ClinGen guidance for use of the PP1/BS4 co-segregation and PP4 phenotype specificity criteria for sequence variant pathogenicity classification.

The 2015 American College of Medical Genetics and Genomics and the Association for Molecular Pathology variant classification publication established a standard employed internationally to guide laboratories in variant assessment. Those recommendations included both pathogenic (PP1) and benign (BS4) criteria for evaluating the inheritance patterns of variants, but details of how to apply those criteria at appropriate evidence levels were sparse. Several publications have since attempted to provide additional guidance, but anecdotally, this issue is still challenging. Additionally, it is not clear that those prior efforts fully distinguished disease-gene identification considerations from variant pathogenicity considerations nor did they address autosomal-recessive and X-linked inheritance. Here, we have taken a mixed inductive and deductive approach to this problem using real diseases as examples. We have developed a practical heuristic for genetic co-segregation evidence and have also determined that the specific phenotype criterion (PP4) is inseparably coupled to the co-segregation criterion. We have also determined that negative evidence at one locus constitutes positive evidence for other loci for disorders with locus heterogeneity. Finally, we provide a points-based system for evaluating phenotype and co-segregation as evidence types to support or refute a locus and show how that can be integrated into the Bayesian framework now used for variant classification and consistent with the 2015 guidelines.

Advanced variant classification framework reduces the false positive rate of predicted loss-of-function variants in population sequencing data.

Predicted loss of function (pLoF) variants are often highly deleterious and play an important role in disease biology, but many pLoF variants may not result in loss of function (LoF). Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines' PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 genes associated with autosomal-recessive disease from the Genome Aggregation Database (gnomAD v.2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in a low proportion expressed across transcripts (pext) scored region, or the presence of cryptic in-frame splice rescues. Variants predicted to evade LoF or to be potential artifacts were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of pLoF variants predicted as likely not LoF/not LoF, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.

Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup.

The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1, PS3, PP3, BS3, BP4, and BP7. However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. We utilized empirically derived splicing evidence to (1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, (2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and (3) exemplify methodology to calibrate splice prediction tools. We propose repurposing the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely, BP7 may be used to capture RNA results demonstrating no splicing impact for intronic and synonymous variants. We propose that the PS3/BS3 codes are applied only for well-established assays that measure functional impact not directly captured by RNA-splicing assays. We recommend the application of PS1 based on similarity of predicted RNA-splicing effects for a variant under assessment in comparison with a known pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA-assay evidence described aim to help standardize variant pathogenicity classification processes when interpreting splicing-based evidence.

Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria.

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

A calibrated functional patch-clamp assay to enhance clinical variant interpretation in KCNH2-related long QT syndrome.

Modern sequencing technologies have revolutionized our detection of gene variants. However, in most genes, including KCNH2, the majority of missense variants are currently classified as variants of uncertain significance (VUSs). The aim of this study was to investigate the utility of an automated patch-clamp assay for aiding clinical variant classification in KCNH2. The assay was designed according to recommendations proposed by the Clinical Genome Sequence Variant Interpretation Working Group. Thirty-one variants (17 pathogenic/likely pathogenic, 14 benign/likely benign) were classified internally as variant controls. They were heterozygously expressed in Flp-In HEK293 cells for assessing the effects of variants on current density and channel gating in order to determine the sensitivity and specificity of the assay. All 17 pathogenic variant controls had reduced current density, and 13 of 14 benign variant controls had normal current density, which enabled determination of normal and abnormal ranges for applying evidence of moderate or supporting strength for VUS reclassification. Inclusion of functional assay evidence enabled us to reclassify 6 out of 44 KCNH2 VUSs as likely pathogenic. The high-throughput patch-clamp assay can provide moderate-strength evidence for clinical interpretation of clinical KCNH2 variants and demonstrates the value of developing automated patch-clamp assays for functional characterization of ion channel gene variants.

Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations.

