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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 &gt;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 &gt; KITLG &gt; CXCL12 &gt; CCR7 = MMP2 &gt; 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.

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