Synonymous variants that disrupt messenger RNA structure are significantly constrained in the human population
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Abstract
Background
The role of synonymous single-nucleotide variants in human health and disease is poorly understood, yet evidence suggests that this class of “silent” genetic variation plays multiple regulatory roles in both transcription and translation. One mechanism by which synonymous codons direct and modulate the translational process is through alteration of the elaborate structure formed by single-stranded mRNA molecules. While tools to computationally predict the effect of non-synonymous variants on protein structure are plentiful, analogous tools to systematically assess how synonymous variants might disrupt mRNA structure are lacking.
Results
We developed novel software using a parallel processing framework for large-scale generation of secondary RNA structures and folding statistics for the transcriptome of any species. Focusing our analysis on the human transcriptome, we calculated 5 billion RNA-folding statistics for 469 million single-nucleotide variants in 45,800 transcripts. By considering the impact of all possible synonymous variants globally, we discover that synonymous variants predicted to disrupt mRNA structure have significantly lower rates of incidence in the human population.
Conclusions
These findings support the hypothesis that synonymous variants may play a role in genetic disorders due to their effects on mRNA structure. To evaluate the potential pathogenic impact of synonymous variants, we provide RNA stability, edge distance, and diversity metrics for every nucleotide in the human transcriptome and introduce a “Structural Predictivity Index” (SPI) to quantify structural constraint operating on any synonymous variant. Because no single RNA-folding metric can capture the diversity of mechanisms by which a variant could alter secondary mRNA structure, we generated a SUmmarized RNA Folding (SURF) metric to provide a single measurement to predict the impact of secondary structure altering variants in human genetic studies.
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Now published in GigaScience doi: 10.1093/gigascience/giab023
Jeffrey B.S. Gaither Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteGrant E. Lammi Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteJames L. Li Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteDavid M. Gordon Computational Genomics Group, The Institute for Genomic Medicine, Nationwide …
Now published in GigaScience doi: 10.1093/gigascience/giab023
Jeffrey B.S. Gaither Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteGrant E. Lammi Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteJames L. Li Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteDavid M. Gordon Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteHarkness C. Kuck Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteBenjamin J. Kelly Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteJames R. Fitch Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this sitePeter White Computational Genomics Group, The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Peter WhiteFor correspondence: peter.white@nationwidechildrens.org
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giab023 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102700 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102701
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