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

全基因组关联研究调查群体中个体DNA的微小变化，这些变化会导致身高和患病倾向等性状的变化。遗传技术的最新进展使研究人员能够测量人群中DNA的这些微小差异（称为单核苷酸多态性或SNP），并且已经发现了与疾病相关的SNP，包括广泛宣传的“FTO”基因，该基因已被证明与2型糖尿病高度相关。然而，单个SNP并不能解释所有被怀疑是遗传的变异，研究人员现在开始研究多个SNP之间相互作用的可能性，以解释这种变异。然而，基因组中可能的配对和三联体的数量是巨大的，因此完全枚举搜索是不可能的，这意味着需要智能技术来处理潜在相互作用的大空间。在这一领域表现出相当大希望的一种方法是蚁群优化（ACO），这是一种受自然启发的搜索技术，基于昆虫在野外找到从巢穴到食物来源的最短路径的方式。该搜索算法具有两个独特的属性，使其成为此任务的理想选择。第一个是局部遗传学可以用来影响搜索，以找到特定的基因-基因相互作用，如上位性，第二个是该算法创建了一个信息素矩阵，提供了一个详细的地图，在搜索过程中发现的变量（SNP）的重要性。该项目将研究使用ACO来搜索SNP相互作用的空间及其与包括2型糖尿病和克罗恩病在内的许多疾病的关联，以及它们解释身高等人类特征的潜力。这些相互作用的发现将推进我们对疾病如何遗传的认识，并可能为基于个体基因组成的高度个性化和先发制人的治疗铺平道路。

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.