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Detecting rare copy number variants (CNVs) from Illumina genotyping arrays with the CamCNV pipeline: segmentation of z-scores improves detection and reliability.

By Joe Dennis, Logan C. Walker, Jonathan Tyrer, Kyriaki Michailidou, Douglas F. Easton

Posted 25 Apr 2020
bioRxiv DOI: 10.1101/2020.04.23.057158 (published DOI: 10.1002/gepi.22367)

Background: The intensities from genotyping array data can be used to detect CNVs but a high level of noise in the data and overlap between different copy-number intensity distributions produces unreliable calls, particularly when only a few probes are covered by the CNV. Results: We present a novel pipeline (CamCNV) with a series of steps to reduce noise and detect more reliably rare CNVs covering as few as three probes. The method uses the information from all samples to convert intensities to z-scores, thus adjusting for variance between probes. We tested the sensitivity of our pipeline by looking for known CNVs from the 1000 Genomes project in our genotyping of 1000 Genomes samples. We also compared the CNV calls for 1,661 pairs of genotyped replicate samples. At the chosen mean z-score cut-off, sensitivity to detect the 1000 Genomes CNVs was approximately 85% for deletions and 65% for duplications. From the replicates we estimate the false discovery rate is controlled at ~10% for deletions (falling to below 3% with more than five probes) and ~28% for duplications. The pipeline demonstrates improved sensitivity when compared to calling with PennCNV, particularly for short deletions covering only a few probes. Conclusion: The CamCNV pipeline provides a reliable method of detecting rare CNVs from Illumina array data and can be used for CNVs that only cover a few probes. For each called CNV the mean z-score is a useful metric for controlling the false discovery rate. ### Competing Interest Statement The authors have declared no competing interest.

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