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Accuracy of CNV Detection from GWAS Data

Overview of attention for article published in PLOS ONE, January 2011
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Title
Accuracy of CNV Detection from GWAS Data
Published in
PLOS ONE, January 2011
DOI 10.1371/journal.pone.0014511
Pubmed ID
Authors

Dandan Zhang, Yudong Qian, Nirmala Akula, Ney Alliey-Rodriguez, Jinsong Tang, Elliot S. Gershon, Chunyu Liu

Abstract

Several computer programs are available for detecting copy number variants (CNVs) using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites--Birdsuite, Partek, HelixTree, and PennCNV-Affy--in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH) followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS) data set (1001 bipolar cases and 1033 controls, all of European ancestry) as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR), while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two "gold standards," the sequence data of Kidd et al., and aCGH data of Conrad et al. Algorithms for calling CNVs especially common ones need substantial improvement, and a "gold standard" for detection of CNVs remains to be established.

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Mendeley readers

The data shown below were compiled from readership statistics for 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 1%
Canada 2 1%
France 2 1%
United States 2 1%
Italy 1 <1%
Brazil 1 <1%
United Kingdom 1 <1%
Turkey 1 <1%
Singapore 1 <1%
Other 1 <1%
Unknown 131 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 28%
Researcher 36 25%
Student > Master 18 12%
Professor > Associate Professor 9 6%
Student > Bachelor 7 5%
Other 23 16%
Unknown 11 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 68 47%
Medicine and Dentistry 23 16%
Biochemistry, Genetics and Molecular Biology 20 14%
Computer Science 6 4%
Mathematics 4 3%
Other 10 7%
Unknown 14 10%