Approved Research

Comparison of computational and statistical methods for identifying genetic risk factors for cognitive decline in Parkinson's Disease

Lay summary

Aim: Aim of the project is to identify genes associated to cognitive decline in Parkinson's Disease.

Scientific rationale: Idiopathic Parkinson's Disease (PD) is influenced by genetic variants. More specifically, there is likely a genetic contribution to the level of cognitive decline, which is frequently observed in PD patients. However, identifying corresponding genetic variants via classical statistical approaches remains challenging, specifically in case of rare genetic variants. Hence, statistical and computational approaches are of interest that aggregate variants, e.g. on gene level. A number of methods have been proposed, but we need to better understand their advantages and limitations, including a systematic power analysis.

Following such an analysis we will apply the most computational promising approach to unravel genes associated with cognitive decline in PD. The knowledge of such genes is an important step towards finding new and better medications in the future.

Project duration: 1 year

Public health impact: This project focuses on identifying genetic factors that contribute to cognitive decline in Parkinson's Disease (PD). Identifying such genetic factors is important to develop novel therapies in the future.

Scope extension:

Current scope:

First research aim of the project is to compare different SNP aggregate scoring approaches. More specifically, we want to compare patients with diagnosis "idiopathic Parkinson's Disease" (PD) and an age and gender matched control group. We want to focus on SNPs that have been associated to PD in existing GWAS studies (retrievable e.g. via DisGeNET and GWASCatalog) or are in strong linkage disequilibrium to those (r^2 > 0.8). Subsequently we want to compare the possibility to discriminate between cases and controls based on classical methods (e.g. gemma) as well as different SNP aggregate scores, e.g.:

- published polygenic risk scores (Ibanz et al., BMC Neurology 2017; Nalls et al., Nat Genetics 2014)

- gene-level scores: GenePy, SKAT (Wu et al., Am J Hum Gen 2011), simple burden score

The second research aim of the project is to identify genes contributing to a cognitive decline in PD patients compared to healthy controls. For this purpose we will specifically employ the statistical approach identified in the first of part of the project.

New scope:

Given the current findings from the project we would like to extend the scope beyond the use of classical statistical methods as follows:

- development of causal AI/ML models to predict PD dementia

- development of causal AI/ML models to predict PD risk