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CAN GENE-GENE INTERACTION BETTER PREDICT THE CORONARY DISEASE RISK?
Session:
Prémio Jovem Investigador
Speaker:
Flávio Mendonça
Congress:
CPC 2021
Topic:
E. Coronary Artery Disease, Acute Coronary Syndromes, Acute Cardiac Care
Theme:
12. Coronary Artery Disease (Chronic)
Subtheme:
12.2 Coronary Artery Disease – Epidemiology, Prognosis, Outcome
Session Type:
Prémios
FP Number:
---
Authors:
Flávio Mendonça; Isabel Mendonça; Marina Santos; Margarida Temtem; Adriano Sousa; Ana Célia Sousa; Eva Henriques; Mariana Rodrigues; Sónia Freitas; Sofia Borges; Graça Guerra; António Drumond; Roberto Palma Dos Reis
Abstract
<p style="text-align:justify"><strong>Introduction</strong>: Multiple genetic variants have been identified in GWAS associated with Coronary Artery Disease (CAD). New computational and statistical methods emerged beyond logistic regression to better analyze the gene-gene interaction.<br /> <strong>Objective:</strong> Study the best gene-gene interaction model and predictor of CAD, using new data mining methods such as Multifactor Dimensionality Reduction (MDR).<br /> <strong>Methods</strong>: We included 3,139 participants (mean age 53.2 ± 7.8 years, 78.1% male), namely 1,723 coronary patients documented by angiography with one or more epicardial stenoses > 75% and 1,416 controls adjusted with cases for age and gender. Taqman SNP genotyping (Applied Biosystems) was used, and then a gene-to-gene analysis was performed between 33 variants associated with CAD. MDR was applied to obtain the best genetic predictor model for CAD by using the 12 most significant variants.<br /> <strong>Results:</strong> In the one-gene model, the MDR projected the TCF21 gene polymorphism as the most significant genetic risk factor for CAD. The model with two genes demonstrated synergistic interaction between the TCF21 and APOE variants. The genetic bivariate model of TCF21 and APOE was the best predictive model with an OR of 1.48 (95%CI: 1.28-1.70; p<0.0001) and with good cross-validation (10/10), with no evidence of overfitting model. The accuracy of the best G-G predictor model of CAD was 0.55. A reasonable sensitivity (60%) and specificity (50%) were obtained from this model.<br /> Conclusions: In our population, the interaction between the genetic variants TCF21 (cell axis) and APOE (lipid axis) showed a consistent CAD association and could be a new marker for CAD prediction. An in-depth investigation of this interaction may lead to the identification of new persons with low conventional but high genetic risk for CAD, as well as to create new therapeutic targets in these patients.</p>
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