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.     

Simple and Versatile Molecular Method of Copy-Number Measurement Using Cloned Competitors

Variations and alterations of copy numbers (CNVs and CNAs) carry disease susceptibility and drug responsiveness implications. Although there are many molecular methods to measure copy numbers, sensitivity, reproducibility, cost, and time issues remain. In the present study, we were able to solve those problems utilizing our modified real competitive PCR method with cloned competitors (mrcPCR). First, the mrcPCR for ERBB2 copy number was established, and the results were comparable to current standard methods but with a shorter assay time and a lower cost. Second, the mrcPCR assays for 24 drug-target genes were established, and the results in a panel of NCI-60 cells were comparable to those from real-time PCR and microarray. Third, the mrcPCR results for FCGR3A and the FCGR3B CNVs were comparable to those by the paralog ratio test (PRT), but without PRT''s limitations. These results suggest that mrcPCR is comparable to the currently available standard or the most sensitive methods. In addition, mrcPCR would be invaluable for measurement of CNVs in genes with variants of similar structures, because combination of the other methods is not necessary, along with its other advantages such as short assay time, small sample amount requirement, and applicability to all sequences and genes.     

A New Method for Detecting Associations with Rare Copy-Number Variants

Copy number variants (CNVs) play an important role in the etiology of many diseases such as cancers and psychiatric disorders. Due to a modest marginal effect size or the rarity of the CNVs, collapsing rare CNVs together and collectively evaluating their effect serves as a key approach to evaluating the collective effect of rare CNVs on disease risk. While a plethora of powerful collapsing methods are available for sequence variants (e.g., SNPs) in association analysis, these methods cannot be directly applied to rare CNVs due to the CNV-specific challenges, i.e., the multi-faceted nature of CNV polymorphisms (e.g., CNVs vary in size, type, dosage, and details of gene disruption), and etiological heterogeneity (e.g., heterogeneous effects of duplications and deletions that occur within a locus or in different loci). Existing CNV collapsing analysis methods (a.k.a. the burden test) tend to have suboptimal performance due to the fact that these methods often ignore heterogeneity and evaluate only the marginal effects of a CNV feature. We introduce CCRET, a random effects test for collapsing rare CNVs when searching for disease associations. CCRET is applicable to variants measured on a multi-categorical scale, collectively modeling the effects of multiple CNV features, and is robust to etiological heterogeneity. Multiple confounders can be simultaneously corrected. To evaluate the performance of CCRET, we conducted extensive simulations and analyzed large-scale schizophrenia datasets. We show that CCRET has powerful and robust performance under multiple types of etiological heterogeneity, and has performance comparable to or better than existing methods when there is no heterogeneity.     

An optimization framework for unsupervised identification of rare copy number variation from SNP array data

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.     

Transformation methods for estimation of subject-specific scapular muscle attachment sites

The parameters that describe the soft tissue structures are among the most important anatomical parameters for subject-specific biomechanical modelling. In this paper, we study one of the soft tissue parameters, namely muscle attachment sites. Two new methods are proposed for transformation of the muscle attachment sites of any reference scapula to any destination scapula based on four palpable bony landmarks. The proposed methods as well as one previously proposed method have been applied for transformation of muscle attachment sites of one reference scapula to seven other scapulae. The transformation errors are compared among the three methods. Both proposed methods yield significantly less (p < 0.05) prediction error as compared to the currently available method. Furthermore, we investigate whether there exists a reference scapula that performs significantly better than other scapulae when used for transformation of muscle attachment sites. Seven different scapulae were used as reference scapula and their resulting transformation errors were compared with each other. In the considered statistical population, no such a thing as an ideal scapula was found. There was, however, one outlier scapula that performed significantly worse than the other scapulae when used as a reference. The effect of perturbations in both muscle attachment sites and other muscle properties is studied by comparing muscle force predictions of a musculoskeletal model between perturbed and non-perturbed versions of the model. It is found that 10 mm variations in muscle attachments have more significant effect on muscle force predictions than 10% variations in any of the other four analysed muscle properties.     

