A Computational Method For Detecting The Associations Between Multiple Loci And Phenotypes

The association analysis between SNP and disease phenotype is of great significance in medical diagnosis and treatment. However, the existing research methods are for a single disease or phenotype, which is a "multiple-loci-single phenotype" situation. It is not suitable for practical conditions which are often "multiple-loci-multiple-phenotypes", and the situation does not take into account the relationship between the internals of the phenotypes. If the existing methods are used to achieve the above purpose, the same processing needs to be done once for each phenotype resulting huge resource cost. When mining the logical relationship between phenotypes, the combination is exponentially increasing. Therefore, its time complexity is also exponentially increasing. In this paper, a method based on particle swarm optimization and hierarchical clustering is proposed. It consists of 4 steps. Firstly, the phenotypic dataset are clustered and then combined, after that the algorithm model is initialized, and the particle swarm optimization (PSO) algorithm is used for extended optimization, and finally the result is filtered to obtain the final result. In this paper, the experiment is carried out by controlling the total number of SNP loci, the number of pathogenic mutation loci to be excavated, the total number of phenotypes in the phenotypic dataset, and the number of phenotypes to be mined. For every configuration of different parameter values, 100 sets of data were simulated for experimentation. The results show that the proposed method adapts to the "multiple-loci-multiplephenotypes" case and obtained highly accurate results. When performing the algorithm, it does not need to loop the routine for each phenotype. The most probable candidates are pre-selected according to the fitness function of PSO algorithm. Therefore, the time and system resource costs are largely reduced comparing with previous methods. The source code and testing datasets are uploaded at https://github.com/GALI17/PSO.