PURPOSE:The Clinical Genome Resource (ClinGen) is creating a central resource of clinically relevant genetic knowledge to improve genomic medicine. Dissemination and use of the ClinGen Resource is essential to ensure broad community uptake. We report on experiences and sustained use of ClinGen tools through engaging international genetics groups based in India, Africa, and Singapore in variant classification training workshops using the ClinGen Variant Curation Interface (VCI). METHODS:We developed pre- and postworkshop questionnaires and analyzed ClinGen tool use following the workshops. We evaluated organizational aspects and costs of creating a dedicated ClinGen VCI instance for each workshop. RESULTS:The workshops yielded >200 participants, with local scientists as essential participants. Although ∼55% of participants were unfamiliar with variant classification, we found that ∼79% were likely to use the VCI after the workshop. Furthermore, we identified that ∼10% of workshop participants created permanent accounts. We estimate costs at ∼$3 per VCI instance. CONCLUSION:Our efforts highlight the yield of international workshops to sustained use of ClinGen's curation tools and identify areas for future consideration, such as creating user groups by experience level, and the importance of local scientist engagement in workshop deployment and organizational aspects.

Clinical genetic laboratories must have access to clinically validated biomedical data for precision medicine. A lack of accessibility, normalized structure, and consistency in evaluation complicates interpretation of disease causality, resulting in confusion in assessing the clinical validity of genes and genetic variants for diagnosis. A key goal of the Clinical Genome Resource (ClinGen) is to fill the knowledge gap concerning the strength of evidence supporting the role of a gene in a monogenic disease, which is achieved through a process known as Gene-Disease Validity curation. Here we review the work of ClinGen in developing a curation infrastructure that supports the standardization, harmonization, and dissemination of Gene-Disease Validity data through the creation of frameworks and the utilization of common data standards. This infrastructure is based on several applications, including the ClinGen GeneTracker, Gene Curation Interface, Data Exchange, GeneGraph, and website.

The Clinical Genome Resource (ClinGen) serves as an authoritative resource on the clinical relevance of genes and variants. In order to support our curation activities and to disseminate our findings to the community, we have developed a Data Platform of informatics resources backed by standardized data models. In this workshop we demonstrate our publicly available resources including curation interfaces, (Variant Curation Interface, CIViC), supporting infrastructure (Allele Registry, Genegraph), and data models (SEPIO, GA4GH VRS, VA).

Precision medicine faces many challenges, including the gap of knowledge between disease genetics and pharmacogenomics (PGx). Disease genetics interprets the pathogenicity of genetic variants for diagnostic purposes, while PGx investigates the genetic influences on drug responses. Ideally, the quality of health care would be improved from the point of disease diagnosis to drug prescribing if PGx is integrated with disease genetics in clinical care. However, PGx genes or variants are usually not reported as a secondary finding even if they are included in a clinical genetic test for diagnostic purposes. This happens even though the detection of PGx variants can provide valuable drug prescribing recommendations. One underlying reason is the lack of systematic classification of the knowledge overlap between PGx and disease genetics. Here, we address this issue by analyzing gene and genetic variant annotations from multiple expert-curated knowledge databases, including PharmGKB, CPIC, ClinGen and ClinVar. We further classified genes based on the strength of evidence supporting a gene's pathogenic role or PGx effect as well as the level of clinical actionability of a gene. Twenty-six genes were found to have pathogenic variation associated with germline diseases as well as strong evidence for a PGx association. These genes were classified into four sub-categories based on the distinct connection between the gene's pathogenic role and PGx effect. Moreover, we have also found thirteen RYR1 genetic variants that were annotated as pathogenic and at the same time whose PGx effect was supported by a preponderance of evidence and given drug prescribing recommendations. Overall, we identified a nontrivial number of gene and genetic variant overlaps between disease genetics and PGx, which laid out a foundation for combining PGx and disease genetics to improve clinical care from disease diagnoses to drug prescribing and adherence.

