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 phenotypemultiple variants or single variantmultiple phenotypes, but not on multiple variantsmultiple 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.

Ant Colony Optimization with SelfEvolving Parameter for Detecting Epistatic Interactions The epistatic interactions of single nucleotide polymorphisms (SNPs) are fundamentally important for understanding the genetic causes of complex diseases. Due to the intensive computational burden and the diversity of disease models, existing methods suffer from low detection power, high computational cost, and preferences for some types of disease models. To tackle these drawbacks, an ant colony optimization with selfevolving parameter (SEPACO) is proposed in the paper. In the proposed algorithm, the selfevolving parameter control (SEPC) strategy is used to select the best parameters of the algorithm during the running process. In this way, SEPACO can set different optimal parameters for different disease models, which leads to the enhancement of the detection ability of the algorithm. Furthermore, the probability distribution function and the pheromone evaporation formula are improved to adapt SEPACO to the detection of epistatic interactions. SEPACO is compared with other recent algorithms on a variety of simulated datasets and a real biological dataset. The experimental results show that our algorithm, compared to the other test algorithms, can improve the average detection power from no more than 32% up to 68%. Moreover, our algorithm uses less running time.