• Amit D Joshi, Sara Lindström, Anika Hüsing, Myrto Barrdahl, Tyler J VanderWeele, Daniele Campa, Federico Canzian, Mia M Gaudet, Jonine D Figueroa, Laura Baglietto, Christine D Berg, Julie E Buring, Stephen J Chanock, María-Dolores Chirlaque, W Ryan Diver, Laure Dossus, Graham G Giles, Christopher A Haiman, Susan E Hankinson, Brian E Henderson, Robert N Hoover, David J Hunter, Claudine Isaacs, Rudolf Kaaks, Laurence N Kolonel, Vittorio Krogh, Loic Le Marchand, I-Min Lee, Eiliv Lund, Catherine A McCarty, Kim Overvad, Petra H Peeters, Elio Riboli, Fredrick Schumacher, Gianluca Severi, Daniel O Stram, Malin Sund, Michael J Thun, Ruth C Travis, Dimitrios Trichopoulos, Walter C Willett, Shumin Zhang, Regina G Ziegler, Peter Kraft, and Breast and Prostate Cancer Cohort Consortium (BPC3).
• Am. J. Epidemiol. 2014 Nov 15; 180 (10): 1018-27.

AbstractAdditive interactions can have public health and etiological implications but are infrequently reported. We assessed departures from additivity on the absolute risk scale between 9 established breast cancer risk factors and 23 susceptibility single-nucleotide polymorphisms (SNPs) identified from genome-wide association studies among 10,146 non-Hispanic white breast cancer cases and 12,760 controls within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium. We estimated the relative excess risk due to interaction and its 95% confidence interval for each pairwise combination of SNPs and nongenetic risk factors using age- and cohort-adjusted logistic regression models. After correction for multiple comparisons, we identified a statistically significant relative excess risk due to interaction (uncorrected P = 4.51 × 10(-5)) between a SNP in the DNA repair protein RAD51 homolog 2 gene (RAD51L1; rs10483813) and body mass index (weight (kg)/height (m)(2)). We also compared additive and multiplicative polygenic risk prediction models using per-allele odds ratio estimates from previous studies for breast-cancer susceptibility SNPs and observed that the multiplicative model had a substantially better goodness of fit than the additive model. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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