Abstract

The availability of high-throughput genomic data has led to several challenges in recent genetic association studies, including the large number of genetic variants that must be considered and the computational complexity in statistical analyses. 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 or SNP-sets should be clustered. A dendrogram can then be constructed based on such distance measure, and the number of clusters can be determined. With the resulting SNP-sets, we next develop an association test HDAT 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. As compared with other existing methods, the clustering algorithm is faster and better at identifying sets containing SNPs exerting a similar effect. In addition, the simulation studies demonstrated that the proposed test works well for SNP-sets containing a large proportion of neutral SNPs. Furthermore, employing the clustering algorithm before testing a large set of data improves the knowledge in confining the genetic regions for susceptible genetic markers.

Highlights

  • With the rapid advancements made in biotechnology, the volume and types of biological data collected have grown at an accelerated rate

  • The procedure we develop is based on the rational that the more individuals carrying the same genotype with respect to two given SNPs, the more similar these two SNPs should be considered, which is exactly what the Hamming distance does by assigning them a smaller value

  • We investigated if the performance of any of the association tests (HDAT, U statistic, and SKAT) can be improved by testing on the Hamming distance clusters

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Summary

Objectives

The aim of our study is to develop a methodology of utilizing the Hamming distance metric to measure the distance between two sets of vectors containing discrete observations, in order to first perform clustering and to use this clustering to conduct association studies

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