CNV Detection Research Articles

Benchmarking of Approaches for Gene Copy-Number Variation Analysis and Its Utility for Genetic Aberration Detection in High-Grade Serous Ovarian Carcinomas.

Objective: The goal of this study was to compare the results of CNV detection by three different methods using 13 paired carcinoma samples, as well as to perform a statistical analysis of the agreement. Methods: CNV was studied using NanoString nCounter v2 Cancer CN Assay (Nanostring), Illumina Infinium CoreExome microarrays (CoreExome microarrays) and digital droplet PCR (ddPCR). Results: There was a good level of agreement (PABAK score > 0.6) between the CoreExome microarrays and the ddPCR results for finding CNVs. There was a moderate level of agreement (PABAK values ≈ 0.3-0.6) between the NanoString Assay results and microarrays or ddPCR. For 83 out of 87 target genes studied (95%), the agreement between the CoreExome microarrays and NanoString nCounter was characterized by PABAK values < 0.75, except for MAGI3, PDGFRA, NKX2-1 and KDR genes (>0.75). The MET, HMGA2, KDR, C8orf4, PAX9, CDK6, and CCND2 genes had the highest agreement among all three approaches. Conclusions: Therefore, to get a better idea of how to genotype an unknown CNV spectrum in tumor or normal tissue samples that are very different molecularly, it makes sense to use at least two CNV detection methods. One of them, like ddPCR, should be able to quantitatively confirm the results of the other.

Genome-Wide Analysis Reveals Copy Number Variant Gene TGFBR3 Regulates Pig Back Fat Deposition.

BFT is closely related to meat quality and lean meat percentage in pigs. The BFT traits of European LW pigs significantly differ from those of Chinese indigenous fatty MZ pigs. CNV is a prevalent genetic variation that plays an important role in economically important traits in pigs. However, the potential contribution of CNV to BFT in LW and MZ pigs remains unclear. In this study, whole-genome CNV detection was performed using next-generation sequencing data from LW and MZ pigs, and transcriptome data from back fat tissue of 180-day-old LW and MZ pigs were integrated for expression quantitative trait loci (eQTL) analysis. We identified a copy number variation in the TGFBR3 gene associated with BFT, showing a dose effect between the genome and transcriptome levels of the TGFBR3 gene. In porcine preadipocytes, TGFBR3 expression continuously increased during differentiation. Knockdown of TGFBR3 using specific siRNA inhibited preadipocyte differentiation and proliferation. Our study provides insights into the genetic regulation of pork quality and offers a theoretical basis for improving carcass quality by modulating BFT in pigs.

A novel method addressing NGS-based mappability bias for sensitive detection of DNA alterations.

A turning point in cancer research is the introduction of massively parallel sequencing technology which greatly reduced the cost and time for genome sequencing. This enhanced the scope for detecting and analyzing the role of structural alterations in cancer. However, certain bias exists in NGS-based approaches, which badly affects the CNV identification process. Moreover, DNA repeats existing in CNV regions need special attention as they will degrade the performance of majority of the existing CNV detection tools, even after applying generalized bias correction method. This motivated this work, where a novel method has been designed to address the issue of DNA repeats and thereby mappability bias existing in regions of CNV.The method consists of three phases, where the first phase computes the alignment information of uniquely mapped DNA reads, considering the base quality and base mismatch parameters at nucleotide level precision. The second and the third phase use a novel approach to allocate the non-uniquely mapped reads to an optimal region of the DNA repeats based on a probabilistic membership model. The proposed method is capable of identifying CNVs present in coding, as well as non-coding region of the DNA, and is also capable of detecting CNVs existing in DNA repeat regions. The methodology achieves a sensitivity greater than [Formula: see text] during the performed simulations, and on real data, the detected variants are validated with the database of genomic variants, where the percentage overlap is also greater than 95%, and has achieved much better breakpoint prediction, as compared with other popular bias correction CNV detection methods.

Utility of genome sequencing in exome-negative pediatric patients with neurodevelopmental phenotypes.

Exome sequencing (ES) has emerged as an essential tool in the evaluation of neurodevelopmental disorders (NDD) of unknown etiology. Genome sequencing (GS) offers advantages over ES due to improved detection of structural, copy number, repeat number and non-coding variants. However, GS is less commonly utilized due to higher cost and more intense analysis. Here, we present nine cases of pediatric NDD that were molecularly diagnosed with GS between 2017 and 2022, following non-diagnostic ES. All individuals presented with global developmental delay or regression. Other features present in our cohort included epilepsy, white matter abnormalities, brain malformation and dysmorphic features. Two cases were diagnosed on GS due to newly described gene-disease relationship or variant reclassification (MAPK8IP3, CHD3). Additional features missed on ES that were later detected on GS were: intermediate-size deletions in three cases who underwent ES that were not validated for CNV detection, pathogenic variants within the non-protein coding genes SNORD118 and RNU7-1, pathogenic variant within the promoter region of GJB1, and a coding pathogenic variant within BCAP31 which was not sufficiently covered on ES. GS following non-diagnostic ES led to the identification of pathogenic variants in this cohort of nine cases, four of which would not have been identified by reanalysis alone.

Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome

BackgroundCopy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome.ResultsWhile consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395).ConclusionsNGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.

Loss of heterozygosity in CCM2 cDNA revealing a structural variant causing multiple cerebral cavernous malformations.

Loss-of-function variants in CCM1/KRIT1, CCM2/MGC4607, and CCM3/PDCD10 genes are identified in the vast majority of familial cases with multiple cerebral cavernous malformations. However, genomic DNA sequencing combined with large rearrangement screening fails to detect a pathogenic variant in 5% of the patients. We report a family with two affected members harboring multiple CCM lesions, one with severe hemorrhages and one asymptomatic. No causative variant was detected using DNA sequencing of the three CCM genes, CNV detection analysis, and RNA sequencing. However, a loss of heterozygosity in CCM2 was observed on cDNA sequences in one of the two affected members, which strongly suggested that this locus might be involved. Whole genome sequencing (WGS) identified a balanced structural variant on chromosome 7 with a breakpoint interrupting the CCM2 gene, preventing normal mRNA synthesis. These data underline the importance of WGS in undiagnosed patients with typical multiple CCM.

B-229 Benchmarking of Bioinformatics and Molecular Tools for Copy Number Variants Calling From Human Genome: A Detailed Look on Single Exons

Abstract Background Copy Number Variants (CNV) are deletions, duplications or insertions larger than 50 base pairs. They account for a large percentage of the normal genome variation and play major roles in human pathology. NGS bears the promise to allow concomitant exploration of CNV and smaller variants. However, accurately calling CNV from NGS data, especially targeted-NGS, remains a difficult computational task, for which a consensus is still lacking. Here, we aim to compare two bioinformatics approaches for CNV calling based on reads count [Dragen CNV caller and Normalized Reads Counts algorithm (NRC)] with legacy molecular tools for CNV detection such as MLPA and qPCR on a set of 20 polymorphic CNV of human genome. Methods DNA extracted from whole blood of 20 participants were enrolled in this validation. All samples were submitted to a clinical exome NGS targeted sequencing performed with lotus DNA library prep, xGEN hybridization capture, xGEN inherited disease panel (all from Integrated DNA Technologies) and sequenced using NextSeq-500 (Illumina). Twenty polymorphic CNV sites of human genome which overlap the exons included in the clinical exome were selected from NCBI dbVar curated common structural variants. Their median (min-max) minor allele frequency was 5% (1–63%). When intersected with regions included in clinical exome these CNV matched regions with 1 to 17 consecutive exons. One exon per CNV was the most prevalent condition (n = 10), followed by 2 exons (n = 4), 3 exons (n = 1), 4 exons (n = 3), 6 exons (n = 1), 8 exons (n = 1), 11 exons (n = 1), and 17 exons (n = 1). A single exon (region) of each included CNV were selected to be genotyped by the four tested methods: MLPA, qPCR, CNV caller implemented on Dragen enrichment v.4.0.3 (Illumina) and a laboratory-developed algorithm which detects CNV based on reads depth count of single exons normalized between samples from the same batch. Dragen was the exception for analysis of only single exons; its segmentation algorithm was not disable. The agreement between each compared method was calculated using absolute and relative frequency and measured using kappa statistics. MLPA was considered the reference method because of its known higher performance over qPCR for CNV calling. MLPA results were presented as copy numbers (CN). Results MLPA and qPCR fail to return results from 1 region each. MLPA called 24 losses with CN = 0, 71 losses with CN = 1, 260 no-CNV (CN = 2), 25 gains with CN = 3, and 0 gains with CN = 4. The CNV calling agreement of MLPA with other tested methods where: MLPA vs Dragen—341/380 (89.7%), Kappa = 0.66 (substantial agreement); MLPA vs NRC 366/380 (96.3%), Kappa = 0.92 (almost perfect agreement); MLPA vs qPCR 329/360 (91.4%), Kappa = 0.82 (almost perfect agreement). Conclusion We observed an overall good concordance between all tested methods when applied to a large number CNV to be interrogated (360–380 CNV callings). qPCR could validate most MLPA calls. Dragen was challenged by the fact that only polymorphic CNV were considered and showed substantial agreement with MLPA. MLPA and NRC showed the highest agreement suggesting that NRC could reliability confirm single exons CNV callings, as MLPA is extensively used to.

OS10.8.A APPLICATIONS OF NOVEL FFPE BASED TECHNOLOGIES FOR THE DIAGNOSTICS OF GLIOMAS

Abstract BACKGROUND Due to the lack of consistent tumour-cell specific markers, the diffuse brain invasion of glioblastoma (GB) presents a significant diagnostic challenge, especially for specimens with a low tumour cell fraction or scarce tissue. The only common alteration found in most GB is the gain of chromosome 7 and loss of chromosome 10. The emergence of new technologies such as spatial and single nucleus transcriptomics that allow for detection of copy number alterations (CNVs) may allow us to push the diagnostic boundaries. MATERIAL AND METHODS We performed 10x Visium spatial transcriptomic analysis of FFPE tissue from bulk resections of 14 FFPE GB and stereotactic biopsies of 6 GB, 7 IDH mutant astrocytomas, 4 oligodendrogliomas and 1 diffuse glioma NOS. Visium tissue spots were then deconvolved using data from single nucleus transcriptomic data from nuclei isolated from FFPE tissue. Fresh frozen and FFPE single nucleus transcriptomic libraries were also generated from 4 GB with matching fresh-frozen and FFPE tissue available. RESULTS The infiltration zone, the tumour core and healthy brain tissue could be differentiated using the inferred CNVs of chromosome 7 and chromosome 10. Additionally, inferred CNVs of tissue fragments smaller than 1 mm2 were in accordance with Infinium MethylationEPIC array analysis (a well-established method for CNV detection) from the same tumour tissue. Using the spatial transcriptomics approach, the loss of chromosome 1p and 19q, which is characteristic for oligodendroglioma was also detected. To overcome the low resolution of Visium, deconvolution approaches using a single cell dataset can be used to determine the tumour content of a given tissue more precisely. To test if the single nuclei data from FFPE samples is suitable for deconvolution of spatial transcriptomics, we compared fresh-frozen and FFPE single nuclei data from matching tumours. As expected, FFPE and fresh frozen nuclei clustered together according to cell types and tumours in the UMAP space. CONCLUSION Spatial transcriptomics can be useful for brain tumour diagnostic workup, especially in cases where a limited amount of tissue prevents analysis by standard molecular diagnostic METHODS . Additionally, CNVs inferred from the spatial data as well as the spatial expression of marker genes help to better distinguish tumour infiltration zones from the core tumour and healthy brain tissue in the case of GB. This analysis can be further supported by deconvolution with a reference set generated from single nucleus transcriptomic analysis of FFPE tissue.

Utility of clinical metagenomics in diagnosing malignancies in a cohort of patients with Epstein-Barr virus positivity.

Differentiation between benign and malignant diseases in EBV-positive patients poses a significant challenge due to the lack of efficient diagnostic tools. Metagenomic Next-Generation Sequencing (mNGS) is commonly used to identify pathogens of patients with fevers of unknown-origin (FUO). Recent studies have extended the application of Next-Generation Sequencing (NGS) in identifying tumors in body fluids and cerebrospinal fluids. In light of these, we conducted this study to develop and apply metagenomic methods to validate their role in identifying EBV-associated malignant disease. We enrolled 29 patients with positive EBV results in the cohort of FUO in the Department of Infectious Diseases of Huashan Hospital affiliated with Fudan University from 2018 to 2019. Upon enrollment, these patients were grouped for benign diseases, CAEBV, and malignant diseases according to their final diagnosis, and CNV analysis was retrospectively performed in 2022 using samples from 2018 to 2019. Among the 29 patients. 16 of them were diagnosed with benign diseases, 3 patients were diagnosed with CAEBV and 10 patients were with malignant diseases. 29 blood samples from 29 patients were tested for mNGS. Among all 10 patients with malignant diagnosis, CNV analysis suggested neoplasms in 9 patients. Of all 19 patients with benign or CAEBV diagnosis, 2 patients showed abnormal CNV results. The sensitivity and specificity of CNV analysis for the identification for tumors were 90% and 89.5%, separately. The application of mNGS could assist in the identification of microbial infection and malignancies in EBV-related diseases. Our results demonstrate that CNV detection through mNGS is faster compared to conventional oncology tests. Moreover, the convenient collection of peripheral blood samples adds to the advantages of this approach.

Solving inherited white matter disorder etiologies in the neurology clinic: Challenges and lessons learned using next-generation sequencing.

Rare neurodevelopmental disorders, including inherited white matter disorders or leukodystrophies, often present a diagnostic challenge on a genetic level given the large number of causal genes associated with a range of disease subtypes. This study aims to demonstrate the challenges and lessons learned in the genetic investigations of leukodystrophies through presentation of a series of cases solved using exome or genome sequencing. Each of the six patients had a leukodystrophy associated with hypomyelination or delayed myelination on MRI, and inconclusive clinical diagnostic genetic testing results. We performed next generation sequencing (case-based exome or genome sequencing) to further investigate the genetic cause of disease. Following different lines of investigation, molecular diagnoses were obtained for each case, with patients harboring pathogenic variants in a range of genes including TMEM106B, GJA1, AGA, POLR3A, and TUBB4A. We describe the lessons learned in reaching the genetic diagnosis, including the importance of (a) utilizing proper multi-gene panels in clinical testing, (b) assessing the reliability of biochemical assays in supporting diagnoses, and (c) understanding the limitations of exome sequencing methods in regard to CNV detection and region coverage in GC-rich areas. This study illustrates the importance of applying a collaborative diagnostic approach by combining detailed phenotyping data and metabolic results from the clinical environment with advanced next generation sequencing analysis techniques from the research environment to increase the diagnostic yield in patients with genetically unresolved leukodystrophies.

Role of chromosomal imbalances in the pathogenesis of DSD: A retrospective analysis of 115 prenatal samples

Differences of sex development (DSDs) are a group of congenital conditions characterized by a discrepancy between chromosomal, gonadal, and genital sex development of an individual, with significant impact on medical, psychological and reproductive life. The genetic heterogeneity of DSDs complicates the diagnosis and almost half of the patients remains undiagnosed. In this context, chromosomal imbalances in syndromic DSD patients may help to identify new genes implicated in DSDs. In this study, we aimed at describing the burden of chromosomal imbalances including submicroscopic ones (copy number variants or CNVs) in a cohort of prenatal syndromic DSD patients, and review their role in DSDs. Our patients carried at least one pathogenic or likely pathogenic chromosomal imbalance/CNV or low-level mosaicism for aneuploidy. Almost half of the cases resulted from an unbalanced chromosomal rearrangement. Chromosome 9p/q, 4p/q, 3q and 11q anomalies were more frequently observed. Review of the literature confirmed the causative role of CNVs in DSDs, either in disruption of known DSD-causing genes (SOX9, NR0B1, NR5A1, AR, ATRX, …) or as a tool to suspect new genes in DSDs (HOXD cluster, ADCY2, EMX2, CAMK1D, …). Recurrent CNVs of regulatory elements without coding sequence content (i.e. duplications/deletions upstream of SOX3 or SOX9) confirm detection of CNVs as a mean to explore our non-coding genome. Thus, CNV detection remains a powerful tool to explore undiagnosed DSDs, either through routine techniques or through emerging technologies such as long-read whole genome sequencing or optical genome mapping.

Performance comparisons of methylation and structural variants from low-input whole-genome methylation sequencing.

Aim: Whole-genome methylation sequencing carries both DNA methylation and structural variant information (single nucleotide variant [SNV]; copy number variant [CNV]); however, limited data is available on the reliability of obtaining this information simultaneously from low-input DNA using various library preparation and sequencing protocols. Methods: A HapMap NA12878 sample was sequenced with three protocols (EM-sequencing, QIA-sequencing and Swift-sequencing) and their performance was compared on CpG methylation measurement and SNV and CNV detection. Results: At low DNA input (10-25ng), EM-sequencing was superior in almost all metrics except CNV detection where all protocols were similar. EM-sequencing captured the highest number of CpGs and true SNVs. Conclusion: EM-sequencing is suitable to detect methylation, SNVs and CNVs from single sequencing with low-input DNA.

Pathogenic/likely pathogenic copy number variations and regions of homozygosity in fetal central nervous system malformations.

To explore pathogenic/likely pathogenic copy number variations (P/LP CNVs) and regions of homozygosity (ROHs) in fetal central nervous system (CNS) malformations. A cohort of 539 fetuses with CNS malformations diagnosed by ultrasound/MRI was retrospectively analyzed between January 2016 and December 2019. All fetuses were analyzed by chromosomal microarray analysis (CMA). Three cases with ROHs detected by CMA were subjected to whole-exome sequencing (WES). The fetuses were divided into two groups according to whether they had other structural abnormalities. The CNS phenotypes of the two groups were further classified as simple (one type) or complicated (≥ 2 types). (1) A total of 35 cases with P/LP CNVs were found. The incidence of P/LP CNVs was higher in the extra-CNS group [18.00% (9/50)] than in the isolated group [5.32% (26/489)] (P < 0.01), while there was no significant difference between the simpletype and complicated-type groups. (2) In the simple-type group, the three most common P/LP CNV phenotypes were holoprosencephaly, Dandy-Walker syndrome, and exencephaly. There were no P/LP CNVs associated with anencephaly, microcephaly, arachnoid cysts, ependymal cysts, or intracranial hemorrhage. (3) Only four cases with ROHs were found, and there were no cases of uniparental disomy or autosomal diseases. The P/LP CNV detection rates varied significantly among the different phenotypes of CNS malformations, although simple CNS abnormalities may also be associated with genetic abnormalities.

PSXII-1 CNV and Cnvr Detection from High-Density SNP Genotyping in Holstein Cattle

Abstract Copy number variation (CNV) represents an important source of genetic variation complementary to single nucleotide polymorphism (SNP) and provides valuable insights into the genetic architecture of complex traits in humans and livestock animals. However, many factors affect CNV detection, including sample size. Previous studies in Holstein cattle have only used hundreds of individuals. Therefore, in the present study, CNVs were identified from high-density genotype array data in 3,529 Holstein cows from 16 herds located in 7 U.S. states. CNVs were derived from 546,802 autosomal SNP markers with MAF &amp;gt; 0.05 and call rate &amp;gt; 0.90 using the intensity signals at each SNP position in a hidden Markov model implemented in PennCNV software. The log R ratio (LRR) values were corrected based on the guanine-cytosine content in the genomic regions located 500 kb upstream and downstream of each marker. After detection, CNVs presenting LRR ≤ 0.30, B allele frequency drift &amp;lt; 0.01, waviness factor ≤ 0.05, number of SNPs &amp;gt; 10 and length &amp;gt; 1 kb were merged into CNV regions (CNVR) using CNVRanger. A total of 1,881 non-redundant CNV events spanning the entire genome were identified in 1,771 cows, with a mean of 10 CNVs per individual and ranging from 1 to 27. The highest percentage of CNV calls was located on BTA7 (16%), while only 9 CNVs were identified on BTA9. The CNV calls were grouped into 685 CNVRs covering 3.07% of the bovine autosomal genome. The number of CNVRs with copy loss and gain were 356 and 310, and both types were observed in 19 regions. The chromosomal distribution of CNVR revealed that BTA7 harbors 38 CNVRs, while only 2 were detected on BTA28. These results will support further research on the contribution of CNVs and CNVRs to the genetic architecture of complex traits in Holstein cattle.

Correlation between serum levels of reproductive hormones and testicular spermatogenic function in men with azoospermia.

To investigate the serum levels of reproductive hormones in men with azoospermia and explore the correlation between abnormal reproductive hormones and the results of testicular biopsy and seminiferous extraction. The serum levels of luteinizing hormone (LH), follicle stimulating hormone (FSH), testosterone (T), and pituitary prolactin (PRL), were measured by Radio immunoassay. Testicular biopsy was used to determine whether patient's had spermatozoa. Spearman's correlation analysis was used to analyse the correlation between LH, FSH, testicular volume, and spermatogenic function. Receiver operator characteristic (ROC) curves were used to evaluate the efficacy and significance of LH and FSH for the detection of spermatozoa and copy number variation sequencing (CNV-seq). Compared with the control group, the serum levels of FSH and LH in the azoospermia group were significantly up-regulated (p < 0.001). Prolactin (PRL) and testosterone levels did not differ significantly between the two groups (p > 0.05). The serum levels of FSH and LH were negatively correlated with testicular spermatogenic function and testicular volume. The areas under the ROC curves for determining FSH and LH potency by testicular biopsy with or without spermatozoa were 0.640 and 0.622, respectively. The areas under the ROC curves for the potency of FSH and LH, as judged by CNV detection results, were 0.523 and 0.534, respectively. Serum levels of FSH and LH are associated with azoospermia and may be of predictive significance in the clinical setting for the acquisition of spermatozoa by testicular biopsy.

Goat SNX29: mRNA expression, InDel and CNV detection, and their associations with litter size.

The sorting nexin 29 (SNX29) gene, a member of the SNX family, is associated with material transport and lipid metabolism. Previous studies have shown that lipid metabolism affects reproductive function in animals. Thus, we hypothesized there is a correlation between the SNX29 gene and reproductive trait. To date, studies on the relationship between the SNX29 gene and reproductive traits are limited. Therefore, the purpose of this study was to examine the polymorphism in the SNX29 gene and its correlation with litter size. Herein, the mRNA expression levels of SNX29 were assayed in various goat tissue. Surprisingly, we found that SNX29 was highly expressed in the corpus luteum, large and small follicles. This result led us to suggest that the SNX29 gene has a critical role in reproduction. We further detected potential polymorphisms in Shaanbei white cashmere (SBWC) goats, including insertion/deletion (InDel, n = 2,057) and copy number variation (CNV, n = 1,402), which were related to fertility. The 17 bp deletion (n = 1004) and the 20 bp deletion (n = 1,053) within the SNX29 gene were discovered to be significantly associated with litter size (P < 0.05), and individuals the ID genotype of P1-Del-17 bp and the DD genotype of P2-Del-20bp had larger litter size. Additionally, the four CNV loci had significant correlations with litter size (P < 0.01) in our detected population. In CNV5, individuals with the median genotype were superior compared to those with loss or gain genotype in term of litter size, and in other three CNVs showed better reproductive trait in the gain genotype. Briefly, these findings suggest that SNX29 could be used as a candidate gene for litter size in goat breeding through marker-assisted selection (MAS).

Omics-informed CNV calls reduce false-positive rates and improve power for CNV-trait associations.

SummaryCopy-number variations (CNV) are believed to play an important role in a wide range of complex traits, but discovering such associations remains challenging. While whole-genome sequencing (WGS) is the gold-standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV); however, these calls suffer from elevated false-positive and -negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNVs) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics, and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (p < 3.0 × 10−90), outperforming other scores. Finally, in an association study of four anthropometric traits in 89,516 Estonian Biobank samples, the use of OQS led to a relative increase in the trait variance explained by CNVs of up to 56% compared with published quality filtering methods or scores. Overall, we put forward a flexible framework to improve any CNV detection method leveraging multi-omics evidence, applied it to improve PennCNV calls, and demonstrated its utility by improving the statistical power for downstream association analyses.

Algorithmic improvements for discovery of germline copy number variants in next-generation sequencing data

BackgroundCopy number variants (CNVs) play a significant role in human heredity and disease. However, sensitive and specific characterization of germline CNVs from NGS data has remained challenging, particularly for hybridization-capture data in which read counts are the primary source of copy number information.ResultsWe describe two algorithmic adaptations that improve CNV detection accuracy in a Hidden Markov Model (HMM) context. First, we present a method for computing target- and copy number-specific emission distributions. Second, we demonstrate that the Pointwise Maximum a posteriori (PMAP) HMM decoding procedure yields improved sensitivity for small CNV calls compared to the more common Viterbi HMM decoder. We develop a prototype implementation, called Cobalt, and compare it to other CNV detection tools using sets of simulated and previously detected CNVs with sizes spanning a single exon to a full chromosome.ConclusionsIn both the simulation and previously detected CNV studies Cobalt shows similar sensitivity but significantly fewer false positive detections compared to other callers. Overall sensitivity is 80–90% for deletion CNVs spanning 1–4 targets and 90–100% for larger deletion events, while sensitivity is somewhat lower for small duplication CNVs.

Investigation of Chromosomal Structural Abnormalities in Patients With Undiagnosed Neurodevelopmental Disorders.

Background: Structural variations (SVs) are various types of the genomic rearrangements encompassing at least 50 nucleotides. These include unbalanced gains or losses of DNA segments (copy number changes, CNVs), balanced rearrangements (such as inversion or translocations), and complex combinations of several distinct rearrangements. SVs are known to play a significant role in contributing to human genomic disorders by disrupting the protein-coding genes or the interaction(s) with cis-regulatory elements. Recently, different types of genome sequencing-based tests have been introduced in detecting various types of SVs other than CNVs and regions with absence of heterozygosity (AOH) with clinical significance. Method: In this study, we applied the mate-pair low pass (∼4X) genome sequencing with large DNA-insert (∼5 kb) in a cohort of 100 patients with neurodevelopmental disorders who did not receive informative results from a routine CNV investigation. Read-depth-based CNV analysis and chimeric-read-pairs analysis were used for CNV and SV analyses. The region of AOH was indicated by a simultaneous decrease in the rate of heterozygous SNVs and increase in the rate of homozygous SNVs. Results: First, we reexamined the 25 previously reported CNVs among 24 cases in this cohort. The boundaries of these twenty-five CNVs including 15 duplications and 10 deletions detected were consistent with the ones indicated by the chimeric-read-pairs analysis, while the location and orientation were determined in 80% of duplications (12/15). Particularly, one duplication was involved in complex rearrangements. In addition, among all the 100 cases, 10% of them were detected with rare or complex SVs (>10 Kb), and 3% were with multiple AOH (≥5 Mb) locating in imprinting chromosomes identified. In particular, one patient with an overall value of 214.5 Mb of AOH identified on 13 autosomal chromosomes suspected parental consanguinity. Conclusion: In this study, mate-pair low-pass GS resolved a significant proportion of CNVs with inconclusive significance, and detected additional SVs and regions of AOH in patients with undiagnostic neurodevelopmental disorders. This approach complements the first-tier CNV analysis for NDDs, not only by increasing the resolution of CNV detection but also by enhancing the characterization of SVs and the discovery of potential causative regions (or genes) contributory to could be complex in composition NDDs.

Towards the detection of copy number variation from single sperm sequencing in cattle

BackgroundCopy number variation (CNV) has been routinely studied using bulk-cell sequencing. However, CNV is not well studied on the single-cell level except for humans and a few model organisms.ResultsWe sequenced 143 single sperms of two Holstein bulls, from which we predicted CNV events using 14 single sperms with deep sequencing. We then compared the CNV results derived from single sperms with the bulk-cell sequencing of one bull’s family trio of diploid genomes. As a known CNV hotspot, segmental duplications were also predicted using the bovine ARS-UCD1.2 genome. Although the trio CNVs validated only some single sperm CNVs, they still showed a distal chromosomal distribution pattern and significant associations with segmental duplications and satellite repeats.ConclusionOur preliminary results pointed out future research directions and highlighted the importance of uniform whole genome amplification, deep sequence coverage, and dedicated software pipelines for CNV detection using single cell sequencing data.