Effect of Combining Multiple CNV Defining Algorithms on the Reliability of CNV Calls from SNP Genotyping Data
In addition to single-nucleotide polymorphisms (SNP), copy number variation (CNV) is a major component of human genetic diversity. Among many whole-genome analysis platforms, SNP arrays have been commonly used for genomewide CNV discovery. Recently, a number of CNV defining algorithms from SNP genot...
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BioMed Central
2012-09-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gni-10-194.pdf |
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| author | Soon-Young Kim Ji-Hong Kim Yeun-Jun Chung |
| author_facet | Soon-Young Kim Ji-Hong Kim Yeun-Jun Chung |
| author_sort | Soon-Young Kim |
| collection | DOAJ |
| description | In addition to single-nucleotide polymorphisms (SNP), copy number variation (CNV) is a major component of human genetic diversity. Among many whole-genome analysis platforms, SNP arrays have been commonly used for genomewide CNV discovery. Recently, a number of CNV defining algorithms from SNP genotyping data have been developed; however, due to the fundamental limitation of SNP genotyping data for the measurement of signal intensity, there are still concerns regarding the possibility of false discovery or low sensitivity for detecting CNVs. In this study, we aimed to verify the effect of combining multiple CNV calling algorithms and set up the most reliable pipeline for CNV calling with Affymetrix Genomewide SNP 5.0 data. For this purpose, we selected the 3 most commonly used algorithms for CNV segmentation from SNP genotyping data, PennCNV, QuantiSNP; and BirdSuite. After defining the CNV loci using the 3 different algorithms, we assessed how many of them overlapped with each other, and we also validated the CNVs by genomic quantitative PCR. Through this analysis, we proposed that for reliable CNV-based genomewide association study using SNP array data, CNV calls must be performed with at least 3 different algorithms and that the CNVs consistently called from more than 2 algorithms must be used for association analysis, because they are more reliable than the CNVs called from a single algorithm. Our result will be helpful to set up the CNV analysis protocols for Affymetrix Genomewide SNP 5.0 genotyping data. |
| format | Article |
| id | doaj-art-e2117c5dcd604e18a50a40d2de7cbc0a |
| institution | Kabale University |
| issn | 1598-866X 2234-0742 |
| language | English |
| publishDate | 2012-09-01 |
| publisher | BioMed Central |
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| spelling | doaj-art-e2117c5dcd604e18a50a40d2de7cbc0a2025-02-02T02:21:56ZengBioMed CentralGenomics & Informatics1598-866X2234-07422012-09-0110319419910.5808/GI.2012.10.3.19414Effect of Combining Multiple CNV Defining Algorithms on the Reliability of CNV Calls from SNP Genotyping DataSoon-Young Kim0Ji-Hong Kim1Yeun-Jun Chung2Integrated Research Center for Genome Polymorphism, The Catholic University of Korea School of Medicine, Seoul 137-701, Korea.Integrated Research Center for Genome Polymorphism, The Catholic University of Korea School of Medicine, Seoul 137-701, Korea.Integrated Research Center for Genome Polymorphism, The Catholic University of Korea School of Medicine, Seoul 137-701, Korea.In addition to single-nucleotide polymorphisms (SNP), copy number variation (CNV) is a major component of human genetic diversity. Among many whole-genome analysis platforms, SNP arrays have been commonly used for genomewide CNV discovery. Recently, a number of CNV defining algorithms from SNP genotyping data have been developed; however, due to the fundamental limitation of SNP genotyping data for the measurement of signal intensity, there are still concerns regarding the possibility of false discovery or low sensitivity for detecting CNVs. In this study, we aimed to verify the effect of combining multiple CNV calling algorithms and set up the most reliable pipeline for CNV calling with Affymetrix Genomewide SNP 5.0 data. For this purpose, we selected the 3 most commonly used algorithms for CNV segmentation from SNP genotyping data, PennCNV, QuantiSNP; and BirdSuite. After defining the CNV loci using the 3 different algorithms, we assessed how many of them overlapped with each other, and we also validated the CNVs by genomic quantitative PCR. Through this analysis, we proposed that for reliable CNV-based genomewide association study using SNP array data, CNV calls must be performed with at least 3 different algorithms and that the CNVs consistently called from more than 2 algorithms must be used for association analysis, because they are more reliable than the CNVs called from a single algorithm. Our result will be helpful to set up the CNV analysis protocols for Affymetrix Genomewide SNP 5.0 genotyping data.http://genominfo.org/upload/pdf/gni-10-194.pdfCNV defining algorithmDNA copy number variationsSNP array |
| spellingShingle | Soon-Young Kim Ji-Hong Kim Yeun-Jun Chung Effect of Combining Multiple CNV Defining Algorithms on the Reliability of CNV Calls from SNP Genotyping Data Genomics & Informatics CNV defining algorithm DNA copy number variations SNP array |
| title | Effect of Combining Multiple CNV Defining Algorithms on the Reliability of CNV Calls from SNP Genotyping Data |
| title_full | Effect of Combining Multiple CNV Defining Algorithms on the Reliability of CNV Calls from SNP Genotyping Data |
| title_fullStr | Effect of Combining Multiple CNV Defining Algorithms on the Reliability of CNV Calls from SNP Genotyping Data |
| title_full_unstemmed | Effect of Combining Multiple CNV Defining Algorithms on the Reliability of CNV Calls from SNP Genotyping Data |
| title_short | Effect of Combining Multiple CNV Defining Algorithms on the Reliability of CNV Calls from SNP Genotyping Data |
| title_sort | effect of combining multiple cnv defining algorithms on the reliability of cnv calls from snp genotyping data |
| topic | CNV defining algorithm DNA copy number variations SNP array |
| url | http://genominfo.org/upload/pdf/gni-10-194.pdf |
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