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Integrative Omics in Parkinson's Disease
- 1st Edition - September 19, 2024
- Editor: Joanne Trinh
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 5 5 0 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 5 5 1 - 4
Integrative Omics in Parkinson’s Disease provides a comprehensive understanding of the current literature on high-throughput technologies relating to discoveries for Parkinson's… Read more
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Request a sales quoteIntegrative 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.
- Reviews evolution of omics technologies from the first identification of monogenic forms of Parkinson’s disease
- Outlines machine learning algorithm application to Parkinson’s disease datasets
- Reviews big datasets on gene-environment interactions, genomics, epigenetics, and transcriptomics
- Identifies how the microbiome influences Parkinson’s disease in mouse models and patients
- Provides outlook for therapies with induced-pluripotent stem cell models
Researchers, Clinicians, and students in Parkinson’s disease
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Editor’s biography
- Preface
- Chapter 1 Integrative omics in Parkinson’s disease: An introduction
- Abstract
- Classical methods and relevance today
- Second-generation sequencing: Short-read sequencing
- Genotyping arrays and genome-wide association studies
- Third-generation sequencing: Long-read sequencing
- Classical labeling methods
- Single-nuclei and spatial transcriptomics
- Epigenetics
- Data analysis and publicly available datasets
- Emerging research questions in Parkinson's disease
- Overarching themes of the book
- References
- Glossary
- Chapter 2 Genetics and pathophysiology of Parkinson’s disease
- Abstract
- α-Synuclein (SNCA)
- LRRK2
- VPS35
- Glucocerebrosidase
- PRKN (Park2), PINK1, and the mitochondria
- References
- Chapter 3 Polygenic scores for Parkinson’s disease
- Abstract
- Acknowledgment
- Introduction to polygenic scores
- Polygenic scores for idiopathic Parkinson’s disease
- Application of PD-PGS
- Discussion and future perspectives
- References
- Chapter 4 Mendelian randomization and Parkinson’s disease
- Abstract
- Acknowledgments
- Introduction
- Connecting the dots: Mendelian randomization and Parkinson’s disease
- Mendelian randomization: Integration in omics
- Future directions and conclusions
- References
- Chapter 5 Methods to investigate somatic structural variants in synucleinopathies
- Abstract
- Acknowledgment
- Introduction
- Combined immunofluorescence-FISH for the detection of somatic SNCA CNVs
- Single-cell whole genome sequencing
- Digital PCR and droplet digital PCR for the detection of low-level SNCA CNVs
- Future directions
- References
- Chapter 6 Mitochondrial genetics in Parkinson’s disease
- Abstract
- Introduction
- Mitochondrial DNA copy number
- Structural variants of the mitochondrial genome
- Germline variants and mitochondrial haplogroups
- Mitochondrial heteroplasmy
- Understudied mitochondrial DNA variations
- Conclusion
- References
- Chapter 7 DNA methylation studies in Parkinson’s disease
- Abstract
- The most studied epigenetic mechanism in complex diseases
- The promise of DNA methylation association studies in Parkinson’s disease
- The early phase: Candidate locus studies of SNCA methylation
- Epigenome-wide studies of PD in brain tissue and cells
- Epigenome-wide studies of PD in blood
- The epigenetic clock and methylation-based scoring algorithms
- General lessons from published PD methylation studies
- The road ahead: Standardized methodology and larger studies
- Sampling the right patients at the right time
- Integration with other types of experimental and epidemiological data
- Experimental studies and therapeutic interventions
- Conclusions
- References
- Chapter 8 The gut microbiome in animal models of Parkinson’s disease
- Abstract
- Acknowledgments
- Introduction
- General features of the gut microbiome in different model organisms
- Profiling the gut microbiome in PD animal models
- Alteration of gut microbiome composition in genetic models of PD
- Alterations of gut microbiome composition in toxicant-based models of PD
- Future perspectives
- References
- Chapter 9 Genetic modifiers of age-related penetrance in X-linked dystonia-parkinsonism
- Abstract
- X-linked dystonia-parkinsonism
- Role of the disease-causing mutation in penetrance modification
- Broadening the investigation from a single-locus to whole-genome analysis
- Follow-up analyses of the ((CCCTCT)n) repeat instability in XDP
- Conclusion
- References
- Chapter 10 Long-read transcriptomics in neurodegeneration
- Abstract
- Introduction
- Alternative splicing and its role in disease
- The new era of transcriptomics: Long-read RNA sequencing
- Applications of long-read sequencing
- Long-read transcriptomics in neurodegenerative disease
- The future of long-read sequencing in neurotranscriptomics
- Challenges in using long-read RNA sequencing when studying neurodegenerative diseases
- Concluding remarks
- References
- Chapter 11 Gene–environment interactions and behavior
- Abstract
- Historical background
- Metaanalyses and systematic reviews investigating environmental factors and PD risk
- Age at onset in PD
- Models to study gene–environment interactions
- Systematic review of gene–environment interactions in the literature
- Conclusion and outlook
- References
- Chapter 12 Introduction to prediction modeling using machine learning and omics data
- Abstract
- Acknowledgment
- Introduction
- Machine learning approaches for prediction modeling
- Training a prediction model
- Measuring prediction performance
- Validating a prediction model
- Explaining a prediction model
- Further reading
- References
- Chapter 13 Merging iPSCs and “omics”: Advances in the field and potential applications to untangle neurodegenerative diseases
- Abstract
- Introduction
- Disease mechanisms identified in iPSC-based studies using omics
- Conclusions and limitations
- Translational potential and future perspectives
- References
- Index
- No. of pages: 300
- Language: English
- Edition: 1
- Published: September 19, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443135507
- eBook ISBN: 9780443135514
JT
Joanne Trinh
Joanne Trinh, Ph.D., is a Heisenberg professor at the Institute of Neurogenetics, University of Lübeck. Dr. Joanne Trinh received her doctorate in medical genetics at the University of British Columbia. She subsequently joined the Institute of Neurogenetics in Lübeck, 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 Lübeck will continue to use big-data approaches to elucidate the causes of neurological disease.
Affiliations and expertise
Institute of Neurogenetics, University of Lübeck, GermanyRead Integrative Omics in Parkinson's Disease on ScienceDirect