MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations.

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.

Variant Selection and Interpretation: An Example of Modified VarSome Classifier of ACMG Guidelines in the Diagnostic Setting.

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.

Genetic test for the personalization of sport training.

Genetic variants may contribute to confer elite athlete status. However, this does not mean that a person with favourable genetic traits would become a champion because multiple genetic interactions and epigenetic contributions coupled with confounding environmental factors shape the overall phenotype. This opens up a new area in sports genetics with respect to commercial genetic testing. The analysis of genetic polymorphisms linked to sport performance would provide insights into the potential of becoming an elite endurance or power performer. This mini-review aims to highlight genetic interactions that are associated with performance phenotypes and their potentials to be used as markers for talent identification and trainability.

Gene-Targeted Analysis of Clinically Diagnosed Long QT Russian Families.

Long QT syndrome (LQTS) has great genetic heterogeneity: more than 500 mutations have been described in several genes. Despite many advances, a genetic diagnosis still cannot be established in 25-30% of patients. The aim of the present study was to perform genetic evaluation in 9 Russian families with LQTS; here we report the results of 4 positive probands and their relatives (a total of 16 individuals). All subjects underwent clinical examination, 12-lead ECG, and Holter monitoring. Genetic analysis of the 14 genes mainly involved in LQTS was performed using a next-generation sequencing approach. We identified two new mutations (KCNQ1 gene) and 6 known mutations (AKAP9, ANK2, KCNE1 and KCNJ2 genes) in 4 out of 9 probands, some of which have already been described in association with LQTS. Segregation studies suggest a possible causative role for KCNQ1 p.(Leu342Pro), AKAP9 p.(Arg1609Lys), KCNE1 p.(Asp85Asn), and KCNJ2 p.(Arg82Gln) variations. Our study confirmed the high genetic heterogeneity of this disease and highlights the difficulties to reveal clear pathogenic genotypes also in large pedigrees. To the best of our knowledge, this is the first genetic study of LQTS patients from Russian families.