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Genetics in parkinson’s disease: From better disease understanding to machine learning based precision medicine

Affiliation
Bonn-Aachen International Center for Information Technology (B-IT) ,Rheinische Friedrich-Wilhelms-Universität Bonn ,Bonn ,Germany
Aborageh, Mohamed;
Affiliation
Institute for Genomic Statistics and Bioinformatics ,University Hospital Bonn ,Bonn ,Germany
Krawitz, Peter;
Affiliation
Bonn-Aachen International Center for Information Technology (B-IT) ,Rheinische Friedrich-Wilhelms-Universität Bonn ,Bonn ,Germany
Fröhlich, Holger

Parkinson’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.

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License Holder: Copyright © 2022 Aborageh, Krawitz and Fröhlich.

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