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Genetics and Genomics of Coronary Artery Disease
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Genetics and Genomics of Coronary Artery Disease

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 3684

Special Issue Editor

Dr. Kumardeep Chaudhary

E-Mail Website
Guest Editor
Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Interests: translational bioinformatics; precision medicine; machine learning

Special Issue Information

Dear Colleagues,

The recent past has witnessed the role of next-generation sequencing technologies expand to dissecting genomic data in order to identify potential novel therapeutic targets and early diagnostic markers in the field of coronary artery disease (CAD). Furthermore, the integration of multi-modal datasets has enabled novel genotype–phenotype associations across different cohorts coming from specific biobanks or public domain resources. Apart from establishing findings in predominantly European cohorts, new studies have come up discovering novel markers in non-European cohorts as well. This would not have been possible without technological advancement, improved statistical methods, and stratification of the samples based on ethnicity. Since there is a paradigm shift from single gene-based approaches to polygenic contributions in understanding the etiology of CAD, the role of advanced statistical and machine-learning methods will be indispensable in delineating this complex trait. This Issue calls for novel research works in CAD using genetic, genomic, and/or multi-omics approaches in combination with phenotypic data coming from electronic health records for their potential role in functional genomics, mechanistic insights, and translational potential.

Dr. Kumardeep Chaudhary
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ischemic heart disease
  • coronary artery disease
  • cardiovascular genetics
  • personalized medicine
  • genetic risk score
  • genotype–phenotype correlation
  • next-generation sequencing
  • machine learning

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Published Papers (1 paper)

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Research

14 pages, 2285 KiB  
Article
A Machine Learning Model Utilizing a Novel SNP Shows Enhanced Prediction of Coronary Artery Disease Severity
by Tanyaporn Pattarabanjird, Corban Cress, Anh Nguyen, Angela Taylor, Stefan Bekiranov and Coleen McNamara
Genes 2020, 11(12), 1446; https://doi.org/10.3390/genes11121446 - 1 Dec 2020
Cited by 12 | Viewed by 3349
Abstract
Background: Machine learning (ML) has emerged as a powerful approach for predicting outcomes based on patterns and inferences. Improving prediction of severe coronary artery disease (CAD) has the potential for personalizing prevention and treatment strategies and for identifying individuals that may benefit from [...] Read more.
Background: Machine learning (ML) has emerged as a powerful approach for predicting outcomes based on patterns and inferences. Improving prediction of severe coronary artery disease (CAD) has the potential for personalizing prevention and treatment strategies and for identifying individuals that may benefit from cardiac catheterization. We developed a novel ML approach combining traditional cardiac risk factors (CRF) with a single nucleotide polymorphism (SNP) in a gene associated with human CAD (ID3 rs11574) to enhance prediction of CAD severity; Methods: ML models incorporating CRF along with ID3 genotype at rs11574 were evaluated. The most predictive model, a deep neural network, was used to classify patients into high (>32) and low level (≤32) Gensini severity score. This model was trained on 325 and validated on 82 patients. Prediction performance of the model was summarized by a confusion matrix and area under the receiver operating characteristics curve (ROC-AUC); and Results: Our neural network predicted severity score with 81% and 87% accuracy for the low and the high groups respectively with an ROC-AUC of 0.84 for 82 patients in the test group. The addition of ID3 rs11574 to CRF significantly enhanced prediction accuracy from 65% to 81% in the low group, and 72% to 84% in the high group. Age, high-density lipoprotein (HDL), and systolic blood pressure were the top 3 contributors in predicting severity score; Conclusions: Our neural network including ID3 rs11574 improved prediction of CAD severity over use of Framingham score, which may potentially be helpful for clinical decision making in patients at increased risk of complications from coronary angiography. Full article
(This article belongs to the Special Issue Genetics and Genomics of Coronary Artery Disease)
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