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[Preprint]. 2024 Mar 7:2024.03.05.24303807.

doi: 10.1101/2024.03.05.24303807.

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

Sarah L Stenton^{1

2}, Vikas Pejaver^{3

4}, Timothy Bergquist³, Leslie G Biesecker⁵, Alicia B Byrne¹, Emily Nadeau⁶, Marc S Greenblatt⁶, Steven Harrison^{1

7}, Sean Tavtigian⁸, Predrag Radivojac⁹, Steven E Brenner¹⁰, Anne O'Donnell-Luria^{1

2}

Affiliations

¹ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
² Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
³ Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
⁴ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
⁵ Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
⁶ Department of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT 05405, USA.
⁷ Ambry Genetics, Aliso Viejo, CA, USA.
⁸ Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
⁹ Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.
¹⁰ Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA.

PMID: 38496501
PMCID: PMC10942508
DOI: 10.1101/2024.03.05.24303807

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

Sarah L Stenton et al. medRxiv. 2024.

[Preprint]. 2024 Mar 7:2024.03.05.24303807.

doi: 10.1101/2024.03.05.24303807.

Authors

Affiliations

¹ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
² Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
³ Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
⁴ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
⁵ Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
⁶ Department of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT 05405, USA.
⁷ Ambry Genetics, Aliso Viejo, CA, USA.
⁸ Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
⁹ Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.
¹⁰ Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA.

PMID: 38496501
PMCID: PMC10942508
DOI: 10.1101/2024.03.05.24303807

Update in

Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations.
Stenton SL, Pejaver V, Bergquist T, Biesecker LG, Byrne AB, Nadeau EAW, Greenblatt MS, Harrison SM, Tavtigian SV, Radivojac P, Brenner SE, O'Donnell-Luria A; ClinGen Sequence Variant Interpretation Working Group. Stenton SL, et al. Genet Med. 2024 Nov;26(11):101213. doi: 10.1016/j.gim.2024.101213. Epub 2024 Jul 25. Genet Med. 2024. PMID: 39030733

Abstract

Purpose: To 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.

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 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 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.

Conclusion: A 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.

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Conflict of interest statement

CONFLICT OF INTEREST Disclosure: L.G.B. receives royalties from Wolters-Kluwer for authorship of UpToDate, is a member of the Illumina Medical Ethics Committee, and receives research support from Merck, Inc. V.P. and P.R. participated in the development of some of the tools assessed in this study. All other authors declare no conflict of interest.

Figures

**Figure 1.**
A. Rare (≤1% AF) missense variants in disease-associated genes per proband by PP3 evidence strength for analyzed computational prediction tools. B. Rare (≤1% AF) missense variants in disease-associated genes with PP3 evidence per proband by evidence strength and reported mode of inheritance (AD-only and AR-only) for analyzed computational tools. Boxplots correspond to the first, second, and third quartile of data, with whiskers denoting 1.5 × IQR. Outliers are displayed as individual points.

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References

1. McInnes G, Sharo AG, Koleske ML, et al. Opportunities and challenges for the computational interpretation of rare variation in clinically important genes. Am J Hum Genet. 2021;108(4):535–548. - PMC - PubMed
1. Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–424. - PMC - PubMed
1. Harrison SM, Dolinsky JS, Knight Johnson AE, et al. Clinical laboratories collaborate to resolve differences in variant interpretations submitted to ClinVar. Genet Med. 2017;19(10):1096–1104. - PMC - PubMed
1. Pejaver V, Byrne AB, Feng BJ, et al. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am J Hum Genet. 2022;109(12):2163–2177. - PMC - PubMed
1. Tavtigian SV, Greenblatt MS, Harrison SM, et al. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet Med. 2018;20(9):1054–1060. - PMC - PubMed

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This is a preprint.

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

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

Authors

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Abstract

Conflict of interest statement

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References

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