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.

Metrics

  • 2,774
    Page views
  • 377
    Downloads
  • 13
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Genetics and Genomics
    2. Neuroscience
    Tomas Andreani et al.
    Research Article

    Homeostatic and circadian processes collaborate to appropriately time and consolidate sleep and wake. To understand how these processes are integrated, we scheduled brief sleep deprivation at different times of day in Drosophila and find elevated morning rebound compared to evening. These effects depend on discrete morning and evening clock neurons, independent of their roles in circadian locomotor activity. In the R5 ellipsoid body sleep homeostat, we identified elevated morning expression of activity dependent and presynaptic gene expression as well as the presynaptic protein BRUCHPILOT consistent with regulation by clock circuits. These neurons also display elevated calcium levels in response to sleep loss in the morning, but not the evening consistent with the observed time-dependent sleep rebound. These studies reveal the circuit and molecular mechanisms by which discrete circadian clock neurons program a homeostatic sleep center.

    1. Genetics and Genomics
    Hanmin Guo et al.
    Research Article Updated

    Exome sequencing on tens of thousands of parent-proband trios has identified numerous deleterious de novo mutations (DNMs) and implicated risk genes for many disorders. Recent studies have suggested shared genes and pathways are enriched for DNMs across multiple disorders. However, existing analytic strategies only focus on genes that reach statistical significance for multiple disorders and require large trio samples in each study. As a result, these methods are not able to characterize the full landscape of genetic sharing due to polygenicity and incomplete penetrance. In this work, we introduce EncoreDNM, a novel statistical framework to quantify shared genetic effects between two disorders characterized by concordant enrichment of DNMs in the exome. EncoreDNM makes use of exome-wide, summary-level DNM data, including genes that do not reach statistical significance in single-disorder analysis, to evaluate the overall and annotation-partitioned genetic sharing between two disorders. Applying EncoreDNM to DNM data of nine disorders, we identified abundant pairwise enrichment correlations, especially in genes intolerant to pathogenic mutations and genes highly expressed in fetal tissues. These results suggest that EncoreDNM improves current analytic approaches and may have broad applications in DNM studies.