Abstract 38: Prediction of Coronary Artery Disease Subtypes With Polygenic Risk Scores

Abstract

Background: Coronary Artery Disease (CAD) is a complex heterogeneous disease with multiple known etiological mechanisms. Consequently, CAD could be considered to have multiple subtypes, reflecting alternative driving etiologies that may warrant distinct therapeutic approaches. Motivated by this, we developed and optimized statistical models to predict CAD subtypes informed by genetic and clinical risk factors. Methods: We developed three models, each using logistic regression: (a) clinical risk factors only, (b) clinical risk factors plus genome-wide polygenic risk scores (PRS), and (c) clinical risk factors and pathway-based PRSs. We defined 5 different CAD subtypes on the basis of the literature: (1) stable vs. unstable, (2) occlusive vs non-occlusive, (3) high vs normal LDL, (4) high vs normal Lp(a), and (5) high vs normal ASCVD score. Clinical risk factors included: ASCVD score, ApoA, ApoB, LDL, HDL, triglycerides, and C-reactive protein. We calculated 4,402 pathway PRSs obtained from six genomic pathway databases (e.g. KEGG). We optimized the models using 80% of the samples as training, and then performed model evaluation (Bonferroni-corrected P-values) in an unseen 20% of the samples as validation. Results: Among 20,935 UK Biobank participants with CAD (mean age 63 years, 58% female), addition of pathway-based PRSs improved prediction of high LDL (P=1e-19) and high Lp(a) subtypes (P=1.8e-78). Using a lasso dimension reduction approach, the pathways most associated with the high Lp(a) subtype of CAD were: Fibronectin Binding (P=6xe-260), Apolipoprotein Binding (P=6.2e-252) and Serine-Type Endopeptidase Activity (P=1.7e-203). Conclusions: We demonstrate that prediction models distinguish CAD subtypes and that a novel pathway PRS approach can increase predictive power and provide insights into the genetic underpinnings of CAD subtypes. Pathway PRSs hold promise for the targeted therapeutic management for subgroups of CAD patients.