Rare variants contribute disproportionately to quantitative trait variation in yeast

  1. Joshua S Bloom  Is a corresponding author
  2. James Boocock
  3. Sebastian Treusch
  4. Meru J Sadhu
  5. Laura Day
  6. Holly Oates-Barker
  7. Leonid Kruglyak  Is a corresponding author
  1. University of California, Los Angeles, United States

Abstract

How variants with different frequencies contribute to trait variation is a central question in genetics. We use a unique model system to disentangle the contributions of common and rare variants to quantitative traits. We generated ~14,000 progeny from crosses among 16 diverse yeast strains and identified thousands of quantitative trait loci (QTLs) for 38 traits. We combined our results with sequencing data for 1,011 yeast isolates to show that rare variants make a disproportionate contribution to trait variation. Evolutionary analyses revealed that this contribution is driven by rare variants that arose recently, and that negative selection has shaped the relationship between variant frequency and effect size. We leveraged the structure of the crosses to resolve hundreds of QTLs to single genes. These results refine our understanding of trait variation at the population level and suggest that studies of rare variants are a fertile ground for discovery of genetic effects.

Data availability

Unless otherwise specified, all computational analyses were performed in R (v3.4.4). Analysis code and processing scripts are available at https://github.com/joshsbloom/yeast-16-parents. Additional links to generated data are also provided in the github repository. The version numbers of R packages used are listed in this repository. Sequencing data has been deposited in the SRA under the accession code PRJNA549760.

The following data sets were generated

Article and author information

Author details

  1. Joshua S Bloom

    Department of Human Genetics, University of California, Los Angeles, Los Angeles, United States
    For correspondence
    jbloom@mednet.ucla.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7241-1648
  2. James Boocock

    Department of Human Genetics, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Sebastian Treusch

    Department of Human Genetics, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Meru J Sadhu

    Department of Human Genetics, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Laura Day

    Department of Human Genetics, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Holly Oates-Barker

    Department of Human Genetics, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Leonid Kruglyak

    Department of Human Genetics, University of California, Los Angeles, Los Angeles, United States
    For correspondence
    LKruglyak@mednet.ucla.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8065-3057

Funding

National Institutes of Health (R01GM102308)

  • Joshua S Bloom
  • Meru J Sadhu
  • Laura Day
  • Holly Oates-Barker
  • Leonid Kruglyak

Howard Hughes Medical Institute

  • Joshua S Bloom
  • Laura Day
  • Holly Oates-Barker
  • Leonid Kruglyak

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

Reviewing Editor

  1. Christian R Landry, Université Laval, Canada

Publication history

  1. Received: June 11, 2019
  2. Accepted: October 23, 2019
  3. Accepted Manuscript published: October 24, 2019 (version 1)
  4. Version of Record published: December 4, 2019 (version 2)

Copyright

© 2019, Bloom et al.

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. Joshua S Bloom
  2. James Boocock
  3. Sebastian Treusch
  4. Meru J Sadhu
  5. Laura Day
  6. Holly Oates-Barker
  7. Leonid Kruglyak
(2019)
Rare variants contribute disproportionately to quantitative trait variation in yeast
eLife 8:e49212.
https://doi.org/10.7554/eLife.49212

Further reading

    1. Genetics and Genomics
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    Rare genetic variants in yeast explain a large amount of phenotypic variation in a complex trait like growth.

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    eLife publishes advances in quantitative genetics, including the genetic basis of complex traits, the maintenance of genetic variation, and their roles in evolution.