Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations

ClinGen Sequence Variant Interpretation Working Group

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: : To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the ClinGen-calibrated PP3/BP4 computational recommendations. Methods: Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools that were able to reach PP3_Strong and BP4_Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide. Results: From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3_Strong, 3-5 PP3_Moderate, and 3-5 PP3_Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of variants reaching PP3_Strong score thresholds increased approximately 2.6-fold compared with disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting. Conclusion: A small number of variants per proband reached PP3_Strong and PP3_Moderate in 3424 disease-associated genes. Although not the intended use of the recommendations, this was also observed genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as pathogenic or likely pathogenic by ACMG/AMP rules.

Original languageEnglish
Article number101213
JournalGenetics in Medicine
Volume26
Issue number11
DOIs
StatePublished - Nov 2024

Keywords

  • ACMG/AMP recommendations
  • Clinical classification
  • Computational predictors
  • PP3/BP4 criteria
  • Variant interpretation

Fingerprint

Dive into the research topics of 'Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations'. Together they form a unique fingerprint.

Cite this