A Deep Learning-based Genome-wide Polygenic Risk Score for Common Diseases Identifies Individuals with Risk

Abstract

AbstractIdentifying individuals at high risk in the population is a key public health need. For many common diseases, individual susceptibility may be influenced by genetic variation. Recently, the clinical potential of polygenic risk score (PRS) has attracted widespread attention. However, the performance of traditional methods is limited in fitting capabilities of the linear model and unable to capture the interaction information between single nucleotide polymorphisms (SNPs). To fill this gap, a novel deep-learning-based model named DeepPRS is developed for scoring the risk of common diseases with genome-wide genotype data. Using the UK Biobank dataset, the evaluation shows that DeepPRS performs better than the other two existing state-of-art methods on Alzheimer’s disease, inflammatory bowel disease, type 2 diabetes and breast cancer. Since DeepPRS does not only rely on the addictive effect of risk SNPs, DeepPRS has the chance to identify high-risk individuals even with few known risk SNPs.