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The Simons Genome Diversity Project: 300 genomes from 142 diverse populations

Nature volume 538, pages 201–206 (2016)Cite this article

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Abstract

Here we report the Simons Genome Diversity Project data set: high quality genomes from 300 individuals from 142 diverse populations. These genomes include at least 5.8 million base pairs that are not present in the human reference genome. Our analysis reveals key features of the landscape of human genome variation, including that the rate of accumulation of mutations has accelerated by about 5% in non-Africans compared to Africans since divergence. We show that the ancestors of some pairs of present-day human populations were substantially separated by 100,000 years ago, well before the archaeologically attested onset of behavioural modernity. We also demonstrate that indigenous Australians, New Guineans and Andamanese do not derive substantial ancestry from an early dispersal of modern humans; instead, their modern human ancestry is consistent with coming from the same source as that of other non-Africans.

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Figure 1: Genetic variation in the SGDP.
Figure 2: Cross-coalescence rates and effective population sizes for selected population pairs.
Figure 3: Present-day populations have negligible ancestry from an early dispersal of modern humans out of Africa.

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Accession codes

Primary accessions

European Nucleotide Archive

Data deposits

Raw data for 279 genomes for which the informed consent documentation is consistent with fully public data release are available through the EBI European Nucleotide Archive under accession numbers PRJEB9586 and ERP010710. For the remaining 21 genomes (designated by code ‘Y’ in the seventh column of Supplementary Data Table 1), data are deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001001959. Data for these 21 genomes can be obtained by submitting to the EGA Data Access Committee a signed letter containing the following text: “(a) I will not distribute the data outside my collaboration; (b) I will not post the data publicly; (c) I will make no attempt to connect the genetic data to personal identifiers for the samples; and (d) I will not use the data for any commercial purposes.” Compact versions of the SGDP dataset and software for accessing it are available at (http://genetics.med.harvard.edu/reichlab/Reich_Lab/Datasets.html). The short tandem repeat (STR) genotypes are available through dbVar under accession number nstd128 (http://www.ncbi.nlm.nih.gov/dbvar).

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Acknowledgements

We thank the volunteers who donated samples. We thank H. Blanche, N. Boivin, H. Cann (deceased), E. Eichler, H. Greely, M. Petraglia, K. Prüfer, A. Rogers, M. Steinrücken, U. Stenzel and P. Sudmant for comments, critiques, discussions, or advice on assembling samples. We thank S. Fan for uploading 21 genomes to the European Genome-phenome archive. The sequencing was funded by the Simons Foundation (SFARI 280376) and the US National Science Foundation (BCS-1032255). I.M. was supported by a Long Term Fellowship grant LT001095/2014 from the Human Frontier Science program. P.S. was supported by the Wenner-Gren foundation and the Swedish Research Council (VR grant 2014-453). T.W. and M.G. were supported by an NIJ grant 2014-DN-BX-K089. Y.E. was supported by a Career Award at the Scientific Interface from the Burroughs Wellcome Fund and by NIJ grant 2014-DN-BX-K089. D.L. was supported by the Natural Sciences and Engineering Research Council of Canada. T.K. was supported by ERC Starting Investigator grant FP7 - 261213. R.S. received support from Russian Foundation for Basic Research (#15-04-02543). S.D. received support from the Russian Foundation for Basic Research (#16-34-00599). R.K., E.K. and S.L. were supported by the Russian Foundation for Basic Research (11-04-00725-a). E.B. was supported by the Russian Foundation for Basic Research (16-06-00303). O.B. was supported by the Russian Scientific Fund (14-04-00827) and by the Russian Foundation for Basic Research (16-04-00890). D.M.B., H.S., E.M., R.V. and M.M. were supported by Institutional Research Funding from the Estonian Research Council IUT24-1 and by the European Regional Development Fund (European Union) through the Centre of Excellence in Genomics to Estonian Biocentre and University of Tartu. D.C. was supported by the Spanish MINECO grant CGL-44351-P. L.B.J. and W.S.W. were supported by NIH grant GM59290. S.A.T. was supported by NIH grants 5DP1ES022577 05, 1R01DK104339-01, and 1R01GM113657-01. C.T.-S. and Y.X. were supported by The Wellcome Trust grant 098051. C.M.B. was supported by NSF grants 0924726 and 1153911. K.T. was supported by CSIR Network Project grant (GENESIS: BSC0121). J.P.S. and Y.S.S. were supported in part by an NIH grant R01-GM094402, and a Packard Fellowship for Science and Engineering. G.R., J.K and S.P. were funded by the Max Planck Society. N.P. and D.R. were supported by NIH grant GM100233 and D.R. is a Howard Hughes Medical Institute investigator.

