Assessing interactions between the associations\ud of common genetic susceptibility variants,\ud reproductive history and body mass index with\ud breast cancer risk in the breast cancer association\ud consortium: a combined case-control study

Introduction: Several common breast cancer genetic susceptibility variants have recently been identified. We\ud aimed to determine how these variants combine with a subset of other known risk factors to influence breast\ud cancer risk in white women of European ancestry using case-control studies participating in the Breast Cancer\ud Association Consortium.\ud Methods: We evaluated two-way interactions between each of age at menarche, ever having had a live birth,\ud number of live births, age at first birth and body mass index (BMI) and each of 12 single nucleotide\ud polymorphisms (SNPs) (10q26-rs2981582 (FGFR2), 8q24-rs13281615, 11p15-rs3817198 (LSP1), 5q11-rs889312\ud (MAP3K1), 16q12-rs3803662 (TOX3), 2q35-rs13387042, 5p12-rs10941679 (MRPS30), 17q23-rs6504950 (COX11), 3p24-rs4973768 (SLC4A7), CASP8-rs17468277, TGFB1-rs1982073 and ESR1-rs3020314). Interactions were tested for by\ud fitting logistic regression models including per-allele and linear trend main effects for SNPs and risk factors,\ud respectively, and single-parameter interaction terms for linear departure from independent multiplicative effects.\ud Results: These analyses were applied to data for up to 26,349 invasive breast cancer cases and up to 32,208\ud controls from 21 case-control studies. No statistical evidence of interaction was observed beyond that expected by\ud chance. Analyses were repeated using data from 11 population-based studies, and results were very similar.\ud Conclusions: The relative risks for breast cancer associated with the common susceptibility variants identified to\ud date do not appear to vary across women with different reproductive histories or body mass index (BMI). The\ud assumption of multiplicative combined effects for these established genetic and other risk factors in risk prediction\ud models appears justified.