PURPOSE: To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the ClinGen-calibrated PP3/BP4 computational recommendations. METHODS: Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools that were able to reach PP3_Strong and BP4_Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide. RESULTS: From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3_Strong, 3-5 PP3_Moderate, and 3-5 PP3_Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of variants reaching PP3_Strong score thresholds increased approximately 2.6-fold compared with disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting. CONCLUSION: A small number of variants per proband reached PP3_Strong and PP3_Moderate in 3424 disease-associated genes. Although not the intended use of the recommendations, this was also observed genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as pathogenic or likely pathogenic by ACMG/AMP rules.

The landscape of reported VUS in multi-gene panel and genomic testing: Time for a change.

PURPOSE: Variants of uncertain significance (VUS) are a common result of diagnostic genetic testing and can be difficult to manage with potential misinterpretation and downstream costs, including time investment by clinicians. We investigated the rate of VUS reported on diagnostic testing via multi-gene panels (MGPs) and exome and genome sequencing (ES/GS) to measure the magnitude of uncertain results and explore ways to reduce their potentially detrimental impact. METHODS: Rates of inconclusive results due to VUS were collected from over 1.5 million sequencing test results from 19 clinical laboratories in North America from 2020 to 2021. RESULTS: We found a lower rate of inconclusive test results due to VUSs from ES/GS (22.5%) compared with MGPs (32.6%; P < .0001). For MGPs, the rate of inconclusive results correlated with panel size. The use of trios reduced inconclusive rates (18.9% vs 27.6%; P < .0001), whereas the use of GS compared with ES had no impact (22.2% vs 22.6%; P = ns). CONCLUSION: The high rate of VUS observed in diagnostic MGP testing warrants examining current variant reporting practices. We propose several approaches to reduce reported VUS rates, while directing clinician resources toward important VUS follow-up.

Harmonizing variant classification for return of results in the All of Us Research Program.

The All of Us Research Program (AoURP) is a historic effort to accelerate research and improve healthcare by generating and collating data from one million people in the United States. Participants will have the option to receive results from their genome analysis, including actionable findings in 59 gene-disorder pairs for which disorder-associated variants are recommended for return by the American College of Medical Genetics and Genomics. To ensure consistent reporting across the AoURP, in a prelaunch study the four participating clinical laboratories shared all variant classifications in the 59 genes of interest from their internal databases. Of the 11,813 unique variants classified by at least two of the four laboratories, classifications were concordant with regard to reportability for 99.1% (11,711), with only 0.9% (102) having reportability differences. Through variant reassessment, data sharing, and discussion of rationale, participating laboratories resolved all 102 reportable differences. These approaches will be maintained during routine AoU reporting to ensure continuous classification harmonization and consistent reporting within AoURP.

Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines.

Recently, we demonstrated that the qualitative American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) guidelines for evaluation of Mendelian disease gene variants are fundamentally compatible with a quantitative Bayesian formulation. Here, we show that the underlying ACMG/AMP "strength of evidence categories" can be abstracted into a point system. These points are proportional to Log(odds), are additive, and produce a system that recapitulates the Bayesian formulation of the ACMG/AMP guidelines. The strengths of this system are its simplicity and that the connection between point values and odds of pathogenicity allows empirical calibration of the strength of evidence for individual data types. Weaknesses include that a narrow range of prior probabilities is locked in and that the Bayesian nature of the system is inapparent. We conclude that a points-based system has the practical attribute of user-friendliness and can be useful so long as the underlying Bayesian principles are acknowledged.

Prune belly syndrome in surviving males can be caused by Hemizygous missense mutations in the X-linked Filamin A gene.

