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Åke Borg

Åke Borg

Principal investigator

Åke Borg

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


  • 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 Blümcke
  • Sandra Bonache
  • Bernardo Bonanni
  • Åke Borg
  • Beatrice Bortesi
  • Joan Brunet
  • Carla Bruzzone
  • Karolin Bucksch
  • Giulia Cagnoli
  • Trinidad Caldés
  • Almuth Caliebe
  • Maria A Caligo
  • Mariarosaria Calvello
  • Gabriele L Capone
  • Sandrine M Caputo
  • Ileana Carnevali
  • Estela Carrasco
  • Virginie Caux-Moncoutier
  • Pietro Cavalli
  • Giulia Cini
  • Edward M Clarke
  • Paola Concolino
  • Elisa J Cops
  • Laura Cortesi
  • Fergus J Couch
  • Esther Darder
  • Miguel de la Hoya
  • Michael Dean
  • Hans Ehrencrona
  • Anders Kvist
  • Therese Törngren

Summary, in English

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 1395 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; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to 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 non-pathogenic 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. This article is protected by copyright. All rights reserved.


  • Breastcancer-genetics
  • Division of Clinical Genetics
  • BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation

Publishing year







Human Mutation

Document type

Journal article


John Wiley & Sons Inc.


  • Medical Genetics




  • ISSN: 1059-7794