There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | zhenlin dai |
| Institution: | Yunnan University |
| Department: | School of Life Sciences |
| Country: | |
| Proposed Analysis: | Proposal Title: Genome-wide Association Study (GWAS) Analysis on Alzheimer's Disease Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. It is characterized by the accumulation of amyloid beta (Aβ) plaques and neurofibrillary tangles (NFTs) in the brain, leading to the loss of neurons and cognitive functions. The etiology of AD is complex, and both genetic and environmental factors contribute to its development. GWAS has been widely used to identify genetic variants associated with AD. Aim: The aim of this proposed analysis is to identify genetic variants associated with AD using a GWAS approach. We will perform a comprehensive analysis of the whole genome using a large sample of AD cases and healthy controls. We will also investigate the functional implications of the identified variants and their potential impact on AD progression. Methods: We will obtain genotyping data from a large cohort of AD cases and healthy controls. Quality control will be performed to exclude low-quality samples and SNPs with low genotyping rates or major allele frequency deviation. Population stratification will be assessed using principal component analysis. Association analysis will be performed using logistic regression, adjusting for age and sex. We will perform both single-SNP and haplotype-based analysis. We will also investigate the association between the identified variants and AD-related phenotypes, such as Aβ levels and cognitive function. Functional annotation of the identified variants will be performed using public databases, such as dbSNP and ENCODE. We will also perform pathway analysis to examine the potential biological pathways and networks involved in AD pathogenesis. Expected Results: We expect to identify new genetic variants associated with AD using GWAS. The identified variants may provide insight into the underlying mechanisms of AD pathogenesis and may represent potential targets for drug discovery. We also expect to identify functional implications of the identified variants, such as their effect on gene expression or protein function. The pathway analysis may uncover biological pathways and networks that are involved in AD development and progression. Conclusion: A GWAS analysis of AD is a powerful approach to identify genetic variants associated with AD. The findings of this study may contribute to our understanding of the genetic basis of AD and provide new insights into its pathogenesis. The results may also have implications for the development of new diagnostics and therapeutics for AD. |
| Additional Investigators |
