Dataset Information

A fast and accurate method to detect allelic genomic imbalances underlying mosaic rearrangements using SNP array data.

ABSTRACT:

Background

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.

Results

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.

Conclusions

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.

SUBMITTER: Gonzalez JR

PROVIDER: S-EPMC3118168 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A fast and accurate method to detect allelic genomic imbalances underlying mosaic rearrangements using SNP array data.

González Juan R JR Rodríguez-Santiago Benjamín B Cáceres Alejandro A Pique-Regi Roger R Rothman Nathaniel N Chanock Stephen J SJ Armengol Lluís L Pérez-Jurado Luis A LA

BMC bioinformatics 20110517

<h4>Background</h4>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 ...[more]

PMID: 21586113

Similar Datasets

Project description:A SNP microarray and FISH-based procedure to detect allelic imbalances in multiple myeloma: an integrated genomics approach reveals a wide dosage effect on gene and microRNA expression Multiple myeloma (MM) is characterized by marked genomic instability. Beyond structural rearrangements, a relevant role in its biology is represented by allelic imbalances leading to significant variations in ploidy status. To better elucidate the genomic complexity of MM, we analyzed a panel of 45 patients using combined FISH and microarray approaches. Using a self-developed procedure to infer exact local copy numbers for each sample, we identified a significant fraction of patients showing marked aneuploidy. A conventional clustering analysis showed that aneuploidy, chromosome 1 alterations, hyperdiploidy and recursive deletions at 1p and chromosomes 13, 14 and 22 were the main aberrations driving samples grouping. Then, we integrated mapping information with gene and microRNAs expression profiles: a multiclass analysis of the identified clusters showed a marked gene-dosage effect, particularly concerning 1q transcripts, also confirmed by correlating gene expression levels and local copy number alterations. A wide dosage effect affected also microRNAs, indicating that structural abnormalities in MM closely reflect in their expression imbalances. Finally, we identified several loci in which genes and microRNAs expression correlated with loss-of-heterozygosity occurrence. Our results provide insights into the composite network linking genome structure and gene/microRNA transcriptional features in MM. Keywords: Integrated genomics approach based on SNP microarray and FISH procedures to detect allelic imbalances in multiple myeloma. Pathological bone marrow specimens from 41 MM and four plasma cell leukemia (PCL) patients at diagnosis. 250 nanograms of genomic DNA was processed and, in accordance with the manufacturer's protocols, 40 micrograms of fragmented biotin-labelled DNA were hybridized on GeneChip Human Mapping 50K XbaI Arrays (Affymetrix Inc.). The arrays were scanned using the GeneChip Scanner 3000 7G. The images were acquired using Affymetrix GeneChip® Operating Software (GCOS version 1.4). Copy number values for individual SNPs were extracted and converted from CEL files into signal intensities using GTYPE 4.1 and Affymetrix Copy Number Analysis Tool (CNAT 4.0.1) softwares. Genomic Smoothing analysis was performed by using the smoothing window of 0 Mb, and inferred copy number states were derived from a Hidden Markov Model (HMM) based algorithm implemented in CNAT 4.0.1. Circular Binary Segmentation (Ohlsen et al., 2004) was applied using DNAcopy package for R Bioconductor on raw data. FBN procedure was finally applied to infer exact local copy number as described in the mentioned Reference.

Project description:Multiple myeloma (MM) is characterized by marked genomic instability. Beyond structural rearrangements, a relevant role in its biology is represented by allelic imbalances leading to significant variations in ploidy status. To better elucidate the genomic complexity of MM, we analyzed a panel of 45 patients using combined FISH and microarray approaches. Using a self-developed procedure to infer exact local copy numbers for each sample, we identified a significant fraction of patients showing marked aneuploidy. A conventional clustering analysis showed that aneuploidy, chromosome 1 alterations, hyperdiploidy and recursive deletions at 1p and chromosomes 13, 14 and 22 were the main aberrations driving samples grouping. Then, we integrated mapping information with gene and microRNAs expression profiles: a multiclass analysis of the identified clusters showed a marked gene-dosage effect, particularly concerning 1q transcripts, also confirmed by correlating gene expression levels and local copy number alterations. A wide dosage effect affected also microRNAs, indicating that structural abnormalities in MM closely reflect in their expression imbalances. Finally, we identified several loci in which genes and microRNAs expression correlated with loss-of-heterozygosity occurrence. Our results provide insights into the composite network linking genome structure and gene/microRNA transcriptional features in MM. Experiment Overall Design: This series of microarray experiments contains the gene expression profiles of purified plasma cells (PCs) obtained from 4 normal donor (N), 11 monoclonal gammopathy of undetermined significance (MGUS), 133 multiple myeloma (MM) and 9 plasma cell leukemia (PCL) at diagnosis. PCs were purified from bone marrow specimens, after red blood cell lysis with 0.86% ammonium chloride, using CD138 immunomagnetic microbeads. The purity of the positively selected PCs was assessed by morphology and flow cytometry and was > 90% in all cases. 5 micrograms of total RNA was processed and, in accordance with the manufacturer's protocols, 15 micrograms of fragmented biotin-labelled cRNA were hybridized on GeneChip Human Genome U133A Arrays (Affymetrix Inc.). The arrays were scanned using the Agilent GeneChip Scanner G2500A. The images were acquired using Affymetrix MicroArray Suite (MAS) 5.0 software and the probe level data converted to expression values using the Bioconductor function for the Robust Multi-Array average (RMA) procedure (Irizarry et al, 2003), in which perfect match intensities are background adjusted, quantile-quantile normalised and log2 transformed.

Dataset Information

A fast and accurate method to detect allelic genomic imbalances underlying mosaic rearrangements using SNP array data.

Background

Results

Conclusions

Publications

A fast and accurate method to detect allelic genomic imbalances underlying mosaic rearrangements using SNP array data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets