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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.

In 2015, professional guidelines defined the term ‘likely pathogenic’ to mean with a 90% chance of pathogenicity. To determine whether current practice reflects this definition, ClinVar classifications were tracked from 2016 to 2019. During that period, between 83.8 and 99.1% of likely pathogenic classifications were reclassified as pathogenic, depending on whether LP to VUS reclassifications are included and on how these classifications are categorized.

Background The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) clinical variant interpretation guidelines established criteria for different types of evidence. This includes the strong evidence codes PS3 and BS3 for “well-established” functional assays demonstrating a variant has abnormal or normal gene/protein function, respectively. However, they did not provide detailed guidance on how functional evidence should be evaluated, and differences in the application of the PS3/BS3 codes are a contributor to variant interpretation discordance between laboratories. This recommendation seeks to provide a more structured approach to the assessment of functional assays for variant interpretation and guidance on the use of various levels of strength based on assay validation. Methods The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation (SVI) Working Group used curated functional evidence from ClinGen Variant Curation Expert Panel-developed rule specifications and expert opinions to refine the PS3/BS3 criteria over multiple in-person and virtual meetings. We estimated the odds of pathogenicity for assays using various numbers of variant controls to determine the minimum controls required to reach moderate level evidence. Feedback from the ClinGen Steering Committee and outside experts were incorporated into the recommendations at multiple stages of development. Results The SVI Working Group developed recommendations for evaluators regarding the assessment of the clinical validity of functional data and a four-step provisional framework to determine the appropriate strength of evidence that can be applied in clinical variant interpretation. These steps are as follows: (1) define the disease mechanism, (2) evaluate the applicability of general classes of assays used in the field, (3) evaluate the validity of specific instances of assays, and (4) apply evidence to individual variant interpretation. We found that a minimum of 11 total pathogenic and benign variant controls are required to reach moderate-level evidence in the absence of rigorous statistical analysis. Conclusions The recommendations and approach to functional evidence evaluation described here should help clarify the clinical variant interpretation process for functional assays. Further, we hope that these recommendations will help develop productive partnerships with basic scientists who have developed functional assays that are useful for interrogating the function of a variety of genes.

Background The majority of clinical genetic testing focuses almost exclusively on regions of the genome that directly encode proteins. The important role of variants in non-coding regions in penetrant disease is, however, increasingly being demonstrated, and the use of whole genome sequencing in clinical diagnostic settings is rising across a large range of genetic disorders. Despite this, there is no existing guidance on how current guidelines designed primarily for variants in protein-coding regions should be adapted for variants identified in other genomic contexts. Methods We convened a panel of nine clinical and research scientists with wide-ranging expertise in clinical variant interpretation, with specific experience in variants within non-coding regions. This panel discussed and refined an initial draft of the guidelines which were then extensively tested and reviewed by external groups. Results We discuss considerations specifically for variants in non-coding regions of the genome. We outline how to define candidate regulatory elements, highlight examples of mechanisms through which non-coding region variants can lead to penetrant monogenic disease, and outline how existing guidelines can be adapted for the interpretation of these variants. Conclusions These recommendations aim to increase the number and range of non-coding region variants that can be clinically interpreted, which, together with a compatible phenotype, can lead to new diagnoses and catalyse the discovery of novel disease mechanisms.

Purpose Integrating 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. Methods Expert 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. Results Adjustments 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. Conclusion These 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.

Purpose: Data sharing through ClinVar offers a unique opportunity to identify interpretation differences between laboratories. As part of a ClinGen initiative, four clinical laboratories (Ambry, GeneDx, Partners Healthcare Laboratory for Molecular Medicine, and University of Chicago Genetic Services Laboratory) collaborated to identify the basis of interpretation differences and to investigate if data sharing and reassessment resolve interpretation differences by analyzing a subset of variants. Methods: ClinVar variants with submissions from at least two of the four participating laboratories were compared. For a subset of identified differences, laboratories documented the basis for discordance, shared internal data, independently reassessed with the American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG-AMP) guidelines, and then compared interpretations. Results: At least two of the participating laboratories interpreted 6,169 variants in ClinVar, of which 88.3% were initially concordant. Laboratories reassessed 242/724 initially discordant variants, of which 87.2% (211) were resolved by reassessment with current criteria and/or internal data sharing; 12.8% (31) of reassessed variants remained discordant owing to differences in the application of the ACMG-AMP guidelines. Conclusion: Participating laboratories increased their overall concordance from 88.3 to 91.7%, indicating that sharing variant interpretations in ClinVar—thereby allowing identification of differences and motivation to resolve those differences—is critical to moving toward more consistent variant interpretations. Genet Med advance online publication 09 March 2017

Background Defining gene-disease relationships (GDRs) influences the clinical utility of hereditary cancer predisposition (HCP) multigene panel testing (MGPT) results, as variant classification relies directly on gene-disease characterization. GDR characterization for HCP is challenging due to disease prevalence, incomplete penetrance, and heterogeneity. There is insufficient data showing how gene-disease validity (GDV) scores of HCP genes affect variant classification and how GDV scores change over time. Though these issues determine the results of HCP-MGPT, their impact on short- and long-term clinical utility has not been explored in-depth. Methods Using an evidence-based GDV framework, genes were classified into five standardized GDV categories at the time of panel addition. We curated changes in GDV scores and classifications for HCP-MGPT over 7 years. The corresponding impact on the frequency of positive and variant of uncertain significance (VUS) results was evaluated by GDV score. Results Positive results were most common in Definitive evidence genes (31.5%), with none in Limited evidence genes (0%). Genes with Definitive GDRs ( n  = 42) remained Definitive, while most genes with Strong (6/10, 60%) and Moderate (19/24, 80%) GDRs changed categories, 8 (23.5%) of which received a clinically significant GDR downgrade. GDRs associated with low-moderate risk of breast cancer were significantly more likely to be downgraded compared to GDRs associated with rarer, high-penetrance specific phenotypes ( p  < 0.0001). Downgrades for all GDRs were due to new published data and updates to the GDV framework (77%, 10/13), with 23% (3/13) due to framework updates alone. Including Limited evidence genes on MGPT increased the VUS frequency by 13.7% percentage points. Conclusions Positive and VUS results varied by GDV category, and Limited evidence genes did not contribute to diagnostic yield. No Limited evidence genes in the category for ≥ 3 years ( n  = 8) were upgraded, indicating that including these genes on HCP-MGPT provides limited long-term clinical utility. Our data highlight that GDV assessment for HCP requires robust evidence and must account for variable disease penetrance and elevated prevalence in the population. Balancing the availability of a comprehensive gene menu and transparency surrounding clinical utility of novel genes will maximize identification of high-risk patients while reducing the risk of misdiagnosis through clinical false-positive results.