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...

Full beskrivning

Sparad:
Bibliografiska uppgifter
Huvudupphovsmän: Klau, Jan Henric, Maj, Carlo, Klinkhammer, Hannah, Krawitz, Peter M., Mayr, Andreas, Hillmer, Axel M., Schumacher, Johannes, Heider, Dominik
Materialtyp: Artikel
Språk:engelska
Publicerad: Philipps-Universität Marburg 2023
Ämnen:
deep learning
machine learning
regression
breast cancer
Technik
polygenic risk score
Technology (Applied sciences)
Länkar:PDF-fulltext
Taggar: Lägg till en tagg
Inga taggar, Lägg till första taggen!
Beskrivning
Sammanfattning: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.
Beskrivning:Gefördert durch den Open-Access-Publikationsfonds der UB Marburg.
DOI:10.3389/fgene.2023.1217860

Liknande verk