High-resolution mapping and analysis of copy number variations in the human genome: A data resource for clinical and research applications

We present a database of copy number variations (CNVs) detected in 2026 disease-free individuals, using high-density, SNP-based oligonucleotide microarrays. This large cohort, comprised mainly of Caucasians (65.2%) and African-Americans (34.2%), was analyzed for CNVs in a single study using a uniform array platform and computational process. We have catalogued and characterized 54,462 individual CNVs, 77.8% of which were identified in multiple unrelated individuals. These nonunique CNVs mapped to 3272 distinct regions of genomic variation spanning 5.9% of the genome; 51.5% of these were previously unreported, and >85% are rare. Our annotation and analysis confirmed and extended previously reported correlations between CNVs and several genomic features such as repetitive DNA elements, segmental duplications, and genes. We demonstrate the utility of this data set in distinguishing CNVs with pathologic significance from normal variants. Together, this analysis and annotation provides a useful resource to assist with the assessment of CNVs in the contexts of human variation, disease susceptibility, and clinical molecular diagnostics.Copy number variation (CNV) in the human genome significantly influences human diversity and predisposition to disease (Sebat et al. 2004, 2007; Sharp et al. 2005; Conrad et al. 2006; Feuk et al. 2006; Hinds et al. 2006; McCarroll et al. 2006; Redon et al. 2006; Kidd et al. 2008; Perry et al. 2008; Walsh et al. 2008). CNVs arise from genomic rearrangements, primarily owing to deletion, duplication, insertion, and unbalanced translocation events. The pathogenic role of CNVs in genetic disorders has been well documented (Lupski and Stankiewicz 2005), yet the extent to which CNVs contribute to phenotypic variation and complex disease predisposition remains poorly understood. CNVs have been known to contribute to genetic disease through different mechanisms, resulting in either imbalance of gene dosage or gene disruption in most cases. In addition to their direct correlation with genetic disorders, CNVs are known to mediate phenotypic changes that can be deleterious (Feuk et al. 2006; Freeman et al. 2006). Recently, several studies have reported an increased burden of rare or de novo CNVs in complex disorders such as Autism, ADHD, and schizophrenia as compared to normal controls, highlighting the potential pathogenicity of rare or unique CNVs (Sebat et al. 2007; International Schizophrenia Consortium 2008; Stefansson et al. 2008; Walsh et al. 2008; Xu et al. 2008; Elia et al. 2009). Thus, more thorough analysis of genomic CNVs is necessary in order to determine their role in conveying disease risk.Several approaches have been used to examine CNVs in the genome, including array CGH and genotyping microarrays (Albertson and Pinkel 2003; Iafrate et al. 2004; Sebat et al. 2004; Sharp et al. 2005; Redon et al. 2006; Wong et al. 2007). Results from more than 30 studies comprising 21,000 CNVs have been reported in public repositories (Iafrate et al. 2004). However, a majority of these studies have been performed on limited numbers of individuals using a variety of nonuniform technologies, reporting methods, and disease states. In addition, these data are both substantially reiterative and enriched in CNV events that are frequently observed in one or more populations. Thus, extreme care is needed in determining whether a particular structural variant plays a role in disease susceptibility or progression. To address these challenges, we identified and characterized the constellation of CNVs observed in a large cohort of healthy children and their parents, when available. This study uses uniform measures to detect and assess CNVs within the context of genomic and functional annotations, as well as to demonstrate the utility of this information in assessing their impact on abnormal phenotypes. Our analysis and annotation provide a useful resource to assist with the assessment of structural variants in the contexts of human variation, disease susceptibility, and clinical molecular diagnostics.