Genome-wide association studies (GWAS) have identified 19 loci associated with the risk of ovarian cancer. In this post-GWAS era interest shifts towards elucidating the interplay of these susceptibility loci and known risk factors for ovarian cancer, i.e. reproductive and hormonal factors, height and smoking.
In this study, we investigate gene-environment interactions between the GWAS loci and single nucleotide polymorphisms (SNPs) in the accompanying genetic pathways and risk factors for ovarian cancer. Currently, a golden standard for the analysis of gene-environment interactions is lacking. Therefore, we use a multifaceted analytical approach by using a series of machine learning methods, including regression analysis, multifactor dimensionality reduction (MDR), entropy-based interaction networks, computational evolution systems and learning classifier systems.
A case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) is being conducted including 590 ovarian cancer cases and 1190 healthy controls. In addition to all known GWAS hits for ovarian cancer, genetic pathways were selected in which the genes of these GWAS hits are potentially involved. A total of 754 SNPs are investigated. The environmental factors of interest are parity, miscarriages, breastfeeding, oral contraceptive (OC) use, age at menarche, age at menopause, height and smoking.
Preliminary results from regression analyses show statistically significant interactions between SNPs identified through GWAS and smoking (in interaction with rs12379687, p=0.0435), height (in interaction with rs2072590, p=0.01563) and OC use (in interaction with rs10098821, p=0.03621). These interactions were confirmed by multifactor dimensionality reduction, based on 10-fold cross-validation and 1000-fold permutation testing. Subsequent analyses are still ongoing and will be presented at the conference. Moreover, the analyses will be supplemented by an additional 289 cases and 578 controls from the Nijmegen Polygene and the Nijmegen Biomedical Study.
The results of this study will provide important insight into the etiology of ovarian cancer and reveal better understanding of the complexity of this disease.
This proffered talk is also presented as Poster 41.
Citation Format: Marieke GM Braem, N. Charlotte Onland-Moret, Jason H. Moore, Petra HM Peeters. Pathway-based gene-environment interactions in ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference on Post-GWAS Horizons in Molecular Epidemiology: Digging Deeper into the Environment; 2012 Nov 11-14; Hollywood, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(11 Suppl):Abstract nr PR2.