Montage

Montage detects mosaic copy number variations (CNVs) in genomic data to enable high-throughput identification and characterization of mosaicism for studies of genetic variation and disease.

Key Features:

• High-Throughput Capability: Processes and screens large cohorts, demonstrated on over 350,000 samples.
• Precision in Detection: Employs a detection threshold of 1% allele frequency to identify low-fraction mosaic CNVs.
• Integration with ParseCNV2: Integrates with the ParseCNV2 algorithm to perform genome-wide association studies (GWAS) linking mosaic events to phenotypes.
• Comprehensive Analysis: Investigates allele imbalance genome-wide to define non-diploid and non-integer copy-number states.
• Curated Callset Output: Generates a curated mosaic CNV callset (for example, 3,716 events in 2,269 samples) for downstream analysis and comparison.
• Computational Efficiency: Provides fast computational runtime suitable for large-scale analyses.

Scientific Applications:

• Genetic diversity and disease mechanisms: Identifying mosaic CNVs to study genetic diversity and the mechanisms underlying disease.
• Association mapping: Mapping genomic loci associated with specific phenotypes through GWAS of mosaic events.
• Clinical and precision genomics: Characterizing mosaicism that may inform diagnosis, prognosis, or individualized therapeutic considerations.

Methodology:

Computational steps include high-throughput screening of samples, applying a 1% allele-frequency detection threshold, genome-wide investigation of allele imbalance to define non-diploid and non-integer copy-number states, integration with ParseCNV2 for GWAS, and generation of a curated mosaic CNV callset.