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.
We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). ...Reported TASs were common median risk allele frequency 36%, interquartile range (IQR) 21%-53% and were associated with modest effect sizes median odds ratio (OR) 1.33, IQR 1.20-1.61. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites OR = 3.9 (2.2-7.0), p = 3.5 x 10⁻⁷ and 5kb-promoter regions OR = 2.3 (1.5-3.6), p = 3 x 10⁻⁴ compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions OR = 0.44 (0.34-0.58), p = 2.0 x 10⁻⁹. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection OR = 1.3 (0.8-2.1), p = 0.2. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.
ClinGen--the Clinical Genome Resource Rehm, Heidi L; Berg, Jonathan S; Brooks, Lisa D ...
New England journal of medicine/The New England journal of medicine,
06/2015, Letnik:
372, Številka:
23
Journal Article
Recenzirano
Odprti dostop
On autopsy, a patient is found to have hypertrophic cardiomyopathy. The patient’s family pursues genetic testing that shows a “likely pathogenic” variant for the condition on the basis of a study in ...an original research publication. Given the dominant inheritance of the condition and the risk of sudden cardiac death, other family members are tested for the genetic variant to determine their risk. Several family members test negative and are told that they are not at risk for hypertrophic cardiomyopathy and sudden cardiac death, and those who test positive are told that they need to be regularly monitored for cardiomyopathy on echocardiography. Five years later, during a routine clinic visit of one of the genotype-positive family members, the cardiologist queries a database for current knowledge on the genetic variant and discovers that the variant is now interpreted as “likely benign” by another laboratory that uses more recently derived population-frequency data. A newly available testing panel for additional genes that are implicated in hypertrophic cardiomyopathy is initiated on an affected family member, and a different variant is found that is determined to be pathogenic. Family members are retested, and one member who previously tested negative is now found to be positive for this new variant. An immediate clinical workup detects evidence of cardiomyopathy, and an intracardiac defibrillator is implanted to reduce the risk of sudden cardiac death.
The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical ...genomics. We present quantitative and qualitative evidence to characterize: (1) acquisition of REA data via clinical laboratory requisition forms, and (2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites ascertained from annotations in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about clinically relevant variants in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with “pathogenic” and “likely pathogenic” expert‐reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population‐level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed through partnerships and collaborations and adopted across clinical genomics.
The Ancestry and Diversity Working Group of the Clinical Genome Resource (ClinGen) presents the results of quantitative and qualitative analyses about race, ethnicity, and ancestry (REA) in clinical genomics. Our findings show great heterogeneity across clinical laboratories in the way race and ethnicity are reported on requisition forms and recommend that standard methods be developed and put into practice through future collaborations. We also demonstrate disparities in the amount of information available for variants at clinically relevant sites across populations.
Rapidly accumulating data from genome-wide association studies (GWASs) and other large-scale studies are most useful when synthesized with existing databases. To address this opportunity, we ...developed the Phenotype-Genotype Integrator (PheGenI), a user-friendly web interface that integrates various National Center for Biotechnology Information (NCBI) genomic databases with association data from the National Human Genome Research Institute GWAS Catalog and supports downloads of search results. Here, we describe the rationale for and development of this resource. Integrating over 66,000 association records with extensive single nucleotide polymorphism (SNP), gene, and expression quantitative trait loci data already available from the NCBI, PheGenI enables deeper investigation and interrogation of SNPs associated with a wide range of traits, facilitating the examination of the relationships between genetic variation and human diseases.
Genome‐scale sequencing creates vast amounts of genomic data, increasing the challenge of clinical sequence variant interpretation. The demand for high‐quality interpretation requires multiple ...specialties to join forces to accelerate the interpretation of sequence variant pathogenicity. With over 600 international members including clinicians, researchers, and laboratory diagnosticians, the Clinical Genome Resource (ClinGen), funded by the National Institutes of Health, is forming expert groups to systematically evaluate variants in clinically relevant genes. Here, we describe the first ClinGen variant curation expert panels (VCEPs), development of consistent and streamlined processes for establishing new VCEPs, and creation of standard operating procedures for VCEPs to define application of the ACMG/AMP guidelines for sequence variant interpretation in specific genes or diseases. Additionally, ClinGen has created user interfaces to enhance reliability of curation and a Sequence Variant Interpretation Working Group (SVI WG) to harmonize guideline specifications and ensure consistency between groups. The expansion of VCEPs represents the primary mechanism by which curation of a substantial fraction of genomic variants can be accelerated and ultimately undertaken systematically and comprehensively. We welcome groups to utilize our resources and become involved in our effort to create a publicly accessible, centralized resource for clinically relevant genes and variants.
ClinGen is organizing Variant Curation Expert Panels (VCEPs) to develop specifications to the ACMG/AMP guidelines for genes or diseases of interest, interpret variants according to these guidelines, and publish the expert interpretations through the publicly available ClinVar database. A stepwise process was iteratively developed for ClinGen VCEPs to apply to submit variant assertions to ClinVar at the Expert Panel level of review. Other groups that wish to assemble as VCEPs are encouraged, though not required, to follow these steps.
The database of Genotypes and Phenotypes (dbGaP) Data Browser (https://www.ncbi.nlm.nih.gov/gap/ddb/) was developed in response to requests from the scientific community for a resource that enable ...view-only access to summary-level information and individual-level genotype and sequence data associated with phenotypic features maintained in the controlled-access tier of dbGaP. Until now, the dbGaP controlled-access environment required investigators to submit a data access request, wait for Data Access Committee review, download each data set and locally examine them for potentially relevant information. Existing unrestricted-access genomic data browsing resources (e.g. http://evs.gs.washington.edu/EVS/, http://exac.broadinstitute.org/) provide only summary statistics or aggregate allele frequencies. The dbGaP Data Browser serves as a third solution, providing researchers with view-only access to a compilation of individual-level data from general research use (GRU) studies through a simplified controlled-access process. The National Institutes of Health (NIH) will continue to improve the Browser in response to user feedback and believes that this tool may decrease unnecessary download requests, while still facilitating responsible genomic data-sharing.
The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies. To facilitate ...replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project. The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains. PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The selected measures are then made freely available to the scientific community via the PhenX Toolkit. Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies. PhenX measures will have the most impact when included at the experimental design stage. The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data. Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions.
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.
Dina Paltoo, Laura Lyman Rodriguez, Michael Feolo and colleagues present their analysis of the usefulness and impact of the first seven years of data sharing via the dbGaP repository and announce the ...extension of data-sharing provisions to other types of research funded by the NIH.
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