Examining SNP-SNP interactions and risk of clinical outcomes in colorectal cancer using multifactor dimensionality reduction based methods

Abstract

Background: SNP interactions may explain the variable outcome risk among colorectal cancer patients. Examining SNP interactions is challenging, especially with large datasets. Multifactor Dimensionality Reduction (MDR)-based programs may address this problem.Objectives: 1) To compare two MDR-based programs for their utility; and 2) to apply these programs to sets of MMP and VEGF-family gene SNPs in order to examine their interactions in relation to colorectal cancer survival outcomes.Methods: This study applied two data reduction methods, Cox-MDR and GMDR 0.9, to study one to three way SNP interactions. Both programs were run using a 5-fold cross validation step and the top models were verified by permutation testing. Prognostic associations of the SNP interactions were verified using multivariable regression methods. Eight datasets, including SNPs from MMP family genes (n = 201) and seven sets of VEGF-family interaction networks (n = 1,517 SNPs) were examined.Results: ∼90 million potential interactions were examined. Analyses in the MMP and VEGF gene family datasets found several novel 1- to 3-way SNP interactions. These interactions were able to distinguish between the patients with different outcome risks (regression p-values 0.03–2.2E-09). The strongest association was detected for a 3-way interaction including CHRM3.rs665159_EPN1.rs6509955_PTGER3.rs1327460 variants.Conclusion: Our work demonstrates the utility of data reduction methods while identifying potential prognostic markers in colorectal cancer.

Multifactor dimensionality reduction method identifies novel SNP interactions in the WNT protein interaction networks that are associated with recurrence risk in colorectal cancer

“…The study focused on common (MAFs >=5%) SNPs from the autosomal chromosomes, and hence, missed examining the associations of rare variables and variables from sex-chromosomes. As we reported earlier, GMDR 0.9 has certain limitations, so it may have missed interactions ( 23 ). However, use of permutation testing, repeating the MDR procedure and choosing the most frequently identified MDR model for each examined interaction, and multivariable regression modeling also have limited the false-positive findings.…”

“…Table S1 shows the genes in each protein interaction network after implementing quality control measures [described in detail in Curtis et al. ( 23 )]. We then used the SNP genotype data and PLINK to retrieve the SNPs located in these genes using the following criteria: Minor Allele Frequency (MAF) >= 0.05; Hardy-Weinberg Equilibrium (HWE) > 0.0001; and missing genotype data = 0%.…”

“…Pruning was performed by PLINK to remove the SNPs in high-LD, as explained in Curtis et al. ( 23 ). As a result we ended up with 6 (in Wnt1 ), 18 (in Wnt2 ), 53 (in Wnt5a ), 4 (in Wnt5b ), and 20 (in Wnt11 ) genes respectively ( Supplementary Table S1 ) , and around 1,000 SNPs in these Wnt interaction networks ( Supplementary Table S2 ).…”

“…All analyses were done as described in Curtis et al. ( 23 ). In brief, we have conducted 1-way interactions (examining the interactions among the three potential genotypes of single SNPs); 2-way interactions (examining the interactions among the genotypes of two SNPs); and 3-way interactions (examining the interactions among the genotypes of three SNPs).…”

“…Methodologies (such as, Multifactor Dimensionality Reduction [MDR]) that address this challenge and tools that utilize these methods (such as GMDR 0.9) have been developed to examine interactions in a relatively feasible way ( 16 – 18 ). We and others have previously shown the utility of MDR and examining interactions among genetic variables in colorectal cancer ( 19 – 23 ). As these studies showed, examining interactions is a promising research area and can reveal new biomarkers that can predict patient outcomes.…”