ClinVar data parsing

Zhang, Xiaolei; Minikel, Eric Vallabh; O’Donnell-Luria, Anne; MacArthur, Daniel G.; Ware, James S.; Weisburd, Ben

doi:10.12688/wellcomeopenres.11640.1

Cited by 19 publications

(25 citation statements)

References 5 publications

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“…GnomAD contains the sequencing data from a variety of large‐scale sequencing projects and is widely used to assess the frequency of a variant in a general population. Hence, we obtained the phenotype‐variant frequency relationship in converged data with 243 477 variants using the ClinVar data parsing tool …”

Section: Resultsmentioning

confidence: 99%

“…GnomAD version r2.0.2 was used as the frequency data in our study (Figure ). We used the ClinVar‐master to generate the connection between frequency data and clinical data based on the human reference genome hg19. Allele frequency of variants in non‐Finnish European (NFE) population was used in phenotype‐variant‐frequency relationship of the converged data due to the maximum population size.…”

Section: Methodsmentioning

confidence: 99%

“…Hence, we obtained the phenotypevariant frequency relationship in converged data with 243 477 variants using the ClinVar data parsing tool. 21 Phenotype features were quantified for additional filtering, which included clinical significance, review status, inheritance modes, age of onset and origin of the variants (Figure 1). We screened variants with all the following clinical conditions to better identify variants that directly respond to negative selection: (a) Variants should have assertion criteria and evidence; (b) the genetic origin of the variants should be germline; and (c) variants should be marked as "pathogenic" or "likely pathogenic" for Mendelian disorders.…”

Section: Clinical Evidence and Allele Frequency Collectionmentioning

confidence: 99%

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Allele frequency of pathogenic variants related to adult‐onset Mendelian diseases

Jin

Yin

2019

Clinical Genetics

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An increasing number of variants related to Mendelian diseases have been discovered through analyses of next‐generation sequencing data, but the results related to adult‐onset Mendelian diseases are insufficient. One possible explanation is that the methods commonly used to evaluate pathogenic variants in patients with congenital Mendelian diseases may not be appropriate for adult‐onset diseases due to differences in selection pressure, particularly when assessing the frequency of variants in the general population. We established a well‐processed and filtered database of pathogenic variants with both phenotype and frequency information based on the ClinVar and GnomAD public database to better explore the genetic features of adult‐onset diseases under real‐world conditions. Compared with the control group, pathogenic variants related to adult‐onset dominant diseases had a higher allele frequency pattern. Further, the allele frequency patterns of both dominant and recessive variants were higher in patients with neurodegenerative diseases than those in patients with intellectual disabilities. Based on the mutation‐selection balance model, the above observation of allele frequency described the lower selection pressure on pathogenic variants related to adult‐onset Mendelian diseases and suggests a lower effectiveness of population and loss‐of‐function evidence in investigations of adult‐onset Mendelian diseases.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Clinical Evidence and Allele Frequency Collectionmentioning

confidence: 99%

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Allele frequency of pathogenic variants related to adult‐onset Mendelian diseases

Jin

Yin

2019

Clinical Genetics

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“…PathoMAN uses ClinVar as one of its knowledge-base. We use the ClinVar data parser tool 53 to extract important fields that will aid in variant classification. PathoMAN takes advantage of the gold star system awarded to variants with highest evidences supporting the assertion of clinical significance.…”

Section: Usage Of Clinvar (Release 12/28/2017) In Pathomanmentioning

confidence: 99%

Towards automation of germline variant curation in clinical cancer genetics

Ravichandran

Shameer

Kemel

et al. 2018

Preprint

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Cancer care professionals are confronted with interpreting results from multiplexed gene sequencing of patients at hereditary risk for cancer. Assessments for variant classification now require orthogonal data searches, requiring aggregation of multiple lines of evidence from diverse resources. The burden of evidence for each variant to meet thresholds for pathogenicity or actionability now poses a growing challenge for those seeking to counsel patients and families following germline genetic testing. A computational algorithm that automates, provides uniformity and significantly accelerates this interpretive process is needed. The tool described here, Pathogenicity of Mutation Analyzer (PathoMAN) automates germline genomic variant curation from clinical sequencing based on ACMG guidelines. PathoMAN aggregates multiple tracks of genomic, protein and disease specific information from public sources. We compared expert manually curated variant data from studies on (i) prostate cancer (ii) breast cancer and (iii) ClinVar to assess performance. PathoMAN achieves high concordance (83.1% pathogenic, 75.5% benign) and negligible discordance (0.04% pathogenic, 0.9% benign) when contrasted against expert curation. Some loss of resolution (8.6% pathogenic, 23.64% benign) and gain of resolution (6.6% pathogenic, 1.6% benign) was also observed. We highlight the advantages and weaknesses related to the programmable automation of variant classification. We also propose a new nosology for the five ACMG classes to facilitate more accurate reporting to ClinVar. The proposed refinements will enhance utility of ClinVar to allow further automation in cancer genetics. PathoMAN will reduce the manual workload of domain level experts. It provides a substantial advance in rapid classification of genetic variants by generating robust models using a knowledge-base of diverse genetic data. https://pathoman.mskcc.org

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“…ClinVar itself has provided a utile web interface and simplified data structures for programmatic use 19 . To the same end, other tools have been developed to address both aims: to easily browse variations and compare curations 20, 21 , or import and manipulate flattened ClinVar data for variant analysis 22, 23 . While the browsing tools allow for user-friendly and web-hosted comparison, they do not provide the throughput to analyze large datasets.…”

Section: Introductionmentioning

confidence: 99%

Clinotator: analyzing ClinVar variation reports to prioritize reclassification efforts

Butler

Gejman

2018

F1000Res

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While ClinVar has become an indispensable resource for clinical variant interpretation, its sophisticated structure provides it with a daunting learning curve. Often the sheer depth of types of information provided can make it difficult to analyze variant information with high throughput. Clinotator is a fast and lightweight tool to extract important aspects of criteria-based clinical assertions; it uses that information to generate several metrics to assess the strength and consistency of the evidence supporting the variant clinical significance. Clinical assertions are weighted by significance type, age of submission and submitter expertise category to filter outdated or incomplete assertions that otherwise confound interpretation. This can be accomplished in batches: either lists of Variation IDs or dbSNP rsIDs, or with vcf files that are additionally annotated. Using sample sets ranging from 15,000–50,000 variants, we slice out problem variants in minutes without extensive computational effort (using only a personal computer) and corroborate recently reported trends of discordance hiding amongst the curated masses. With the rapidly growing body of variant evidence, most submitters and researchers have limited resources to devote to variant curation. Clinotator provides efficient, systematic prioritization of discordant variants in need of reclassification. The hope is that this tool can inform ClinVar curation and encourage submitters to keep their clinical assertions current by focusing their efforts. Additionally, researchers can utilize new metrics to analyze variants of interest in pursuit of new insights into pathogenicity.

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ClinVar data parsing

Cited by 19 publications

References 5 publications

Allele frequency of pathogenic variants related to adult‐onset Mendelian diseases

Allele frequency of pathogenic variants related to adult‐onset Mendelian diseases

Towards automation of germline variant curation in clinical cancer genetics

Clinotator: analyzing ClinVar variation reports to prioritize reclassification efforts

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