A genome-wide SNP-SNP interaction analysis exploring novel interacting loci associated with the risk of recurrence in colorectal cancer

Background: Genetic factors can influence and predict patient outcomes. The association of interactions of germline SNPs with patient outcomes is an understudied area of prognostic research. In this study, we applied the first genome-wide SNP-SNP interaction analysis in relation to colorectal cancer outcomes. Objectives: Our objective was to explore interacting SNP loci at the genome-wide level that predict the risk of local or distant recurrence (RMFS) in a cohort of stage I-III colorectal cancer patients from the Canadian province of Newfoundland and Labrador. Methods: The patient cohort consisted of 430 unrelated Caucasian patients. Genetic and medical data was collected previously and the genetic data consisted of a total of 384,415 genotyped SNPs. The PLINK epistasis function was utilized to examine pairwise SNP interactions. Select interactions were assessed by multivariable Cox-regression models, adjusting for established clinical covariates. Genomic regions identified were explored for additional interactions. Published databases were utilized to retrieve biological information about the loci identified. Results: After Bonferroni correction for multiple testing, no interaction remained significant. We present the top 20 interactions. The interaction p-values ranged from p = 1.37E-8 to p = 2.14E-9 in this set. Interactions were also tested by multivariable Cox regression models including established clinical covariates. Many of the SNPs were intronic and some of them were functional (e.g., expression quantitative expression loci). Analysis of the other SNPs in the same genomic regions as the interacting SNPs led to the identification of three additional interaction models. Conclusions: We present the results of the first genome-wide SNP-SNP interaction analysis in colorectal cancer outcomes. While no SNP-SNP interaction remained significant after correction for multiple testing, our methodology emphasizes the additional knowledge that can be obtained using interaction analyses while studying prognostic markers.