Performance of In Silico Tools for the Evaluation of UGT1A1 Missense 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. Here we report the study of the the prediction ability of 16 in silico tools to determine the pathogenicity of human UGT1A1 nsSNPs, that have been phenotypically characterized by in vivo and in vitro studies. The results reveal that eight of these algorithms reached or exceeded 90% sensitivity and six presented a Matthews correlation coefficient above 0.46. Our data suggest that the selection of user inputs improves the performance of these methods.