Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification: Human Mutation

Michael T. Parsons, Emma Tudini, Hongyan Li, Eric Hahnen, Barbara Wappenschmidt, Lidia Feliubadaló, Cora M. Aalfs, Simona Agata, Kristiina Aittomäki, Elisa Alducci, María Concepción Alonso-Cerezo, Norbert Arnold, Bernd Auber, Rachel Austin, Jacopo Azzollini, Judith Balmaña, Elena Barbieri, Claus R. Bartram, Ana Blanco, Britta BlümckeSandra Bonache, Bernardo Bonanni, Åke Borg, Beatrice Bortesi, Joan Brunet, Carla Bruzzone, Karolin Bucksch, Giulia Cagnoli, Trinidad Caldés, Almuth Caliebe, Mariarosaria Calvello, Giulia Cini, Paola Concolino, Laura Cortesi, Irene Feroce, Viviana Gismondi, Aliana Guerrieri-Gonzaga, Siranoush Manoukian, Monica Marabelli, Laura Matricardi, Marco Montagna, Lidia Moserle, Bernard Peissel, Daniela Rivera, Silvia Tognazzo, Liliana Varesco, Alessandra Viel, Valentina Zampiga, Arcangela De Nicolo, Paolo Radice

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1557-1578
Number of pages22
JournalHuman Mutation
Issue number9
Publication statusPublished - 2019


  • classification
  • BRCA1
  • BRCA2
  • clinical
  • multifactorial
  • quantitative
  • uncertain significance
  • variant


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