Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN
- Author(s)
- Fayer, S; Horton, C; Dines, JN; Rubin, AF; Richardson, ME; McGoldrick, K; Hernandez, F; Pesaran, T; Karam, R; Shirts, BH; Fowler, DM; Starita, LM;
- Details
- Publication Year 2021-11-13,Volume 108,Issue #12,Page 2248-2258
- Journal Title
- American Journal of Human Genetics
- Abstract
- Clinical interpretation of missense variants is challenging because the majority identified by genetic testing are rare and their functional effects are unknown. Consequently, most variants are of uncertain significance and cannot be used for clinical diagnosis or management. Although not much can be done to ameliorate variant rarity, multiplexed assays of variant effect (MAVEs), where thousands of single-nucleotide variant effects are simultaneously measured experimentally, provide functional evidence that can help resolve variants of unknown significance (VUSs). However, a rigorous assessment of the clinical value of multiplexed functional data for variant interpretation is lacking. Thus, we systematically combined previously published BRCA1, TP53, and PTEN multiplexed functional data with phenotype and family history data for 324 VUSs identified by a single diagnostic testing laboratory. We curated 49,281 variant functional scores from MAVEs for these three genes and integrated four different TP53 multiplexed functional datasets into a single functional prediction for each variant by using machine learning. We then determined the strength of evidence provided by each multiplexed functional dataset and reevaluated 324 VUSs. Multiplexed functional data were effective in driving variant reclassification when combined with clinical data, eliminating 49% of VUSs for BRCA1, 69% for TP53, and 15% for PTEN. Thus, multiplexed functional data, which are being generated for numerous genes, are poised to have a major impact on clinical variant interpretation.
- Publisher
- Elsevier
- Research Division(s)
- Bioinformatics
- PubMed ID
- 34793697
- Publisher's Version
- https://doi.org/10.1016/j.ajhg.2021.11.001
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2021-12-07 12:03:59
Last Modified: 2021-12-07 12:19:23