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

PURPOSE:According to the American College of Medical Genetics and Genomics/Association of Medical Pathology (ACMG/AMP) guidelines, in silico evidence is applied at the supporting strength level for pathogenic (PP3) and benign (BP4) evidence. Although PP3 is commonly used, less is known about the effect of these criteria on variant classification outcomes.METHODS:A total of 727 missense variants curated by Clinical Genome Resource expert groups were analyzed to determine how often PP3 and BP4 were applied and their impact on variant classification. The ACMG/AMP categorical system of variant classification was compared with a quantitative point-based system. The pathogenicity likelihood ratios of REVEL, VEST, FATHMM, and MPC were calibrated using a gold standard set of 237 pathogenic and benign variants (classified independent of the PP3/BP4 criteria).RESULTS:The PP3 and BP4 criteria were applied by Variant Curation Expert Panels to 55% of missense variants. Application of those criteria changed the classification of 15% of missense variants for which either criterion was applied. The point-based system resolved borderline classifications. REVEL and VEST performed best at a strength level consistent with moderate evidence.CONCLUSION:We show that in silico criteria are commonly applied and often affect the final variant classifications. When appropriate thresholds for in silico predictors are established, our results show that PP3 and BP4 can be used at a moderate strength.

AbstractBackgroundIn silicoevidence is important to consider when interpreting genetic variants. According to the ACMG/AMP,in silicoevidence is applied at the supporting strength level using the PP3 and BP4 criteria, for pathogenic and benign evidence, respectively. While PP3 has been determined to be one of the most commonly applied criteria, less is known about the effect of these two criteria on variant classification outcomes.MethodsIn this study, a total of 727 missense variants curated by Clinical Genome Resource (ClinGen) Variant Curation Expert Panels (VCEPs) were analyzed to determine how often PP3 and BP4 were applied and how often they influenced final variant classifications. The current categorical system of variant classification was compared with a point-based system being developed by the ClinGen Sequence Variant Interpretation Working Group. In addition, the performance of fourin silicotools (REVEL, VEST, FATHMM, and MPC) was assessed by using a gold set of 237 variants (classified as benign or pathogenic independent of PP3 or BP4) to calculate pathogenicity likelihood ratios.ResultsCollectively, the PP3 and BP4 criteria were applied by ClinGen VCEPs to 55% of missense variants in this data set. Removingin silicocriteria from variants where they were originally applied caused variants to change classification from pathogenic to likely pathogenic (14%), likely pathogenic to variant of uncertain significance (VUS) (24%), or likely benign to VUS (64%). The proportion of downgrades with the categorical classification system was similar to that of the point-based system, though the latter resolved borderline classifications. REVEL and VEST performed at a level consistent with moderate strength towards either benign or pathogenic evidence, while FATHMM performed at the supporting level.ConclusionsOverall, this study demonstrates thatin silicocriteria PP3 and BP4 are commonly applied in variant classification and often affect the final classification. Our results suggest that when sufficient thresholds forin silicopredictors are established, PP3 and BP4 may be appropriate to use at a moderate strength. However, further calibration with larger datasets is needed to optimize the performance of currentin silicotools given the impact they have on clinical variant classification.

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.

As genetic sequencing costs decrease, the lack of clinical interpretation of variants has become the bottleneck in using genetics data. A major rate limiting step in clinical interpretation is the manual curation of evidence in the genetic literature by highly trained biocurators. What makes curation particularly time-consuming is that the curator needs to identify papers that study variant pathogenicity using different types of approaches and evidences-e.g. biochemical assays or case control analysis. In collaboration with the Clinical Genomic Resource (ClinGen)-the flagship NIH program for clinical curation-we propose the first machine learning system, LitGen, that can retrieve papers for a particular variant and filter them by specific evidence types used by curators to assess for pathogenicity. LitGen uses semi-supervised deep learning to predict the type of evi+dence provided by each paper. It is trained on papers annotated by ClinGen curators and systematically evaluated on new test data collected by ClinGen. LitGen further leverages rich human explanations and unlabeled data to gain 7.9%-12.6% relative performance improvement over models learned only on the annotated papers. It is a useful framework to improve clinical variant curation.

