CardioVAI: An automatic implementation of ACMG-AMP variant interpretation guidelines in the diagnosis of cardiovascular diseases

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project

BACKGROUND

A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting.

METHODS

We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values.

RESULTS

Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency.

CONCLUSIONS

Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases

Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an exome/genome based on the probability of being disease-causing. To do so, our method leverages standard guidelines for germline variant interpretation as defined by the American College of Human Genomics (ACMG) and the Association for Molecular Pathology (AMP), inheritance information, phenotypic similarity, and variant quality. Starting from (1) the VCF file containing proband's variants, (2) the list of proband's phenotypes encoded in Human Phenotype Ontology terms, and optionally (3) the information about family members (if available), the "Suggested Diagnosis" ranks all the variants according to their machine learning prediction. This method significantly reduces the number of variants that need to be evaluated by geneticists by pinpointing causative variants in the very first positions of the prioritized list. Most importantly, our approach proved to be among the top performers within the CAGI6 Rare Genome Project Challenge, where it was able to rank the true causative variant among the first positions and, uniquely among all the challenge participants, increased the diagnostic yield of 12.5% by solving 2 undiagnosed cases.

Unraveling Complexities in Genetically Elusive Long QT Syndrome

Genetic testing has become standard of care for patients with long QT syndrome (LQTS), providing diagnostic, prognostic, and therapeutic information for both probands and their family members. However, up to a quarter of patients with LQTS do not have identifiable Mendelian pathogenic variants in the currently known LQTS-associated genes. This absence of genetic confirmation, intriguingly, does not lessen the severity of LQTS, with the prognosis in these gene-elusive patients with unequivocal LQTS mirroring genotype-positive patients in the limited data available. Such a conundrum instigates an exploration into the causes of corrected QT interval (QTc) prolongation in these cases, unveiling a broad spectrum of potential scenarios and mechanisms. These include multiple environmental influences on QTc prolongation, exercise-induced repolarization abnormalities, and the profound implications of the constantly evolving nature of genetic testing and variant interpretation. In addition, the rapid advances in genetics have the potential to uncover new causal genes, and polygenic risk factors may aid in the diagnosis of high-risk patients. Navigating this multifaceted landscape requires a systematic approach and expert knowledge, integrating the dynamic nature of genetics and patient-specific influences for accurate diagnosis, management, and counseling of patients. The role of a subspecialized expert cardiogenetic clinic is paramount in evaluation to navigate this complexity. Amid these intricate aspects, this review outlines potential causes of gene-elusive LQTS. It also provides an outline for the evaluation of patients with negative and inconclusive genetic test results and underscores the need for ongoing adaptation and reassessment in our understanding of LQTS, as the complexities of gene-elusive LQTS are increasingly deciphered.

Toward advanced diagnosis and management of inherited arrhythmia syndromes: Harnessing the capabilities of artificial intelligence and machine learning

The use of advanced computational technologies, such as artificial intelligence (AI), is now exerting a significant influence on various aspects of life, including health care and science. AI has garnered remarkable public notice with the release of deep learning models that can model anything from artwork to academic papers with minimal human intervention. Machine learning, a method that uses algorithms to extract information from raw data and represent it in a model, and deep learning, a method that uses multiple layers to progressively extract higher-level features from the raw input with minimal human intervention, are increasingly leveraged to tackle problems in the health sector, including utilization for clinical decision support in cardiovascular medicine. Inherited arrhythmia syndromes are a clinical domain where multiple unanswered questions remain despite unprecedented progress over the past 2 decades with the introduction of large panel genetic testing and the first steps in precision medicine. In particular, AI tools can help address gaps in clinical diagnosis by identifying individuals with concealed or transient phenotypes; enhance risk stratification by elevating recognition of underlying risk burden beyond widely recognized risk factors; improve prediction of response to therapy, and further prognostication. In this contemporary review, we provide a summary of the AI models developed to solve challenges in inherited arrhythmia syndromes and also outline gaps that can be filled with the development of intelligent AI models.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project

Background

A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery. Families are consented for sharing of sequence and phenotype data with researchers, allowing development of a Critical Assessment of Genome Interpretation (CAGI) community challenge, placing variant prioritization models head-to-head in a real-life clinical diagnostic setting.

