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Hum Mutat. 2018 Dec;39(12):1835-1846. doi: 10.1002/humu.23665. Epub 2018 Oct 19.

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

Author information

1
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
2
enGenome srl, via Ferrata 5, Pavia, Italy.
3
Laboratory of Molecular Cardiology, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy.
4
Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri, Pavia, Italy.

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.

KEYWORDS:

ACMG; Decision support system; guidelines; interpretation; variant

PMID:
30298955
DOI:
10.1002/humu.23665

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