e20066 Background: As lung cancer incidence and mortality increase, methods of early diagnosis are needed to increase survival. The validated HUNT Lung Cancer Model (HUNT LCM) predicts individual 6-year risk of lung cancer in ever smokers with an AUC of 0.87 based on eight clinical variables: sex, age, BMI, pack-years, smoking intensity number of cigarettes per day, quit time, daily cough, and daily indoors smoke exposure. We aimed to explore whether the performance of the HUNT model can be improved by adding genetic information, and in what group of ever smokers these differences in performance emerges. Methods: Based on the eight clinical variables of the HUNT LCM and 22 single nucleotide polymorphisms (SNPs) previously identified in the literature as highly associated with lung cancer risk, the novel lung cancer risk prediction model HUNT Lung-SNP was developed. Clinical and genetical data of ever-smokers from the HUNT2 population study (n = 30746, median follow-up 15.26 years) where 160 individuals were diagnosed with lung cancer within six years were used for the model fitting. External validation was performed in another population-based cohort, the Tromsø Study (n = 2663) where 39 were diagnosed with lung cancer within 6 years. Results: The integrated discrimination improvement index (IDI) between the HUNT Lung-SNP and the HUNT LCM shows that the HUNT Lung-SNP significantly improve the LC risk stratification within six years with an IDI of 0.019 (95% CI 0.015-0.025), p < 0·001 and of 0.013 (95% CI 0.008-0.018), p < 0·001 in the HUNT2 and Tromsø cohorts, respectively. Of the cases, 21/160 (13%) and 10/39 (25%) in the HUNT and Tromsø cohort respectively, were low risk by the HUNT LCM but high risk by the HUNT Lung-SNP model. The main difference in characteristics among cases predicted by the HUNT Lung-SNP model, but missed by the HUNT LCM, was the average number of pack-years: 8.22 vs. 29.8 (p < 0.001) in HUNT2 and 7.7 vs. 33.7 (p < 0.001) in Tromsø cohort. Conclusions: A genetic approach combined with clinical variables of the HUNT LCM can improve the lung cancer risk prediction significantly by increasing the sensitivity among light smokers. Thus, we identified a high-risk population in two independent cohorts that would not be eligible for screening by clinical cutoffs nor with a clinical risk prediction model. This novel HUNT Lung-SNP needs further validation in other populations to evaluate its clinical utility.