Performance of In Silico Tools for the Evaluation ofUGT1A1Missense Variants

Abstract

Variations in the gene encoding uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) are particularly important because they have been associated with hyperbilirubinemia in Gilbert's and Crigler-Najjar syndromes as well as with changes in drug metabolism. Several variants associated with these phenotypes are nonsynonymous single-nucleotide polymorphisms (nsSNPs). Bioinformatics approaches have gained increasing importance in predicting the functional significance of these variants. This study was focused on the predictive ability of bioinformatics approaches to determine the pathogenicity of human UGT1A1 nsSNPs, which were previously characterized at the protein level by in vivo and in vitro studies. Using 16 Web algorithms, we evaluated 48 nsSNPs described in the literature and databases. Eight of these algorithms reached or exceeded 90% sensitivity and six presented a Matthews correlation coefficient above 0.46. The best-performing method was MutPred, followed by Sorting Intolerant from Tolerant (SIFT). The prediction measures varied significantly when predictors such us SIFT, polyphen-2, and Prediction of Pathological Mutations on Proteins were run with their native alignment generated by the tool, or with an input alignment that was strictly built with UGT1A1 orthologs and manually curated. Our results showed that the prediction performance of some methods based on sequence conservation analysis can be negatively affected when nsSNPs are positioned at the hypervariable or constant regions of UGT1A1 ortholog sequences.

Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics

“…The study by Luxembourg et al [ 13 ] reported an increased number of misclassifications in cases where mutations were localized in the α -helix of a corresponding protein. Rodrigues et al [ 15 ] found that genomic regions of strong conservation as well as hypervariability may negatively affect prediction results. Grimm et al [ 11 ] noted that the evaluation of several tools suffered from overfitting, as variants used to train the methods also appeared in the evaluation set.…”

Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene

“…Furthermore, the sequence homology based approach and the structural homology based approach of SNP prediction are used in a complimentary way, wherein sequence homology based approach predicts SNPs using genetic point of view; whereas, the structural homology based approach predicts SNPs using functional point of view. The same combination has already been used in same manner previously by many other studies conducted by other research groups 36–38 . In fact, clinical laboratories also employ in-silico prediction tools, either alone or in combination, to predict missense single nucleotide variants of uncertain pathogenicity 39, 40 .…”

Benchmarking analysis of deleterious SNP prediction tools on CYP2D6 enzyme

“…Other independent analyses also reported similar results. In predicting the effect of missense SNPs on UGT1A1, Rodrigues et al (2015) demonstrated that 13 of the 20 tools had an MCC close to zero, and only the predictor of MutPred reached a value of MCC ≥ 0.7 (Rodrigues et al, 2015). Kerr et al (2017) used 7 tools to predict the functional impact of variants of the BRCA1, BRCA2, MLH1 and MSH2 genes associated with hereditary cancer, and demonstrated that within the evaluated set, 5 algorithms had coefficients below 0.35 and only two tools (Align -GVGD (0.65) and MAPP-MMR (0.59)) had MCCs greater than 0.59 (Kerr et al, 2017).…”