cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate

Quantitative analyses of next-generation sequencing (NGS) data, such as the detection of copy number variations (CNVs), remain challenging. Current methods detect CNVs as changes in the depth of coverage along chromosomes. Technological or genomic variations in the depth of coverage thus lead to a high false discovery rate (FDR), even upon correction for GC content. In the context of association studies between CNVs and disease, a high FDR means many false CNVs, thereby decreasing the discovery power of the study after correction for multiple testing. We propose 'Copy Number estimation by a Mixture Of PoissonS' (cn.MOPS), a data processing pipeline for CNV detection in NGS data. In contrast to previous approaches, cn.MOPS incorporates modeling of depths of coverage across samples at each genomic position. Therefore, cn.MOPS is not affected by read count variations along chromosomes. Using a Bayesian approach, cn.MOPS decomposes variations in the depth of coverage across samples into integer copy numbers and noise by means of its mixture components and Poisson distributions, respectively. The noise estimate allows for reducing the FDR by filtering out detections having high noise that are likely to be false detections. We compared cn.MOPS with the five most popular methods for CNV detection in NGS data using four benchmark datasets: (i) simulated data, (ii) NGS data from a male HapMap individual with implanted CNVs from the X chromosome, (iii) data from HapMap individuals with known CNVs, (iv) high coverage data from the 1000 Genomes Project. cn.MOPS outperformed its five competitors in terms of precision (1-FDR) and recall for both gains and losses in all benchmark data sets. The software cn.MOPS is publicly available as an R package at http://www.bioinf.jku.at/software/cnmops/ and at Bioconductor.     

PattRec: An easy-to-use CNV detection tool optimized for targeted NGS assays with diagnostic purposes

Genetic laboratories use custom-commercial targeted next-generation sequencing (tg-NGS) assays to identify disease-causing variants. Although the high coverage achieved with these tests allows for the detection of copy number variants (CNVs), which account for an important proportion of the genetic burden in human diseases, an easy-to-use tool for automatic CNV detection is still lacking. This article presents a new CNV detection tool optimized for tg-NGS data: PattRec. PattRec was evaluated using a wide range of data, and its performance compared with those of other CNV detection tools. The software includes features for selecting optimal controls, discarding polymorphic CNVs prior to analysis, and filtering out deletions based on SNV zygosity, and automatically creates an in-house CNV database. There is no need for high level bioinformatic expertise and users can choose color-coded xlsx output that helps to prioritize potentially pathogenic CNVs. PattRec is presented as a Java based GUI, freely available online: https://github.com/irotero/PattRec.     

Outlier-Based Identification of Copy Number Variations Using Targeted Resequencing in a Small Cohort of Patients with Tetralogy of Fallot

Copy number variations (CNVs) are one of the main sources of variability in the human genome. Many CNVs are associated with various diseases including cardiovascular disease. In addition to hybridization-based methods, next-generation sequencing (NGS) technologies are increasingly used for CNV discovery. However, respective computational methods applicable to NGS data are still limited. We developed a novel CNV calling method based on outlier detection applicable to small cohorts, which is of particular interest for the discovery of individual CNVs within families, de novo CNVs in trios and/or small cohorts of specific phenotypes like rare diseases. Approximately 7,000 rare diseases are currently known, which collectively affect ∼6% of the population. For our method, we applied the Dixon’s Q test to detect outliers and used a Hidden Markov Model for their assessment. The method can be used for data obtained by exome and targeted resequencing. We evaluated our outlier- based method in comparison to the CNV calling tool CoNIFER using eight HapMap exome samples and subsequently applied both methods to targeted resequencing data of patients with Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease. In both the HapMap samples and the TOF cases, our method is superior to CoNIFER, such that it identifies more true positive CNVs. Called CNVs in TOF cases were validated by qPCR and HapMap CNVs were confirmed with available array-CGH data. In the TOF patients, we found four copy number gains affecting three genes, of which two are important regulators of heart development (NOTCH1, ISL1) and one is located in a region associated with cardiac malformations (PRODH at 22q11). In summary, we present a novel CNV calling method based on outlier detection, which will be of particular interest for the analysis of de novo or individual CNVs in trios or cohorts up to 30 individuals, respectively.     

A Bayesian Analysis for Identifying DNA Copy Number Variations Using a Compound Poisson Process

To study chromosomal aberrations that may lead to cancer formation or genetic diseases, the array-based Comparative Genomic Hybridization (aCGH) technique is often used for detecting DNA copy number variants (CNVs). Various methods have been developed for gaining CNVs information based on aCGH data. However, most of these methods make use of the log-intensity ratios in aCGH data without taking advantage of other information such as the DNA probe (e.g., biomarker) positions/distances contained in the data. Motivated by the specific features of aCGH data, we developed a novel method that takes into account the estimation of a change point or locus of the CNV in aCGH data with its associated biomarker position on the chromosome using a compound Poisson process. We used a Bayesian approach to derive the posterior probability for the estimation of the CNV locus. To detect loci of multiple CNVs in the data, a sliding window process combined with our derived Bayesian posterior probability was proposed. To evaluate the performance of the method in the estimation of the CNV locus, we first performed simulation studies. Finally, we applied our approach to real data from aCGH experiments, demonstrating its applicability.     

Sample degradation leads to false-positive copy number variation calls in multiplex real-time polymerase chain reaction assays

The recent implication of genomic copy number variations (CNVs) in multiple human genetic disorders has led to increased interest in CNV discovery technologies. There is a growing consensus that, in addition to the method used for detection, at least one additional technology should be employed for validation. Real-time quantitative polymerase chain reaction (qPCR) analysis, incorporating a normal (2N) copy number standard, is commonly used as a means of validating CNVs. Whereas it has previously been reported that formalin-fixed paraffin-embedded (FFPE) DNA samples can yield spurious CNV calls in real-time qPCR assays, here we report that sample degradation under standard laboratory storage conditions generates a significant increase in false-positive CNV results. Results suggest the possibility of biased degradation among genomic regions and emphasize the need to assess sample integrity immediately prior to real-time qPCR experiments.     

GPA: A Microbial Genetic Polymorphisms Assignments Tool in Metagenomic Analysis by Bayesian Estimation

Identifying antimicrobial resistant(AMR) bacteria in metagenomics samples is essential for public health and food safety. Next-generation sequencing(NGS) technology has provided a powerful tool in identifying the genetic variation and constructing the correlations between genotype and phenotype in humans and other species. However, for complex bacterial samples, there lacks a powerful bioinformatic tool to identify genetic polymorphisms or copy number variations(CNVs) for given genes. Here we provide a Bayesian framework for genotype estimation for mixtures of multiple bacteria, named as Genetic Polymorphisms Assignments(GPA). Simulation results showed that GPA has reduced the false discovery rate(FDR) and mean absolute error(MAE) in CNV and single nucleotide variant(SNV) identification. This framework was validated by whole-genome sequencing and Pool-seq data from Klebsiella pneumoniae with multiple bacteria mixture models, and showed the high accuracy in the allele fraction detections of CNVs and SNVs in AMR genes between two populations. The quantitative study on the changes of AMR genes fraction between two samples showed a good consistency with the AMR pattern observed in the individual strains. Also, the framework together with the genome annotation and population comparison tools has been integrated into an application, which could provide a complete solution for AMR gene identification and quantification in unculturable clinical samples. The GPA package is available at https://github.com/IID-DTH/GPA-package.     

Genome-wide CNV analysis revealed variants associated with growth traits in African indigenous goats

Using the CaprineSNP50 data generated by the AGIN consortium, we detected common CNVs in 126 samples from four African indigenous goat breeds. A total of 30 CNVs ranging from 30,237 bp to 4,910,757 bp were identified. These CNVs were then associated with six growth traits by a linear regression analysis. Three significant associations were identified between two CNVs and two body traits after false discovery rate (FDR) correction (P < .05). One of them (CNV27) was significantly associated with both chest width and width of pin bones. It overlaps the SNX29 gene, the Gene Ontology (GO) annotations of which indicate CNV27 could be a potential functional candidate for meat production, health and reproduction traits. To our knowledge, this study is the first CNV-based association test of growth traits using SNP chip data in African meat goats.     

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization

Background

Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined.

Results

We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses.

Conclusion

Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.     

The effect of algorithms on copy number variant detection

Background

The detection of copy number variants (CNVs) and the results of CNV-disease association studies rely on how CNVs are defined, and because array-based technologies can only infer CNVs, CNV-calling algorithms can produce vastly different findings. Several authors have noted the large-scale variability between CNV-detection methods, as well as the substantial false positive and false negative rates associated with those methods. In this study, we use variations of four common algorithms for CNV detection (PennCNV, QuantiSNP, HMMSeg, and cnvPartition) and two definitions of overlap (any overlap and an overlap of at least 40% of the smaller CNV) to illustrate the effects of varying algorithms and definitions of overlap on CNV discovery.

