Evidence map›Paper›PMID 37443081›Full record

ArticleGenome medicine2023

ClinVar and HGMD genomic variant classification accuracy has improved over time, as measured by implied disease burden.

Andrew G Sharo, Yangyun Zou, Aashish N Adhikari, Steven E Brenner

Abstract read
In one paragraph

Article in Genome medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed.

  1. Review
  2. Article
  3. Heterogeneity of monogenic epilepsy in loci, phenotypes, and treatment approaches.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    Review
  4. Article
  5. Frontiers in oncology · 2026
    Article
  6. Article
  7. Alport Syndrome-Associated PathogenicInternational journal of genomics · 2026
    Article
  8. Article
  9. Article
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  11. Article
  12. Article
  13. Computational Evidence for Digenic Contribution ofInternational journal of molecular sciences · 2025
    Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Review
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Andrew G SharoBiophysics Graduate Group, University of California, Berkeley, CA, 94720, USA. sharo@compbio.berkeley.edu.ORCID 0000-0001-6533-8385
Yangyun ZouCenter for Computational Biology, University of California, Berkeley, CA, 94720, USA.ORCID 0000-0001-5034-9938
Aashish N AdhikariCenter for Computational Biology, University of California, Berkeley, CA, 94720, USA.ORCID 0000-0003-4305-9494
Steven E BrennerBiophysics Graduate Group, University of California, Berkeley, CA, 94720, USA. brenner@compbio.berkeley.edu.ORCID 0000-0001-7559-6185

Funding

MOLECULAR BIOPHYSICS TRAINING GRANTT32GM008295 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI HURLEY, JAMES H, WEMMER, DAVID E · 1989 to 2022
$8.1M
Sequencing of Newborn Blood Spot DNA to Improve and Expand Newborn ScreeningU19HD077627 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KOENIG, BARBARA A., KWOK, PUI-YAN · 2013 to 2018
$6.6M
Center for Critical Assessment of Genome InterpretationU24HG007346 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI BRENNER, STEVEN E, IOANNIDIS, NILAH MONNIER · 2020 to 2025
$3.8M
Component 2 - Resource ProjectU41HG007346 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI BRENNER, STEVEN E · 2015 to 2017
$1.7M
NHGRI NIH HHS U24 HG007346NHGRI NIH HHS U41 HG007346NICHD NIH HHS U19 HD077627NIGMS NIH HHS T32 GM008295NIH HHS HD077627
6 · The paper itself

Abstract

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.

Indexed as

Databases, GeneticGenetic VariationGene FrequencyGenomicsGenotypeClinVarHGMDInborn errors of metabolismPathogenic variantVariant classificationVariant curation

Identifiers

PMID37443081
PMCPMC10347827

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.