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

Breast and Prostate Cancer Cohort Consortium (BPC3), 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 HankinsonBrian 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

Research output: Contribution to journalArticlepeer-review

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)1018-27
Number of pages10
JournalAmerican Journal of Epidemiology
Volume180
Issue number10
Early online date25 Sep 2014
DOIs
Publication statusPublished - 15 Nov 2014

Keywords

  • Alleles
  • Australia
  • Biomarkers, Tumor
  • Body Mass Index
  • Breast Neoplasms
  • Case-Control Studies
  • Cohort Studies
  • DNA-Binding Proteins
  • Europe
  • European Continental Ancestry Group
  • Female
  • Genetic Predisposition to Disease
  • Genome-Wide Association Study
  • Humans
  • Male
  • Middle Aged
  • Odds Ratio
  • Polymorphism, Single Nucleotide
  • Prostatic Neoplasms
  • Rad51 Recombinase
  • Risk Assessment
  • Risk Factors
  • United States

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