Spontaneous mutations and the origin and maintenance of quantitative genetic variation
- North Carolina State University, United States
- Washington University in St. Louis, United States
- National Heart Lung and Blood Institute, United States
- Syngenta, United States
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
Reviewing Editor
- Molly Przeworski, Columbia University, United States
Publication history
- Received: January 21, 2016
- Accepted: May 21, 2016
- Accepted Manuscript published: May 23, 2016 (version 1)
- Version of Record published: June 16, 2016 (version 2)
- 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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Spontaneous mutations and the origin and maintenance of quantitative genetic variation
eLife 5:e14625.
https://doi.org/10.7554/eLife.14625
Further reading
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- Genetics and Genomics
Upstream open reading frames (uORFs) are potent cis-acting regulators of mRNA translation and nonsense-mediated decay (NMD). While both AUG- and non-AUG initiated uORFs are ubiquitous in ribosome profiling studies, few uORFs have been experimentally tested. Consequently, the relative influences of sequence, structural, and positional features on uORF activity have not been determined. We quantified thousands of yeast uORFs using massively parallel reporter assays in wildtype and ∆upf1 yeast. While nearly all AUG uORFs were robust repressors, most non-AUG uORFs had relatively weak impacts on expression. Machine learning regression modeling revealed that both uORF sequences and locations within transcript leaders predict their effect on gene expression. Indeed, alternative transcription start sites highly influenced uORF activity. These results define the scope of natural uORF activity, identify features associated with translational repression and NMD, and suggest that the locations of uORFs in transcript leaders are nearly as predictive as uORF sequences.
