The coalescent with selection on copy number variants

We develop a coalescent-based simulation tool to generate patterns of single nucleotide polymorphisms (SNPs) in a wide region encompassing both the original and duplicated genes. Selection on the new duplicated copy and interlocus gene conversion between the two copies are incorporated. This simulation enables us to explore how selection on duplicated copies affects the pattern of SNPs. The fixation of an advantageous duplicated copy causes a strong reduction in polymorphism not only in the duplicated copy but also in its flanking regions, which is a typical signature of a selective sweep by positive selection. After fixation, polymorphism gradually increases by accumulating neutral mutations and eventually reaches the equilibrium value if there is no gene conversion. When gene conversion is active, the number of SNPs in the duplicated copy quickly increases by transferring SNPs from the original copy; therefore, the time when we can recognize the signature of selection is decreased. Because this effect of gene conversion is restricted only to the duplicated region, more power to detect selection is expected if a flanking region to the duplicated copy is used.     

On the frequency of copy number variants

MOTIVATION: Estimating the frequency distribution of copy number variants (CNVs) is an important aspect of the effort to characterize this new type of genetic variation. Currently, most studies report a strong skew toward low-frequency CNVs. In this article, our goal is to investigate the frequencies of CNVs. We employ a two-step procedure for the CNV frequency estimation process. We use family information a posteriori to select only the most reliable CNV regions, i.e. those showing high rates of Mendelian transmission. RESULTS: Our results suggest that the current skew toward low-frequency CNVs may not be representative of the true frequency distribution, but may be due, among other reasons, to the non-negligible false negative rates that characterize CNV detection methods. Moreover, false positives are also likely, as low-frequency CNVs are hard to detect with small sample sizes and technologies that are not ideally suited for their detection. Without appropriate validation methods, such as incorporation of biologically relevant information (for example, in our case, the transmission of heritable CNVs from parents to offspring), it is difficult to assess the validity of specific CNVs, and even harder to obtain reliable frequency estimates.     

Genetic variation in human disease and a new role for copy number variants

While complex diseases, such as inflammatory bowel disease, do not follow distinctive Mendelian inheritance patterns, there is now considerable evidence from twin and pedigree studies to show that there are significant genetic influences in the development of many such diseases. In times past, this type of information was considered to be interesting, and was used mainly to alert other members of the families that they may also be at increased risk of developing the disease. However, with the ability to evaluate the genetic basis of common disease, this information will have important consequences for the diagnosis, prevention and treatment of the disorder. The genetic basis for common disease is likely to be more complicated than we had previously anticipated, since we now recognise epigenetic causes of disease, and other subtle gene regulatory mechanisms. Copy number variants have been highlighted in this review, as being a phenomenon that we have known about for a long time, but that has not previously been clearly associated with human disease. As complex disease is related to changes in gene expression, any variation in the human genome that alters gene expression is now a candidate for being involved in the disease process.     

A classification model for distinguishing copy number variants from cancer-related alterations

Background  

Both somatic copy number alterations (CNAs) and germline copy number variants (CNVs) that are prevalent in healthy individuals can appear as recurrent changes in comparative genomic hybridization (CGH) analyses of tumors. In order to identify important cancer genes CNAs and CNVs must be distinguished. Although the Database of Genomic Variants (DGV) contains a list of all known CNVs, there is no standard methodology to use the database effectively.     

Genetic copy number variants in sib pairs both affected with schizophrenia

Background  

Schizophrenia is a complex disorder with involvement of multiple genes.     

A framework for exhaustively mapping functional missense variants

Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here, we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin‐like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.     

