ArticleHuman genomics2025
Toward streamline variant classification: discrepancies in variant nomenclature and syntax for ClinVar pathogenic variants across annotation tools.
Article in Human genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Who cites it
3 citing papers in PubMed.
- APOE ε4 variant interpretation and protein numbering discrepancy: a case report.Molecular biology reports · 2026Article
- To be or not to be a protein coding mutation, that's the question!NAR genomics and bioinformatics · 2025Review
- Application of whole genome sequencing for carrier and diagnostic assessment of spinal muscular atrophy in Taiwan.NPJ genomic medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundHigh-throughput sequencing has revolutionized genetic disorder diagnosis, but variant pathogenicity interpretation is still challenging. Even though the human genome variation society (HGVS) provides recommendations for variant nomenclature, discrepancies in annotation remain a significant hurdle.
resultsIn this study, we evaluated the annotation concordance between three tools-ANNOVAR, SnpEff, and variant effect predictor (VEP)-using 164,549 two-star variants from ClinVar. The analysis used HGVS nomenclature string-match comparisons to assess annotation consistency from each tool, corresponding coding impacts, and associated ACMG criteria inferred from the annotations. The analysis revealed variable concordance rates, with 58.52% agreement for HGVSc, 84.04% for HGVSp, and 85.58% for the coding impact. SnpEff showed the highest match for HGVSc (0.988), while VEP bettered for HGVSp (0.977). The substantial discrepancies were noted in the loss-of-function (LoF) category. Incorrect PVS1 interpretations affected the final pathogenicity and downgraded PLP variants (ANNOVAR 55.9%, SnpEff 66.5%, VEP 67.3%), risking false negatives of clinically relevant variants in reports.
conclusionsThese findings highlight the critical challenges in accurately interpreting variant pathogenicity due to discrepancies in annotations. To enhance the reliability of genetic variant interpretation in clinical practice, standardizing transcript sets and systematically cross-validating results across multiple annotation tools is essential.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.