Evaluation of CNV detection tools for NGS panel data in genetic diagnostics

The yield of genetic workup for middle-aged and elderly patients with neurological disorders in a real-world setting

Genetic workup is becoming increasingly common in the clinical assessment of neurological disorders. We evaluated its yield among middle-aged and elderly neurological patients, in a real-world context. This retrospective study included 368 consecutive Israeli patients aged 50 years and older (202 [54.9%] males), who were referred to a single neurogenetics clinic between 2017 and mid-2023. All had neurological disorders, without a previous molecular diagnosis. Demographic, clinical and genetic data were collected from medical records. The mean age at first genetic counseling at the clinic was 62.3 ± 7.8 years (range 50-85 years), and the main indications for referral were neuromuscular, movement and cerebrovascular disorders, as well as cognitive impairment and dementia. Out of the 368 patients, 245 (66.6%) underwent genetic testing that included exome sequencing (ES), analysis of nucleotide repeat expansions, detection of specific mutations, targeted gene panel sequencing or chromosomal microarray analysis. Overall, 80 patients (21.7%) received a molecular diagnosis due to 36 conditions, accounting for 32.7% of the patients who performed genetic testing. The diagnostic rates were highest for neuromuscular (58/186 patients [31.2%] in this group, 39.2% of 148 tested individuals) and movement disorders (14/79 [17.7%] patients, 29.2% of 48 tested), but lower for other disorders. Testing of nucleotide repeat expansions and ES provided a diagnosis to 28/73 (38.4%) and 19/132 (14.4%) individuals, respectively. Based on our findings, genetic workup and testing are useful in the diagnostic process of neurological patients aged ≥50 years, in particular for those with neuromuscular and movement disorders.

A novel copy number variant in the murine Cdh23 gene gives rise to profound deafness and vestibular dysfunction

Hearing loss is the most common congenital sensory deficit worldwide and exhibits high genetic heterogeneity, making molecular diagnoses elusive for most individuals. Detecting novel mutations that contribute to hearing loss is crucial to providing accurate personalized diagnoses, tailored interventions, and improving prognosis. Copy number variants (CNVs) are structural mutations that are understudied, potential contributors to hearing loss. Here, we present the Abnormal Wobbly Gait (AWG) mouse, the first documented mutant exhibiting waltzer-like locomotor dysfunction, hyperactivity, circling behaviour, and profound deafness caused by a spontaneous CNV deletion in cadherin 23 (Cdh23). We were unable to identify the causative mutation through a conventional whole-genome sequencing (WGS) and variant detection pipeline, but instead found a linked variant in hexokinase 1 (Hk1) that was insufficient to recapitulate the AWG phenotype when introduced into C57BL/6J mice using CRISPR-Cas9. Investigating nearby deafness-associated genes revealed a pronounced downregulation of Cdh23 mRNA and a complete absence of full-length CDH23 protein, which is critical for the development and maintenance of inner ear hair cells, in whole head extracts from AWG neonates. Manual inspection of WGS read depth plots of the Cdh23 locus revealed a putative 10.4 kb genomic deletion of exons 11 and 12 that was validated by PCR and Sanger sequencing. This study underscores the imperative to refine variant detection strategies to permit identification of pathogenic CNVs easily missed by conventional variant calling to enhance diagnostic precision and ultimately improve clinical outcomes for individuals with genetically heterogenous disorders such as hearing loss.

Identification of the molecular etiology in rare congenital hemolytic anemias using next-generation sequencing with exome-based copy number variant analysis

OBJECTIVES

In congenital hemolytic anemias (CHA), it is not always possible to determine the specific diagnosis by evaluating clinical findings and conventional laboratory tests. The aim of this study is to evaluate the utility of next-generation sequencing (NGS) and clinical-exome-based copy number variant (CNV) analysis in patients with CHA.

METHODS

One hundred and forty-three CHA cases from 115 unrelated families referred for molecular analysis were enrolled in the study. Molecular analysis was performed using two different clinical exome panels in 130 patients, and whole-exome sequencing in nine patients. Exome-based CNV calling was incorporated into the traditional single-nucleotide variant and small insertion/deletion analysis pipeline for NGS data in 92 cases. In four patients from the same family, the PK Gypsy variant was investigated using long-range polymerase chain reaction.

RESULTS

Molecular diagnosis was established in 86% of the study group. The most frequently mutated genes were SPTB (31.7%) and PKLR (28.5%). CNV analysis of 92 cases revealed that three patients had different sizes of large deletions in the SPTB and six patients had a deletion in the PKLR.

CONCLUSIONS

In this study, NGS provided a high molecular diagnostic rate in cases with rare CHA. Analysis of the CNVs contributed to the diagnostic success.

Copy number variant detection using next-generation sequencing in EYS-associated retinitis pigmentosa

Retinitis pigmentosa (RP) is the most common inherited retinal dystrophy and a major cause of blindness. RP is caused by several variants of multiple genes, and genetic diagnosis by identifying these variants is important for optimizing treatment and estimating patient prognosis. Next-generation sequencing (NGS), which is currently widely used for diagnosis, is considered useful but is known to have limitations in detecting copy number variations (CNVs). In this study, we re-evaluated CNVs in EYS, the main causative gene of RP, identified via NGS using multiplex ligation-dependent probe amplification (MLPA). CNVs were identified in NGS samples of eight patients. To identify potential CNVs, MLPA was also performed on samples from 42 patients who were undiagnosed by NGS but carried one of the five major pathogenic variants reported in Japanese EYS-RP cases. All suspected CNVs based on NGS data in the eight patients were confirmed via MLPA. CNVs were found in 2 of the 42 NGS-undiagnosed RP cases. Furthermore, results showed that 121 of the 661 patients with RP had EYS as the causative gene, and 8.3% (10/121 patients with EYS-RP) had CNVs. Although NGS using the CNV calling criteria utilized in this study failed to identify CNVs in two cases, no false-positive results were detected. Collectively, these findings suggest that NGS is useful for CNV detection during clinical diagnosis of RP.

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

BACKGROUND

Copy 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.

RESULTS

While 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).

CONCLUSIONS

NGS 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.

The expanding diagnostic toolbox for rare genetic diseases

Genomic technologies, such as targeted, exome and short-read genome sequencing approaches, have revolutionized the care of patients with rare genetic diseases. However, more than half of patients remain without a diagnosis. Emerging approaches from research-based settings such as long-read genome sequencing and optical genome mapping hold promise for improving the identification of disease-causal genetic variants. In addition, new omic technologies that measure the transcriptome, epigenome, proteome or metabolome are showing great potential for variant interpretation. As genetic testing options rapidly expand, the clinical community needs to be mindful of their individual strengths and limitations, as well as remaining challenges, to select the appropriate diagnostic test, correctly interpret results and drive innovation to address insufficiencies. If used effectively - through truly integrative multi-omics approaches and data sharing - the resulting large quantities of data from these established and emerging technologies will greatly improve the interpretative power of genetic and genomic diagnostics for rare diseases.

