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 BlümckeSandra 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, Irmgard Debatin, Jesús Del Valle, Capucine Delnatte, Nicolas Derive, Orland Diez, Nina Ditsch, Susan M. Domchek, Véronique Dutrannoy, Diana M. Eccles, Hans Ehrencrona, Ute Enders, D. Gareth Evans, Chantal Farra, Ulrike Faust, Ute Felbor, Irene Feroce, Miriam Fine, William D. Foulkes, Henrique C.R. Galvao, Gaetana Gambino, Andrea Gehrig, Francesca Gensini, Anne Marie Gerdes, Aldo Germani, Jutta Giesecke, Viviana Gismondi, Carolina Gómez, Encarna B. Gómez Garcia, Sara González, Elia Grau, Sabine Grill, Eva Gross, Aliana Guerrieri-Gonzaga, Marine Guillaud-Bataille, Sara Gutiérrez-Enríquez, Thomas Haaf, Karl Hackmann, Thomas V.O. Hansen, Marion Harris, Jan Hauke, Tilman Heinrich, Heide Hellebrand, Karen N. Herold, Ellen Honisch, Judit Horvath, Claude Houdayer, Verena Hübbel, Silvia Iglesias, Angel Izquierdo, Paul A. James, Linda A.M. Janssen, Udo Jeschke, Silke Kaulfuß, Katharina Keupp, Marion Kiechle, Alexandra Kölbl, Sophie Krieger, Torben A. Kruse, Anders Kvist, Fiona Lalloo, Mirjam Larsen, Vanessa L. Lattimore, Charlotte Lautrup, Susanne Ledig, Elena Leinert, Alexandra L. Lewis, Joanna Lim, Markus Loeffler, Adrià López-Fernández, Emanuela Lucci-Cordisco, Nicolai Maass, Siranoush Manoukian, Monica Marabelli, Laura Matricardi, Alfons Meindl, Rodrigo D. Michelli, Setareh Moghadasi, Alejandro Moles-Fernández, Marco Montagna, Gemma Montalban, Alvaro N. Monteiro, Eva Montes, Luigi Mori, Lidia Moserle, Clemens R. Müller, Christoph Mundhenke, Nadia Naldi, Katherine L. Nathanson, Matilde Navarro, Heli Nevanlinna, Cassandra B. Nichols, Dieter Niederacher, Henriette R. Nielsen, Kai Ren Ong, Nicholas Pachter, Edenir I. Palmero, Laura Papi, Inge Sokilde Pedersen, Bernard Peissel, Pedro Perez-Segura, Katharina Pfeifer, Marta Pineda, Esther Pohl-Rescigno, Nicola K. Poplawski, Berardino Porfirio, Anne S. Quante, Juliane Ramser, Rui M. Reis, Françoise Revillion, Kerstin Rhiem, Barbara Riboli, Julia Ritter, Daniela Rivera, Paula Rofes, Andreas Rump, Monica Salinas, Ana María Sánchez de Abajo, Gunnar Schmidt, Ulrike Schoenwiese, Jochen Seggewiß, Ares Solanes, Doris Steinemann, Mathias Stiller, Dominique Stoppa-Lyonnet, Kelly J. Sullivan, Rachel Susman, Christian Sutter, Sean V. Tavtigian, Soo H. Teo, Alex Teulé, Mads Thomassen, Maria Grazia Tibiletti, Marc Tischkowitz, Silvia Tognazzo, Amanda E. Toland, Eva Tornero, Therese Törngren, Sara Torres-Esquius, Angela Toss, Alison H. Trainer, Katherine M. Tucker, Christi J. van Asperen, Marion T. van Mackelenbergh, Liliana Varesco, Gardenia Vargas-Parra, Raymonda Varon, Ana Vega, Ángela Velasco, Anne Sophie Vesper, Alessandra Viel, Maaike P.G. Vreeswijk, Sebastian A. Wagner, Anke Waha, Logan C. Walker, Rhiannon J. Walters, Shan Wang-Gohrke, Bernhard H.F. Weber, Wilko Weichert, Kerstin Wieland, Lisa Wiesmüller, Isabell Witzel, Achim Wöckel, Emma R. Woodward, Silke Zachariae, Valentina Zampiga, Christine Zeder-Göß, KCon Fab Investigators, Conxi Lázaro, Arcangela De Nicolo, Paolo Radice, Christoph Engel, Rita K. Schmutzler, David E. Goldgar, Amanda B. Spurdle

Research output: Contribution to journalArticle

Abstract

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
Volume40
Issue number9
DOIs
Publication statusPublished - Sep 1 2019

Keywords

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

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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    Parsons, M. T., Tudini, E., Li, H., Hahnen, E., Wappenschmidt, B., Feliubadaló, L., Aalfs, C. M., Agata, S., Aittomäki, K., Alducci, E., Alonso-Cerezo, M. C., Arnold, N., Auber, B., Austin, R., Azzollini, J., Balmaña, J., Barbieri, E., Bartram, C. R., Blanco, A., ... Spurdle, A. B. (2019). Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification. Human Mutation, 40(9), 1557-1578. https://doi.org/10.1002/humu.23818