A survey assessing adoption of the ACMG-AMP guidelines for interpreting sequence variants and identification of areas for continued improvement

Recommendations for risk allele evidence curation, classification, and reporting from the ClinGen Low Penetrance/Risk Allele Working Group

PURPOSE

Genetic variants at the low end of the penetrance spectrum have historically been challenging to interpret because their high population frequencies exceed the disease prevalence of the associated condition, leading to a lack of clear segregation between the variant and disease. There is currently substantial variation in the classification of these variants, and no formal classification framework has been widely adopted. The Clinical Genome Resource Low Penetrance/Risk Allele Working Group was formed to address these challenges and promote harmonization within the clinical community.

METHODS

The work presented here is the product of internal and community Likert-scaled surveys in combination with expert consensus within the Working Group.

RESULTS

We formally recognize risk alleles and low-penetrance variants as distinct variant classes from those causing highly penetrant disease that require special considerations regarding their clinical classification and reporting. First, we provide a preferred terminology for these variants. Second, we focus on risk alleles and detail considerations for reviewing relevant studies and present a framework for the classification these variants. Finally, we discuss considerations for clinical reporting of risk alleles.

CONCLUSION

These recommendations support harmonized interpretation, classification, and reporting of variants at the low end of the penetrance spectrum.

An overview of actionable and potentially actionable TSC1 and TSC2 germline variants in an online Database

Tuberous Sclerosis Complex (TSC) is caused by loss of function germline variants in the TSC1 or TSC2 tumor suppressor genes. Genetic testing for the detection of pathogenic variants in either TSC1 or TSC2 was implemented as a diagnostic criterion for TSC. However, TSC molecular diagnosis can be challenging due to the absence of variant hotspots and the high number of variants described. This review aimed to perform an overview of TSC1/2 variants submitted in the ClinVar database. Variants of uncertain significance (VUS), missense and single nucleotide variants were the most frequent in clinical significance (37-40%), molecular consequence (37%-39%) and variation type (82%-83%) categories in ClinVar in TSC1 and TSC2 variants, respectively. Frameshift and nonsense VUS have potential for pathogenic reclassification if further functional and segregation studies were performed. Indeed, there were few functional assays deposited in the database and literature. In addition, we did not observe hotspots for variation and many variants presented conflicting submissions regarding clinical significance. This study underscored the importance of disseminating molecular diagnostic results in a public database to render the information largely accessible and promote accurate diagnosis. We encourage the performance of functional studies evaluating the pathogenicity of TSC1/2 variants.

Evaluating ClinGen variant curation expert panels' application of PVS1 code

BACKGROUND

The 2015 American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP) guidelines articulates that the effects of certain types of variants on gene function can often be seen as a complete absence of the gene product by leading to a lack of transcription or nonsense-mediated decay(NMD). However, detailed information considering different types of loss of function(LOF) variants, refined steps assimilating details concerning location of variant, changes in strength levels, NMD boundary, or any additional information pointing to a true null effect, were all left to expert judgement. As part of its Clinical Genome Resource (ClinGen) initiative, Variant Curation Expert Panels (VCEPs) are designated to make gene/disease-centric specifications in accordance with the ACMG/AMP guidelines, including a more detailed definition of what constitutes an appropriate LOF evidence. Our goal was to evaluate the current LOF guidelines developed by the VCEPs and analyse the prior curated variants concerning the PVS1 criteria, bringing people occupied in genetic data analysis a comprehensive understanding of this code.

METHODS

Our study evaluated 7 VCEPs for their LOF criteria (PVS1). Subsequently, we assessed the predictive criteria by considering the underlying disease mechanism, protein transcript, and variant types delineated. Then, we meticulously curated the LOF evidence referenced by each VCEP in their preliminary variant classification, thereby scrutinizing the recommendations put forth by VCEPs and their application in the interpretation of the aforementioned predictive criteria. Based on these, an extensive curation of evidence summary considering PVS1 applied by VCEPs according to their classification of pilot variants for the purpose of analyzing VCEP criteria specifications and their use in the understanding of LOF was conducted.

RESULTS

We observed in this article that the VCEPs discussed followed the majority of Sequence Variant Interpretation (SVI) recommendations concerning the application of this LOF criteria, except for some disease/gene specific considerations. We highlighted the wide range of PVS1 strength levels approved by VCEP, reflecting the diversity of evidence for each variants type. In addition, we observed substantial differences in the approach used to determine relative strengths for different types of null variants and in the attitude towards these principles concerning variant location, NMD and influence to protein function between VCEPs.

CONCLUSIONS

It is difficult to understand the intricacies of the predictive data(PVS1), which often requires expert-level knowledge of disease/gene. The VCEP criteria specifications for the predictive evidence play an important role in making it more accessible for the curators to apply the predictive data by providing details concerning this complex criteria. Despite this, we believe there is a need for more guidance on standardizing this process and ensuring consistency in the application of this predictive evidence.

Genetic Defects in Early-Onset Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) represents a spectrum of disease, which is characterized by chronic gastrointestinal inflammation. Monogenic mutations driving IBD pathogenesis are more highly represented in early-onset compared to adult-onset disease. The pathogenic genes which dysregulate host immune responses in monogenic IBD affect both the innate (ie, intestinal barrier, phagocytes) and adaptive immune systems (ie, T cells, B cells). Advanced genomic and targeted functional testing can improve clinical decision making and present increased opportunities for precision medicine approaches in this important patient population.

Platelet genetic testing by next-generation sequencing: A practical update

Inherited platelet disorders (IPDs) are a heterogeneous group of disorders characterized by normal or reduced platelet counts, bleeding diatheses of varying severities, and the presence (syndromic) or absence (non-syndromic) of involvement of other organs. Due to the lack of highly specific platelet function tests and overlapping clinical and laboratory features, diagnosing the underlying cause of IPDs remains challenging. In recent years, genetic testing via next-generation sequencing (NGS) technologies to rapidly analyze multiple genes has gradually emerged as an important part of the laboratory investigation of patients with IPDs. A systemic clinical and laboratory testing approach and thorough phenotype and genotype correlation studies of both patients and their family members are crucial for accurate diagnoses of IPDs.

DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data

DECIPHER (Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case-control data that provide evidence of gene-disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research.

ClinVar and HGMD genomic variant classification accuracy has improved over time, as measured by implied disease burden

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.

An additional whole-exome sequencing study in 102 panel-undiagnosed patients: A retrospective study in a Chinese craniosynostosis cohort

Craniosynostosis (CRS) is a disease with prematurely fused cranial sutures. In the last decade, the whole-exome sequencing (WES) was widely used in Caucasian populations. The WES largely contributed in genetic diagnosis and exploration on new genetic mechanisms of CRS. In this study, we enrolled 264 CRS patients in China. After a 17-gene-panel sequencing designed in the previous study, 139 patients were identified with pathogenic/likely pathogenic (P/LP) variants according to the ACMG guideline as positive genetic diagnosis. WES was then performed on 102 patients with negative genetic diagnosis by panel. Ten P/LP variants were additionally identified in ten patients, increasing the genetic diagnostic yield by 3.8% (10/264). The novel variants in ANKH, H1-4, EIF5A, SOX6, and ARID1B expanded the mutation spectra of CRS. Then we designed a compatible research pipeline (RP) for further exploration. The RP could detect all seven P/LP SNVs and InDels identified above, in addition to 15 candidate variants found in 13 patients with worthy of further study. In sum, the 17-gene panel and WES identified positive genetic diagnosis for 56.4% patients (149/264) in 16 genes. At last, in our estimation, the genetic testing strategy of “Panel-first” saves 24.3% of the cost compared with “WES only”, suggesting the “Panel-first” is an economical strategy.

