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.

Figure 1

Algorithm process to detect genomic imbalances using SNP array data. (a) B allele frequency (BAF, red dots) from heterozygous clusters different to non altered probes (BAF 0.5) can be used to iddentify genomic imbalances. (b) b-deviation, b_dev, is computed to detect altered regions. (c) Probit transformation, Φ^-1(b_dev), is used to achieve normallity before applying a segmentation algorithm. (d) Segmentation algorithm detects altered regions. False discovery rate (FDR) can be controlled in this step. (e) After calling segments by using BAF values, LRR allows to classify altered regions in different kind of mosaic structural variations.

Figure 2

Example of mosaic rearrangement detected in HapMap individuals. The plot shows a trisomy in chromosome 14 for the individual NA12248 detected using MAD (not detected using BAFsegmentation). The same alteration was also identified when analysing a replicated experiment of the same HapMap individual. Red dots represent B-allele frequency (BAF), while black dots show log2ratio (LRR) values.

Figure 3

ROC curve obtained from simulated data. Four scenarios were considered depending on i) the percentage of mosaic cells in the altered region: 10% (blue lines) and 20% (red lines) and ii) the quality of data: good (solid lines) and noisy (dashed lines). Each line gives the True-positive rate for a given False-Positive rate level. This example corresponds to a case with moderate/large altered region.

Figure 4

False discovery rate (FDR) validation. Each figure compares the simulated FDR vs. the estimated FDR using the approach given in the Methods Section for different scenarios. The percentage of mosaic cells in the altered region as well as the quality of the data were changed.

Figure 5

Sensitivity as a function of mosaic cell proportion. Low proportion of cells affected with the abnormality reduces the sensitivity to identify a 1 kB mosaic alteration, in a 20 kB region of 200 simulated individuals. Overall MAD showed a better performance when compared to BAFsegmentation.