Tag SNP selection using similarity associations between SNPs

Abstract

Genetic changes that may be associated with complex diseases are tried to be determined by means of many genome-wide association studies. Single Nucleotide Polymorphisms (SNPs) are used primarily in these studies since they comprise a large part of these genetic changes. Statistical importance of the genome-wide association study is directly related to the number of individuals and SNPs. However, it is still very costly and time-consuming to genotype all SNPs inside the candidate area for many individuals in very large-scale association studies. For this reason, with a small error, it is necessary to select an appropriate subset of all SNPs that will represent the rest of SNPs. These selected SNPs are called tag SNPs or haplotype tag SNPs (tag SNPs or htSNPs). It is essential in tag SNP selection to determine minimum tag SNP set with very good prediction accuracy. In this study, while Clonal Selection Algorithm (CLONALG) was used as tag SNP selection method, a new method named CLONSim, in which similarity association between SNPs was used as the prediction method for the rest of SNPs was proposed. The proposed method was compared with BPSO (Binary Particle Swarm Optimization) and CLONTagger methods with parameter optimization using datasets of different sizes. Experiment results showed that the proposed method could identify tag SNPs significantly faster.