Academic Journal
Variant Classification Using Proteomics-Informed Large Language Models Increases Power of Rare Variant Association Studies and Enhances Target Discovery.
| Τίτλος: | Variant Classification Using Proteomics-Informed Large Language Models Increases Power of Rare Variant Association Studies and Enhances Target Discovery. |
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| Συγγραφείς: | Gillies CE; Regeneron Genetics Center, Tarrytown, New York, USA., Mbatchou J; Regeneron Genetics Center, Tarrytown, New York, USA., Habegger L; Regeneron Genetics Center, Tarrytown, New York, USA., Kessler MD; Regeneron Genetics Center, Tarrytown, New York, USA., Bao S; Regeneron Genetics Center, Tarrytown, New York, USA., Balasubramanian S; Regeneron Genetics Center, Tarrytown, New York, USA., Delaneau O; Regeneron Genetics Center, Tarrytown, New York, USA., Kosmicki JA; Regeneron Genetics Center, Tarrytown, New York, USA., Willer CJ; Regeneron Genetics Center, Tarrytown, New York, USA., Kang HM; Regeneron Genetics Center, Tarrytown, New York, USA., Baras A; Regeneron Genetics Center, Tarrytown, New York, USA., Reid JG; Regeneron Genetics Center, Tarrytown, New York, USA., Marchini J; Regeneron Genetics Center, Tarrytown, New York, USA., Abecasis GR; Regeneron Genetics Center, Tarrytown, New York, USA., Ghoussaini M; Regeneron Genetics Center, Tarrytown, New York, USA. |
| Πηγή: | Genetic epidemiology [Genet Epidemiol] 2025 Dec; Vol. 49 (8), pp. e70023. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley-Liss Country of Publication: United States NLM ID: 8411723 Publication Model: Print Cited Medium: Internet ISSN: 1098-2272 (Electronic) Linking ISSN: 07410395 NLM ISO Abbreviation: Genet Epidemiol Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York, NY : Wiley-Liss Original Publication: New York, N.Y. : Alan R. Liss, c1984- |
| Ιατρικοί όροι (MeSH): | Proteomics*/methods , Genetic Association Studies*/methods , Genetic Variation*, Humans ; Machine Learning ; Models, Genetic ; Phenotype ; Genome-Wide Association Study ; Deep Learning ; Language ; Mutation, Missense ; Large Language Models |
| Περίληψη: | Rare variant association analysis, which assesses the aggregate effect of rare damaging variants within a gene, is a powerful strategy for advancing knowledge of human biology. Numerous models have been proposed to identify damaging coding variants, with the most recent ones employing deep learning and large language models (LLMs) to predict the impact of changes in coding sequences. Here, we use newly available proteomics data on 2898 proteins across 46,665 individuals to evaluate and refine LLM predictors of damaging variants. Using one of these refined models, we evaluate the association between rare damaging variants and human phenotypes at 241 positive control gene-trait pairs. Among these gene-trait pairs, our proteomics-guided model outperforms an ensemble of conventional approaches including PolyPhen2, MutationTaster, SIFT, and LRT, as well as newer machine learning approaches for identifying damaging missense variants, such as CADD, ESM-1v, ESM-1b, and AlphaMissense. When attempting to recover known associations by correctly separating damaging singleton missense variants from other singleton variants, our approach recapitulates 36.5% of gene-trait pairs with known associations, exceeding all the alternatives we considered. Furthermore, when we apply our model to 10 example traits from the UK Biobank, we identify 177 gene-trait associations-again exceeding all other approaches. Our results demonstrate that summary statistics from large-scale human proteomics data enable evaluation and refinement of coding variant classification LLMs, improving discovery potential in human genetic studies. (© 2025 Regeneron Pharmaceuticals. Genetic Epidemiology published by Wiley Periodicals LLC.) |
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| Entry Date(s): | Date Created: 20251103 Date Completed: 20251103 Latest Revision: 20260707 |
| Update Code: | 20260708 |
| DOI: | 10.1002/gepi.70023 |
| PMID: | 41178319 |
| Βάση Δεδομένων: | MEDLINE |
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