ClinTAD: a tool for copy number variant interpretation in the context of topologically associated domains

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

POSTRE: a tool to predict the pathological effects of human structural variants

Understanding the pathological impact of non-coding genetic variation is a major challenge in medical genetics. Accumulating evidences indicate that a significant fraction of genetic alterations, including structural variants (SVs), can cause human disease by altering the function of non-coding regulatory elements, such as enhancers. In the case of SVs, described pathomechanisms include changes in enhancer dosage and long-range enhancer-gene communication. However, there is still a clear gap between the need to predict and interpret the medical impact of non-coding variants, and the existence of tools to properly perform these tasks. To reduce this gap, we have developed POSTRE (Prediction Of STRuctural variant Effects), a computational tool to predict the pathogenicity of SVs implicated in a broad range of human congenital disorders. By considering disease-relevant cellular contexts, POSTRE identifies SVs with either coding or long-range pathological consequences with high specificity and sensitivity. Furthermore, POSTRE not only identifies pathogenic SVs, but also predicts the disease-causative genes and the underlying pathological mechanism (e.g, gene deletion, enhancer disconnection, enhancer adoption, etc.). POSTRE is available at https://github.com/vicsanga/Postre.

TADA-a machine learning tool for functional annotation-based prioritisation of pathogenic CNVs

Few methods have been developed to investigate copy number variants (CNVs) based on their predicted pathogenicity. We introduce TADA, a method to prioritise pathogenic CNVs through assisted manual filtering and automated classification, based on an extensive catalogue of functional annotation supported by rigourous enrichment analysis. We demonstrate that our classifiers are able to accurately predict pathogenic CNVs, outperforming current alternative methods, and produce a well-calibrated pathogenicity score. Our results suggest that functional annotation-based prioritisation of pathogenic CNVs is a promising approach to support clinical diagnostics and to further the understanding of mechanisms controlling the disease impact of larger genomic alterations.

Automated prediction of the clinical impact of structural copy number variations

Copy number variants (CNVs) play an important role in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analyses. The interpretation of the effect of these structural variants is a challenging problem due to highly variable numbers of gene, regulatory, or other genomic elements affected by the CNV. This led to the demand for the interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors, and clinical geneticists from the laborious process of annotation and classification of CNVs. We designed and validated a prediction method (ISV; Interpretation of Structural Variants) that is based on boosted trees which takes into account annotations of CNVs from several publicly available databases. The presented approach achieved more than 98% prediction accuracy on both copy number loss and copy number gain variants while also allowing CNVs being assigned "uncertain" significance in predictions. We believe that ISV's prediction capability and explainability have a great potential to guide users to more precise interpretations and classifications of CNVs.

SVInterpreter: A Comprehensive Topologically Associated Domain-Based Clinical Outcome Prediction Tool for Balanced and Unbalanced Structural Variants

With the advent of genomic sequencing, a number of balanced and unbalanced structural variants (SVs) can be detected per individual. Mainly due to incompleteness and the scattered nature of the available annotation data of the human genome, manual interpretation of the SV's clinical significance is laborious and cumbersome. Since bioinformatic tools developed for this task are limited, a comprehensive tool to assist clinical outcome prediction of SVs is warranted. Herein, we present SVInterpreter, a free Web application, which analyzes both balanced and unbalanced SVs using topologically associated domains (TADs) as genome units. Among others, gene-associated data (as function and dosage sensitivity), phenotype similarity scores, and copy number variants (CNVs) scoring metrics are retrieved for an informed SV interpretation. For evaluation, we retrospectively applied SVInterpreter to 97 balanced (translocations and inversions) and 125 unbalanced (deletions, duplications, and insertions) previously published SVs, and 145 SVs identified from 20 clinical samples. Our results showed the ability of SVInterpreter to support the evaluation of SVs by (1) confirming more than half of the predictions of the original studies, (2) decreasing 40% of the variants of uncertain significance, and (3) indicating several potential position effect events. To our knowledge, SVInterpreter is the most comprehensive TAD-based tool to identify the possible disease-causing candidate genes and to assist prediction of the clinical outcome of SVs. SVInterpreter is available at http://dgrctools-insa.min-saude.pt/cgi-bin/SVInterpreter.py.

AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs

Background

The American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) presented technical standards for interpretation and reporting of constitutional copy-number variants in 2019 (the standards). Although ClinGen developed a web-based CNV classification calculator based on scoring metrics, it can only track and tally points that have been assigned based on observed evidence. Here, we developed AutoCNV (a semiautomatic automated CNV interpretation system) based on the standards, which can automatically generate predictions on 18 and 16 criteria for copy number loss and gain, respectively.

