(RESOURCE PROJECT) Here, we describe a set of integrated resources developed by Stanford and Baylor in concert with ClinGen leadership to address pressing needs in the clinical and medical genomics community. These include (1) the need to share case-level genotype and phenotype data for variant interpretation and research, (2) the need to improve the accuracy of clinical genetic testing in underrepresented minority population, where VUS rates are higher than in the general U.S. white population, (3) the need for standards in annotation and organization of teams of experts to facilitate interpretation of clinically relevant genes and variants at scale, (4) the need for clinical-grade standards on how computational tools for pathogenicity prediction are trained, evaluated, reported, and incorporated into clinical genomic workflows. These resources we develop here address these needs as follows: (1) The Case Level Evidence Aggregation and Reporting NETwork (CLEARNET) is a secure and easy-to-use cloud-based portal which will standardize the use of case-level evidence in ClinGen variant interpretation. (2) A new Working Group chaired by Dr. Bustamante (PI) and Dr. Nussbaum (Medical Director, InVitae) on the use of Genetic Ancestry in Clinical Genomics. We will also create new bioinformatics resources in support of this goal, including improved population reference sets, and estimates of pathogenic allelic frequency for ten?s of thousands of variants across diverse population datasets to improve accuracy of variant classification. (3) Multiple activities in support of the biocuration activities across ClinGen and support of clinical domain working groups (CDWG), particularly for non-Mendelian disorders. These include: (a) developing standards for biocuration at scale and community curators, (b) standardization of the requirements for the extent of pre-test genetic counseling across the spectrum of genetic tests available today, (c) leadership in the inborn errors of metabolism, cardiovascular and coordination of Somatic cancer (SC-WG), and Hereditary cancer (HC-CDWG), (d) creation of new working groups for Complex Disease and Regulatory Variation, and (e) collaborative network of activities across Pharmacogenomics which leverages investments in PharmGKB and PGN. (4) A comprehensive new effort through a new Computational Predictors WG with expert representation from all stakeholders (computational developers, curation scientists, clinicians) to improve concordance, transparency and usability of in-silico pathogenicity prediction tools in clinical workflows. This will include (a) metadata vocabularies and clear reporting standards for predictors, (a) an expert guideline around honest evaluation of predictor performance and hidden-bias controls, and (c) maintaining a portfolio of clinical-grade predictors with gene-, disease- or domain- specific requirements for the community.
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