Detecting common copy number variants in high-throughput sequencing data by using JointSLM algorithm

The discovery of genomic structural variants (SVs), such as copy number variants (CNVs), is essential to understand genetic variation of human populations and complex diseases. Over recent years, the advent of new high-throughput sequencing (HTS) platforms has opened many opportunities for SVs discovery, and a very promising approach consists in measuring the depth of coverage (DOC) of reads aligned to the human reference genome. At present, few computational methods have been developed for the analysis of DOC data and all of these methods allow to analyse only one sample at time. For these reasons, we developed a novel algorithm (JointSLM) that allows to detect common CNVs among individuals by analysing DOC data from multiple samples simultaneously. We test JointSLM performance on synthetic and real data and we show its unprecedented resolution that enables the detection of recurrent CNV regions as small as 500 bp in size. When we apply JointSLM to analyse chromosome one of eight genomes with different ancestry, we identify 3000 regions with recurrent CNVs of different frequency and size: hierarchical clustering on these regions segregates the eight individuals in two groups that reflect their ancestry, demonstrating the potential utility of JointSLM for population genetics studies.     

wuHMM: a robust algorithm to detect DNA copy number variation using long oligonucleotide microarray data

Copy number variants (CNVs) are currently defined as genomic sequences that are polymorphic in copy number and range in length from 1000 to several million base pairs. Among current array-based CNV detection platforms, long-oligonucleotide arrays promise the highest resolution. However, the performance of currently available analytical tools suffers when applied to these data because of the lower signal:noise ratio inherent in oligonucleotide-based hybridization assays. We have developed wuHMM, an algorithm for mapping CNVs from array comparative genomic hybridization (aCGH) platforms comprised of 385 000 to more than 3 million probes. wuHMM is unique in that it can utilize sequence divergence information to reduce the false positive rate (FPR). We apply wuHMM to 385K-aCGH, 2.1M-aCGH and 3.1M-aCGH experiments comparing the 129X1/SvJ and C57BL/6J inbred mouse genomes. We assess wuHMM's performance on the 385K platform by comparison to the higher resolution platforms and we independently validate 10 CNVs. The method requires no training data and is robust with respect to changes in algorithm parameters. At a FPR of <10%, the algorithm can detect CNVs with five probes on the 385K platform and three on the 2.1M and 3.1M platforms, resulting in effective resolutions of 24 kb, 2–5 kb and 1 kb, respectively.     

EXCAVATOR: detecting copy number variants from whole-exome sequencing data

We developed a novel software tool, EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.     

Segmental duplications are hot spots of copy number variants affecting barley gene content

Copy number variants (CNVs) are pervasive in several animal and plant genomes and contribute to shaping genetic diversity. In barley, there is evidence that changes in gene copy number underlie important agronomic traits. The recently released reference sequence of barley represents a valuable genomic resource for unveiling the incidence of CNVs that affect gene content and for identifying sequence features associated with CNV formation. Using exome sequencing and read count data, we detected 16 605 deletions and duplications that affect barley gene content by surveying a diverse panel of 172 cultivars, 171 landraces, 22 wild relatives and other 32 uncategorized domesticated accessions. The quest for segmental duplications (SDs) in the reference sequence revealed many low‐copy repeats, most of which overlap predicted coding sequences. Statistical analyses revealed that the incidence of CNVs increases significantly in SD‐rich regions, indicating that these sequence elements act as hot spots for the formation of CNVs. The present study delivers a comprehensive genome‐wide study of CNVs affecting barley gene content and implicates SDs in the molecular mechanisms that lead to the formation of this class of CNVs.     

Reference-unbiased copy number variant analysis using CGH microarrays

Comparative genomic hybridization (CGH) microarrays have been used to determine copy number variations (CNVs) and their effects on complex diseases. Detection of absolute CNVs independent of genomic variants of an arbitrary reference sample has been a critical issue in CGH array experiments. Whole genome analysis using massively parallel sequencing with multiple ultra-high resolution CGH arrays provides an opportunity to catalog highly accurate genomic variants of the reference DNA (NA10851). Using information on variants, we developed a new method, the CGH array reference-free algorithm (CARA), which can determine reference-unbiased absolute CNVs from any CGH array platform. The algorithm enables the removal and rescue of false positive and false negative CNVs, respectively, which appear due to the effects of genomic variants of the reference sample in raw CGH array experiments. We found that the CARA remarkably enhanced the accuracy of CGH array in determining absolute CNVs. Our method thus provides a new approach to interpret CGH array data for personalized medicine.     

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.     

Clinical interpretation of copy number variants in the human genome

Molecular methods, by which copy number variants (CNVs) detection is available, have been gradually introduced into routine diagnostics over the last 15 years. Despite this, some CNVs continue to be a huge challenge when it comes to clinical interpretation. CNVs are an important source of normal and pathogenic variants, but, in many cases, their impact on human health depends on factors that are not yet known. Therefore, perception of their clinical consequences can change over time, as our knowledge grows. This review summarises guidelines that facilitate correct classification of identified changes and discusses difficulties with the interpretation of rare, small CNVs.     

