Integrating Neuroimaging and Genetics Data in a Bioinformatics-based Approach for the Study of Alzheimer’s Disease
Year: 2019
Type: dissertation
Abstract: Recent advances in the imaging-genetics field within Alzheimer’s disease (AD) have resulted in large amounts of data; however the challenge is filtering through the data and identifying meaningful genotype-phenotype relationships within AD. The overall goal of this thesis is to integrate existing tools to prioritize influential genetic variants and associated neuroanatomical measures in order to tease apart the etiology of AD and be able to use these markers in a predictive analysis of AD progression. The thesis is divided into four independent projects. The first project aims to reduce the burden of multiple testing correction by using a false discovery rate approach within strata defined by a novel biologically driven gene prioritization methodology. The overall aim of projects two and three is to estimate the heritability of neuroanatomical measures by preselection of regions with high heritability, allowing for reduction of the multiple testing burden. In project two, I investigated the influence of whole hippocampal volume and total brain volume on the heritability estimates of hippocampal subfields. In project three, I investigated heritability estimates of cortical thickness and surface area using both a vertex-wise and region of interest approach determining unreliability of smaller regions. The fourth project was a pilot study to investigate if combining genome wide association study data with neuroanatomical measures would improve the performance of common machine learning models in predicting cognitive decline in AD. These methods will aid in the identification of measurable robust and reliable quantitative markers, and have the potential to help predict AD onset and reveal more about the etiology of AD. In the future, such findings may allow targeted drug treatments to be delivered before the onset of clinical symptoms.
Source: TSpace (University of Toronto)
Author: Sejal Patel
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2973809448