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.

Author(s): Joshi AD,  Lindström S,  Hüsing A,  Barrdahl M,  VanderWeele TJ,  Campa D,  Canzian F,  Gaudet MM,  Figueroa JD,  Baglietto L,  Berg CD,  Buring JE,  Chanock SJ,  Chirlaque MD,  Diver WR,  Dossus L,  Giles GG,  Haiman CA,  Hankinson SE,  Henderson BE,  Hoover RN,  Hunter DJ,  Isaacs C,  Kaaks R,  Kolonel LN,  Krogh V,  Le Marchand L,  Lee IM,  Lund E,  McCarty CA,  Overvad K,  Peeters PH,  Riboli E,  Schumacher F,  Severi G,  Stram DO,  Sund M,  Thun MJ,  Travis RC,  Trichopoulos D,  Willett WC,  Zhang S,  Ziegler RG,  Kraft P,  Breast and Prostate Cancer Cohort Consortium (BPC3)

Journal: Am J Epidemiol

Date: 2014 Nov 15

Major Program(s) or Research Group(s): EDRG, PLCO

PubMed ID: 25255808

PMC ID: PMC4224360

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.