CardioVAI: An automatic implementation of ACMG-AMP variant interpretation guidelines in the diagnosis of cardiovascular diseases

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Abstract

Variant interpretation for the diagnosis of genetic diseases is a complex process. The American College of Medical Genetics and Genomics, with the Association for Molecular Pathology, have proposed a set of evidence-based guidelines to support variant pathogenicity assessment and reporting in Mendelian diseases. Cardiovascular disorders are a field of application of these guidelines, but practical implementation is challenging due to the genetic disease heterogeneity and the complexity of information sources that need to be integrated. Decision support systems able to automate variant interpretation in the light of specific disease domains are demanded. We implemented CardioVAI (Cardio Variant Interpreter), an automated system for guidelines based variant classification in cardiovascular-related genes. Different omics-resources were integrated to assess pathogenicity of every genomic variant in 72 cardiovascular diseases related genes. We validated our method on benchmark datasets of high-confident assessed variants, reaching pathogenicity and benignity concordance up to 83 and 97.08%, respectively. We compared CardioVAI to similar methods and analyzed the main differences in terms of guidelines implementation. We finally made available CardioVAI as a web resource (http://cardiovai.engenome.com/) that allows users to further specialize guidelines recommendations.

Original languageEnglish
Pages (from-to)1835-1846
Number of pages12
JournalHuman Mutation
Volume39
Issue number12
DOIs
Publication statusPublished - Dec 2018

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Adenosine Monophosphate
Cardiovascular Diseases
Guidelines
Virulence
Inborn Genetic Diseases
Benchmarking
Genetic Heterogeneity
Molecular Pathology
Genomics
Genes

Cite this

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title = "CardioVAI: An automatic implementation of ACMG-AMP variant interpretation guidelines in the diagnosis of cardiovascular diseases",
abstract = "Variant interpretation for the diagnosis of genetic diseases is a complex process. The American College of Medical Genetics and Genomics, with the Association for Molecular Pathology, have proposed a set of evidence-based guidelines to support variant pathogenicity assessment and reporting in Mendelian diseases. Cardiovascular disorders are a field of application of these guidelines, but practical implementation is challenging due to the genetic disease heterogeneity and the complexity of information sources that need to be integrated. Decision support systems able to automate variant interpretation in the light of specific disease domains are demanded. We implemented CardioVAI (Cardio Variant Interpreter), an automated system for guidelines based variant classification in cardiovascular-related genes. Different omics-resources were integrated to assess pathogenicity of every genomic variant in 72 cardiovascular diseases related genes. We validated our method on benchmark datasets of high-confident assessed variants, reaching pathogenicity and benignity concordance up to 83 and 97.08{\%}, respectively. We compared CardioVAI to similar methods and analyzed the main differences in terms of guidelines implementation. We finally made available CardioVAI as a web resource (http://cardiovai.engenome.com/) that allows users to further specialize guidelines recommendations.",
author = "Giovanna Nicora and Ivan Limongelli and Patrick Gambelli and Mirella Memmi and Alberto Malovini and Andrea Mazzanti and Carlo Napolitano and Silvia Priori and Riccardo Bellazzi",
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year = "2018",
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T2 - An automatic implementation of ACMG-AMP variant interpretation guidelines in the diagnosis of cardiovascular diseases

AU - Nicora, Giovanna

AU - Limongelli, Ivan

AU - Gambelli, Patrick

AU - Memmi, Mirella

AU - Malovini, Alberto

AU - Mazzanti, Andrea

AU - Napolitano, Carlo

AU - Priori, Silvia

AU - Bellazzi, Riccardo

N1 - © 2018 Wiley Periodicals, Inc.

PY - 2018/12

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N2 - Variant interpretation for the diagnosis of genetic diseases is a complex process. The American College of Medical Genetics and Genomics, with the Association for Molecular Pathology, have proposed a set of evidence-based guidelines to support variant pathogenicity assessment and reporting in Mendelian diseases. Cardiovascular disorders are a field of application of these guidelines, but practical implementation is challenging due to the genetic disease heterogeneity and the complexity of information sources that need to be integrated. Decision support systems able to automate variant interpretation in the light of specific disease domains are demanded. We implemented CardioVAI (Cardio Variant Interpreter), an automated system for guidelines based variant classification in cardiovascular-related genes. Different omics-resources were integrated to assess pathogenicity of every genomic variant in 72 cardiovascular diseases related genes. We validated our method on benchmark datasets of high-confident assessed variants, reaching pathogenicity and benignity concordance up to 83 and 97.08%, respectively. We compared CardioVAI to similar methods and analyzed the main differences in terms of guidelines implementation. We finally made available CardioVAI as a web resource (http://cardiovai.engenome.com/) that allows users to further specialize guidelines recommendations.

AB - Variant interpretation for the diagnosis of genetic diseases is a complex process. The American College of Medical Genetics and Genomics, with the Association for Molecular Pathology, have proposed a set of evidence-based guidelines to support variant pathogenicity assessment and reporting in Mendelian diseases. Cardiovascular disorders are a field of application of these guidelines, but practical implementation is challenging due to the genetic disease heterogeneity and the complexity of information sources that need to be integrated. Decision support systems able to automate variant interpretation in the light of specific disease domains are demanded. We implemented CardioVAI (Cardio Variant Interpreter), an automated system for guidelines based variant classification in cardiovascular-related genes. Different omics-resources were integrated to assess pathogenicity of every genomic variant in 72 cardiovascular diseases related genes. We validated our method on benchmark datasets of high-confident assessed variants, reaching pathogenicity and benignity concordance up to 83 and 97.08%, respectively. We compared CardioVAI to similar methods and analyzed the main differences in terms of guidelines implementation. We finally made available CardioVAI as a web resource (http://cardiovai.engenome.com/) that allows users to further specialize guidelines recommendations.

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