99 Associations of polymorphisms in circadian genes, shift work and breast cancer in the German GENICA study. (11th October 2013) - Rabstein, S; Harth, V; Pesch, B; Justenhoven, C; Baisch, C; Schiffermann, M; Heinze, E; Brauch, H; Hamann, U; Ko, Y; Brüning, T
Abstract : Objectives: Recently, the role of night-shift work in breast cancer development has been intensively discussed. Common variants in genes that regulate the circadian system may modify the observed risks of shift work. Here, we hypothesised that circadian genes influence breast cancer risk and may modify the risk of night shift work to develop breast cancer. Material and Methods: The population based case-control study Gene-Environment Interaction and Breast Cancer (GENICA) was conducted in the Greater Region of Bonn, Germany. Shift work and detailed shift work characteristics were assessed in subsequent telephone interviews. Thirteen polymorphisms in circadian genes AANAT, ARNTL, CLOCK, CRY2, MTNR1B, NPAS2, PER2, UGT1A, UGT1A6, UGT2B7, and UGT2B15 were genotyped. Associations between polymorphisms, shift work and breast cancer could be investigated for 1022 controls and 1014 cases. Risk estimates were calculated as odds ratios (ORs) with 95% confidence intervals (CIs) conditional on age and adjusted for hormone replacement therapy, number of mammograms and familial breast cancer. Test for interactions as well as methods for Multifactor Dimensionality Reduction will be presented. Results: First results indicate elevated risk estimates for polymorphism rs8150 of gene AANAT (GC + CC vs. GG: OR 1.17; 95% CI 1.01–1.36). In women that ever worked in shift for at least one year we found an elevated risk estimate for polymorphism rs10462028 in CLOCK gene (OR 3.53; 95% CI 1.09–11.42). Discussion: Our study suggests that polymorphisms in circadian genes may be associated with breast cancer and may also modify the risks of shift work for breast cancer. However, the results are limited by low prevalence of night work and variant genotypes. Therefore a pooling of studies would improve the statistical power to analyse the influence of circadian genes in breast cancer development.