Results

We assessed the performance of AutoCNV using 72 CNVs evaluated by external independent reviewers and 20 illustrative case examples. Using AutoCNV, it showed that 100 % (72/72) and 95 % (19/20) of CNVs were consistent with the reviewers’ and ClinGen-verified classifications, respectively. AutoCNV only required an average of less than 5 milliseconds to obtain the result for one CNV with automated scoring. We also applied AutoCNV for the interpretation of CNVs from the ClinVar database and the dbVar database. We also developed a web-based version of AutoCNV (wAutoCNV).

Conclusions

AutoCNV may serve to assist users in conducting in-depth CNV interpretation, to accelerate and facilitate the interpretation process of CNVs and to improve the consistency and reliability of CNV interpretation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08011-4.

A Reassessment of Copy Number Variations in Congenital Heart Defects: Picturing the Whole Genome

Copy number variations (CNVs) can modulate phenotypes by affecting protein-coding sequences directly or through interference of gene expression. Recent studies in cancer and limb defects pinpointed the relevance of non-coding gene regulatory elements such as long non-coding RNAs (lncRNAs) and topologically associated domain (TAD)-related gene-enhancer interactions. The contribution of such non-coding elements is largely unexplored in congenital heart defects (CHD). We performed a retrospective analysis of CNVs reported in a cohort of 270 CHD patients. We reviewed the diagnostic yield of pathogenic CNVs, and performed a comprehensive reassessment of 138 CNVs of unknown significance (CNV-US), evaluating protein-coding genes, lncRNA genes, and potential interferences with TAD-related gene-enhancer interactions. Fifty-two of the 138 CNV-US may relate to CHD, revealing three candidate CHD regions, 19 candidate CHD genes, 80 lncRNA genes of interest, and six potentially CHD-related TAD interferences. Our study thus indicates a potential relevance of non-coding gene regulatory elements in CNV-related CHD pathogenesis. Shortcomings in our current knowledge on genomic variation call for continuous reporting of CNV-US in international databases, careful patient counseling, and additional functional studies to confirm these preliminary findings.

Chromosomes in the genomic age. Preserving cytogenomic competence of diagnostic genome laboratories

Participation of clinical genetic laboratories in External Quality Assessment schemes (EQAs) is a powerful method to ascertain if any improvement or additional training is required in the diagnostic service. Here, we provide evidence from recent EQAs that the competence in recognizing and interpreting cytogenetic aberrations is variable and could impact patient management. We identify several trends that could affect cytogenomic competence. Firstly, as a result of the age distribution among clinical laboratory geneticists (CLGs) registered at the European Board of Medical Genetics, about 25-30% of those with experience in cytogenetics will retire during the next decade. At the same time, there are about twice as many molecular geneticists to cytogeneticists among the younger CLGs. Secondly, when surveying training programs for CLG, we observed that not all programs guarantee that candidates gather sufficient experience in clinical cytogenomics. Thirdly, we acknowledge that whole genome sequencing (WGS) has a great attraction to biomedical scientists that wish to enter a training program for CLG. This, with a larger number of positions available, makes a choice for specialization in molecular genetics logical. However, current WGS technology cannot provide a diagnosis in all cases. Understanding the etiology of chromosomal rearrangements is essential for appropriate follow-up and for ascertaining recurrence risks. We define the minimal knowledge a CLG should have about cytogenomics in a world dominated by WGS, and discuss how laboratory directors and boards of professional organizations in clinical genetics can uphold cytogenomic competence by providing adequate CLG training programs and attracting sufficient numbers of trainees.

Copy Number Variation: Methods and Clinical Applications

Gains and losses of large segments of genomic DNA, known as copy number variants (CNVs) gained considerable interest in clinical diagnostics lately, as particular forms may lead to inherited genetic diseases. In recent decades, researchers developed a wide variety of cytogenetic and molecular methods with different detection capabilities to detect clinically relevant CNVs. In this review, we summarize methodological progress from conventional approaches to current state of the art techniques capable of detecting CNVs from a few bases up to several megabases. Although the recent rapid progress of sequencing methods has enabled precise detection of CNVs, determining their functional effect on cellular and whole-body physiology remains a challenge. Here, we provide a comprehensive list of databases and bioinformatics tools that may serve as useful assets for researchers, laboratory diagnosticians, and clinical geneticists facing the challenge of CNV detection and interpretation.

