Abstract Objectives To analyze the efficiency of a multigenic targeted massively parallel sequencing panel related to endocrine disorders for molecular diagnosis of patients assisted in a tertiary hospital involved in the training of medical faculty. Material and methods Retrospective analysis of the clinical diagnosis and genotype obtained from 272 patients in the Endocrine unit of a tertiary hospital was performed using a custom panel designed with 653 genes, most of them already associated with the phenotype (OMIM) and some candidate genes that englobes developmental, metabolic and adrenal diseases. The enriched DNA libraries were sequenced in NextSeq 500. Variants found were then classified according to ACMG/AMP criteria, with Varsome and InterVar. Results Three runs were performed; the mean coverage depth of the targeted regions in panel sequencing data was 249×, with at least 96.3% of the sequenced bases being covered more than 20-fold. The authors identified 66 LP/P variants (24%) and 27 VUS (10%). Considering the solved cases, 49 have developmental diseases, 12 have metabolic and 5 have adrenal diseases. Conclusion The application of a multigenic panel aids the training of medical faculty in an academic hospital by showing the picture of the molecular pathways behind each disorder. This may be particularly helpful in developmental disease cases. A precise genetic etiology provides an improvement in understanding the disease, guides decisions about prevention or treatment, and allows genetic counseling.

OBJECTIVES: Single nucleotide variants (SNVs) are the most common type of genetic variation among humans. High-throughput sequencing methods have recently characterized millions of SNVs in several thousand individuals from various populations, most of which are benign polymorphisms. Identifying rare disease-causing SNVs remains challenging, and often requires functional in vitro studies. Prioritizing the most likely pathogenic SNVs is of utmost importance, and several computational methods have been developed for this purpose. However, these methods are based on different assumptions, and often produce discordant results. The aim of the present study was to evaluate the performance of 11 widely used pathogenicity prediction tools, which are freely available for identifying known pathogenic SNVs: Fathmn, Mutation Assessor, Protein Analysis Through Evolutionary Relationships (Phanter), Sorting Intolerant From Tolerant (SIFT), Mutation Taster, Polymorphism Phenotyping v2 (Polyphen-2), Align Grantham Variation Grantham Deviation (Align-GVGD), CAAD, Provean, SNPs&GO, and MutPred. METHODS: We analyzed 40 functionally proven pathogenic SNVs in four different genes associated with differences in sex development (DSD): 17β-hydroxysteroid dehydrogenase 3 (HSD17B3), steroidogenic factor 1 (NR5A1), androgen receptor (AR), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). To evaluate the false discovery rate of each tool, we analyzed 36 frequent (MAF>0.01) benign SNVs found in the same four DSD genes. The quality of the predictions was analyzed using six parameters: accuracy, precision, negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient (MCC). Overall performance was assessed using a receiver operating characteristic (ROC) curve. RESULTS: Our study found that none of the tools were 100% precise in identifying pathogenic SNVs. The highest specificity, precision, and accuracy were observed for Mutation Assessor, MutPred, SNP, and GO. They also presented the best statistical results based on the ROC curve statistical analysis. Of the 11 tools evaluated, 6 (Mutation Assessor, Phanter, SIFT, Mutation Taster, Polyphen-2, and CAAD) exhibited sensitivity >0.90, but they exhibited lower specificity (0.42-0.67). Performance, based on MCC, ranged from poor (Fathmn=0.04) to reasonably good (MutPred=0.66). CONCLUSION: Computational algorithms are important tools for SNV analysis, but their correlation with functional studies not consistent. In the present analysis, the best performing tools (based on accuracy, precision, and specificity) were Mutation Assessor, MutPred, and SNPs&GO, which presented the best concordance with functional studies.

INTRODUÇÃO: Aproximadamente 50% dos pacientes com síndrome de Noonan (SN) apresentam mutações em heterozigose no gene PTPN11. OBJETIVO: Avaliar a freqüência de mutações no PTPN11 em pacientes com SN e analisar a correlação fenótipo-genótipo. PACIENTES: 33 pacientes com SN. MÉTODO: Extração de DNA de leucócitos periféricos e seqüenciamento dos 15 exons do PTPN11. RESULTADOS: Nove diferentes mutações missense no PTPN11, incluindo a mutação P491H, ainda não descrita, foram encontradas em 16 dos 33 pacientes. As características clínicas mais freqüentes dos pacientes com SN foram: pavilhão auricular com rotação incompleta e espessamento da helix (85%), baixa estatura (79%), prega cervical (77%) e criptorquidismo nos meninos (60%). O Z da altura foi de -2,7 ± 1,2 e o do IMC foi de -1 ± 1,4. Os pacientes com mutação no PTPN11 apresentaram maior freqüência de estenose pulmonar do que os pacientes sem mutação (38% vs. 6%, p< 0,05). Pacientes com ou sem mutação no PTPN11 não diferiram em relação à média do Z da altura, Z do IMC, freqüência de alterações torácicas, características faciais, criptorquidia, retardo mental, dificuldade de aprendizado, pico de GH ao teste de estímulo e Z de IGF-1 ou IGFBP-3. CONCLUSÃO: Identificamos mutações no PTPN11 em 48,5% dos pacientes com SN, os quais apresentaram maior freqüência de estenose pulmonar.

INTRODUCTION: Around 50% of Noonan syndrome (NS) patients present heterozygous mutations in the PTPN11 gene. AIM: To evaluate the frequency of mutations in the PTPN11 in patients with NS, and perform phenotype-genotype correlation. PATIENTS: 33 NS patients (23 males). METHODS: DNA was extracted from peripheral blood leukocytes, and all 15 PTPN11 exons were directly sequenced. RESULTS: Nine different missense mutations, including the novel P491H, were found in 16 of 33 NS patients. The most frequently observed features in NS patients were posteriorly rotated ears with thick helix (85%), short stature (79%), webbed neck (77%) and cryptorchidism (60%) in boys. The mean height SDS was -2.7 ± 1.2 and BMI SDS was -1 ± 1.4. Patients with PTPN11 mutations presented a higher incidence of pulmonary stenosis than patients without mutations (38% vs. 6%, p< 0.05). Patients with and without mutations did not present differences regarding height SDS, BMI SDS, frequency of thorax deformity, facial characteristics, cryptorchidism, mental retardation, learning disabilities, GH peak at stimulation test and IGF-1 or IGFBP-3 SDS. CONCLUSION: We identified missense mutations in 48.5% of the NS patients. There was a positive correlation between the presence of PTPN11 mutations and pulmonary stenosis frequency in NS patients.