Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays.
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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 differences 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.
DNA Copy Number Variations
Oligonucleotide Array Sequence Analysis
Polymorphism, Single Nucleotide
Published Version (Please cite this version)10.1093/nar/gkq040
Publication InfoDellinger, Andrew E; Saw, Seang-Mei; Goh, Liang K; Seielstad, Mark; Young, Terri L; & Li, Yi-Ju (2010). Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays. Nucleic Acids Res, 38(9). pp. e105. 10.1093/nar/gkq040. Retrieved from https://hdl.handle.net/10161/10630.
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Professor of Biostatistics and Bioinformatics
My research interest is in statistical genetics, primarily in statistical method development and its application for genetic research of human complex diseases. Here is the list of research topics: Statistical genetics: I have involved in the development of several family-based association methods for quantitative traits with or without censoring, and for detecting X-linked genes. With the availability of next generation sequencing data, we have ongoing projects to develop
Adjunct Professor in Ophthalmology
Terri L. Young, M.D., M.B.A. is a board-certified clinician-scientist ophthalmologist. She is a Professor of Ophthalmology, Pediatrics, and Medicine at Duke University School of Medicine. She is a Professor of Neuroscience at the Duke- National University of Singapore Graduate Medical School, with adjunct appointments at the Singapore Eye Research Institute and the Saw Swee Hock School of Public Health, National University of Singapore. She is the founding Director of the Duke
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