The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co‐segregation, family cancer history profile, co‐occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case‐control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene‐specific calibration of evidence types used for variant classification.

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Strategies in relation to CPGs, conduct of professionals, conduct of patients, or organization of health care processes improve the use of tests in relation to clinical processes. Laboratory professionals have the appropriate knowledge and can improve the quality and efficacy of health care.

Many BRCA1 and BRCA2 (BRCA1/2) genetic variants have been studied at mRNA level and linked to hereditary breast and ovarian cancer due to splicing alteration. In silico tools are reliable when assessing variants located in consensus splice sites, but we may identify variants in complex genomic contexts for which bioinformatics is not precise enough. In this study, we characterize BRCA2 c.7976 + 5G > T variant located in intron 17 which has an atypical donor site (GC). This variant was identified in three unrelated Spanish families and we have detected exon 17 skipping as the predominant transcript occurring in carriers. We have also detected several isoforms (Δ16-18, Δ17,18, Δ18, and ▼17q ) at different expression levels among carriers and controls. This study remarks the challenge of interpreting genetic variants when multiple alternative isoforms are present, and that caution must be taken when using in silico tools to identify potential spliceogenic variants located in GC-AG introns.

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