Genetics researchers and clinical professionals rely on diversity measures such as race, ethnicity, and ancestry (REA) to stratify study participants and patients for a variety of applications in research and precision medicine. However, there are no comprehensive, widely accepted standards or guidelines for collecting and using such data in clinical genetics practice. Two NIH-funded research consortia, the Clinical Genome Resource (ClinGen) and Clinical Sequencing Evidence-generating Research (CSER), have partnered to address this issue and report how REA are currently collected, conceptualized, and used. Surveying clinical genetics professionals and researchers (n = 448), we found heterogeneity in the way REA are perceived, defined, and measured, with variation in the perceived importance of REA in both clinical and research settings. The majority of respondents (>55%) felt that REA are at least somewhat important for clinical variant interpretation, ordering genetic tests, and communicating results to patients. However, there was no consensus on the relevance of REA, including how each of these measures should be used in different scenarios and what information they can convey in the context of human genetics. A lack of common definitions and applications of REA across the precision medicine pipeline may contribute to inconsistencies in data collection, missing or inaccurate classifications, and misleading or inconclusive results. Thus, our findings support the need for standardization and harmonization of REA data collection and use in clinical genetics and precision health research.

The linear production of consumer goods is characterised by mass manufacture by multinational enterprises and globally dispersed supply chains. The current centralised model has created a distance between the manufacturer and end user, limiting the opportunity for intelligent circular approaches for production and consumption. Through a mixed method approach, opportunities of circularity are explored for the consumer goods sector. The study presents four lenses to analyse three enterprises through a multi-case study approach to explore the potential of digital intelligence and redistributed manufacturing (RDM) as enablers of circular business models. In addition, the study examines whether Discrete Event Simulation can be used to evaluate the circular scenarios identified through quantifying flows of material that determine traditional economic value (cost/tonne). The mixed method approach demonstrates that, a qualitative systemic analysis can reveal opportunities for circularity, gained through implementing 'digital intelligence' and distributed models of production and consumption. Furthermore, simulations can provide a quantified evaluation on the effects of introducing circular activities across a supply chain.

On the Cover: This cover image is based on the Special Article Unique aspects of sequence variant interpretation for inborn errors of metabolism (IEM): The ClinGen IEM Working Group and the Phenylalanine Hydroxylase Gene by Diane B. Zastrow et al., Pages 1569–1580. DOI: 10.1002/humu.23649.

Effective exchange of information about genetic variants is currently hampered by the lack of readily available globally unique variant identifiers that would enable aggregation of information from different sources. The ClinGen Allele Registry addresses this problem by providing (1) globally unique “canonical” variant identifiers (CAids) on demand, either individually or in large batches; (2) access to variant-identifying information in a searchable Registry; (3) links to allele-related records in many commonly used databases; and (4) services for adding links to information about registered variants in external sources. A core element of the Registry is a canonicalization service, implemented using in-memory sequence alignment-based index, which groups variant identifiers denoting the same nucleotide variant and assigns unique and dereferenceable CAids. More than 650 million distinct variants are currently registered, including those from gnomAD, ExAC, dbSNP, and ClinVar, including a small number of variants registered by Registry users. The Registry is accessible both via a web interface and programmatically via well-documented Hypertext Transfer Protocol (HTTP) Representational State Transfer Application Programming Interface (REST-APIs). For programmatic interoperability, the Registry content is accessible in the JavaScript Object Notation for Linked Data (JSON-LD) format. We present several use cases and demonstrate how the linked information may provide raw material for reasoning about variant's pathogenicity.

The ClinGen Inborn Errors of Metabolism Working Group was tasked with creating a comprehensive, standardized knowledge base of genes and variants for metabolic diseases. Phenylalanine hydroxylase (PAH) deficiency was chosen to pilot development of the Working Group's standards and guidelines. A PAH variant curation expert panel (VCEP) was created to facilitate this process. Following ACMG-AMP variant interpretation guidelines, we present the development of these standards in the context of PAH variant curation and interpretation. Existing ACMG-AMP rules were adjusted based on disease (6) or strength (5) or both (2). Disease adjustments include allele frequency thresholds, functional assay thresholds, and phenotype-specific guidelines. Our validation of PAH-specific variant interpretation guidelines is presented using 85 variants. The PAH VCEP interpretations were concordant with existing interpretations in ClinVar for 69 variants (81%). Development of biocurator tools and standards are also described. Using the PAH-specific ACMG-AMP guidelines, 714 PAH variants have been curated and will be submitted to ClinVar. We also discuss strategies and challenges in applying ACMG-AMP guidelines to autosomal recessive metabolic disease, and the curation of variants in these genes.

