Using Dissimilarity Information to Cluster SNP Sets and Test for Disease Association

• Activity Number: 558
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Chuhsing Kate Hsiao* and Charlotte Wang and Jung-Ying Tzeng and Wen-Hsin Kao
• Companies: National Taiwan University and National Taiwan University and North Carolina State University and National Taiwan University
• Keywords: association studies ; clustering ; dendrogram ; Hamming distance ; similarity ; SNP-set

Abstract

The availability of high-throughput genomic data has led to several challenges in recent genetic association studies. Tackling these problems with a marker-set study such as SNP-set analysis can be an efficient solution. To construct SNP-sets, we first propose a clustering algorithm, which employs Hamming distance to measure the similarity between strings of SNP genotypes and evaluates whether the given SNPs should be clustered. A dendrogram can then be constructed and the number of clusters can be determined. With the resulting SNP-sets, we next develop an association test to examine susceptibility to the disease of interest. This proposed test assesses, based on Hamming distance, whether the similarity between a diseased and a normal individual differs from the similarity between two individuals of the same disease status. In our proposed methodology, only genotype information is needed. No inference of haplotypes is required, and SNPs under consideration do not need to locate in nearby regions. The proposed clustering algorithm and association test are illustrated with applications and simulation studies.