Copy number variation (CNV) identification,interpretation, and database from Brazilian patients

Copy number variations (CNVs) constitute an important class of variation in the human genome and the interpretation of their pathogenicity considering different frequencies across populations is still a challenge for geneticists. Since the CNV databases are predominantly composed of European and non-admixed individuals, and Brazilian genetic constitution is admixed and ethnically diverse, diagnostic screenings on Brazilian variants are greatly difficulted by the lack of populational references. We analyzed a clinical sample of 268 Brazilian individuals, including patients with neurodevelopment disorders and/or congenital malformations. The pathogenicity of CNVs was classified according to their gene content and overlap with known benign and pathogenic variants. A total of 1,504 autosomal CNVs (1,207 gains and 297 losses) were classified as benign (92.9%), likely benign (1.6%), VUS (2.6%), likely pathogenic (0.2%) and pathogenic (2.7%). Some of the CNVs were recurrent and with frequency increased in our sample, when compared to populational open resources of structural variants: 14q32.33, 22q11.22, 1q21.1, and 1p36.32 gains. Thus, these highly recurrent CNVs classified as likely benign or VUS were considered non-pathogenic in our Brazilian sample. This study shows the relevance of introducing CNV data from diverse cohorts to improve on the interpretation of clinical impact of genomic variations.     

Interpretation of array comparative genome hybridization data: a major challenge

The advent and application of high-resolution array-based comparative genome hybridization (array CGH) has led to the detection of large numbers of copy number variants (CNVs) in patients with developmental delay and/or multiple congenital anomalies as well as in healthy individuals. The notion that CNVs are also abundantly present in the normal population challenges the interpretation of the clinical significance of detected CNVs in patients. In this review we will illustrate a general clinical workflow based on our own experience that can be used in routine diagnostics for the interpretation of CNVs.     

High resolution array in the clinical approach to chromosomal phenotypes

Array genomic hybridization (AGH) has recently been implemented as a diagnostic tool for the detection of submicroscopic copy number variants (CNVs) in patients with developmental disorders. However, there is no consensus regarding the choice of the platform, the minimal resolution needed and systematic interpretation of CNVs. We report our experience in the clinical diagnostic use of high resolution AGH up to 100 kb on 131 patients with chromosomal phenotypes but previously normal karyotype. We evaluated the usefulness in our clinics and laboratories by the detection rate of causal CNVs and CNVs of unknown clinical significance and to what extent their interpretation would challenge the systematic use of high-resolution arrays in clinical application. Prioritizing phenotype-genotype correlation in our interpretation strategy to criteria previously described, we identified 33 (25.2%) potentially pathogenic aberrations. 16 aberrations were confirmed pathogenic (16.4% syndromic, 8.5% non-syndromic patients); 9 were new and individual aberrations, 3 of them were pathogenic although inherited and one is as small as approx 200 kb. 13 of 16 further CNVs of unknown significance were classified likely benign, for 3 the significance remained unclear. High resolution array allows the detection of up to 12.2% of pathogenic aberrations in a diagnostic clinical setting. Although the majority of aberrations are larger, the detection of small causal aberrations may be relevant for family counseling. The number of remaining unclear CNVs is limited. Careful phenotype-genotype correlations of the individual CNVs and clinical features are challenging but remain a hallmark for CNV interpretation.     

Identification of Rare Recurrent Copy Number Variants in High-Risk Autism Families and Their Prevalence in a Large ASD Population

Structural variation is thought to play a major etiological role in the development of autism spectrum disorders (ASDs), and numerous studies documenting the relevance of copy number variants (CNVs) in ASD have been published since 2006. To determine if large ASD families harbor high-impact CNVs that may have broader impact in the general ASD population, we used the Affymetrix genome-wide human SNP array 6.0 to identify 153 putative autism-specific CNVs present in 55 individuals with ASD from 9 multiplex ASD pedigrees. To evaluate the actual prevalence of these CNVs as well as 185 CNVs reportedly associated with ASD from published studies many of which are insufficiently powered, we designed a custom Illumina array and used it to interrogate these CNVs in 3,000 ASD cases and 6,000 controls. Additional single nucleotide variants (SNVs) on the array identified 25 CNVs that we did not detect in our family studies at the standard SNP array resolution. After molecular validation, our results demonstrated that 15 CNVs identified in high-risk ASD families also were found in two or more ASD cases with odds ratios greater than 2.0, strengthening their support as ASD risk variants. In addition, of the 25 CNVs identified using SNV probes on our custom array, 9 also had odds ratios greater than 2.0, suggesting that these CNVs also are ASD risk variants. Eighteen of the validated CNVs have not been reported previously in individuals with ASD and three have only been observed once. Finally, we confirmed the association of 31 of 185 published ASD-associated CNVs in our dataset with odds ratios greater than 2.0, suggesting they may be of clinical relevance in the evaluation of children with ASDs. Taken together, these data provide strong support for the existence and application of high-impact CNVs in the clinical genetic evaluation of children with ASD.     