BACKGROUND: Prune belly syndrome (PBS) is a rare, multi-system congenital myopathy primarily affecting males that is poorly described genetically. Phenotypically, its morbidity spans from mild to lethal, however, all isolated PBS cases manifest three cardinal pathological features: 1) wrinkled flaccid ventral abdominal wall with skeletal muscle deficiency, 2) urinary tract dilation with poorly contractile smooth muscle, and 3) intra-abdominal undescended testes. Despite evidence for a genetic basis, previously reported PBS autosomal candidate genes only account for one consanguineous family and single cases. METHODS: We performed whole exome sequencing (WES) of two maternal adult half-brothers with syndromic PBS (PBS + Otopalatodigital spectrum disorder [OPDSD]) and two unrelated sporadic individuals with isolated PBS and further functionally validated the identified mutations. RESULTS: We identified three unreported hemizygous missense point mutations in the X-chromosome gene Filamin A (FLNA) (c.4952 C > T (p.A1448V), c.6727C > T (p.C2160R), c.5966 G > A (p.G2236E)) in two related cases and two unrelated sporadic individuals. Two of the three PBS mutations map to the highly regulatory, stretch-sensing Ig19-21 region of FLNA and enhance binding to intracellular tails of the transmembrane receptor ß-integrin 1 (ITGß1). CONCLUSIONS: FLNA is a regulatory actin-crosslinking protein that functions in smooth muscle cells as a mechanosensing molecular scaffold, transmitting force signals from the actin-myosin motor units and cytoskeleton via binding partners to the extracellular matrix. This is the first evidence for an X-linked cause of PBS in multiple unrelated individuals and expands the phenotypic spectrum associated with FLNA in males surviving even into adulthood.

Updated recommendation for the benign stand-alone ACMG/AMP criterion.

The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation Working Group set out to refine the American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG/AMP) variant pathogenicity recommendations for stand-alone rule BA1 (a variant with minor allele frequency [MAF] > 0.05 is benign), by clarifying how it should be used and specifying a set of variants that should be exempted from this rule. We cross-referenced ClinVar and Exome Aggregation Consortium data to identify variants for which there was a plausible argument for pathogenicity and the variant exists in one or more population data sets at MAF > 0.05. We identified nine such variants that were present in these data sets that may not be benign. The ACMG/AMP criteria were applied to these variants that resulted in four pathogenic and five variants of uncertain significance. We have refined benign rule BA1 by clarifying terms used to describe its use, which databases we recommend using, and assumptions made about this rule. We also recognized an initial list of nine variants for which there was some evidence of pathogenicity even though the MAF was high for these variants. We specify processes whereby individuals can petition ClinGen for amendments to our variant-specific assertions and the criteria experts should use when setting a numerically lower threshold for BA1 for specific genes.

Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion.

The 2015 ACMG/AMP sequence variant interpretation guideline provided a framework for classifying variants based on several benign and pathogenic evidence criteria, including a pathogenic criterion (PVS1) for predicted loss of function variants. However, the guideline did not elaborate on specific considerations for the different types of loss of function variants, nor did it provide decision-making pathways assimilating information about variant type, its location, or any additional evidence for the likelihood of a true null effect. Furthermore, this guideline did not take into account the relative strengths for each evidence type and the final outcome of their combinations with respect to PVS1 strength. Finally, criteria specifying the genes for which PVS1 can be applied are still missing. Here, as part of the ClinGen Sequence Variant Interpretation (SVI) Workgroup's goal of refining ACMG/AMP criteria, we provide recommendations for applying the PVS1 criterion using detailed guidance addressing the above-mentioned gaps. Evaluation of the refined criterion by seven disease-specific groups using heterogeneous types of loss of function variants (n = 56) showed 89% agreement with the new recommendation, while discrepancies in six variants (11%) were appropriately due to disease-specific refinements. Our recommendations will facilitate consistent and accurate interpretation of predicted loss of function variants.

ClinVar Miner: Demonstrating utility of a Web-based tool for viewing and filtering ClinVar data.

ClinVar Miner is a Web-based suite that utilizes the data held in the National Center for Biotechnology Information's ClinVar archive. The goal is to render the data more accessible to processes pertaining to conflict resolution of variant interpretation as well as tracking details of data submission and data management for detailed variant curation. Here, we establish the use of these tools to address three separate use cases and to perform analyses across submissions. We demonstrate that the ClinVar Miner tools are an effective means to browse and consolidate data for variant submitters, curation groups, and general oversight. These tools are also relevant to the variant interpretation community in general.

