 4). Sample sizes are described in Supplementary Note 4.
Extended Data Fig. 3 Match rate of introgressed S′ haplotypes in Pacific populations to the Vindija Neanderthal and Altai Denisovan genomes.
The match rate is the proportion of putative archaic alleles matching a given archaic genome, excluding sites at masked positions. Only S′ haplotypes with more than 40 sites outside archaic genome masks were included in the analysis. The numbers indicate the height of the density corresponding to each contour line. Contour lines are shown for multiples of 1 (solid lines) and multiples of 0.1 between 0.3 and 0.9 (dashed lines).
Extended Data Fig. 4 Detection of introgressed haplotypes from an unknown archaic hominin.
a, Cumulative length of S′ haplotypes retrieved among modern human populations (S′), after removing Neanderthal CRF haplotypes (S′NoNeanderthal) or Denisovan CRF haplotypes (S′NoDenisova) or both (S′NoArchaic), and removing from the S′NoArchaic haplotypes those with a match rate higher than 1% to either the Vindija Neanderthal or Altai Denisovan genomes (S′NoArchaicLowMatch). These S′ haplotypes are, therefore, putatively introgressed haplotypes from hominins outside of the Neanderthal and Denisovan branch (Supplementary Note 13). b, Proportion of S′NoArchaicLowMatch haplotypes common or private (that is, unique) to populations. Total numbers of S′NoArchaicLowMatch haplotypes are shown above the population labels.
Extended Data Fig. 5 Examples of candidate loci for adaptive introgression in Pacific populations.
a, Adaptive introgression of Denisovan origin at the CD33 locus. b, Adaptive introgression of Denisovan origin at the IRF4 locus. c, Adaptive introgression of Neanderthal origin at the KRT80 locus. d, Adaptive introgression of Neanderthal origin at the TBC1D1 locus. e, Adaptive introgression of Denisovan origin at the JAK1 locus. f, Adaptive introgression of Denisovan origin at the BANK1 locus. a–f, Left, local Manhattan plot showing the derived allele frequency of archaic SNPs (aSNPs), the proportion of high-confidence introgressed haplotypes (HC CRF) and the gene isoforms at the locus (in Mb, based on hg19 coordinates). Middle, derived allele frequencies of the top archaic SNP in 1000 Genomes Project phase 3 populations (excluding recently admixed populations). Right, derived allele frequencies of the top archaic SNP in populations from this study. Colours in the left panels indicate populations as in Fig. 1. Pie charts indicate the derived allele frequency in purple, and are centred on the approximate geographical location of each population. Maps were generated using the maps R package51.
Extended Data Fig. 6 Classic sweep signals detected in Papuan-related populations.
a–d, Manhattan plots of classic sweep signals in Papua New Guinean Highlanders (a), Solomon Islanders (b), ni-Vanuatu (c) and Philippine Agta (d). a–d, The y axis shows the −log10(P value) for the number of outlier SNPs per window. Each point is a 100-kb window. The names of genes associated with windows with significant sweep signals are shown.
Extended Data Fig. 7 Classic sweeps and adaptive archaic introgression.
a, b, Coloured squares indicate genomic regions displaying signals of both a selective sweep and adaptive introgression from Neanderthals (a) or Denisovans (b). Yellow and blue frames indicate genomic regions identified in East-Asian- and Papuan-related populations, respectively. AGT, Philippine Agta; PHP, Philippine individuals.
Extended Data Fig. 8 Examples of candidate loci for classic sweeps in Pacific populations.
a, c, Sweep signals detected in Papuan-related populations at the GABRP locus (a) and in Polynesian populations at the LHFPL2 locus (c). Manhattan plots shows the −log10(P value) of the Fisher’s scores for each SNP (Supplementary Note 16). b, d, Maps showing the population allele frequencies for candidate SNPs rs79997355 (GABRP) (b) and rs117421341 (LHFPL2) (d). Pie charts indicate the derived allele frequency in purple, in which the radius is proportional to the sample size (Supplementary Table 1). The pie charts for the populations of Santa Cruz and Vanuatu were moved from their sampling locations for convenience. Maps were generated using the maps R package51.
Extended Data Fig. 9 Classic sweep signals detected in East-Asian-related populations.
Manhattan plots of classic sweep signals in East Asian individuals (a), Taiwanese Indigenous peoples (b), Philippine Cebuano (c) and Polynesian individuals (d). a–d, The y axis shows the −log10(P value) for the number of outlier SNPs per window. Each point is a 100-kb window. The names of genes associated with windows with significant sweep signals are shown.
Extended Data Fig. 10 Schematic model of the history of archaic introgression in modern humans.
The phylogenetic tree depicts relationships among archaic and modern humans. Estimates for the splits between archaic, introgressing populations and for introgression episodes are shown. Five introgression events are consistent with our data: a Neanderthal introgression event into the common ancestors of non-African individuals around 61 ka; a Denisovan introgression event into the ancestors of Papuan individuals approximately 46 ka, which is shared with the ancestral Indigenous Australian individuals and Philippine Agta populations14,15,17,97; a Denisovan introgression event that occurred only in the ancestors of Papuan individuals around 25 ka; a Denisovan introgression event in the ancestors of East Asian individuals around 21 ka, the legacy of which is also observed in Philippine Agta and western Eurasian individuals due to subsequent gene flow (solid purple arrows); and a Denisovan introgression event into the ancestors of the Philippine Agta at an unknown date.
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Supplementary Information
This file contains Supplementary Notes 1-18, which provide additional information on the methodology, results and discussion of the main text; Supplementary Figs. 1-82, which present additional results and quality checks; and Supplementary References.
Supplementary Tables
This file contains Supplementary Tables 1-25 (referred to in the main Supplementary Information file).
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Choin, J., Mendoza-Revilla, J., Arauna, L.R. et al. Genomic insights into population history and biological adaptation in Oceania. Nature 592, 583–589 (2021). https://doi.org/10.1038/s41586-021-03236-5
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DOI: https://doi.org/10.1038/s41586-021-03236-5
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