Prediction of the functional impact of missense variants in BRCA1 and BRCA2 with BRCA-ML

被引:18
作者
Hart, Steven N. [1 ]
Polley, Eric C. [1 ]
Shimelis, Hermella [2 ]
Yadav, Siddhartha [3 ]
Couch, Fergus J. [1 ,2 ]
机构
[1] Mayo Clin, Dept Hlth Sci Res, Rochester, MN 55905 USA
[2] Mayo Clin, Dept Lab Med & Pathol, Rochester, MN USA
[3] Mayo Clin, Dept Oncol, Rochester, MN USA
基金
美国国家卫生研究院;
关键词
D O I
10.1038/s41523-020-0159-x
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
In silico predictions of missense variants is an important consideration when interpreting variants of uncertain significance (VUS) in the BRCA1 and BRCA2 genes. We trained and evaluated hundreds of machine learning algorithms based on results from validated functional assays to better predict missense variants in these genes as damaging or neutral. This new optimal "BRCA-ML" model yielded a substantially more accurate method than current algorithms for interpreting the functional impact of variants in these genes, making BRCA-ML a valuable addition to data sources for VUS classification.
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页数:4
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