Detecting SNPs-disease associations using Bayesian networks

Epistatic interactions play a significant role in improving pathogenesis, prevention, diagnosis and treatment of complex human diseases. A recent study in automatic detection of epistatic interactions showed that Markov Blanket-based methods are capable of finding SNPs (single-nucleotide polymorphism) that have a strong association with common diseases and of reducing false positives when the number of instances is large. Unfortunately, a typical SNP dataset consists of very limited number of examples, where current methods including Markov Blanket-based methods perform poorly. To address small sample problems, we propose a Bayesian network-based approach to detect epistatic interactions. The proposed method also employs a Branch-and-Bound technique for learning. We apply the proposed method to simulated datasets based on four disease models and a real dataset. Experimental results show that our method significantly outperforms Markov Blanket-based methods and other commonly-used methods, especially when the number of samples is small.