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Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization

John C Marioni12, Natalie P Thorne12, Armand Valsesia3, Tomas Fitzgerald3, Richard Redon3, Heike Fiegler3, T Daniel Andrews3, Barbara E Stranger3, Andrew G Lynch2, Emmanouil T Dermitzakis3, Nigel P Carter3, Simon Tavaré12 and Matthew E Hurles3*

Author Affiliations

1 Computational Biology Group, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, UK

2 Computational Biology Group, Department of Oncology, University of Cambridge, Cancer Research UK Cambridge Research Institute, Robinson Way, Cambridge CB2 0RE, UK

3 The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK

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Genome Biology 2007, 8:R228  doi:10.1186/gb-2007-8-10-r228

Published: 25 October 2007



Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined.


We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses.


Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.