A fast and accurate method to detect allelic genomic imbalances underlying mosaic rearrangements using SNP array data
1 Center for Research in Environmental Epidemiology (CREAL), Doctor Aiguader 88, Barcelona 08003, Spain
2 Institut Municipal d'Investigació Mèdica (IMIM), Doctor Aiguader 88, Barcelona 08003, Spain
3 CIBER Epidemiología y Salud Pública (CIBERESP), Spain
4 Dept. de Ciències Experimentals i de la Salut, UPF, Barcelona 08003, Spain
5 CIBER de Enfermedades Raras, CIBERER, Spain
6 Department of Human Genetics, University of Chicago, IL 60637, USA
7 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20852-4907, USA
8 Core Genotyping Facility, SAIC-Frederick, Frederick, MD 21702, USA
9 Quantitative Genomic Medicine Laboratories, Ltd (qGenomics), Doctor Aiguader 88, Barcelona 08003, Spain
BMC Bioinformatics 2011, 12:166 doi:10.1186/1471-2105-12-166Published: 17 May 2011
Mosaicism for copy number and copy neutral chromosomal rearrangements has been recently identified as a relatively common source of genetic variation in the normal population. However its prevalence is poorly defined since it has been only studied systematically in one large-scale study and by using non optimal ad-hoc SNP array data analysis tools, uncovering rather large alterations (> 1 Mb) and affecting a high proportion of cells. Here we propose a novel methodology, Mosaic Alteration Detection-MAD, by providing a software tool that is effective for capturing previously described alterations as wells as new variants that are smaller in size and/or affecting a low percentage of cells.
The developed method identified all previously known mosaic abnormalities reported in SNP array data obtained from controls, bladder cancer and HapMap individuals. In addition MAD tool was able to detect new mosaic variants not reported before that were smaller in size and with lower percentage of cells affected. The performance of the tool was analysed by studying simulated data for different scenarios. Our method showed high sensitivity and specificity for all assessed scenarios.
The tool presented here has the ability to identify mosaic abnormalities with high sensitivity and specificity. Our results confirm the lack of sensitivity of former methods by identifying new mosaic variants not reported in previously utilised datasets. Our work suggests that the prevalence of mosaic alterations could be higher than initially thought. The use of appropriate SNP array data analysis methods would help in defining the human genome mosaic map.