English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/207200
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:

Title

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

AuthorsMondal, Mayukh ; Bertranpetit, Jaume ; Lao, Oscar
KeywordsGenetic variation
Issue Date16-Jan-2019
PublisherSpringer Nature
CitationNature Communications 10: 246 (2019)
AbstractSince 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.
Publisher version (URL)http://doi.org/10.1038/s41467-018-08089-7
URIhttp://hdl.handle.net/10261/207200
Identifiersdoi: 10.1038/s41467-018-08089-7
e-issn: 2041-1723
Appears in Collections:(IBE) Artículos
Files in This Item:
File Description SizeFormat 
bayesian_computation_learning_introgression_Asia_Oceania.pdf888,23 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.