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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Published inNature communications Vol. 10; no. 1; pp. 246 - 9
Main Authors Mondal, Mayukh, Bertranpetit, Jaume, Lao, Oscar
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 16.01.2019
Nature Publishing Group
Nature Research
Nature Portfolio
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ISSN2041-1723
2041-1723
DOI10.1038/s41467-018-08089-7

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Summary: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. Introgression of Neanderthals and Denisovans left genomic signals in anatomically modern human after Out-of-Africa event. Here, the authors identify a third archaic introgression common to all Asian and Oceanian human populations by applying an approximate Bayesian computation with a Deep Learning framework.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-018-08089-7