The Role of Constitutional Copy Number Variants in Breast Cancer
Abstract
:1. Introduction
2. Single Nucleotide Polymorphism (SNP)-Array Platforms to Assess Breast Cancer Risk
3. Copy Number Variant (CNV) Prediction Algorithms for SNP Array Data
ACCURACY of CNV Predictions from SNP Arrays
Software | Algorithm | Code | Platform | Year a | Reference | Citations b | Software URL |
---|---|---|---|---|---|---|---|
PennCNV | HMM | Perl | Multiple | 2007 | [43] | 300 | http://penncnv.openbioinformatics.org |
Birdsuite (Birdseye, Canary) | Mixture models | Java/Python/R | Affymetrix | 2008 | [44] | 300 | http://www.broadinstitute.org |
Nexus Copy Number | Proprietary (Segmentation) | windows executable | Multiple | - | - | 100 | http://www.biodiscovery.com |
QuantiSNP | HMM | MATLAB | Multiple | 2007 | [45] | 100 | http://sites.google.com/site/quantisnp |
CNVPartition | Proprietary | windows executable | Illumina | 2006 | - | 100 | http://support.illumina.com |
Partek Genomics Suite | Proprietary (Segmentation or HMM) | windows executable | Multiple | - | - | 30 | http://www.partek.com/pgs |
CNVFinder | Experimental variability | perl | Array CGH | 2006 | [46] | 30 | http://www.sanger.ac.uk/resources/software/cnvfinder/ |
CGHCall | segmentation and mixture model | R | Array CGH | 2007 | [47] | 30 | http://www.few.vu.nl/~mavdwiel/CGHcall.html |
GenoCNV | HMM | R | Multiple | 2009 | [48] | 30 | http://www.bios.unc.edu/~weisun/software/genoCN.htm |
SW-ARRAY | Smith Waterman | R | Array CGH | 2005 | [49] | 30 | Not available |
HMMSeg | HMM wavelet smoothing | Java | Multiple | 2007 | [50] | 10 | http://noble.gs.washington.edu/proj/hmmseg |
VanillaICE | HMM | R | Affymetrix | 2008 | [51] | 10 | http://cran.r-project.org |
CNVHap | HMM, Haplotype | Java | Multiple | 2010 | [52] | 10 | http://www.imperial.ac.uk/people/l.coin |
dChip | Multiple | R | Multiple | 2008 | [53] | 10 | http://sites.google.com/site/dchipsoft |
GADA | Bayesian | R | Multiple | 2010 | [54] | 10 | http://cran.r-project.org |
CNV Workshop | Segmentation | complete VM | Multiple | 2010 | [55] | 10 | http://sourceforge.net/projects/cnv |
Algorithm(s) | Platform | Validation Method | Accuracy | Study Conclusion | Reference |
---|---|---|---|---|---|
Adapted method on SW-ARRAY and GIM | Affymetrix | qPCR or Mass Spec Validation | 2.5% false positives, ~90% singleton validation | Developed a multistep algorithm to better call CNVs. | [41] |
Birdsuite, CNAT, CNVPartition, GADA, Nexus, PennCNV and QuantiSNP | Affymetrix, Illumina | Comparison of HapMap samples to Kidd et al., Korbel et al. and Redon et al., data [5,40,56] | Assay sensitivity ranged 20%−49% with some algorithms predicting more events (i.e., GADA, 546 predicted CNVs). | PennCNV had the greatest sensitivity (49%). Little agreement between studies and within studies. | [37] |
cnvHap, CNVPartition, PennCNV and QuantiSNP | Aglient, Illumnina | Compared samples either with previously characterized (by aCGH) CNVs or HapMap samples from Kidd et al. [40] | cnvHap had very good sensitivity (68%) for larger CNVs (>10kb) in Kidd et al. This reduced to 31% for smaller CNVs (<5kb). | cnvHap has increased sensitivity compared with other CNV algorithms. | [52] |
PennCNV, Aroma.Affymetrix, APT and CRLMM | Affymetrix | Compared concordance between calling algorithms. | Greater concordance in deletion (51.5%) than duplications (47.9%). The probable false positive rates for CRLMM and PennCNV were 26% and 24%. | PennCNV appeared to detect all the CNV and more than CRLMM predicted | [57] |
CNVPartition, PennCNV and QuantiSNP | Illumnina | Agreement between algorithms | Agreement varied from 59%−62% for deletions, to 43%−57% for duplications. | Use of multiple algorithms increased the positive predictive value, as did the number of probes and the minimum size (kb). | [35] |
CNVPartition, PennCNV and QuantiSNP | Illumnina | MLPA validation, measures were taken to reduce false positive calls. | All algorithms show better specificity than sensitivity. QuantiSNP was the most sensitive, predicting 28% of CNVs. PennCNV was better at discriminating copy number state. | Applying methods to reduce false positives results in low sensitivity. | [42] |