Methodology and Principal Findings

We used a 56 K Illumina genotyping array enriched for CNV regions to generate hybridization intensities and allele frequencies for 48 Caucasian schizophrenia cases and 48 age-, ethnicity-, and gender-matched control subjects. No algorithm found a difference in CNV burden between the two groups. However, the total number of CNVs called ranged from 102 to 3,765 across algorithms. The mean CNV size ranged from 46 kb to 787 kb, and the average number of CNVs per subject ranged from 1 to 39. The number of novel CNVs not previously reported in normal subjects ranged from 0 to 212.

Conclusions and Significance

Motivated by the availability of multiple publicly available genome-wide SNP arrays, investigators are conducting numerous analyses to identify putative additional CNVs in complex genetic disorders. However, the number of CNVs identified in array-based studies, and whether these CNVs are novel or valid, will depend on the algorithm(s) used. Thus, given the variety of methods used, there will be many false positives and false negatives. Both guidelines for the identification of CNVs inferred from high-density arrays and the establishment of a gold standard for validation of CNVs are needed.     

SCNVSim: somatic copy number variation and structure variation simulator

Background

Somatically acquired structure variations (SVs) and copy number variations (CNVs) can induce genetic changes that are directly related to tumor genesis. Somatic SV/CNV detection using next-generation sequencing (NGS) data still faces major challenges introduced by tumor sample characteristics, such as ploidy, heterogeneity, and purity. A simulated cancer genome with known SVs and CNVs can serve as a benchmark for evaluating the performance of existing somatic SV/CNV detection tools and developing new methods.

Results

SCNVSim is a tool for simulating somatic CNVs and structure variations SVs. Other than multiple types of SV and CNV events, the tool is capable of simulating important features related to tumor samples including aneuploidy, heterogeneity and purity.

Conclusions

SCNVSim generates the genomes of a cancer cell population with detailed information of copy number status, loss of heterozygosity (LOH), and event break points, which is essential for developing and evaluating somatic CNV and SV detection methods in cancer genomics studies.     

What a difference copy number variation makes

DNA copy number variation (CNV) represents a considerable source of human genetic diversity. Recently,1 a global map of copy number variation in the human genome has been drawn up which reveals not only the ubiquity but also the complexity of this type of variation. Thus, two human genomes may differ by more than 20 Mb and it is likely that the full extent of CNV still remains to be discovered. Nearly 3000 genes are associated with CNV. This high degree of variability with regard to gene copy number between two individuals challenges definitions of normality. Many CNVs are located in regions of complex genomic structure and this currently limits the extent to which these variants can be genotyped by using tagging SNPs. However, some CNVs are already amenable to genome-wide association studies so that their influence on human phenotypic diversity and disease susceptibility may soon be determined.     

Modeling genetic inheritance of copy number variations

Copy number variations (CNVs) are being used as genetic markers or functional candidates in gene-mapping studies. However, unlike single nucleotide polymorphism or microsatellite genotyping techniques, most CNV detection methods are limited to detecting total copy numbers, rather than copy number in each of the two homologous chromosomes. To address this issue, we developed a statistical framework for intensity-based CNV detection platforms using family data. Our algorithm identifies CNVs for a family simultaneously, thus avoiding the generation of calls with Mendelian inconsistency while maintaining the ability to detect de novo CNVs. Applications to simulated data and real data indicate that our method significantly improves both call rates and accuracy of boundary inference, compared to existing approaches. We further illustrate the use of Mendelian inheritance to infer SNP allele compositions in each of the two homologous chromosomes in CNV regions using real data. Finally, we applied our method to a set of families genotyped using both the Illumina HumanHap550 and Affymetrix genome-wide 5.0 arrays to demonstrate its performance on both inherited and de novo CNVs. In conclusion, our method produces accurate CNV calls, gives probabilistic estimates of CNV transmission and builds a solid foundation for the development of linkage and association tests utilizing CNVs.     

