Evidence map›Paper›PMID 33822938›Full record

ArticleGigaScience2021

Synonymous variants that disrupt messenger RNA structure are significantly constrained in the human population.

Jeffrey B S Gaither, Grant E Lammi, James L Li, David M Gordon, Harkness C Kuck, Benjamin J Kelly, James R Fitch, Peter White

Open access · goldAbstract read
In one paragraph

Article in GigaScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 30 citations in OpenAlex.

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  15. RNA encodes physical information.bioRxiv : the preprint server for biology · 2024
    Article
  16. Article
  17. Cannabis Pharmacogenomics: A Path to Personalized Medicine.Current issues in molecular biology · 2023
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Jeffrey B S GaitherComputational Genomics Group, The Institute for Genomic Medicine, Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43215, USA.
Grant E LammiComputational Genomics Group, The Institute for Genomic Medicine, Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43215, USA.
James L LiComputational Genomics Group, The Institute for Genomic Medicine, Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43215, USA.
David M GordonComputational Genomics Group, The Institute for Genomic Medicine, Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43215, USA.
Harkness C KuckComputational Genomics Group, The Institute for Genomic Medicine, Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43215, USA.
Benjamin J KellyComputational Genomics Group, The Institute for Genomic Medicine, Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43215, USA.
James R FitchComputational Genomics Group, The Institute for Genomic Medicine, Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43215, USA.
Peter WhiteComputational Genomics Group, The Institute for Genomic Medicine, Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43215, USA.
Nationwide Children's Hospital · US

Funding

Exome sequencing and functional studies in familial CHDR01HL109758 · NHLBI · RESEARCH INST NATIONWIDE CHILDREN'S HOSP · PI GARG, VIDU, MCBRIDE, KIM LEWIS · 2012 to 2015
$2.7M
NHLBI NIH HHS R01 HL109758
6 · The paper itself

Abstract

backgroundThe 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.

resultsWe 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.

conclusionsThese 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.

Indexed as

Protein BiosynthesisRNA StabilityCodonHumansNucleotidesRNA, MessengerCodonNucleotidesRNA, MessengerApache Sparkgenetic diseasegenomicsmRNA stabilityRNA structuresynonymous variant

Identifiers

PMID33822938
PMCPMC8023685
OpenAlexW3142412216

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.