Utility and Outcomes of the 2019 American College of Medical Genetics and Genomics-Clinical Genome Resource Guidelines for Interpretation of Copy Number Variants with Borderline Classifications at an Academic Clinical Diagnostic Laboratory

In 2019, American College of Medical Genetics and Genomics and the Clinical Genome Resource published updated technical standards for the interpretation and reporting of copy number variants (CNVs), introducing a semiquantitative classification system that aims to foster greater standardization and consistency between laboratories. Evaluation of these guidelines' performance will inform laboratories about the impact of their implementation into clinical practice. A total of 145 difficult-to-classify CNVs, originally assessed by an academic molecular diagnostic laboratory, were re-interpreted/classified according to the American College of Medical Genetics and Genomics-Clinical Genome Resource guidelines. Classifications between interpretation systems were then compared. The concordance rate was 60.7%, and significantly more variants of uncertain significance were obtained when using the guidelines (n = 98) versus the laboratory's classification system (n = 49; P < 0.001). The concordance rate was presumably impacted by the intentionally unclear nature of the selected variants. The difference in variant of uncertain significance rate was largely due to laboratory-specific practices for variant interpretation and reporting, as well as differences in utilization of general population data. Laboratory-specific policies and practices may need to be addressed for true standardization to be achieved. Challenges to consistent guideline utilization are centered around the general lack of high-quality curated data available for CNV interpretations and the inherent subjectivity in the selection of evidence criteria and application of evidence points. Multiple aspects of the guidelines were highlighted as potential opportunities for subsequent refinements to further improve classification standardization.

Interpretation and actionability of genetic variants in cardiomyopathies: a position statement from the European Society of Cardiology Council on cardiovascular genomics

This document describes the contribution of clinical criteria to the interpretation of genetic variants using heritable Mendelian cardiomyopathies as an example. The aim is to assist cardiologists in defining the clinical contribution to a genetic diagnosis and the interpretation of molecular genetic reports. The identification of a genetic variant of unknown or uncertain significance is a limitation of genetic testing, but current guidelines for the interpretation of genetic variants include essential contributions from clinical family screening that can establish a de novo assignment of the variant or its segregation with the phenotype in the family. A partnership between clinicians and patients helps to solve major uncertainties and provides reliable and clinically actionable information.

Adaptation of ACMG-ClinGen Technical Standards for Copy Number Variant Interpretation Concordance

This study aimed to evaluate inter-laboratory classification concordance for copy number variants (CNVs) with a semiquantitative point-based scoring metric recommended by the American College of Medical Genetics and Genomics (ACMG) and Clinical Genome Resources (ClinGen). A total of 234 CNVs distributed by the National Center of Clinical Laboratories (NCCLs), and 72 CNVs submitted by different laboratories, were distributed to nine clinical laboratories performing routine clinical CNV testing in China and independently classified across laboratories. The overall inter-laboratory complete classification concordance rate of the 234 distributed CNVs increased from 18% (41/234) to 76% (177/234) using the scoring metric compared to the laboratory's previous method. The overall inter-laboratory complete classification concordance rate of the 72 submitted CNVs was 65% (47/72) using the scoring metrics. The 82 variants that initially did not reach complete concordance classification and 1 additional CNV deletion were reviewed; 34 reached complete agreement, and the overall post-review complete concordance rate was 85% (260/306). Additionally, the overall percentage of classification discordance possibly impacting medical management [i.e., pathogenic (P) or likely pathogenic (LP) vs. variant of uncertain significance (VUS)] was 11% (35/306). The causes of initial and final discordance in the classification were identified. The ACMG-ClinGen framework has promoted consistency in interpreting the clinical significance of CNVs. Continuous training among laboratories, further criteria and additional clarification of the standards, sharing classifications and supporting evidence through public database, and ongoing work for dosage sensitive genes/regions curation will be beneficial for harmonization of CNVs classification.

