A novel scatterplot-based method to detect copy number variation (CNV)
(2023) In Frontiers in Genetics 14.- Abstract
Objective: Most methods to detect copy number variation (CNV) have high false positive rates, especially for small CNVs and in real-life samples from clinical studies. In this study, we explored a novel scatterplot-based method to detect CNVs in microarray samples. Methods: Illumina SNP microarray data from 13,254 individuals were analyzed with scatterplots and by PennCNV. The data were analyzed without the prior exclusion of low-quality samples. For CNV scatterplot visualization, the median signal intensity of all SNPs located within a CNV region was plotted against the median signal intensity of the flanking genomic region. Since CNV causes loss or gain of signal intensities, carriers of different CNV alleles pop up in clusters.... (More)
Objective: Most methods to detect copy number variation (CNV) have high false positive rates, especially for small CNVs and in real-life samples from clinical studies. In this study, we explored a novel scatterplot-based method to detect CNVs in microarray samples. Methods: Illumina SNP microarray data from 13,254 individuals were analyzed with scatterplots and by PennCNV. The data were analyzed without the prior exclusion of low-quality samples. For CNV scatterplot visualization, the median signal intensity of all SNPs located within a CNV region was plotted against the median signal intensity of the flanking genomic region. Since CNV causes loss or gain of signal intensities, carriers of different CNV alleles pop up in clusters. Moreover, SNPs within a deletion are not heterozygous, whereas heterozygous SNPs within a duplication show typical 1:2 signal distribution between the alleles. Scatterplot-based CNV calls were compared with standard results of PennCNV analysis. All discordant calls as well as a random selection of 100 concordant calls were individually analyzed by visual inspection after noise-reduction. Results: An algorithm for the automated scatterplot visualization of CNVs was developed and used to analyze six known CNV regions. Use of scatterplots and PennCNV yielded 1019 concordant and 108 discordant CNV calls. All concordant calls were evaluated as true CNV-findings. Among the 108 discordant calls, 7 were false positive findings by the scatterplot method, 80 were PennCNV false positives, and 21 were true CNVs detected by the scatterplot method, but missed by PennCNV (i.e., false negative findings). Conclusion: CNV visualization by scatterplots allows for a reliable and rapid detection of CNVs in large studies. This novel method may thus be used both to confirm the results of genome-wide CNV detection software and to identify known CNVs in hitherto untyped samples.
(Less)
- author
- author collaboration
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- copy number variation (CNV), filtering, pennCNV, quality control, scatterplot
- in
- Frontiers in Genetics
- volume
- 14
- article number
- 1166972
- publisher
- Frontiers Media S. A.
- external identifiers
-
- pmid:37485343
- scopus:85165391974
- ISSN
- 1664-8021
- DOI
- 10.3389/fgene.2023.1166972
- language
- English
- LU publication?
- yes
- id
- 7032f41e-c1c9-4f02-b2cc-421f943aac17
- date added to LUP
- 2023-09-19 15:03:08
- date last changed
- 2024-04-19 01:26:41
@article{7032f41e-c1c9-4f02-b2cc-421f943aac17,
abstract = {{<p>Objective: Most methods to detect copy number variation (CNV) have high false positive rates, especially for small CNVs and in real-life samples from clinical studies. In this study, we explored a novel scatterplot-based method to detect CNVs in microarray samples. Methods: Illumina SNP microarray data from 13,254 individuals were analyzed with scatterplots and by PennCNV. The data were analyzed without the prior exclusion of low-quality samples. For CNV scatterplot visualization, the median signal intensity of all SNPs located within a CNV region was plotted against the median signal intensity of the flanking genomic region. Since CNV causes loss or gain of signal intensities, carriers of different CNV alleles pop up in clusters. Moreover, SNPs within a deletion are not heterozygous, whereas heterozygous SNPs within a duplication show typical 1:2 signal distribution between the alleles. Scatterplot-based CNV calls were compared with standard results of PennCNV analysis. All discordant calls as well as a random selection of 100 concordant calls were individually analyzed by visual inspection after noise-reduction. Results: An algorithm for the automated scatterplot visualization of CNVs was developed and used to analyze six known CNV regions. Use of scatterplots and PennCNV yielded 1019 concordant and 108 discordant CNV calls. All concordant calls were evaluated as true CNV-findings. Among the 108 discordant calls, 7 were false positive findings by the scatterplot method, 80 were PennCNV false positives, and 21 were true CNVs detected by the scatterplot method, but missed by PennCNV (i.e., false negative findings). Conclusion: CNV visualization by scatterplots allows for a reliable and rapid detection of CNVs in large studies. This novel method may thus be used both to confirm the results of genome-wide CNV detection software and to identify known CNVs in hitherto untyped samples.</p>}},
author = {{Qiao, Jia Lu and Levinson, Rebecca T. and Chen, Bowang and Engelter, Stefan T. and Erhart, Philipp and Gaynor, Brady J. and McArdle, Patrick F. and Schlicht, Kristina and Krawczak, Michael and Stenman, Martin and Lindgren, Arne G. and Cole, John W. and Grond-Ginsbach, Caspar}},
issn = {{1664-8021}},
keywords = {{copy number variation (CNV); filtering; pennCNV; quality control; scatterplot}},
language = {{eng}},
publisher = {{Frontiers Media S. A.}},
series = {{Frontiers in Genetics}},
title = {{A novel scatterplot-based method to detect copy number variation (CNV)}},
url = {{http://dx.doi.org/10.3389/fgene.2023.1166972}},
doi = {{10.3389/fgene.2023.1166972}},
volume = {{14}},
year = {{2023}},
}