Author information

Author notes

  1. Sriram Sankararaman & Lalji Singh

    Present address: †Present addresses: Department of Computer Science, University of California at Los Angeles, California 90095, USA and Department of Human Genetics Science, University of California at Los Angeles, California 90095, USA (S.S.); Genome Foundation, Hyderabad 500076, India (L.S).,

  2. Heng Li, Mark Lipson and Iain Mathieson: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Genetics, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Swapan Mallick, Mark Lipson, Iain Mathieson, Mengyao Zhao, Niru Chennagiri, Susanne Nordenfelt, Arti Tandon, Pontus Skoglund, Iosif Lazaridis, Sriram Sankararaman, Qiaomei Fu, Nadin Rohland & David Reich

  2. Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, USA

    Swapan Mallick, Heng Li, Melissa Gymrek, Mengyao Zhao, Niru Chennagiri, Susanne Nordenfelt, Arti Tandon, Pontus Skoglund, Iosif Lazaridis, Sriram Sankararaman, Qiaomei Fu, Nadin Rohland, Nick Patterson & David Reich

  3. Howard Hughes Medical Institute, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Swapan Mallick, Mengyao Zhao, Niru Chennagiri, Susanne Nordenfelt & David Reich

  4. Whitehead Institute for Biomedical Research, Cambridge, 02142, Massachusetts, USA

    Melissa Gymrek

  5. Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, 02139, Massachusetts, USA

    Melissa Gymrek

  6. New York Genome Center, New York, 10013, New York, USA

    Melissa Gymrek, Yaniv Erlich, Thomas Willems & William Klitz

  7. Department of Integrative Biology, University of California, Berkeley, 94720-3140, California, USA

    Fernando Racimo

  8. Key Laboratory of Vertebrate Evolution and Human Origins of Chinese Academy of Sciences, IVPP, CAS, Beijing, 100044, China

    Qiaomei Fu

  9. Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, D-04103, Germany

    Gabriel Renaud, Svante Pääbo & Janet Kelso

  10. Department of Computer Science, Columbia University, New York, 10027, New York, USA

    Yaniv Erlich

  11. Center for Computational Biology and Bioinformatics, Columbia University, New York, 10032, New York, USA

    Yaniv Erlich

  12. Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA

    Thomas Willems

  13. Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 15102, Perú

    Carla Gallo & Giovanni Poletti

  14. Computational Biology Graduate Group, University of California, Berkeley, 94720, California, USA

    Jeffrey P. Spence

  15. Computer Science Division, University of California, Berkeley, 94720, California, USA

    Yun S. Song

  16. Department of Statistics, University of California, Berkeley, 94720, California, USA

    Yun S. Song

  17. Department of Mathematics and Department of Biology, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA

    Yun S. Song

  18. Genetics Institute, University College London, Gower Street, London, WC1E 6BT, UK

    Francois Balloux

  19. Institute of Linguistics, University of Bern, Bern, CH-3012, Switzerland

    George van Driem

  20. Department of Human and Clinical Genetics, Postzone S5-P, Leiden University Medical Center, ZA, 2333, Leiden, Netherlands

    Peter de Knijff

  21. School of Biological Sciences, Nanyang Technological University, 637551, Singapore

    Irene Gallego Romero

  22. Lee Kong Chian School of Medicine, Nanyang Technological University, 636921, Singapore

    Irene Gallego Romero

  23. Department of Human Genetics, University of Chicago, Chicago, 60637, Illinois, USA

    Aashish R. Jha, Anna Di Rienzo & Choongwon Jeong

  24. Estonian Biocentre, Evolutionary Biology group, Tartu, 51010, Estonia

    Doron M. Behar, Hovhannes Sahakyan, Ene Metspalu, Jüri Parik, Richard Villems, Sergey Litvinov, Toomas Kivisild & Mait Metspalu

  25. Laboratorio de Genética Molecular Poblacional, Instituto Multidisciplinario de Biología Celular (IMBICE), CCT-CONICET La Plata/CIC Buenos Aires/Universidad Nacional de La Plata, La Plata B1906APO, Argentina

    Claudio M. Bravi

  26. Department of Zoology, University of Oxford, Oxford, 3PS, OX1, UK

    Cristian Capelli

  27. Department of Clinical Science, University of Bergen, Bergen, 5021, Norway

    Tor Hervig

  28. National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, 36821, Guanajuato, Mexico

    Andres Moreno-Estrada

  29. Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, 630090, Russia

    Olga L. Posukh

  30. Novosibirsk State University, Novosibirsk, 630090, Russia

    Olga L. Posukh

  31. Research Centre for Medical Genetics, Moscow, 115478, Russia

    Elena Balanovska & Oleg Balanovsky

  32. Vavilov Institute for General Genetics, Moscow, 119991, Russia

    Oleg Balanovsky

  33. Moscow Institute for Physics and Technology, Dolgoprudniy, 141700, Russia

    Oleg Balanovsky

  34. Department of Medical Genetics, National Human Genome Center, Medical University Sofia, Sofia, 1431, Bulgaria

    Sena Karachanak-Yankova & Draga Toncheva

  35. Laboratory of Ethnogenomics, Institute of Molecular Biology, National Academy of Sciences of Armenia, Yerevan, 0014, Armenia

