Predicting Lung Cancer in Korean Never-Smokers with Polygenic Risk Scores

Abstract

In the last few decades, genome-wide association studies (GWAS) with more than 10,000 subjects have identified several loci associated with lung cancer. Hence, recently, genetic data have been used to develop novel risk prediction tools for cancer. The present study aimed to establish a lung cancer prediction model for Korean never-smokers using polygenic risk scores (PRSs). PRSs were calculated using a thresholding-pruning-based approach based on 11 genome-wide significant single nucleotide polymorphisms (SNPs). Overall, the odds ratios tended to increase as PRSs were larger, with the odds ratio of the top 5% PRSs being 1.71 (95% confidence interval: 1.31-2.23), and the area under the curve (AUC) of the prediction model being of 0.76 (95% confidence interval: 0.747-0.774). The receiver operating characteristic (ROC) curves of the prediction model with and without PRSs as covariates were compared using DeLongs test, and a significant difference was observed. Our results suggest that PRSs can be valuable tools for predicting the risk of lung cancer.