Identification of genetic risk factors for Parkinson’s disease

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Dokumentart: PhDThesis
Date: 2023-04-03
Language: English
Faculty: 4 Medizinische Fakultät
Department: Medizin
Advisor: Heutink, Peter (Prof. Dr.)
Day of Oral Examination: 2023-03-15
DDC Classifikation: 570 - Life sciences; biology
Keywords: Parkinson-Krankheit , Genetik
Other Keywords:
Structural variants
neurodegenerative disorders
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Parkinson's disease (PD) is a common progressive neurodegenerative disorder with a complex and heterogeneous genetic landscape. Approximately 90% of all PD cases are driven by the cumulative effect of several common low-risk genetic variants. Over the last years, genetic studies of familial and sporadic PD cases identified a range of high and low-risk variants, representing approximately 40% of estimated heritability. However, the role of structural variants (SV) in the PD missing heritability remains understudied. Therefore, we investigated SVs in the human cohort enriched for the PD phenotype to expand our knowledge about the putative PD genetic risk factors. We leveraged the matching omics datasets obtained from 95 iPSC lines differentiated into the dopaminergic neuronal-like state to run the SV calling and to directly assess their impact on the gene and transcript expression. We demonstrated a conceptual approach for the genome-wide SV annotation and pathogenicity assessment, addressing the challenges of functional SV effect prediction based on the known properties of genome regions and available multi-omics data. Using this approach, we prioritized a group of non-coding SVs absent in the healthy controls with a strong association with the differential expression of genes whose dysregulation can trigger the development of PD or PD-related phenotype. Discovered variation impacts molecular mechanisms involved in the regulation of signaling processes, oxidative stress response, and neuronal DNA reparation. Additional analysis on the larger PD patient and control cohort has to be conducted for variant-expression association validation and exploration of the allele effect size and penetrance of the prioritized hits. The dataset is publicly available to facilitate the further discovery of SV PD risk association as well as to study sequence signatures and neurological disease-specific SV hot spots.

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