Methods

Predictors were provided a dataset of phenotype terms and variant calls from GS of 175 RGP individuals (65 families), including 35 solved training set families, with causal variants specified, and 30 test set families (14 solved, 16 unsolved). The challenge tasked teams with identifying the causal variants in as many test set families as possible. Ranked variant predictions were submitted with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on rank position of true positive causal variants and maximum F-measure, based on precision and recall of causal variants across EPCR thresholds.

Results

Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performing teams recalled the causal variants in up to 13 of 14 solved families by prioritizing high quality variant calls that were rare, predicted deleterious, segregating correctly, and consistent with reported phenotype. In unsolved families, newly discovered diagnostic variants were returned to two families following confirmatory RNA sequencing, and two prioritized novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant, in an unsolved proband with phenotype overlap with asparagine synthetase deficiency.

Conclusions

By objective assessment of variant predictions, we provide insights into current state-of-the-art algorithms and platforms for genome sequencing analysis for rare disease diagnosis and explore areas for future optimization. Identification of diagnostic variants in unsolved families promotes synergy between researchers with clinical and computational expertise as a means of advancing the field of clinical genome interpretation.

Success and Pitfalls of Genetic Testing in Undiagnosed Diseases: Whole Exome Sequencing and Beyond

Novel approaches to uncover the molecular etiology of neurodevelopmental disorders (NDD) are highly needed. Even using a powerful tool such as whole exome sequencing (WES), the diagnostic process may still prove long and arduous due to the high clinical and genetic heterogeneity of these conditions. The main strategies to improve the diagnostic rate are based on family segregation, re-evaluation of the clinical features by reverse-phenotyping, re-analysis of unsolved NGS-based cases and epigenetic functional studies. In this article, we described three selected cases from a cohort of patients with NDD in which trio WES was applied, in order to underline the typical challenges encountered during the diagnostic process: (1) an ultra-rare condition caused by a missense variant in MEIS2, identified through the updated Solve-RD re-analysis; (2) a patient with Noonan-like features in which the NGS analysis revealed a novel variant in NIPBL causing Cornelia de Lange syndrome; and (3) a case with de novo variants in genes involved in the chromatin-remodeling complex, for which the study of the epigenetic signature excluded a pathogenic role. In this perspective, we aimed to (i) provide an example of the relevance of the genetic re-analysis of all unsolved cases through network projects on rare diseases; (ii) point out the role and the uncertainties of the reverse phenotyping in the interpretation of the genetic results; and (iii) describe the use of methylation signatures in neurodevelopmental syndromes for the validation of the variants of uncertain significance.

HTAADVar: Aggregation and fully automated clinical interpretation of genetic variants in heritable thoracic aortic aneurysm and dissection

PURPOSE

Early detection and pathogenicity interpretation of disease-associated variants are crucial but challenging in molecular diagnosis, especially for insidious and life-threatening diseases, such as heritable thoracic aortic aneurysm and dissection (HTAAD). In this study, we developed HTAADVar, an unbiased and fully automated system for the molecular diagnosis of HTAAD.

METHODS

We developed HTAADVar (http://htaadvar.fwgenetics.org) under the American College of Medical Genetics and Genomics/Association for Molecular Pathology framework, with optimizations based on disease- and gene-specific knowledge, expert panel recommendations, and variant observations. HTAADVar provides variant interpretation with a self-built database through the web server and the stand-alone programs.

RESULTS

We constructed an expert-reviewed database by integrating 4373 variants in HTAAD genes, with comprehensive metadata curated from 697 publications and an in-house study of 790 patients. We further developed an interpretation system to assess variants automatically. Notably, HTAADVar showed a multifold increase in performance compared with public tools, reaching a sensitivity of 92.64% and specificity of 70.83%. The molecular diagnostic yield of HTAADVar among 790 patients (42.03%) also matched the clinical data, independently demonstrating its good performance in clinical application.

CONCLUSION

HTAADVar represents the first fully automated system for accurate variant interpretation for HTAAD. The framework of HTAADVar could also be generalized for the molecular diagnosis of other genetic diseases.

