An optimization framework for unsupervised identification of rare copy number variation from SNP array data.
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ABSTRACT:
SUBMITTER: Yavas G
PROVIDER: S-EPMC2784334 | biostudies-literature | 2009
REPOSITORIES: biostudies-literature
Publications
An optimization framework for unsupervised identification of rare copy number variation from SNP array data.
Yavas Gökhan G Koyutürk Mehmet M Ozsoyoğlu Meral M Gould Meetha P MP LaFramboise Thomas T
Genome biology 20091023 10
Copy number variants (CNVs) have roles in human disease, and DNA microarrays are important tools for identifying them. In this paper, we frame CNV identification as an objective function optimization problem. We apply our method to data from hundreds of samples, and demonstrate its ability to detect CNVs at a high level of sensitivity without sacrificing specificity. Its performance compares favorably with currently available methods and it reveals previously unreported gains and losses. ...[more]
Similar Datasets
Project description:BackgroundCopy Number Variations (CNVs) are usually inferred from Single Nucleotide Polymorphism (SNP) arrays by use of some software packages based on given algorithms. However, there is no clear understanding of the performance of these software packages; it is therefore difficult to select one or several software packages for CNV detection based on the SNP array platform.We selected four publicly available software packages designed for CNV calling from an Affymetrix SNP array, including Birdsuite, dChip, Genotyping Console (GTC) and PennCNV. The publicly available dataset generated by Array-based Comparative Genomic Hybridization (CGH), with a resolution of 24 million probes per sample, was considered to be the "gold standard". Compared with the CGH-based dataset, the success rate, average stability rate, sensitivity, consistence and reproducibility of these four software packages were assessed compared with the "gold standard". Specially, we also compared the efficiency of detecting CNVs simultaneously by two, three and all of the software packages with that by a single software package.ResultsSimply from the quantity of the detected CNVs, Birdsuite detected the most while GTC detected the least. We found that Birdsuite and dChip had obvious detecting bias. And GTC seemed to be inferior because of the least amount of CNVs it detected. Thereafter we investigated the detection consistency produced by one certain software package and the rest three software suits. We found that the consistency of dChip was the lowest while GTC was the highest. Compared with the CNVs detecting result of CGH, in the matching group, GTC called the most matching CNVs, PennCNV-Affy ranked second. In the non-overlapping group, GTC called the least CNVs. With regards to the reproducibility of CNV calling, larger CNVs were usually replicated better. PennCNV-Affy shows the best consistency while Birdsuite shows the poorest.ConclusionWe found that PennCNV outperformed the other three packages in the sensitivity and specificity of CNV calling. Obviously, each calling method had its own limitations and advantages for different data analysis. Therefore, the optimized calling methods might be identified using multiple algorithms to evaluate the concordance and discordance of SNP array-based CNV calling.
| S-EPMC4015297 | biostudies-literature
Project description:The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose a novel ensemble learning framework, ensembleCNV, to detect and genotype CNVs using single nucleotide polymorphism (SNP) array data. EnsembleCNV (a) identifies and eliminates batch effects at raw data level; (b) assembles individual CNV calls into CNV regions (CNVRs) from multiple existing callers with complementary strengths by a heuristic algorithm; (c) re-genotypes each CNVR with local likelihood model adjusted by global information across multiple CNVRs; (d) refines CNVR boundaries by local correlation structure in copy number intensities; (e) provides direct CNV genotyping accompanied with confidence score, directly accessible for downstream quality control and association analysis. Benchmarked on two large datasets, ensembleCNV outperformed competing methods and achieved a high call rate (93.3%) and reproducibility (98.6%), while concurrently achieving high sensitivity by capturing 85% of common CNVs documented in the 1000 Genomes Project. Given this CNV call rate and accuracy, which are comparable to SNP genotyping, we suggest ensembleCNV holds significant promise for performing genome-wide CNV association studies and investigating how CNVs predispose to human diseases.
| S-EPMC6468244 | biostudies-literature
Project description:The use of array technology to define chromosome deletions and duplications is bringing us closer to establishing a genotype/phenotype map of genomic copy number alterations. We studied 21 patients and five relatives with deletions of the short arm of chromosome 20 using the Illumina HumanHap550 SNP array to: 1) more accurately determine the deletion sizes; 2) identify and compare breakpoints; 3) establish genotype/phenotype correlations; and 4) investigate the use of the HumanHap550 platform for analysis of chromosome deletions. Deletions ranged from 95 kb to 14.62 Mb, and all of the breakpoints were unique. Eleven patients had deletions between 95 kb and 4 Mb and these individuals had normal development, with no anomalies outside of those associated with Alagille syndrome (AGS). The proximal and distal boundaries of these 11 deletions constitute a 5.4-Mb region, and we propose that haploinsufficiency for only 1 of the 12 genes in this region causes phenotypic abnormalities. This defines the JAG1-associated critical region, in which deletions do not confer findings other than those associated with AGS. The other 10 patients had deletions between 3.28 Mb and 14.62 Mb, which extended outside the critical region, and, notably, all of these patients had developmental delay. This group had other findings such as autism, scoliosis, and bifid uvula. We identified 47 additional polymorphic genome-wide copy number variants (>20 SNPs), with 0 to 5 variants called per patient. Deletions of the short arm of chromosome 20 are associated with relatively mild and limited clinical anomalies. The use of SNP arrays provides accurate high-resolution definition of genomic abnormalities.
| S-EPMC2650004 | biostudies-literature
Project description:BackgroundCopy number data are routinely being extracted from genome-wide association study chips using a variety of software. We empirically evaluated and compared four freely-available software packages designed for Affymetrix SNP chips to estimate copy number: Affymetrix Power Tools (APT), Aroma.Affymetrix, PennCNV and CRLMM. Our evaluation used 1,418 GENOA samples that were genotyped on the Affymetrix Genome-Wide Human SNP Array 6.0. We compared bias and variance in the locus-level copy number data, the concordance amongst regions of copy number gains/deletions and the false-positive rate amongst deleted segments.ResultsAPT had median locus-level copy numbers closest to a value of two, whereas PennCNV and Aroma.Affymetrix had the smallest variability associated with the median copy number. Of those evaluated, only PennCNV provides copy number specific quality-control metrics and identified 136 poor CNV samples. Regions of copy number variation (CNV) were detected using the hidden Markov models provided within PennCNV and CRLMM/VanillaIce. PennCNV detected more CNVs than CRLMM/VanillaIce; the median number of CNVs detected per sample was 39 and 30, respectively. PennCNV detected most of the regions that CRLMM/VanillaIce did as well as additional CNV regions. The median concordance between PennCNV and CRLMM/VanillaIce was 47.9% for duplications and 51.5% for deletions. The estimated false-positive rate associated with deletions was similar for PennCNV and CRLMM/VanillaIce.ConclusionsIf the objective is to perform statistical tests on the locus-level copy number data, our empirical results suggest that PennCNV or Aroma.Affymetrix is optimal. If the objective is to perform statistical tests on the summarized segmented data then PennCNV would be preferred over CRLMM/VanillaIce. Specifically, PennCNV allows the analyst to estimate locus-level copy number, perform segmentation and evaluate CNV-specific quality-control metrics within a single software package. PennCNV has relatively small bias, small variability and detects more regions while maintaining a similar estimated false-positive rate as CRLMM/VanillaIce. More generally, we advocate that software developers need to provide guidance with respect to evaluating and choosing optimal settings in order to obtain optimal results for an individual dataset. Until such guidance exists, we recommend trying multiple algorithms, evaluating concordance/discordance and subsequently consider the union of regions for downstream association tests.
| S-EPMC3146450 | biostudies-literature
Project description:MotivationDNA copy number aberration--both inherited and sporadic--is a significant contributor to a variety of human diseases. Copy number characterization is therefore an area of intense research. Probe hybridization-based arrays are important tools used to measure copy number in a high-throughput manner.ResultsIn this article, we present a simple but powerful nonparametric rank-based approach to detect deletions and gains from raw array copy number measurements. We use three different rank-based statistics to detect three separate molecular phenomena-somatic lesions, germline deletions and germline gains. The approach is robust and rigorously grounded in statistical theory, thereby enabling the meaningful assignment of statistical significance to each putative aberration. We demonstrate the flexibility of our approach by applying it to data from three different array platforms. We show that our method compares favorably with established approaches by applying it to published well-characterized samples. Power simulations demonstrate exquisite sensitivity for array data of reasonable quality.ConclusionsOur flexible rank-based framework is suitable for multiple platforms including single nucleotide polymorphism arrays and array comparative genomic hybridization, and can reliably detect gains or losses of genomic DNA, whether inherited, de novo, or somatic.AvailabilityAn R package RankCopy containing the methods described here, and is freely available from the author's web site (http://mendel.gene.cwru.edu/laframboiselab/).Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC2732300 | biostudies-literature
Project description:BackgroundSNP arrays output two signals that reflect the total genomic copy number (LRR) and the allelic ratio (BAF), which in combination allow the characterisation of allele-specific copy numbers (ASCNs). While methods based on hidden Markov models (HMMs) have been extended from array comparative genomic hybridisation (aCGH) to jointly handle the two signals, only one method based on change-point detection, ASCAT, performs bivariate segmentation.ResultsIn the present work, we introduce a generic framework for bivariate segmentation of SNP array data for ASCN analysis. For the matter, we discuss the characteristics of the typically applied BAF transformation and how they affect segmentation, introduce concepts of multivariate time series analysis that are of concern in this field and discuss the appropriate formulation of the problem. The framework is implemented in a method named CnaStruct, the bivariate form of the structural change model (SCM), which has been successfully applied to transcriptome mapping and aCGH.ConclusionsOn a comprehensive synthetic dataset, we show that CnaStruct outperforms the segmentation of existing ASCN analysis methods. Furthermore, CnaStruct can be integrated into the workflows of several ASCN analysis tools in order to improve their performance, specially on tumour samples highly contaminated by normal cells.
