TY - JOUR T1 - Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status JF - Journal of Medical Genetics JO - J Med Genet SP - 601 LP - 608 DO - 10.1136/jmedgenet-2011-100716 VL - 49 IS - 9 AU - Anika Hüsing AU - Federico Canzian AU - Lars Beckmann AU - Montserrat Garcia-Closas AU - W Ryan Diver AU - Michael J Thun AU - Christine D Berg AU - Robert N Hoover AU - Regina G Ziegler AU - Jonine D Figueroa AU - Claudine Isaacs AU - Anja Olsen AU - Vivian Viallon AU - Heiner Boeing AU - Giovanna Masala AU - Dimitrios Trichopoulos AU - Petra H M Peeters AU - Eiliv Lund AU - Eva Ardanaz AU - Kay-Tee Khaw AU - Per Lenner AU - Laurence N Kolonel AU - Daniel O Stram AU - Loïc Le Marchand AU - Catherine A McCarty AU - Julie E Buring AU - I-Min Lee AU - Shumin Zhang AU - Sara Lindström AU - Susan E Hankinson AU - Elio Riboli AU - David J Hunter AU - Brian E Henderson AU - Stephen J Chanock AU - Christopher A Haiman AU - Peter Kraft AU - Rudolf Kaaks Y1 - 2012/09/01 UR - http://jmg.bmj.com/content/49/9/601.abstract N2 - Objective There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status. Material and methods Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROCa). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction. Results We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROCa going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status. Discussion and conclusions Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application. ER -