Parsons MT, Tudini E, Li H, Hahnen E, Wappenschmidt B, Feliubadalo L, Aalfs CM, Agata S, Aittomaki K, Alducci E, Concepcion Alonso-Cerezo M, Arnold N, Auber B, Austin R, Azzollini J, Balmana J, Barbieri E, Bartram CR, Blanco A, Bluemcke B, Bonache S, Bonanni B, Borg A, Bortesi B, Brunet J, Bruzzone C, Bucksch K, Cagnoli G, Caldes T, Caliebe A, Caligo MA, Calvello M, Capone GL, Caputo SM, Carnevali I, Carrasco E, Caux-Moncoutier V, Cavalli P, Cini G, Clarke EM, Concolino P, Cops EJ, Cortesi L, Couch FJ, Darder E, De La Hoya M, Dean M, Debatin I, Del Valle J, Delnatte C, Derive N, Diez O, Ditsch N, Domchek SM, Dutrannoy V, Eccles DM, Ehrencrona H, Enders U, Evans DG, Farra C, Faust U, Felbor U, Feroce I, Fine M, Foulkes WD, Galvao HC, Gambino G, Gehrig A, Gensini F, Gerdes AM, Germani A, Giesecke J, Gismondi V, Gomez C, Garcia EBG, Gonzalez S, Grau E, Grill S, Gross E, Guerrieri-Gonzaga A, Guillaud-Bataille M, Gutierrez-Enriquez S, Haaf T, Hackmann K, Hansen TV, Harris M, Hauke J, Heinrich T, Hellebrand H, Herold KN, Honisch E, Horvath J, Houdayer C, Huebbel V, Iglesias S, Izquierdo A, James PA, Janssen LA, Jeschke U, Kaulfuss S, Keupp K, Kiechle M, Koelbl A, Krieger S, Kruse TA, Kvist A, Lalloo F, Larsen M, Lattimore VL, Lautrup C, Ledig S, Leinert E, Lewis AL, Lim J, Loeffler M, Lopez-Fernandez A, Lucci-Cordisco E, Maass N, Manoukian S, Marabelli M, Matricardi L, Meindl A, Michelli RD, Moghadasi S, Moles-Fernandez A, Montagna M, Montalban G, Monteiro AN, Montes E, Mori L, Moserle L, Mueller CR, Mundhenke C, Naldi N, Nathanson KL, Navarro M, Nevanlinna H, Nichols CB, Niederacher D, Nielsen HR, Ong KR, Pachter N, Palmero E, Papi L, Pedersen IS, Peissel B, Perez-Segura P, Pfeifer K, Pineda M, Pohl-Rescigno E, Poplawski NK, Porfirio B, Quante AS, Ramser J, Reis RM, Revillion F, Rhiem K, Riboli B, Ritter J, Rivera D, Rofes P, Rump A, Salinas M, Sanchez De Abajo AM, Schmidt G, Schoenwiese U, Seggewiss J, Solanes A, Steinemann D, Stiller M, Stoppa-Lyonnet D, Sullivan KJ, Susman R, Sutter C, Tavtigian S, Teo SH, Teule A, Thomassen M, Tibiletti MG, Tischkowitz M, Tognazzo S, Toland AE, Tornero E, Torngren T, Torres-Esquius S, Toss A, Trainer AH, Tucker KM, Van Asperen CJ, Van Mackelenbergh MT, Varesco L, Vargas-Parra G, Varon R, Vega A, Velasco A, Vesper AS, Viel A, Vreeswijk MPG, Wagner SA, Waha A, Walker LC, Walters RJ, Wang-Gohrke S, Weber BHF, Weichert W, Wieland K, Wiesmueller L, Witzel I, Woeckel A, Woodward ER, Zachariae S, Zampiga V, Zeder-Goss C, Lazaro C, De Nicolo A, Radice P, Engel C, Schmutzler RK, Goldgar DE, Spurdle AB (2019)
Publication Type: Journal article
Publication year: 2019
Book Volume: 40
Pages Range: 1557-1578
Journal Issue: 9
DOI: 10.1002/humu.23818
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.
APA:
Parsons, M.T., Tudini, E., Li, H., Hahnen, E., Wappenschmidt, B., Feliubadalo, L.,... 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://dx.doi.org/10.1002/humu.23818
MLA:
Parsons, Michael T., et al. "Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification." Human Mutation 40.9 (2019): 1557-1578.
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