Minimal-assumption inference from population-genomic data

  1. Daniel B Weissman  Is a corresponding author
  2. Oskar Hallatschek  Is a corresponding author
  1. Emory University, United States
  2. University of California, Berkeley, United States

Abstract

Samples of multiple complete genome sequences contain vast amounts of information about the evolutionary history of populations, much of it in the associations among polymorphisms at different loci. We introduce a method, Minimal-Assumption Genomic Inference of Coalescence (MAGIC), that reconstructs key features of the evolutionary history, including the distribution of coalescence times, by integrating information across genomic length scales without using an explicit model of coalescence or recombination, allowing it to analyze arbitrarily large samples without phasing while making no assumptions about ancestral structure, linked selection, or gene conversion. Using simulated data, we show that the performance of MAGIC is comparable to that of PSMC' even on single diploid samples generated with standard coalescent and recombination models. Applying MAGIC to a sample of human genomes reveals evidence of non-demographic factors driving coalescence.

Data availability

The following previously published data sets were used
    1. Drmanac R
    2. Sparks AB
    3. Callow MJ
    4. Halpern AL
    5. et al.
    (2010) 69 Genomes
    Publicly available at the 69 Genomes Data website (download link: ftp://ftp2.completegenomics.com/).

Article and author information

Author details

  1. Daniel B Weissman

    Department of Physics, Emory University, Atlanta, United States
    For correspondence
    dbweissman@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7799-1573
  2. Oskar Hallatschek

    Department of Physics, University of California, Berkeley, Berkeley, United States
    For correspondence
    ohallats@berkeley.edu
    Competing interests
    The authors declare that no competing interests exist.

Funding

Simons Foundation (Simons Investigator Award)

  • Oskar Hallatschek

National Institute of General Medical Sciences (R01GM115851)

  • Oskar Hallatschek

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Magnus Nordborg, Vienna Biocenter, Austria

Publication history

  1. Received: January 2, 2017
  2. Accepted: July 1, 2017
  3. Accepted Manuscript published: July 3, 2017 (version 1)
  4. Version of Record published: July 18, 2017 (version 2)

Copyright

© 2017, Weissman & Hallatschek

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Daniel B Weissman
  2. Oskar Hallatschek
(2017)
Minimal-assumption inference from population-genomic data
eLife 6:e24836.
https://doi.org/10.7554/eLife.24836

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