Functional characterization of all CDKN2A missense variants and comparison to in silico models of pathogenicity

Interpretation of variants identified during genetic testing is a significant clinical challenge. In this study, we developed a high-throughput CDKN2A functional assay and characterized all possible human CDKN2A missense variants. We found that 17.7% of all missense variants were functionally deleterious. We also used our functional classifications to assess the performance of in silico models that predict the effect of variants, including recently reported models based on machine learning. Notably, we found that all in silico models performed similarly when compared to our functional classifications with accuracies of 39.5–85.4%. Furthermore, while we found that functionally deleterious variants were enriched within ankyrin repeats, we did not identify any residues where all missense variants were functionally deleterious. Our functional classifications are a resource to aid the interpretation of CDKN2A variants and have important implications for the application of variant interpretation guidelines, particularly the use of in silico models for clinical variant interpretation.

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Publication Details

Journal
eLife
Published
2025-04-16
DOI
https://doi.org/10.7554/elife.95347.4
Citations
1
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
1.82

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article

Functional characterization of all CDKN2A missense variants and comparison to in silico models of pathogenicity

Cristian Tomasetti, Kamel Lahouel, Nicholas J. Roberts, Hirokazu Kimura
1 citations
eLife
Genomics and Rare Diseases
1.82
article

Functional characterization of all CDKN2A missense variants and comparison to in silico models of pathogenicity

Cristian Tomasetti, Kamel Lahouel, Nicholas J. Roberts, Hirokazu Kimura
article en
1 citations

Abstract

Interpretation of variants identified during genetic testing is a significant clinical challenge. In this study, we developed a high-throughput CDKN2A functional assay and characterized all possible human CDKN2A missense variants. We found that 17.7% of all missense variants were functionally deleterious. We also used our functional classifications to assess the performance of in silico models that predict the effect of variants, including recently reported models based on machine learning. Notably, we found that all in silico models performed similarly when compared to our functional classifications with accuracies of 39.5–85.4%. Furthermore, while we found that functionally deleterious variants were enriched within ankyrin repeats, we did not identify any residues where all missense variants were functionally deleterious. Our functional classifications are a resource to aid the interpretation of CDKN2A variants and have important implications for the application of variant interpretation guidelines, particularly the use of in silico models for clinical variant interpretation.

eLifeVol. 13
Translational Genomics Research Institute (US), Johns Hopkins University (US), Johns Hopkins Medicine (US), City of Hope (US), Igenbio (United States) (US), Beckman Research Institute
Rolfe Pancreatic Cancer Foundation, Japan Society for the Promotion of Science
Openalex Percentile: Top 17%
Genomics and Rare Diseases
1.82
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