Dataset Information

AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification.

ABSTRACT:

Summary

With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics-Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 strength adjustments, removal of PP5/BP6). This tool facilitates large-scale, clinically focused classification of germline sequence variants in a research setting.

Availability and implementation

AutoGVP is an open source dockerized workflow implemented in R and freely available on GitHub at https://github.com/diskin-lab-chop/AutoGVP.

SUBMITTER: Kim J

PROVIDER: S-EPMC10955249 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification.

Kim Jung J Naqvi Ammar S AS Corbett Ryan J RJ Kaufman Rebecca S RS Vaksman Zalman Z Brown Miguel A MA Miller Daniel P DP Phul Saksham S Geng Zhuangzhuang Z Storm Phillip B PB Resnick Adam C AC Stewart Douglas R DR Rokita Jo Lynne JL Diskin Sharon J SJ

Bioinformatics (Oxford, England) 20240301 3

<h4>Summary</h4>With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics-Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 stre ...[more]

PMID: 38426335

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Project description:BackgroundCurated databases of genetic variants assist clinicians and researchers in interpreting genetic variation. Yet, these databases contain some misclassified variants. It is unclear whether variant misclassification is abating as these databases rapidly grow and implement new guidelines.MethodsUsing archives of ClinVar and HGMD, we investigated how variant misclassification has changed over 6 years, across different ancestry groups. We considered inborn errors of metabolism (IEMs) screened in newborns as a model system because these disorders are often highly penetrant with neonatal phenotypes. We used samples from the 1000 Genomes Project (1KGP) to identify individuals with genotypes that were classified by the databases as pathogenic. Due to the rarity of IEMs, nearly all such classified pathogenic genotypes indicate likely variant misclassification in ClinVar or HGMD.ResultsWhile the false-positive rates of both ClinVar and HGMD have improved over time, HGMD variants currently imply two orders of magnitude more affected individuals in 1KGP than ClinVar variants. We observed that African ancestry individuals have a significantly increased chance of being incorrectly indicated to be affected by a screened IEM when HGMD variants are used. However, this bias affecting genomes of African ancestry was no longer significant once common variants were removed in accordance with recent variant classification guidelines. We discovered that ClinVar variants classified as Pathogenic or Likely Pathogenic are reclassified sixfold more often than DM or DM? variants in HGMD, which has likely resulted in ClinVar's lower false-positive rate.ConclusionsConsidering misclassified variants that have since been reclassified reveals our increasing understanding of rare genetic variation. We found that variant classification guidelines and allele frequency databases comprising genetically diverse samples are important factors in reclassification. We also discovered that ClinVar variants common in European and South Asian individuals were more likely to be reclassified to a lower confidence category, perhaps due to an increased chance of these variants being classified by multiple submitters. We discuss features for variant classification databases that would support their continued improvement.

Dataset Information

AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification.

Summary

Availability and implementation

Publications

AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification.

Similar Datasets

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