Evaluation of an algorithm of tagging SNPs selection by linkage disequilibrium

Abstract

Background: Single nucleotide polymorphisms (SNPs) are the most abundant kind of genetic polymorphism in the human genome. They are important in both genetic research and genetic testing in a clinical setting, such as in the area of pharmacogenetics. In order to improve efficiency, tagging SNPs (tagSNPs) are selected in genes of interest to represent other co-related SNPs in linkage disequilibrium (LD) with the tagSNPs. Various algorithms have been proposed to identify a subset of single nucleotide polymorphisms as tagSNPs. Most algorithms of tagSNPs selection are haplotype-based, in which the spatial relationship between SNPs is considered. Currently, a more efficient cluster-based algorithm is proposed which clusters SNPs solely by a LD parameter, such as r 2. Here, we evaluated the sample distribution of r 2 and its effect on the cluster-based tagSNPs selection. Design and methods: The genotype data of 198 individual within a 500-kb region on 5q31 was used to evaluate the sample distribution of r 2 and its effect on the cluster-based tagSNPs selection. Results: It was found that the degree of variation of LD depends on the LD structure of genes. Conclusion: As a cluster-based tagSNPs selection algorithm does not take into account the spatial position of SNPs, a more stringent r 2 threshold is required to achieve more reliable tagSNPs selection.