Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework

Sarah E. Brnich, Ahmad N. Abou Tayoun, Fergus J. Couch, Garry R. Cutting, Marc S. Greenblatt, Christopher D. Heinen, Dona M. Kanavy, Xi Luo, Shannon M. McNulty, Lea M. Starita, Sean V. Tavtigian, Matt W. Wright, Steven M. Harrison, Leslie G. Biesecker, Jonathan S. Berg, Ahmad N. Abou Tayoun, Jonathan S. Berg, Leslie G. Biesecker, Steven E. Brenner, Garry R. CuttingSian Ellard, Marc S. Greenblatt, Steven M. Harrison, Izabela Karbassi, Rachel Karchin, Jessica L. Mester, Anne O'Donnell-Luria, Tina Pesaran, Sharon E. Plon, Heidi Rehm, Sean V. Tavtigian, Scott Topper

Research output: Contribution to journalReview article

Abstract

Background: The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) clinical variant interpretation guidelines established criteria for different types of evidence. This includes the strong evidence codes PS3 and BS3 for "well-established" functional assays demonstrating a variant has abnormal or normal gene/protein function, respectively. However, they did not provide detailed guidance on how functional evidence should be evaluated, and differences in the application of the PS3/BS3 codes are a contributor to variant interpretation discordance between laboratories. This recommendation seeks to provide a more structured approach to the assessment of functional assays for variant interpretation and guidance on the use of various levels of strength based on assay validation. Methods: The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation (SVI) Working Group used curated functional evidence from ClinGen Variant Curation Expert Panel-developed rule specifications and expert opinions to refine the PS3/BS3 criteria over multiple in-person and virtual meetings. We estimated the odds of pathogenicity for assays using various numbers of variant controls to determine the minimum controls required to reach moderate level evidence. Feedback from the ClinGen Steering Committee and outside experts were incorporated into the recommendations at multiple stages of development. Results: The SVI Working Group developed recommendations for evaluators regarding the assessment of the clinical validity of functional data and a four-step provisional framework to determine the appropriate strength of evidence that can be applied in clinical variant interpretation. These steps are as follows: (1) define the disease mechanism, (2) evaluate the applicability of general classes of assays used in the field, (3) evaluate the validity of specific instances of assays, and (4) apply evidence to individual variant interpretation. We found that a minimum of 11 total pathogenic and benign variant controls are required to reach moderate-level evidence in the absence of rigorous statistical analysis. Conclusions: The recommendations and approach to functional evidence evaluation described here should help clarify the clinical variant interpretation process for functional assays. Further, we hope that these recommendations will help develop productive partnerships with basic scientists who have developed functional assays that are useful for interrogating the function of a variety of genes.

Original languageEnglish (US)
Article number3
JournalGenome Medicine
Volume12
Issue number1
DOIs
StatePublished - Dec 31 2019

Keywords

  • Functional assays
  • Guidelines
  • Variant interpretation

ASJC Scopus subject areas

  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Genetics(clinical)

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    Brnich, S. E., Abou Tayoun, A. N., Couch, F. J., Cutting, G. R., Greenblatt, M. S., Heinen, C. D., Kanavy, D. M., Luo, X., McNulty, S. M., Starita, L. M., Tavtigian, S. V., Wright, M. W., Harrison, S. M., Biesecker, L. G., Berg, J. S., Abou Tayoun, A. N., Berg, J. S., Biesecker, L. G., Brenner, S. E., ... Topper, S. (2019). Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Medicine, 12(1), [3]. https://doi.org/10.1186/s13073-019-0690-2