Nucleic Acids Research Advance Access originally published online on September 10, 2008
Nucleic Acids Research 2008 36(19):e126; doi:10.1093/nar/gkn556
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Nucleic Acids Research, 2008, Vol. 36, No. 19 e126
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms
1Center for Childhood Cancer Research, Children's Hospital of Philadelphia, 2Genomics and Computational Biology, University of Pennsylvania, 3Department of Pediatrics, University of Pennsylvania, 4Department of Biostatistics and Epidemiology, University of Pennsylvania, 5Department of Genetics, University of Pennsylvania and 6Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
*To whom correspondence should be addressed. Tel: +1 215 590 5244. Email: maris{at}chop.edu
Correspondence may also be addressed to Kai Wang. Tel: +1 267 426 2378; Email: wangk{at}chop.edu
Received April 19, 2008. Revised August 13, 2008. Accepted August 18, 2008.
Whole-genome microarrays with large-insert clones designed to determine DNA copy number often show variation in hybridization intensity that is related to the genomic position of the clones. We found these ‘genomic waves’ to be present in Illumina and Affymetrix SNP genotyping arrays, confirming that they are not platform-specific. The causes of genomic waves are not well-understood, and they may prevent accurate inference of copy number variations (CNVs). By measuring DNA concentration for 1444 samples and by genotyping the same sample multiple times with varying DNA quantity, we demonstrated that DNA quantity correlates with the magnitude of waves. We further showed that wavy signal patterns correlate best with GC content, among multiple genomic features considered. To measure the magnitude of waves, we proposed a GC-wave factor (GCWF) measure, which is a reliable predictor of DNA quantity (correlation coefficient = 0.994 based on samples with serial dilution). Finally, we developed a computational approach by fitting regression models with GC content included as a predictor variable, and we show that this approach improves the accuracy of CNV detection. With the wide application of whole-genome SNP genotyping techniques, our wave adjustment method will be important for taking full advantage of genotyped samples for CNV analysis.
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