A method to comprehensively identify germline SNVs, INDELs and CNVs from whole exome sequencing data of BRCA1/2 negative breast cancer patients

In the rapidly evolving field of genomics, understanding the genetic basis of complex diseases like breast cancer, particularly its familial/hereditary forms, is crucial. Current methods often examine genomic variants-such as Single Nucleotide Variants (SNVs), insertions/deletions (Indels), and Copy Number Variations (CNVs)-separately, lacking an integrated approach. Here, we introduced a robust, flexible methodology for a comprehensive variants' analysis using Whole Exome Sequencing (WES) data. Our approach uniquely combines meticulous validation with an effective variant filtering strategy. By reanalyzing two germline WES datasets from BRCA1/2 negative breast cancer patients, we demonstrated our tool's efficiency and adaptability, uncovering both known and novel variants. This contributed new insights for potential diagnostic, preventive, and therapeutic strategies. Our method stands out for its comprehensive inclusion of key genomic variants in a unified analysis, and its practical resolution of technical challenges, offering a pioneering solution in genomic research. This tool presents a breakthrough in providing detailed insights into the genetic alterations in genomes, with significant implications for understanding and managing hereditary breast cancer.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Bioinformatics of germline variant discovery for rare disease diagnostics: current approaches and remaining challenges

Next-generation sequencing (NGS) has revolutionized the field of rare disease diagnostics. Whole exome and whole genome sequencing are now routinely used for diagnostic purposes; however, the overall diagnosis rate remains lower than expected. In this work, we review current approaches used for calling and interpretation of germline genetic variants in the human genome, and discuss the most important challenges that persist in the bioinformatic analysis of NGS data in medical genetics. We describe and attempt to quantitatively assess the remaining problems, such as the quality of the reference genome sequence, reproducible coverage biases, or variant calling accuracy in complex regions of the genome. We also discuss the prospects of switching to the complete human genome assembly or the human pan-genome and important caveats associated with such a switch. We touch on arguably the hardest problem of NGS data analysis for medical genomics, namely, the annotation of genetic variants and their subsequent interpretation. We highlight the most challenging aspects of annotation and prioritization of both coding and non-coding variants. Finally, we demonstrate the persistent prevalence of pathogenic variants in the coding genome, and outline research directions that may enhance the efficiency of NGS-based disease diagnostics.

Copy Number Variations in Hereditary Spastic Paraplegia-Related Genes: Evaluation of an Iranian Hereditary Spastic Paraplegia Cohort and Literature Review

Introduction

In human genetic disorders, copy number variations (CNVs) are considered a considerable underlying cause. CNVs are generally detected by array-based methods but can also be discovered by read-depth analysis of whole-exome sequencing (WES) data. We performed WES-based CNV identification in a cohort of 35 Iranian families with hereditary spastic paraplegia (HSP) patients.

Methods

Thirty-five patients whose routine single-nucleotide variants (SNVs) and insertion/deletion analyses from exome data were unrevealing underwent a pipeline of CNV analysis using the read-depth detection method. Subsequently, a comprehensive search about the existence of CNVs in all 84 known HSP-causing genes was carried out in all reported HSP cases, so far.

Results and Discussion

CNV analysis of exome data indicated that 1 patient harbored a heterozygous deletion in exon 17 of the SPAST gene. Multiplex ligation-dependent probe amplification analysis confirmed this deletion in the proband and his affected father. Literature review demonstrated that, to date, pathogenic CNVs have been identified in 30 out of 84 HSP-causing genes (∼36%). However, CNVs in only 17 of these genes were specifically associated with the HSP phenotype. Among them, CNVs were more common in L1CAM, PLP1, SPAST, SPG7, SPG11, and REEP1 genes. The identification of the CNV in 1 of our patients suggests that WES allows the detection of both SNVs and CNVs from a single method without additional costs and execution time. However, because of intrinsic issues of WES in the detection of large rearrangements, it may not yet be exploited to replace the CNV detection methods in standard clinical practice.

Molecular Challenges in the Diagnosis of X-Linked Chronic Granulomatous Disease: CNVs, Intronic Variants, Skewed X-Chromosome Inactivation, and Gonosomal Mosaicism

Chronic granulomatous disease (CGD) is a prototypical inborn error of immunity affecting phagocytes, in which these cells are unable to produce reactive oxygen species. CGD is caused by defects in genes encoding subunits of the NADPH oxidase enzyme complex (CYBA, CYBB, CYBC1, NCF1, NCF2, NCF4); inflammatory responses are dysregulated, and patients are highly susceptible to recurrent severe bacterial and fungal infections. X-linked CGD (XL-CGD), caused by mutations in the CYBB gene, is the most common and severe form of CGD. In this study, we describe the analytical processes undertaken in 3 families affected with XL-CGD to illustrate several molecular challenges in the genetic diagnosis of this condition: in family 1, a girl with a heterozygous deletion of CYBB exon 13 and skewed X-chromosome inactivation (XCI); in family 2, a boy with a hemizygous deletion of CYBB exon 7, defining its consequences at the mRNA level; and in family 3, 2 boys with the same novel intronic variant in CYBB (c.1151 + 6 T > A). The variant affected the splicing process, although a small fraction of wild-type mRNA was produced. Their mother was a heterozygous carrier, while their maternal grandmother was a carrier in form of gonosomal mosaicism. In summary, using a variety of techniques, including an NGS-based targeted gene panel and deep amplicon sequencing, copy number variation calling strategies, microarray-based comparative genomic hybridization, and cDNA analysis to define splicing defects and skewed XCI, we show how to face and solve some uncommon genetic mechanisms in the diagnosis of XL-CGD.

Germline CNV Detection through Whole-Exome Sequencing (WES) Data Analysis Enhances Resolution of Rare Genetic Diseases

Whole-Exome Sequencing (WES) has proven valuable in the characterization of underlying genetic defects in most rare diseases (RDs). Copy Number Variants (CNVs) were initially thought to escape detection. Recent technological advances enabled CNV calling from WES data with the use of accurate and highly sensitive bioinformatic tools. Amongst 920 patients referred for WES, 454 unresolved cases were further analysed using the ExomeDepth algorithm. CNVs were called, evaluated and categorized according to ACMG/ClinGen recommendations. Causative CNVs were identified in 40 patients, increasing the diagnostic yield of WES from 50.7% (466/920) to 55% (506/920). Twenty-two CNVs were available for validation and were all confirmed; of these, five were novel. Implementation of the ExomeDepth tool promoted effective identification of phenotype-relevant and/or novel CNVs. Among the advantages of calling CNVs from WES data, characterization of complex genotypes comprising both CNVs and SNVs minimizes cost and time to final diagnosis, while allowing differentiation between true or false homozygosity, as well as compound heterozygosity of variants in AR genes. The use of a specific algorithm for calling CNVs from WES data enables ancillary detection of different types of causative genetic variants, making WES a critical first-tier diagnostic test for patients with RDs.

Detection of germline variants with pathogenic potential in 48 patients with familial colorectal cancer by using whole exome sequencing

BACKGROUND

Hereditary genetic mutations causing predisposition to colorectal cancer are accountable for approximately 30% of all colorectal cancer cases. However, only a small fraction of these are high penetrant mutations occurring in DNA mismatch repair genes, causing one of several types of familial colorectal cancer (CRC) syndromes. Most of the mutations are low-penetrant variants, contributing to an increased risk of familial colorectal cancer, and they are often found in additional genes and pathways not previously associated with CRC. The aim of this study was to identify such variants, both high-penetrant and low-penetrant ones.

METHODS

We performed whole exome sequencing on constitutional DNA extracted from blood of 48 patients suspected of familial colorectal cancer and used multiple in silico prediction tools and available literature-based evidence to detect and investigate genetic variants.

RESULTS

We identified several causative and some potentially causative germline variants in genes known for their association with colorectal cancer. In addition, we identified several variants in genes not typically included in relevant gene panels for colorectal cancer, including CFTR, PABPC1 and TYRO3, which may be associated with an increased risk for cancer.

CONCLUSIONS

Identification of variants in additional genes that potentially can be associated with familial colorectal cancer indicates a larger genetic spectrum of this disease, not limited only to mismatch repair genes. Usage of multiple in silico tools based on different methods and combined through a consensus approach increases the sensitivity of predictions and narrows down a large list of variants to the ones that are most likely to be significant.

Systematic assessment of the contribution of structural variants to inherited retinal diseases

Despite increasing success in determining genetic diagnosis for patients with inherited retinal diseases (IRDs), mutations in about 30% of the IRD cases remain unclear or unsettled after targeted gene panel or whole exome sequencing. In this study, we aimed to investigate the contributions of structural variants (SVs) to settling the molecular diagnosis of IRD with whole-genome sequencing (WGS). A cohort of 755 IRD patients whose pathogenic mutations remain undefined were subjected to WGS. Four SV calling algorithms including include MANTA, DELLY, LUMPY and CNVnator were used to detect SVs throughout the genome. All SVs identified by any one of these four algorithms were included for further analysis. AnnotSV was used to annotate these SVs. SVs that overlap with known IRD-associated genes were examined with sequencing coverage, junction reads and discordant read pairs. Polymerase Chain Reaction (PCR) followed by Sanger sequencing was used to further confirm the SVs and identify the breakpoints. Segregation of the candidate pathogenic alleles with the disease was performed when possible. A total of 16 candidate pathogenic SVs were identified in 16 families, including deletions and inversions, representing 2.1% of patients with previously unsolved IRDs. Autosomal dominant, autosomal recessive and X-linked inheritance of disease-causing SVs were observed in 12 different genes. Among these, SVs in CLN3, EYS and PRPF31 were found in multiple families. Our study suggests that the contribution of SVs detected by short-read WGS is about 0.25% of our IRD patient cohort and is significantly lower than that of single nucleotide changes and small insertions and deletions.

Identification of a Novel IQCE Large Deletion through Copy Number Variant Analysis from Whole-Exome Sequencing Data of a Patient with Postaxial Polydactyly Type A7

Introduction

Non-syndromic polydactyly has been associated with pathogenic variants in 11 genes until today, including IQCE gene. More precisely, loss-of-function of IQCE is associated with the autosomal recessive disorder postaxial polydactyly type A7 (PAPA7, MIM #617642).

Case Presentation

A 3-year-old female patient was referred to our genetics department with postaxial polydactyly, syndactyly, brachydactyly, and hypoplastic teeth. Through whole-exome sequencing (WES), a pathogenic IQCE variant was identified (c.895_904del) in the homozygous state, which adequately explained the disease phenotype of our patient. However, copy number variant (CNV) analysis from WES data, using ExomeDepth, revealed a novel, likely pathogenic large deletion involving IQCE genomic regions (DEL:chr7:2606751_2641098) encompassing exons 2-18 of the gene.

Conclusion

IQCE gene codes for a 695-amino acid protein located at the base of the primary cilia that positively regulates the Hedgehog signaling pathway. This case report represents the first description of a large deletion in IQCE and indicates that implementation of ExomeDepth in routine WES analysis can contribute valuable information toward elucidating the correct etiology of rare genetic diseases, increasing the diagnostic yield, and minimizing the need for additional tests.

Revisiting Genetic Epidemiology with a Refined Targeted Gene Panel for Hereditary Hearing Impairment in the Taiwanese Population

Hearing impairment is one of the most common sensory disorders in children, and targeted next-generation sequencing (NGS)-based genetic examinations can assist in its prognostication and management. In 2020, we developed a simplified 30-gene NGS panel from the original 214-gene NGS version based on Taiwanese genetic epidemiology data to increase the accessibility of NGS-based examinations. In this study, we evaluated the diagnostic performance of the 30-gene NGS panel and compared it with that of the original 214-gene NGS panel in patient subgroups with different clinical features. Data on the clinical features, genetic etiologies, audiological profiles, and outcomes were collected from 350 patients who underwent NGS-based genetic examinations for idiopathic bilateral sensorineural hearing impairment between 2020 and 2022. The overall diagnostic yield was 52%, with slight differences in genetic etiology between patients with different degrees of hearing impairment and ages of onset. No significant difference was found in the diagnostic yields between the two panels, regardless of clinical features, except for a lower detection rate of the 30-gene panel in the late-onset group. For patients with negative genetic results, where the causative variant is undetectable on current NGS-based methods, part of the negative results may be due to genes not covered by the panel or yet to be identified. In such cases, the hearing prognosis varies and may decline over time, necessitating appropriate follow-up and consultation. In conclusion, genetic etiologies can serve as references for refining targeted NGS panels with satisfactory diagnostic performance.

Identification of Extremely Rare Pathogenic CNVs by Array CGH in Saudi Children with Developmental Delay, Congenital Malformations, and Intellectual Disability

Chromosomal imbalance is implicated in developmental delay (DD), congenital malformations (CM), and intellectual disability (ID), and, thus, precise identification of copy number variations (CNVs) is essential. We therefore aimed to investigate the genetic heterogeneity in Saudi children with DD/CM/ID. High-resolution array comparative genomic hybridization (array CGH) was used to detect disease-associated CNVs in 63 patients. Quantitative PCR was done to confirm the detected CNVs. Giemsa banding-based karyotyping was also performed. Array CGH identified chromosomal abnormalities in 24 patients; distinct pathogenic and/or variants of uncertain significance CNVs were found in 19 patients, and aneuploidy was found in 5 patients including 47,XXY (n = 2), 45,X (n = 2) and a patient with trisomy 18 who carried a balanced Robertsonian translocation. CNVs including 9p24p13, 16p13p11, 18p11 had gains/duplications and CNVs, including 3p23p14, 10q26, 11p15, 11q24q25, 13q21.1q32.1, 16p13.3p11.2, and 20q11.1q13.2, had losses/deletions only, while CNVs including 8q24, 11q12, 15q25q26, 16q21q23, and 22q11q13 were found with both gains or losses in different individuals. In contrast, standard karyotyping detected chromosomal abnormalities in ten patients. The diagnosis rate of array CGH (28%, 18/63 patients) was around two-fold higher than that of conventional karyotyping (15.87%, 10/63 patients). We herein report, for the first time, the extremely rare pathogenic CNVs in Saudi children with DD/CM/ID. The reported prevalence of CNVs in Saudi Arabia adds value to clinical cytogenetics.

The evolution of comprehensive genetic analysis in neurology: Implications for precision medicine

Technological advancements have facilitated the availability of reliable and thorough genetic analysis in many medical fields, including neurology. In this review, we focus on the importance of selecting the appropriate genetic test to aid in the accurate identification of disease utilizing currently employed technologies for analyzing monogenic neurological disorders. Moreover, the applicability of comprehensive analysis via NGS for various genetically heterogeneous neurological disorders is reviewed, revealing its efficiency in clarifying a frequently cloudy diagnostic picture and delivering a conclusive and solid diagnosis that is essential for the proper management of the patient. The feasibility and effectiveness of medical genetics in neurology require interdisciplinary cooperation among several medical specialties and geneticists, to select and perform the most relevant test according to each patient's medical history, using the most appropriate technological tools. The prerequisites for a comprehensive genetic analysis are discussed, highlighting the utility of appropriate gene selection, variant annotation, and classification. Moreover, genetic counseling and interdisciplinary collaboration could improve diagnostic yield further. Additionally, a sub-analysis is conducted on the 1,502,769 variation records with submitted interpretations in the Clinical Variation (ClinVar) database, with a focus on neurology-related genes, to clarify the value of suitable variant categorization. Finally, we review the current applications of genetic analysis in the diagnosis and personalized management of neurological patients and the advances in the research and scientific knowledge of hereditary neurological disorders that are evolving the utility of genetic analysis towards the individualization of the treatment strategy.

The genetic alterations of rectal neuroendocrine tumor and indications for therapy and prognosis: a systematic review

Neuroendocrine tumors (NETs) are a type of rare tumor that can occur at multiple organs. Rectal NETs are the most common NETs in gastrointestinal tract. Due to the rarity of rectal NETs in rectal cancer, the molecular features and the correlation with patient therapeutic response and prognosis have not been investigated in detail. In this review, we focused on the molecular features, potential therapeutic targets and prognosis of rectal NETs. By summarizing the relevant studies, we established the mutational landscape of rectal NETs and identified a series of large fragment variations. Driver genes including TP53, APC, KRAS, BRAF, RB1, CDKN2A and PTEN were found as the top mutated genes. Large fragment alterations mainly involved known driver genes, including APC, TP53, CCNE1, MYC, TERT, RB1 and ATM. Germline mutations of APC, MUTYH, MSH6, MLH1 and MSH2 associated with Lynch syndrome or FAP were also found in rectal NETs. The BRAF-V600E mutation was reported as an actionable target in rectal NETs, and the combined BRAF/MEK inhibitors were found to be effective targeting BRAF-V600E in advanced or metastatic NETs. The known prognostic risk factors of rectal adenocarcinoma, including a series of demographic and clinicopathological factors were also prognostic factors for rectal NETs. Furthermore, three types of markers, including genetic alterations, protein expression levels and methylation, were also suggested as prognostic factors for rectal NETs. In summary, we established the landscape of mutations and large-fragment alterations of rectal NETs, and identified potential therapeutic targets and a series of prognostic factors. Future studies may focus on the optimization of therapeutic strategies based on potential actionable biomarkers.

Systematic assessment of the contribution of structural variants to inherited retinal diseases

Despite increasing success in determining genetic diagnosis for patients with inherited retinal diseases (IRDs), mutations in about 30% of the IRD cases remain unclear or unsettled after targeted gene panel or whole exome sequencing. In this study, we aimed to investigate the contributions of structural variants (SVs) to settling the molecular diagnosis of IRD with whole-genome sequencing (WGS). A cohort of 755 IRD patients whose pathogenic mutations remain undefined was subjected to WGS. Four SV calling algorithms including include MANTA, DELLY, LUMPY, and CNVnator were used to detect SVs throughout the genome. All SVs identified by any one of these four algorithms were included for further analysis. AnnotSV was used to annotate these SVs. SVs that overlap with known IRD-associated genes were examined with sequencing coverage, junction reads, and discordant read pairs. PCR followed by Sanger sequencing was used to further confirm the SVs and identify the breakpoints. Segregation of the candidate pathogenic alleles with the disease was performed when possible. In total, sixteen candidate pathogenic SVs were identified in sixteen families, including deletions and inversions, representing 2.1% of patients with previously unsolved IRDs. Autosomal dominant, autosomal recessive, and X-linked inheritance of disease-causing SVs were observed in 12 different genes. Among these, SVs in CLN3, EYS, PRPF31 were found in multiple families. Our study suggests that the contribution of SVs detected by short-read WGS is about 0.25% of our IRD patient cohort and is significantly lower than that of single nucleotide changes and small insertions and deletions.

Detection of the Copy Number Variants of Genes in Patients with Familial Cardiac Diseases by Massively Parallel Sequencing

INTRODUCTION

The implication of copy number variations in familial heart disease is known, although in-depth knowledge is lacking; hence, more studies are needed to further our understanding. Massively parallel sequencing, thanks to its recent surge in use, is emerging as a valid tool for the detection of this type of variant, through the use of appropriate software.

METHODS

We conducted a study with 182 patients diagnosed with mendelian cardiovascular diseases who underwent sequencing using a cardiac gene panel and then a specific calling process for copy number variations (CNVs) with ExomeDepth software, which provides us with a Bayes factor (BF), a score of the probability that a CNV detected is true.

RESULTS

After a rigorous CNV prioritization process, we confirmed the variants obtained by MLPA or SNP-based array, finding three real CNVs in five individuals in the MYH11, FBN1 and PDMI7 genes.

CONCLUSION

The confirmed CNVs present in all cases BF values > 60, thus establishing a threshold to consider real CNVs in the calling process carried out by ExomeDepth on our gene panel.

Copy-number analysis by base-level normalization: An intuitive visualization tool for evaluating copy number variations

Next-generation sequencing (NGS) facilitates comprehensive molecular analyses that help with diagnosing unsolved disorders. In addition to detecting single-nucleotide variations and small insertions/deletions, bioinformatics tools can identify copy number variations (CNVs) in NGS data, which improves the diagnostic yield. However, due to the possibility of false positives, subsequent confirmation tests are generally performed. Here, we introduce Copy-number Analysis by BAse-level NormAlization (CABANA), a visualization tool that allows users to intuitively identify candidate CNVs using the normalized single-base-level read depth calculated from NGS data. To demonstrate how CABANA works, NGS data were obtained from 474 patients with neuromuscular disorders. CNVs were screened using a conventional bioinformatics tool, ExomeDepth, and then we normalized and visualized those data at the single-base level using CABANA, followed by manual inspection by geneticists to filter out false positives and determine candidate CNVs. In doing so, we identified 31 candidate CNVs (7%) in 474 patients and subsequently confirmed all of them to be true using multiplex ligation-dependent probe amplification. The performance of CABANA was deemed acceptable by comparing its diagnostic yield with previous data about neuromuscular disorders. Despite some limitations, we expect CABANA to help researchers accurately identify CNVs and reduce the need for subsequent confirmation testing.

EOSAL-CNV for Easy and Rapid Detection of CNVs by Fragment Analysis : EOSAL: A Fast and Reliable New Method for CNV Detection

Copy number variations (CNVs) are a type of genetic variation involving from 50 base pairs (bps) to millions of bps and, in a general point of view, can include alterations of complete chromosomes. As CNVs mean the gain or loss of DNA sequences, their detection requires specific techniques and analysis. We have developed Easy One-Step Amplification and Labeling for CNV Detection (EOSAL-CNV) by fragment analysis in a DNA sequencer. The procedure is based on a single PCR reaction for amplification and labeling of all fragments included. The protocol includes specific primers for the amplification of the regions of interest with a tail in each of the primers (one for forward and another for the reverse primers) together with primers for tail amplification. One of the primers for tail amplification is labeled with a fluorophore, allowing the amplification and labeling in the same reaction. Combination of several tail pairs and labels allows the detection of DNA fragment by different fluorophores and increases the number of fragments that can be analyzed in one reaction. PCR products can be analyzed without any purification on a DNA sequencer for fragment detection and quantification. Finally, simple and easy calculations allow the detection of fragments with deletions or extra copies. The use of EOSAL-CNV allows simplifying and reducing costs in sample analysis for CNV detection.

varAmpliCNV: analyzing variance of amplicons to detect CNVs in targeted NGS data

MOTIVATION

Computational identification of copy number variants (CNVs) in sequencing data is a challenging task. Existing CNV-detection methods account for various sources of variation and perform different normalization strategies. However, their applicability and predictions are restricted to specific enrichment protocols. Here, we introduce a novel tool named varAmpliCNV, specifically designed for CNV-detection in amplicon-based targeted resequencing data (Haloplex™ enrichment protocol) in the absence of matched controls. VarAmpliCNV utilizes principal component analysis (PCA) and/or metric dimensional scaling (MDS) to control variances of amplicon associated read counts enabling effective detection of CNV signals.

RESULTS

Performance of VarAmpliCNV was compared against three existing methods (ConVaDING, ONCOCNV and DECoN) on data of 167 samples run with an aortic aneurysm gene panel (n = 30), including 9 positive control samples. Additionally, we validated the performance on a large deafness gene panel (n = 145) run on 138 samples, containing 4 positive controls. VarAmpliCNV achieved higher sensitivity (100%) and specificity (99.78%) in comparison to competing methods. In addition, unsupervised clustering of CNV segments and visualization plots of amplicons spanning these regions are included as a downstream strategy to filter out false positives.

AVAILABILITY AND IMPLEMENTATION

The tool is freely available through galaxy toolshed and at: https://hub.docker.com/r/cmgantwerpen/varamplicnv. Supplementary Data File S1: https://tinyurl.com/2yzswyhh; Supplementary Data File S2: https://tinyurl.com/ycyf2fb4.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

BAP1 Tumour Predisposition Syndrome Due to Whole BAP1 Gene Deletion

BRCA-1-associated protein-1 (BAP1) tumour predisposition syndrome (BAP1-TPDS) is a dominant hereditary cancer syndrome. The full spectrum of associated malignancies is yet to be fully characterised. We detail the phenotypic features of the first reported family with a whole BAP1 gene deletion. This report also adds to the emerging evidence that the rhabdoid subtype of meningioma is a part of the clinical spectrum of this tumour predisposition syndrome.

