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Review
. 2022 Oct 3:2:933383.
doi: 10.3389/fmmed.2022.933383. eCollection 2022.

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

Mohamed Aborageh  1 , Peter Krawitz  2 , Holger Fröhlich  1   3
Affiliations
Review

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

Mohamed Aborageh et al. Front Mol Med. .

Abstract

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.

Keywords: Parkinson disease; genome-wide association study; machine learning; polygenic risk score; risk.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Advantages and disadvantages of different association tests.
FIGURE 2
FIGURE 2
An illustration of Mendelian Randomization framework.
See this image and copyright information in PMC

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