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Assessing the Performance of qpAdm: A Statistical Tool for Studying Population Admixture

View ORCID ProfileÉadaoin Harney, Nick Patterson, View ORCID ProfileDavid Reich, John Wakeley
doi: https://doi.org/10.1101/2020.04.09.032664
Éadaoin Harney
*Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
†The Max Planck-Harvard Research Center for the Archaeoscience of the Ancient Mediterranean, Cambridge, MA, 02138, USA and Jena, D-07745, Germany
‡Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
§Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
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  • ORCID record for Éadaoin Harney
  • For correspondence: eadaoinharney@gmail.com
Nick Patterson
§Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
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David Reich
‡Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
§Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
**Broad Institute of Harvard and MIT, Cambridge, MA 02142 USA
††Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, 02115, USA
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John Wakeley
*Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
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ABSTRACT

qpAdm is a statistical tool for studying the ancestry of populations with histories that involve admixture between two or more source populations. Using qpAdm, it is possible to identify plausible models of admixture that fit the population history of a group of interest and to calculate the relative proportion of ancestry that can be ascribed to each source population in the model. Although qpAdm is widely used in studies of population history of human (and non-human) groups, relatively little has been done to assess its performance. We performed a simulation study to assess the behavior of qpAdm under various scenarios in order to identify areas of potential weakness and establish recommended best practices for use. We find that qpAdm is a robust tool that yields accurate results in many cases, including when data coverage is low, there are high rates of missing data or ancient DNA damage, or when diploid calls cannot be made. However, we caution against co-analyzing ancient and present-day data, the inclusion of an extremely large number of reference populations in a single model, and analyzing population histories involving extended periods of gene flow. We provide a user guide suggesting best practices for the use of qpAdm.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 10, 2020.
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Assessing the Performance of qpAdm: A Statistical Tool for Studying Population Admixture
Éadaoin Harney, Nick Patterson, David Reich, John Wakeley
bioRxiv 2020.04.09.032664; doi: https://doi.org/10.1101/2020.04.09.032664
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Assessing the Performance of qpAdm: A Statistical Tool for Studying Population Admixture
Éadaoin Harney, Nick Patterson, David Reich, John Wakeley
bioRxiv 2020.04.09.032664; doi: https://doi.org/10.1101/2020.04.09.032664

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