A study of CNVs as trait-associated polymorphisms and as expression quantitative trait loci

We conducted a comprehensive study of copy number variants (CNVs) well-tagged by SNPs (r(2)≥ 0.8) by analyzing their effect on gene expression and their association with disease susceptibility and other complex human traits. We tested whether these CNVs were more likely to be functional than frequency-matched SNPs as trait-associated loci or as expression quantitative trait loci (eQTLs) influencing phenotype by altering gene regulation. Our study found that CNV-tagging SNPs are significantly enriched for cis eQTLs; furthermore, we observed that trait associations from the NHGRI catalog show an overrepresentation of SNPs tagging CNVs relative to frequency-matched SNPs. We found that these SNPs tagging CNVs are more likely to affect multiple expression traits than frequency-matched variants. Given these findings on the functional relevance of CNVs, we created an online resource of expression-associated CNVs (eCNVs) using the most comprehensive population-based map of CNVs to inform future studies of complex traits. Although previous studies of common CNVs that can be typed on existing platforms and/or interrogated by SNPs in genome-wide association studies concluded that such CNVs appear unlikely to have a major role in the genetic basis of several complex diseases examined, our findings indicate that it would be premature to dismiss the possibility that even common CNVs may contribute to complex phenotypes and at least some common diseases.     

Copy number variation and association over T-cell receptor genes—influence of DNA source

Genomic copy number variants (CNVs) are a common, heritable source of inter-individual differences in genomic sequence. Their influence on phenotypic variability and their involvement in the pathogenesis of several common diseases is well established and the object of many current studies. In the course of examining CNV association to various quantitative traits in a general population, we have detected a strong association of CNVs over the four TCR genes to lymphocyte and neutrophil numbers in blood. In a small replication series, we have further characterized the nature of these CNVs and found them not to be germline, but dependent on the origin of analysed DNA. Germline deletion and rearrangement around the T-cell receptor (TCR) genes naturally occurs in white blood cells. Blood DNA derived from persons with high lymphocyte counts generates variable intensity signals which behave like germline CNVs over these genes. As DNA containing a relative high proportion of these CNV-like events involving the TCR genes has the ability to influence genotype counts of SNPs in the regions of these genes, care should be taken in interpreting and replicating association signals on variants within these genes when blood-derived DNA is the only source of data.     

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.     

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.     

Accurate Distinction of Pathogenic from Benign CNVs in Mental Retardation

Copy number variants (CNVs) have recently been recognized as a common form of genomic variation in humans. Hundreds of CNVs can be detected in any individual genome using genomic microarrays or whole genome sequencing technology, but their phenotypic consequences are still poorly understood. Rare CNVs have been reported as a frequent cause of neurological disorders such as mental retardation (MR), schizophrenia and autism, prompting widespread implementation of CNV screening in diagnostics. In previous studies we have shown that, in contrast to benign CNVs, MR-associated CNVs are significantly enriched in genes whose mouse orthologues, when disrupted, result in a nervous system phenotype. In this study we developed and validated a novel computational method for differentiating between benign and MR-associated CNVs using structural and functional genomic features to annotate each CNV. In total 13 genomic features were included in the final version of a Naïve Bayesian Tree classifier, with LINE density and mouse knock-out phenotypes contributing most to the classifier''s accuracy. After demonstrating that our method (called GECCO) perfectly classifies CNVs causing known MR-associated syndromes, we show that it achieves high accuracy (94%) and negative predictive value (99%) on a blinded test set of more than 1,200 CNVs from a large cohort of individuals with MR. These results indicate that this classification method will be of value for objectively prioritizing CNVs in clinical research and diagnostics.     

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.     

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.     

