Published online 8 February 2010 Nucleic Acids Research, 2010, Vol. 38, No. 9 e105 doi:10.1093/nar/gkq040 Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays Andrew E. Dellinger1, Seang-Mei Saw2, Liang K. Goh 3 , Mark Seielstad 4 , Terri L. Young1,3,5 and Yi-Ju Li1,6,* 1Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA, 2Department of Epidemiology and Public Health, National University of Singapore, 3Duke-NUS Singapore Graduate Medical School, Singapore, 4Genome Institute of Singapore, 138672, Singapore, 5Department of Ophthalmology, Duke Eye Center and 6Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham 27710, USA Received July 17, 2009; Revised January 14, 2010; Accepted January 17, 2010 ABSTRACT Determination of copy number variants (CNVs) inferred in genome wide single nucleotide polymorphism arrays has shown increasing utility in genetic variant disease associations. Several CNV detection methods are available, but differ- ences in CNV call thresholds and characteristics exist. We evaluated the relative performance of seven methods: circular binary segmentation, CNVFinder, cnvPartition, gain and loss of DNA, Nexus algorithms, PennCNV and QuantiSNP. Tested data included real and simulated Illumina HumHap 550 data from the Singapore cohort study of the risk factors for Myopia (SCORM) and simulated data from Affymetrix 6.0 and platform- independent distributions. The normalized singleton ratio (NSR) is proposed as a metric for parameter optimization before enacting full analysis. We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples. The statistical power, false positive rates, and receiver operating characteristic (ROC) curve residuals were evaluated by simulation studies. Optimal parameters, as determined by NSR and ROC curve residuals, were consistent across datasets. QuantiSNP outperformed other methods based on ROC curve residuals over most datasets. Nexus Rank and SNPRank have low specificity and high power. Nexus Rank calls oversized CNVs. PennCNV detects one of the fewest numbers of CNVs. INTRODUCTION Copy number variants (CNVs) are duplications, insertions or deletions of chromosomal segments that are 1 kb (1,2). Multiple experimental techniques can detect CNVs, including bacterial artiﬁcial chromosome (BAC) arrays, paired end mapping, ﬂuorescent in situ hybridiza- tion, representational oligonucleotide microarray analysis (ROMA) and whole genome single nucleotide poly- morphism (SNP) arrays (3). Due to increased use of genome wide association (GWA) studies, SNP arrays with suﬃciently high-density (>300 K SNPs) have become a convenient tool for studying CNVs. Accurate CNV detection in SNP arrays requires sophisticated algorithms or statistical methods. The accuracy of CNV boundaries derived from SNP arrays is inﬂu- enced by multiple factors such as the robustness of the statistical method, batch eﬀects, population stratiﬁcation and diﬀerences between experiments (4; http://www. goldenhelix.com/Downloads/login.html?product=SVS& view=./Events/recordings/wgacnv2008/wgacnv2008.html). Experimental validation is therefore important to conﬁrm the accuracy of CNVs derived from SNP array platforms. To date, several detection methods are available for identifying CNVs from genome-wide SNP array data. Most were initially developed for array comparative genomic hybridization (aCGH) platforms. The statistical models underlying these approaches include hidden Markov models (HMMs) (5,6), segmentation algorithms (7,8), t-tests and standard deviations (SDs) of the log R ratio (9). While these free or commercial programs are available for detecting CNVs from SNP arrays, a thorough comparison of these methods, particularly, the recently developed ones, has not been conducted. The most recent comprehensive survey of the performance of CNV detection methods was performed in 2005, in which *To whom correspondence should be addressed. Tel: +1 919 684 0604; Fax: +1 919 684 0921; Email: yiju.li@duke.edu The Author(s) 2010. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.