DNA from an ancient, unidentified ancestor was passed down to humans living today

August 06, 2020

A new analysis of ancient genomes suggests that different branches of the human family tree interbred multiple times, and that some humans carry DNA from an archaic, unknown ancestor. Melissa Hubisz and Amy Williams of Cornell University and Adam Siepel of Cold Spring Harbor Laboratory report these findings in a study published 6th August in PLOS Genetics.

Roughly 50,000 years ago, a group of humans migrated out of Africa and interbred with Neanderthals in Eurasia. But that's not the only time that our ancient human ancestors and their relatives swapped DNA. The sequencing of genomes from Neanderthals and a less well-known ancient group, the Denisovans, has yielded many new insights into these interbreeding events and into the movement of ancient human populations. In the new paper, the researchers developed an algorithm for analyzing genomes that can identify segments of DNA that came from other species, even if that gene flow occurred thousands of years ago and came from an unknown source. They used the algorithm to look at genomes from two Neanderthals, a Denisovan and two African humans. The researchers found evidence that 3 percent of the Neanderthal genome came from ancient humans, and estimate that the interbreeding occurred between 200,000 and 300,000 years ago. Furthermore, 1 percent of the Denisovan genome likely came from an unknown and more distant relative, possibly Homo erectus, and about 15% of these "super-archaic" regions may have been passed down to modern humans who are alive today.

The new findings confirm previously reported cases of gene flow between ancient humans and their relatives, and also point to new instances of interbreeding. Given the number of these events, the researchers say that genetic exchange was likely whenever two groups overlapped in time and space. Their new algorithm solves the challenging problem of identifying tiny remnants of gene flow that occurred hundreds of thousands of years ago, when only a handful of ancient genomes are available. This algorithm may also be useful for studying gene flow in other species where interbreeding occurred, such as in wolves and dogs.

"What I think is exciting about this work is that it demonstrates what you can learn about deep human history by jointly reconstructing the full evolutionary history of a collection of sequences from both modern humans and archaic hominins," said author Adam Siepel. "This new algorithm that Melissa has developed, ARGweaver-D, is able to reach back further in time than any other computational method I've seen. It seems to be especially powerful for detecting ancient introgression."
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Peer-reviewed / Observational study / People

In your coverage please use this URL to provide access to the freely available article in PLOS Genetics:

http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008895

Citation: Hubisz MJ, Williams AL, Siepel A (2020) Mapping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph. PLoS Genet 16(8): e1008895. https://doi.org/10.1371/journal.pgen.1008895

Funding: MJH and AS were supported by US National Institutes of Health grant R35-GM127070 (to AS) (https://www.nih.gov), and MJH was additionally supported by National Science Foundation GRFP DGE-1650441 (https://www.nsf.gov). ALW was supported by an Alfred P. Sloan Research Fellowship (https://sloan.org/fellowships/) and a seed grant from Nancy and Peter Meinig. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562 (https://www.nsf.gov). The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: No authors have competing interests.

PLOS

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