Mitochondrial and ion channel gene alterations in autism

To evaluate the potential importance in autistic subjects of copy number variants (CNVs) that alter genes of relevance to bioenergetics, ionic metabolism, and synaptic function, we conducted a detailed microarray analysis of 69 autism probands and 35 parents, compared to 89 CEU HapMap controls. This revealed that the frequency CNVs of≥100kb and CNVs of≥10 Kb were markedly increased in probands over parents and in probands and parents over controls. Evaluation of CNVs≥1Mb by chromosomal FISH confirmed the molecular identity of a subset of the CNVs, some of which were associated with chromosomal rearrangements. In a number of the cases, CNVs were found to alter the copy number of genes that are important in mitochondrial oxidative phosphorylation (OXPHOS), ion and especially calcium transport, and synaptic structure. Hence, autism might result from alterations in multiple bioenergetic and metabolic genes required for mental function. This article is part of a Special Issue entitled: 17th European Bioenergetics Conference (EBEC 2012).     

A high-resolution map of segmental DNA copy number variation in the mouse genome

Submicroscopic (less than 2 Mb) segmental DNA copy number changes are a recently recognized source of genetic variability between individuals. The biological consequences of copy number variants (CNVs) are largely undefined. In some cases, CNVs that cause gene dosage effects have been implicated in phenotypic variation. CNVs have been detected in diverse species, including mice and humans. Published studies in mice have been limited by resolution and strain selection. We chose to study 21 well-characterized inbred mouse strains that are the focus of an international effort to measure, catalog, and disseminate phenotype data. We performed comparative genomic hybridization using long oligomer arrays to characterize CNVs in these strains. This technique increased the resolution of CNV detection by more than an order of magnitude over previous methodologies. The CNVs range in size from 21 to 2,002 kb. Clustering strains by CNV profile recapitulates aspects of the known ancestry of these strains. Most of the CNVs (77.5%) contain annotated genes, and many (47.5%) colocalize with previously mapped segmental duplications in the mouse genome. We demonstrate that this technique can identify copy number differences associated with known polymorphic traits. The phenotype of previously uncharacterized strains can be predicted based on their copy number at these loci. Annotation of CNVs in the mouse genome combined with sequence-based analysis provides an important resource that will help define the genetic basis of complex traits.     

Using ERDS to Infer Copy-Number Variants in High-Coverage Genomes

Although there are many methods available for inferring copy-number variants (CNVs) from next-generation sequence data, there remains a need for a system that is computationally efficient but that retains good sensitivity and specificity across all types of CNVs. Here, we introduce a new method, estimation by read depth with single-nucleotide variants (ERDS), and use various approaches to compare its performance to other methods. We found that for common CNVs and high-coverage genomes, ERDS performs as well as the best method currently available (Genome STRiP), whereas for rare CNVs and high-coverage genomes, ERDS performs better than any available method. Importantly, ERDS accommodates both unique and highly amplified regions of the genome and does so without requiring separate alignments for calling CNVs and other variants. These comparisons show that for genomes sequenced at high coverage, ERDS provides a computationally convenient method that calls CNVs as well as or better than any currently available method.     

Copy Number Variants in German Patients with Schizophrenia

Large rare copy number variants (CNVs) have been recognized as significant genetic risk factors for the development of schizophrenia (SCZ). However, due to their low frequency (1∶150 to 1∶1000) among patients, large sample sizes are needed to detect an association between specific CNVs and SCZ. So far, the majority of genome-wide CNV analyses have focused on reporting only CNVs that reached a significant P-value within the study cohort and merely confirmed the frequency of already-established risk-carrying CNVs. As a result, CNVs with a very low frequency that might be relevant for SCZ susceptibility are lost for secondary analyses. In this study, we provide a concise collection of high-quality CNVs in a large German sample consisting of 1,637 patients with SCZ or schizoaffective disorder and 1,627 controls. All individuals were genotyped on Illumina''s BeadChips and putative CNVs were identified using QuantiSNP and PennCNV. Only those CNVs that were detected by both programs and spanned ≥30 consecutive SNPs were included in the data collection and downstream analyses (2,366 CNVs, 0.73 CNVs per individual). The genome-wide analysis did not reveal a specific association between a previously unknown CNV and SCZ. However, the group of CNVs previously reported to be associated with SCZ was more frequent in our patients than in the controls. The publication of our dataset will serve as a unique, easily accessible, high-quality CNV data collection for other research groups. The dataset could be useful for the identification of new disease-relevant CNVs that are currently overlooked due to their very low frequency and lack of power for their detection in individual studies.     

