Objective: Defects in the human thyroid peroxidase (TPO) gene are reported to be one of the causes of congenital hypothyroidism (CH) due to a total iodide organification defect. The aim of the present study was to determine the nature and frequency of TPO gene mutations in patients with CH, characterised by elevated TSH levels and orthotopic thyroid gland, identified in the Portuguese National Neonatal Screening Programme.Subjects and methods: The sample comprised 55 patients, from 53 unrelated families, with follow-up in the endocrinology clinics of the treatment centres of Porto and Lisbon. Mutation screening in the TPO gene (exons 1–17) was performed by single-strand conformational analysis followed by sequencing of fragments with abnormal migration patterns.Results: Eight different mutations were detected in 13 patients (seven homozygotes and six compound heterozygotes). Novel mutations included three missense mutations, namely 391T > C (S131P), 1274A > G (N425S) and 2512T > A (C838S), as well as the predictable splice mutation 2748G > A (Q916Q/spl?). The undocumented polymorphism 180-47A > C was also detected.Conclusion: The results are in accordance with previous observations confirming the genetic heterogeneity of TPO defects. The proportion of patients in which the aetiology was determined justifies the implementation of this molecular testing in our CH patients with dyshormonogenesis.

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.