Phenotype-Specific Recalibration of MAVE Data Enables Repurposing of BAP1 Functional Assays for Kury-Isidor Syndrome
Gupta, P.; Balton, E. V.; Tejura, M.; Kumar, R. D.; Snyder, M. W.; Stone, J.; Villani, R. M.; Peter, B. H.; Sirisak, C.; Ian, G. A.; Martha, H.-P.; Danny, M. E.; Jane, R.; Elisabeth, R. A.; Andrew, S. H.; Mark, W.; Undiagnosed Diseases Network (UDN), ; Kathleen, L. A.; Matthew, B. D.; Melissa, M. J.; Gail, J. P.; Katrina, D. M.; Elizabeth, B. E.; Fowler, D. M.; Starita, L. M.; McEwen, A. E.; Stergachis, A. B.
Show abstract
Purpose Multiplexed assays of variant effect (MAVEs) are transforming clinical variant interpretation. However, many genes are associated with more than one disease, making it unclear whether functional data generated in one disease context may be directly applicable to another. For example, germline BAP1 missense variants are associated with both BAP1 tumor predisposition syndrome (BAP1-TPDS) and Kury-Isidor syndrome (KURIS), a rare neurodevelopmental disorder. Here, we demonstrate how phenotype-specific calibration of BAP1 MAVE data enables disease-specific variant classification. Methods Saturation genome editing (SGE) data for BAP1 were recalibrated using either BAP1-TPDS- or KURIS-associated missense variants as pathogenic controls. Functional evidence strength was quantified using the Odds of Pathogenicity (OddsPath) framework and mapped to ACMG/AMP PS3/BS3 criteria. Recalibrated functional evidence was integrated with standard clinical criteria for variant classification. A workshop was developed to teach phenotype-specific MAVE recalibration to clinicians and variant curators and evaluated for educational impact. Results Phenotype-specific recalibration using BAP1-TPDS and KURIS controls yielded OddsPath values consistent with PS3_Strong evidence in both contexts. Application of KURIS-specific recalibration enabled the diagnosis of KURIS in an individual with a previously uncertain BAP1 missense variant. The educational workshop enabled quantitatively improved understanding in applying functional evidence. Conclusion Phenotype-specific recalibration enables appropriately calibrated reuse of MAVE datasets across distinct disease contexts, increasing the clinical utility of MAVE datasets and the interpretability of variants in pleiotropic genes. This framework expands the diagnostic utility of existing functional datasets without requiring new experimental assays.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Detecting cryptic clinically-relevant structural variation in exome sequencing data increases diagnostic yield for developmental disorders 97%
- Extracting and calibrating evidence of variant pathogenicity from population biobank data 96%
- Non-coding variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms 95%
Similar papers in this journal
- A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project. 96%
- Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of diverse feature set 95%
- Evaluating Genome Sequencing Strategies: Trio, Singleton, and Standard Testing in Rare Disease Diagnosis 95%
Similar papers in this journal
- Long-read genome sequencing for the diagnosis of neurodevelopmental disorders 96%
- IMPROVE-DD: Integrating Multiple Phenotype Resources Optimises Variant Evaluation in genetically determined Developmental Disorders 94%
- Identification and validation of novel candidate risk genes in endocytic vesicular trafficking associated with esophageal atresia and tracheoesophageal fistulas 94%
Similar papers in this journal
- Detection and characterisation of copy number variants from exome sequencing in the DDD study 96%
- The impact of the Turkish (TK) population variome on the genomic architecture of rare disease traits 94%
- Genetic Diagnosis of Facioscapulohumeral Muscular Dystrophy Type 1 Using Rare Variant Linkage Analysis and Long Read Genome Sequencing 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.