Novel likely pathogenic SLC20A variant in primary familial brain calcification

A woman in her 30s was referred to our neurology outpatient clinic following an incidental finding of significant bilateral and symmetric basal ganglia, thalamic, cerebellar and subcortical white matter calcification on brain CT and MRI. A diagnosis of asymptomatic primary familial brain calcification (PFBC) was made. Targeted genetic testing revealed a likely pathogenic variant in the SLC20A2 gene, the most common gene in which pathogenic variants have been implicated in PFBC. These findings prompted genetic testing and brain CT of our patient's asymptomatic 64-year-old father. These tests revealed the same variant in SLC20A2 and similar brain calcification on CT in the patient's father.

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.

Reanalysis of eMERGE phase III sequence variants in 10,500 participants and infrastructure to support the automated return of knowledge updates

PURPOSE

The clinical genomics knowledgebase is dynamic with variant classifications changing as newly identified cases, additional population data, and other evidence become available. This is a challenge for the clinical laboratory because of limited resource availability for variant reassessment.

METHODS

Throughout the Electronic Medical Records and Genomics phase III program, clinical sites associated with the Mass General Brigham/Broad sequencing center received automated, real-time notifications when reported variants were reclassified. In this study, we summarized the nature of these reclassifications and described the proactive reassessment framework we used for the Electronic Medical Records and Genomics program data set to identify variants most likely to undergo reclassification.

RESULTS

Reanalysis of 1855 variants led to the reclassification of 2% (n = 45) of variants, affecting 0.6% (n = 67) of participants. Of these reclassifications, 78% (n = 35) were high-impact changes affecting reportability, with 8 variants downgraded from likely pathogenic/pathogenic to variants of uncertain significance (VUS) and 27 variants upgraded from VUS to likely pathogenic/pathogenic. Most upgraded variants (67%) were initially classified as VUS-Favor Pathogenic, highlighting the benefit of VUS subcategorization. The most common reason for reclassification was new published case data and/or functional evidence.

CONCLUSION

Our results highlight the importance of periodic sequence variant reevaluation and the need for automated approaches to advance routine implementation of variant reevaluations in clinical practice.

ClinGen Variant Curation Interface: a variant classification platform for the application of evidence criteria from ACMG/AMP guidelines

BACKGROUND

Identification of clinically significant genetic alterations involved in human disease has been dramatically accelerated by developments in next-generation sequencing technologies. However, the infrastructure and accessible comprehensive curation tools necessary for analyzing an individual patient genome and interpreting genetic variants to inform healthcare management have been lacking.

RESULTS

Here we present the ClinGen Variant Curation Interface (VCI), a global open-source variant classification platform for supporting the application of evidence criteria and classification of variants based on the ACMG/AMP variant classification guidelines. The VCI is among a suite of tools developed by the NIH-funded Clinical Genome Resource (ClinGen) Consortium and supports an FDA-recognized human variant curation process. Essential to this is the ability to enable collaboration and peer review across ClinGen Expert Panels supporting users in comprehensively identifying, annotating, and sharing relevant evidence while making variant pathogenicity assertions. To facilitate evidence-based improvements in human variant classification, the VCI is publicly available to the genomics community. Navigation workflows support users providing guidance to comprehensively apply the ACMG/AMP evidence criteria and document provenance for asserting variant classifications.

CONCLUSIONS

The VCI offers a central platform for clinical variant classification that fills a gap in the learning healthcare system, facilitates widespread adoption of standards for clinical curation, and is available at https://curation.clinicalgenome.org.

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.

