Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants
<h3>Background</h3><p dir="ltr">Existing BRCA2-specific variant pathogenicity prediction algorithms focus on the prediction of the functional impact of a subtype of variants alone. General variant effect predictors are applicable to all subtypes, but are trained on putati...
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| مؤلفون آخرون: | |
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2022
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| _version_ | 1864513519122120704 |
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| author | Mohannad N. Khandakji (13885434) |
| author2 | Borbala Mifsud (3907267) |
| author2_role | author |
| author_facet | Mohannad N. Khandakji (13885434) Borbala Mifsud (3907267) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohannad N. Khandakji (13885434) Borbala Mifsud (3907267) |
| dc.date.none.fl_str_mv | 2022-09-30T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fgene.2022.982930 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Gene-specific_machine_learning_model_to_predict_the_pathogenicity_of_BRCA2_variants/25658874 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Genetics breast cancer variant pathogenicity in-silico predictions variant prioritization VUS |
| dc.title.none.fl_str_mv | Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Existing BRCA2-specific variant pathogenicity prediction algorithms focus on the prediction of the functional impact of a subtype of variants alone. General variant effect predictors are applicable to all subtypes, but are trained on putative benign and pathogenic variants and do not account for gene-specific information, such as hotspots of pathogenic variants. Local, gene-specific information have been shown to aid variant pathogenicity prediction; therefore, our aim was to develop a BRCA2-specific machine learning model to predict pathogenicity of all types of BRCA2 variants.</p><p><br></p><h3>Methods</h3><p dir="ltr">We developed an XGBoost-based machine learning model to predict pathogenicity of BRCA2 variants. The model utilizes general variant information such as position, frequency, and consequence for the canonical BRCA2 transcript, as well as deleteriousness prediction scores from several tools. We trained the model on 80% of the expert reviewed variants by the Evidence-Based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium and tested its performance on the remaining 20%, as well as on an independent set of variants of uncertain significance with experimentally determined functional scores.</p><p><br></p><h3>Results</h3><p dir="ltr">The novel gene-specific model predicted the pathogenicity of ENIGMA BRCA2 variants with an accuracy of 99.9%. The model also performed excellently on predicting the functional consequence of the independent set of variants (accuracy was up to 91.3%).</p><p><br></p><h3>Conclusion</h3><p dir="ltr">This new, gene-specific model is an accurate method for interpreting the pathogenicity of variants in the BRCA2 gene. It is a valuable addition for variant classification and can prioritize unreviewed variants for functional analysis or expert review.</p><p dir="ltr"><br></p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Genetics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fgene.2022.982930" target="_blank">https://dx.doi.org/10.3389/fgene.2022.982930</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_917a0aa2b10d29bc002bddf97f9091bb |
| identifier_str_mv | 10.3389/fgene.2022.982930 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25658874 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Gene-specific machine learning model to predict the pathogenicity of BRCA2 variantsMohannad N. Khandakji (13885434)Borbala Mifsud (3907267)Biological sciencesGeneticsbreast cancervariant pathogenicityin-silico predictionsvariant prioritizationVUS<h3>Background</h3><p dir="ltr">Existing BRCA2-specific variant pathogenicity prediction algorithms focus on the prediction of the functional impact of a subtype of variants alone. General variant effect predictors are applicable to all subtypes, but are trained on putative benign and pathogenic variants and do not account for gene-specific information, such as hotspots of pathogenic variants. Local, gene-specific information have been shown to aid variant pathogenicity prediction; therefore, our aim was to develop a BRCA2-specific machine learning model to predict pathogenicity of all types of BRCA2 variants.</p><p><br></p><h3>Methods</h3><p dir="ltr">We developed an XGBoost-based machine learning model to predict pathogenicity of BRCA2 variants. The model utilizes general variant information such as position, frequency, and consequence for the canonical BRCA2 transcript, as well as deleteriousness prediction scores from several tools. We trained the model on 80% of the expert reviewed variants by the Evidence-Based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium and tested its performance on the remaining 20%, as well as on an independent set of variants of uncertain significance with experimentally determined functional scores.</p><p><br></p><h3>Results</h3><p dir="ltr">The novel gene-specific model predicted the pathogenicity of ENIGMA BRCA2 variants with an accuracy of 99.9%. The model also performed excellently on predicting the functional consequence of the independent set of variants (accuracy was up to 91.3%).</p><p><br></p><h3>Conclusion</h3><p dir="ltr">This new, gene-specific model is an accurate method for interpreting the pathogenicity of variants in the BRCA2 gene. It is a valuable addition for variant classification and can prioritize unreviewed variants for functional analysis or expert review.</p><p dir="ltr"><br></p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Genetics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fgene.2022.982930" target="_blank">https://dx.doi.org/10.3389/fgene.2022.982930</a></p>2022-09-30T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fgene.2022.982930https://figshare.com/articles/journal_contribution/Gene-specific_machine_learning_model_to_predict_the_pathogenicity_of_BRCA2_variants/25658874CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256588742022-09-30T03:00:00Z |
| spellingShingle | Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants Mohannad N. Khandakji (13885434) Biological sciences Genetics breast cancer variant pathogenicity in-silico predictions variant prioritization VUS |
| status_str | publishedVersion |
| title | Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants |
| title_full | Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants |
| title_fullStr | Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants |
| title_full_unstemmed | Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants |
| title_short | Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants |
| title_sort | Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants |
| topic | Biological sciences Genetics breast cancer variant pathogenicity in-silico predictions variant prioritization VUS |