An Artificial Fish Swarm Algorithm for Identifying Associations between Multiple Variants and Multiple Phenotypes

Identifying associations between genomic variants and phenotypes has always been an interesting research field of population genetics, which is of great significance for studying the pathogenesis of complex diseases and supporting clinical assistant decision making. Nowadays, many identification methods have been proposed to find the associations between variants and phenotypes, such as GWAS and pheWAS, and have made excellent achievements in pathological research and clinical practice. However, the existing methods only focus on single phenotype-multiple variants or single variant-multiple phenotypes, but not on multiple variants-multiple phenotypes. In the view of the fact that complex diseases often have several subtypes which differ greatly in variants and phenotypes, focusing only on single variant or single phenotype is far from enough and limits the ability of identification of those methods. Therefore, we propose a heuristic method with an AFSA framework on the solution space to identify associations between multiple variants and multiple phenotypes. In our method, each fish carries two logic trees that respectively represent the associations between variants and the associations between phenotypes. The logic trees will be iteratively updated to find a better solution according to the preset update strategies. When the iteration stop condition is reached, the algorithm will stop and output the optimal fish. The logical expression represented by the logic trees carried by the optimal fish is the associations we find. We validated the proposed method on the simulation data generated by hapgen2 and PhenotypeSimulator, and took the ratio of the number of people that can be explained by the found logical expression as the index to evaluate the performance, which was called Coverage. We conducted 9 groups of experiments, each of which was different in the number of variants and phenotypes. The best Coverage of was from the group including 500 variants and 10 phenotypes, which reached 72.12%, and the worst result is from the group including 100 variants and 20 phenotypes, 31.73%. We also exhausted the simulation data to find the optimal logical expression and several most important logic rules to evaluate the results obtained by the method.