Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects
Abstract Computational methods for estimating missense variant impact suffer from inconsistent performance across genes, which poses a major challenge for their reliable use in clinical practice. While ensemble scores leverage multiple prediction methods to enhance consistency, the overrepresentatio...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55066-4 |
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| _version_ | 1841559259299446784 |
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| author | Panagiotis Katsonis Olivier Lichtarge |
| author_facet | Panagiotis Katsonis Olivier Lichtarge |
| author_sort | Panagiotis Katsonis |
| collection | DOAJ |
| description | Abstract Computational methods for estimating missense variant impact suffer from inconsistent performance across genes, which poses a major challenge for their reliable use in clinical practice. While ensemble scores leverage multiple prediction methods to enhance consistency, the overrepresentation of certain genes in the training data can bias their outcomes. To address this critical limitation, we propose a gene-specific ensemble framework trained on reference computational annotations rather than on clinical or experimental data. Accordingly, we generate Meta-EA ensemble scores that achieve comparable performance to the top individual predicting method for each gene set. Incorporating the effects of splicing and the allele frequency of human polymorphisms further enhances the performance of Meta-EA, achieving an area under the receiver operating characteristic curve of 0.97 for both gene-balanced and imbalanced clinical assessments. In conclusion, this work leverages the wealth of existing variant impact prediction approaches to generate improved estimations for clinical interpretation. |
| format | Article |
| id | doaj-art-fdbb20e9b7f9463ca9ce8f31227649b9 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-fdbb20e9b7f9463ca9ce8f31227649b92025-01-05T12:39:59ZengNature PortfolioNature Communications2041-17232025-01-0116111310.1038/s41467-024-55066-4Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effectsPanagiotis Katsonis0Olivier Lichtarge1Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor PlazaDepartment of Molecular and Human Genetics, Baylor College of Medicine, One Baylor PlazaAbstract Computational methods for estimating missense variant impact suffer from inconsistent performance across genes, which poses a major challenge for their reliable use in clinical practice. While ensemble scores leverage multiple prediction methods to enhance consistency, the overrepresentation of certain genes in the training data can bias their outcomes. To address this critical limitation, we propose a gene-specific ensemble framework trained on reference computational annotations rather than on clinical or experimental data. Accordingly, we generate Meta-EA ensemble scores that achieve comparable performance to the top individual predicting method for each gene set. Incorporating the effects of splicing and the allele frequency of human polymorphisms further enhances the performance of Meta-EA, achieving an area under the receiver operating characteristic curve of 0.97 for both gene-balanced and imbalanced clinical assessments. In conclusion, this work leverages the wealth of existing variant impact prediction approaches to generate improved estimations for clinical interpretation.https://doi.org/10.1038/s41467-024-55066-4 |
| spellingShingle | Panagiotis Katsonis Olivier Lichtarge Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects Nature Communications |
| title | Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects |
| title_full | Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects |
| title_fullStr | Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects |
| title_full_unstemmed | Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects |
| title_short | Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects |
| title_sort | meta ea a gene specific combination of available computational tools for predicting missense variant effects |
| url | https://doi.org/10.1038/s41467-024-55066-4 |
| work_keys_str_mv | AT panagiotiskatsonis metaeaagenespecificcombinationofavailablecomputationaltoolsforpredictingmissensevarianteffects AT olivierlichtarge metaeaagenespecificcombinationofavailablecomputationaltoolsforpredictingmissensevarianteffects |