Cited by 129Forensic and Genetic ResearchPleistocene-Era Hominins and ArchaeologyGenetic diversity and population structure

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Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania

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Mondal, Mayukh, Bertranpetit, Jaume, Lao, Oscar·2019·Nature Communications

DOI 10.1038/s41467-018-08089-7PMC PMC6335398

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Abstract

Since anatomically modern humans dispersed Out of Africa, the evolutionary history of Eurasian populations has been marked by introgressions from presently extinct hominins. Some of these introgressions have been identified using sequenced ancient genomes (Neanderthal and Denisova). Other introgressions have been proposed for still unidentified groups using the genetic diversity present in current human populations. We built a demographic model based on deep learning in an Approximate Bayesian Computation framework to infer the evolutionary history of Eurasian populations including past introgression events in Out of Africa populations fitting the current genetic evidence. In addition to the reported Neanderthal and Denisovan introgressions, our results support a third introgression in all Asian and Oceanian populations from an archaic population. This population is either related to the Neanderthal-Denisova clade or diverged early from the Denisova lineage. We propose the use of deep learning methods for clarifying situations with high complexity in evolutionary genomics.

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Fig. 1 Increase of Neanderthal ancestry in Asians compared to Europeans. The introgression amount is calculated by the F4 ratio test. F4 (Outgroup, Altai; Europeans, X)/F4(Outgroup, Altai; Africa, Vindjia) where Outgroup can be either Denisova or Ancestral alleles from 1000 Genome and X is either East Asian (ASN), Indian Tribal (IND) or Andamanese (AND). In the y axis to make it short we only mention F4(Outgroup, X) as the other populations remain constant for all the F4 ratio tests. In the x axis we have the amount of F4 ratio and the standard error is denoted by bars (standard error was calculated by Jackknife method using 555 blocks). The blue line signifies 0 value for F4 ratio testCC BY · PubMed Central

Fig. 2 Demographic models implemented for explaining the genetic variation present in current African (A), European (E), East Asian (Ea), Andamanese (An), Indian (I), Papuan (P) and Australian (Au) populations, in relation with Altai Neanderthal (N) and Denisovan (D) archaic populations. The process of archaic introgression in Asian populations is modelled either by different introgression events from a anatomically modern human ghost population (Xn), b, c, d known archaic populations such as N and D and or e, f, g, h by an unknown archaic extinct (Xe) population. An archaic population (Xd) introgressing to Denisovans is also modelled. Turquoise arrows indicate single pulse events of archaic introgression. All models include recent continuous migrations between African and European, European and East Asian, and Papuan and Australian populationsCC BY · PubMed Central

Fig. 3 Graphical representation of the two best supported model (Model F and Model H) with mean posterior distribution of each parameter. a Mean of the posterior distributions of each parameter calculated by means of ABC-DL in model F for Africans (A), Europeans (E), East Asians (Ea), Andamanese (An), Indians (I), Papuans (P), Aboriginal Australians (Au), Altai Neanderthal (N) and Denisovan (D). The model includes an extinct hominin population (Xe) descendent from the Denisovan population. b Mean of the posterior distributions of each parameter calculated by means of ABC-DL in model H. Xe population is an admixture between Neanderthals and Denisovans which interbreeds with Asian populations and a Homo erectus-like population (Xd) that interbreeds with Denisovan. In blue, some of the recent continuous migrations between populations which were taken into consideration in the model. In turquoise, the different introgressionsCC BY · PubMed Central