General assessment of copy number variation in normal and tumor tissues of the domestic dog (Canis lupus familiaris)

In recent years, characterization of a copy number variation (CNV) of the genomic DNA has provided evidence for the relationship of this type of genetic variation with the occurrence of a broad spectrum of diseases, including cancer lesions. Copy number variants (CNVs) also occur in the genomes of healthy individuals as a result of abnormal recombination processes in germ cells and have a hereditary character contributing to the natural genetic diversity. Recent image analysis methods and advanced computational techniques allow for identification of CNVs using SNPs genotyping microarrays based on the analysis of signal intensity observed for markers located in the specific genomic regions. In this study we used CanineHD BeadChip assay (Illumina) to identify both natural and cancer-induced CNVs in the genomes of different dog breeds and in different cancer types occurring in this species. The obtained results showed that structural aberrations are a common phenomenon arising during a tumor progression and are more complex and widespread in tumors of mesenchymal tissue origin than in epithelial tissue originating tumors. The tumor derived CNVs, in comparison to healthy samples, were characterized by larger sizes of regions, higher number of amplifications, and in some cases encompassed genes with potential effect on tumor progression.     

Prioritizing Clinically Relevant Copy Number Variation from Genetic Interactions and Gene Function Data

It is becoming increasingly necessary to develop computerized methods for identifying the few disease-causing variants from hundreds discovered in each individual patient. This problem is especially relevant for Copy Number Variants (CNVs), which can be cheaply interrogated via low-cost hybridization arrays commonly used in clinical practice. We present a method to predict the disease relevance of CNVs that combines functional context and clinical phenotype to discover clinically harmful CNVs (and likely causative genes) in patients with a variety of phenotypes. We compare several feature and gene weighing systems for classifying both genes and CNVs. We combined the best performing methodologies and parameters on over 2,500 Agilent CGH 180k Microarray CNVs derived from 140 patients. Our method achieved an F-score of 91.59%, with 87.08% precision and 97.00% recall. Our methods are freely available at https://github.com/compbio-UofT/cnv-prioritization. Our dataset is included with the supplementary information.     

An integrated framework for discovery and genotyping of genomic variants from high-throughput sequencing experiments

Recent advances in high-throughput sequencing (HTS) technologies and computing capacity have produced unprecedented amounts of genomic data that have unraveled the genetics of phenotypic variability in several species. However, operating and integrating current software tools for data analysis still require important investments in highly skilled personnel. Developing accurate, efficient and user-friendly software packages for HTS data analysis will lead to a more rapid discovery of genomic elements relevant to medical, agricultural and industrial applications. We therefore developed Next-Generation Sequencing Eclipse Plug-in (NGSEP), a new software tool for integrated, efficient and user-friendly detection of single nucleotide variants (SNVs), indels and copy number variants (CNVs). NGSEP includes modules for read alignment, sorting, merging, functional annotation of variants, filtering and quality statistics. Analysis of sequencing experiments in yeast, rice and human samples shows that NGSEP has superior accuracy and efficiency, compared with currently available packages for variants detection. We also show that only a comprehensive and accurate identification of repeat regions and CNVs allows researchers to properly separate SNVs from differences between copies of repeat elements. We expect that NGSEP will become a strong support tool to empower the analysis of sequencing data in a wide range of research projects on different species.     

On Estimation of Allele Frequencies via Next-Generation DNA Resequencing with Barcoding

Next Generation Sequencing (NGS) has revolutionized biomedical research in recent years. It is now commonly used to identify rare variants through resequencing individual genomes. Due to the cost of NGS, researchers have considered pooling samples as a cost-effective alternative to individual sequencing. In this article, we consider the estimation of allele frequencies of rare variants through the NGS technologies with pooled DNA samples with or without barcodes. We consider three methods for estimating allele frequencies from such data, including raw sequencing counts, inferred genotypes, and expected minor allele counts, and compare their performance. Our simulation results suggest that the estimator based on inferred genotypes overall performs better than or as well as the other two estimators. When the sequencing coverage is low, biases and MSEs can be sensitive to the choice of the prior probabilities of genotypes for the estimators based on inferred genotypes and expected minor allele counts so that more accurate specification of prior probabilities is critical to lower biases and MSEs. Our study shows that the optimal number of barcodes in a pool is relatively robust to the frequencies of rare variants at a specific coverage depth. We provide general guidelines on using DNA pooling with barcoding for the estimation of allele frequencies of rare variants.     