Review of genetic testing in kidney disease patients: Diagnostic yield of single nucleotide variants and copy number variations evaluated across and within kidney phenotype groups

Genetic kidney disease comprises a diverse group of disorders. These can roughly be divided in the phenotype groups congenital anomalies of the kidney and urinary tract, ciliopathies, glomerulopathies, stone disorders, tubulointerstitial kidney disease, and tubulopathies. Many etiologies can lead to chronic kidney disease that can progress to end-stage kidney disease. Despite each individual disease being rare, together these genetic disorders account for a large proportion of kidney disease cases. With the introduction of massively parallel sequencing, genetic testing has become more accessible, but a comprehensive analysis of the diagnostic yield is lacking. This review gives an overview of the diagnostic yield of genetic testing across and within the full range of kidney disease phenotypes through a systematic literature search that resulted in 115 included articles. Patient, test, and cohort characteristics that can influence the diagnostic yield are highlighted. Detection of copy number variations and their contribution to the diagnostic yield is described for all phenotype groups. Also, the impact of a genetic diagnosis for a patient and family members, which can be diagnostic, therapeutic, and prognostic, is shown through the included articles. This review will allow clinicians to estimate an a priori probability of finding a genetic cause for the kidney disease in their patients.

Identification of New Copy Number Variation and the Evaluation of a CNV Detection Tool for NGS Panel Data in Polish Familial Hypercholesterolemia Patients

Familial hypercholesterolemia (FH) is an inherited, autosomal dominant metabolic disorder mostly associated with disease-causing variant in LDLR, APOB or PCSK9. Although the dominant changes are small-scale missense, frameshift and splicing variants, approximately 10% of molecularly defined FH cases are due to copy number variations (CNVs). The first-line strategy is to identify possible pathogenic SNVs (single nucleotide variants) using multiple PCR, Sanger sequencing, or with more comprehensive approaches, such as NGS (next-generation sequencing), WES (whole-exome sequencing) or WGS (whole-genome sequencing). The gold standard for CNV detection in genetic diagnostics are MLPA (multiplex ligation-dependent amplification) or aCGH (array-based comparative genome hybridization). However, faster and simpler analyses are needed. Therefore, it has been proposed that NGS data can be searched to analyze CNV variants. The aim of the study was to identify novel CNV changes in FH patients without detected pathogenic SNVs using targeted sequencing and evaluation of CNV calling tool (DECoN) working on gene panel NGS data; the study also assesses its suitability as a screening step in genetic diagnostics. A group of 136 adult and child patients were recruited for the present study. The inclusion criteria comprised at least “possible FH” according to the Simon Broome diagnostic criteria in children and the DLCN (Dutch Lipid Clinical Network) criteria in adults. NGS analysis revealed potentially pathogenic SNVs in 57 patients. Thirty selected patients without a positive finding from NGS were subjected to MLPA analysis; ten of these revealed possibly pathogenic CNVs. Nine patients were found to harbor exons 4−8 duplication, two harbored exons 6−8 deletion and one demonstrated exon 9−10 deletion in LDLR. To test the DECoN program, the whole study group was referred for bioinformatic analysis. The DECoN program detected duplication of exons 4−8 in the LDLR gene in two patients, whose genetic analysis was stopped after the NGS step. The integration of the two methods proved to be particularly valuable in a five-year-old girl presenting with extreme hypercholesterolemia, with both a pathogenic missense variant (c.1747C>T) and exons 9−10 deletion in LDLR. This is the first report of a heterozygous deletion of exons 9 and 10 co-occurring with SNV. Our results suggest that the NGS-based approach has the potential to identify large-scale variation in the LDLR gene and could be further applied to extend CNV screening in other FH-related genes. Nevertheless, the outcomes from the bioinformatic approach still need to be confirmed by MLPA; hence, the latter remains the reference method for assessing CNV in FH patients.

CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank

Copy number variation (CNV) is known to influence human traits, having a rich history of research into common and rare genetic disease, and although CNV is accepted as an important class of genomic variation, progress on copy-number-based genome-wide association studies (GWASs) from next-generation sequencing (NGS) data has been limited. Here we present a novel method for large-scale copy number analysis from NGS data generating robust copy number estimates and allowing copy number GWASs (CN-GWASs) to be performed genome-wide in discovery mode. We provide a detailed analysis in the UK Biobank resource and a specifically designed software package. We use these methods to perform CN-GWAS analysis across 78 human traits, discovering over 800 genetic associations that are likely to contribute strongly to trait distributions. Finally, we compare CNV and SNP association signals across the same traits and samples, defining specific CNV association classes.

ClearCNV: CNV calling from NGS panel data in the presence of ambiguity and noise

MOTIVATION

While the identification of small variants in panel sequencing data can be considered a solved problem, the identification of larger, multi-exon copy number variants (CNVs) still poses a considerable challenge. Thus, CNV calling has not been established in all laboratories performing panel sequencing. At the same time, such laboratories have accumulated large datasets and thus have the need to identify CNVs on their data to close the diagnostic gap.

RESULTS

In this article, we present our method clearCNV that addresses this need in two ways. First, it helps laboratories to properly assign datasets to enrichment kits. Based on homogeneous subsets of data, clearCNV identifies CNVs affecting the targeted regions. Using real-world datasets and validation, we show that our method is highly competitive with previous methods and preferable in terms of specificity.

AVAILABILITY AND IMPLEMENTATION

The software is available for free under a permissible license at https://github.com/bihealth/clear-cnv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Different Strategies for Counting the Depth of Coverage in Copy Number Variation Calling Tools

There are many copy number variation (CNV) detection tools based on the depth of coverage. A characteristic feature of all tools based on the depth of coverage is the first stage of data processing—counting the depth of coverage in the investigated sequencing regions. However, each tool implements this stage in a slightly different way. Herein, we used data from the 1000 Genomes Project to present the impact of another depth of coverage counting strategies on the results of the CNVs detection process. In the study, we used 7 CNV calling tools: CODEX, CANOES, exomeCopy, ExomeDepth, CLAMMS, CNVkit, and CNVind; from each of these applications, we separated the process of counting the depth of coverage into independent modules. Then, we counted the depth of coverage by mentioned modules, and finally, the obtained depth of coverage tables were used as the input data set to other CNV calling tools. The performed experiments showed that the best methods of counting the depth of coverage are the algorithms implemented in the CLAMMS and CNVkit applications. Both ways allow obtaining much better sets of detected CNVs compared to counting the depth of coverage implemented in other tools. What is more, some CNV detection tools are reasonably resistant to changing the input depth of coverage table. In this study, we proved that the exomeCopy application gives an approximately similar set of the resulting rare CNVs, regardless of the method of counting the depth of coverage table.

Utility and Outcomes of the 2019 American College of Medical Genetics and Genomics-Clinical Genome Resource Guidelines for Interpretation of Copy Number Variants with Borderline Classifications at an Academic Clinical Diagnostic Laboratory

In 2019, American College of Medical Genetics and Genomics and the Clinical Genome Resource published updated technical standards for the interpretation and reporting of copy number variants (CNVs), introducing a semiquantitative classification system that aims to foster greater standardization and consistency between laboratories. Evaluation of these guidelines' performance will inform laboratories about the impact of their implementation into clinical practice. A total of 145 difficult-to-classify CNVs, originally assessed by an academic molecular diagnostic laboratory, were re-interpreted/classified according to the American College of Medical Genetics and Genomics-Clinical Genome Resource guidelines. Classifications between interpretation systems were then compared. The concordance rate was 60.7%, and significantly more variants of uncertain significance were obtained when using the guidelines (n = 98) versus the laboratory's classification system (n = 49; P < 0.001). The concordance rate was presumably impacted by the intentionally unclear nature of the selected variants. The difference in variant of uncertain significance rate was largely due to laboratory-specific practices for variant interpretation and reporting, as well as differences in utilization of general population data. Laboratory-specific policies and practices may need to be addressed for true standardization to be achieved. Challenges to consistent guideline utilization are centered around the general lack of high-quality curated data available for CNV interpretations and the inherent subjectivity in the selection of evidence criteria and application of evidence points. Multiple aspects of the guidelines were highlighted as potential opportunities for subsequent refinements to further improve classification standardization.