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Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
Markov Chain Monte Carlo in Practice
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
A draft sequence of the Neandertal genome
Green, R. E., Krause, J., Briggs, A. W., Maricic, T., Stenzel, U., Kircher, M., Patterson, N., Li, H., Zhai, W., Fritz, M. H.-Y., et al. (2010) · Science · doi:10.1126/science.1188021 · Cited by 4,318
Ancient Admixture in Human History
Patterson, N., Moorjani, P., Luo, Y., Mallick, S., Rohland, N., Zhan, Y., Genschoreck, T., Webster, T., & Reich, D. (2012) · Genetics · doi:10.1534/genetics.112.145037 · Cited by 2,848
Inference of human population history from individual whole-genome sequences
Li, H., & Durbin, R. (2011) · Nature · doi:10.1038/nature10231 · Cited by 2,590
Generating samples under a Wright–Fisher neutral model of genetic variation
Hudson, R. R. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.2.337 · Cited by 2,397
The complete genome sequence of a Neanderthal from the Altai Mountains
Prüfer, K., Racimo, F., Patterson, N., Jay, F., Sankararaman, S., Sawyer, S., Heinze, A., Renaud, G., Sudmant, P. H., de Filippo, C., et al. (2014) · Nature · doi:10.1038/nature12886 · Cited by 2,254
Genetic history of an archaic hominin group from Denisova Cave in Siberia
Reich, D., Green, R. E., Kircher, M., Krause, J., Patterson, N., Durand, E. Y., Viola, B., Briggs, A. W., Stenzel, U., Johnson, P. L. F., et al. (2010) · Nature · doi:10.1038/nature09710 · Cited by 1,991
A high-coverage genome sequence from an archaic denisovan individual
Meyer, M., Kircher, M., Gansauge, M.-T., Li, H., Racimo, F., Mallick, S., Schraiber, J. G., Jay, F., Prüfer, K., de Filippo, C., et al. (2012) · Science · doi:10.1126/science.1224344 · Cited by 1,967
Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data
Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H., & Bustamante, C. D. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000695 · Cited by 1,906
The Simons Genome Diversity Project: 300 genomes from 142 diverse populations
Mallick, S., Li, H., Lipson, M., Mathieson, I., Gymrek, M., Racimo, F., Zhao, M., Chennagiri, N., Nordenfelt, S., Tandon, A., et al. (2016) · Nature · doi:10.1038/nature18964 · Cited by 1,584
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Excoffier, L., Dupanloup, I., Huerta‐Sánchez, E., Sousa, V. C., & Foll, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003905 · Cited by 1,480
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Fu, Q., Posth, C., Hajdinjak, M., Petr, M., Mallick, S., Fernandes, D., Furtwängler, A., Haak, W., Meyer, M., Mittnik, A., et al. (2016) · Nature · doi:10.1038/nature17993 · Cited by 959
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Lazaridis, I., Nadel, D., Rollefson, G., Merrett, D. C., Rohland, N., Mallick, S., Fernandes, D., Novak, M., Gamarra, B., Sirak, K., et al. (2016) · Nature · doi:10.1038/nature19310 · Cited by 955
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Rasmussen, M., Guo, X., Wang, Y., Lohmueller, K. E., Rasmussen, S., Albrechtsen, A., Skotte, L., Lindgreen, S., Metspalu, M., Jombart, T., et al. (2011) · Science · doi:10.1126/science.1211177 · Cited by 787
Correcting for Purifying Selection: An Improved Human Mitochondrial Molecular Clock
Soares, P. A., Ermini, L., Thomson, N., Mormina, M., Rito, T., Röhl, A., Salas, A., Oppenheimer, S., Macaulay, V. A., & Richards, M. B. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.05.001 · Cited by 729
A high-coverage Neandertal genome from Vindija Cave in Croatia
Prüfer, K., de Filippo, C., Grote, S., Mafessoni, F., Korlević, P., Hajdinjak, M., Vernot, B., Skov, L., Hsieh, P., Peyrégne, S., et al. (2017) · Science · doi:10.1126/science.aao1887 · Cited by 719
abc: an R package for approximate Bayesian computation (ABC)
Csilléry, K., François, O., & Blum, M. G. B. (2012) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2011.00179.x · Cited by 709
A genomic history of Aboriginal Australia
Malaspinas, A.-S., Westaway, M. C., Muller, C., Sousa, V. C., Lao, O., Alves, I., Bergström, A., Athanasiadis, G., Cheng, J. Y., Crawford, J. E., et al. (2016) · Nature · doi:10.1038/nature18299 · Cited by 650
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Sunnåker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., & Dessimoz, C. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002803 · Cited by 636
Ancient gene flow from early modern humans into Eastern Neanderthals
Kuhlwilm, M., Gronau, I., Hubisz, M. J., de Filippo, C., Prado-Martinez, J., Kircher, M., Fu, Q., Burbano, H. A., Lalueza-Fox, C., de la Rasilla, M., et al. (2016) · Nature · doi:10.1038/nature16544 · Cited by 577
Southern African ancient genomes estimate modern human divergence to 350,000 to 260,000 years ago
Schlebusch, C. M., Malmström, H., Günther, T., Sjödin, P., Coutinho, A., Edlund, H., Munters, A. R., Vicente, M., Steyn, M., Soodyall, H., et al. (2017) · Science · doi:10.1126/science.aao6266 · Cited by 519
Cross-cultural estimation of the human generation interval for use in genetics- based population divergence studies
Fenner, J. N. (2005) · American Journal of Physical Anthropology · doi:10.1002/ajpa.20188 · Cited by 508
ABC as a flexible framework to estimate demography over space and time: some cons, many pros
Bertorelle, G., Benazzo, A., & Mona, S. (2010) · Molecular Ecology · doi:10.1111/j.1365-294x.2010.04690.x · Cited by 465
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Sankararaman, S., Mallick, S., Patterson, N., & Reich, D. (2016) · Current Biology · doi:10.1016/j.cub.2016.03.037 · Cited by 465
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Pagani, L., Lawson, D. J., Jagoda, E., Mörseburg, A., Eriksson, A., Mitt, M., Clemente, F., Hudjashov, G., DeGiorgio, M., Saag, L., et al. (2016) · Nature · doi:10.1038/nature19792 · Cited by 459
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Skoglund, P., Thompson, J. C., Prendergast, M. E., Mittnik, A., Sirak, K. A., Hajdinjak, M., Salie, T., Rohland, N., Mallick, S., Peltzer, A., et al. (2017) · Cell · doi:10.1016/j.cell.2017.08.049 · Cited by 422
The genome of the offspring of a Neanderthal mother and a Denisovan father
Slon, V., Mafessoni, F., Vernot, B., de Filippo, C., Grote, S., Viola, B., Hajdinjak, M., Peyrégne, S., Nagel, S., Brown, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0455-x · Cited by 395
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Browning, S. R., Browning, B. L., Zhou, Y., Tucci, S., & Akey, J. M. (2018) · Cell · doi:10.1016/j.cell.2018.02.031 · Cited by 381
U-series and ESR analyses of bones and teeth relating to the human burials from Skhul
Grün, R., Stringer, C., McDermott, F., Nathan, R., Porat, N., Robertson, S., Taylor, L., Mortimer, G., Eggins, S., & McCulloch, M. (2005) · Journal of Human Evolution · doi:10.1016/j.jhevol.2005.04.006 · Cited by 338
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Simonti, C. N., Vernot, B., Bastarache, L., Bottinger, E., Carrell, D. S., Chisholm, R. L., Crosslin, D. R., Hebbring, S. J., Jarvik, G. P., Kullo, I. J., et al. (2016) · Science · doi:10.1126/science.aad2149 · Cited by 330
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Deeply divergent archaic mitochondrial genome provides lower time boundary for African gene flow into Neanderthals
Posth, C., Wißing, C., Kitagawa, K., Pagani, L., van Holstein, L. A., Racimo, F., Wehrberger, K., Conard, N. J., Kind, C.-J., Bocherens, H., et al. (2017) · Nature Communications · doi:10.1038/ncomms16046 · Cited by 320
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Sheehan, S., & Song, Y. S. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004845 · Cited by 259
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Wall, J. D., Yang, M. A., Jay, F., Kim, S. K., Durand, E. Y., Stevison, L. S., Gignoux, C., Woerner, A., Hammer, M. F., & Slatkin, M. (2013) · Genetics · doi:10.1534/genetics.112.148213 · Cited by 249
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Vernot, B., & Akey, J. M. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.01.006 · Cited by 149
Genomic analysis of Andamanese provides insights into ancient human migration into Asia and adaptation
Mondal, M., Casals, F., Xu, T., Dall'Olio, G. M., Pybus, M., Netea, M. G., Comas, D., Laayouni, H., Li, Q., Majumder, P. P., et al. (2016) · Nature Genetics · doi:10.1038/ng.3621 · Cited by 149
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Research works printed but not linked: 1

