PAGE 3 OF 14 developed for Illumina and Aﬀymetrix SNP arrays. CNVFinder was also developed for Aﬀymetrix SNP arrays. cnvPartition and QuantiSNP were developed for Illumina SNP arrays. Optimization of method parameters We propose using the NSR as a summary metric for determining the optimal parameter setting for each method. The NSR is deﬁned as NSR ¼ pu cs where pu is the proportion of unique CNV SNPs (i.e. the proportion of the number of CNV SNPs found in only one sample of the dataset), and mCS is the average number of CNV SNPs called per sample. The NSR hypothesizes that CNV SNPs called in only one sample are more likely to be false positives than CNV SNPs that are called in multiple samples, thus the smaller the NSR, the better the method. Since types of parameters vary among CNV detection methods, the parameters adjusted in this optimization study included the following: SD for CBS, SDe for CNVFinder, conﬁdence for cnvPartition, smoothed segment log R ratio for GLAD, segment thresh- old for Nexus, conﬁdence and CNV length in SNPs for PennCNV and log Bayes Ratio and CNV length in SNPs for QuantiSNP. Comparative statistics The SNP intensity data (log R ratios) were obtained from the Illumina BeadStudio 3.1 program. To compare the characteristics and quality of these CNV detection methods, we used three reference datasets from public databases: CNVs from SNP studies in the Database of Genomic Variants (DGV; http://projects.tcag.ca/ variation) (3) (7950 CNVs; 99 645 CNV SNPs); the set of HapMap CNVs in Asian populations reported by Redon et al. (11) (5753 CNVs; 25 799 CNV SNPs) and the subset of all experimentally conﬁrmed CNVs from Redon et al. (11) (275 CNVs; 4984 CNV SNPs). For each CNV detection method, the CNV results for the tested dataset (e.g. SCORM dataset) were compared to the reference dataset for each sample. Assuming that all CNVs in the reference datasets are true, the sensitivity, speciﬁcity and kappa statistics were computed from the CNV results for all datasets. Since some CNV detection methods such as Nexus cannot correctly handle CNVs in chromosomes X or Y, these chromosomes and the mitochondrial genome were excluded from the analyses. Accounting for CNV SNPs and CNVs detected in multiple methods The performance of the seven CNV detection methods was evaluated at both SNP and CNV levels. The CBS, cnvPartition, Nexus Rank and SNPRank, PennCNV and QuantiSNP programs produced CNV boundaries directly, providing the start and end base pair map loca- tions for the CNV detected. The CNVFinder and GLAD Nucleic Acids Research, 2010, Vol. 38, No. 9 e105 programs directly designated the gain or loss of informa- tion of each CNV SNP rather than indicating CNV boundaries. For these latter two methods, we deﬁned a CNV as three or more consecutive CNV SNP calls and excluded regions with one or two CNV SNP calls from the analysis. Correlation between chips We computed pair-wise Pearson correlations among a set of 20 samples, in which 10 samples were randomly chosen from each BeadArray platform to evaluate sample quality and eﬀects caused by diﬀerences between the HumanHap 500 and 500 Duo arrays (Supplementary Table S1). Pearson correlation tests were also conducted on all emmetropic individuals in the SCORM dataset, and samples with pair-wise correlation values <0.75 with more than ﬁve other samples were excluded in the three SCORM subsets presented here. Receiver operating characteristic curve residuals Methods at optimal parameters were compared by using both ROC curves and ROC curve residuals, the distances from a point on the ROC curve to the diagonal line of y = x for each method. The residuals are calculated using the equation: pﬃ2ﬃðsensitivity ð 1 specificityÞÞ : 2 The largest ROC curve residuals are optimal. Simulated datasets Simulated CNV data were generated to compare the power, false positive rates and boundary calling properties of the CNV detection methods. cnvPartition data were unable to be simulated because of the limitations of data input into BeadStudio. We compared the parameter settings based on the optimal ROC curve residual and on the optimal NSR. Exactly matched boundary calling refers to the CNV region reported by the detection method having the same starting and ending SNPs as the one predesignated in the simulation. For the simulations mimicking Illumina arrays, we directly used the log R ratio data in the SCORM sample to generate the log R ratio of non-CNV SNPs rather than generating from a given distribution. Among samples in the training and analysis datasets, we chose one sample with mean log R ratio of 0.01 and SD of 0.15, which is the average log R ratio and SD of total samples of these two datasets. We randomly sampled a log R ratio from this sample to assign to the non-CNV SNPs in the simulated datasets. Two sets of Illumina simulations were conducted to generate the log R ratio of the CNV SNP. In simulation 1, we assigned CNVs to the positions shown in Figure 1, mimicking chromosome 1. The CNV at each position was assigned a speciﬁc copy number (0, 1, 3 or 4) and size (10, 20 or 30 SNPs). The log R ratio of each CNV SNP was equal to a random number drawn within the interval designated for each CNV: 2 ± SD for 0 copy CNVs,