A Systematic Review of Polygenic Models for Predicting Drug Outcomes
Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understa...
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MDPI AG
2022-08-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/12/9/1394 |
| _version_ | 1797486156689965056 |
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| author | Angela Siemens Spencer J. Anderson S. Rod Rassekh Colin J. D. Ross Bruce C. Carleton |
| author_facet | Angela Siemens Spencer J. Anderson S. Rod Rassekh Colin J. D. Ross Bruce C. Carleton |
| author_sort | Angela Siemens |
| collection | DOAJ |
| description | Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (<i>n</i> = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research. |
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| format | Article |
| id | doaj.art-5b1b5c95ed4b4793a5102ab811b37b9c |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T23:29:11Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-5b1b5c95ed4b4793a5102ab811b37b9c2023-11-23T17:12:24ZengMDPI AGJournal of Personalized Medicine2075-44262022-08-01129139410.3390/jpm12091394A Systematic Review of Polygenic Models for Predicting Drug OutcomesAngela Siemens0Spencer J. Anderson1S. Rod Rassekh2Colin J. D. Ross3Bruce C. Carleton4Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, CanadaDepartment of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, CanadaDivision of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3V4, CanadaDepartment of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, CanadaDepartment of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, CanadaPolygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (<i>n</i> = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.https://www.mdpi.com/2075-4426/12/9/1394pharmacogenomicspolygenic modelsdrug outcomes |
| spellingShingle | Angela Siemens Spencer J. Anderson S. Rod Rassekh Colin J. D. Ross Bruce C. Carleton A Systematic Review of Polygenic Models for Predicting Drug Outcomes Journal of Personalized Medicine pharmacogenomics polygenic models drug outcomes |
| title | A Systematic Review of Polygenic Models for Predicting Drug Outcomes |
| title_full | A Systematic Review of Polygenic Models for Predicting Drug Outcomes |
| title_fullStr | A Systematic Review of Polygenic Models for Predicting Drug Outcomes |
| title_full_unstemmed | A Systematic Review of Polygenic Models for Predicting Drug Outcomes |
| title_short | A Systematic Review of Polygenic Models for Predicting Drug Outcomes |
| title_sort | systematic review of polygenic models for predicting drug outcomes |
| topic | pharmacogenomics polygenic models drug outcomes |
| url | https://www.mdpi.com/2075-4426/12/9/1394 |
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