Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data

We evaluated four publicly available software packages for high throughput copy number analysis using synthetic and empirical 100 K SNP array data sets, the latter obtained from 107 mental retardation (MR) patients and their unaffected parents and siblings. We evaluated the software with regards to overall suitability for high-throughput 100 K SNP array data analysis, as well as effectiveness of normalization, scaling with various reference sets and feature extraction, as well as true and false positive rates of genomic copy number variant (CNV) detection.We observed considerable variation among the numbers and types of candidate CNVs detected by different analysis approaches, and found that multiple programs were needed to find all real aberrations in our test set. The frequency of false positive deletions was substantial, but could be greatly reduced by using the SNP genotype information to confirm loss of heterozygosity.Chromosomal abnormalities frequently contribute to human disorders including cancer [1-3] and mental retardation (MR) [4-6], and characterization of these DNA alterations is important for both diagnosis and understanding of disease mechanisms. A surprising recent finding has been the extent to which genomic copy number variants (CNVs) also exist in the normal population [7-13]. Such variation may represent an important class of mutations that predispose to disease.Conventional cytogenetic studies such as karyotyping are routinely used to detect genomic deletions and duplications involving more than 5–10 Mb, but detection of submicroscopic aberrations requires higher resolution approaches. Oligonucleotide microarray technologies offer high resolution, scalable methods for whole genome screening and can detect previously unidentified CNVs [6,14-17]. Among these approaches, the Affymetrix GeneChip？ Mapping Assay [18,19] is increasingly used for detecting CNVs in human DNA. This method involves a whole genome sampling analysis (WGSA) combined with hig