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Forer, L.* ; Schönherr, S.* ; Weissensteiner, H.* ; Haider, F.* ; Kluckner, T.* ; Gieger, C. ; Wichmann, H.-E. ; Specht, G.* ; Kronenberg, F.* ; Kloss-Brandstätter, A.*

CONAN: Copy number variation analysis software for genome-wide association studies.

BMC Bioinformatics 11:318 (2010)
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Open Access Gold
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BACKGROUND: Genome-wide association studies (GWAS) based on single nucleotide polymorphisms (SNPs) revolutionized our perception of the genetic regulation of complex traits and diseases. Copy number variations (CNVs) promise to shed additional light on the genetic basis of monogenic as well as complex diseases and phenotypes. Indeed, the number of detected associations between CNVs and certain phenotypes are constantly increasing. However, while several software packages support the determination of CNVs from SNP chip data, the downstream statistical inference of CNV-phenotype associations is still subject to complicated and inefficient in-house solutions, thus strongly limiting the performance of GWAS based on CNVs. RESULTS: CONAN is a freely available client-server software solution which provides an intuitive graphical user interface for categorizing, analyzing and associating CNVs with phenotypes. Moreover, CONAN assists the evaluation process by visualizing detected associations via Manhattan plots in order to enable a rapid identification of genome-wide significant CNV regions. Various file formats including the information on CNVs in population samples are supported as input data. CONCLUSIONS: CONAN facilitates the performance of GWAS based on CNVs and the visual analysis of calculated results. CONAN provides a rapid, valid and straightforward software solution to identify genetic variation underlying the 'missing' heritability for complex traits that remains unexplained by recent GWAS. The freely available software can be downloaded at http://genepi-conan.i-med.ac.at.
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Publication type Article: Journal article
Document type Scientific Article
Keywords HIDDEN-MARKOV MODEL; SNP GENOTYPING DATA; POPULATION; ECOMPAGT
ISSN (print) / ISBN 1471-2105
e-ISSN 1471-2105
Quellenangaben Volume: 11, Issue: , Pages: , Article Number: 318 Supplement: ,
Publisher BioMed Central
Reviewing status Peer reviewed