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Hybridization and amplification rate correction for affymetrix SNP arrays
Quan Wang1, Peichao Peng, Minping Qian
1Center for Theoretical Biology, Peking University, Beijing 100871, People's Republic of China.
BMC Medical Genomics
|June 14, 2012
Summary
We developed CNVhac, a new pipeline for copy number variation (CNV) detection using SNP arrays. CNVhac corrects for biases, improving accuracy and reducing false discoveries in complex disease studies.
Area of Science:
- Genomics
- Bioinformatics
Background:
- Copy number variation (CNV) is crucial for understanding complex diseases.
- Accurate copy number (CN) estimation from SNP arrays is challenged by cross-hybridization, batch effects, and genomic waves.
- Existing algorithms often fail to address all bias factors, leading to high false discovery rates (FDR).
Purpose of the Study:
- To develop a novel CNV detection pipeline, CNVhac, that addresses key biases in SNP array data.
- To improve the accuracy and reduce the FDR of CNV identification.
Main Methods:
- CNVhac estimates allelic concentrations (ACs) using sample-independent parameters derived from physicochemical hybridization laws.
- Raw CN is calculated as the ratio of AC to average AC from reference samples.
- A hidden Markov model (HMM) is employed for CNV region segmentation.
Main Results:
- CNVhac effectively smooths genomic waves and enhances raw CN estimates compared to existing methods.
- The pipeline reduces sample-dependent biases in CNV inference.
- CNVhac achieves a significantly lower FDR for CNV calling.
Conclusions:
- CNVhac offers an effective solution for common challenges in SNP array analysis.
- The methodology is adaptable and can be extended to other platforms for CNV detection.
