Additional file 1 of An early-onset specific polygenic risk score optimizes age-based risk estimate and stratification of prostate cancer: population-based cohort study

Additional file 1: Fig. S1. Flowchart of this study. Exit-age: age at diagnosis or censoring. Fig. S2. Population ascertainment of the UK Biobank cohort. Fig. S3. Age-specific incidence of prostate cancer per 100,000 for White ancestry population from the CDC US Cancer Statistics. Fig. S4. Estimate risk for PCa associated a 269-PRS for age groups with weighted Cox proportional hazard models. Fig. S5. Forest plot of the heterogeneity analyses between the EOPC and LOPC risk measured by weighted Cox proportional hazard models. Fig. S6. Risk estimates for PCa associated with PRSs from the case–control population. Fig. S7. Forest plot of the heterogeneity analyses between the EOPC and LOPC risk. Fig. S8. Genome-wide association studies (GWAS) for PCa risk in General-population, EO-population and LO-population using Cox proportional hazard models. Fig. S9. Forest plot of the heterogeneity analyses between the EOPC and LOPC risk. Fig. S10. Risk estimates for PCa associated with PRSs from the case–control population. Fig. S11. Forest plot of the heterogeneity analyses between the EOPC and LOPC risk measured by logistic regression models. Fig. S12. Flowchart of merging reported variants and GWAS top variants. Fig. S13. Forest plot of the heterogeneity analyses between the EOPC and LOPC risk measured by weighted Cox proportional hazard models. Fig. S14. Risk estimates for PCa associated with PRSs from the case–control population. Fig. S15. Forest plot of the heterogeneity analyses between the EOPC and LOPC risk. Fig. S16. Population structure demonstrated by principal component analysis based on all high-quality SNPs. Fig. S17. The area under the receiver operating characteristic (ROC) curve (AUC) evaluating the predictive accuracy of EOPC-PRS (A), 54-PRS (B) and 110-PRS (C) for EOPC in a European ancestry population generated from the PLCO cohort and TCGA program. Fig. S18. Time-dependent receiver operating characteristic (ROC) curves and area under the curves (AUC) from censored diagnosis data at 60-year of PSA and PSA + EOPC-PRS for prediction of PCa. Fig. S19. Risk estimates for early-onset prostate cancer (EOPC) associated with EOPC-PRS stratified by clinical variables (Gleason score, T stage and M stage). Fig. S20. Flowchart of two-sample Mendelian randomization analyses. Fig. S21. An example for the use of ProAP (Prostate cancer Age-based PheWAS)

Supplementary Materials from CYP2A6 Activity and Cigarette Consumption Interact in Smoking-Related Lung Cancer Susceptibility

Supplementary Methods and Figures</p

Supplementary Tables1-11 from CYP2A6 Activity and Cigarette Consumption Interact in Smoking-Related Lung Cancer Susceptibility

Supplementary Tables1-11</p