Background: A novel validated model for risk prediction of lung cancer, the HUNT Lung Cancer Model predicts 6- and 16-year risk of lung cancer with a C-index = 0.879 and 6-year AUC = 0.87. The model is valid for smokers and ex-smokers of any intensity and quit time and includes seven variables; age, BMI, pack-years, smoking intensity (cigarettes per day), quit time, daily cough in periods of the year and hours of daily indoors smoke exposure. Genome-wide association studies (GWAS) have consistently identified specific lung cancer susceptibility regions. We aimed to improve performance of the HUNT model by integrating the most significant Single Nucleotide Polymorphisms (SNPs). Methods: Lung cancer cases (n = 484) and controls without other cancer (n = 50337) were genotyped for 22 SNPs located in GWAS-identified lung cancer susceptibility regions. Variable selection and model development used backwards feature selection with Akaike Information Criterion in multivariable Cox regression models. Internal validation used bootstrap to assess the change in area under the receiver operator characteristic curve (AUC) in order to compare nested models with and without genetic variables in the ever-smokers´ population (n = 456 cases, n = 28633 controls). We also used likelihood based methods for significance testing. Results: Variable selection and model development in the general population yielded six SNPs, rs1051730, rs11571833, rs13314271, rs2131877, rs2736100 and rs4488809. The added genetic information from these SNPs to the HUNT model, resulted in an improvement according to F test of the nested models (ANOVA p-value 0.000002425). The AUC of the augmented model was 0.881 (95% CI [0.869 0.892]) vs 0.869 without SNPs. Conclusions: In a highly predictive clinical risk prediction model, the integration of SNPs could further improve model performance according to likelihood based methods. Further refinement and validation of this integrated model is needed for clinical use.