A Decade's Experience in Pediatric Chromosomal Microarray Reveals Distinct Characteristics Across Ordering Specialties

Chromosomal microarray (CMA) is a testing modality frequently used in pediatric patients; however, previously published data on its utilization are limited to the genetic setting. Herein, we performed a database search for all CMA testing performed from 2010 to 2020, and delineated the diagnostic yield based on patient characteristics, including sex, age, clinical specialty of providers, indication of testing, and pathogenic finding. The indications for testing were further categorized into Human Phenotype Ontology categories for analysis. This study included a cohort of 14,541 patients from 29 different medical specialties, of whom 30% were from the genetics clinic. The clinical indications for testing suggested that neonatology patients demonstrated the greatest involvement of multiorgan systems, involving the most Human Phenotype Ontology categories, compared with developmental behavioral pediatrics and neurology patients being the least. The top pathogenic findings for each specialty differed, likely due to the varying clinical features and indications for testing. Deletions involving the 22q11.21 locus were the top pathogenic findings for patients presenting to genetics, neonatology, cardiology, and surgery. Our data represent the largest pediatric cohort published to date. This study is the first to demonstrate the diagnostic utility of this assay for patients seen in the setting of different specialties, and it provides normative data of CMA results among a general pediatric population referred for testing because of variable clinical presentations.

Copy Number Variation (CNV): A New Genomic Insight in Horses

Simple Summary

This study aimed to contribute to our knowledge of CNVs, a type of genomic marker in equines, by producing, for the first time, a fine-scale characterization of the CNV regions (CNVRs) in the Pura Raza Española horse breed. We found not only the existence of a unique pattern of genomic regions enriched in CNVs in the PRE in comparison with the data available from other breeds but also the incidence of CNVs across the entire genome. Since these regions could affect the structure and dose of the genes involved, we also performed a gene ontology analysis which revealed that most of the genes overlapping in CNVRs were related to the olfactory pathways and immune response.

Abstract

Copy number variations (CNVs) are a new-fangled source of genetic variation that can explain changes in the phenotypes in complex traits and diseases. In recent years, their study has increased in many livestock populations. However, the study and characterization of CNVs in equines is still very limited. Our study aimed to investigate the distribution pattern of CNVs, characterize CNV regions (CNVRs), and identify the biological pathways affected by CNVRs in the Pura Raza Española (PRE) breed. To achieve this, we analyzed high-density SNP genotyping data (670,804 markers) from a large cohort of 654 PRE horses. In total, we identified 19,902 CNV segments and 1007 CNV regions in the whole population. The length of the CNVs ranged from 1.024 kb to 4.55 Mb, while the percentage of the genome covered by CNVs was 4.4%. Interestingly, duplications were more abundant than deletions and mixed CNVRs. In addition, the distribution of CNVs across the chromosomes was not uniform, with ECA12 being the chromosome with the largest percentage of its genome covered (19.2%), while the highest numbers of CNVs were found in ECA20, ECA12, and ECA1. Our results showed that 71.4% of CNVRs contained genes involved in olfactory transduction, olfactory receptor activity, and immune response. Finally, 39.1% of the CNVs detected in our study were unique when compared with CNVRs identified in previous studies. To the best of our knowledge, this is the first attempt to reveal and characterize the CNV landscape in PRE horses, and it contributes to our knowledge of CNVs in equines, thus facilitating the understanding of genetic and phenotypic variations in the species. However, further research is still needed to confirm if the CNVs observed in the PRE are also linked to variations in the specific phenotypical differences in the breed.

Copy number variation analysis using next-generation sequencing identifies the CFHR3/CFHR1 deletion in atypical hemolytic uremic syndrome: a case report

OBJECTIVES

Atypical hemolytic uremic syndrome (aHUS) is characterized by a triad of thrombocytopenia, microangiopathic hemolytic anemia, and acute renal failure resulting from platelet thrombi in the microcirculation of the kidney and other organs, in the absence of a preceding diarrheal illness. This report describes a case in which copy number variation (CNV) analysis using next-generation sequencing (NGS) identified the CFHR3/CFHR1 deletion in a patient with aHUS.

METHODS

A 49-year-old Korean female was diagnosed with aHUS based on clinical findings, including schistocytes in peripheral blood and marked thrombocytopenia, suggesting the presence of thrombotic microangiopathy, elevated serum lactate dehydrogenase, and acute kidney injury. Sequence variants and CNV generated from NGS data were estimated to determine if there was a potential genetic cause. Multiplex ligation-dependent probe amplification (MLPA) was conducted to confirm the CFHR3/CFHR1 deletion identified by NGS with CNV analysis.

RESULTS

No known or novel pathogenic single nucleotide variant or small insertion/deletion that would be predicted to have damaging effects that could lead to aHUS were identified. However, CNV analysis of NGS data identified the heterozygous CFHR3/CFHR1 deletion. MLPA confirmed this loss of one copy number between the CFHR3 and the CFHR1 genes on chromosome 1q31.3.

CONCLUSION

We genetically diagnosed a Korean woman harboring a heterozygous CFHR3/CFHR1 deletion of a known causative gene for aHUS. Our report emphasizes the need for CNV analysis of NGS data and gene dosage assays, such as MLPA, to evaluate large-scale deletions or duplications and generate hybrid CFH genes in patients with suspected aHUS.

Apparent regional differences in the spectrum of BARD1 pathogenic variants in Spanish population and importance of copy number variants

Only up to 25% of the cases in which there is a familial aggregation of breast and/or ovarian cancer are explained by germline mutations in the well-known BRCA1 and BRCA2 high-risk genes. Recently, the BRCA1-associated ring domain (BARD1), that partners BRCA1 in DNA repair, has been confirmed as a moderate-risk breast cancer susceptibility gene. Taking advantage of next-generation sequencing techniques, and with the purpose of defining the whole spectrum of possible pathogenic variants (PVs) in this gene, here we have performed a comprehensive mutational analysis of BARD1 in a cohort of 1946 Spanish patients who fulfilled criteria to be tested for germline pathogenic mutations in BRCA1 and BRCA2. We identified 22 different rare germline variants, being 5 of them clearly pathogenic or likely pathogenic large deletions, which account for 0.26% of the patients tested. Our results show that the prevalence and spectrum of mutations in the BARD1 gene might vary between different regions of Spain and expose the relevance to test for copy number variations.

CNVind: an open source cloud-based pipeline for rare CNVs detection in whole exome sequencing data based on the depth of coverage

Background

A typical Copy Number Variations (CNVs) detection process based on the depth of coverage in the Whole Exome Sequencing (WES) data consists of several steps: (I) calculating the depth of coverage in sequencing regions, (II) quality control, (III) normalizing the depth of coverage, (IV) calling CNVs. Previous tools performed one normalization process for each chromosome—all the coverage depths in the sequencing regions from a given chromosome were normalized in a single run.

Methods

Herein, we present the new CNVind tool for calling CNVs, where the normalization process is conducted separately for each of the sequencing regions. The total number of normalizations is equal to the number of sequencing regions in the investigated dataset. For example, when analyzing a dataset composed of n sequencing regions, CNVind performs n independent depth of coverage normalizations. Before each normalization, the application selects the k most correlated sequencing regions with the depth of coverage Pearson’s Correlation as distance metric. Then, the resulting subgroup of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k+1$$\end{document}k+1 sequencing regions is normalized, the results of all n independent normalizations are combined; finally, the segmentation and CNV calling process is performed on the resultant dataset.

Results and conclusions

We used WES data from the 1000 Genomes project to evaluate the impact of independent normalization on CNV calling performance and compared the results with state-of-the-art tools: CODEX and exomeCopy. The results proved that independent normalization allows to improve the rare CNVs detection specificity significantly. For example, for the investigated dataset, we reduced the number of FP calls from over 15,000 to around 5000 while maintaining a constant number of TP calls equal to about 150 CNVs. However, independent normalization of each sequencing region is a computationally expensive process, therefore our pipeline is customized and can be easily run in the cloud computing environment, on the computer cluster, or the single CPU server. To our knowledge, the presented application is the first attempt to implement an innovative approach to independent normalization of the depth of WES data coverage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04617-x.

Determining the Accuracy of Next Generation Sequencing Based Copy Number Variation Analysis in Hereditary Breast and Ovarian Cancer

BACKGROUND

Copy number variations (CNVs) are commonly associated with malignancies, including hereditary breast and ovarian cancers. Next generation sequencing (NGS) provides solutions for CNV detection in a single run. This study aimed to compare the accuracy of CNV detection by NGS analysing tool against Multiplex Ligation Dependent Probe Amplification (MLPA).

