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


RESULTS
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.


CONCLUSIONS
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.
Marioni
John C
JC
Computational Biology Group, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, UK. J.Marioni@damtp.cam.ac.uk
Thorne
Natalie P
NP
Valsesia
Armand
A
Fitzgerald
Tomas
T
Redon
Richard
R
Fiegler
Heike
H
Andrews
T Daniel
TD
Stranger
Barbara E
BE
Lynch
Andrew G
AG
Dermitzakis
Emmanouil T
ET
Carter
Nigel P
NP
Tavaré
Simon
S
Hurles
Matthew E
ME
D000465:Algorithms; D003627:Data Interpretation, Statistical; D018628:Gene Dosage / Q000235:genetics*; D014644:Genetic Variation; D046228:Microarray Analysis / Q000379:methods*; D009693:Nucleic Acid Hybridization / Q000235:genetics*