Anke Wesselius

Although GWASs have been conducted to investigate genetic variation of bladder tumorigenesis, little is known about genetic interactions that may influence bladder cancer (BC) risk. By leveraging large‐scale participants from UK Biobank, we established a discovery database with 4000 Caucasian participants (2000 cases vs 2000 non‐cases), a database with 1648 Caucasian participants (824 cases vs 824 non‐cases) and 856 non‐Caucasian participants (428 cases vs 428 non‐cases) as validation. We then performed a genome‐wide SNP‐SNP interaction investigation related to BC risk based a machine learning approach (ie, GenEpi). Moreover, we used the selected interactions to build a BC screening model with an integrated interaction‐empowered polygenic risk score (iPRS) based on Cox proportional hazard model. With Bonferroni correction, we identified 10 statistically significant pairs of SNPs, which located in 17 chromosomes. Of these, four SNP‐SNP interactions were found to be positively associated with BC risk among Caucasian participants (ORs 1.57‐2.03), while six SNP‐SNP interactions showed negatively associated with BC risk (ORs 0.54‐0.65). Only four of the SNP‐SNP interactions were consistently identified in non‐Caucasian participants located in ST7L ‐ ADSS2, FHIT ‐ CHDH, LARP4B ‐ LHPP and RBFOX3 ‐ MPRIP . In addition, the iPRS showed a HR of 1.81 (95% CI: 1.46‐2.09) compared the highest tertile to the lowest tertile, with an enhanced AUC (0.91; 95% CI:0.85‐0.97) than PRS (AUC: 0.86; 95% CI:0.76‐0.95; P ‐ DeLong test = 2.2 × 10 −4 ). In summary, this study identified several important SNP‐SNP interactions for BC risk, and developed an iPRS model for BC screening, which may help to identify the people at high‐risk state of BC before early manifestation.