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Microarray-based maps of copy-number variant regions in European and sub-saharan populations
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 1095034
Author(s) Vogler, Christian; Gschwind, Leo; Röthlisberger, Benno; Huber, Andreas; Filges, Isabel; Miny, Peter; Auschra, Bianca; Stetak, Attila; Demougin, Philippe; Vukojevic, Vanja; Kolassa, Iris-Tatjana; Elbert, Thomas; de Quervain, Dominique J.-F.; Papassotiropoulos, Andreas
Author(s) at UniBasel Papassotiropoulos, Andreas
Vogler, Christian
Gschwind, Leo
Filges, Isabel
Miny, Peter
Auschra, Bianca
Stetak, Attila
Demougin, Philippe
de Quervain, Dominique
Year 2010
Title Microarray-based maps of copy-number variant regions in European and sub-saharan populations
Journal PLoS ONE
Volume 5
Number 12
Pages / Article-Number e15246
Mesh terms African Continental Ancestry Group, genetics; Algorithms; Cluster Analysis; European Continental Ancestry Group, genetics; Gene Dosage; Genetic Variation; Genome, Human; Genotype; Humans; Models, Genetic; Oligonucleotide Array Sequence Analysis; Polymorphism, Single Nucleotide; Rwanda; Sensitivity and Specificity; Switzerland
Abstract The genetic basis of phenotypic variation can be partially explained by the presence of copy-number variations (CNVs). Currently available methods for CNV assessment include high-density single-nucleotide polymorphism (SNP) microarrays that have become an indispensable tool in genome-wide association studies (GWAS). However, insufficient concordance rates between different CNV assessment methods call for cautious interpretation of results from CNV-based genetic association studies. Here we provide a cross-population, microarray-based map of copy-number variant regions (CNVRs) to enable reliable interpretation of CNV association findings. We used the Affymetrix Genome-Wide Human SNP Array 6.0 to scan the genomes of 1167 individuals from two ethnically distinct populations (Europe, N = 717; Rwanda, N = 450). Three different CNV-finding algorithms were tested and compared for sensitivity, specificity, and feasibility. Two algorithms were subsequently used to construct CNVR maps, which were also validated by processing subsamples with additional microarray platforms (Illumina 1M-Duo BeadChip, Nimblegen 385K aCGH array) and by comparing our data with publicly available information. Both algorithms detected a total of 42669 CNVs, 74% of which clustered in 385 CNVRs of a cross-population map. These CNVRs overlap with 862 annotated genes and account for approximately 3.3% of the haploid human genome.We created comprehensive cross-populational CNVR-maps. They represent an extendable framework that can leverage the detection of common CNVs and additionally assist in interpreting CNV-based association studies.
Publisher Public Library of Science
ISSN/ISBN 1932-6203
edoc-URL http://edoc.unibas.ch/dok/A5841735
Full Text on edoc No
Digital Object Identifier DOI 10.1371/journal.pone.0015246
PubMed ID http://www.ncbi.nlm.nih.gov/pubmed/21179565
ISI-Number WOS:000285381200019
Document type (ISI) Article
 
   

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19/03/2024