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

作者：Joshi Amit D^*; Lindstrom Sara; Husing Anika; Barrdahl Myrto; VanderWeele Tyler J; Campa Daniele; Canzian Federico; Gaudet Mia M; Figueroa Jonine D; Baglietto Laura; Berg Christine D; Buring Julie E; Chanock Stephen J; Chirlaque Maria Dolores; Diver W Ryan; Dossus Laure; Giles Graham G; Haiman Christopher A; Hankinson Susan E; Henderson Brian E; Hoover Robert N; Hunter David J; Isaacs Claudine; Kaaks Rudolf; Kolonel Laurence N; Krogh Vittorio

来源：American Journal of Epidemiology, 2014, 180(10): 1018-1027.

DOI：10.1093/aje/kwu214

• additive interactions
• breast cancer
• genome-wide association studies
• single-nucleotide polymorphisms

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%26apos;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 x 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.

• 出版日期2014-11-15