ADM-2, Birdsuite, CNVfinder, CNVPartition, dCHIP, GTC, iPattern, Nexus, Partek, PennCNV, QuantiSNP | CGH arrays and SNP arrays (Affymetrix and Illumina) | Experiments were repeated in triplicate and CNV calls were compared. CNV calls were also compared to 5 references (‘gold standards’). | Algorithm replication has <70% reproducibility. CNV calls between any two algorithms is typically low (25%–50%) within a platform. Overlap with DGV was high, whereas overlap with references [39,40] was low. | Newer high resolution arrays outperform older arrays in both CNVs’ call and reproducibility. Algorithms developed for specific array platforms outperformed adapted and independent algorithms. | [58] |
Birdsuite, Partek, Genomics Suite, HelixTree and PennCNV | Affymetrix | Comparison with HapMap CNV in two studies [39,40]. | Overlap ranged between 42% and 70% when including 20 probes for Kidd et al. [40] and 26%−48% in Conrad et al. [39] | Birdsuite outperformed the other 3 algorithms over multiple permutation. | [38] |
qPCR validation of rare CNVs (a single CNV event in >1000 bipolar samples) | For each algorithm between 10 or 11, CNVs were tested. Partek and Birdsuite both validated all (5/5) deletion events tested. | Birduite and Partek had high positive predictive values, particularly with deletions. HelixTree performed poorly. | |||
CNVPartition, PennCNV and QuantiSNP | Illumnina | Comparison to a previous CGH study [59]. qPCR validation of 3 candidate loci in 717 horses. | 50 CNVs were called by all 3 algorithms. QuantiSNP had the highest overlap with CNVs predicted from CGH arrays (25%). Validation rates were greater than 80% for the 3 loci. | CNVPartition predicted the least CNVs, suggesting a high false negative rate. | [60] |
GenoCN, PennCNV and QuantiSNP | Illumnina | Comparison of HapMap sample to Conrad et al.[39] Compared both CNVs (i.e. Gain or Loss) and normal calls. | All algorithms show much better specificity than sensitivity. PennCNV had the worst sensitivity, predicting <15% of Conrad et al. [39] CNVs in 3 samples | The three HMM algorithms all performed with varied results. They were all highly specific (>98%), but sensitivity remains to be an issue for all three algorithms. | [36] |
cnvHap, COKGEN, GenoCNV, HaplotypeCN, PennCNV and QuantiSNP | Affymetrix | Compared 270 HapMap samples which have been previously described. Compared simulated data to test haplotype phasing between cnvHap and HaplotypeCNV. | GenoCNV has the most sensitivity (28%) when using Kidd et al. [40]; however, the concordance rate in PennCNV was greater (36% and 9%, respectively). | Algorithm performance varied with reference study. GenoCNV was the most sensitive but had the lowest concordance rate. HaplotypeCNV, cnvHap and PennCNV (under a specific permutation) were compared separately, with HaplotypeCN outperforming the other two. | [61] |
Birdsuite, dCHIP, GTC and PennCNV | Affymetrix | Comparison to a previous CGH study [62]. | GTC had the highest portion of CNV matching (50% overlap) to CGH, 66%. Larger CNVs were called with greater accuracy. | Birdsuite called the most CNVs; however, PennCNV outperformed all algorithms with greater specificity and sensitivity. | [63] |
4. Functional Annotation of CNVs
5. Application of SNP Arrays for Profiling CNVs in Breast Cancer
5.1. Inherited Copy Number Polymorphisms and Breast Cancer Risk
5.2. Inherited and de novo Rare CNVs and Breast Cancer Risk
5.3. Is There a Relationship between Germline CNVs and Breast Tumourigenesis
6. Conclusion
Supplementary Files
Supplementary File 1Acknowledgments
Author Contributions
Conflicts of Interest
References
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Walker, L.C.; Wiggins, G.A.R.; Pearson, J.F. The Role of Constitutional Copy Number Variants in Breast Cancer. Microarrays 2015, 4, 407-423. https://doi.org/10.3390/microarrays4030407
Walker LC, Wiggins GAR, Pearson JF. The Role of Constitutional Copy Number Variants in Breast Cancer. Microarrays. 2015; 4(3):407-423. https://doi.org/10.3390/microarrays4030407
Chicago/Turabian StyleWalker, Logan C., George A.R. Wiggins, and John F. Pearson. 2015. "The Role of Constitutional Copy Number Variants in Breast Cancer" Microarrays 4, no. 3: 407-423. https://doi.org/10.3390/microarrays4030407