Critical to the field of clinical genomics is an understanding of the strength of correlation between genotype and phenotype. The central mission of ClinGen (The Clinical Genome Resource, clinicalgenome.org) is to define the “clinical relevance of genes and variants for use in precision medicine and research.” Inherent to this mission is the ability to access and evaluate evidence for a gene’s and/or variant’s role in disease in a consistent and efficient manner. Towards this goal, ClinGen has built interconnected curation interfaces, one for gene curation and one for variant curation. The rich evidence captured by both interfaces is available in a structured format that allows it to be easily accessed via ClinGen’s public portal (clinicalgenome.org). The ClinGen gene and variant curation interfaces have been developed according to the following important specifications: 1) variant curation follows the ACMG-AMP Standards and Guidelines (Richards et al 2015, PMID:25741868), while gene curation follows the Clinical Validity Classifications framework established by ClinGen’s Gene Curation Work Group, 2) both interfaces centralize evidence from relevant resources in order to enable efficient and consistent curation, 3) curated literature evidence and evidence retrieved from external resources are shared between the gene and variant curation tools, 4) the interfaces are designed to guide biocurators through the curation process, 5) controlled vocabularies and ontologies are an important part of the design in order to promote the capture of discrete evidence, facilitate connections, and promote consistency, 6) external and curated evidence is viewable by all biocurators, while curated evidence can only be edited by its creator, 7) the interfaces support expert review of provisional classifications and interpretations by allowing access to all evidence, 8) contextual help and documentation are included to assist the biocurator, and 9) data is stored in standard JSON-LD format to define rich relationships and facilitate data exchange. Both tools are now in production (curation.clinicalgenome.org) and currently accessible to ClinGen biocurators and approved groups from the broader community. A demo version of the interfaces (curation-test.clinicalgenome.org), which does not permanently save data, is available via both registered and generic (non-registered) access to allow exploration. We expect active use of the ClinGen curation interfaces will facilitate the implementation of the ACMG variant classification guidelines across diverse disease genes.

With advances in genomic sequencing technology, the number of reported gene-disease relationships has rapidly expanded. However, the evidence supporting these claims varies widely, confounding accurate evaluation of genomic variation in a clinical setting. Despite the critical need to differentiate clinically valid relationships from less well-substantiated relationships, standard guidelines for such evaluation do not currently exist. The NIH-funded Clinical Genome Resource (ClinGen) has developed a framework to define and evaluate the clinical validity of gene-disease pairs across a variety of Mendelian disorders. In this manuscript we describe a proposed framework to evaluate relevant genetic and experimental evidence supporting or contradicting a gene-disease relationship and the subsequent validation of this framework using a set of representative gene-disease pairs. The framework provides a semiquantitative measurement for the strength of evidence of a gene-disease relationship that correlates to a qualitative classification: "Definitive," "Strong," "Moderate," "Limited," "No Reported Evidence," or "Conflicting Evidence." Within the ClinGen structure, classifications derived with this framework are reviewed and confirmed or adjusted based on clinical expertise of appropriate disease experts. Detailed guidance for utilizing this framework and access to the curation interface is available on our website. This evidence-based, systematic method to assess the strength of gene-disease relationships will facilitate more knowledgeable utilization of genomic variants in clinical and research settings.