MARGINAL: An Automatic Classification of Variants in BRCA1 and BRCA2 Genes Using a Machine Learning Model

Implementation of next-generation sequencing (NGS) for the genetic analysis of hereditary diseases has resulted in a vast number of genetic variants identified daily, leading to inadequate variant interpretation and, consequently, a lack of useful clinical information for treatment decisions. Herein, we present MARGINAL 1.0.0, a machine learning (ML)-based software for the interpretation of rare BRCA1 and BRCA2 germline variants. MARGINAL software classifies variants into three categories, namely, (likely) pathogenic, of uncertain significance and (likely) benign, implementing the criteria established by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP). We first annotated BRCA1 and BRCA2 variants using various sources. Then, we automatically implemented the ACMG-AMP criteria, and we finally constructed the ML model for variant classification. To maximize accuracy, we compared the performance of eight different ML algorithms in a classification scheme based on a serial combination of two classifiers. The model showed high predictive abilities with maximum accuracy of 92% and 98%, recall of 92% and 98% and specificity of 90% and 98% for the first and second classifiers, respectively. Our results indicate that using a gene and disease-specific ML automated software for clinical variant evaluation can minimize conflicting interpretations.

GenOtoScope: Towards automating ACMG classification of variants associated with congenital hearing loss

Since next-generation sequencing (NGS) has become widely available, large gene panels containing up to several hundred genes can be sequenced cost-efficiently. However, the interpretation of the often large numbers of sequence variants detected when using NGS is laborious, prone to errors and is often difficult to compare across laboratories. To overcome this challenge, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) have introduced standards and guidelines for the interpretation of sequencing variants. Additionally, disease-specific refinements have been developed that include accurate thresholds for many criteria, enabling highly automated processing. This is of particular interest for common but heterogeneous disorders such as hearing impairment. With more than 200 genes associated with hearing disorders, the manual inspection of possible causative variants is particularly difficult and time-consuming. To this end, we developed the open-source bioinformatics tool GenOtoScope, which automates the analysis of all ACMG/AMP criteria that can be assessed without further individual patient information or human curator investigation, including the refined loss of function criterion ("PVS1"). Two types of interfaces are provided: (i) a command line application to classify sequence variants in batches for a set of patients and (ii) a user-friendly website to classify single variants. We compared the performance of our tool with two other variant classification tools using two hearing loss data sets, which were manually annotated either by the ClinGen Hearing Loss Gene Curation Expert Panel or the diagnostics unit of our human genetics department. GenOtoScope achieved the best average accuracy and precision for both data sets. Compared to the second-best tool, GenOtoScope improved the accuracy metric by 25.75% and 4.57% and precision metric by 52.11% and 12.13% on the two data sets, respectively. The web interface is accessible via: http://genotoscope.mh-hannover.de:5000 and the command line interface via: https://github.com/damianosmel/GenOtoScope.

Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants

Hypertrophic cardiomyopathy (HCM) is a common heart disease associated with sudden cardiac death. Early diagnosis is critical to identify patients who may benefit from implantable cardioverter defibrillator therapy. Although genetic testing is an integral part of the clinical evaluation and management of patients with HCM and their families, in many cases the genetic analysis fails to identify a disease-causing mutation. This is in part due to difficulties in classifying newly detected rare genetic variants as well as variants-of-unknown-significance (VUS). Multiple computational algorithms have been developed to predict the potential pathogenicity of genetic variants, but their relative performance in HCM has not been comprehensively assessed. Here, we compared the performance of 39 currently available prediction tools in distinguishing between high-confidence HCM-causing missense variants and benign variants, and we developed an easy-to-use-tool to perform variant prediction benchmarks based on annotated VCF files (VETA). Our results show that tool performance increases after HCM-specific calibration of thresholds. After excluding potential biases due to circularity type I issues, we identified ClinPred, MISTIC, FATHMM, MPC and MetaLR as the five best performer tools in discriminating HCM-associated variants. We propose combining these tools in order to prioritize unknown HCM missense variants that should be closely followed-up in the clinic.

