Predicting Functional Effects of Synonymous Variants: A Systematic Review and Perspectives

Abstract

Recent advances in high-throughput experimentation have put the exploration of genome sequences at the forefront of precision medicine. In an effort to interpret the sequencing data, numerous computational methods have been developed for evaluating the effects of genome variants. Interestingly, despite the fact that every person has as many synonymous (sSNV) as non-synonymous single nucleotide variants, our ability to predict their effects is limited. The paucity of experimentally tested sSNV effects appears to be the limiting factor in development of such methods. Here, we summarize the details and evaluate the performance of nine existing computational methods capable of predicting sSNV effects. We used a set of observed and artificially generated variants to approximate large scale performance expectations of these tools. We note that the distribution of these variants across amino acid and codon types suggests purifying evolutionary selection retaining generated variants out of the observed set; i.e., we expect the generated set to be enriched for deleterious variants. Closer inspection of the relationship between the observed variant frequencies and the associated prediction scores identifies predictor-specific scoring thresholds of reliable effect predictions. Notably, across all predictors, the variants scoring above these thresholds were significantly more often generated than observed. which confirms our assumption that the generated set is enriched for deleterious variants. Finally, we find that while the methods differ in their ability to identify severe sSNV effects, no predictor appears capable of definitively recognizing subtle effects of such variants on a large scale.

Dysregulated PDGFR alpha expression and novel somatic mutations in colorectal cancer: association to RAS wild type status and tumor size

“…Similar results were reported for other synonymous variation in CRC and other malignancy via the same mechanisms mentioned above [32][33][34]. This would have phenotypic effect on protein expression [35,36]. Our analysis showed a non-significant but a tendency toward association between the presence of synonymous variations and the PDGFRα overexpression (P = 0.069; data not shown).…”

Rheostat positions: A new classification of protein positions relevant to pharmacogenomics

“…Clearly, computational methods to predict outcomes of protein substitutions are vital. However, despite decades of study, available algorithms still lack reliable performance (Miller et al 2017;Dong et al 2015;Andreoletti et al 2019;Zeng and Bromberg 2019).…”

Genome-Wide Identification of RNA Editing Sites Affecting Intramuscular Fat in Pigs

“…In the results of SIFT prediction, the editing of the sites in USP38 was deleterious to its protein function ( p < 0.05). As synonymous variants can disrupt transcription, splicing and mRNA stability [ 38 ], we used mfold [ 39 ] to predict the stability of the RNA structure of the genes containing the synonymous editing sites. Unfortunately, the three synonymous RNA editing sites could not cause any stability changes.…”