MOCSphaser: a haplotype inference tool from a mixture of copy number variation and single nucleotide polymorphism data

Detailed analyses of the population-genetic nature of copy number variations (CNVs) and the linkage disequilibrium between CNV and single nucleotide polymorphism (SNP) loci from high-throughput experimental data require a computational tool to accurately infer alleles of CNVs and haplotypes composed of both CNV alleles and SNP alleles. Here we developed a new tool to infer population frequencies of such alleles and haplotypes from observed copy numbers and SNP genotypes, using the expectation-maximization algorithm. This tool can also handle copy numbers ambiguously determined, such as 2 or 3 copies, due to experimental noise. AVAILABILITY: http://emu.src.riken.jp/MOCSphaser/MOCSphaser.zip.     

Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants

We have systematically compared copy number variant (CNV) detection on eleven microarrays to evaluate data quality and CNV calling, reproducibility, concordance across array platforms and laboratory sites, breakpoint accuracy and analysis tool variability. Different analytic tools applied to the same raw data typically yield CNV calls with <50% concordance. Moreover, reproducibility in replicate experiments is <70% for most platforms. Nevertheless, these findings should not preclude detection of large CNVs for clinical diagnostic purposes because large CNVs with poor reproducibility are found primarily in complex genomic regions and would typically be removed by standard clinical data curation. The striking differences between CNV calls from different platforms and analytic tools highlight the importance of careful assessment of experimental design in discovery and association studies and of strict data curation and filtering in diagnostics. The CNV resource presented here allows independent data evaluation and provides a means to benchmark new algorithms.     

Origins and breakpoint analyses of copy number variations: up close and personal

Array-based methods have enabled the detection of many genomic gains and losses. These are stated as copy number variants (CNVs) and comprise up to 13% of the human genome. Based on their breakpoints and modes of formation CNVs are termed recurrent or nonrecurrent. Recurrent CNVs are flanked by low copy repeats and are of a fixed size. They arise as a result of misalignment during meiosis by a mechanism named nonallelic homologous recombination. Several of such recurrent CNVs have been linked to human diseases. Nonrecurrent CNVs, which are not flanked by low copy repeats, are of variable size and may arise via mechanisms like nonhomologous end joining and replication-based mechanisms described by the fork stalling and template switching and microhomology-mediated break-induced replication models. It is becoming clear that most disease-causing CNVs are nonrecurrent and generally arise via replication-based mechanisms. Furthermore, it is now appreciated that genomic features other than low copy repeats play a role in the formation of nonrecurrent CNVs. This review will discuss the different mechanisms of CNV formation and how high resolution analyses of CNV breakpoints have added to our knowledge of their precise structure.     

Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays

The detection of copy number variants (CNV) by array-based platforms provides valuable insight into understanding human diversity. However, suboptimal study design and data processing negatively affect CNV assessment. We quantitatively evaluate their impact when short-sequence oligonucleotide arrays are applied (Affymetrix Genome-Wide Human SNP Array 6.0) by evaluating 42 HapMap samples for CNV detection. Several processing and segmentation strategies are implemented, and results are compared to CNV assessment obtained using an oligonucleotide array CGH platform designed to query CNVs at high resolution (Agilent). We quantitatively demonstrate that different reference models (e.g. single versus pooled sample reference) used to detect CNVs are a major source of inter-platform discrepancy (up to 30%) and that CNVs residing within segmental duplication regions (higher reference copy number) are significantly harder to detect (P < 0.0001). After adjusting Affymetrix data to mimic the Agilent experimental design (reference sample effect), we applied several common segmentation approaches and evaluated differential sensitivity and specificity for CNV detection, ranging 39–77% and 86–100% for non-segmental duplication regions, respectively, and 18–55% and 39–77% for segmental duplications. Our results are relevant to any array-based CNV study and provide guidelines to optimize performance based on study-specific objectives.     

An efficient method for measuring copy number variation applied to improvement of nematode resistance in soybean

Copy number variation (CNV) is implicated in important traits in multiple crop plants, but can be challenging to genotype using conventional methods. The Rhg1 locus of soybean, which confers resistance to soybean cyst nematode (SCN), is a CNV of multiple 31.2‐kb genomic units each containing four genes. Reliable, high‐throughput methods to quantify Rhg1 and other CNVs for selective breeding were developed. The CNV genotyping assay described here uses a homeologous gene copy within the paleopolyploid soybean genome to provide the internal control for a single‐tube TaqMan copy number assay. Using this assay, CNV in breeding populations can be tracked with high precision. We also show that extensive CNV exists within Fayette, a released, inbred SCN‐resistant soybean cultivar with a high copy number at Rhg1 derived from a single donor parent. Copy number at Rhg1 is therefore unstable within a released variety over a relatively small number of generations. Using this assay to select for individuals with altered copy number, plants were obtained with both increased copy number and increased SCN resistance relative to control plants. Thus, CNV genotyping technologies can be used as a new type of marker‐assisted selection to select for desirable traits in breeding populations, and to control for undesirable variation within cultivars.     