Synergistic Interactions between Drosophila Orthologues of Genes Spanned by De Novo Human CNVs Support Multiple-Hit Models of Autism

Autism spectrum disorders (ASDs) are highly heritable and characterised by deficits in social interaction and communication, as well as restricted and repetitive behaviours. Although a number of highly penetrant ASD gene variants have been identified, there is growing evidence to support a causal role for combinatorial effects arising from the contributions of multiple loci. By examining synaptic and circadian neurological phenotypes resulting from the dosage variants of unique human:fly orthologues in Drosophila, we observe numerous synergistic interactions between pairs of informatically-identified candidate genes whose orthologues are jointly affected by large de novo copy number variants (CNVs). These CNVs were found in the genomes of individuals with autism, including a patient carrying a 22q11.2 deletion. We first demonstrate that dosage alterations of the unique Drosophila orthologues of candidate genes from de novo CNVs that harbour only a single candidate gene display neurological defects similar to those previously reported in Drosophila models of ASD-associated variants. We then considered pairwise dosage changes within the set of orthologues of candidate genes that were affected by the same single human de novo CNV. For three of four CNVs with complete orthologous relationships, we observed significant synergistic effects following the simultaneous dosage change of gene pairs drawn from a single CNV. The phenotypic variation observed at the Drosophila synapse that results from these interacting genetic variants supports a concordant phenotypic outcome across all interacting gene pairs following the direction of human gene copy number change. We observe both specificity and transitivity between interactors, both within and between CNV candidate gene sets, supporting shared and distinct genetic aetiologies. We then show that different interactions affect divergent synaptic processes, demonstrating distinct molecular aetiologies. Our study illustrates mechanisms through which synergistic effects resulting from large structural variation can contribute to human disease.     

Enhanced Methods for Local Ancestry Assignment in Sequenced Admixed Individuals

Inferring the ancestry at each locus in the genome of recently admixed individuals (e.g., Latino Americans) plays a major role in medical and population genetic inferences, ranging from finding disease-risk loci, to inferring recombination rates, to mapping missing contigs in the human genome. Although many methods for local ancestry inference have been proposed, most are designed for use with genotyping arrays and fail to make use of the full spectrum of data available from sequencing. In addition, current haplotype-based approaches are very computationally demanding, requiring large computational time for moderately large sample sizes. Here we present new methods for local ancestry inference that leverage continent-specific variants (CSVs) to attain increased performance over existing approaches in sequenced admixed genomes. A key feature of our approach is that it incorporates the admixed genomes themselves jointly with public datasets, such as 1000 Genomes, to improve the accuracy of CSV calling. We use simulations to show that our approach attains accuracy similar to widely used computationally intensive haplotype-based approaches with large decreases in runtime. Most importantly, we show that our method recovers comparable local ancestries, as the 1000 Genomes consensus local ancestry calls in the real admixed individuals from the 1000 Genomes Project. We extend our approach to account for low-coverage sequencing and show that accurate local ancestry inference can be attained at low sequencing coverage. Finally, we generalize CSVs to sub-continental population-specific variants (sCSVs) and show that in some cases it is possible to determine the sub-continental ancestry for short chromosomal segments on the basis of sCSVs.     

Efficient algorithms for tandem copy number variation reconstruction in repeat-rich regions

MOTIVATION: Structural variations and in particular copy number variations (CNVs) have dramatic effects of disease and traits. Technologies for identifying CNVs have been an active area of research for over 10 years. The current generation of high-throughput sequencing techniques presents new opportunities for identification of CNVs. Methods that utilize these technologies map sequencing reads to a reference genome and look for signatures which might indicate the presence of a CNV. These methods work well when CNVs lie within unique genomic regions. However, the problem of CNV identification and reconstruction becomes much more challenging when CNVs are in repeat-rich regions, due to the multiple mapping positions of the reads. RESULTS: In this study, we propose an efficient algorithm to handle these multi-mapping reads such that the CNVs can be reconstructed with high accuracy even for repeat-rich regions. To our knowledge, this is the first attempt to both identify and reconstruct CNVs in repeat-rich regions. Our experiments show that our method is not only computationally efficient but also accurate.     

SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features

Whole-genome and exome sequencing studies reveal many genetic variants between individuals, some of which are linked to disease. Many of these variants lead to single amino acid variants (SAVs), and accurate prediction of their phenotypic impact is important. Incorporating sequence conservation and network-level features, we have developed a method, SuSPect (Disease-Susceptibility-based SAV Phenotype Prediction), for predicting how likely SAVs are to be associated with disease. SuSPect performs significantly better than other available batch methods on the VariBench benchmarking dataset, with a balanced accuracy of 82%. SuSPect is available at www.sbg.bio.ic.ac.uk/suspect. The Web site has been implemented in Perl and SQLite and is compatible with modern browsers. An SQLite database of possible missense variants in the human proteome is available to download at www.sbg.bio.ic.ac.uk/suspect/download.html.