RESEARCH DESIGN AND METHODS

In total, 1276 cases were studied by targeted NGS panels and 691 cases (61 calls in 58 NGS-CNV positive and 633 NGS-CNV negative cases) were validated by MLPA.

RESULTS

Twenty-eight (46%) NGS-CNV positive calls were consistent, whereas 33 (54%) calls showed discordance with MLPA. Two cases were detected as SNV by the NGS and CNV by the MLPA analysis. In total, 2% of the cases showed an MLPA confirmed CNV region in BRCA1/2. The results of this study showed that despite the high false positive call rate of the NGS-CNV algorithm, there were no false negative calls. The cases that were determined to be negative by the NGS and positive by the MLPA were actually carrying SNVs that were located on the MLPA probe binding sites.

CONCLUSION

The diagnostic performance of NGS-CNV analysis is promising; however, the need for confirmation by different methods remains.

Epilepsy Genetics and Precision Medicine in Adults: A New Landscape for Developmental and Epileptic Encephalopathies

This review aims to provide an updated perspective of epilepsy genetics and precision medicine in adult patients, with special focus on developmental and epileptic encephalopathies (DEEs), covering relevant and controversial issues, such as defining candidates for genetic testing, which genetic tests to request and how to interpret them. A literature review was conducted, including findings in the discussion and recommendations. DEEs are wide and phenotypically heterogeneous electroclinical syndromes. They generally have a pediatric presentation, but patients frequently reach adulthood still undiagnosed. Identifying the etiology is essential, because there lies the key for precision medicine. Phenotypes modify according to age, and although deep phenotyping has allowed to outline certain entities, genotype-phenotype correlations are still poor, commonly leading to long-lasting diagnostic odysseys and ineffective therapies. Recent adult series show that the target patients to be identified for genetic testing are those with epilepsy and different risk factors. The clinician should take active part in the assessment of the pathogenicity of the variants detected, especially concerning variants of uncertain significance. An accurate diagnosis implies precision medicine, meaning genetic counseling, prognosis, possible future therapies, and a reduction of iatrogeny. Up to date, there are a few tens of gene mutations with additional concrete treatments, including those with restrictive/substitutive therapies, those with therapies modifying signaling pathways, and channelopathies, that are worth to be assessed in adults. Further research is needed regarding phenotyping of adult syndromes, early diagnosis, and the development of targeted therapies.

Family with congenital contractural arachnodactyly due to a novel multiexon deletion of the FBN2 gene

Congenital contractural arachnodactyly (CCA) is caused by pathogenic FBN2 variants; however, the contributions of copy number variations (CNVs) to CCA are still unknown. Here, we report on a familial case of CCA, in which a novel multiexon deletion of exons 35-39 in FBN2 was identified after simple CNV prediction.

An Easy-to-Use Approach to Detect CNV From Targeted NGS Data: Identification of a Novel Pathogenic Variant in MO Disease

BACKGROUND

Despite the new next-generation sequencing (NGS) molecular approaches implemented the genetic testing in clinical diagnosis, copy number variation (CNV) detection from NGS data remains difficult mainly in the absence of bioinformatics personnel (not always available among laboratory resources) and when using very small gene panels that do not meet commercial software criteria. Furthermore, not all large deletions/duplications can be detected with the Multiplex Ligation-dependent Probe Amplification (MLPA) technique due to both the limitations of the methodology and no kits available for the most of genes.

AIM

We propose our experience regarding the identification of a novel large deletion in the context of a rare skeletal disease, multiple osteochondromas (MO), using and validating a user-friendly approach based on NGS coverage data, which does not require any dedicated software or specialized personnel.

METHODS

The pipeline uses a simple algorithm comparing the normalized coverage of each amplicon with the mean normalized coverage of the same amplicon in a group of "wild-type" samples representing the baseline. It has been validated on 11 samples, previously analyzed by MLPA, and then applied on 20 patients with MO but negative for the presence of pathogenic variants in EXT1 or EXT2 genes. Sensitivity, specificity, and accuracy were evaluated.

RESULTS

All the 11 known CNVs (exon and multi-exon deletions) have been detected with a sensitivity of 97.5%. A novel EXT2 partial exonic deletion c. (744-122)-?_804+?del -out of the MLPA target regions- has been identified. The variant was confirmed by real-time quantitative Polymerase Chain Reaction (qPCR).

CONCLUSION

In addition to enhancing the variant detection rate in MO molecular diagnosis, this easy-to-use approach for CNV detection can be easily extended to many other diagnostic fields-especially in resource-limited settings or very small gene panels. Notably, it also allows partial-exon deletion detection.

VarGenius-HZD Allows Accurate Detection of Rare Homozygous or Hemizygous Deletions in Targeted Sequencing Leveraging Breadth of Coverage

Homozygous deletions (HDs) may be the cause of rare diseases and cancer, and their discovery in targeted sequencing is a challenging task. Different tools have been developed to disentangle HD discovery but a sensitive caller is still lacking. We present VarGenius-HZD, a sensitive and scalable algorithm that leverages breadth-of-coverage for the detection of rare homozygous and hemizygous single-exon deletions (HDs). To assess its effectiveness, we detected both real and synthetic rare HDs in fifty exomes from the 1000 Genomes Project obtaining higher sensitivity in comparison with state-of-the-art algorithms that each missed at least one event. We then applied our tool on targeted sequencing data from patients with Inherited Retinal Dystrophies and solved five cases that still lacked a genetic diagnosis. We provide VarGenius-HZD either stand-alone or integrated within our recently developed software, enabling the automated selection of samples using the internal database. Hence, it could be extremely useful for both diagnostic and research purposes.

isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data

Background

Accurate copy number variant (CNV) detection is especially challenging for both targeted sequencing (TS) and whole‐exome sequencing (WES) data. To maximize the performance, the parameters of the CNV calling algorithms should be optimized for each specific dataset. This requires obtaining validated CNV information using either multiplex ligation-dependent probe amplification (MLPA) or array comparative genomic hybridization (aCGH). They are gold standard but time-consuming and costly approaches.

Results

We present isoCNV which optimizes the parameters of DECoN algorithm using only NGS data. The parameter optimization process is performed using an in silico CNV validated dataset obtained from the overlapping calls of three algorithms: CNVkit, panelcn.MOPS and DECoN. We evaluated the performance of our tool and showed that increases the sensitivity in both TS and WES real datasets.

Conclusions

isoCNV provides an easy-to-use pipeline to optimize DECoN that allows the detection of analysis-ready CNV from a set of DNA alignments obtained under the same conditions. It increases the sensitivity of DECoN without the need for orthogonal methods. isoCNV is available at https://gitlab.com/sequentiateampublic/isocnv.

Risk prediction for metastasis of clear cell renal cell carcinoma using digital multiplex ligation-dependent probe amplification

Precise quantification of copy‐number alterations (CNAs) in a tumor genome is difficult. We have applied a comprehensive copy‐number analysis method, digital multiplex ligation‐dependent probe amplification (digitalMLPA), for targeted gene copy‐number analysis in clear cell renal cell carcinoma (ccRCC). Copy‐number status of all chromosomal arms and 11 genes was determined in 60 ccRCC samples. Chromosome 3p loss and 5q gain, known as early changes in ccRCC development, as well as losses at 9p and 14q were detected in 56/60 (93.3%), 31/60 (51.7%), 11/60 (18.3%), and 33/60 (55%), respectively. Through gene expression analysis, a significant positive correlation was detected in terms of 14q loss determined using digitalMLPA and downregulation of mRNA expression ratios with HIF1A and L2HGDH (P = .0253 and .0117, respectively). Patients with early metastasis (<1 y) (n = 18) showed CNAs in 6 arms (in median), whereas metastasis‐free patients (n = 34) showed those in significantly less arms (3 arms in median) (P = .0289). In particular, biallelic deletion of CDKN2A/2B was associated with multiple CNAs (≥7 arms) in 3 tumors. Together with sequence‐level mutations in genes VHL, PBRM1, SETD2, and BAP1, we performed multiple correspondence analysis, which identified the association of 9p loss and 4q loss with early metastasis (both P < .05). This analysis indicated the association of 4p loss and 1p loss with poor survival (both, P < .05). These findings suggest that CNAs have essential roles in aggressiveness of ccRCC. We showed that our approach of measuring CNA through digitalMLPA will facilitate the selection of patients who may develop metastasis.

