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Background: Variants within factor VIII (F8) are associated with sex-linked hemophilia A and thrombosis, with gene therapy approaches being available for pathogenic variants. Many variants within F8 remain variants of uncertain significance (VUS) or are under-explored as to their connections to phenotypic outcomes. Methods: We assessed data on F8 expression while screening the UniProt, ClinVar, Geno2MP, and gnomAD databases for F8 missense variants; these collectively represent the sequencing of more than a million individuals. Results: For the two F8 isoforms coding for different protein lengths (2351 and 216 amino acids), we observed noncoding variants influencing expression which are also associated with thrombosis risk, with uncertainty as to differences in females and males. Variant analysis identified a severe stratification of potential annotation issues for missense variants in subjects of non-European ancestry, suggesting a need for further defining the genetics of diverse populations. Additionally, few heterozygous female carriers of known pathogenic variants have sufficiently confident phenotyping data, leaving researchers unable to determine subtle, less defined phenotypes. Using structure movement correlations to known pathogenic variants for the VUS, we determined seven clusters of likely pathogenic variants based on screening work. Conclusions: This work highlights the need to define missense variants, especially those for VUS and from subjects of non-European ancestry, as well as the roles of these variants in women’s physiology.
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ClinVar and HGMD genomic variant classification accuracy has improved over time, as measured by implied disease burden
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.
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- Award ID(s):
- 2109912
- PAR ID:
- 10431579
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Genome Medicine
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 1756-994X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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