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Background Multiple different evidence types as well as gene-specific variant classification guidelines need to be considered during the classification of variants, making the process complex. Therefore, tools that support variant classification by experts are urgently needed. Methods We present HerediVar a web application and HerediClassify a variant classification algorithm. The performance of HerediClassify was validated and compared to other variant classification tools. HerediClassify implements 19/28 variant classification criteria by the American College of Medical Genetics and gene-specific recommendations for ATM , BRCA1 , BRCA2 , CDH1 , PALB2 , PTEN , and TP53 . Results HerediVar offers modular annotation services and allows for collaboration in the classification of variants. On the validation dataset, HerediClassify shows an average F1-Score of 93% across all criteria. HerediClassify outperforms other automated variant classification tools like vaRHC and Cancer SIGVAR. Conclusion In HerediVar and HerediClassify we present a powerful solution to support variant classification in HBOC. Through their modular design, HerediVar and HerediClassify are easily extendable to other use cases and human genetic diagnostics as a whole.

Background Automation has been introduced into variant interpretation, but it is not known how automated variant interpretation performs on a stand‐alone basis. The purpose of this study was to evaluate a fully automated computerized approach. Method We reviewed all variants encountered in a set of carrier screening panels over a 1‐year interval. Observed variants with high‐confidence ClinVar interpretations were included in the analysis; those without high‐confidence ClinVar entries were excluded. Results Discrepancy rates between automated interpretations and high‐confidence ClinVar entries were analyzed. Of the variants interpreted as positive (likely pathogenic or pathogenic) based on ClinVar information, 22.6% were classified as negative (variants of uncertain significance, likely benign or benign) variants by the automated method. Of the ClinVar negative variants, 1.7% were classified as positive by the automated software. On a per‐case basis, which accounts for variant frequency, 63.4% of cases with a ClinVar high‐confidence positive variant were classified as negative by the automated method. Conclusion While automation in genetic variant interpretation holds promise, there is still a need for manual review of the output. Additional validation of automated variant interpretation methods should be conducted. Here, we performed a comparative analysis of a fully automated curation of variant classification versus a process that included an additional manual component. The comparison was carried out for a set of variants where there was high confidence in their pathogenic classification, based on ClinVar entries. We found that a high proportion of automated interpretations (22.6% of positive and 1.7% of negative variants) were reclassified when there was a manual review. We conclude that manual review of the output from the automated variant classifier is currently essential.

Purpose We evaluated the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines for internal consistency and compatibility with Bayesian statistical reasoning. Methods The ACMG/AMP criteria were translated into a naive Bayesian classifier, assuming four levels of evidence and exponentially scaled odds of pathogenicity. We tested this framework with a range of prior probabilities and odds of pathogenicity. Results We modeled the ACMG/AMP guidelines using biologically plausible assumptions. Most ACMG/AMP combining criteria were compatible. One ACMG/AMP likely pathogenic combination was mathematically equivalent to pathogenic and one ACMG/AMP pathogenic combination was actually likely pathogenic. We modeled combinations that include evidence for and against pathogenicity, showing that our approach scored some combinations as pathogenic or likely pathogenic that ACMG/AMP would designate as variant of uncertain significance (VUS). Conclusion By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only 2 of the 18 existing ACMG/AMP evidence combinations were mathematically inconsistent with the overall framework. Mixed combinations of pathogenic and benign evidence could yield a likely pathogenic, likely benign, or VUS result. This quantitative framework validates the approach adopted by the ACMG/AMP, provides opportunities to further refine evidence categories and combining rules, and supports efforts to automate components of variant pathogenicity assessments.

