Identification and validation of copy number variants using SNP genotyping arrays from a large clinical cohort.

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Serval ID
serval:BIB_B4A4552A9706
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Identification and validation of copy number variants using SNP genotyping arrays from a large clinical cohort.
Journal
Bmc Genomics
Author(s)
Valsesia A., Stevenson B.J., Waterworth D., Mooser V., Vollenweider P., Waeber G., Jongeneel C.V., Beckmann J.S., Kutalik Z., Bergmann S.
ISSN
1471-2164 (Electronic)
ISSN-L
1471-2164
Publication state
Published
Issued date
2012
Peer-reviewed
Oui
Volume
13
Number
241
Pages
241
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Abstract
BACKGROUND: Genotypes obtained with commercial SNP arrays have been extensively used in many large case-control or population-based cohorts for SNP-based genome-wide association studies for a multitude of traits. Yet, these genotypes capture only a small fraction of the variance of the studied traits. Genomic structural variants (GSV) such as Copy Number Variation (CNV) may account for part of the missing heritability, but their comprehensive detection requires either next-generation arrays or sequencing. Sophisticated algorithms that infer CNVs by combining the intensities from SNP-probes for the two alleles can already be used to extract a partial view of such GSV from existing data sets.
RESULTS: Here we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs.
CONCLUSION: Our new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits.
Pubmed
Web of science
Open Access
Yes
Create date
27/06/2012 15:41
Last modification date
20/08/2019 15:23
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