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http://hdl.handle.net/10261/207200
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dc.contributor.author | Mondal, Mayukh | - |
dc.contributor.author | Bertranpetit, Jaume | - |
dc.contributor.author | Lao, Oscar | - |
dc.date.accessioned | 2020-04-13T07:12:17Z | - |
dc.date.available | 2020-04-13T07:12:17Z | - |
dc.date.issued | 2019-01-16 | - |
dc.identifier | doi: 10.1038/s41467-018-08089-7 | - |
dc.identifier | e-issn: 2041-1723 | - |
dc.identifier.citation | Nature Communications 10: 246 (2019) | - |
dc.identifier.uri | http://hdl.handle.net/10261/207200 | - |
dc.description.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. | - |
dc.description.sponsorship | M.M was supported by the European Union through the European Regional Development Fund (Project No. 2014-2020.4.01.16-0030). For J.B, this study has been possible thanks to grant BFU2016-77961-P (AEI/FEDER, UE) awarded by the Agencia Estatal de Investigación (MINECO, Spain) and with the support of Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 702). Part of the “Unidad de Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370). O.L. was supported by a Ramón y Cajal grant from the Spanish Ministerio de Economia y Competitividad (MINECO) with reference RYC-2013-14797, a BFU2015-68759-P (MINECO/FEDER) grant and the support of Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 937). O.L. also acknowledges the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership; Centro de Excelencia Severo Ochoa; CERCA Programme/Generalitat de Catalunya; the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) through the Instituto de Salud Carlos III; Generalitat de Catalunya through Departament de Salut and Departament d’Empresa i Coneixement; the co-financing by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) with funds from the European Regional Development Fund (ERDF) corresponding to the 2014-2020 Smart Growth Operating Program. | - |
dc.language | eng | - |
dc.publisher | Springer Nature | - |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BFU2016-77961-P | - |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MDM-2014-0370 | - |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/RYC-2013-14797 | - |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BFU2015-68759-P | - |
dc.relation.isversionof | Publisher's version | - |
dc.rights | openAccess | - |
dc.subject | Genetic variation | - |
dc.title | Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania | - |
dc.type | artículo | - |
dc.identifier.doi | 10.1038/s41467-018-08089-7 | - |
dc.relation.publisherversion | http://doi.org/10.1038/s41467-018-08089-7 | - |
dc.date.updated | 2020-04-13T07:12:17Z | - |
dc.rights.license | http://creativecommons.org/licenses/by/4.0/ | - |
dc.contributor.funder | European Commission | - |
dc.contributor.funder | Agencia Estatal de Investigación (España) | - |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | - |
dc.contributor.funder | Generalitat de Catalunya | - |
dc.contributor.funder | Instituto de Salud Carlos III | - |
dc.relation.csic | Sí | - |
dc.identifier.funder | http://dx.doi.org/10.13039/501100002809 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100000780 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100011033 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100003329 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100004587 | es_ES |
dc.identifier.pmid | 30651539 | - |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.openairetype | artículo | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
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bayesian_computation_learning_introgression_Asia_Oceania.pdf | 888,23 kB | Adobe PDF | Visualizar/Abrir |
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