Integrative Omics in Parkinson’s Disease provides a comprehensive understanding of the current literature on high-throughput technologies relating to discoveries for Parkinson's disease etiology. This emerging field uses large omics datasets to investigate the etiology of Parkinson’s disease and other forms of parkinsonism. The book traces the evolution of omics technologies from the discovery of monogenic Parkinson's disease forms. Chapters delve into genomics, transcriptomics, epigenomics, artificial intelligence, and gene-environment interactions. Furthermore, it examines the potential therapeutic applications of these advancements and provides insights into the future of omics research in Parkinson's disease.
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Table of Contents
1. Introduction: evolution of omics2. Monogenic and Complex genetics PD
3. Genetic risk scores
4. Mendelian Randomization
5. Methods to investigate somatic structural variants in synucleinopathies
6. Mitochondrial genetics
7. Epigenetics in PD genes
8. The microbiome
9. Genetic modifiers in reduced penetrance: X-linked dystonia
10. Long-read transcriptomics in neurodegeneration
11. Gene-environment interactions and behaviour
12. Introduction to prediction modeling using machine learning and omics data
13. IPSCs and OMICs merging
Authors
Joanne Trinh Institute of Neurogenetics, University of L�beck, Germany.Joanne Trinh, Ph.D., is a Heisenberg professor at the Institute of Neurogenetics, University of L�beck. Dr. Joanne Trinh received her doctorate in medical genetics at the University of British Columbia. She subsequently joined the Institute of Neurogenetics in L�beck, where she obtained a faculty position. She is now head of the "Integrative Omics in Parkinson's Disease� research group, which investigates the role of mosaic variants, nuclear and mitochondrial genome sequences, and lifestyle and environmental factors in parkinsonism. She is on the editorial board of Annals of Neurology and an associate editor of Frontiers in Neurology. Her research group in L�beck will continue to use big-data approaches to elucidate the causes of neurological disease.