Challenges of interpreting copy number variation in syndromic and non-syndromic congenital heart defects

Array comparative genomic hybridization (aCGH) has led to an increased detection of causal chromosomal imbalances in individuals with congenital heart defects (CHD). The introduction of aCGH as a diagnostic tool in a clinical cardiogenetic setting entails numerous challenges. Based on our own experience as well as those of others described in the literature, we outline the state of the art and attempt to answer a number of outstanding questions such as the detection frequency of causal imbalances in different patient populations, the added value of higher-resolution arrays, and the existence of predictive factors in syndromic cases. We introduce a step-by-step approach for clinical interpretation of copy number variants (CNV) detected in CHD, which is primarily based on gene content and overlap with known chromosomal syndromes, rather than on CNV inheritance and size. Based on this algorithm, we have reclassified the detected aberrations in aCGH studies for their causality for syndromic and non-syndromic CHD. From this literature overview, supplemented with own investigations in a cohort of 46 sporadic patients with severe non-syndromic CHD, it seems clear that the frequency of causal CNVs in non-syndromic CHD populations is lower than that in syndromic CNV populations (3.6 vs. 19%). Moreover, causal CNVs in non-syndromic CHD mostly involve imbalances with a moderate effect size and reduced penetrance, whereas the majority of causal imbalances in syndromic CHD consistently affects human development and significantly reduces reproductive fitness.     

Population Analysis of Large Copy Number Variants and Hotspots of Human Genetic Disease

Copy number variants (CNVs) contribute to human genetic and phenotypic diversity. However, the distribution of larger CNVs in the general population remains largely unexplored. We identify large variants in ~2500 individuals by using Illumina SNP data, with an emphasis on “hotspots” prone to recurrent mutations. We find variants larger than 500 kb in 5%–10% of individuals and variants greater than 1 Mb in 1%–2%. In contrast to previous studies, we find limited evidence for stratification of CNVs in geographically distinct human populations. Importantly, our sample size permits a robust distinction between truly rare and polymorphic but low-frequency copy number variation. We find that a significant fraction of individual CNVs larger than 100 kb are rare and that both gene density and size are strongly anticorrelated with allele frequency. Thus, although large CNVs commonly exist in normal individuals, which suggests that size alone can not be used as a predictor of pathogenicity, such variation is generally deleterious. Considering these observations, we combine our data with published CNVs from more than 12,000 individuals contrasting control and neurological disease collections. This analysis identifies known disease loci and highlights additional CNVs (e.g., 3q29, 16p12, and 15q25.2) for further investigation. This study provides one of the first analyses of large, rare (0.1%–1%) CNVs in the general population, with insights relevant to future analyses of genetic disease.     

Implications of copy number variation in people with chromosomal abnormalities: potential for greater variation in copy number state may contribute to variability of phenotype

Copy number variation is common in the human genome with many regions, overlapping thousands of genes, now known to be deleted or amplified. Aneuploidies and other forms of chromosomal imbalance have a wide range of adverse phenotypes and are a common cause of birth defects resulting in significant morbidity and mortality. “Normal” copy number variants (CNVs) embedded within the regions of chromosome imbalance may affect the clinical outcomes by altering the local copy number of important genes or regulatory regions: this could alleviate or exacerbate certain phenotypes. In this way CNVs may contribute to the clinical variability seen in many disorders caused by chromosomal abnormalities, such as the congenital heart defects (CHD) seen in ~40% of Down’s syndrome (DS) patients. Investigation of CNVs may therefore help to pinpoint critical genes or regulatory elements, elucidating the molecular mechanisms underlying these conditions, also shedding light on the aetiology of such phenotypes in people without major chromosome imbalances, and ultimately leading to their improved detection and treatment.     

Exploring the role of copy number variants in human adaptation

Over the past decade, the ubiquity of copy number variants (CNVs, the gain or loss of genomic material) in the genomes of healthy humans has become apparent. Although some of these variants are associated with disorders, a handful of studies documented an adaptive advantage conferred by CNVs. In this review, we propose that CNVs are substrates for human evolution and adaptation. We discuss the possible mechanisms and evolutionary processes in which CNVs are selected, outline the current challenges in identifying these loci, and highlight that copy number variable regions allow for the creation of novel genes that may diversify the repertoire of such genes in response to rapidly changing environments. We expect that many more adaptive CNVs will be discovered in the coming years, and we believe that these new findings will contribute to our understanding of human-specific phenotypes.