ClinGen Variant Curation Expert Panel experiences and standardized processes for disease and gene-level specification of the ACMG/AMP guidelines for sequence variant interpretation.

Genome-scale sequencing creates vast amounts of genomic data, increasing the challenge of clinical sequence variant interpretation. The demand for high-quality interpretation requires multiple specialties to join forces to accelerate the interpretation of sequence variant pathogenicity. With over 600 international members including clinicians, researchers, and laboratory diagnosticians, the Clinical Genome Resource (ClinGen), funded by the National Institutes of Health, is forming expert groups to systematically evaluate variants in clinically relevant genes. Here, we describe the first ClinGen variant curation expert panels (VCEPs), development of consistent and streamlined processes for establishing new VCEPs, and creation of standard operating procedures for VCEPs to define application of the ACMG/AMP guidelines for sequence variant interpretation in specific genes or diseases. Additionally, ClinGen has created user interfaces to enhance reliability of curation and a Sequence Variant Interpretation Working Group (SVI WG) to harmonize guideline specifications and ensure consistency between groups. The expansion of VCEPs represents the primary mechanism by which curation of a substantial fraction of genomic variants can be accelerated and ultimately undertaken systematically and comprehensively. We welcome groups to utilize our resources and become involved in our effort to create a publicly accessible, centralized resource for clinically relevant genes and variants.

Scaling resolution of variant classification differences in ClinVar between 41 clinical laboratories through an outlier approach.

ClinVar provides open access to variant classifications shared from many clinical laboratories. Although most classifications are consistent across laboratories, classification differences exist. To facilitate resolution of classification differences on a large scale, clinical laboratories were encouraged to reassess outlier classifications of variants with medically significant differences (MSDs). Outliers were identified by first comparing ClinVar submissions from 41 clinical laboratories to detect variants with MSDs between the laboratories (650 variants). Next, MSDs were filtered for variants with ≥3 classifications (244 variants), of which 87.6% (213 variants) had a majority consensus in ClinVar, thus allowing for identification of outlier classifications in need of reassessment. Laboratories with outlier classifications were sent a custom report and encouraged to reassess variants. Results were returned for 204 (96%) variants, of which 62.3% (127) were resolved. Of those 127, 64.6% (82) were resolved due to reassessment prompted by this study and 35.4% (45) resolved by a previously completed reassessment. This study demonstrates a scalable approach to classification resolution and capitalizes on the value of data sharing within ClinVar. These activities will help the community move toward more consistent variant classifications, which will improve the care of patients with, or at risk for, genetic disorders.

Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework.

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.

Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen's Inherited Cardiomyopathy Expert Panel.

PurposeIntegrating genomic sequencing in clinical care requires standardization of variant interpretation practices. The Clinical Genome Resource has established expert panels to adapt the American College of Medical Genetics and Genomics/Association for Molecular Pathology classification framework for specific genes and diseases. The Cardiomyopathy Expert Panel selected MYH7, a key contributor to inherited cardiomyopathies, as a pilot gene to develop a broadly applicable approach.MethodsExpert revisions were tested with 60 variants using a structured double review by pairs of clinical and diagnostic laboratory experts. Final consensus rules were established via iterative discussions.ResultsAdjustments represented disease-/gene-informed specifications (12) or strength adjustments of existing rules (5). Nine rules were deemed not applicable. Key specifications included quantitative frameworks for minor allele frequency thresholds, the use of segregation data, and a semiquantitative approach to counting multiple independent variant occurrences where fully controlled case-control studies are lacking. Initial inter-expert classification concordance was 93%. Internal data from participating diagnostic laboratories changed the classification of 20% of the variants (n = 12), highlighting the critical importance of data sharing.ConclusionThese adapted rules provide increased specificity for use in MYH7-associated disorders in combination with expert review and clinical judgment and serve as a stepping stone for genes and disorders with similar genetic and clinical characteristics.