Prangle, D. in Handbook of Approximate Bayesian Computation (ABC) (eds Sisson, S. A., Fan, Y. & Beaumont, B.) 320 (Taylor & Francis, Abingdon, 2108).

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Authors: Mondal, M., Bertranpetit, J., & Lao, O.
Affiliations: University of Tartu (EE)
Universitat Pompeu Fabra (ES)
Corresponding author: Bertranpetit, Jaume
ORCID: Mondal, Mayukh
Bertranpetit, Jaume
Lao, Oscar
Venue: Nature Communications
Publisher: Springer Science and Business Media LLC
Published: 2019-01-16
Volume: 10
Issue: 1
Pages: 246-246
ISSN: 2041-1723 (ISSN-L)
DOI: 10.1038/s41467-018-08089-7
PMC: PMC6335398
OpenAlex: W2909640254
Cited by: 124
References: 51
Citations by year: 2026: 7 · 2025: 6 · 2024: 11 · 2023: 9 · 2022: 15 · 2021: 29 · 2020: 38 · 2019: 9
FWCI: 13.59
Type: journal article
Language: en
Open access: gold
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Licence: cc-by
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Funders: Centres de Recerca de Catalunya
European Commission (2014‐2020, 2020 ‘, 2014-2020.4.01.16-0030)
Generalitat de Catalunya (GRC 2017 SGR 937, 2017-SGR, MDM-2014-0370)
Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España (RYC-2013-14797, MDM-2014-0370, BFU2015-68759-P)
Instituto de Salud Carlos III (MDM-2014-0370, 2014/2020)
European Regional Development Fund (BFU2016, 2014-2020’, 2014-2020.4.01.16-0030, MDM-2014-0370, 14797, BFU2016-77961-P)
Agencia Estatal de Investigación (MDM-2014-0370, BFU2016-77961-P)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
Social Sciences › Anthropology › Pleistocene-Era Hominins and Archaeology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
Keywords: archaic introgression, Neanderthal introgression, Denisovan introgression, approximate Bayesian computation, dispersal out of Africa, human evolution

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Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania

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