Exact and Heuristic Approaches for Identifying Disease-Associated SNP Motifs

Gaofeng Huang, Peter Jeavons and Dominic Kwiatkowski

abstract

A Single Nucleotide Polymorphism (SNP) is a small DNA variation which occurs naturally between different individuals of the same species. Some combinations of SNPs in the human genome are known to increase the risk of certain complex genetic diseases. This paper formulates the problem of identifying such disease-associated SNP motifs as a combinatorial optimization problem and shows it to be mathcal-hard. Both exact and heuristic approaches for this problem are developed and tested on simulated data and real clinical data. Computational results indicate that our algorithms are efficient AI tools which can support ongoing biological research.

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