Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies

Area of Science:

• Genetics
• Bioinformatics
• Statistical genomics

Background:

• High-throughput genomic data presents challenges in genetic association studies due to numerous variants and computational complexity.
• Marker-set studies, like SNP-set analysis, offer an efficient approach to address these challenges.

Purpose of the Study:

• To develop an efficient SNP-set clustering algorithm and a novel association test (HDAT) for genetic association studies.
• To improve the identification of genetic variants associated with disease susceptibility.

Main Methods:

• A novel clustering algorithm using Hamming distance to group single nucleotide polymorphisms (SNPs) based on genotype similarity.
• Construction of a dendrogram to determine optimal SNP-set clusters.
• Development of the Hamming Distance Association Test (HDAT) to assess disease susceptibility using genotype data.

Main Results:

• The proposed clustering algorithm is faster and more effective at identifying SNP sets with similar effects compared to existing methods.
• Simulation studies show HDAT performs well, even with SNP sets containing many neutral SNPs.
• The methodology successfully confines genetic regions for susceptible markers.

Conclusions:

• The developed SNP-set analysis method provides an efficient and effective approach for genetic association studies.
• This method simplifies analysis by not requiring haplotype inference or proximity of SNPs.
• The approach enhances the ability to identify genetic markers associated with disease susceptibility.