Makarious, Mary Botros; (2024) Genomics of Parkinson's Disease: Global and Scalable Approaches Towards Precision Medicine. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

In the field of Parkinson's disease (PD) research, the study of genomics has become increasingly important, focusing on global and scalable approaches to leverage massive amounts of data. Neurodegenerative diseases, including PD, are complex and present challenges in identifying biomarkers for diagnosis and prognosis. Genetic factors play a crucial role in PD risk, and the exploration of rare and common genetic variations has provided valuable insights. I have conducted population-scale analyses reaffirming rare variations in SNCA, GBA1, and LRRK2 and their association with PD as well as potential novel rare variant associations with genes involved in neuroinflammation. Additionally, studying diverse ancestries, particularly African and African admixed populations, I have uncovered novel genetic risk factors specific to these populations. To effectively leverage these insights for clinical applications, we must address the critical need for diversity in genetic research. Ensuring equitable inclusion of ancestrally diverse groups is essential to uncover population-specific genetic risk factors and assess local ancestry per individual. This approach allows us to discern individual-level attributable risk, enhancing the precision of genetic risk assessments and promoting more inclusive genetic studies. The transition to more inclusive research methodologies sets the stage for the integration of advanced technologies in genomics. Advances in multi-omics and machine learning have revolutionized the field, enabling the integration of diverse data modalities for improved PD risk assessment. I, alongside others, have developed GenoML, an automated machine learning package, that facilitates the development and deployment of machine learning models in genomics research, promoting accessibility and replicability. Multi-modality machine learning models have shown promise in predicting PD risk, incorporating various data types to enhance accuracy. This progress in predictive modeling paves the way for deeper exploration into the molecular mechanisms underlying PD. As we build on these foundational advances, my research will explore community clustering and network analyses to gain deeper insights into the complex interactions within PD-related biological networks and pathways. Further investigations into GBA1 and other networks and mechanisms will provide valuable information for the development of targeted therapies. Computational approaches, including CRISPR and perturb simulations, will be useful in precision drug development. These technologies offer the potential to accelerate the discovery of personalized treatment strategies by enabling the systematic exploration of genetic alterations and their therapeutic implications. In conclusion, these global and scalable approaches in genomics research hold tremendous potential in advancing our understanding of PD, facilitating early diagnosis, and paving the way for tailored therapeutic interventions. Overall, these global and scalable approaches in genomics research hold tremendous potential in advancing our understanding of PD, facilitating early diagnosis, and paving the way for tailored therapeutic interventions.

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