Somatic mosaics in hereditary tumor predisposition syndromes

Historically, it is estimated that 5-10% of cancer patients carry a causative genetic variant for a tumor predisposition syndrome. These conditions have high clinical relevance as they are actionable regarding risk-specific surveillance, predictive genetic testing, reproductive options, and - in some cases - risk reducing surgery or targeted therapy. Every individual is born with on average 0.5-1 exonic mosaic variants prevalent in single or multiple tissues. Depending on the tissues affected, mosaic conditions can abrogate the clinical phenotype of a tumor predisposition syndrome and can even go unrecognized, because it can be impossible or difficult to detect them with routine genetic testing in blood/leucocytes. On the other hand, it is estimated that at least 4% of presumed de novo variants are the result of low-level mosaicism (variant allele frequency <10%) in a parent, while around 7% are true mosaic variants with a higher variant allele frequency, which can sometimes be confused for heterozygous variants. Clonal hematopoiesis however can simulate a mosaic tumor predisposition in genetic diagnostics and has to be taken into account, especially for TP53 variants. Depending on the technique, variant allele frequencies of 2-3% can be detected for single nucleotide variants by next generation sequencing, copy number variants with variant allele frequencies of 5-30% can be detected by array-based technologies or MLPA. Mosaic tumor predisposition syndromes are more common than previously thought and may often remain undiagnosed. The clinical suspicion and diagnostic procedure for several cases with mosaic tumor predisposition syndromes are presented.

Response to Comment on "Reintroduction of the archaic variant of NOVA1 in cortical organoids alters neurodevelopment"

Maricic et al. performed an undisclosed in silico-only whole-exome sequencing analysis of our data and found genomic alterations previously undetected in some clones. Some of the predicted alterations, if true, could change the original genotype of the clones. We failed to experimentally validate all but one of these genomic alterations, which did not affect our previous results or data interpretation.

A Novel LMX1B Variant Identified in a Patient Presenting with Severe Renal Involvement and Thin Glomerular Basement Membrane

We report a case of nail-patella syndrome (NPS) with unusual thinning of the glomerular basement membrane (GBM) associated with a novel heterozygous variant in the LMX1B gene. A 43-year-old female patient with a previous diagnosis of NPS, referred to our hospital for persistent proteinuria, underwent a renal biopsy, which revealed minor glomerular abnormalities. She underwent a second renal biopsy at the age of 56 owing to the presence of persistent proteinuria and decline in serum albumin, meeting the diagnostic criteria for nephrotic syndrome. Light microscopy demonstrated glomerulosclerosis and cystic dilatation of the renal tubules. Notably, electron microscopy revealed unusual thinning of the GBM, which is quite different from typical biopsy findings observed in patients with NPS, characterized by thick GBM with fibrillary material and electron-lucent structures. Comprehensive genetic screening for 168 known genes responsible for inherited kidney diseases using a next-generation sequencing panel identified a novel heterozygous in-frame deletion-insertion (c.723_729delinsCAAC: p.[Ser242_Lys243delinsAsn]) in exon 4 of the LMX1B gene, which may account for the disrupted GBM structure. Further studies are warranted to elucidate the complex genotype-phenotype relationship between LMX1B and proper GBM morphogenesis.

Detecting copy number variation in next generation sequencing data from diagnostic gene panels

BACKGROUND

Detection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples.

RESULTS

Our computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests.

CONCLUSIONS

With this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

High-Frequency Exon Deletion of DNA Cross-Link Repair 1C Accounting for Severe Combined Immunodeficiency May Be Missed by Whole-Exome Sequencing

Next-generation sequencing (NGS) has been used to detect severe combined immunodeficiency (SCID) in patients, and some patients with DNA cross-link repair 1C (DCLRE1C) variants have been identified. Moreover, some compound variants, such as copy number variants (CNV) and single nucleotide variants (SNV), have been reported. The purpose of this study was to expand the genetic data related to patients with SCID carrying the compound DCLRE1C variant. Whole-exome sequencing (WES) was performed for genetic analysis, and variants were verified by performing Sanger sequencing or quantitative PCR. Moreover, we searched PubMed and summarized the data of the reported variants. Four SCID patients with DCLRE1C variants were identified in this study. WES revealed a homozygous deletion in the DCLRE1C gene from exons 1–5 in patient 1, exons 1–3 deletion and a novel rare variant (c.92T>C, p.L31P) in patient 2, exons 1–3 deletion and a novel rare variant (c.328C>G, p.L110V) in patient 3, and exons 1–4 deletion and a novel frameshift variant (c.449dup, p.His151Alafs*20) in patient 4. Based on literature review, exons 1–3 was recognized as a hotspot region for deletion variation. Moreover, we found that compound variations (CNV + SNV) accounted for approximately 7% variations in all variants. When patients are screened for T-cell receptor excision circles (TRECs), NGS can be used to expand genetic testing. Deletion of the DCLRE1C gene should not be ignored when a variant has been found in patients with SCID.

Applying Bioinformatic Platforms, In Vitro, and In Vivo Functional Assays in the Characterization of Genetic Variants in the GH/IGF Pathway Affecting Growth and Development

Heritability accounts for over 80% of adult human height, indicating that genetic variability is the main determinant of stature. The rapid technological development of Next-Generation Sequencing (NGS), particularly Whole Exome Sequencing (WES), has resulted in the characterization of several genetic conditions affecting growth and development. The greatest challenge of NGS remains the high number of candidate variants identified. In silico bioinformatic tools represent the first approach for classifying these variants. However, solving the complicated problem of variant interpretation requires the use of experimental approaches such as in vitro and, when needed, in vivo functional assays. In this review, we will discuss a rational approach to apply to the gene variants identified in children with growth and developmental defects including: (i) bioinformatic tools; (ii) in silico modeling tools; (iii) in vitro functional assays; and (iv) the development of in vivo models. While bioinformatic tools are useful for a preliminary selection of potentially pathogenic variants, in vitro—and sometimes also in vivo—functional assays are further required to unequivocally determine the pathogenicity of a novel genetic variant. This long, time-consuming, and expensive process is the only scientifically proven method to determine causality between a genetic variant and a human genetic disease.

The Emerging Genetic Landscape of Hirschsprung Disease and Its Potential Clinical Applications

Hirschsprung disease (HSCR) is the leading cause of neonatal functional intestinal obstruction. It is a rare congenital disease with an incidence of one in 3,500-5,000 live births. HSCR is characterized by the absence of enteric ganglia in the distal colon, plausibly due to genetic defects perturbing the normal migration, proliferation, differentiation, and/or survival of the enteric neural crest cells as well as impaired interaction with the enteric progenitor cell niche. Early linkage analyses in Mendelian and syndromic forms of HSCR uncovered variants with large effects in major HSCR genes including RET, EDNRB, and their interacting partners in the same biological pathways. With the advances in genome-wide genotyping and next-generation sequencing technologies, there has been a remarkable progress in understanding of the genetic basis of HSCR in the past few years, with common and rare variants with small to moderate effects being uncovered. The discovery of new HSCR genes such as neuregulin and BACE2 as well as the deeper understanding of the roles and mechanisms of known HSCR genes provided solid evidence that many HSCR cases are in the form of complex polygenic/oligogenic disorder where rare variants act in the sensitized background of HSCR-associated common variants. This review summarizes the roadmap of genetic discoveries of HSCR from the earlier family-based linkage analyses to the recent population-based genome-wide analyses coupled with functional genomics, and how these discoveries facilitated our understanding of the genetic architecture of this complex disease and provide the foundation of clinical translation for precision and stratified medicine.