Bardet–Biedl syndrome (BBS) is a rare inherited multisystemic disorder with autosomal recessive or complex digenic triallelic inheritance. There is currently no treatment for BBS, but some morbidities can be managed. Accurate molecular diagnosis is often crucial for the definition of appropriate patient management and for the development of a potential personalized therapy. We developed a next-generation-sequencing (NGS) protocol for the screening of the 18 most frequently mutated genes to define the genotype and clarify the mutation spectrum of a cohort of 20 BBS Italian patients. We defined the causative variants in 60% of patients; four of those are novel. 33% of patients also harboured variants in additional gene/s, suggesting possible oligogenic inheritance. To explore the function of different genes, we looked for correlations between genotype and phenotype in our cohort. Hypogonadism was more frequently detected in patients with variants in BBSome proteins, while renal abnormalities in patients with variations in BBSome chaperonin genes. NGS is a powerful tool that can help understanding BBS patients’ phenotype through the identification of mutations that could explain differences in phenotype severity and could provide insights for the development of targeted therapy. Furthermore, our results support the existence of additional BBS loci yet to be identified.

We have developed MAGI-ACMG, a classification algorithm that allows the classification of sequencing variants (single nucleotide or small indels) according to the recommendations of the American College of Medical Genetics (ACMG) and the Association for Clinical Genomic Science (ACGS). The MAGI-ACMG classification algorithm uses information retrieved through the VarSome Application Programming Interface (API), integrates the AutoPVS1 tool in order to evaluate more precisely the attribution of the PVS1 criterion, and performs the customized assignment of specific criteria. In addition, we propose a sub-classification scheme for variants of uncertain significance (VUS) according to their proximity either towards the “likely pathogenic” or “likely benign” classes. We also conceived a pathogenicity potential criterion (P_POT) as a proxy for segregation criteria that might be added to a VUS after posterior testing, thus allowing it to upgrade its clinical significance in a diagnostic reporting setting. Finally, we have developed a user-friendly web application based on the MAGI-ACMG algorithm, available to geneticists for variant interpretation.

Infertility is a significant clinical problem. It affects 8-12% of couples worldwide, about 30% of whom are diagnosed with idiopathic infertility (infertility lacking any obvious cause). In 2010, the World Health Organization calculated that 1.9% of child-seeking women aged 20-44 years were unable to have a first live birth (primary infertility), and 10.5% of child-seeking women with a prior live birth were unable to have an additional live birth (secondary infertility). About 50% of all infertility cases are due to female reproductive defects. Several chromosome aberrations, diagnosed by karyotype analysis, have long been known to be associated with female infertility and monogenic mutations have also recently been found. Female infertility primarily involves oogenesis. The following phenotypes are associated with monogenic female infertility: premature ovarian failure, ovarian dysgenesis, oocyte maturation defects, early embryo arrest, polycystic ovary syndrome and recurrent pregnancy loss. Here we summarize the genetic causes of non-syndromic monogenic female infertility and the genes analyzed by our genetic test.

Congenital hypothyroidism is a condition in which the thyroid gland does not produce enough thyroid hormones. It occurs in 1:2000-4000 newborns. Common clinical features include decreased activity and increased sleep, feeding difficulty, constipation, prolonged jaundice, myxedematous facies, large fontanels (especially posterior), macroglossia, distended abdomen with umbilical hernia, and hypotonia. Slow linear growth and developmental delay are usually apparent by 4-6 months of age. Without treatment, congenital hypothyroidism leads to severe intellectual deficit and short stature. Congenital hyperthyroidism occurs when the thyroid gland produces too much of the hormone thyroxine, which can accelerate body metabolism, causing unintentional weight loss and a rapid or irregular heartbeat. Hyperthyroidism is very rare and its prevalence is unknown. Common clinical features include unintentional weight loss, tachycardia, arrhythmia, palpitations, anxiety, tremor and sweating. Here we summarize the genes involved in congenital hypo- and hyperthyroidism and the tests we use for genetic analysis.

Background: Up to 15% of couples are infertile and male factor infertility accounts for approximately 50% of these cases. Male infertility is a multifactorial pathological condition. The genetic of male infertility is very complex and at least 2000 genes are involved in its etiology. Genetic testing by next-generation sequencing (NGS) technologies can be relevant for its diagnostic value in male infertile patients. Therefore, the aim of this study was to implement the diagnostic offer with the use of an NGS panel for the identification of genetic variants. Methods: We developed an NGS gene panel that we used in 22 male infertile patients. The panel consisted of 110 genes exploring the genetic causes of male infertility; namely spermatogenesis failure due to single-gene mutations, central hypogonadism, androgen insensitivity syndrome, congenital hypopituitarism, and primary ciliary dyskinesia. Results: NGS and a subsequent sequencing of the positive pathogenic or likely pathogenic variants, 5 patients (23%) were found to have a molecular defect. In particular, pathogenic variants were identified in TEX11, CCDC39, CHD7, and NR5A1 genes. Moreover, 14 variants of unknown significance and 7 novel variants were found that require further functional studies and family segregation. Conclusion: This extended NGS-based diagnostic approach may represent a useful tool for the diagnosis of male infertility. The development of a custom-made gene panel by NGS seems capable of reducing the proportion of male idiopathic infertility.

Variant interpretation is challenging as it involves combining different levels of evidence in order to evaluate the role of a specific variant in the context of a patient’s disease. Many in-depth refinements followed the original 2015 American College of Medical Genetics (ACMG) guidelines to overcome subjective interpretation of criteria and classification inconsistencies. Here, we developed an ACMG-based classifier that retrieves information for variant interpretation from the VarSome Stable-API environment and allows molecular geneticists involved in clinical reporting to introduce the necessary changes to criterion strength and to add or exclude criteria assigned automatically, ultimately leading to the final variant classification. We also developed a modified ACMG checklist to assist molecular geneticists in adjusting criterion strength and in adding literature-retrieved or patient-specific information, when available. The proposed classifier is an example of integration of automation and human expertise in variant curation, while maintaining the laboratory analytical workflow and the established bioinformatics pipeline.