Improved detection of global copy number variation using high density, non-polymorphic oligonucleotide probes

Area of Science:

• Genomics
• Human Genetics
• Molecular Biology

Background:

• Copy number variations (CNVs) are increasingly recognized as significant contributors to human genome diversity, potentially exceeding the impact of single nucleotide polymorphisms (SNPs).
• Current methods for CNV identification, often reliant on SNP genotyping arrays, face limitations in probe coverage and assay dependency.
• Optimizing CNV detection is crucial for advancing genome-wide association studies (GWAS).

Purpose of the Study:

• To design and evaluate a high-density array for CNV detection utilizing non-polymorphic oligonucleotide probes.
• To decouple copy number variation analysis from single nucleotide polymorphism genotyping for enhanced accuracy and coverage.
• To develop and validate a novel algorithm for precise CNV region and boundary extraction.

Main Methods:

• Development of a high-density array with over 1.3 million independent NspI restriction enzyme fragments queried by non-polymorphic probes.
• Application of a PCR-based, complexity-reduced DNA target for improved probe performance.
• Implementation and validation of a novel algorithm for CNV identification and boundary delineation.

Main Results:

• The new array design significantly increases marker density compared to previous SNP arrays.
• A novel algorithm effectively identified CNV regions and boundaries.
• Close to 200 CNVs were identified in well-characterized DNA samples, with nearly 50% validated as novel.

Conclusions:

• Non-polymorphic probes offer a robust and effective strategy for CNV identification, independent of SNP genotyping.
• The developed array and algorithm enhance probe coverage and precision in CNV boundary analysis.
• This approach facilitates a more comprehensive understanding of the genomic organization and impact of CNVs.