ParseCNV2: efficient sequencing tool for copy number variation genome-wide association studies

Area of Science:

• Genetics and Bioinformatics
• Genomic Analysis
• Computational Biology

Background:

• Copy number variation (CNV) detection and association studies are crucial for understanding genetic contributions to disease.
• Current methods often focus on candidate CNVs, limiting hypothesis-free genome-wide association studies (GWAS).
• There is a need for robust, scalable tools that integrate diverse CNV data formats for comprehensive analysis.

Purpose of the Study:

• To introduce ParseCNV2, a next-generation tool for CNV association analysis.
• To provide a unified platform supporting VCF and PennCNV formats for sequencing and SNP array data.
• To enable efficient analysis of large-scale cohorts for hypothesis-free GWAS.

Main Methods:

• Developed ParseCNV2, a single, efficient tool supporting VCF and PennCNV formats.
• Implemented rigorous CNV curation pre- and post-association for reliable results.
• Benchmarked ParseCNV2 on large datasets: UK Biobank (>450,000 samples) and CAG Biobank (>350,000 samples).

Main Results:

• ParseCNV2 demonstrates efficiency for large cohort analysis (>100,000 samples) without data partitioning.
• The tool facilitates formal CNV association for inclusion in GWAS Catalog alongside SNP associations.
• New features include clinical CNV prioritization, interactive quality control, and covariate adjustment.

Conclusions:

• ParseCNV2 significantly advances CNV association studies by providing a unified, efficient, and feature-rich platform.
• The tool supports the inclusion of CNVs in large-scale GWAS, expanding the scope of genetic discovery.
• ParseCNV2 promotes best practices in CNV analysis, yielding high-quality, reliable results for disease association research.