Abstract
The intensities from genotyping array data can be used to detect copy number variants (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. We present a novel pipeline (CamCNV) with a series of steps to reduce noise and detect more reliably CNVs covering as few as three probes. The pipeline aims to detect rare CNVs (below 1% frequency) for association tests in large cohorts. 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 1661 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. For each called CNV, the mean z-score is a useful metric for controlling the false discovery rate.
Highlights
Genotyping experiments with large case control studies have been effective at identifying SNPs associated with disease
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
Summary
Genotyping experiments with large case control studies have been effective at identifying SNPs associated with disease. Associations with common CNVs can be detected by imputation of their genotypes using reference datasets with sequence data. At two GWAS loci associated with breast cancer, the APOBEC locus [2, 3] and 2q35 [4], deletions tagged by SNPs are associated with risk, and the CNV has been identified as the probable causal variant in each case. With current imputation reference panel sizes it is not possible to reliably impute rare CNVs. With increasing sample sizes genotyped on arrays there is an opportunity to detect novel associations with rare CNVs that potentially have large effects on disease risk. 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, when only a few probes are covered by the CNV
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