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

Abstract

BackgroundMosaicism 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.ResultsThe 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.ConclusionsThe 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.

Detectable clonal mosaicism in blood as a biomarker of cancer risk in Fanconi anemia

“…SNP arrays were used to build molecular karyotypes that were analyzed with the MAD software. 6 In total, 16 out of 130 FA patients (12.3%) had detectable CMEs that were 4.4 to 159 Mb in size ( Figure 1, Table 1; supplemental Table 1). Five of the CME carriers (5/91, 5.5%) were children or adolescents ,18 years old, whereas 11 (11/39, 28.2%) were in the 19-to 50-years-old range.…”

“…The detection of mosaic rearrangements was based on the assessment of allelic imbalance and copy number changes using the Mosaic Alteration Detection (MAD) algorithm implemented in R Genomic Alteration Detection Analysis software. 6 We defined the breakpoint interval for each CME as the region located between 2 informative SNP probes (the first probe within and outside the event). We restricted the search to breakpoints mapped to intervals ,200 Kb in order to compare our results with reported data generated with SNP arrays of lower probe density.…”

“…2,3,5 Although the identification of mosaic events will greatly benefit from single-cell analyses, chromosomal rearrangements .1 to 2Mb can also be inferred from studies of DNA from cell populations by genome-wide single-nucleotide polymorphism (SNP) genotypes when the proportion of clonally affected cells is larger than a certain threshold, that is, 7% to 18% depending on the type of rearrangement and the quality of the array. 6 In this context, the term "detectable clonal mosaicism" refers to chromosomal mosaic events (CMEs) that occur in a proportion of cells sufficient for their detection. This operational term underestimates the true rate of CMEs.…”

Detection of structural mosaicism from targeted and whole-genome sequencing data

“…Segmentation was performed using the GADA algorithm (González et al 2011), using the parameters values as follows: SBL step: maxit of 1 × 10 7 ; Backward Elimination step: Tvalue of 10 and MinSegLen value of 15. This step generates contiguous segments of putative chromosomal abnormalities.…”

“…Illumina GenomeStudio software was used to generate log R ratio and BAF metrics, and Illumina Gencall software was used to calculate genotypes. Structural mosaic detection was performed using MAD (González et al 2011). Initial mosaic events were merged if events were within 1 Mb and were the same type (loss, gain, or LOH) of mosaic event.…”