PAGE 13 OF 14 technique that requires an initial few samples to test multiple parameter settings in order to ﬁnd the optimal parameters. On the other hand, this strategy should be able to ensure that the optimal parameter setting deter- mined in the training dataset is also optimal for the full dataset. The method used in this study accomplishes both goals. In our real data example, 10 training samples were suﬃcient to ﬁnd the same optimal parameters as those found for the analysis dataset of 100 samples. As a sec- ondary conﬁrmation of optimal parameters on a training dataset, the ROC curve residual can be used with the NSR to determine if the two metrics agree. Overall, the NSR and the ROC curve residuals can provide good guidance for tuning parameters to the optimal level. Although blood-derived DNA is considered the best choice for genotyping, not all studies can obtain blood samples, particularly for studies involving child subjects. Our analysis of DNA types on the pilot dataset concluded that buccal, saliva and blood DNA samples were compa- rable in CNV detection thus, these DNA sources are adequate for CNV detection. However, this conclusion may be limited to this set of small pilot samples that were selected because of known high genotype call rates. The only DNA type that showed poor performance for CNV detection by all methods was the ampliﬁed buccal DNA, as has been seen in previous studies (17,18). All methods detected too many CNV SNPs in the ampliﬁed buccal DNA compared to other DNA types ( 10 times as many), even though genotype call rates were still above 98%. Therefore, ampliﬁed buccal DNA was determined to be unreliable for CNV detection, possibly due to the large variation in the log R distribution of these samples (data not shown). This comparison is limited in that: more parameters could be tested, especially less conservative parameters than were found to be optimal; marker density of the one million marker array will provide more accurate CNV detection and may alter relative outcomes among the methods; and there are many new methods not tested in this study. However, many more parameters were run than are shown here, and optimal parameters were often the least conservative parameter available to the method. Also, increased marker density is not likely to change the core characteristics of the methods found in this study or to radically change the rankings of the methods tested. For example, weakness in detecting CNVs with few SNPs and in boundary calling conditions should not change with an increase in marker density. Finally, this study can be used as a template for easy future studies of the many new CNV detection methods that are available now and in the future. In summary, the utility of determining CNVs from SNP arrays usually occurs in two steps: (i) determine the genome location, and number and size of the CNVs and (ii) relate these CNVs with phenotypes of interest using methods such as association analyses. The detection of CNVs plays an important role in the ﬁnal conclusion of the study. Among the seven methods evaluated in this report, we conclude that the QuantiSNP program outperformed the other methods. We also presented the limitations of each method in terms of failure of CNV Nucleic Acids Research, 2010, Vol. 38, No. 9 e105 detection. The NSR can be used as a valid evaluation parameter of method quality in CNV detection methods. Use of the NSR and a training dataset as demonstrated herein serves as a valid template for determining optimal parameter setting for the method of choice prior to the full data analysis. Web resources Database of Genomic Variants, http://projects.tcag .ca/variation/. Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/. Redon databases, http://www.nature.com/nature/ journal/v444/n7118/full/nature05329.html. SUPPLEMENTARY DATA Supplementary Data are available at NAR Online. ACKNOWLEDGEMENTS The authors thank all the individuals and families who participated in the SCORM study. They also thank the clinical staﬀ for helping with recruitment and the labora- tory staﬀ at the Genome Institute of Singapore for per- forming the genotyping. They thank Dr Ioannis Ragoussis for allowing us trial use of the QuantiSNP program. They also thank Dr Elizabeth Hauser for her valuable sugges- tions to improve this manuscript. FUNDING US National Institute of Health/National Eye Institute (1R21-EY-019086-01 to Y.-J.L.); Singapore BioMedical Research Council 06/1/21/19/466 to S.-M.S.; Singapore Tissue Network. Funding for open access charge: US National Institute of Health/National Eye Institute (1R21-EY-019086-01 to Y.-J.L). 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