1. Genetics and Genomics
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Spontaneous mutations and the origin and maintenance of quantitative genetic variation

  1. Wen Huang
  2. Richard F Lyman
  3. Rachel A Lyman
  4. Mary Anna Carbone
  5. Susan T Harbison
  6. Michael M Magwire
  7. Trudy FC Mackay  Is a corresponding author
  1. North Carolina State University, United States
  2. Washington University in St. Louis, United States
  3. National Heart Lung and Blood Institute, United States
  4. Syngenta, United States
Research Article
  • Cited 28
  • Views 7,632
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Cite this article as: eLife 2016;5:e14625 doi: 10.7554/eLife.14625

Abstract

Mutation and natural selection shape the genetic variation in natural populations. Here, we directly estimated the spontaneous mutation rate by sequencing new Drosophila mutation accumulation lines maintained with minimal natural selection. We inferred strong stabilizing natural selection on quantitative traits because genetic variation among wild-derived inbred lines was much lower than predicted from a neutral model and the mutational effects were much larger than allelic effects of standing polymorphisms. Stabilizing selection could act directly on the traits, or indirectly from pleiotropic effects on fitness. However, our data are not consistent with simple models of mutation-stabilizing selection balance; therefore, further empirical work is needed to assess the balance of evolutionary forces responsible for quantitative genetic variation.

Article and author information

Author details

  1. Wen Huang

    Program in Genetics, North Carolina State University, Raleigh, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Richard F Lyman

    Program in Genetics, North Carolina State University, Raleigh, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Rachel A Lyman

    Department of Biology, Washington University in St. Louis, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Mary Anna Carbone

    Program in Genetics, North Carolina State University, Raleigh, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Susan T Harbison

    Laboratory of Systems Genetics, National Heart Lung and Blood Institute, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Michael M Magwire

    Syngenta, Research Triangle Park, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Trudy FC Mackay

    Program in Genetics, North Carolina State University, Raleigh, United States
    For correspondence
    trudy_mackay@ncsu.edu
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. Molly Przeworski, Columbia University, United States

Publication history

  1. Received: January 21, 2016
  2. Accepted: May 21, 2016
  3. Accepted Manuscript published: May 23, 2016 (version 1)
  4. Version of Record published: June 16, 2016 (version 2)
  5. Version of Record updated: October 19, 2016 (version 3)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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Further reading

    1. Genetics and Genomics
    Jose D Aponte et al.
    Research Article Updated

    Realistic mappings of genes to morphology are inherently multivariate on both sides of the equation. The importance of coordinated gene effects on morphological phenotypes is clear from the intertwining of gene actions in signaling pathways, gene regulatory networks, and developmental processes underlying the development of shape and size. Yet, current approaches tend to focus on identifying and localizing the effects of individual genes and rarely leverage the information content of high-dimensional phenotypes. Here, we explicitly model the joint effects of biologically coherent collections of genes on a multivariate trait – craniofacial shape – in a sample of n = 1145 mice from the Diversity Outbred (DO) experimental line. We use biological process Gene Ontology (GO) annotations to select skeletal and facial development gene sets and solve for the axis of shape variation that maximally covaries with gene set marker variation. We use our process-centered, multivariate genotype-phenotype (process MGP) approach to determine the overall contributions to craniofacial variation of genes involved in relevant processes and how variation in different processes corresponds to multivariate axes of shape variation. Further, we compare the directions of effect in phenotype space of mutations to the primary axis of shape variation associated with broader pathways within which they are thought to function. Finally, we leverage the relationship between mutational and pathway-level effects to predict phenotypic effects beyond craniofacial shape in specific mutants. We also introduce an online application that provides users the means to customize their own process-centered craniofacial shape analyses in the DO. The process-centered approach is generally applicable to any continuously varying phenotype and thus has wide-reaching implications for complex trait genetics.