    Hovhannes Sahakyan & Levon Yepiskoposyan

  36. The Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, Cambridgeshire, UK

    Chris Tyler-Smith & Yali Xue

  37. RIPAS Hospital, Bandar Seri Begawan, Brunei

    M. Syafiq Abdullah

  38. Department of Genetics, Evolution and Environment, University College London WC1E 6BT, UK

    Andres Ruiz-Linares

  39. Department of Anthropology, Case Western Reserve University, Cleveland, 44106-7125, Ohio, USA

    Cynthia M. Beall

  40. Laboratory of Human Molecular Genetics, Institute of Molecular and Cellular Biology, Siberian Branch of Russian Academy of Sciences, Novosibirsk, 630090, Russia

    Elena B. Starikovskaya, Stanislav Dryomov & Rem Sukernik

  41. Department of Evolutionary Biology, University of Tartu, Tartu, 51010, Estonia

    Ene Metspalu & Richard Villems

  42. Estonian Academy of Sciences, Tallinn, 10130, Estonia

    Richard Villems

  43. Department of Ecology and Evolution, Stony Brook University, Stony Brook, 11794, New York, USA

    Brenna M. Henn

  44. NextBio, Illumina, Santa Clara, California, 95050, USA

    Ugur Hodoglugil

  45. Gladstone Institutes, San Francisco, 94158, California, USA

    Robert Mahley

  46. Department of Forensic Medicine, University of Helsinki, Helsinki, 00014, Finland

    Antti Sajantila

  47. Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, 98195, Washington, USA

    George Stamatoyannopoulos

  48. National Cancer Centre Singapore, 169610, Singapore

    Joseph T. S. Wee

  49. Institute of Biochemistry and Genetics, Ufa Research Centre, Russian Academy of Sciences, Ufa, 450054, Russia

    Rita Khusainova, Elza Khusnutdinova & Sergey Litvinov

  50. Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, 450074, Russia

    Rita Khusainova, Elza Khusnutdinova & Sergey Litvinov

  51. Jaramogi Oginga Odinga University of Science and Technology, Bondo, 40601, Kenya

    George Ayodo

  52. Institut de Biologia Evolutiva (CSIC-UPF), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, 08003, Spain

    David Comas

  53. ARL Division of Biotechnology, University of Arizona, Tucson, 85721, Arizona, USA

    Michael F. Hammer

  54. Division of Biological Anthropology, University of Cambridge, Fitzwilliam Street, Cambridge, CB2 1QH, UK

    Toomas Kivisild

  55. Basic Research Laboratory, Center for Cancer Research, NCI, Leidos Biomedical Research, Inc., Frederick National Laboratory, Frederick, 21702, Maryland, USA

    Cheryl A. Winkler

  56. CHU Sainte-Justine, Pediatrics Departement, Université de Montréal, Québec, H3T 1C5, Canada

    Damian Labuda

  57. Department of Pediatrics, University of Washington, Seattle, 98119, Washington, USA

    Michael Bamshad

  58. Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, 84112, Utah, USA

    Lynn B. Jorde

  59. Departments of Genetics and Biology, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA

    Sarah A. Tishkoff

  60. Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, 84112, Utah, USA

    W. Scott Watkins

  61. Department of Paleolithic Archaeology, Institute of Archaeology and Ethnography, Siberian Branch of Russian Academy of Sciences, Novosibirsk, 630090, Russia

    Stanislav Dryomov

  62. Altai State University, Barnaul, 656000, Russia

    Rem Sukernik

  63. CSIR-Centre for Cellular and Molecular Biology, Hyderabad, 500 007, India

    Lalji Singh & Kumarasamy Thangaraj

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Contributions

S.M., Y.E., Y.S.S., S.P., J.K., N.P. and D.R. supervised the study. S.N., N.R., C.G., G.P., F.B., G.D., I.G.R., A.R.J., P.D., D.M.B., C.M.B., C.C., T.H., A.M.-E., O.L.P., E.B., O.B., S.K.-Y., H.S., D.T., L.Y., C.T.-S., Y.X., M.S.A., A.R.-L., C.B., A.D.R., C.J., E.B.S., E.M., J.P., R.V., B.M.H., U.H., R.W.M., A.S., G.S., J.T.S.W., R.K., E.K., S.L., G.A., D.C., M.H., T.K., W.K., C.A.W., D.L., M.B., L.B.J., S.A.T., W.S.W., M.M., S.D., R.S., L.S., K.T. and D.R. assembled samples. S.M., H.L., M.L., I.M., M.G., F.R., J.P.S., M.Z., N.C., A.T., P.S., I.L., S.S., Q.F., G.R., Y.S., N.P. and D.R. performed analyses. S.M., H.L., M.L., I.M., M.G., F.R., M.Z., N.P. and D.R. wrote the manuscript with help from all co-authors.

