A CNN deep learning model to improve SNP-based hypertension risk prediction accuracy

Abstract

Hypertension is a modifiable factor for cardiovascular diseases such as ischemic heart disease, one of the leading causes of death worldwide, known as the silent killer. Therefore, especially at a young age, method development to detect the risk of hypertension is essential. Most models for predicting disease risk are primarily based on lifestyle factors. Recently, considering the risk of genetic factors, including disease-related SNPs, has improved the accuracy of individual disease prediction. SNP is a small genetic change in DNA and is the most common genetic variation in humans. Four approaches are used to predict hypertension with genomic markers analysis: a statistical, meta-analysis, machine learning, and clinical modeling. The most critical issue in these models is the high number of input SNPs and their relationship. In the present study, a deep learning method with the CNN approach uses multiple SNPs and hypertension labels in a longitudinal cohort study for comparison; PRS was calculated using plink and gcta64 software. First, the genomic data is converted into an image and entered into the CNN model, whose layers include the convolution layer, pooled layer, fully connected layer, and output layer. Data contains three sections: genomic data, age, and longitudinal data of hypertension based on the study of cardiac-metabolic genetics in Tehran. AUC was used to compare the performance of the model. The CNN model with an AUC value of 0.877 shows better performance than the PRS and the latest models presented in the literature.