AncesHC

AncesHC infers ancestral haplotype clusters from genotype data and tests their associations with binary and quantitative phenotypes.

Key Features:

• Ancestral Haplotype Clustering: Clusters haplotypes into groups reflecting predicted common ancestral origins to address rare haplotype abundance and ambiguous block boundaries.
• Flexible Marker Compatibility: Supports biallelic and multiallelic markers and applies to haploid, diploid, and multiploid organisms.
• Handling of Missing Data: Manages missing genotype data during analysis to retain genomic information for clustering and testing.
• Hidden Markov Model (HMM) Utilization: Uses HMMs to cluster haplotypes into ancestral groups.
• Block Structure Recognition: Dynamically recognizes block-like structures in the data rather than assuming rigid block boundaries.
• Association Testing: Tests each haplotype cluster for association with phenotypes by comparing chromosomal configurations (0, 1, or 2 chromosomes) between groups.

Scientific Applications:

• Genome-wide association studies (GWAS): Aggregates haplotype information to detect associations with complex traits that may be missed by single-SNP analyses.
• Disease association detection: Demonstrates increased power to detect disease associations compared to single-SNP analyses.
• Simulation-based evaluation: Applied to simulation studies including case-control status in datasets such as 1500 outcrossed mice from eight inbred lines.
• Comparative performance: Shown to offer superior power and flexibility relative to methods such as CLADHC in comparative analyses.

Methodology:

Haplotypes are clustered using Hidden Markov Models, and each cluster is tested for association with phenotypes by comparing the distribution of cases and controls across chromosomal configurations (0, 1, or 2 chromosomes).