A Guide to Using ClinTAD for Interpretation of DNA Copy Number Variants in the Context of Topologically Associated Domains

DNA copy number variants (CNVs) are routinely evaluated as part of clinical diagnosis in both the prenatal and postnatal genetic settings. Current guidelines for interpreting the potential clinical significance of these CNVs, typically identified by chromosomal microarray, focus entirely on genes localized within the CNV region. However, recent work has suggested that some CNVs can actually produce clinical impacts by influencing transcription of genes outside the CNV region. These alterations of transcription appear to occur by disrupting the composition of DNA topologically associated domains (TADs), which strongly influence contacts between gene promoters and their associated enhancers. Here we present a set of detailed protocols for the use of the free software tool ClinTAD (https://www.clintad.com). This decision-support software allows for prediction as to whether a given CNV may potentially disrupt a TAD boundary, and offers phenotype matching to genes near, but not within the CNV region, whose expression could be influenced by altered TAD architecture and that have phenotypic impacts related to that reported in a given patient. Our protocols here provide specific examples of how to implement these tools. In addition, the software has the capability to impact genomic research by evaluating multiple cases in parallel. We propose that this decision-support tool can benefit and improve genetic diagnosis. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Evaluating a single case using ClinTAD Basic Protocol 2: Evaluating a single case with multiple variants using ClinTAD Basic Protocol 3: Evaluating multiple cases using ClinTAD Basic Protocol 4: Creating tracks with custom data.

REDBot: Natural language process methods for clinical copy number variation reporting in prenatal and products of conception diagnosis

Background

Current copy number variation (CNV) identification methods have rapidly become mature. However, the postdetection processes such as variant interpretation or reporting are inefficient. To overcome this situation, we developed REDBot as an automated software package for accurate and direct generation of clinical diagnostic reports for prenatal and products of conception (POC) samples.

Methods

We applied natural language process (NLP) methods for analyzing 30,235 in‐house historical clinical reports through active learning, and then, developed clinical knowledge bases, evidence‐based interpretation methods and reporting criteria to support the whole postdetection pipeline.

Results

Of the 30,235 reports, we obtained 37,175 CNV‐paragraph pairs. For these pairs, the active learning approaches achieved a 0.9466 average F1‐score in sentence classification. The overall accuracy for variant classification was 95.7%, 95.2%, and 100.0% in retrospective, prospective, and clinical utility experiments, respectively.

Conclusion

By integrating NLP methods in CNVs postdetection pipeline, REDBot is a robust and rapid tool with clinical utility for prenatal and POC diagnosis.

Compound craniosynostosis, intellectual disability, and Noonan-like facial dysmorphism associated with 7q32.3-q35 deletion

OBJECTIVE

Craniosynostosis (CS) is the premature fusion of the cranial sutures, occurring either in isolated or syndromic form. Syndromic CS, which was described in over 180 genetic syndromes, accounts for 15-30% of all CS cases and usually originates from mutations within the FGFR1, FGFR2, FGFR3, and TWIST1 genes. However, causative alterations in other genes, or rarely copy number variations (CNVs) were also reported. In this article, we describe a patient with Noonan-like facial dysmorphism accompanied by intellectual disability and compound CS, involving coronal, sagittal, and squamous sutures.

METHODS

We applied karyotyping, copy number variations analysis using array comparative genomic hybridization, and microarray-based genes expresion analysis.

RESULTS

We have shown that the index carried a large and rare heterozygous deletion, which encompassed 12.782 Mb and mapped to a chromosomal region of 7q32.3-q35 (HG38 - chr7:131837067-144607071). The aberration comprised 109 protein-coding genes, including BRAF, that encodes serine/threonine-protein kinase B-Raf, being a part of the RAS/MAPK signaling pathway.

DISCUSSION

The RAS/MAPK pathway plays an essential role in human development; hence, its dysregulation not surprisingly results in severe congenital anomalies, such as phenotypically overlapping syndromes termed RASopathies. To our best knowledge, we report here the first CNV causing haploinsufficiency of BRAF, resulting in dysregulation of the RAS/MAPK cascade, and consequently, in the phenotype observed in our patient. To conclude, with this report, we have pointed to the involvement of the RAS/MAPK signaling pathway in CS development. Moreover, we have shown that the molecular analysis based on both DNA and RNA profiling, undoubtedly constitutes a comprehensive diagnostic and research strategy for elucidating a cause of genetic diseases.

Sudden Cardiac Death and Copy Number Variants: What Do We Know after 10 Years of Genetic Analysis?

Over the last ten years, analysis of copy number variants has increasingly been applied to the study of arrhythmogenic pathologies associated with sudden death, mainly due to significant advances in the field of massive genetic sequencing. Nevertheless, few published reports have focused on the prevalence of copy number variants associated with sudden cardiac death. As a result, the frequency of these genetic alterations in arrhythmogenic diseases as well as their genetic interpretation and clinical translation has not been established. This review summarizes the current available data concerning copy number variants in sudden cardiac death-related diseases.