Genetic studies of sporadic Parkinson’s disease: On the identification of genetic risk factors and the path towards a better understanding of underlying pathogenic mechanisms - PhDData

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Genetic studies of sporadic Parkinson’s disease: On the identification of genetic risk factors and the path towards a better understanding of underlying pathogenic mechanisms

The thesis was published by Berge-Seidl, Victoria, in January 2022, University in Oslo.

Abstract:

Parkinson’s disease (PD) is a common neurological movement disorder that mainly affects individuals in higher age groups. While treatment exists that alleviates some symptoms, there is currently no curative therapy that can affect the progressive nature of the disease. Development of new therapeutic strategies depends upon improved understanding of disease mechanisms. Mapping of genetic causes of PD is likely to be central to acquire this insight. Large-scale genetic investigations such as genome-wide association studies have identified multiple gene regions affecting the risk of PD. It has however proven difficult to identify the exact genetic variants and biological mechanisms underlying these genetic associations.
The work presented in this thesis explores the contribution of genetic risk factors in PD, first by seeking to clarify the involvement of specific genetic variants. GBA has emerged as a candidate gene for targeted therapies in PD. In the first study, genetic variants in the GBA gene were analyzed in Scandinavian patients and controls. We found that E326K is a susceptibility allele for PD and that this variant may fully account for a nearby genome-wide association signal. Genetic variability in the DNM3 gene that had been reported to modify age at onset of LRRK2-associated PD, was investigated by us in a meta-analysis of seven datasets finding no evidence of this effect being transferable to the majority of PD patients that have no known disease-causing mutation. Then, aiming to translate genetic risk into biological mechanisms, genome-wide association signals were combined with epigenomic data to identify transcriptional networks potentially involved in PD risk mechanisms. Taken together, these studies involve a broad range of methods to help expand our understanding of genetic risk factors in PD.



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