AI-based multi-PRS models outperform classical single-PRS models

AI-based multi-PRS models outperform classical single-PRS models

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, in...

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Bibliografiske detaljer
Autoren: Klau, Jan Henric, Maj, Carlo, Klinkhammer, Hannah, Krawitz, Peter M., Mayr, Andreas, Hillmer, Axel M., Schumacher, Johannes, Heider, Dominik
Format: Artikel
Sprog:engelsk
Udgivet: Philipps-Universität Marburg 2023
Fag:
deep learning
machine learning
regression
breast cancer
Technik
polygenic risk score
Technology (Applied sciences)
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Beskrivelse
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.
Emne beskrivelse:Gefördert durch den Open-Access-Publikationsfonds der UB Marburg.
DOI:10.3389/fgene.2023.1217860

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