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Background <p>High-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.</p> Results <p>In 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.</p> Conclusions <p>These 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.</p> Graphical Abstract <p></p>

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Toward streamline variant classification: discrepancies in variant nomenclature and syntax for ClinVar pathogenic variants across annotation tools

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月21日

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  • Yu-An Chen,
  • Tzu-Hang Yuan,
  • Jia-Hsin Huang,
  • Yu-Bin Wang,
  • Tzu-Mao Hung,
  • Chien-Yu Chen,
  • Pei-Lung Chen,
  • Jacob Shujui Hsu

摘要

Background

High-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.

Results

In 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.

Conclusions

These 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.

Graphical Abstract