Purpose The 2015 American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG–AMP) guidelines were a major step toward establishing a common framework for variant classification. In practice, however, several aspects of the guidelines lack specificity, are subject to varied interpretations, or fail to capture relevant aspects of clinical molecular genetics. A simple implementation of the guidelines in their current form is insufficient for consistent and comprehensive variant classification. Methods We undertook an iterative process of refining the ACMG–AMP guidelines. We used the guidelines to classify more than 40,000 clinically observed variants, assessed the outcome, and refined the classification criteria to capture exceptions and edge cases. During this process, the criteria evolved through eight major and minor revisions. Results Our implementation: (i) separated ambiguous ACMG–AMP criteria into a set of discrete but related rules with refined weights; (ii) grouped certain criteria to protect against the overcounting of conceptually related evidence; and (iii) replaced the “clinical criteria” style of the guidelines with additive, semiquantitative criteria. Conclusion Sherloc builds on the strong framework of 33 rules established by the ACMG–AMP guidelines and introduces 108 detailed refinements, which support a more consistent and transparent approach to variant classification.

The ability of a single technology, next-generation sequencing, to provide both sequence and copy number variant (CNV) results has driven the merger of clinical cytogenetics and molecular genetics. Consequently, the distinction between the definition of a sequence variant and a CNV is blurry. As the 2015 American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) standards and guidelines for interpretation of sequence variants address CNV classification only sparingly, this study focused on adapting ACMG/AMP criteria for single-gene CNV interpretation. CNV-specific modifications of the 2015 ACMG/AMP criteria were developed and their utility was independently tested by three diagnostic laboratories. Each laboratory team interpreted the same 12 single-gene CNVs using three systems: (1) without ACMG/AMP guidance, (2) with ACMG/AMP criteria,and (3) with new modifications. A replication study of 12 different CNVs validated the modified criteria. The adapted criteria system presented here showed improved concordance and usability for single-gene CNVs compared with using the ACMG/AMP interpretation guidelines focused on sequence variants. These single-gene CNV criteria modifications could be used as a supplement to the ACMG/AMP guidelines for sequence variants, allowing for a streamlined workflow and a step toward a uniform classification system for both sequence and copy number alterations.

The promising expectations about personalized medicine have opened the path to routine large-scale sequencing and increased the importance of genetic counseling for hereditary cancers, among which hereditary breast and ovary cancers (HBOC) have a major impact. High-throughput sequencing, or Next-Generation Sequencing (NGS), has improved cancer patient management, ameliorating diagnosis and treatment decisions. In addition to its undeniable clinical utility, NGS is also unveiling a large number of variants that we are still not able to clearly define and classify, the variants of uncertain significance (VUS), which account for about 40% of total variants. At present, VUS use in the clinical context is challenging. Medical reports may omit this kind of data and, even when included, they limit the clinical utility of genetic information. This has prompted the scientific community to seek easily applicable tests to accurately classify VUS and increase the amount of usable information from NGS data. In this review, we will focus on NGS and classification systems for VUS investigation, with particular attention on HBOC-related genes and in vitro functional tests developed for ameliorating and accelerating variant classification in cancer.

Purpose Pathogenic variants in GJB2 are the most common cause of autosomal recessive sensorineural hearing loss. The classification of c.101T>C/p.Met34Thr and c.109G>A/p.Val37Ile in GJB2 are controversial. Therefore, an expert consensus is required for the interpretation of these two variants. Methods The ClinGen Hearing Loss Expert Panel collected published data and shared unpublished information from contributing laboratories and clinics regarding the two variants. Functional, computational, allelic, and segregation data were also obtained. Case–control statistical analyses were performed. Results The panel reviewed the synthesized information, and classified the p.Met34Thr and p.Val37Ile variants utilizing professional variant interpretation guidelines and professional judgment. We found that p.Met34Thr and p.Val37Ile are significantly overrepresented in hearing loss patients, compared with population controls. Individuals homozygous or compound heterozygous for p.Met34Thr or p.Val37Ile typically manifest mild to moderate hearing loss. Several other types of evidence also support pathogenic roles for these two variants. Conclusion Resolving controversies in variant classification requires coordinated effort among a panel of international multi-institutional experts to share data, standardize classification guidelines, review evidence, and reach a consensus. We concluded that p.Met34Thr and p.Val37Ile variants in GJB2 are pathogenic for autosomal recessive nonsyndromic hearing loss with variable expressivity and incomplete penetrance.

Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by the mutations in survival motor neuron 1 gene (SMN1). The molecular pathology of missense mutations in SMN1 is not thoroughly investigated so far. Therefore, we collected all missense mutations in the SMN1 protein, using all possible search terms, from three databases (PubMed, PMC and Google Scholar). All missense mutations were subjected to in silico pathogenicity, conservation, and stability analysis tools. We used statistical analysis as a QC measure for validating the specificity and accuracy of these tools. PolyPhen-2 demonstrated the highest specificity and accuracy. While PolyPhen-1 showed the highest sensitivity; overall, PolyPhen2 showed better measures in comparison to other in silico tools. Three mutations (D44V, Y272C, and Y277C) were identified as the most pathogenic and destabilizing. Further, we compared the physiochemical properties of the native and the mutant amino acids and observed loss of H-bonds and aromatic stacking upon the cysteine to tyrosine substitution, which led to the loss of aromatic rings and may reduce protein stability. The three mutations were further subjected to Molecular Dynamics Simulation (MDS) analysis using GROMACS to understand the structural changes. The Y272C and Y277C mutants exhibited maximum deviation pattern from the native protein as compared to D44V mutant. Further MDS analysis predicted changes in the stability that may have been contributed due to the loss of hydrogen bonds as observed in intramolecular hydrogen bond analysis and physiochemical analysis. A loss of function/structural impact was found to be severe in the case of Y272C and Y277C mutants in comparison to D44V mutation. Correlating the results from in silico predictions, physiochemical analysis, and MDS, we were able to observe a loss of stability in all the three mutants. This combinatorial approach could serve as a platform for variant interpretation and drug design for spinal muscular dystrophy resulting from missense mutations.

Multiplexed assays of variant effect (MAVEs) are a critical tool for researchers and clinicians to understand genetic variants. Here we describe the 2024 update to MaveDB ( https://www.mavedb.org/ ) with four key improvements to the MAVE community’s database of record: more available data including over 7 million variant effect measurements, an improved data model supporting assays such as saturation genome editing, new built-in exploration and visualization tools, and powerful APIs for data federation and streamlined submission and access. Together these changes support MaveDB’s role as a hub for the analysis and dissemination of MAVEs now and into the future.

Background Curated databases of genetic variants assist clinicians and researchers in interpreting genetic variation. Yet, these databases contain some misclassified variants. It is unclear whether variant misclassification is abating as these databases rapidly grow and implement new guidelines. Methods Using archives of ClinVar and HGMD, we investigated how variant misclassification has changed over 6 years, across different ancestry groups. We considered inborn errors of metabolism (IEMs) screened in newborns as a model system because these disorders are often highly penetrant with neonatal phenotypes. We used samples from the 1000 Genomes Project (1KGP) to identify individuals with genotypes that were classified by the databases as pathogenic. Due to the rarity of IEMs, nearly all such classified pathogenic genotypes indicate likely variant misclassification in ClinVar or HGMD. Results While the false-positive rates of both ClinVar and HGMD have improved over time, HGMD variants currently imply two orders of magnitude more affected individuals in 1KGP than ClinVar variants. We observed that African ancestry individuals have a significantly increased chance of being incorrectly indicated to be affected by a screened IEM when HGMD variants are used. However, this bias affecting genomes of African ancestry was no longer significant once common variants were removed in accordance with recent variant classification guidelines. We discovered that ClinVar variants classified as Pathogenic or Likely Pathogenic are reclassified sixfold more often than DM or DM? variants in HGMD, which has likely resulted in ClinVar’s lower false-positive rate. Conclusions Considering misclassified variants that have since been reclassified reveals our increasing understanding of rare genetic variation. We found that variant classification guidelines and allele frequency databases comprising genetically diverse samples are important factors in reclassification. We also discovered that ClinVar variants common in European and South Asian individuals were more likely to be reclassified to a lower confidence category, perhaps due to an increased chance of these variants being classified by multiple submitters. We discuss features for variant classification databases that would support their continued improvement.