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Copy number variants (CNVs) account for more polymorphic base pairs in thehuman genome than do single nucleotide polymorphisms (SNPs). CNVs encompassgenes as well as noncoding DNA, making these polymorphisms good candidates forfunctional variation. Consequently, most modern genome-wide association studiestest CNVs along with SNPs, after inferring copy number status from the datagenerated by high-throughput genotyping platforms. Here we give an overview ofCNV genomics in humans, highlighting patterns that inform methods foridentifying CNVs. We describe how genotyping signals are used to identify CNVsand provide an overview of existing statistical models and methods used toinfer location and carrier status from such data, especially the most commonlyused methods exploring hybridization intensity. We compare the power of suchmethods with the alternative method of using tag SNPs to identify CNV carriers.As such methods are only powerful when applied to common CNVs, we describe twoalternative approaches that can be informative for identifying rare CNVscontributing to disease risk. We focus particularly on methods identifying denovo CNVs and show that such methods can be more powerful than case-controldesigns. Finally we present some recommendations for identifying CNVscontributing to common complex disorders.

Using GWAS Data to Identify Copy Number Variants Contributing to Common Complex Diseases