A CNN Approach to Polygenic Risk Prediction of Kidney Stone Formation

Abstract

Kidney stones are a common and debilitating health issue, and genetic factors play a crucial role in determining susceptibility. While Genome-Wide Association Studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) linked to kidney stone risk, translating these findings into effective clinical tools remains a challenge. In this study, we explore the potential of deep learning techniques, particularly Convolutional Neural Networks (CNNs), to enhance Polygenic Risk Score (PRS) models for predicting kidney stone susceptibility. Using a curated dataset of kidney stone-associated SNPs from a recent GWAS, we apply CNNs to model non-linear genetic interactions and improve prediction accuracy. Our approach includes SNP selection, genotype filtering, and model training using a dataset of 560 individuals, divided into training and testing subsets. We compare our CNN-based model with traditional machine learning models, including logistic regression, random forest, and support vector machines, demonstrating that the CNN outperforms these models in terms of classification accuracy and ROC-AUC. The proposed model achieved a validation accuracy of 62%, with an ROC-AUC of 0.68, suggesting its potential for improving genetic-based risk prediction for kidney stones. This study contributes to the growing field of genomics-driven precision medicine and highlights the promise of deep learning in enhancing PRS models for complex diseases.