Accuracy of CNV Detection from GWAS Data

Several computer programs are available for detecting copy number variants (CNVs) using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites--Birdsuite, Partek, HelixTree, and PennCNV-Affy--in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH) followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS) data set (1001 bipolar cases and 1033 controls, all of European ancestry) as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR), while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two "gold standards," the sequence data of Kidd et al., and aCGH data of Conrad et al. Algorithms for calling CNVs especially common ones need substantial improvement, and a "gold standard" for detection of CNVs remains to be established.     

Comparison of Sequencing Based CNV Discovery Methods Using Monozygotic Twin Quartets

Background

The advent of high throughput sequencing methods breeds an important amount of technical challenges. Among those is the one raised by the discovery of copy-number variations (CNVs) using whole-genome sequencing data. CNVs are genomic structural variations defined as a variation in the number of copies of a large genomic fragment, usually more than one kilobase. Here, we aim to compare different CNV calling methods in order to assess their ability to consistently identify CNVs by comparison of the calls in 9 quartets of identical twin pairs. The use of monozygotic twins provides a means of estimating the error rate of each algorithm by observing CNVs that are inconsistently called when considering the rules of Mendelian inheritance and the assumption of an identical genome between twins. The similarity between the calls from the different tools and the advantage of combining call sets were also considered.

Results

ERDS and CNVnator obtained the best performance when considering the inherited CNV rate with a mean of 0.74 and 0.70, respectively. Venn diagrams were generated to show the agreement between the different algorithms, before and after filtering out familial inconsistencies. This filtering revealed a high number of false positives for CNVer and Breakdancer. A low overall agreement between the methods suggested a high complementarity of the different tools when calling CNVs. The breakpoint sensitivity analysis indicated that CNVnator and ERDS achieved better resolution of CNV borders than the other tools. The highest inherited CNV rate was achieved through the intersection of these two tools (81%).

Conclusions

This study showed that ERDS and CNVnator provide good performance on whole genome sequencing data with respect to CNV consistency across families, CNV breakpoint resolution and CNV call specificity. The intersection of the calls from the two tools would be valuable for CNV genotyping pipelines.     

Precision-mapping and statistical validation of quantitative trait loci by machine learning

Background

DNA sequence diversity within the human genome may be more greatly affected by copy number variations (CNVs) than single nucleotide polymorphisms (SNPs). Although the importance of CNVs in genome wide association studies (GWAS) is becoming widely accepted, the optimal methods for identifying these variants are still under evaluation. We have previously reported a comprehensive view of CNVs in the HapMap DNA collection using high density 500 K EA (Early Access) SNP genotyping arrays which revealed greater than 1,000 CNVs ranging in size from 1 kb to over 3 Mb. Although the arrays used most commonly for GWAS predominantly interrogate SNPs, CNV identification and detection does not necessarily require the use of DNA probes centered on polymorphic nucleotides and may even be hindered by the dependence on a successful SNP genotyping assay.

Results

In this study, we have designed and evaluated a high density array predicated on the use of non-polymorphic oligonucleotide probes for CNV detection. This approach effectively uncouples copy number detection from SNP genotyping and thus has the potential to significantly improve probe coverage for genome-wide CNV identification. This array, in conjunction with PCR-based, complexity-reduced DNA target, queries over 1.3 M independent NspI restriction enzyme fragments in the 200 bp to 1100 bp size range, which is a several fold increase in marker density as compared to the 500 K EA array. In addition, a novel algorithm was developed and validated to extract CNV regions and boundaries.

Conclusion

Using a well-characterized pair of DNA samples, close to 200 CNVs were identified, of which nearly 50% appear novel yet were independently validated using quantitative PCR. The results indicate that non-polymorphic probes provide a robust approach for CNV identification, and the increasing precision of CNV boundary delineation should allow a more complete analysis of their genomic organization.