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Interpreting f-Statistics and Admixture Graphs: Theory and Examples
Version 1
: Received: 12 March 2020 / Approved: 15 March 2020 / Online: 15 March 2020 (02:01:35 CET)
Version 2 : Received: 7 May 2020 / Approved: 8 May 2020 / Online: 8 May 2020 (04:05:41 CEST)
Version 2 : Received: 7 May 2020 / Approved: 8 May 2020 / Online: 8 May 2020 (04:05:41 CEST)
How to cite: Lipson, M. Interpreting f-Statistics and Admixture Graphs: Theory and Examples. Preprints 2020, 2020030237. https://doi.org/10.20944/preprints202003.0237.v1 Lipson, M. Interpreting f-Statistics and Admixture Graphs: Theory and Examples. Preprints 2020, 2020030237. https://doi.org/10.20944/preprints202003.0237.v1
Abstract
A popular approach to learning about admixture from population genetic data is by computing the allele-sharing summary statistics known as f-statistics. Compared to some methods in population genetics, f-statistics are relatively simple, but interpreting them can still be complicated at times. In addition, f-statistics can be used to build admixture graphs (multi-population trees allowing for admixture events), which provide more explicit and thorough modeling capabilities but are correspondingly more complex to work with. Here, I discuss some of these issues to provide users of these tools with a basic guide for protocols and procedures. My focus is on the kinds of conclusions that can or cannot be drawn from the results of f4-statistics and admixture graphs, illustrated with real-world examples involving human populations.
Keywords
f-statistics; admixture graphs; admixture; parameter estimation
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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