Genome-wide association studies (GWASs) of breast cancer (BC) have identified multiple risk variants. However, the multiple interactions among these variants are still not well established. In this study, we utilized the multi-analytic strategy combing random forest (RF), multifactor dimensionality reduction (MDR), and logistic regression approaches to investigate the high-order interactions among ten genetic variants recently identified by GWAS in 477 BC patients and 534 healthy controls. Expectedly, six variants, rs1219648, rs3757318, rs1926657, rs6556756, rs2046210, and rs4973768, were significantly associated with BC risk under independent analysis. In RF analysis, rs3757318, rs2046210, and rs4973768 were ranked as the top three important risk factors and were selected as the best set which taking interactions into consideration. Subsequently, the MDR analysis of the ten variants found that the three-factor model including rs3757318, rs2046210, and rs4973768 interpret the best interaction model with the maximized testing accuracy of 0.6183 and cross-validation consistency of 10/10. Intriguingly, cumulative effect was observed in the manner of dose-dependent with increasing numbers of risk alleles (P(trend) = 9.80 × 10(-5)), and the individuals carrying 4-6 risk alleles had a threefold higher risk of BC than carrying 0 risk alleles (OR 3.27, 95 % CI 1.96-5.48). Our findings emphasized the proof of principle that multiple interactions of genetic variants, including rs3757318, rs2046210, and rs4973768 may play important roles in the susceptibility of BC though the biological mechanisms underlying the observed associations need to be elucidated.