1. Evolutionary Biology
  2. Genetics and Genomics
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Mutation saturation for fitness effects at human CpG sites

  1. Ipsita Agarwal  Is a corresponding author
  2. Molly Przeworski  Is a corresponding author
  1. Columbia University, United States
Research Article
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Cite this article as: eLife 2021;10:e71513 doi: 10.7554/eLife.71513

Abstract

Whole exome sequences have now been collected for millions of humans, with the related goals of identifying pathogenic mutations in patients and establishing reference repositories of data from unaffected individuals. As a result, we are approaching an important limit, in which datasets are large enough that, in the absence of natural selection, every highly mutable site will have experienced at least one mutation in the genealogical history of the sample. Here, we focus on CpG sites that are methylated in the germline and experience mutations to T at an elevated rate of ~10-7 per site per generation; considering synonymous mutations in a sample of 390,000 individuals, ~99% of such CpG sites harbor a C/T polymorphism. Methylated CpG sites provide a natural mutation saturation experiment for fitness effects: as we show, at current sample sizes, not seeing a non-synonymous polymorphism is indicative of strong selection against that mutation. We rely on this idea in order to directly identify a subset of CpG transitions that are likely to be highly deleterious, including ~27% of possible loss-of-function mutations, and up to 20% of possible missense mutations, depending on the type of functional site in which they occur. Unlike methylated CpGs, most mutation types, with rates on the order of 10-8 or 10-9, remain very far from saturation. We discuss what these findings imply for interpreting the potential clinical relevance of mutations from their presence or absence in reference databases and for inferences about the fitness effects of new mutations.

Data availability

All source data are freely available to researchers, with sources provided in the manuscript. Data and code to generate the figures is available at https://github.com/agarwal-i/cpg_saturation.

The following previously published data sets were used

Article and author information

Author details

  1. Ipsita Agarwal

    Department of Biological Sciences, Columbia University, New York, United States
    For correspondence
    ia2337@columbia.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8537-0008
  2. Molly Przeworski

    Department of Systems Biology, Columbia University, New York, United States
    For correspondence
    mp3284@columbia.edu
    Competing interests
    Molly Przeworski, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5369-9009

Funding

National Institutes of Health (GM122975)

  • Molly Przeworski

National Institutes of Health (GM121372)

  • Molly Przeworski

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

Reviewing Editor

  1. Jeffrey Ross-Ibarra, University of California, Davis, United States

Publication history

  1. Received: June 22, 2021
  2. Accepted: November 21, 2021
  3. Accepted Manuscript published: November 22, 2021 (version 1)

Copyright

© 2021, Agarwal & Przeworski

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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