Detection Of Copy Number Variations Research Articles

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.

Detection and characterization of copy number variation in three differentially-selected Nellore cattle populations.

Introduction: Nellore cattle (Bos taurus indicus) is the main beef cattle breed raised in Brazil. This breed is well adapted to tropical conditions and, more recently, has experienced intensive genetic selection for multiple performance traits. Over the past 43years, an experimental breeding program has been developed in the Institute of Animal Science (IZ, Sertaozinho, SP, Brazil), which resulted in three differentially-selected lines known as Nellore Control (NeC), Nellore Selection (NeS), and Nellore Traditional (NeT). The primary goal of this selection experiment was to determine the response to selection for yearling weight (YW) and residual feed intake (RFI) on Nellore cattle. The main objectives of this study were to: 1) identify copy number variation (CNVs) in Nellore cattle from three selection lines; 2) identify and characterize CNV regions (CNVR) on these three lines; and 3) perform functional enrichment analyses of the CNVR identified. Results: A total of 14,914 unique CNVs and 1,884 CNVRs were identified when considering all lines as a single population. The CNVRs were non-uniformly distributed across the chromosomes of the three selection lines included in the study. The NeT line had the highest number of CNVRs (n = 1,493), followed by the NeS (n = 823) and NeC (n = 482) lines. The CNVRs covered 23,449,890 bp (0.94%), 40,175,556 bp (1.61%), and 63,212,273 bp (2.54%) of the genome of the NeC, NeS, and NeT lines, respectively. Two CNVRs were commonly identified between the three lines, and six, two, and four exclusive regions were identified for NeC, NeS, and NeT, respectively. All the exclusive regions overlap with important genes, such as SMARCD3, SLC15A1, and MAPK1. Key biological processes associated with the candidate genes were identified, including pathways related to growth and metabolism. Conclusion: This study revealed large variability in CNVs and CNVRs across three Nellore lines differentially selected for YW and RFI. Gene annotation and gene ontology analyses of the exclusive CNVRs to each line revealed specific genes and biological processes involved in the expression of growth and feed efficiency traits. These findings contribute to the understanding of the genetic mechanisms underlying the phenotypic differences among the three Nellore selection lines.

Copy number variations in malignant melanoma: genomic regions, biomarkers, and therapeutic targets.

Malignant melanoma is a skin tumor arising from melanocytes, occurring mostly in predisposed individuals. Melanomas are frequently present with copy number variations (CNVs), i.e., gains or losses of specific DNA regions that have provided immense potential for disease diagnosis and classification. The methodology of CNV detection has revolutionized in past decades, and current high throughput technologies enable us to analyze the entire spectrum of CNV alterations at the whole genome scale. Thus, identifying novel CNV biomarkers and evaluating their applicability in biomedicine are becoming increasingly important. The aim of this review was to summarize copy number changes occurring in malignant melanomas. We made an overview of specific genes and chromosomal locations affected in sporadic and familial melanoma and also of known germline alterations in melanoma-prone families. We summarized genomic regions aberrant in malignant melanoma and highlighted those frequently discussed in the literature, suggesting 7q, 11q, 12q, 9p, and 1q, but also others, as the most affected ones.

Copy number variation detection based on constraint least squares

Copy number variation detection based on constraint least squares

Prenatal Exome Sequencing Analysis in Fetuses with Various Ultrasound Findings.

