PubMed Highlight: Prioritization of synonymous variants

The final step of variant prioritization is a key point in NGS studies focused on identification of disease causing mutations. By now all the tools developed in this area consider only missense mutations, relying on various algorithms and integration with known information to suggest the best causative variants within a list of candidates. However, recent studies showed that also synonymous mutations could be responsible for disease. The new Silent Variant Analyzer (SilVA), describes by Buske et al. on Bioinformatics, is the first effort to prioritize synonymous variants and identify the ones that may be deleterious. I'm sorry, it seems that we can't anymore throw away synonymous SNVs to simplify data analysis...

Identification of deleterious synonymous variants in human genomes

Orion J. Buske, AshokKumar Manickaraj, Seema Mital, Peter N. Ray and Michael Brudno

Abstract

Motivation: The prioritization and identification of disease-causing mutations is one of the most significant challenges in medical genomics. Currently available methods address this problem for non-synonymous single nucleotide variants (SNVs) and variation in promoters/enhancers; however, recent research has implicated synonymous (silent) exonic mutations in a number of disorders.

Results: We have curated 33 such variants from literature and developed the Silent Variant Analyzer (SilVA), a machine-learning approach to separate these from among a large set of rare polymorphisms. We evaluate SilVA’s performance on in silico ‘infection’ experiments, in which we implant known disease-causing mutations into a human genome, and show that for 15 of 33 disorders, we rank the implanted mutation among the top five most deleterious ones. Furthermore, we apply the SilVA method to two additional datasets: synonymous variants associated with Meckel syndrome, and a collection of silent variants clinically observed and stratified by a molecular diagnostics laboratory, and show that SilVA is able to accurately predict the harmfulness of silent variants in these datasets.

Availability: SilVA is open source and is freely available from the project website: http://compbio.cs.toronto.edu/silva