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

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

SUBMITTER: Dennis J 

PROVIDER: S-EPMC8005414 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature