Detection Of Copy Number Variations Research Articles

Abstract 2337: Accelerated optical genome mapping analysis with Stratys Compute and Guided Assembly

Abstract Background Optical genome maps (OGM) from Bionano enable the detection of genomic structural and copy number variants that cannot be detected by next-generation sequencing (NGS) technologies and are often missed by conventional cytogenetic techniques. Bionano has developed bioinformatics pipelines for calling structural and copy number variants including the Bionano Solve de novo assembly pipeline for constitutional analysis and the Rare Variant Analysis (RVA) pipeline for low-allele-fraction cancer applications. Both pipelines are computationally intensive and currently take 5-10 hours on the latest generation of the Bionano Saphyr® Compute which is deployed as a four-node compute cluster and requires significant IT resources. Methods To increase throughput and simplify the deployment of the compute resources needed to support the Stratys™ optical genome mapping instrument, Bionano has developed Stratys™ Compute. Stratys Compute improves both compute and analytical performance by adapting compute-intensive stages in the Solve pipeline to run on GPUs and by developing the Guided Assembly pipeline. Stratys Compute is powered by state-of-the-art NVIDIA RTX 6000 Ada generation cards and CUDA-optimized refinement, alignment, and structural variation detection kernels to accelerate OGM analysis. Stratys Compute is a standalone workstation placed adjacent to Stratys instrument. This will minimize the IT footprint and bypass the integration with the customer site’s data center. We have incorporated these optimizations into the new Guided Assembly pipeline, which aims to combine the low-allele fraction detection capability of the RVA pipeline with the whole genome coverage and ability to detect smaller structural variants enabled by the de novo assembly pipeline. The Guided Assembly pipeline uses the reference genome as an initial seed followed by extension, refinement, and structural variant calling. This new analysis method has been evaluated through comparison to previous results from both existing pipelines and standard benchmarking datasets used to estimate structural variant calling performance and is deployed on Stratys Compute for both constitutional and low allele-fraction applications. Results Guided Assembly has been adapted to run on GPU hardware in a simplified compute tower that can be deployed to a lab along with the Stratys instrument without the need for a dedicated server room. We found concordance between the guided assembly results and our previous de novo and RVA pipeline results. We also found increased sensitivity at low allele fractions for detecting insertion variants smaller than 5 kb and larger than 200 kb while finding equivalent performance for other variant types with the updated methods. The accelerated Guided Assembly for constitutional and low-allele-fraction applications will be available to early access customers in Q4 2023 with full commercial release in Q2 2024. Citation Format: Damla Senol Cali, Thomas Anantharaman, Martin Muggli, Samer Al-Saffar, Charles Schoonover, Neil Miller. Accelerated optical genome mapping analysis with Stratys Compute and Guided Assembly [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2337.

Abstract 3694: Genomic profiling of single cancer cells using the novel single-cell integrated mutational profiling of actionable cancer targets (scIMPACT) assay

Abstract Liquid biopsy provides opportunities to guide therapy decisions using biomarkers such as circulating tumor cells (CTCs) even years after the primary tumor resection. For this, novel methods permitting the genomic profiling of CTCs are needed to characterize systemic cancer spread and identify clinically targetable genomic alterations. Hence, we developed an assay based on the FDA-approved MSK-IMPACT® assay to analyze the genomes of single-cells. We present a series of benchmarking experiments using well-characterized cell lines to demonstrate the performance of our new Single-Cell Integrated Mutation Profiling of Actionable Cancer Targets (scIMPACT) assay.The scIMPACT assay was developed using Ampli1® WGA products of single-cell and pool samples. The workflow consists of an optimized library preparation protocol followed by targeted sequencing with the scIMPACT panel. A bioinformatics mutation calling workflow facilitates the detection of single nucleotide variations (SNVs), indels, and copy number variations (CNVs). To account for WGA associated bias, we developed an in-house proprietary machine learning based classifier algorithm. Two sample collectives were prepared and used for training and validating the workflow. The first collective, including genomic libraries of MDA-MB-453, BT474, BT549 and ZR-75.1 samples, was used for training of the mutation detection workflow. The second collective, consisting of HCC1395/HCC1395BL cell lines representing matched tumor and normal cells, was used to validate the performance of the assay. The scIMPACT assay identified somatic mutations in all WGA products as predicted from bulk gDNA samples and published literature. Notably, evidence of genetic heterogeneity was reported at the single cell level. Pertaining to the SNV detection, the training collective displayed 91% sensitivity and 96% specificity (AUC=0.98) while the validation collective demonstrated similar performance with 84% sensitivity and 96% specificity (AUC=0.90) proving the applicability of our workflow across different datasets. Furthermore, CNV analysis showed high concordance between matching gDNA and WGA samples. Our workflow enables mutational profiling with high specificity and sensitivity and accurate CNV detection in single cells. The scIMPACT assay has been successfully adapted to analyze samples at the single-cell level. Therefore, our method can be applied for the genomic profiling of patient-derived CTCs. Citation Format: Clara Chaiban, Adithi Ravikumar Varadarajan, Jonas Grote, Thomas Ragg, Isabell Blochberger, Vadim Dechand, Jens Warfsmann, Christoph A. Klein, Zbigniew T. Czyż. Genomic profiling of single cancer cells using the novel single-cell integrated mutational profiling of actionable cancer targets (scIMPACT) assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3694.

Abstract 318: Demystifying tumor heterogeneity with a fully automated, high-throughput single-cell DNA-Seq (scDNA-seq) workflow

Abstract Objective: Genetic heterogeneity is a key factor underlying tumor drug resistance and metastatic potential. Understanding this heterogeneity is therefore vital to improving both prognosis and treatment. Next-generation sequencing is a valuable tool for analyzing the genetic makeup of tumors. However, bulk sequencing methods lack the sensitivity to fully resolve tumor heterogeneity. While single cell methods provide a powerful approach to dissect such heterogeneity, to date, these methods have been limited in their throughput. To address this bottleneck, we have developed a fully automated workflow for generating scDNA-seq libraries based on PicoPLEX® whole genome amplification (WGA) technology. This high-throughput method, which has been optimized on our ICELL8® cx Single-Cell System, enables the generation of WGA libraries for >1,000 single cells within one day. Methods: We first sought to demonstrate the ability of this new high-throughput method to generate WGA libraries of comparable quality to the standard PicoPLEX workflow in terms of genome coverage, GC bias, and other typical quality metrics. We additionally assessed copy number variant (CNV) detection sensitivity using two cell lines with well-characterized small segmental CNVs: GM22601 (~25Mb deletion on chromosome 4) and GM05067 (~45Mb gain on chromosome 9). We also analyzed a lymphoblastoid line (K562) that carries a range of chromosomal aneuploidies. A total of 1,288 single-cell WGA libraries were generated and sequenced to a depth of 250K paired-end reads per cell. Data analysis was done using the Ginkgo CNV pipeline with an average bin size of 500 kb. As a final proof of principal, we also generated single cell data using tumor and adjacent normal tissue from two clear cell renal cell carcinoma (ccRCC) samples. Results: Libraries generated using our high-throughput workflow had a high mapping rate, with 94.1% of the reads being uniquely mapped. Additionally, the libraries were comparable to those generated with the standard PicoPLEX workflow in terms of coverage uniformity, GC bias and other metrics. Moreover, the segmental aneuploidies in both GM22601 and GM05067 were reliably detected in >90% of cells at a read depth as low as 250,000 reads per cell. Analysis of the ccRCC samples revealed subclonal heterogeneity with various CNVs common to ccRCC, including deletion of chr. 3p, amplification of chr. 5q, and duplication of chr. 2. Conclusion: By adapting PicoPLEX technology to high throughput using the ICELL8 cx Single-Cell System, we have obtained single-cell WGA libraries from up to 1,200 cells at once and enabled the reliable detection of CNVs and tumor subclones at a shallow sequencing depth. In addition, by leveraging the automated nanoliter-dispensing capabilities of ICELL8 cx system this method provides a significant reduction in reagent use and labor compared to plate-based methods. Citation Format: Xuan Li, Raymond Mendoza, Samantha Leong, Hima Anbunathan, Mike Covington, Mohammad Fallahi, Bryan Bell, Shuwen Chen, Yue Yun, Andrew Farmer. Demystifying tumor heterogeneity with a fully automated, high-throughput single-cell DNA-Seq (scDNA-seq) workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 318.

Genetics of inborn errors of immunity: Diagnostic strategies and new approaches to CNV detection.

Genetic diagnosis of inborn errors of immunity (IEI) is complex due to the large number of genes involved and their molecular features. Missense variants have been reported as the most common cause of IEI. However, the frequency of copy number variants (CNVs) may be underestimated since their detection requires specific quantitative techniques. At this point, the use of Next Generation Sequencing (NGS) is acquiring relevance. In this article, we present our experience in the genetic diagnosis of IEI based on three diagnostic algorithms that allowed the detection of single nucleotide variants (SNVs) and CNVs. Following this approximation, 703 index cases were evaluated between 2014 and 2021. Sanger sequencing, MLPA, CGH array, breakpoint spanning PCR or a customized NGS-based multigene-targeted panel were performed. A genetic diagnosis was reached in 142 of the 703 index cases (20%), 19 of them presented deletions as causal variants. Deletions were also detected in 5 affected relatives and 16 healthy carriers during the family studies. Additionally, we compile, characterize and present all the CNVs detected by our diagnostic algorithms, representing the largest cohort of deletions related to IEI to date. Furthermore, three bioinformatic tools (LACONv, XHMM, VarSeq™) based on NGS data were evaluated. VarSeq™ was the most sensitive and specific bioinformatic tool; detecting 21/23 (91%) deletions located in captured regions. Based on our results, we propose a strategy to guide the molecular diagnosis that can be followed by expert and non-expert centres in the field of IEI.

The Genetic Approach to Stillbirth: A Systematic Review

ABSTRACT Stillbirth represents a significant burden to individuals and families and has a great variety of causes, but much is still unknown about the underlying etiologies. The overall contribution of genetic abnormalities to stillbirth is not well known; this study was designed to evaluate known genetic causes of unexplained stillbirths, as well as understand the current position and future direction for using genetic and genomic testing in this field. This study was a systematic review of literature published between 1953 and 2020 on genetic testing and stillbirth. Inclusion criteria for studies were human cases, details and specific data on cohorts that included proportion of unexplained fetal deaths, and data on genetic testing and the subsequent results. Exclusion criteria were pregnancy loss before 22 weeks of gestation, focus on known causes of stillbirth, focus on fetal pathology or abnormalities, investigating neonatal death, genetic testing in populations with congenital anomalies or suspected specific syndromes, and those without details on genetic testing. The final review included 9 studies, 1 comparing karyotyping and chromosomal microarray analysis (CMA); 1 comparing karyotyping, CMA, and quantitative fluorescence–polymerase chain reaction; 2 with CMA only; and 5 with exome sequencing data. The studies that examined only CMA found 1 case of copy number variants (CNVs) in 24 cases and 31 CNVs in 96 cases. The largest study included here analyzed 532 stillbirths and compared CMA and karyotyping. Chromosomal microarray analysis showed a higher rate of detection of CNVs, increasing from 5.8% to 8.3% (P = 0.007) when compared with karyotype analysis. One challenge presented by this topic is that some disorders that could contribute to unexplained stillbirth are typically diagnosed when symptoms appear later in life and so are not usually associated with stillbirth or the risk of stillbirth. One example of this could be cardiac rhythm anomalies or other rare genetic variants that lead to stillbirth when appearing in a particular variation. One study included a cohort of unexplained stillbirth after postmortem examination that excluded obvious causes. Genetic analysis showed that several cases carried a variant that was functionally disruptive that may have contributed to stillbirth, but several additional cases that were functionally normal. Genetic variations relating to unexplained stillbirth are a broad and largely unexplored topic. Genetics play a vital role in clinical exploration and treatment of many disorders and in diagnosing causes of fetal mortality and morbidity. This review shows that genetic testing for stillbirth varies greatly according to test availability and guidelines of use, with some tests showing greater diagnostic yield than others. Although these studies incorporated large cohorts of unexplained stillbirths, genetic analysis identified potential causes for only a small percentage. Future research is needed to explain potential genetic causes for stillbirth.

Exome sequencing improves the molecular diagnostics of paediatric unexplained neurodevelopmental disorders

BackgroundNeurodevelopmental disorders (NDDs) and/or associated multiple congenital abnormalities (MCAs) represent a genetically heterogeneous group of conditions with an adverse prognosis for the quality of intellectual and social abilities and common daily functioning. The rapid development of exome sequencing (ES) techniques, together with trio-based analysis, nowadays leads to up to 50% diagnostic yield. Therefore, it is considered as the state-of-the-art approach in these diagnoses.ResultsIn our study, we present the results of ES in a cohort of 85 families with 90 children with severe NDDs and MCAs. The interconnection of the in-house bioinformatic pipeline and a unique algorithm for variant prioritization resulted in a diagnostic yield of up to 48.9% (44/90), including rare and novel causative variants (41/90) and intragenic copy-number variations (CNVs) (3/90). Of the total number of 47 causative variants, 53.2% (25/47) were novel, highlighting the clinical benefit of ES for unexplained NDDs. Moreover, trio-based ES was verified as a reliable tool for the detection of rare CNVs, ranging from intragenic exon deletions (GRIN2A, ZC4H2 genes) to a 6-Mb duplication. The functional analysis using PANTHER Gene Ontology confirmed the involvement of genes with causative variants in a wide spectrum of developmental processes and molecular pathways, which form essential structural and functional components of the central nervous system.ConclusionTaken together, we present one of the first ES studies of this scale from the central European region. Based on the high diagnostic yield for paediatric NDDs in this study, 48.9%, we confirm trio-based ES as an effective and reliable first-tier diagnostic test in the genetic evaluation of children with NDDs.

Comparison of real-time quantitative PCR and two digital PCR platforms to detect copy number variation in FCGR3B

The importance of structural genetic variants, such as copy number variations (CNVs), in modulating human disease is being increasingly recognized. Several clinical conditions require investigation of human neutrophil antigen (HNA-1), which is encoded by the Fc gamma receptor IIIb gene (FCGR3B), including suspicion of neutropenia, infections, and proactive testing of blood component donors to reduce the potential risk in transfusion. In this study, we compared real-time quantitative polymerase chain reaction (qPCR) with two digital PCR (dPCR) platforms, namely droplet digital PCR and an array-based platform, to determine copy numbers (CNs) in FCGR3B. We initially tested 400 anonymous blood donors with qPCR using a commercially available TaqMan probe assay (Applied Biosystems) on a Quant Studio 12 Flex. CNs was determined for all 400 tested individuals with CNs ranging from zero to four. Zero copies were detected in 0.2% (1/400), one copy was detected in 3.8% (15/400), two copies were detected in 87.8% (351/400), three copies were detected in 8.0% (32/400), and four copies were detected in 0.2% (1/400) of tested individuals. From this cohort, we selected 32 donors with CNs from zero to four for analyses with Digital Real-Time PCR (dPCR) using Lab on an array (LOAA) on an On-Point analyzer from Optolane Technologies Inc. and the Droplet Digital PCR (ddPCR) platform from Bio-Rad Laboratories. We compared the obtained CNs of FCGR3B on the three platforms and found full concordance between the CNs obtained. We therefore conclude that all three platforms can be used for quantification of CNs for FCGR3B, and although dPCR has some advantages over qPCR, it was not necessary for reliably estimating CNs of the FCGR3B gene.

Detection of Copy-Number Variation in Circulating Cell-Free DNA in Patients With Uveal Melanoma.

Somatic chromosomal alterations, particularly monosomy 3 and 8q gains, have been associated with metastatic risk in uveal melanoma (UM). Whole genome-scale evaluation of detectable alterations in cell-free DNA (cfDNA) in UM could provide valuable prognostic information. Our pilot study evaluates the correlation between genomic information using ultra-low-pass whole-genome sequencing (ULP-WGS) of cfDNA in UM and associated clinical outcomes. ULP-WGS of cfDNA was performed on 29 plasma samples from 16 patients, 14 metastatic UM (mUM) and two non-metastatic, including pre- and post-treatment mUM samples from 10 patients treated with immunotherapy and one with liver-directed therapy. We estimated tumor fraction (TFx) and detected copy-number alterations (CNAs) using ichorCNA. Presence of 8q amplification was further analyzed using the likelihood ratio test (LRT). Eleven patients with mUM (17 samples) of 14 had detectable circulating tumor DNA (ctDNA). 8q gain was detected in all 17, whereas monosomy 3 was detectable in 10 of 17 samples. TFx generally correlated with disease status, showing an increase at the time of disease progression (PD). 8q gain detection sensitivity appeared greater with the LRT than with ichorCNA at lower TFxs. The only patient with mUM with partial response on treatment had a high pretreatment TFx and undetectable on-treatment ctDNA, correlating with her profound response and durable survival. ctDNA can be detected in mUM using ULP-WGS, and the TFx correlates with DS. 8q gain was consistently detectable in mUM, in line with previous studies indicating 8q gains early in primary UM and higher amplification with PD. Our work suggests that detection of CNAs by ULP-WGS, particularly focusing on 8q gain, could be a valuable blood biomarker to monitor PD in UM.

OTSUCNV: an adaptive segmentation and OTSU-based anomaly classification method for CNV detection using NGS data

Copy-number variations (CNVs), which refer to deletions and duplications of chromosomal segments, represent a significant source of variation among individuals, contributing to human evolution and being implicated in various diseases ranging from mental illness and developmental disorders to cancer. Despite the development of several methods for detecting copy number variations based on next-generation sequencing (NGS) data, achieving robust detection performance for CNVs with arbitrary coverage and amplitude remains challenging due to the inherent complexity of sequencing samples. In this paper, we propose an alternative method called OTSUCNV for CNV detection on whole genome sequencing (WGS) data. This method utilizes a newly designed adaptive sequence segmentation algorithm and an OTSU-based CNV prediction algorithm, which does not rely on any distribution assumptions or involve complex outlier factor calculations. As a result, the effective detection of CNVs is achieved with lower computational complexity. The experimental results indicate that the proposed method demonstrates outstanding performance, and hence it may be used as an effective tool for CNV detection.

Validation of low-pass genome sequencing for prenatal diagnosis.

Chromosomal microarray (CMA), while considered the gold standard for detecting copy number variants (CNVs) in prenatal diagnostics, has its limitations, including the necessity to replace aging microarray equipment, low throughput, a static design, and an inefficient multi-day workflow. This study evaluates the feasibility of low-pass genome sequencing (LP-GS) as a potential replacement for CMA in prenatal diagnostics. We comprehensively compared LP-GS at 10x and 5x average depths with CMA in a prenatal laboratory. We examined parameters, including concordance, sensitivity, specificity, workflow efficiency, and cost-effectiveness. We found a high degree of agreement between LP-GS and CMA for detecting CNVs and absence of heterozygosity. Furthermore, compared to CMA, LP-GS increased workflow efficiency and proved to be cost-neutral at 10x and cost-effective at 5x. Our study suggests that LP-GS is a promising alternative to CMA in prenatal diagnostics, offering advantages, including a more efficient workflow and scalability for larger testing volumes. Importantly, for clinical laboratories that have adopted next-generation sequencing in a separate capacity, LP-GS facilitates a unified NGS-centric approach, enabling workflow consolidation. By offering a single, streamlined platform for detecting a broad range of genetic variants, LP-GS may represent a critical step toward enhancing the diagnostic capabilities of prenatal laboratories.

On the core segmentation algorithms of copy number variation detection tools.

Shotgun sequencing is a high-throughput method used to detect copy number variants (CNVs). Although there are numerous CNV detection tools based on shotgun sequencing, their quality varies significantly, leading to performance discrepancies. Therefore, we conducted a comprehensive analysis of next-generation sequencing-based CNV detection tools over the past decade. Our findings revealed that the majority of mainstream tools employ similar detection rationale: calculates the so-called read depth signal from aligned sequencing reads and then segments the signal by utilizing either circular binary segmentation (CBS) or hidden Markov model (HMM). Hence, we compared the performance of those two core segmentation algorithms in CNV detection, considering varying sequencing depths, segment lengths and complex types of CNVs. To ensure a fair comparison, we designed a parametrical model using mainstream statistical distributions, which allows for pre-excluding bias correction such as guanine-cytosine (GC) content during the preprocessing step. The results indicate the following key points: (1) Under ideal conditions, CBS demonstrates high precision, while HMM exhibits a high recall rate. (2) For practical conditions, HMM is advantageous at lower sequencing depths, while CBS is more competitive in detecting small variant segments compared to HMM. (3) In case involving complex CNVs resembling real sequencing, HMM demonstrates more robustness compared with CBS. (4) When facing large-scale sequencing data, HMM costs less time compared with the CBS, while their memory usage is approximately equal. This can provide an important guidance and reference for researchers to develop new tools for CNV detection.

Impact of copy number variants in epilepsy plus neurodevelopment disorders

IntroductionEpilepsy, a neurological disorder characterized by recurring unprovoked seizures due to excessive neuronal excitability, is primarily attributed to genetic factors, accounting for an estimated 70 % of cases. Array-comparative genomic hybridization (aCGH) is a crucial genetic test for detecting copy number variants (CNVs) associated with epilepsy. This study aimed to analyze a cohort of epilepsy patients with CNVs detected through aCGH to enhance our understanding of the genetic underpinnings of epilepsy. MethodsA retrospective cross-sectional study was conducted using the aCGH database from the Genetics Department of the Faculty of Medicine of the University of Porto, encompassing 146 patients diagnosed with epilepsy, epileptic encephalopathy, or seizures. Clinical data were collected, and aCGH was performed following established guidelines. CNVs were classified based on ACMG standards, and patients were categorized into four groups according to their clinical phenotype. ResultsAmong the 146 included patients, 94 (64 %) had at least one CNV, with 22 (15.1 %) classified as pathogenic or likely pathogenic. Chromosomes 1, 2, 16, and X were frequently implicated, with Xp22.33 being the most reported region (8 CNVs). The phenotype "Epilepsy and global developmental delay/intellectual disability" showed the highest prevalence of clinically relevant CNVs. Various CNVs were identified across different groups, suggesting potential roles in epilepsy. ConclusionsThis study highlights the significance of aCGH in unraveling the genetic basis of epilepsy and tailoring treatment strategies. It contributes valuable insights to the expanding knowledge in the field, emphasizing the need for research to elucidate the diverse genetic causes of epilepsy.

A MALDI-TOF mass spectrometry-based method for detection of copy number variations in BRCA1 and BRCA2 genes.

Background: Identifying germline mutations in BRCA1 and BRCA2 genes (BRCAs) would benefit the carriers in multiple aspects. In addition to single-nucleotide variations and small indels, copy number variations (CNVs) is also an indispensable component of identifiable mutations in BRCAs. A sensitive, rapid and throughput-flexible method to detect CNVs would be preferred to meet the rising clinical requirements for BRCAs testing. Methods: We developed a MALDI-TOF-MS-based method (MS assay) which included three steps: first, multiplex end-point PCR followed by a single base extension reaction; second, automated analyte transfer and data acquisition; third, data analysis. We applied MS assay to detect CNVs in BRCAs in 293 Chinese patients with ovarian or pancreatic cancer. All the samples were examined by targeted next-generation sequencing (TS) simultaneously. Samples were further cross-validated by multiplex ligation-dependent probe amplification (MLPA) if the results from MS assay and TS were inconsistent. Long range PCR was then applied to identify the exact breakpoints in BRCAs. Results: MS assay introduced highly multiplexed panels to detect CNVs of BRCAs semi-quantitatively. Simplified on-board data analysis was available for MS assay and no complex bioinformatics was needed. The turnaround time of MS assay was less than 8hours with a hands-on time of only 40min. Compared to TS, MS assay exhibited higher sensitivity (100% vs. 75%) and was more flexible in throughput, with the reagent cost per sample remaining constant no matter how many samples were examined per assay. A total of eight CNVs in BRCAs were detected from the 293 samples, and the molecular breakpoints were successfully identified in five samples through long-range PCR followed by Sanger sequencing. Conclusion: Our results suggested that MS assay might be an effective method in primary screening for CNVs in genes such as BRCAs, especially when short turnaround time and/or high sensitivity is a top priority.

An evaluation of carrier detection for Spinal muscular atrophy using digital PCR assay

To assess the effectiveness and feasibility of carrier detection for Spinal muscular atrophy (SMA) by using digital PCR assay. Peripheral blood samples were collected from 214 pregnant women who were routinely screened for SMA carriers, of which 204 were randomly selected samples and 10 were samples with known copy numbers of SMN1 exons 7 and 8. Samples with known copy numbers of SMN1 exons 7 and 8 were randomly mixed into the experiment to validate the performance of the digital PCR assay. The copy numbers of SMN1 exons 7 and 8 and SMN2 exons 7 and 8 in peripheral blood samples were detected by digital PCR assay. The results of SMN1 exons 7 and 8 were compared with those of the quantitative PCR method to assess the reliability and clinical performance of the digital PCR assay. Among the 204 random samples, digital PCR has detected five samples with simultaneous heterozygous deletion of SMN1 exons 7 and 8, three samples with heterozygous deletion of SMN1 exon 8 only, and 196 samples with no deletion of SMN1 exons 7 and 8. Ten samples with known SMN1 exons 7 and 8 copy numbers were detected with the expected values. The digital PCR test results were fully consistent with that of the quantitative PCR. The results of digital PCR for the detection of copy number variation of SMN1 exons 7 and 8 were consistent with qPCR. Digital PCR assay was able to clearly distinguish the copy number of the target genes, therefore can be used for SMA carrier screening. Moreover, it can also detect copy number of SMN2 exons 7 and 8, which can provide more information for genetic counseling.

Genome-wide investigation to assess copy number variants in the Italian local chicken population.

Copy number variants (CNV) hold significant functional and evolutionary importance. Numerous ongoing CNV studies aim to elucidate the etiology of human diseases and gain insights into the population structure of livestock. High-density chips have enabled the detection of CNV with increased resolution, leading to the identification of even small CNV. This study aimed to identify CNV in local Italian chicken breeds and investigate their distribution across the genome. Copy number variants were mainly distributed across the first six chromosomes and primarily associated with loss type CNV. The majority of CNV in the investigated breeds were of types 0 and 1, and the minimum length of CNV was significantly larger than that reported in previous studies. Interestingly, a high proportion of the length of chromosome 16 was covered by copy number variation regions (CNVR), with the major histocompatibility complex being the likely cause. Among the genes identified within CNVR, only those present in at least five animals across breeds (n = 95) were discussed to reduce the focus on redundant CNV. Some of these genes have been associated to functional traits in chickens. Notably, several CNVR on different chromosomes harbor genes related to muscle development, tissue-specific biological processes, heat stress resistance, and immune response. Quantitative trait loci (QTL) were also analyzed to investigate potential overlapping with the identified CNVR: 54 out of the 95 gene-containing regions overlapped with 428 QTL associated to body weight and size, carcass characteristics, egg production, egg components, fat deposition, and feed intake. The genomic phenomena reported in this study that can cause changes in the distribution of CNV within the genome over time and the comparison of these differences in CNVR of the local chicken breeds could help in preserving these genetic resources.

ECOLE: Learning to call copy number variants on whole exome sequencing data.

Copy number variants (CNV) are shown to contribute to the etiology of several genetic disorders. Accurate detection of CNVs on whole exome sequencing (WES) data has been a long sought-after goal for use in clinics. This was not possible despite recent improvements in performance because algorithms mostly suffer from low precision and even lower recall on expert-curated gold standard call sets. Here, we present a deep learning-based somatic and germline CNV caller for WES data, named ECOLE. Based on a variant of the transformer architecture, the model learns to call CNVs per exon, using high-confidence calls made on matched WGS samples. We further train and fine-tune the model with a small set of expert calls via transfer learning. We show that ECOLE achieves high performance on human expert labelled data for the first time with 68.7% precision and 49.6% recall. This corresponds to precision and recall improvements of 18.7% and 30.8% over the next best-performing methods, respectively. We also show that the same fine-tuning strategy using tumor samples enables ECOLE to detect RT-qPCR-validated variations in bladder cancer samples without the need for a control sample. ECOLE is available at https://github.com/ciceklab/ECOLE .

Application of Chromosomal Microarray Analysis in Genetic Reasons of Miscarriage Tissues.

The potential causes of miscarriage are very complex, including genetic, immune, infectious, and endocrine factors. 50%-60% of miscarriages are caused by chromosomal abnormalities. Chromosomal microarray analysis (CMA) is a key tool in this context, capable of detecting not only copy number variations (CNV) but also loss of heterozygosity (LOH). CMA has been used as a tool to investigate the genetic reasons for miscarriage. In our study, chromosomal microarray analysis (CMA) conducted 1220 miscarriage villous tissues. The results from this technology were used to identify the genetic reasons for miscarriage and evaluated strategies for subsequent pre-pregnancy planning. Here, the abnormality rate of miscarriage was 56.07%(684/1220). The aneuploidy rate accounted for 81.14%(555/684), and was significantly higher in group >35-year-old age. The second most common genetic reason for miscarriage was polyploidy, accounting for 10.09%(69/684). Additionally, we discovered loss of heterozygosity (LOH) in a small percentage of cases, accounting for 2.20%(15/684) reason for miscarriage genetic reasons, due to the advantage of CMA can detect isodisomy (a kind of uniparental disomy). 45 cases (6.58%) with copy number variants, which due to the CMA can detect copy number variations. Our study indicated that miscarriage villous tissues should be performed genetic analysis, seek help from professional genetic counseling.

