DL-PRS: a novel deep learning approach to polygenic risk scores

Abstract Background COPD is a complex heterogeneous disease influenced by both environmental and genetic risk factors. Traditional genome wide association studies (GWAS) have been successful in identifying many reproducible risk variants of moderate to small effect. Polygenic risk scores (PRS) were developed as way to aggregate risk alleles weighted by their effect size to produce a score which could be used in clinical practice to identify individuals at high risk of disease. A limitation of both GWAS and PRS is that they make the important assumption that the effect of each allele is independent and not modified by other genetic or environmental factors. Machine learning methods such as deep learning (DL) neural networks complement the GWAS and PRS paradigm by making fewer assumptions about the nature of the genetic effects being modeled. For example, the hidden layers of a DL model have the potential to model gene-gene interactions with non-additive effects on disease risk. The goal of the present study was to develop a DL neural network approach to GWAS and PRS and to compare it to the prevailing paradigm based on modeling independent effects. We applied our DL-PRS method to genetic association data from several GWAS studies of chronic obstructive pulmonary disease (COPD).Results We developed a DL learning algorithm for modeling the relationship between genetic variation from GWAS and risk of COPD in several population-based studies. We then developed a DL-PRS based on nodes and associated weights from the first and second layer of the DL neural network. Our DL-PRS framework has overall satisfactory performance in the prediction of COPD and provides significant contribution to prediction in addition to the current PRS methods. Moreover, regarding the clinical relevance of COPD, our DL-PRS has a consistent and closer relationship regarding individual deciles and lung functions such as FEV1/FVC and predicted FEV1%. Conclusions Not only does DL-PRS show favorable predictive performance with current benchmark PRS methods, but it also extends the ranges of PRS deciles in predicting different stages of COPD. Moreover, our DL-PRS results were replicated in an independent cohort. This study opens the door to the use of machine learning for developing risk scores from models developed using fewer assumptions about the nature of the genetic effects.