Dataset Information

Scaling resolution of variant classification differences in ClinVar between 41 clinical laboratories through an outlier approach.

ABSTRACT: ClinVar provides open access to variant classifications shared from many clinical laboratories. Although most classifications are consistent across laboratories, classification differences exist. To facilitate resolution of classification differences on a large scale, clinical laboratories were encouraged to reassess outlier classifications of variants with medically significant differences (MSDs). Outliers were identified by first comparing ClinVar submissions from 41 clinical laboratories to detect variants with MSDs between the laboratories (650 variants). Next, MSDs were filtered for variants with ?3 classifications (244 variants), of which 87.6% (213 variants) had a majority consensus in ClinVar, thus allowing for identification of outlier classifications in need of reassessment. Laboratories with outlier classifications were sent a custom report and encouraged to reassess variants. Results were returned for 204 (96%) variants, of which 62.3% (127) were resolved. Of those 127, 64.6% (82) were resolved due to reassessment prompted by this study and 35.4% (45) resolved by a previously completed reassessment. This study demonstrates a scalable approach to classification resolution and capitalizes on the value of data sharing within ClinVar. These activities will help the community move toward more consistent variant classifications, which will improve the care of patients with, or at risk for, genetic disorders.

SUBMITTER: Harrison SM

PROVIDER: S-EPMC6188667 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Scaling resolution of variant classification differences in ClinVar between 41 clinical laboratories through an outlier approach.

Harrison Steven M SM Dolinksy Jill S JS Chen Wenjie W Collins Christin D CD Das Soma S Deignan Joshua L JL Garber Kathryn B KB Garcia John J Jarinova Olga O Knight Johnson Amy E AE Koskenvuo Juha W JW Lee Hane H Mao Rong R Mar-Heyming Rebecca R McFaddin Andrew S AS Moyer Krista K Nagan Narasimhan N Rentas Stefan S Santani Avni B AB Seppälä Eija H EH Shirts Brian H BH Tidwell Timothy T Topper Scott S Vincent Lisa M LM Vinette Kathy K Rehm Heidi L HL

Human mutation 20181101 11

ClinVar provides open access to variant classifications shared from many clinical laboratories. Although most classifications are consistent across laboratories, classification differences exist. To facilitate resolution of classification differences on a large scale, clinical laboratories were encouraged to reassess outlier classifications of variants with medically significant differences (MSDs). Outliers were identified by first comparing ClinVar submissions from 41 clinical laboratories to d ...[more]

PMID: 30311378

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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

Scaling resolution of variant classification differences in ClinVar between 41 clinical laboratories through an outlier approach.

Publications

Scaling resolution of variant classification differences in ClinVar between 41 clinical laboratories through an outlier approach.

Similar Datasets

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