A unified genealogy of modern and ancient genomes.

Autor: Wohns AW; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK., Wong Y; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK., Jeffery B; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK., Akbari A; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.; Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA., Mallick S; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA., Pinhasi R; Department of Evolutionary Anthropology, University of Vienna, 1090 Vienna, Austria., Patterson N; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.; Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA., Reich D; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.; Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA., Kelleher J; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK., McVean G; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
Jazyk: angličtina
Zdroj: Science (New York, N.Y.) [Science] 2022 Feb 25; Vol. 375 (6583), pp. eabi8264. Date of Electronic Publication: 2022 Feb 25.
DOI: 10.1126/science.abi8264
Abstrakt: The sequencing of modern and ancient genomes from around the world has revolutionized our understanding of human history and evolution. However, the problem of how best to characterize ancestral relationships from the totality of human genomic variation remains unsolved. Here, we address this challenge with nonparametric methods that enable us to infer a unified genealogy of modern and ancient humans. This compact representation of multiple datasets explores the challenges of missing and erroneous data and uses ancient samples to constrain and date relationships. We demonstrate the power of the method to recover relationships between individuals and populations as well as to identify descendants of ancient samples. Finally, we introduce a simple nonparametric estimator of the geographical location of ancestors that recapitulates key events in human history.
Databáze: MEDLINE
Nepřihlášeným uživatelům se plný text nezobrazuje