EFSA Supporting PublicationsVolume 13, Issue 12 1129E Event reportOpen Access EFSA Scientific Colloquium 22 – Epigenetics and Risk Assessment: Where do we stand? European Food Safety Authority, European Food Safety AuthoritySearch for more papers by this authorTina Bahadori, Tina Bahadori Environmental Protection Agency, USASearch for more papers by this authorDavid Bell, David Bell European Chemicals AgencySearch for more papers by this authorSandra Ceccatelli, Sandra Ceccatelli Karolinska Institute, SESearch for more papers by this authorRaffaella Corvi, Raffaella Corvi Joint Research Centre, European CommissionSearch for more papers by this authorChrister Hogstrand, Christer Hogstrand University College London, UKSearch for more papers by this authorSharon Munn, Sharon Munn Joint Research Centre, European CommissionSearch for more papers by this authorEric Nilsson, Eric Nilsson Washington State University, USASearch for more papers by this authorDavid Spurgeon, David Spurgeon Centre for Ecology and Hydrology, UKSearch for more papers by this authorJochen Vom Brocke, Jochen Vom Brocke European Chemicals AgencySearch for more papers by this authorDiane Wray-Cahen, Diane Wray-Cahen Foreign Agricultural Service of United States Department of Agriculture, USASearch for more papers by this authorMatt Wright, Matt Wright Newcastle University, UKSearch for more papers by this authorMarco Binaglia, Marco Binaglia European Food Safety AuthoritySearch for more papers by this authorJean-Lou Dorne, Jean-Lou Dorne European Food Safety AuthoritySearch for more papers by this authorNikolaos Georgiadis, Nikolaos Georgiadis European Food Safety AuthoritySearch for more papers by this authorAndrea Germini, Andrea Germini European Food Safety AuthoritySearch for more papers by this authorGeorge Kass, George Kass European Food Safety AuthoritySearch for more papers by this authorTobin Robinson, Tobin Robinson European Food Safety AuthoritySearch for more papers by this authorAnnamaria Rossi, Annamaria Rossi European Food Safety AuthoritySearch for more papers by this authorReinhilde Schoonjans, Reinhilde Schoonjans European Food Safety AuthoritySearch for more papers by this authorAndrea Terron, Andrea Terron European Food Safety AuthoritySearch for more papers by this authorHubert Noteborn, Hubert Noteborn Netherlands Food and Consumer Product Safety Authority, NLSearch for more papers by this author European Food Safety Authority, European Food Safety AuthoritySearch for more papers by this authorTina Bahadori, Tina Bahadori Environmental Protection Agency, USASearch for more papers by this authorDavid Bell, David Bell European Chemicals AgencySearch for more papers by this authorSandra Ceccatelli, Sandra Ceccatelli Karolinska Institute, SESearch for more papers by this authorRaffaella Corvi, Raffaella Corvi Joint Research Centre, European CommissionSearch for more papers by this authorChrister Hogstrand, Christer Hogstrand University College London, UKSearch for more papers by this authorSharon Munn, Sharon Munn Joint Research Centre, European CommissionSearch for more papers by this authorEric Nilsson, Eric Nilsson Washington State University, USASearch for more papers by this authorDavid Spurgeon, David Spurgeon Centre for Ecology and Hydrology, UKSearch for more papers by this authorJochen Vom Brocke, Jochen Vom Brocke European Chemicals AgencySearch for more papers by this authorDiane Wray-Cahen, Diane Wray-Cahen Foreign Agricultural Service of United States Department of Agriculture, USASearch for more papers by this authorMatt Wright, Matt Wright Newcastle University, UKSearch for more papers by this authorMarco Binaglia, Marco Binaglia European Food Safety AuthoritySearch for more papers by this authorJean-Lou Dorne, Jean-Lou Dorne European Food Safety AuthoritySearch for more papers by this authorNikolaos Georgiadis, Nikolaos Georgiadis European Food Safety AuthoritySearch for more papers by this authorAndrea Germini, Andrea Germini European Food Safety AuthoritySearch for more papers by this authorGeorge Kass, George Kass European Food Safety AuthoritySearch for more papers by this authorTobin Robinson, Tobin Robinson European Food Safety AuthoritySearch for more papers by this authorAnnamaria Rossi, Annamaria Rossi European Food Safety AuthoritySearch for more papers by this authorReinhilde Schoonjans, Reinhilde Schoonjans European Food Safety AuthoritySearch for more papers by this authorAndrea Terron, Andrea Terron European Food Safety AuthoritySearch for more papers by this authorHubert Noteborn, Hubert Noteborn Netherlands Food and Consumer Product Safety Authority, NLSearch for more papers by this author First published: 14 December 2016 https://doi.org/10.2903/sp.efsa.2016.EN-1129Citations: 1 Question number: EFSA-Q-2016-00555 Disclaimer: The views or positions expressed in this publication do not necessarily represent in legal terms the official position of the European Food Safety Authority (EFSA). EFSA assumes no responsibility or liability for any errors or inaccuracies that may appear. Amendment: Editorial corrections were carried out on page 1-2 (authors list), 22 (references) and 24-25 (county of origin of participants) that does not materially affect the contents or outcome of this scientific output. To avoid confusion the older version has been removed from the EFSA Journal, but is available on request, as is the version showing all the changes made. Updated: 21 December 2016; 7 April 2017 AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. 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