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

## TL;DR

This paper reviews how genetics and machine learning can improve understanding and personalized treatment of Parkinson’s disease.

## Contribution

The paper introduces machine learning as a novel approach to better quantify Parkinson’s disease phenotypes for precision medicine.

## Key findings

- Existing statistical methods in PD GWAS have struggled to robustly identify genetic factors.
- Machine learning offers potential to better quantify disease phenotypes and personalize treatment.
- Moving beyond traditional GWAS could enhance disease understanding and therapeutic approaches.

## 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.