e105 Nucleic Acids Research, 2010, Vol. 38, No. 9 Lai et al. (10) tested 11 methods using receiver operating characteristic (ROC) curves and found that segmentation algorithms performed consistently well. However, a few nonsegmentation methods proposed recently such as the QuantiSNP (5) and PennCNV (6) programs were not included. Furthermore, although utilization of high- density SNP arrays to infer CNVs is increasing, the appli- cation of these methods on real data is in its infancy. Many practical considerations need further exploration, such as the determination of optimal parameters for each method, parameter setting impact on CNV detection and CNV size, and method adjustments with various CNV sizes, signal levels and signal variations. In this study, we compared seven frequently used CNV detection methods: circular binary segmentation (CBS) (8), CNVFinder (9), cnvPartition, gain and loss of DNA (GLAD) (7), Nexus segmentation methods Rank and SNPRank, PennCNV (6) and QuantiSNP (5) for the fol- lowing aspects: (i) optimal parameter settings for each method; (ii) sensitivity, speciﬁcity, power and false positive rates of each calling algorithm and (iii) conditions where a method failed to call correct boundaries and where a method detected diﬀerent CNV sizes. MATERIALS AND METHODS Datasets We used both genome wide SNP arrays and simulated data (described later) to evaluate the performance of these CNV detection methods. The SNP array data were obtained from the GWA study of the Singapore cohort study of the risk factors for Myopia (SCORM). SCORM is a longitudinal cohort designed to evaluate the environ- mental and genetic risk factors for myopia in Singapore Chinese schoolchildren. A total of 1979 school children from Grades 1–3 in Singapore were followed up yearly by ophthalmologists and optometrists, who measured refractive error, keratometry, axial length, anterior chamber depth, lens thickness and vitreous chamber depth. Buccal samples were collected from 1875 children (aged 8–12 years), in which 1116 samples from Chinese participants were genotyped using Illumina HumanHap 550 and 550 Duo BeadArrays. The study protocol was approved by the Institutional Review Boards of the National University of Singapore and the Singapore Eye Research Institute. In this study, we analyzed the following three subsets of SNP arrays from the SCORM GWA study: (i) a training dataset of 10 unrelated control samples (ﬁve males and ﬁve females) from the 550 nonduo chips who had no myopia or hyperopia (emmetropic, spherical equivalent between 0.50 and +0.50 diopters in both eyes) and had the highest genotyping quality (call rate 0.98); (ii) a pilot dataset of 16 SNP arrays generated from diﬀerent sources of DNA specimens from ﬁve individuals, all with buccal, whole genome ampliﬁed buccal and saliva-derived DNA samples, in which one individual also had an array genotyped from the blood-derived DNA sample; (iii) an analysis dataset of 100 unrelated emmetropic control samples independent from the training dataset that had PAGE 2 OF 14 high-genotyping quality (call rate 98%), SD of the log R ratio distribution <0.3 and high correlation with at least 95% of the total analysis samples (Pearson corre- lation coeﬃcient >0.75). Throughout this work, SNPs located within the CNV boundaries detected by each detection method are referred as CNV SNPs. We used the training dataset to standardize parameter settings for each method. To conﬁrm the optimal param- eters chosen by the normalized singleton ratio (NSR) described below, two additional 10 sample datasets were constructed from emmetropic individuals: one chosen from 550 duo chips with the same criteria as the training dataset above and one chosen from samples of moderate genotyping quality (rank 51–60 of 112) regardless of gender or chip type. The optimal parameter setting derived from the training dataset was applied to both the pilot and analysis datasets. The pilot dataset was used to compare the performance of each CNV detection method using diﬀerent DNA sources. The analysis dataset was used to draw conclusions for the performance of each method. The outcomes from the SCORM CNV data were then evaluated in a simulated dataset. CNV detection methods The underlying statistical models for the seven CNV detection methods evaluated in this study diﬀer by varying degrees. The primary raw data used for detecting CNVs from SNP arrays are the SNP intensity measured by log R ratios. Some methods also used B allele frequencies to enhance detection. CBS (8), Nexus 4.1 Rank and Nexus 4.1 SNPRank (http://www .biodiscovery.com) use the same segmentation algorithm that recursively divides chromosomes into segments of common intensity distribution functions, but CBS has no inherent method of determining segment signiﬁcance while Nexus uses an unknown equation to compute segment signiﬁcance (Dr Soheil Shams, CSO of Biodiscovery, Developer of Nexus, personal communica- tion). GLAD uses a version of adaptive weights smoothing to build segments by adding neighboring SNPs to the existing set of SNPs in the segment. The QuantiSNP (5) and PennCNV (6) programs use diﬀerent HMMs. The PennCNV program uses the combined log R ratio and B allele frequency, while the QuantiSNP program treats them independently. The PennCNV program generates a hidden state for copy neutral loss of heterozygosity (LOH) and uses each population-based B allele frequency of the SNP to infer CNVs, while the QuantiSNP program uses a ﬁxed rate of heterozygosity for each SNP. The CNVFinder (9) program uses experi- mental variability, termed SDe, in the log R ratio distribu- tion. The cnvPartition program implemented in the Illumina BeadStudio software uses an undocumented method of CNV detection. The experimental platforms for which the CNV detec- tion methods were developed also diﬀer. Several methods were developed for CGH of various types: CBS for BACs and ROMA, GLAD for BACs, PennCNV and Nexus for Agilent and Nimblegen platforms and CNVFinder for whole genome tile path. Nexus and PennCNV were also