Clustering of SNP Data with Application to Genomics

Single nucleotide polymorphisms (SNPs) are very common throughout the genome and hence are potentially valuable for mapping disease susceptibility loci by detecting association between SNP markers and disease. Many methods may only be applicable when marker haplotypes, rather than genotypes (categorical data), are available for analysis. In this paper, we explore the properties of k-modes (categorical data) clustering algorithms to SNP data for detecting association between SNP markers and disease. Sub-space k-modes clustering properties are also considered and tested