Aborageh, MohamedMohamedAboragehKrawitz, PeterPeterKrawitzFröhlich, HolgerHolgerFröhlich2024-03-112024-06-192024-03-112022-10-03https://publica.fraunhofer.de/handle/publica/46397110.3389/fmmed.2022.933383Parkinson’s Disease (PD) is a neurodegenerative disorder with highly heterogeneous phenotypes. Accordingly, it has been challenging to robustly identify genetic factors associated with disease risk, prognosis and therapy response via genome-wide association studies (GWAS). In this review we first provide an overview of existing statistical methods to detect associations between genetic variants and the disease phenotypes in existing PD GWAS. Secondly, we discuss the potential of machine learning approaches to better quantify disease phenotypes and to move beyond disease understanding towards a better-personalized treatment of the disease.enParkinson diseaseriskgenome-wide association studymachine learningpolygenic risk scoreDDC::000 Informatik, Informationswissenschaft, allgemeine WerkeDDC::000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung; Informatikjournal article