Copy Number Variants Calls Research Articles

NanoStringNormCNV: pre-processing of NanoString CNV data.

The NanoString System is a well-established technology for measuring RNA and DNA abundance. Although it can estimate copy number variation, relatively few tools support analysis of these data. To address this gap, we created NanoStringNormCNV, an R package for pre-processing and copy number variant calling from NanoString data. This package implements algorithms for pre-processing, quality-control, normalization and copy number variation detection. A series of reporting and data visualization methods support exploratory analyses. To demonstrate its utility, we apply it to a new dataset of 96 genes profiled on 41 prostate tumour and 24 matched normal samples. NanoStringNormCNV is implemented in R and is freely available at http://labs.oicr.on.ca/boutros-lab/software/nanostringnormcnv. paul.boutros@oicr.on.ca. Supplementary data are available at Bioinformatics online.

PennCNV in whole-genome sequencing data

BackgroundThe use of high-throughput sequencing data has improved the results of genomic analysis due to the resolution of mapping algorithms. Although several tools for copy-number variation calling in whole genome sequencing have been published, the noisy nature of sequencing data is still a limitation for accuracy and concordance among such tools. To assess the performance of PennCNV original algorithm for array data in whole genome sequencing data, we processed mapping (BAM) files to extract coverage, representing log R ratio (LRR) of signal intensity, and B allele frequency (BAF).ResultsWe used high quality sample NA12878 from the recently reported NIST database and created 10 artificial samples with several CNVs spread along all chromosomes. We compared PennCNV-Seq with other tools with general deletions and duplications, as well as for different number of copies and copy-neutral loss-of-heterozygosity (LOH).ConclusionPennCNV-Seq was able to find correct CNVs and can be integrated in existing CNV calling pipelines to report accurately the number of copies in specific genomic regions.

The Identification of Copy Number Variation of Cnv Esv27061 among Young Adults with Hypertension: Preliminary Findings

Introduction: Hypertension related morbidities and mortalities around the world show a gradual increase and early detection and prevention are advocated. The Database of Genomic Variants (DGV) has associated variation in DNA sequences called copy number variation (CNV) with susceptibility to common diseases. However, little is known about CNV role in essential hypertension. Thus, this study aimed to characterize the CNV esv27061 among prehypertensive and hypertensive young adults in Malaysia. Materials and method: In this comparative cross-sectional study, 104 subjects living in Kuantan who gave voluntary consent to participate are recruited and divided into three groups; control (43 subjects), prehypertensive (38 subjects) and mild hypertensive (23 subjects). An optimized droplet digital polymerase chain reaction (ddPCR) was used in the determination of CNV esv27061 in this study. Results: All subjects in the control (n=38; 88.4% gain), prehypertensive (n=33; 86.8% gain) and mild hypertensive (n=21; 91.3% gain) groups had CNV gain (copy number > 2) while 11.6% of control, 13.2% of prehypertensive and 8.7% of mild hypertensive subjects exhibited normal copies (copy number = 2). Conclusion: The present preliminary finding was consistent with the Database of Genomic Variants (DGV) which stated that CNV esv27061 showed more gain than loss.

Copy number variant calling on a 177 gene expanded carrier screening panel reveals impact of hbb deletions

Copy number variant calling on a 177 gene expanded carrier screening panel reveals impact of hbb deletions

Genome-wide meta-analysis of copy number variations with alcohol dependence.

Genetic association studies and meta-analyses of alcohol dependence (AD) have reported AD-associated single nucleotide polymorphisms (SNPs). These SNPs collectively account for a small portion of estimated heritability in AD. Recent genome-wide copy number variation (CNV) studies have identified CNVs associated with AD and substance dependence, suggesting that a portion of the missing heritability is explained by CNV. We applied PennCNV and QuantiSNP CNV calling algorithms to identify consensus CNVs in five AD cohorts of European and African origins. After rigorous quality control, genome-wide meta-analyses of CNVs were carried out in 3243 well-diagnosed AD cases and 2802 controls. We identified nine CNV regions, including a deletion in chromosome 5q21.3 with a suggestive association with AD (OR=2.15 (1.41-3.29) and P=3.8 × 10-4) and eight nominally significant CNV regions. All regions were replicated with consistent effect sizes across studies and populations. Pathway and gene-drug interaction enrichment analyses based on the resulting genes indicated the mitogen-activated protein kinase signaling pathway and the recombinant insulin and hyaluronidase drugs, which were relevant to AD biology or treatment. To our knowledge, this is the first genome-wide meta-analysis of CNVs with addiction. Further investigation of the AD-associated CNV regions will provide better understanding of the AD genetic mechanism.

Abstract 396: Detecting copy number variations using WES datasets in patient derived xenografts

Abstract Amplification or deletion of oncogenes and tumor suppressors can be oncogenic, which may serve as drug targets and biomarkers for disease prognosis and drug response. Traditionally, array-based assays, e.g. Affymetrix SNP6.0® and lately OncoScan®, have been used to detect DNA copy number variations (CNVs). These assays have limitations including insufficient probe availability for some genes, cross hybridization, imprecise measurement of fluorescent signal, less suitability of tumor samples, and high cost. Whole exome sequencing (WES) is now widely used to profile tumor samples, and provides a fast and efficient determination for point mutations, insertions and deletions at the DNA level. Recently, WES is being used to profile CNVs with some success, but also suffers from many drawbacks, such as the requirement of paired normal tissues, the need of a large batch of samples, the inadequacy of detecting chromosomal-level CNVs, the incapability to detect CNVs in low coverage genomic regions. We have developed a CNV detection pipeline on both genomic (segment) level and gene level from WES data using the concept of off-target and on-target reads1, and evaluated it in a set of 155 patient-derived xenografts (PDXs), with head-to-head comparison to Affymetrix SNP6.0® and OncoScan® on 5 models with 2 passages from each. PDX is a well-accepted experimental model mimicking original patient in histo- & molecular pathology2. Reads derived from mouse contaminants were removed from WES datasets to avoid mouse signal interference, which cannot be done in array-based techniques. RT-PCR was used to experimentally validate CNVs for selected genes. We found that the average off-target rate for our PDX models is approximately 15%, and off-target reads were uniformly distributed across genome. The comparison with array-based technologies indicates that 1) WES has the highest resolution (20kb) while OncoScan® is the second (50-100 kb), followed by SNP6.0® (100-200kb), 2) the OncoScan® CNV calls are very similar to our WES methods at the genome level, yet 3) WES gives a much higher accuracy on CNV inference for genes flanking the ~900 cancer related genes with enhanced probe densities on OncoScan®, suggesting that our WES method has the highest accuracy. RT-PCR results confirmed the observations. In summary, our WES-based approach gives a better solution in CNV detection in PDX models.

An evaluation of copy number variation detection tools for cancer using whole exome sequencing data

BackgroundRecently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors’ complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer.ResultsIn this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly.ConclusionsThe limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.

Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans

BackgroundHigh-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data.ResultsThe arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4–489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0–86%). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters.ConclusionsHigh-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies.

Increased genomic burden of germline copy number variants is associated with early onset breast cancer: Australian breast cancer family registry

BackgroundWomen with breast cancer who have multiple affected relatives are more likely to have inherited genetic risk factors for the disease. All the currently known genetic risk factors for breast cancer account for less than half of the average familial risk. Furthermore, the genetic factor(s) underlying an increased cancer risk for many women from multiple-case families remain unknown. Rare genomic duplications and deletions, known as copy number variants (CNVs), cover more than 10% of a human genome, are often not assessed in studies of genetic predisposition, and could account for some of the so-called “missing heritability”.MethodsWe carried out a hypothesis-generating case-control study of breast cancer diagnosed before age 40 years (200 cases, 293 controls) using population-based cases from the Australian Breast Cancer Family Study. Genome-wide scanning for CNVs was performed using the Human610-Quad BeadChip and fine-mapping was conducted using PennCNV.ResultsWe identified deletions overlapping two known cancer susceptibility genes, (BRCA1 and BLM), and a duplication overlapping SMARCB1, associated with risk. The number of deletions across the genome was 1.5-fold higher for cases than controls (P = 10-16), and 2-fold higher when only rare deletions overlapping genes (frequency <1%) were assessed (P = 5 × 10-4). Association tests of CNVs, followed by experimental validation of CNV calls, found deletions overlapping the OR4C11 and OR4P4 genes were associated with breast cancer (P = 0.02 and P = 0.03, respectively).ConclusionThese results suggest rare CNVs might have a role in breast cancer susceptibility, at least for disease at a young age.

SeqCNV: a novel method for identification of copy number variations in targeted next-generation sequencing data

BackgroundTargeted next-generation sequencing (NGS) has been widely used as a cost-effective way to identify the genetic basis of human disorders. Copy number variations (CNVs) contribute significantly to human genomic variability, some of which can lead to disease. However, effective detection of CNVs from targeted capture sequencing data remains challenging.ResultsHere we present SeqCNV, a novel CNV calling method designed to use capture NGS data. SeqCNV extracts the read depth information and utilizes the maximum penalized likelihood estimation (MPLE) model to identify the copy number ratio and CNV boundary. We applied SeqCNV to both bacterial artificial clone (BAC) and human patient NGS data to identify CNVs. These CNVs were validated by array comparative genomic hybridization (aCGH).ConclusionsSeqCNV is able to robustly identify CNVs of different size using capture NGS data. Compared with other CNV-calling methods, SeqCNV shows a significant improvement in both sensitivity and specificity.

Genomic and genetic variability of six chicken populations using single nucleotide polymorphism and copy number variants as markers

Genomic and genetic variation among six Italian chicken native breeds (Livornese, Mericanel della Brianza, Milanino, Bionda Piemontese, Bianca di Saluzzo and Siciliana) were studied using single nucleotide polymorphism (SNP) and copy number variants (CNV) as markers. A total of 94 DNA samples genotyped with Axiom® Genome-Wide Chicken Genotyping Array (Affymetrix) were used in the analyses. The results showed the genetic and genomic variability occurring among the six Italian chicken breeds. The genetic relationship among animals was established with a principal component analysis. The genetic diversity within breeds was calculated using heterozygosity values (expected and observed) and with Wright's F-statistics. The individual-based CNV calling, based on log R ratio and B-allele frequency values, was done by the Hidden-Markov Model (HMM) of PennCNV software on autosomes. A hierarchical agglomerative clustering was applied in each population according to the absence or presence of definite CNV regions (CNV were grouped by overlapping of at least 1 bp). The CNV map was built on a total of 1003 CNV found in individual samples, after grouping by overlaps, resulting in 564 unique CNV regions (344 gains, 213 losses and 7 complex), for a total of 9.43 Mb of sequence and 1.03% of the chicken assembly autosome. All the approaches using SNP data showed that the Siciliana breed clearly differentiate from other populations, the Livornese breed separates into two distinct groups according to the feather colour (i.e. white and black) and the Bionda Piemontese and Bianca di Saluzzo breeds are closely related. The genetic variability found using SNP is comparable with that found by other authors in the same breeds using microsatellite markers. The CNV markers analysis clearly confirmed the SNP results.

Rare CNVs in Suicide Attempt include Schizophrenia-Associated Loci and Neurodevelopmental Genes: A Pilot Genome-Wide and Family-Based Study.

Suicidal behavior (SB) has a complex etiology involving genes and environment. One of the genetic components in SB could be copy number variations (CNVs), as CNVs are implicated in neurodevelopmental disorders. However, a recently published genome-wide and case-control study did not observe any significant role of CNVs in SB. Here we complemented these initial observations by instead using a family-based trio-sample that is robust to control biases, having severe suicide attempt (SA) in offspring as main outcome (n = 660 trios). We first tested for CNV associations on the genome-wide Illumina 1M SNP-array by using FBAT-CNV methodology, which allows for evaluating CNVs without reliance on CNV calling algorithms, analogous to a common SNP-based GWAS. We observed association of certain T-cell receptor markers, but this likely reflected inter-individual variation in somatic rearrangements rather than association with SA outcome. Next, we used the PennCNV software to call 385 putative rare (<1%) and large (>100 kb) CNVs, observed in n = 225 SA offspring. Nine SA offspring had rare CNV calls in a set of previously schizophrenia-associated loci, indicating the importance of such CNVs in certain SA subjects. Several additional, very large (>1MB) sized CNV calls in 15 other SA offspring also spanned pathogenic regions or other neural genes of interest. Overall, 45 SA had CNVs enriched for 65 medically relevant genes previously shown to be affected by CNVs, which were characterized by a neurodevelopmental biology. A neurodevelopmental implication was partly congruent with our previous SNP-based GWAS, but follow-up analysis here indicated that carriers of rare CNVs had a decreased burden of common SNP risk-alleles compared to non-carriers. In conclusion, while CNVs did not show genome-wide association by the FBAT-CNV methodology, our preliminary observations indicate rare pathogenic CNVs affecting neurodevelopmental functions in a subset of SA, who were distinct from SA having increased SNP risk-allele burden. These observations may open up new avenues in the genetic etiology of SB.

185: The impact of copy number analysis in expanded carrier screening

To examine the impact of copy number variants (CNVs) on detection rates in expanded carrier screening (ECS) panels. ECS panels typically include analysis of copy number variation (CNV) for only a small subset of genes and exons, often to the detriment of detection rate. To assess the impact of panel-wide CNV analysis, a preliminary analysis was performed on 56,267 de-identified ECS tests performed by full-exon next-generation sequencing at Counsyl’s laboratory. A 93 gene subset (consisting of severe and profound diseases) of Counsyl’s ECS panel was analyzed to estimate the additional benefit of copy number analysis. The disease risk, i.e., the probability that a hypothetical child from randomly selected parents is affected by disease, was used as a metric for detection rate. No variant curation was performed on the detected CNVs and all detected deletions/duplications were assumed pathogenic. To calculate the disease risk, historical NGS data at Counsyl was used to estimate deleterious allele frequencies (AFs). The AFs were then used to calculate the disease risk with CNV estimates either excluded or included. Without CNV analysis, the disease risk of the 93 gene subpanel was 123 affected children per 100,000. The addition of CNV analysis increased the detection rate by 2.0%, to 125 affected children per 100,000. This change in disease risk is roughly equivalent to calling non-CNV variants via NGS in the 54 least prevalent genes, which contribute 2.0% of the observed disease risk. Panel-wide CNV analysis increases detection rate and provides more value than the addition of low-prevalence genes. CNV calling may be important to further reduce a patient’s residual risks for specific genes due to certain scenarios, e.g., family history of a genetic disease and partner testing of known carriers for recessive diseases. For example, in HBB, CFTR, and ATM, CNVs account for up to 9% of the total detection rate. Further work to assess the clinical impact of the discovered variants is ongoing.

Detecting Accumulated Somatic Mutations in Healthy Aged Female Myeloid Cells

Abstract Background: Acute Myeloid Leukemia and Myelodysplasia are diseases associated with age and with genetic mutations driving evolution of clonal dominance. The earliest steps leading to genetic variation occur in a milieu of normal hematopoiesis by as a matter of course because genomic divergence is a fundamental element of biology. Organism speciation implicates genetic variation is the first step of evolution, followed by resource restriction pressure (a bottleneck). Such conditions create an environment where competitive advantages arise for genetically unique sub-species. Our work is based on the hypothesis that these rules also apply to the rise of pathologic clonal dominance. Consequentially, a low level of mutations in normal hematopoietic stem cells should be detectable and increase with age. Mutations can serve as lineage markers to allow the definition of the kinetics of competing myeloid sub-clones. Also, individual clones with higher rates of mutation might serve as a pre-condition for pathological clonal dominance and disease initiation. Methods: Healthy 78 year old female myeloid progenitors were single cell sorted and cultured for 6 weeks in expansion media. Clones were harvested and typed apropos X-Chromosome Inactivation state (XCI state) as either "Xa" or "Xb" using a methylation specific polymerase chain reaction strategy after DNA was bisulfite modified. Remaining unmodified DNA was whole genome amplified and placed on an Affymetrix 6.0 Single Nucleotide Polymorphism (SNP) array (2.5 million SNP calls/array). Partek Genome Suite software interpreted raw SNP intensity signals as copy number variants (CNV) or loss of heterozygosity (LOH) calls using a stringent search strategy of a &gt; 30 SNP markers, and an average variance of 0.5 (p&lt;0.001). Mutations were compared between XCI states and within XCI states (i.e: individual Xa or Xb clones). Results: SNP comparison between XCI defined phylogenetic lineages showed significant somatic LOH and CNV variance, as did comparison of clones within specific XCI states (e.g: between Xa clones). Mutations represent accumulated genetic drift over the 78.75 years since subject conception and are summarized in Table 1. Regarding call parameters, LOH analysis was unpaired, using Partek internal references. For CNV calls, subtractive comparison was made between related data sets. For example, raw Xa data from clones A1 and A2 were averaged, CNV calls were generated and compared to Xb averaged clone data (B1 and B2) to reveal specific XCI state variance (i.e.: A-Ave vs B-Ave). Individual clones were compared within the XCI state (i.e.: clone A1 compared directly to A2 to reveal unique A1 calls). An example of our data interpretation: 8 LOH and 558 CNV are specific to and shared among XCI state Xa clones (i.e.: A-both). Clone A1 had 35 specific, unique LOH and 1402 CNV calls, while clone A2 had 15 unique, specific LOH and 650 CNV calls. Xb clone data (B1, B2, B-both) are displayed in Table 1. Conclusions: Recent studies reveal frequent mutations in the blood of healthy individuals; however within a person there a no model to identify normal myeloid sub-clones. Therefore we have no accurate data on stem cell kinetics or clonal evolution in healthy marrow. Herein we describe the feasibility of using SNP calls variation in un-diseased aged human myeloid cells to define stem cell specific progeny. To our knowledge, this is the first use of both inclusive (somatic mutations) and exclusive (XCI state) markers to develop a phylogenetic lineage map of normal aged marrow. Importantly, all clones had progenitors with an identical genetic background (the fertilized egg) at a single time point (conception). Therefore, this innovative approach allows for the somatic variation rate to be monitored in primary cells occupying the same organism environment as a function of age and stress events (i.e.: chemo, radiation, etc). Disclosures No relevant conflicts of interest to declare.

Mutation Burden in Multiple Myeloma Is Captured By Gene Expression Profiles

Mutation Burden in Multiple Myeloma Is Captured By Gene Expression Profiles

Identification of Heterozygous Single- and Multi-exon Deletions in IL7R by Whole Exome Sequencing

PurposeWe aimed to achieve a retrospective molecular diagnosis by applying state-of-the-art genomic sequencing methods to past patients with T-B+NK+ severe combined immunodeficiency (SCID). We included identification of copy number variations (CNVs) by whole exome sequencing (WES) using the CNV calling method ExomeDepth to detect gene alterations for which routine Sanger sequencing analysis is not suitable, such as large heterozygous deletions.MethodsOf a total of 12 undiagnosed patients with T-B+NK+ SCID, we analyzed eight probands by WES, using GATK to detect single nucleotide variants (SNVs) and small insertions and deletions (INDELs) and ExomeDepth to detect CNVs.ResultsWe found heterozygous single- or multi-exon deletions in IL7R, a known disease gene for autosomal recessive T-B+NK+ SCID, in four families (seven patients). In three families (five patients), these deletions coexisted with a heterozygous splice site or nonsense mutation elsewhere in the same gene, consistent with compound heterozygosity. In our cohort, about a quarter of T-B+NK+ SCID patients (26%) had such compound heterozygous IL7R deletions.ConclusionsWe show that heterozygous IL7R exon deletions are common in T-B+NK+ SCID and are detectable by WES. They should be considered if Sanger sequencing fails to detect homozygous or compound heterozygous IL7R SNVs or INDELs.

A Genome-Wide Association Study to Identify Potential Germline Copy Number Variants for Sporadic Breast Cancer Susceptibility

Breast cancer (BC) predisposition in populations arises from both genetic and nongenetic risk factors. Structural variations such as copy number variations (CNVs) are heritable determinants for disease susceptibility. The primary objectives of this study are (1) to identify CNVs associated with sporadic BC using a genome-wide association study (GWAS) design; (2) to utilize 2 distinct CNV calling algorithms to identify concordant CNVs as a strategy to reduce false positive associations in the hypothesis-generating GWAS discovery phase, and (3) to identify potential candidate CNVs for follow-up replication studies. We used Affymetrix SNP Array 6.0 data profiled on Caucasian subjects (422 cases/348 controls) to call CNVs using algorithms implemented in Nexus Copy Number and Partek Genomics Suite software. Nexus algorithm identified CNVs associated with BC (731 autosomal CNVs with >5% frequency in the total sample and Q < 0.05). Thirteen CNVs were identified when Partek algorithm-called CNVs were overlapped with Nexus-identified CNVs; these CNVs showed concordances for frequency, effect size, and direction. Coding genes present within BC-associated CNVs were known to play a role in disease etiology and prognosis. Long noncoding RNAs identified within CNVs showed tissue-specific expression, indicating potential functional relevance of the findings. The identified candidate CNVs warrant independent replication.

De novo large rare copy-number variations contribute to conotruncal heart disease in Chinese patients.

Conotruncal heart anomalies (CTDs) are particularly prevalent congenital heart diseases (CHD) in Hong Kong. We surveyed large (>500 kb), rare (<1% frequency in controls) copy-number variations (CNVs) in Chinese patients with CTDs to identify potentially disease-causing variations. Adults who tested negative for 22q11.2 deletions were recruited from the adult CHD clinic in Hong Kong. Using a stringent calling criteria, high-confidence CNV calls were obtained, and a large control set comprising 3,987 Caucasian and 1,945 Singapore Chinese subjects was used to identify rare CNVs. Ten large rare CNVs were identified, and 3 in 108 individuals were confirmed to harbour de novo CNVs. All three patients were syndromic with a more complex phenotype, and each of these CNVs overlapped regions likely to be important in CHD. One was a 611 kb deletion at 17p13.3, telomeric to the Miller–Dieker syndrome (MDS) critical region, overlapping the NXN gene. Another was a 5 Mb deletion at 13q33.3, within a previously described critical region for CHD. A third CNV, previously unreported, was a large duplication at 2q22.3 overlapping the ZEB2 gene. The commonly reported 1q21.1 recurrent duplication was not observed in this Chinese cohort. We provide detailed phenotypic and genotypic descriptions of large rare genic CNVs that may represent CHD loci in the East Asian population. Larger samples of Chinese origin will be required to determine whether the genome-wide distribution differs from that found in predominantly European CHD cohorts.

THapMix: simulating tumour samples through haplotype mixtures.

Large-scale rearrangements and copy number changes combined with different modes of clonal evolution create extensive somatic genome diversity, making it difficult to develop versatile and scalable variant calling tools and create well-calibrated benchmarks. We developed a new simulation framework tHapMix that enables the creation of tumour samples with different ploidy, purity and polyclonality features. It easily scales to simulation of hundreds of somatic genomes, while re-use of real read data preserves noise and biases present in sequencing platforms. We further demonstrate tHapMix utility by creating a simulated set of 140 somatic genomes and showing how it can be used in training and testing of somatic copy number variant calling tools. tHapMix is distributed under an open source license and can be downloaded from https://github.com/Illumina/tHapMix CONTACT: sivakhno@illumina.comSupplementary information: Supplementary data are available at Bioinformatics online.

Assessing the reproducibility of exome copy number variations predictions.

BackgroundReproducibility is receiving increased attention across many domains of science and genomics is no exception. Efforts to identify copy number variations (CNVs) from exome sequence (ES) data have been increasing. Many algorithms have been published to discover CNVs from exomes and a major challenge is the reproducibility in other datasets. Here we test exome CNV calling reproducibility under three conditions: data generated by different sequencing centers; varying sample sizes; and varying capture methodology.MethodsFour CNV tools were tested: eXome Hidden Markov Model (XHMM), Copy Number Inference From Exome Reads (CoNIFER), EXCAVATOR, and Copy Number Analysis for Targeted Resequencing (CONTRA). To examine the reproducibility, we ran the callers on four datasets, varying sample sizes of N = 10, 30, 75, 100, 300, and data with different capture methodology. We examined the false negative (FN) calls and false positive (FP) calls for potential limitations of the CNV callers. The positive predictive value (PPV) was measured by checking the CNV call concordance against single nucleotide polymorphism array.ResultsUsing independently generated datasets, we examined the PPV for each dataset and observed wide range of PPVs. The PPV values were highly data dependent (p <0.001). For the sample sizes and capture method analyses, we tested the callers in triplicates. Both analyses resulted in wide ranges of PPVs, even for the same test. Interestingly, negative correlations between the PPV and the sample sizes were observed for CoNIFER (ρ = –0.80). Further examination of FN calls showed that 44 % of these were missed by all callers and were attributed to the CNV size (46 % spanned ≤3 exons). Overlap of the FP calls showed that FPs were unique to each caller, indicative of algorithm dependency.ConclusionsOur results demonstrate that further improvements in CNV callers are necessary to improve reproducibility and to include wider spectrum of CNVs (including the small CNVs). These CNV callers should be evaluated on multiple independent, heterogeneously generated datasets of varying size to increase robustness and utility. These approaches to the evaluation of exome CNV are essential to support wide utility and applicability of CNV discovery in exome studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0336-6) contains supplementary material, which is available to authorized users.