Background: Epistasis makes complex diseases difficult to understand, especially when heterogeneity
also exists. Heterogeneity of complex diseases makes the distribution of case population more
confused. However, the traditional methods proposed to detect epistasis often ignore heterogeneity, resulting
in low power of association studies.
Method: In this study, we firstly use rank information in the classification decision tree and mutual entropy
(CTME) to construct two different evaluation scores, namely multiple objectives. In addition, we
improve the calculation of joint entropy between SNPs and disease label, which elevates the efficiency of
CTME. Then, the ant colony algorithm is applied to search two-locus epistatic combination space. To
handle the potential heterogeneity, all candidate two-locus SNPs are merged to recognize multiple different
epistatic combinations. Finally, all these solutions are tested by χ2 test.
Conclusion: Experiments show that our method CTME improves the power of association study. More
importantly, CTME also detects multiple epistatic SNPs contributing to heterogeneity. The experimental
results show that CTME has advantages on power and efficiency.