Single Nucleotide Polymorphisms Data Analysis using Improved Ant Colony Algorithm

Abstract

Abstract. Improved classical ant colony clustering algorithm (LF algorithm), applied to salt sensitive hypertension SNPs data analysis, in order to explore the high throughput SNPs statistical analysis to provide new ideas. The LF algorithm was improved, and the improved algorithm was programmed with Mat 1ab8.0 software. The cluster analysis was performed on 335 samples of salt sensitive hypertension. The LF algorithm was successfully improved and the software interface was realized. Using the new algorithm, all samples are divided into 2 categories, the first class of 169 samples, second of 166 samples of consistency test and latent class analysis results, the Kappa value is 0.93, P<0.001, and the two kinds of differences in population SNPs probability distribution statistical test, we selected 3 SNPs:rs848307, rs1739843, rs1010069, clear it plays an important role in the classification of. Conclusion: ant colony clustering algorithm has the characteristics of unique thinking, automatic calculation, easy to improve, etc. it has broad application prospects in the field of high throughput SNPs data analysis and other related fields of genomics.