Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Copy number variants (CNVs) are crucial in genetic variation and disease.
• Standardized methods for CNV detection across different microarray platforms are lacking.
• Variability in CNV calling can impact research and clinical diagnostics.

Purpose of the Study:

• To systematically compare CNV detection across eleven microarrays.
• To evaluate data quality, CNV calling, reproducibility, and concordance.
• To assess breakpoint accuracy and the impact of analysis tool variability.

Main Methods:

• Comparative analysis of CNV detection using eleven microarray platforms.
• Evaluation of data quality, reproducibility, and concordance metrics.
• Assessment of breakpoint accuracy and analysis tool performance.

Main Results:

• CNV calling concordance between different analytic tools was typically less than 50%.
• Reproducibility in replicate experiments was below 70% for most platforms.
• Large CNVs, primarily in complex genomic regions, showed poor reproducibility but are manageable via clinical data curation.

Conclusions:

• Significant variability exists in CNV detection across platforms and tools, emphasizing the need for careful experimental design and data curation.
• Despite variability, large CNVs can be reliably detected for clinical diagnostics.
• The presented CNV resource facilitates independent data evaluation and algorithm benchmarking.