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

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 introgress...

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Autores principales: Mondal, Mayukh, Bertranpetit, Jaume, Lao, Oscar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6335398/
https://www.ncbi.nlm.nih.gov/pubmed/30651539
http://dx.doi.org/10.1038/s41467-018-08089-7
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author Mondal, Mayukh
Bertranpetit, Jaume
Lao, Oscar
author_facet Mondal, Mayukh
Bertranpetit, Jaume
Lao, Oscar
author_sort Mondal, Mayukh
collection PubMed
description 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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spelling pubmed-63353982019-01-18 Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania Mondal, Mayukh Bertranpetit, Jaume Lao, Oscar Nat Commun Article 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. Nature Publishing Group UK 2019-01-16 /pmc/articles/PMC6335398/ /pubmed/30651539 http://dx.doi.org/10.1038/s41467-018-08089-7 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Mondal, Mayukh
Bertranpetit, Jaume
Lao, Oscar
Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania
title Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania
title_full Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania
title_fullStr Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania
title_full_unstemmed Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania
title_short Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania
title_sort approximate bayesian computation with deep learning supports a third archaic introgression in asia and oceania
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6335398/
https://www.ncbi.nlm.nih.gov/pubmed/30651539
http://dx.doi.org/10.1038/s41467-018-08089-7
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