A genome-wide study reveals copy number variants exclusive to childhood obesity cases

The prevalence of obesity in children and adults in the United States has increased dramatically over the past decade. Genomic copy number variations (CNVs) have been strongly implicated in subjects with extreme obesity and coexisting developmental delay. To complement these previous studies, we addressed CNVs in common childhood obesity by examining children with a BMI in the upper 5(th) percentile but excluding any subject greater than three standard deviations from the mean in order to reduce severe cases in the cohort. We performed a whole-genome CNV survey of our cohort of 1080 defined European American (EA) childhood obesity cases and 2500 lean controls (< 50(th) percentile BMI) who were genotyped with 550,000 SNP markers. Positive findings were evaluated in an independent African American (AA) cohort of 1479 childhood obesity cases and 1575 lean controls. We identified 17 CNV loci that were unique to at least three EA cases and were both previously unreported in the public domain and validated via quantitative PCR. Eight of these loci (47.1%) also replicated exclusively in AA cases (six deletions and two duplications). Replicated deletion loci consisted of EDIL3, S1PR5, FOXP2, TBCA, ABCB5, and ZPLD1, whereas replicated duplication loci consisted of KIF2B and ARL15. We also observed evidence for a deletion at the EPHA6-UNQ6114 locus when the AA cohort was investigated as a discovery set. Although these variants may be individually rare, our results indicate that CNVs contribute to the genetic susceptibility of common childhood obesity in subjects of both European and African ancestry.     

Read count approach for DNA copy number variants detection

MOTIVATION: The advent of high-throughput sequencing technologies is revolutionizing our ability in discovering and genotyping DNA copy number variants (CNVs). Read count-based approaches are able to detect CNV regions with an unprecedented resolution. Although this computational strategy has been recently introduced in literature, much work has been already done for the preparation, normalization and analysis of this kind of data. RESULTS: Here we face the many aspects that cover the detection of CNVs by using read count approach. We first study the characteristics and systematic biases of read count distributions, focusing on the normalization methods designed for removing these biases. Subsequently, we compare the algorithms designed to detect the boundaries of CNVs and we investigate the ability of read count data to predict the exact number of DNA copy. Finally, we review the tools publicly available for analysing read count data. To better understand the state of the art of read count approaches, we compare the performance of the three most widely used sequencing technologies (Illumina Genome Analyzer, Roche 454 and Life Technologies SOLiD) in all the analyses that we perform.     

Analysis of copy number variants in the cattle genome

Copy number variation (CNV) is likely to be an important component of heritable variation in livestock. To characterise CNVs in cattle, we performed a genome wide survey to determine the number, location and gene content of these genomic features. A tiling oligonucleotide array with ~385,000 probes was used for comparative genomic hybridisation of both taurine and zebu cattle. Using a conservative set of calling criteria, a total of 51 CNV were detected that collectively spanned approximately half of one percent of the bovine genome. The size of the average CNV within each animal ranged from 213 kb up to 335 kb. Half of the CNV were detected in a single animal only, whilst the remainder was independently identified in multiple individuals. Analysis was performed to determine the gene content for each CNV region. This revealed that the majority of CNV (82%) spanned at least one gene, with a number of CNV containing genes which are known to control aspects of phenotypic variation in cattle. Whilst additional studies are required to determine the impact of individual CNV, this study confirmed them as an important class of genomic variation in cattle.     

Identification of copy number variants from exome sequence data

Background

With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1–4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1–4 exons), combining computational prediction algorithms and a high-resolution custom CGH array.

Results

We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate.

Conclusions

In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1–4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-661) contains supplementary material, which is available to authorized users.     

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.     

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.     

Genome-wide analysis of copy number variants in age-related macular degeneration

Age-related macular degeneration (AMD) is a complex genetic disease, with many loci demonstrating appreciable attributable disease risk. Despite significant progress toward understanding the genetic and environmental etiology of AMD, identification of additional risk factors is necessary to fully appreciate and treat AMD pathology. In this study, we investigated copy number variants (CNVs) as potential AMD risk variants in a cohort of 400 AMD patients and 500 AMD-free controls ascertained at the University of Iowa. We used three publicly available copy number programs to analyze signal intensity data from Affymetrix GeneChip SNP Microarrays. CNVs were ranked based on prevalence in the disease cohort and absence from the control group; high interest CNVs were subsequently confirmed by qPCR. While we did not observe a single-locus "risk CNV" that could account for a major fraction of AMD, we identified several rare and overlapping CNVs containing or flanking compelling candidate genes such as NPHP1 and EFEMP1. These and other candidate genes highlighted by this study deserve further scrutiny as sources of genetic risk for AMD.     

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/.