Dataset Information

Assessing the gene-disease association of 19 genes with the RASopathies using the ClinGen gene curation framework.

ABSTRACT: The RASopathies are a complex group of conditions regarding phenotype and genetic etiology. The ClinGen RASopathy Expert Panel (RAS EP) assessed published and other publicly available evidence supporting the association of 19 genes with RASopathy conditions. Using the semiquantitative literature curation method developed by the ClinGen Gene Curation Working Group, evidence for each gene was curated and scored for Noonan syndrome (NS), Costello syndrome, cardiofaciocutaneous syndrome, NS with multiple lentigines, and Noonan-like syndrome with loose anagen hair. The curated evidence supporting each gene-disease relationship was then discussed and approved by the ClinGen RASopathy Expert Panel. Each association's strength was classified as definitive, strong, moderate, limited, disputed, or no evidence. Eleven genes were classified as definitively associated with at least one RASopathy condition. Two genes classified as strong for association with at least one RASopathy condition while one gene was moderate and three were limited. The RAS EP also disputed the association of two genes for all RASopathy conditions. Overall, our results provide a greater understanding of the different gene-disease relationships within the RASopathies and can help in guiding and directing clinicians, patients, and researchers who are identifying variants in individuals with a suspected RASopathy.

SUBMITTER: Grant AR

PROVIDER: S-EPMC6326381 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Assessing the gene-disease association of 19 genes with the RASopathies using the ClinGen gene curation framework.

Grant Andrew R AR Cushman Brandon J BJ Cavé Hélène H Dillon Mitchell W MW Gelb Bruce D BD Gripp Karen W KW Lee Jennifer A JA Mason-Suares Heather H Rauen Katherine A KA Tartaglia Marco M Vincent Lisa M LM Zenker Martin M

Human mutation 20181101 11

The RASopathies are a complex group of conditions regarding phenotype and genetic etiology. The ClinGen RASopathy Expert Panel (RAS EP) assessed published and other publicly available evidence supporting the association of 19 genes with RASopathy conditions. Using the semiquantitative literature curation method developed by the ClinGen Gene Curation Working Group, evidence for each gene was curated and scored for Noonan syndrome (NS), Costello syndrome, cardiofaciocutaneous syndrome, NS with mul ...[more]

PMID: 30311384

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Project description:BACKGROUND:The 2015 American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines for clinical sequence variant interpretation state that "well-established" functional studies can be used as evidence in variant classification. These guidelines articulated key attributes of functional data, including that assays should reflect the biological environment and be analytically sound; however, details of how to evaluate these attributes were left to expert judgment. The Clinical Genome Resource (ClinGen) designates Variant Curation Expert Panels (VCEPs) in specific disease areas to make gene-centric specifications to the ACMG/AMP guidelines, including more specific definitions of appropriate functional assays. We set out to evaluate the existing VCEP guidelines for functional assays. METHODS:We evaluated the functional criteria (PS3/BS3) of six VCEPs (CDH1, Hearing Loss, Inherited Cardiomyopathy-MYH7, PAH, PTEN, RASopathy). We then established criteria for evaluating functional studies based on disease mechanism, general class of assay, and the characteristics of specific assay instances described in the primary literature. Using these criteria, we extensively curated assay instances cited by each VCEP in their pilot variant classification to analyze VCEP recommendations and their use in the interpretation of functional studies. RESULTS:Unsurprisingly, our analysis highlighted the breadth of VCEP-approved assays, reflecting the diversity of disease mechanisms among VCEPs. We also noted substantial variability between VCEPs in the method used to select these assays and in the approach used to specify strength modifications, as well as differences in suggested validation parameters. Importantly, we observed discrepancies between the parameters VCEPs specified as required for approved assay instances and the fulfillment of these requirements in the individual assays cited in pilot variant interpretation. CONCLUSIONS:Interpretation of the intricacies of functional assays often requires expert-level knowledge of the gene and disease, and current VCEP recommendations for functional assay evidence are a useful tool to improve the accessibility of functional data by providing a starting point for curators to identify approved functional assays and key metrics. However, our analysis suggests that further guidance is needed to standardize this process and ensure consistency in the application of functional evidence.

Project description:PurposeThe curation of images using human resources is time intensive but an essential step for developing artificial intelligence (AI) algorithms. Our goal was to develop and implement an AI algorithm for image curation in a high-volume setting. We also explored AI tools that will assist in deploying a tiered approach, in which the AI model labels images and flags potential mislabels for human review.DesignImplementation of an AI algorithm.ParticipantsSeven-field stereoscopic images from multiple clinical trials.MethodsThe 7-field stereoscopic image protocol includes 7 pairs of images from various parts of the central retina along with images of the anterior part of the eye. All images were labeled for field number by reading center graders. The model output included classification of the retinal images into 8 field numbers. Probability scores (0-1) were generated to identify misclassified images, with 1 indicating a high probability of a correct label.Main outcome measuresAgreement of AI prediction with grader classification of field number and the use of probability scores to identify mislabeled images.ResultsThe AI model was trained and validated on 17 529 images and tested on 3004 images. The pooled agreement of field numbers between grader classification and the AI model was 88.3% (kappa, 0.87). The pooled mean probability score was 0.97 (standard deviation [SD], 0.08) for images for which the graders agreed with the AI-generated labels and 0.77 (SD, 0.19) for images for which the graders disagreed with the AI-generated labels (P < 0.0001). Using receiver operating characteristic curves, a probability score of 0.99 was identified as a cutoff for distinguishing mislabeled images. A tiered workflow using a probability score of < 0.99 as a cutoff would include 27.6% of the 3004 images for human review and reduce the error rate from 11.7% to 1.5%.ConclusionsThe implementation of AI algorithms requires measures in addition to model validation. Tools to flag potential errors in the labels generated by AI models will reduce inaccuracies, increase trust in the system, and provide data for continuous model development.

Dataset Information

Assessing the gene-disease association of 19 genes with the RASopathies using the ClinGen gene curation framework.

Publications

Assessing the gene-disease association of 19 genes with the RASopathies using the ClinGen gene curation framework.

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