Integrative Omics in Parkinson's Disease

Joanne Trinh

• Publisher: Academic Press
• Year: 2024
• Topic: Biological Sciences
• eBook ISBN:
  9780443135514
• Subtopic: Neuroscience
• Index: Biological Sciences

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. - 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

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Contributors
• Editor’s biography
• Preface
• List of Illustrations
• List of Tables
• Chapter 1 : Integrative omics in Parkinson’s disease: An introduction
• Chapter 2 : Genetics and pathophysiology of Parkinson’s disease
• Chapter 3 : Polygenic scores for Parkinson’s disease
• Chapter 4 : Mendelian randomization and Parkinson’s disease
• Chapter 5 : Methods to investigate somatic structural variants in synucleinopathies
• Chapter 6 : Mitochondrial genetics in Parkinson’s disease
• Chapter 7 : DNA methylation studies in Parkinson’s disease
• Chapter 8 : The gut microbiome in animal models of Parkinson’s disease
• Chapter 9 : Genetic modifiers of age-related penetrance in X-linked dystonia-parkinsonism
• Chapter 10 : Long-read transcriptomics in neurodegeneration
• Chapter 11 : Gene–environment interactions and behavior
• Chapter 12 : Introduction to prediction modeling using machine learning and omics data
• Chapter 13 : Merging iPSCs and “omics”: Advances in the field and potential applications to untangle neurodegenerative diseases
• Index
• A