| S-EPMC3599505 | biostudies-literature
Project description:Charcot-Marie-Tooth disease type 1A (CMT1A) is caused by an abnormal copy number variation (CNV) with a trisomy of chromosome 17p12. The increase of the DNA-segment copy number is expected to alter the allele frequency of single nucleotide polymorphism (SNP) within the duplicated region. We tested whether SNP allele frequency determined by a Sequenom MassArray can be used to detect the CMT1A mutation. Our results revealed distinct patterns of SNP allele frequency distribution, which reliably differentiated CMT1A patients from controls. This finding suggests that this technique may serve as an alternative approach to identifying CNV in certain diseases, including CMT1A.
| S-EPMC4254366 | biostudies-literature
Project description:Recent studies have established the role of rare copy number variants (CNVs) in several neurological disorders but the contribution of rare CNVs to cerebral palsy (CP) is not known. Fifty Caucasian families having children with CP were studied using two microarray designs. Potentially pathogenic, rare (<1% population frequency) CNVs were identified, and their frequency determined, by comparing the CNVs found in cases with 8329 adult controls with no known neurological disorders. Ten of the 50 cases (20%) had rare CNVs of potential relevance to CP; there were a total of 14 CNVs, which were observed in <0.1% (<8/8329) of the control population. Eight inherited from an unaffected mother: a 751-kb deletion including FSCB, a 1.5-Mb duplication of 7q21.13, a 534-kb duplication of 15q11.2, a 446-kb duplication including CTNND2, a 219-kb duplication including MCPH1, a 169-kb duplication of 22q13.33, a 64-kb duplication of MC2R, and a 135-bp exonic deletion of SLC06A1. Three inherited from an unaffected father: a 386-kb deletion of 12p12.2-p12.1, a 234-kb duplication of 10q26.13, and a 4-kb exonic deletion of COPS3. The inheritance was unknown for three CNVs: a 157-bp exonic deletion of ACOX1, a 693-kb duplication of 17q25.3, and a 265-kb duplication of DAAM1. This is the first systematic study of CNVs in CP, and although it did not identify de novo mutations, has shown inherited, rare CNVs involving potentially pathogenic genes and pathways requiring further investigation.
| S-EPMC3865415 | biostudies-literature
Project description:Autoimmune Addison's disease (AAD) is a rare but life-threatening endocrine disorder caused by an autoimmune destruction of the adrenal cortex. A previous genome-wide association study (GWAS) has shown that common variants near immune-related genes, which mostly encode proteins participating in the immune response, affect the risk of developing this condition. However, little is known about the contribution of copy number variations (CNVs) to AAD susceptibility. We used the genome-wide genotyping data from Norwegian and Swedish individuals (1,182 cases and 3,810 controls) to investigate the putative role of CNVs in the AAD aetiology. Although the frequency of rare CNVs was similar between cases and controls, we observed that larger deletions (>1,000 kb) were more common among patients (OR = 4.23, 95% CI 1.85-9.66, p = 0.0002). Despite this, none of the large case-deletions were conclusively pathogenic, and the clinical presentation and an AAD-polygenic risk score were similar between cases with and without the large CNVs. Among deletions exclusive to individuals with AAD, we highlight two ultra-rare deletions in the genes LRBA and BCL2L11, which we speculate might have contributed to the polygenic risk in these carriers. In conclusion, rare CNVs do not appear to be a major cause of AAD but further studies are needed to ascertain the potential contribution of rare deletions to the polygenic load of AAD susceptibility.
| S-EPMC10982488 | biostudies-literature
Project description:Posttraumatic stress disorder (PTSD) is a heritable (h2 = 24-71%) psychiatric illness. Copy number variation (CNV) is a form of rare genetic variation that has been implicated in the etiology of psychiatric disorders, but no large-scale investigation of CNV in PTSD has been performed. We present an association study of CNV burden and PTSD symptoms in a sample of 114,383 participants (13,036 cases and 101,347 controls) of European ancestry. CNVs were called using two calling algorithms and intersected to a consensus set. Quality control was performed to remove strong outlier samples. CNVs were examined for association with PTSD within each cohort using linear or logistic regression analysis adjusted for population structure and CNV quality metrics, then inverse variance weighted meta-analyzed across cohorts. We examined the genome-wide total span of CNVs, enrichment of CNVs within specified gene-sets, and CNVs overlapping individual genes and implicated neurodevelopmental regions. The total distance covered by deletions crossing over known neurodevelopmental CNV regions was significant (beta = 0.029, SE = 0.005, P = 6.3 × 10-8). The genome-wide neurodevelopmental CNV burden identified explains 0.034% of the variation in PTSD symptoms. The 15q11.2 BP1-BP2 microdeletion region was significantly associated with PTSD (beta = 0.0206, SE = 0.0056, P = 0.0002). No individual significant genes interrupted by CNV were identified. 22 gene pathways related to the function of the nervous system and brain were significant in pathway analysis (FDR q < 0.05), but these associations were not significant once NDD regions were removed. A larger sample size, better detection methods, and annotated resources of CNV are needed to explore this relationship further.
| S-EPMC9763110 | biostudies-literature