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Joint nonparametric coalescent inference of mutation spectrum history and demography

View ORCID ProfileWilliam S. DeWitt, View ORCID ProfileKameron Decker Harris, View ORCID ProfileKelley Harris
doi: https://doi.org/10.1101/2020.06.16.153452
William S. DeWitt
1Department of Genome Sciences, University of Washington, Seattle, WA
4Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA
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  • For correspondence: wsdewitt@uw.edu harriske@uw.edu
Kameron Decker Harris
2Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA
3Department of Biology, University of Washington, Seattle, WA
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Kelley Harris
1Department of Genome Sciences, University of Washington, Seattle, WA
4Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA
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  • For correspondence: wsdewitt@uw.edu harriske@uw.edu
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Abstract

Booming and busting populations modulate the accumulation of genetic diversity, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs the mutation spectrum: the relative mutation rates in different local nucleotide contexts. Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We describe mushi: a method to perform fast, nonparametric joint inference of demographic and mutation spectrum histories from allele frequency data. We use mushi to reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and produce more accurate time calibration for a previously-reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be productively incorporated in a well-studied theoretical setting, and rigorously inferred from genomic variation data like other features of evolutionary history.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://harrispopgen.github.io/mushi/

  • https://github.com/harrispopgen/mushi

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 4.0 International license.
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Posted June 16, 2020.
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Joint nonparametric coalescent inference of mutation spectrum history and demography
William S. DeWitt, Kameron Decker Harris, Kelley Harris
bioRxiv 2020.06.16.153452; doi: https://doi.org/10.1101/2020.06.16.153452
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Joint nonparametric coalescent inference of mutation spectrum history and demography
William S. DeWitt, Kameron Decker Harris, Kelley Harris
bioRxiv 2020.06.16.153452; doi: https://doi.org/10.1101/2020.06.16.153452

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