Dokument
| Titel: | AI-based multi-PRS models outperform classical single-PRS models |
| Autor: | Klau, Jan Henric |
| Weitere Verfasser: | Maj, Carlo; Klinkhammer, Hannah; Krawitz, Peter M.; Mayr, Andreas; Hillmer, Axel M.; Schumacher, Johannes; Heider, Dominik |
| Veröffentlicht: | 2023 |
| URI: | https://archiv.ub.uni-marburg.de/es/2024/0441 |
| DOI: | https://doi.org/10.3389/fgene.2023.1217860 |
| DDC: | 600 Technik |
| Publikationsdatum: | 2024-01-17 |
| Lizenz: | https://creativecommons.org/licenses/by/4.0 |
| Schlagwörter: |
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| polygenic risk score, regression, machine learning, breast cancer, deep learning |
Summary:
Polygenic risk scores (PRS) calculate the risk for a specific disease based on the
weighted sum of associated alleles from different genetic loci in the germline
estimated by regression models. Recent advances in genetics made it possible to
create polygenic predictors of complex human traits, including risks for many
important complex diseases, such as cancer, diabetes, or cardiovascular diseases,
typically influenced by many genetic variants, each of which has a negligible effect
on overall risk. In the current study, we analyzed whether adding additional PRS
from other diseases to the prediction models and replacing the regressions with
machine learning models can improve overall predictive performance. Results
showed that multi-PRS models outperform single-PRS models significantly on
different diseases. Moreover, replacing regression models with machine learning
models, i.e., deep learning, can also improve overall accuracy.
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