cnvHap

cnvHap enhances genotyping of copy number variations (CNVs) by integrating chromosome-wide haplotype models with cluster-based allele intensity models across multiple genotyping platforms to improve detection of small and rare deletions and amplifications.

Key Features:

• Chromosome-wide haplotype model: Employs a chromosome-wide haplotype representation of CNVs to capture patterns of copy number variation across samples.
• Multi-platform data integration: Integrates probe intensity data from multiple genotyping platforms to increase sensitivity and accuracy of CNV calls.
• Cluster-based allele intensity modeling: Uses cluster-based models to analyze allele intensity at each probe for more precise genotype assignments.
• Joint model learning: Jointly learns haplotype models and cluster-based intensity models across multiple samples and platforms.
• Improved detection for small aberrations: Benchmarked against PennCNV and QuantiSNP, reporting an 82% improvement in detecting deletions and a 115% improvement in identifying amplifications for aberrations containing fewer than ten probes.
• Detection of deleted and amplified genotypes: Capable of identifying both deletions and amplifications, including rare and small CNVs.

Scientific Applications:

• Genome-wide association studies (GWAS): Identifies CNVs associated with complex diseases and aids investigation of 'missing heritability' not captured by SNPs alone.
• Population-based studies: Applied to population datasets (e.g., 50 French individuals) to detect deleted and amplified genotypes and characterize CNV distribution.

Methodology:

cnvHap jointly learns chromosome-wide haplotype models and cluster-based allele intensity models across multiple samples and genotyping platforms.