Phenotypic Variation in Two Siblings Affected with Shwachman-Diamond Syndrome: The Use of Expert Variant Interpreter (eVai) Suggests Clinical Relevance of a Variant in the KMT2A Gene

Introduction. Shwachman-Diamond Syndrome (SDS) is an autosomal-recessive disorder characterized by neutropenia, pancreatic exocrine insufficiency, skeletal dysplasia, and an increased risk for leukemic transformation. Biallelic mutations in the SBDS gene have been found in about 90% of patients. The clinical spectrum of SDS in patients is wide, and variability has been noticed between different patients, siblings, and even within the same patient over time. Herein, we present two SDS siblings (UPN42 and UPN43) carrying the same SBDS mutations and showing relevant differences in their phenotypic presentation. Study aim. We attempted to understand whether other germline variants, in addition to SBDS, could explain some of the clinical variability noticed between the siblings. Methods. Whole-exome sequencing (WES) was performed. Human Phenotype Ontology (HPO) terms were defined for each patient, and the WES data were analyzed using the eVai and DIVAs platforms. Results. In UPN43, we found and confirmed, using Sanger sequencing, a novel de novo variant (c.10663G > A, p.Gly3555Ser) in the KMT2A gene that is associated with autosomal-dominant Wiedemann−Steiner Syndrome. The variant is classified as pathogenic according to different in silico prediction tools. Interestingly, it was found to be related to some of the HPO terms that describe UPN43. Conclusions. We postulate that the KMT2A variant found in UPN43 has a concomitant and co-occurring clinical effect, in addition to SBDS mutation. This dual molecular effect, supported by in silico prediction, could help to understand some of the clinical variations found among the siblings. In the future, these new data are likely to be useful for personalized medicine and therapy for selected cases.

Burden of rare variants in arrhythmogenic cardiomyopathy with right dominant form associated genes provides new insights for molecular diagnosis and clinical management

Arrhythmogenic cardiomyopathy with right dominant form (ACR) is a rare heritable cardiac cardiomyopathy disorder associated with sudden cardiac death. Pathogenic variants in desmosomal genes have been causally related to ACR in 40% of cases. Other genes encoding non desmosomal proteins have been described in ACR but their contribution in this pathology is still debated. A panel of 71 genes associated with inherited cardiopathies was screened in an ACR population of 172 probands and 856 individuals from the general population. Pathogenic variants (PV) and variants of uncertain significance (VUS) have been identified in 36% and 18.6% of patients respectively. Among the cardiopathy associated genes, burden tests show a significant enrichment in PV and VUS only for desmosomal genes PKP2, DSP, DSC2 and DSG2. Importantly, VUS may account for 15% of ACR cases and should then be considered for molecular diagnosis. Among the other genes, no evidence of enrichment was detected, suggesting an extreme caution in the interpretation of these genetic variations without associated functional or segregation data. Genotype-phenotype correlation points to 1) a more severe and earlier onset of the disease in PV and VUS carriers, underlying the importance to carry out presymptomatic diagnosis in relatives and 2) to a more prevalent left ventricular dysfunction in DSP variant carriers. This article is protected by copyright. All rights reserved.

Novel Pathogenetic Variants in PTHLH and TRPS1 Genes Causing Syndromic Brachydactyly

Skeletal disorders, including both isolated and syndromic brachydactyly type E, derive from genetic defects affecting the fine tuning of the network of pathways involved in skeletogenesis and growth-plate development. Alterations of different genes of this network may result in overlapping phenotypes, as exemplified by disorders due to the impairment of the parathyroid hormone/parathyroid hormone-related protein pathway, and obtaining a correct diagnosis is sometimes challenging without a genetic confirmation. Five patients with Albright's hereditary osteodystrophy (AHO)-like skeletal malformations without a clear clinical diagnosis were analyzed by whole-exome sequencing (WES) and novel potentially pathogenic variants in parathyroid hormone like hormone (PTHLH) (BDE with short stature [BDE2]) and TRPS1 (tricho-rhino-phalangeal syndrome [TRPS]) were discovered. The pathogenic impact of these variants was confirmed by in vitro functional studies. This study expands the spectrum of genetic defects associated with BDE2 and TRPS and demonstrates the pathogenicity of TRPS1 missense variants located outside both the nuclear localization signal and the GATA ((A/T)GATA(A/G)-binding zinc-containing domain) and Ikaros-like binding domains. Unfortunately, we could not find distinctive phenotypic features that might have led to an earlier clinical diagnosis, further highlighting the high degree of overlap among skeletal syndromes associated with brachydactyly and AHO-like features, and the need for a close interdisciplinary workout in these rare patients. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization

Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Penalized Logistic Regression, to support variant classification and prioritization. Our approach combines ACMG/AMP guidelines for germline variant interpretation as well as variant annotation features and provides a probabilistic score of pathogenicity, thus supporting the prioritization and classification of variants that would be interpreted as uncertain by the ACMG/AMP guidelines. We compared different approaches in terms of variant prioritization and classification on different datasets, showing that our data-driven approach is able to solve more variant of uncertain significance (VUS) cases in comparison with guidelines-based approaches and in silico prediction tools.

VIP-HL: Semi-automated ACMG/AMP variant interpretation platform for genetic hearing loss

The American College of Medical Genetics and Genomics, and the Association for Molecular Pathology (ACMG/AMP) have proposed a set of evidence-based guidelines to support sequence variant interpretation. The ClinGen hearing loss expert panel (HL-EP) introduced further specifications into the ACMG/AMP framework for genetic hearing loss. This study developed a tool named Variant Interpretation Platform for genetic Hearing Loss (VIP-HL), aiming to semi-automate the HL ACMG/AMP rules. VIP-HL aggregates information from external databases to automate 13 out of 24 ACMG/AMP rules specified by HL-EP, namely PVS1, PS1, PM1, PM2, PM4, PM5, PP3, BA1, BS1, BS2, BP3, BP4, and BP7. We benchmarked VIP-HL using 50 variants in which 82 rules were activated by the ClinGen HL-EP. VIP-HL concordantly activated 93% (76/82) rules, significantly higher than that of by InterVar (48%; 39/82). VIP-HL is an integrated online tool for reliable automated variant classification in hearing loss genes. It assists curators in variant interpretation and provides a platform for users to share classifications with each other. VIP-HL is available with a user-friendly web interface at http://hearing.genetics.bgi.com/.

Genetic evaluation of cardiomyopathies in Qatar identifies enrichment of pathogenic sarcomere gene variants and possible founder disease mutations in the Arabs

Background

Hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM) are serious inherited heart diseases with various causative mutations identified. The full spectrum of causative mutations remains to be discovered, especially in understudied populations.

Methods

Here, we established the DOHA Registry and Biobank for cardiomyopathies in Qatar, followed by sequencing of 174 genes on 51 HCM and 53 DCM patients, and 31 relatives.

Results

In HCM, the analysis of 25 HCM‐associated genes showed that 20% of HCM cases had putative pathogenic variants for cardiomyopathy, mainly in sarcomere genes. Additional 49% of HCM cases had variants of uncertain significance, while 31% of HCM cases had likely benign variant(s) or had no variants identified within the analyzed HCM genes. In DCM, 56 putative DCM genes were analyzed. Eight percent of DCM cases had putative pathogenic variants for DCM, in the TTN gene while 70% of cases had variants of uncertain significance, in the analyzed DCM genes, that will need further pathogenicity assessment. Moreover, 22% of DCM cases remain unexplained, by having likely benign variant(s) or having no variants detected in any of the analyzed DCM genes.

Conclusion

We identified or replicated at least four recurrent variants among cardiomyopathy patients, which could be founder disease mutations in the Arabic population, including a frameshift variant (c.1371_1381dupTATCCAGTTAT) of unknown significance in the FKTN gene which seems to cause DCM in homozygosity, and HCM in heterozygosity. In vivo and/or in vitro functional validation need to be pursued in order to assess the pathogenicity of the identified variants.