To evaluate the use of Exome Sequencing (ES) for the detection of genome-wide Copy Number Variants (CNVs) and the frequency of SNVs-InDels in selected genes related to developmental disorders in a cohort of consecutive pregnancies undergoing invasive diagnostic procedures for minor or simple ultrasound findings with no indication of ES. Women undergoing invasive diagnostic testing (chorionic villus sampling or amniocentesis) for QF-PCR and chromosomal microarray analysis (CMA) due to prenatal ultrasound findings without an indication for ES were selected over a five-month period (May-September 2021). ES was performed to compare the efficiency of genome-wide CNV detection against CMA analysis and to detect monogenic disorders. Virtual gene panels were selected to target genes related to ultrasound findings and bioinformatic analysis was performed, prioritizing variants based on the corresponding HPO terms. The broad Fetal Gene panel for developmental disorders developed by the PAGE group was also included in the analysis. A total of 59 out of 61 women consented to participate in this study. There were 36 isolated major fetal anomalies, 11 aneuploidy markers, 6 minor fetal anomalies, 4 multiple anomalies, and 2 other ultrasound signs. Following QF-PCR analysis, two uncultured samples were excluded from this study, and six (10%) common chromosome aneuploidies were detected. In the remaining 51 cases, no pathogenic CNVs were detected at CMA, nor were any pathogenic variants observed in gene panels only targeting the ultrasound indications. Two (3.9%) monogenic diseases, apparently unrelated to the fetal phenotype, were detected: blepharo-cheilo-odontic syndrome (spina bifida) and Duchenne muscular dystrophy (pyelocaliceal dilation). In our series of pregnancies with ultrasound findings, common aneuploidies were the only chromosomal abnormalities present, which were detected in 10% of cases. ES CNV analysis was concordant with CMA results in all cases. No additional findings were provided by only targeting selected genes based on ultrasound findings. Broadening the analysis to a larger number of genes involved in fetal developmental disorders revealed monogenic diseases in 3.9% of cases, which, although apparently not directly related to the indications, were clinically relevant.

Incorporating CNV analysis improves the yield of exome sequencing for rare monogenic disorders-an important consideration for resource-constrained settings.

Exome sequencing (ES) is a recommended first-tier diagnostic test for many rare monogenic diseases. It allows for the detection of both single-nucleotide variants (SNVs) and copy number variants (CNVs) in coding exonic regions of the genome in a single test, and this dual analysis is a valuable approach, especially in limited resource settings. Single-nucleotide variants are well studied; however, the incorporation of copy number variant analysis tools into variant calling pipelines has not been implemented yet as a routine diagnostic test, and chromosomal microarray is still more widely used to detect copy number variants. Research shows that combined single and copy number variant analysis can lead to a diagnostic yield of up to 58%, increasing the yield with as much as 18% from the single-nucleotide variant only pipeline. Importantly, this is achieved with the consideration of computational costs only, without incurring any additional sequencing costs. This mini review provides an overview of copy number variant analysis from exome data and what the current recommendations are for this type of analysis. We also present an overview on rare monogenic disease research standard practices in resource-limited settings. We present evidence that integrating copy number variant detection tools into a standard exome sequencing analysis pipeline improves diagnostic yield and should be considered a significantly beneficial addition, with relatively low-cost implications. Routine implementation in underrepresented populations and limited resource settings will promote generation and sharing of CNV datasets and provide momentum to build core centers for this niche within genomic medicine.

Comparison of the accuracy of multiplex digital PCR versus multiplex ligation-dependent probe amplification in quantification of the survival of motor neuron genes copy numbers

For over two decades, multiplex ligation-dependent probe amplification (MLPA) has served as the gold standard for genetic testing of spinal muscular atrophy. However, there is emerging evidence questioning the reliability of MLPA in determining the copy numbers (CNs) of the survival of motor neuron (SMN) gene in certain cases. Recently, digital polymerase chain reaction (dPCR) has shown potential for better performance in copy number variant detection. This study aimed to compare MLPA and dPCR in quantifying SMN1 and SMN2 CNs, identify reasons for observed discrepancies, and explore the clinical implications of false results.A total of 733 DNA samples, previously subjected to MLPA analysis, were tested using multiplex droplet dPCR assays. Samples exhibiting inconsistent results between the two methods underwent repeated dPCR assays. When inconsistencies persisted, a third method was employed for verification. Digital PCR yielded results consistent with those of MLPA in 94.4% (692/733) of samples. Forty-one cases exhibited quantitative disparities in SMN1 and/or SMN2 CNs between the two methods. Confirmatory tests revealed that 37 inaccurate results were produced by the MLPA analysis, whereas four were attributed to the dPCR method. The dPCR technique exhibits better accuracy than MLPA and is qualified for SMA genetic testing across various clinical scenarios.