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

, ; Parsons, Michael T; Tudini, Emma; Li, Hongyan; Hahnen, Eric; Wappenschmidt, Barbara; Feliubadaló, Lidia; Aalfs, Cora M; Agata, Simona and Aittomäki, Kristiina, et al. (2019) In Human Mutation
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 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior... (More)

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.

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Human Mutation
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John Wiley & Sons
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1059-7794
DOI
10.1002/humu.23818
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English
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6ac49438-147a-4d67-8b7b-e375a208b383
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2019-06-14 14:07:09
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2019-07-26 11:40:57
@article{6ac49438-147a-4d67-8b7b-e375a208b383,
  abstract     = {<p>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.</p>},
  author       = {,  and Parsons, Michael T and Tudini, Emma and Li, Hongyan and Hahnen, Eric and Wappenschmidt, Barbara and Feliubadaló, Lidia and Aalfs, Cora M and Agata, Simona and Aittomäki, Kristiina and Alducci, Elisa and Alonso-Cerezo, María Concepción and Arnold, Norbert and Auber, Bernd and Austin, Rachel and Azzollini, Jacopo and Balmaña, Judith and Barbieri, Elena and Bartram, Claus R and Blanco, Ana and Blümcke, Britta and Bonache, Sandra and Bonanni, Bernardo and Borg, Åke and Bortesi, Beatrice and Brunet, Joan and Bruzzone, Carla and Bucksch, Karolin and Cagnoli, Giulia and Caldés, Trinidad and Caliebe, Almuth and Caligo, Maria A and Calvello, Mariarosaria and Capone, Gabriele L and Caputo, Sandrine M and Carnevali, Ileana and Carrasco, Estela and Caux-Moncoutier, Virginie and Cavalli, Pietro and Cini, Giulia and Clarke, Edward M and Concolino, Paola and Cops, Elisa J and Cortesi, Laura and Couch, Fergus J and Darder, Esther and de la Hoya, Miguel and Dean, Michael and Ehrencrona, Hans and Kvist, Anders and Törngren, Therese},
  issn         = {1059-7794},
  language     = {eng},
  month        = {05},
  publisher    = {John Wiley & Sons},
  series       = {Human Mutation},
  title        = {Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants : An ENIGMA resource to support clinical variant classification},
  url          = {http://dx.doi.org/10.1002/humu.23818},
  year         = {2019},
}