GigaScienceGenomicsPeer-reviewed journal article
Global Analysis of Human mRNA Folding Disruptions in Synonymous Variants Demonstrates Significant Population Constraint
Jeff GaitheriD, Grant LammiiD, James LiiD, David M Gordon, Harkness C. KuckiD, Benjamin KellyiD, James FitchiD, Peter WhiteiD
Abstract
BackgroundIn most organisms the structure of an mRNA molecule is crucial in determining speed of translation, half-life, splicing propensities and final protein configuration. Synonymous variants which distort this wildtype mRNA structure may be pathogenic as a consequence. However, current clinical guidelines classify synonymous or "silent" single nucleotide variants (sSNVs) as largely benign unless a role in RNA splicing can be demonstrated. ResultsWe developed novel software to conduct a global transcriptome study in which RNA folding statistics were computed for 469 million SNVs in 45,800 transcripts using an Apache Spark implementation of ViennaRNA in the cloud. Focusing our analysis on the subset of 17.9 million sSNVs, we discover that variants predicted to disrupt mRNA structure have lower rates of incidence in the human population. Given that the community lacks tools to evaluate the potential pathogenic impact of sSNVs, we introduce a "Structural Predictivity Index" (SPI) to quantify this constraint due to mRNA structure. ConclusionsOur findings support the hypothesis that sSNVs may play a role in genetic disorders due to their effects on mRNA structure. Our RNA-folding scores provide a means of gauging the structural constraint operating on any sSNV in the human genome. Given that the majority of patients with rare or as yet to be diagnosed disease lack a molecular diagnosis, these scores have the potential to enable discovery of novel genetic etiologies. Our RNA Stability Pipeline as well as ViennaRNA structural metrics and SPI scores for all human synonymous variants can be downloaded from GitHub https://github.com/nch-igm/rna-stability.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 36
- FWCI
- 1.49
- Field percentile
- top 18%
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