Additive interactions between susceptibility single-nucleotide polymorphisms identified in genome-wide association studies and breast cancer risk factors in the Breast and Prostate Cancer Cohort Consortium

Amit S Joshi, Sara Lindstrom, Anika Husing, 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, Maria-Dolores Chirlaque, William Ryan Diver, Laure Dossus, Graham Gerald Giles, Christopher A Haiman, Susan E Hankinson, Brian E HendersonRobert N Hoover, David John Hunter, Claudine Janet Diana Isaacs, Rudolf J Kaaks, Laurence N Kolonel, Vittorio Krogh, Loic Le Marchand, I-Min Lee, Eiliv Eylin Lund, Catherine A McCarty, Kim Overvad, Petra H M Peeters, Elio B Riboli, Fredrick Schumacher, Gianluca Severi, Daniel O Stram, Malin Sund, Michael J Thun, Ruth C Travis, Dimitrios Trichopoulos, Walter Churchill Willett, Shumin Zhang, Regina G Ziegler, Peter Kraft

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25 Citations (Scopus)

Abstract

Additive 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.
Original languageEnglish
Pages (from-to)1018 - 1027
Number of pages10
JournalAmerican Journal of Epidemiology
Volume180
Issue number10
DOIs
Publication statusPublished - 2014

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