Ant Colony Optimisation for the Discovery of Gene-Gene Interactions in Genome-Wide Association Studies

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Genome-wide association studies investigate the small changes in DNA among individuals in a population that lead to variations in traits such as height and the propensity to suffer from diseases. Recent advances in genetic technology allow researchers to measure these small differences in DNA in a population (known as single-nucleotide polymorphisms or SNPs) and have already discovered SNPs that are associated with diseases including the widely publicised 'FTO' gene which has been shown to be highly associated with type 2 diabetes. However, single SNPs do not account for all of the variation that is suspected to be inherited and researchers are now beginning to investigate the potential for interactions between multiple SNPs to explain this variation. The number of possible pairs and triplets in the genome though is vast and so a full enumeration search is not possible, meaning that intelligent techniques are required to process the large space of potential interactions. A method that has shown considerable promise in this area is ant colony optimisation (ACO), a nature-inspired search technique based on the way that insects find the shortest path from a nest to a food source in the wild. This search algorithm has two unique properties that make it ideal for this task. The first is that local heuristics can be used to influence the search to find specific gene-gene interactions such as epistasis and the second is that the algorithm creates a pheromone matrix that provides a detailed map of the importance of variables (SNPs) found during the search. This project will investigate the use of ACO to search the space of SNP interactions and their association with a number of diseases including type 2 diabetes and Crohn's disease and also the potential for them to explain human traits such as height. The discovery of these interactions will advance our knowledge of how disease is inherited and could pave the way for highly personalised and pre-emptive treatment based on an individual's genetic makeup.