Genome Sequencing Unveils the Role of Copy Number Variants in Hearing Loss and Identifies Novel Deletions With Founder Effect in the DFNB1 Locus

Sensorineural hearing loss is a prevalent disorder with significant genetic involvement, which is often challenging to diagnose due to genetic heterogeneity. Exome sequencing (ES) has been a standard diagnostic tool for sensorineural hearing loss, but its limitations in detecting copy number variants (CNVs) and intronic variants have prompted the exploration of genome sequencing (GS) for improved diagnostic yield. We conducted GS on 46 hearing loss families with previously negative ES results and an additional cohort of 36 patients with a monoallelic pathogenic variant in GJB2 (the most common deafness gene). Additionally, the impact of a previously unrecognized novel 125‐kb deletion in the DFNB1 locus on GJB2 expression was assessed using quantitative polymerase chain reaction (qPCR), and haplotype analysis was performed to characterize the deletion. GS diagnosed eight cases (17%, 8/46) in the ES‐negative cohort, primarily attributed to CNVs (6/8). Notably, a previously unrecognized 125 kb deletion in the DFNB1 region was identified, affecting GJB2 expression and characterizing it as a founder effect in East Asian. In 47 patients with a monoallelic GJB2 variant, 15% (95% CI, 7.4%–28%) were diagnosed with DFNB1 deletions. Analysis of the gnomAD database revealed the prevalence and ethnic diversity of DFNB1 deletions, with the novel 125 kb deletion emerging as a prominent pathogenic variant in East Asian, non‐Finnish European, and admixed American populations. Our study highlights the utility of GS in diagnosing sensorineural hearing loss. The identification of DFNB1 deletions underscores their significant contribution to hearing loss etiology, advocating for their inclusion in routine diagnostic testing. We propose GS as a primary genetic testing approach for patients with hearing loss, offering comprehensive genomic analysis and the potential for improved diagnostic accuracy.

Next-generation variant exon screening: Moving forward in routine genetic disease investigations

PurposePatients with genetic diseases often seek testing to reach a firm diagnosis. Based on clinical phenotypes, exome sequencing for small-nucleotide variations or array-based methods for copy-number variations (CNVs) are commonly offered to identify the underlying causative genetic variants. In this study, we investigated whether data from a standard ES test could be used to additionally identify pathogenic CNVs and increase diagnostic yield. MethodsProspectively, 134 patients presenting with a skin condition suspected of being genetic in origin were offered the next-generation variant exon screening (ngVES) test. Sequencing data were analyzed for both single-nucleotide variants and CNVs using established algorithms. ResultsThe positive detection rate for skin diseases using ngVES was 66% (88/134) with the most common diagnoses being neurofibromatosis type1 (n = 48) and tuberous sclerosis type2 (n = 12). The diagnostic increased yield from 58% to 66% was the result of additional detection of pathogenic CNVs. Each of the 9 CNVs were verified by independent genetic tests. ConclusionThe advances in the ngVES bioinformatics pipeline are proofs of concept, which improved identification of genetic variants associated with skin disease. Simultaneous single-nucleotide variants/INDEL and CNV detection by this approach demonstrates ngVES potential as a first-tier screen for any suspected genetic disease.

Detection and characterization of copy-number variants from exome sequencing in the DDD study

PurposeStructural variants such as multiexon deletions and duplications are an important cause of disease but are often overlooked in standard exome/genome sequencing analysis. We aimed to evaluate the detection of copy-number variants (CNVs) from exome sequencing (ES) in comparison with genome-wide low-resolution and exon-resolution chromosomal microarrays (CMAs) and to characterize the properties of de novo CNVs in a large clinical cohort. MethodsWe performed CNV detection using ES of 9859 parent-offspring trios in the Deciphering Developmental Disorders (DDD) study and compared them with CNVs detected from exon-resolution array comparative genomic hybridization in 5197 probands from the DDD study. ResultsIntegrating calls from multiple ES-based CNV algorithms using random forest machine learning generated a higher quality data set than using individual algorithms. Both ES- and array comparative genomic hybridization–based approaches had the same sensitivity of 89% and detected the same number of unique pathogenic CNVs not called by the other approach. Of DDD probands prescreened with low-resolution CMAs, 2.6% had a pathogenic CNV detected by higher-resolution assays. De novo CNVs were strongly enriched in known DD-associated genes and exhibited no bias in parental age or sex. ConclusionES-based CNV calling has higher sensitivity than low-resolution CMAs currently in clinical use and comparable sensitivity to exon-resolution CMA. With sufficient investment in bioinformatic analysis, exome-based CNV detection could replace low-resolution CMA for detecting pathogenic CNVs.