Corresponding authors

Correspondence to Swapan Mallick or David Reich.

Ethics declarations

Competing interests

U.H. is employed by NextBio, a division of Illumina Ltd.

Additional information

Reviewer Information Nature thanks P. Bellwood and S. Ramachandran and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Heat map of fraction of heterozygous sites missed in the 1000 Genomes project.

For each sample, we examine all heterozygous sites passing filter level 1, and compute the fraction included as known polymorphisms in the 1000 Genomes project.

Extended Data Figure 2 Worldwide variation in human short tandem repeats.

a, Mean STR length is reported as the average of the length difference (in base pairs) from the GRCh37 reference for each genotype. Bubble area scales with the number of calls compared at each point. b, c, The first two principal components after performing principal component analysis on tetranucleotide and homopolymer genotypes, respectively. Colours represent the region of origin of each sample. d, Pairwise FST values between populations computed using only SNPs versus using combined SNP + STR loci. e, Block jackknife standard errors for the SNP versus SNP + STR FST analysis. The red dashed lines give the best-fit line, described by the formula in red. The black dashed line denotes the diagonal.

Extended Data Figure 3 ADMIXTURE analysis.

We carried out unsupervised ADMIXTURE 1.238,43 analysis over the 300 SGDP individuals in 20 replicates with randomly chosen initial seeds, varying the number of ancestral populations between K = 2 and K = 12 and using default fivefold cross-validation (–cv flag). We used genotypes of at least filter level 1, and restricted analysis to sites where at least two individuals carried the variant allele (as singleton variants are non-informative for population clustering). After further filtering of sites with at least 99% completeness and performing linkage-disequilibrium-based pruning in PLINK 1.944,45 with parameters (–indep-pairwise 1000 100 0.2), a total of 482,515 single nucleotide polymorphisms remained. This figure shows the highest likelihood replicate for each value of K. We found that log likelihood monotonically increases with K, while the value K = 5 minimizes cross-validation error (not shown). The solution at K = 5 corresponds to major continental groups (Sub-Saharan Africans, Oceanians, East Asians, Native Americans, and West Eurasians), but we show the full range of K here as they illustrate finer-scale population structure that may be useful to users of the data.

Extended Data Figure 4 Principal component analysis and neighbour joining tree.

a, Principal component analysis. b, Neighbour-joining tree based on FST values for all populations with at least two samples.

Extended Data Figure 5 Fewer accumulated mutations in Africans than in non-Africans confirmed by mapping to chimpanzee.

We compute a statistic D (Population A, Population B, Chimp), measuring the difference in the rate of matching to chimpanzee in Population A compared to Population B. The evidence of mismatching to chimpanzee is seen when we restrict to the male X chromosome to eliminate possible effects due to differences in heterozygosity across populations, and map to the chimpanzee genome which is phylogenetically symmetrically related to all present-day humans. We find that in 78 randomly chosen Population A = African and Population B = non-African pairs of males, transversion substitutions show no consistent skew from zero, but transition substitutions do.

Extended Data Figure 6 3P-CLR scan for positive selection.

The red line denotes the 99.9% quantile cut-off. The genes in the top five regions are labelled. a, Scan for selection on the San terminal branch. b, Scan for selection on the non-San terminal branch. c, Scan for selection on the ancestral modern human branch.

Extended Data Figure 7 Scan for genomic locations where the great majority of present-day humans share a recent common ancestor.

We carried out PSMC analysis on 40 pairs of haploid genomes chosen to sample some of the most deeply divergent present-day human lineages. We recorded the time since the most recent common ancestor (TMRCA) at each position, and rescaled to obtain an estimate of absolute time (Supplementary Information section 12). a, Distribution across the genome of the fraction of TMRCAs below specified date cut-offs. For the 100 kya cut-off, the maximum fraction observed anywhere in the genome is 68%. b, Distribution across the genome of the date T at which specified fractions of sample pairs are inferred to have a TMRCA less than T. c, Percentile points of the cumulative distribution function of B.

Extended Data Table 1 Fewer accumulated mutations in Africans than in non-Africans
Full size table

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Supplementary information

Supplementary Information

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Supplementary Table 1

This file shows the data by each sample studied. (XLSX 124 kb)

Supplementary Table 2

This table shows the top hits for 3P-CLR run. (XLSX 71 kb)

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Mallick, S., Li, H., Lipson, M. et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538, 201–206 (2016). https://doi.org/10.1038/nature18964

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