ABSTRACT
BACKGROUND: Next-generation sequencing has had a significant impact on genetic disease diagnosis, but the interpretation of the vast amount of genomic data it generates can be challenging. To address this, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology have established guidelines for standardized variant interpretation. In this manuscript, we present the updated Hospital Israelita Albert Einstein Standards for Constitutional Sequence Variants Classification, incorporating modifications from leading genetics societies and the ClinGen initiative. RESULTS: First, we standardized the scientific publications, documents, and other reliable sources for this document to ensure an evidence-based approach. Next, we defined the databases that would provide variant information for the classification process, established the terminology for molecular findings, set standards for disease-gene associations, and determined the nomenclature for classification criteria. Subsequently, we defined the general rules for variant classification and the Bayesian statistical reasoning principles to enhance this process. We also defined bioinformatics standards for automated classification. Our workgroup adhered to gene-specific rules and workflows curated by the ClinGen Variant Curation Expert Panels whenever available. Additionally, a distinct set of specifications for criteria modulation was created for cancer genes, recognizing their unique characteristics. CONCLUSIONS: The development of an internal consensus and standards for constitutional sequence variant classification, specifically adapted to the Brazilian population, further contributes to the continuous refinement of variant classification practices. The aim of these efforts from the workgroup is to enhance the reliability and uniformity of variant classification.
Subject(s)
Genetic Testing , Genetic Variation , Humans , United States , Mutation , Reproducibility of Results , Bayes Theorem , Genome, HumanABSTRACT
Molecular biology is currently a fast-advancing science. Sequencing techniques are getting cheaper, but the interpretation of genetic variants requires expertise and computational power, therefore is still a challenge. Next-generation sequencing releases thousands of variants and to classify them, researchers propose protocols with several parameters. Here we present a review of several in silico pathogenicity prediction tools involved in the variant prioritization/classification process used by some international protocols for variant analysis and studies evaluating their efficiency.
ABSTRACT
BACKGROUND: The role of deep intronic variants in hereditary cancer susceptibility has been largely understudied. Previously, the BRCA2 c.6937 + 594T>G variant has been shown to preferentially promote the inclusion of a 95 nucleotide cryptic exon and to introduce a premature termination codon. Our objective was to further assess the pathogenicity of the BRCA2 c.6937 + 594T>G deep intronic variant. PATIENTS AND METHODS: We examined the association between BRCA2 c.6937 + 594T>G and breast cancer (BC) risk in 464 BC cases and 497 noncancer controls from Puerto Rico. RESULTS: The overall frequency of the G allele was 2.1% in this population. There was no association between the TG/GG genotypes and BC risk in the uncorrected model and after correcting for confounders. There was only one carrier of the GG genotype. This individual did not have personal or family history of cancer and did not meet the National Comprehensive Cancer Network criteria for hereditary cancer genetic testing. CONCLUSIONS: Although previous work has demonstrated that the BRCA2 c.6937 + 594T>G variant affects splicing, this association study does not support a pathogenic role for the BRCA2 c.6937 + 594T>G intronic variant in breast and ovarian cancer syndrome susceptibility. Furthermore, it emphasizes the need to take into account multiple diverse populations in association studies for the assessment of variant pathogenicity.