Variant Classification Concordance using the ACMG-AMP Variant Interpretation Guidelines across Nine Genomic Implementation Research Studies

Harmonization of variant pathogenicity classification across laboratories is important for advancing clinical genomics. The two CLIA-accredited Electronic Medical Record and Genomics Network sequencing centers and the six CLIA-accredited laboratories and one research laboratory performing genome or exome sequencing in the Clinical Sequencing Evidence-Generating Research Consortium collaborated to explore current sources of discordance in classification. Eight laboratories each submitted 20 classified variants in the ACMG secondary finding v.2.0 genes. After removing duplicates, each of the 158 variants was annotated and independently classified by two additional laboratories using the ACMG-AMP guidelines. Overall concordance across three laboratories was assessed and discordant variants were reviewed via teleconference and email. The submitted variant set included 28 P/LP variants, 96 VUS, and 34 LB/B variants, mostly in cancer (40%) and cardiac (27%) risk genes. Eighty-six (54%) variants reached complete five-category (i.e., P, LP, VUS, LB, B) concordance, and 17 (11%) had a discordance that could affect clinical recommendations (P/LP versus VUS/LB/B). 21% and 63% of variants submitted as P and LP, respectively, were discordant with VUS. Of the 54 originally discordant variants that underwent further review, 32 reached agreement, for a post-review concordance rate of 84% (118/140 variants). This project provides an updated estimate of variant concordance, identifies considerations for LP classified variants, and highlights ongoing sources of discordance. Continued and increased sharing of variant classifications and evidence across laboratories, and the ongoing work of ClinGen to provide general as well as gene- and disease-specific guidance, will lead to continued increases in concordance.

An Initial Survey of the Performances of Exome Variant Analysis and Clinical Reporting Among Diagnostic Laboratories in China

Exome sequencing has become an effective diagnostic method for Mendelian disorders. But the quality of services differs widely across laboratories in China, particularly in variant classification, even with the adoption of the ACMG guidelines. As an effort of quality control and improvement for better clinical utilization of exome sequencing, we assessed the exome data analysis and clinical reporting among Chinese laboratories. Five raw datasets of real clinical samples with associated phenotypes were sent to 53 laboratories. The participants independently performed secondary analysis, variant classification, and reporting. The first round of results was used for identifying problems associated with these aspects. Subsequently, we implemented several corrective actions and a training program was designed based on the identified issues. A second round of five datasets were sent to the same participants. We compared the performances in variant interpretation and reporting. A total of 85.7% (42/49) of participants correctly identified all the variants related with phenotype. Many lines of evidence using the ACMG guidelines were incorrectly utilized, which resulted in a large inter-laboratory discrepancy. After training, the evidence usage problems significantly improved, leading to a more consistent outcome. Participants improved their exome data analysis and clinical reporting capability. Targeted training and a deeper understanding of the ACMG guidelines helped to improve the clinical exome sequencing service in terms of consistency and accuracy in variant classification in China.

Overview of Specifications to the ACMG/AMP Variant Interpretation Guidelines

The 2015 ACMG/AMP guidelines established a classification system for sequence variants; however, the broad scope of these guidelines necessitates specification of evidence types for specific genes or diseases of interest. Since publication of the guidelines, both general use and disease-focused specifications have emerged to aid in accurate application of ACMG/AMP evidence types. This article summarizes the approaches to, and rationale for, specifying three evidence categories (population frequency data, variant type and location, and case-level data), including available resources and a quantitative framework that can inform the specification process. © 2019 by John Wiley & Sons, Inc.

Is 'likely pathogenic' really 90% likely? Reclassification data in ClinVar

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.

Toward Clinical Implementation of Next-Generation Sequencing-Based Genetic Testing in Rare Diseases: Where Are We?

Next-generation sequencing (NGS) technologies have changed the landscape of genetic testing in rare diseases. However, the rapid evolution of NGS technologies has outpaced its clinical adoption. Here, we re-evaluate the critical steps in the clinical application of NGS-based genetic testing from an informatics perspective. We suggest a 'fit-for-purpose' triage of current NGS technologies. We also point out potential shortcomings in the clinical management of genetic variants and offer ideas for potential improvement. We specifically emphasize the importance of ensuring the accuracy and reproducibility of NGS-based genetic testing in the context of rare disease diagnosis. We highlight the role of artificial intelligence (AI) in enhancing understanding and prioritization of variance in the clinical setting and propose deep learning frameworks for further investigation.