Genetics in MedicineGenetic & Genomic MedicinePeer-reviewed journal article
Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations
Sarah L. StentoniD, Vikas Rao PejaveriD, Timothy R. BergquistiD, Leslie G. BieseckeriD, Alicia B. ByrneiD, Emily A.W. NadeauiD, Marc S. GreenblattiD, Steven M. HarrisoniD, Sean V. TavtigianiD, Predrag Radivojac AiD, Steven E. BrenneriD, Anne O’Donnell‐LuriaiD, Ahmad A. Tayoun, Jonathan S. BergiD, Garry R. CuttingiD, Sian EllardiD, Peter Kang, Izabela Karbassi, Rachel KarchiniD, Jessica Mester, Anne O’Donnell-Luria, Tina Pesaran, Sharon E. PloniD, Heidi L. RehmiD, Natasha T. StrandeiD, Scott TopperiD
Abstract
PurposeTo investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the calibrated PP3/BP4 computational recommendations. MethodsMissense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools 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. ResultsFrom 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 PP3_Strong variants increased approximately 2.6-fold compared to disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting. ConclusionA small number of variants per proband reached PP3_Strong and PP3_Moderate in 3,424 disease-associated genes, and though not the intended use of the recommendations, also 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.
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 18
- FWCI
- 4.76
- Field percentile
- top 4%
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