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

Steven N. Hart; Eric C. Polley; Hermella Shimelis; Siddhartha Yadav; Fergus J. Couch

doi:10.1038/s41523-020-0159-x

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

Steven N. Hart, Eric C. Polley, Hermella Shimelis, Siddhartha Yadav, Fergus J. Couch

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

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.

Original language	English (US)
Article number	13
Journal	npj Breast Cancer
Volume	6
Issue number	1
DOIs	https://doi.org/10.1038/s41523-020-0159-x
State	Published - Dec 1 2020

ASJC Scopus subject areas

Oncology
Radiology Nuclear Medicine and imaging
Pharmacology (medical)

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abstract = "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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AU - Polley, Eric C.

AU - Shimelis, Hermella

AU - Yadav, Siddhartha

AU - Couch, Fergus J.

PY - 2020/12/1

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

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