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Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

PURPOSE: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequence...

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Autores principales: Zhang, Xiaolei, Walsh, Roddy, Whiffin, Nicola, Buchan, Rachel, Midwinter, William, Wilk, Alicja, Govind, Risha, Li, Nicholas, Ahmad, Mian, Mazzarotto, Francesco, Roberts, Angharad, Theotokis, Pantazis I., Mazaika, Erica, Allouba, Mona, de Marvao, Antonio, Pua, Chee Jian, Day, Sharlene M., Ashley, Euan, Colan, Steven D., Michels, Michelle, Pereira, Alexandre C., Jacoby, Daniel, Ho, Carolyn Y., Olivotto, Iacopo, Gunnarsson, Gunnar T., Jefferies, John L., Semsarian, Chris, Ingles, Jodie, O’Regan, Declan P., Aguib, Yasmine, Yacoub, Magdi H., Cook, Stuart A., Barton, Paul J. R., Bottolo, Leonardo, Ware, James S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790749/
https://www.ncbi.nlm.nih.gov/pubmed/33046849
http://dx.doi.org/10.1038/s41436-020-00972-3
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author Zhang, Xiaolei
Walsh, Roddy
Whiffin, Nicola
Buchan, Rachel
Midwinter, William
Wilk, Alicja
Govind, Risha
Li, Nicholas
Ahmad, Mian
Mazzarotto, Francesco
Roberts, Angharad
Theotokis, Pantazis I.
Mazaika, Erica
Allouba, Mona
de Marvao, Antonio
Pua, Chee Jian
Day, Sharlene M.
Ashley, Euan
Colan, Steven D.
Michels, Michelle
Pereira, Alexandre C.
Jacoby, Daniel
Ho, Carolyn Y.
Olivotto, Iacopo
Gunnarsson, Gunnar T.
Jefferies, John L.
Semsarian, Chris
Ingles, Jodie
O’Regan, Declan P.
Aguib, Yasmine
Yacoub, Magdi H.
Cook, Stuart A.
Barton, Paul J. R.
Bottolo, Leonardo
Ware, James S.
author_facet Zhang, Xiaolei
Walsh, Roddy
Whiffin, Nicola
Buchan, Rachel
Midwinter, William
Wilk, Alicja
Govind, Risha
Li, Nicholas
Ahmad, Mian
Mazzarotto, Francesco
Roberts, Angharad
Theotokis, Pantazis I.
Mazaika, Erica
Allouba, Mona
de Marvao, Antonio
Pua, Chee Jian
Day, Sharlene M.
Ashley, Euan
Colan, Steven D.
Michels, Michelle
Pereira, Alexandre C.
Jacoby, Daniel
Ho, Carolyn Y.
Olivotto, Iacopo
Gunnarsson, Gunnar T.
Jefferies, John L.
Semsarian, Chris
Ingles, Jodie
O’Regan, Declan P.
Aguib, Yasmine
Yacoub, Magdi H.
Cook, Stuart A.
Barton, Paul J. R.
Bottolo, Leonardo
Ware, James S.
author_sort Zhang, Xiaolei
collection PubMed
description PURPOSE: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. METHODS: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. RESULTS: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. CONCLUSIONS: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity.
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spelling pubmed-77907492021-01-15 Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions Zhang, Xiaolei Walsh, Roddy Whiffin, Nicola Buchan, Rachel Midwinter, William Wilk, Alicja Govind, Risha Li, Nicholas Ahmad, Mian Mazzarotto, Francesco Roberts, Angharad Theotokis, Pantazis I. Mazaika, Erica Allouba, Mona de Marvao, Antonio Pua, Chee Jian Day, Sharlene M. Ashley, Euan Colan, Steven D. Michels, Michelle Pereira, Alexandre C. Jacoby, Daniel Ho, Carolyn Y. Olivotto, Iacopo Gunnarsson, Gunnar T. Jefferies, John L. Semsarian, Chris Ingles, Jodie O’Regan, Declan P. Aguib, Yasmine Yacoub, Magdi H. Cook, Stuart A. Barton, Paul J. R. Bottolo, Leonardo Ware, James S. Genet Med Article PURPOSE: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. METHODS: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. RESULTS: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. CONCLUSIONS: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity. Nature Publishing Group US 2020-10-13 2021 /pmc/articles/PMC7790749/ /pubmed/33046849 http://dx.doi.org/10.1038/s41436-020-00972-3 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhang, Xiaolei
Walsh, Roddy
Whiffin, Nicola
Buchan, Rachel
Midwinter, William
Wilk, Alicja
Govind, Risha
Li, Nicholas
Ahmad, Mian
Mazzarotto, Francesco
Roberts, Angharad
Theotokis, Pantazis I.
Mazaika, Erica
Allouba, Mona
de Marvao, Antonio
Pua, Chee Jian
Day, Sharlene M.
Ashley, Euan
Colan, Steven D.
Michels, Michelle
Pereira, Alexandre C.
Jacoby, Daniel
Ho, Carolyn Y.
Olivotto, Iacopo
Gunnarsson, Gunnar T.
Jefferies, John L.
Semsarian, Chris
Ingles, Jodie
O’Regan, Declan P.
Aguib, Yasmine
Yacoub, Magdi H.
Cook, Stuart A.
Barton, Paul J. R.
Bottolo, Leonardo
Ware, James S.
Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
title Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
title_full Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
title_fullStr Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
title_full_unstemmed Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
title_short Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
title_sort disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790749/
https://www.ncbi.nlm.nih.gov/pubmed/33046849
http://dx.doi.org/10.1038/s41436-020-00972-3
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