H-Clust

H-Clust partitions genomic regions by linkage disequilibrium (LD) and haplotype diversity to distinguish blocked and unblocked regions and select tag SNPs for association studies.

Key Features:

• Partitioning Genomic Regions: Uses an approximate likelihood model combined with a parsimony criterion to partition genomic regions into blocks reflecting haplotype diversity.
• Handling Complex Patterns: Identifies adjacent, distinct haplotype blocks and multiple independent single nucleotide polymorphisms (SNPs) that separate these blocks, addressing spatial heterogeneity in LD.
• Efficient SNP Selection: Selects tag SNPs chosen to represent haplotype diversity within a block to reduce the number of markers needed for association analysis.

Scientific Applications:

• Genomic Research: Characterizes LD structure and haplotype block architecture to assist studies of genetic variation and disease-associated regions.
• Association Studies: Provides tag SNP selection to support genome-wide association studies (GWAS) by capturing essential genetic variation with fewer markers.

Methodology:

Analyzes haplotype diversity across genomic regions using an approximate likelihood model with a parsimony criterion to partition regions into blocks and facilitate tag SNP selection.