Fig. 1

Computational framework comparing different feature selection strategies for the selection of risk loci panels for CAD. A–C The data collected from the UKB is subjected to preprocessing to extract the phenotype of interest (CAD vs. non-CAD) and generate high-quality genotype imputed data. D, E To identify the most relevant covariates for association tests, we performed an analysis of CAD-associated risk factors and conducted principal component analysis on the genotype data. F tenfold cross-validation was used to perform a fair comparison between ML-based methods and PRSs. G Genomic variants for predicting CAD were selected based on three feature selection strategies encompassing filter-based and embedded methods. H, I Three PRS methods were implemented and combined with Logistic Regression-based classifier for the classification. J Genomic variants selected through FS were systematically uses to train three different classification algorithms: Lasso, RF and SVM. K The Area Under the ROC Curve (AUC) statistics as the main accuracy metric. Moreover, we also recorded the frequency of each feature being selected across different training set and feature selection methods. L The most informative SNPs were further analyzed to assess their biological relevance