Introduction
Human genome variation has facilitated the understanding of inter-individual phenotypic differences [1, 2]. In addition to single-nucleotide polymorphisms (SNPs), it is widely accepted that large-scale DNA structural variation, termed copy number variation (CNV), is a major component of human genetic diversity [2, 3]. Genomewide SNP genotyping data can be used for CNV calling [4]; therefore, if we can get reliable CNV calls from the SNP genotyping data, a CNV-based genomewide association study (GWAS) can be realized. Among many whole-genome CNV analysis platforms, SNP arrays have been suggested as a resource for CNV discovery due to their ubiquitous genome coverage and relatively advantageous resolution. However, despite the importance of CNV-disease association analysis, CNV calling from SNP genotyping data has not been well established. Affymetrix Genomewide SNP 5.0 is one of the commonly used SNP array platforms for SNP-GWAS as well as CNV analysis [5]. We previously validated the accuracy and reproducibility of CNVs called from Affymetrix SNP array 5.0 data by comparing the CNV calls from 3 different array platforms using NEXUS software: Affymetrix SNP array 5.0, Agilent 2X244K CNV array, and NimbleGen 2.1M CNV array [6].
Recently, a number of CNV defining algorithms have been developed, which have facilitated the CNV-based GWAS [7-14]. However, due to the fundamental limitation of SNP genotyping data for the measurement of signal intensity, there are still concerns regarding the possibility of false discovery or low sensitivity for detecting CNVs [15, 16]. Indeed, CNV calling is dependent on the types of array platforms and analytic tools. Each platform and calling algorithm has its own advantages and disadvantages; so, one single algorithm or array platform is not always best for determination of CNVs [17-19]. Recently Pinto et al. [20] showed that different analytic tools applied to the same raw data typically yielded CNV calls with <50% concordance and, using multiple algorithms, minimize the number of false discoveries. To remedy the potential limitations of SNP array for CNV detection, more than one way of CNV calling by using several different segmentation algorithms are performed, and overlapped calls are used for GWAS analysis [21-24].
In this study, we tried to verify the effect of adopting multiple CNV calling algorithms and set up the most reliable pipeline for CNV calling with Affymetrix Genomewide SNP 5.0 data. We selected the 3 most commonly used algorithms for CNV segmentation from SNP genotyping data, PennCNV, QuantiSNP, and BirdSuite. After defining the CNV loci using the 3 different algorithms, we assessed how many of them overlapped with each other, and we also validated the CNVs by genomic quantitative PCR (qPCR). Finally we concluded that CNVs that were consistently called from more than 2 different calling algorithms are more reliable than the CNVs called from a single algorithm. Our result will be helpful to set up the CNV analysis protocols for Affymetrix Genomewide SNP 5.0 genotyping data.