Stepwise ABC system for classification of any type of genetic variant

The American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP) system for variant classification is score based with five classes: benign, likely benign, variant of unknown significance (VUS), likely pathogenic, and pathogenic. Here, we present a variant classification model that can be an add-on or alternative to ACMG classification: A stepwise system that can classify any type of genetic variant (e.g., hypomorphic alleles, imprinted alleles, copy number variants, runs of homozygosity, enhancer variants, and variants related to traits). We call it the ABC system because classification is first functional (A), then clinical (B), and optionally a standard comment that fits the clinical question is selected (C). Both steps A and B have 1–5 grading when knowledge is sufficient, if not, class “zero” is assigned. Functional grading (A) only concerns biological consequences with the stages normal function (1), likely normal function (2), hypothetical functional effect (3), likely functional effect (4), and proven functional effect (5). Clinical grading (B) is genotype–phenotype focused with the stages “right type of gene” (1), risk factor (2), and pathogenic (3–5, depending on penetrance). Both grades are listed for each variant and combined to generate a joint class ranging from A to F. Importantly, the A–F classes are linked to standard comments, reflecting laboratory or national policy. In step A, the VUS class is split into class 0 (true unknown) and class 3 (hypothetical functional effect based on molecular predictions or de novo occurrence), providing a rationale for variant-of-interest reporting when the clinical picture could fit the finding. The system gives clinicians a better guide to variant significance.

Cancer SIGVAR: A semiautomated interpretation tool for germline variants of hereditary cancer-related genes

Cancer is one of the most important health issues globally and the accuracy of interpretation of cancer-related variants is critical for the clinical management of hereditary cancer. ClinGen Sequence Variant Interpretation Working Groups have developed many adaptations of American College of Medical Genetics and Genomics and the Association of Molecular Pathologists guidelines to improve the consistency of interpretation. We combined the most recent adaptations to expand the number of the criteria from 28 to 48 and developed a tool called Cancer SIGVAR to help genetic counselors interpret the clinical significance of cancer germline variants. Our tool can accept VCF files as input and realize fully automated interpretation based on 21 criteria and semiautomated interpretation based on 48 criteria. We validated the performance of our tool with the ClinVar and CLINVITAE benchmark databases, achieving an average consistency for pathogenic and benign assessment up to 93.71% and 79.38%, respectively. We compared Cancer SIGVAR with two similar tools, InterVar and PathoMAN, and analyzed the main differences in criteria and implementation. Furthermore, we selected 911 variants from another two in-house benchmark databases, and semiautomated interpretation reached an average classification consistency of 98.35%. Our findings highlight the need to optimize automated interpretation tools based on constantly updated guidelines. Cancer SIGVAR is publicly available at http://cancersigvar.bgi.com/.

One4Two®: An Integrated Molecular Approach to Optimize Infertile Couples' Journey

The current diagnostic path of infertile couples is long lasting and often ineffective. Genetic tests, in particular, appear as a limiting step due to their jeopardized use on one side, and to the limited number of genes evaluated on the other. In this context, the development and diffusion, also in routine diagnostic settings, of next generation sequencing (NGS)-based methods for the analyses of several genes in multiple subjects at a time is improving the diagnostic sensitivity of molecular analyses. Thus, we developed One4Two^®, a custom NGS panel to optimize the diagnostic journey of infertile couples. The panel validation was carried out in three steps analyzing a total of 83 subjects. Interestingly, all the previously identified variants were confirmed, assessing the analytic sensitivity of the method. Moreover, additional pathogenic variants have been identified underlying the diagnostic efficacy of the proposed method. One4Two^® allows the simultaneous analysis of infertility-related genes, disease-genes of common inherited diseases, and of polymorphisms related to therapy outcome. Thus, One4Two^® is able to improve the diagnostic journey of infertile couples by simplifying the whole process not only for patients, but also for laboratories and reproduction specialists moving toward an even more personalized medicine.

AutoPVS1: An automatic classification tool for PVS1 interpretation of null variants

Null variants are prevalent within the human genome, and their accurate interpretation is critical for clinical management. In 2018, the ClinGen Sequence Variant Interpretation (SVI) Working Group refined the only criterion with a very strong pathogenicity rating (PVS1). To streamline PVS1 interpretation, we have developed an automatic classification tool with a graphical user interface called AutoPVS1. The performance of AutoPVS1 was assessed using 56 variants manually curated by the ClinGen's SVI Working Group; it achieved an interpretation concordance of 93% (52/56). A further analysis of 28,586 putative loss-of-function variants by AutoPVS1 demonstrated that at least 27.7% of them do not reach a very strong strength level, 17.5% because of variant-specific issues and 10.2% due to disease mechanism considerations. Notably, 41.0% (1,936/4,717) of splicing variants were assigned a decreased preliminary PVS1 strength level, a significantly greater fraction than in frameshift variants (13.2%) and nonsense variants (10.8%). Our results reinforce the necessity of considering variant-specific issues and disease mechanisms in variant interpretation and demonstrate that AutoPVS1 meets an urgent need by enabling biocurators to easily assign accurate, reliable and reproducible PVS1 strength levels in the process of variant interpretation. AutoPVS1 is publicly available at http://autopvs1.genetics.bgi.com/.

