Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER)

In association studies, the combined effects of single nucleotide polymorphism (SNP)-SNP interactions and the problem of imbalanced data between cases and controls are frequently ignored. In the present study, we used an improved multifactor dimensionality reduction (MDR) approach namely MDR-ER to detect the high order SNP-SNP interaction in an imbalanced breast cancer data set containing seven SNPs of chemokine CXCL12/CXCR4 pathway genes. Most individual SNPs were not significantly associated with breast cancer. After MDR-ER analysis, six significant SNP-SNP interaction models with seven genes (highest cross-validation consistency, 10; classification error rates, 41.3-21.0; and prediction error rates, 47.4-55.3) were identified. CD4 and VEGFA genes were associated in a 2-loci interaction model (classification error rate, 41.3; prediction error rate, 47.5; odds ratio (OR), 2.069; 95% bootstrap CI, 1.40-2.90; P=1.71E-04) and it also appeared in all the best 2-7-loci models. When the loci number increased, the classification error rates and P-values decreased. The powers in 2-7-loci in all models were >0.9. The minimum classification error rate of the MDR-ER-generated model was shown with the 7-loci interaction model (classification error rate, 21.0; OR=15.282; 95% bootstrap CI, 9.54-23.87; P=4.03E-31). In the epistasis network analysis, the overall effect with breast cancer susceptibility was identified and the SNP order of impact on breast cancer was identified as follows: CD4 = VEGFA > KITLG > CXCL12 > CCR7 = MMP2 > CXCR4. In conclusion, the MDR-ER can effectively and correctly identify the best SNP-SNP interaction models in an imbalanced data set for breast cancer cases.