A Comparative Study of Tag SNP Selection Using Clustering

摘要

The immense volume and rapid growth of human genomic data, especially single nucleotide polymorphisms (SNPs), present special challenges for both biomedical researchers and automatic algorithms. SNPs are confirmed as a major factor in human genome polymorphisms, and are found to be suitable as a genetic marker for disease characteristics. SNPs hold much promise as a basis for genome-wide disease-gene association. Determining the relationship between disease complexity and SNPs requires complex genotyping for large SNP data sets, and is thus very expensive and labor-intensive. In this paper, we attempt two novel approaches to solve the problem of tag SNP selection, one using selforganizing maps (SOM) for clustering the SNPs and the other using Fuzzy C Means clustering. Both the above methods have been shown to select a more optimal set of tag SNPs which capture the remaining SNPs more efficiently as compared to Haploview Tagger, thus satisfying the goal of tag SNP selection in a more suitable way.