Contemporary Insights Into the Genetics of Hypertrophic Cardiomyopathy: Toward a New Era in Clinical Testing?

Genetic testing for hypertrophic cardiomyopathy (HCM) is an established clinical technique, supported by 30 years of research into its genetic etiology. Although pathogenic variants are often detected in patients and used to identify at-risk relatives, the effectiveness of genetic testing has been hampered by ambiguous genetic associations (yielding uncertain and potentially false-positive results), difficulties in classifying variants, and uncertainty about genotype-negative patients. Recent case-control studies on rare variation, improved data sharing, and meta-analysis of case cohorts contributed to new insights into the genetic basis of HCM. In particular, although research into new genes and mechanisms remains essential, reassessment of Mendelian genetic associations in HCM argues that current clinical genetic testing should be limited to a small number of validated disease genes that yield informative and interpretable results. Accurate and consistent variant interpretation has benefited from new standardized variant interpretation guidelines and innovative approaches to improve classification. Most cases lacking a pathogenic variant are now believed to indicate non-Mendelian HCM, with more benign prognosis and minimal risk to relatives. Here, we discuss recent advances in the genetics of HCM and their application to clinical genetic testing together with practical issues regarding implementation. Although this review focuses on HCM, many of the issues discussed are also relevant to other inherited cardiac diseases.

Variant filtering, digenic variants, and other challenges in clinical sequencing: a lesson from fibrillinopathies

Genome-scale high-throughput sequencing enables the detection of unprecedented numbers of sequence variants. Variant filtering and interpretation are facilitated by mutation databases, in silico tools, and population-based reference datasets such as ExAC/gnomAD, while variants are classified using the ACMG/AMP guidelines. These methods, however, pose clinically relevant challenges. We queried the gnomAD dataset for (likely) pathogenic variants in genes causing autosomal-dominant disorders. Furthermore, focusing on the fibrillinopathies Marfan syndrome (MFS) and congenital contractural arachnodactyly (CCA), we screened 500 genomes of our patients for co-occurring variants in FBN1 and FBN2. In gnomAD, we detected 2653 (likely) pathogenic variants in 253 genes associated with autosomal-dominant disorders, enabling the estimation of variant-filtering thresholds and disease predisposition/prevalence rates. In our database, we discovered two families with hitherto unreported co-occurrence of FBN1/FBN2 variants causing phenotypes with mixed or modified MFS/CCA clinical features. We show that (likely) pathogenic gnomAD variants may be more frequent than expected and are challenging to classify according to the ACMG/AMP guidelines as well as that fibrillinopathies are likely underdiagnosed and may co-occur. Consequently, selection of appropriate frequency cutoffs, recognition of digenic variants, and variant classification represent considerable challenges in variant interpretation. Neglecting these challenges may lead to incomplete or missed diagnoses.

Practical Aspects in Genetic Testing for Cardiomyopathies and Channelopathies

Genetic testing has an increasingly important role in the diagnosis and management of cardiac disorders, where it confirms the diagnosis, aids prognostication and risk stratification and guides treatment. A genetic diagnosis in the proband also enables clarification of the risk for family members by cascade testing. Genetics in cardiac disorders is complex where epigenetic and environmental factors might come into interplay. Incomplete penetrance and variable expressivity is also common. Genetic results in cardiac conditions are mostly probabilistic and should be interpreted with all available clinical information. With this complexity in cardiac genetics, testing is only indicated in patients with a strong suspicion of an inheritable cardiac disorder after a full clinical evaluation. In this review we discuss the genetics underlying the major cardiomyopathies and channelopathies, and the practical aspects of diagnosing these conditions in the laboratory.