Author ORCID Identifier
Date Available
8-6-2025
Year of Publication
2023
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Public Health
Department/School/Program
Epidemiology and Biostatistics
Advisor
Dr. David W. Fardo
Abstract
Aged individuals often accumulate multiple brain pathologies that contribute individually and synergistically to cognitive decline and dementia. Alzheimer’s disease (AD) is the most commonly diagnosed form of dementia, constituting over 50% of dementia diagnoses, and poses an enormous burden on human health and well-being. In the United States alone, over six million individuals are living with AD, and the disease imposes financial costs of treatment and care of over $300 billion annually. The most common form of AD, late-onset AD (LOAD), presents in individuals aged 65 years and older and is highly heritable, with twin and family studies estimating its heritability at ~60%. Genome-wide association studies (GWAS), in which millions of genetic variants are individually assessed for association with a trait through generalized linear regression models, have been an enormously successful approach for studying the genetic risk of LOAD. Recent GWAS of AD have included upwards of 700,000-1,200,000 participants and have identified more than 70 individual genetic risk loci for LOAD. However, the simple clinical or proxy definitions of LOAD used in these studies do not reflect the complexity of the underlying disease. In addition to the pathognomonic LOAD neuropathologies of amyloid-beta plaques and tau neurofibrillary tangles, the majority of dementia patients also have TDP-43, alpha-synuclein, cerebrovascular pathology, or some combination thereof. Individual neuropathologies likely have both independent and shared genetic risk factors, and studying precisely defined neuropathologic phenotypes through GWAS can act as a complementary approach to large case-control based studies; however, neuropathologic endophenotypes have been relatively neglected in GWAS relative to clinical phenotypes.
Brain volumes are another class of endophenotypes associated with a variety of important life and health outcomes, including LOAD. GWAS have identified genetic loci associated with brain MRI volumes measured via magnetic resonance imaging (MRI), although these studies have either exclusively studied genetic variants with minor allele frequencies (MAF) ≥1% or have primarily focused on participants of European ancestry. Investigating the relationship between rare genetic variants (MAF
In the first study, we performed a genome-wide association study of brain arteriolosclerosis. We then performed a gene-based analysis to prioritize genes and Bayesian colocalization analyses to identify functional effects of risk loci. In the second study, we expanded our investigation, performing GWAS and functional analyses on 11 neuropathologic endophenotypes. We then performed targeted analyses to confirm association between DNA methylation and RNA expression with neuropathologic endophenotypes. In the third study, we investigated the genetic factors of four brain volume endophenotypes (intracranial volume, total brain volume, hippocampal volume, and lateral ventricular volume) using whole-genome sequence data in a cohort of participants with diverse ancestry.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2023.345
Funding Information
This work was supported by the National Institutes of Health, including the National Institute of Neurological Disorders and Stroke (NINDS) [F30NS124136] from 2022-2023, the National Institute on Aging (NIA) [the University of Kentucky Sanders-Brown Center on Aging T32AG057461: Training in Translational Research in Alzheimer's and Related Dementias (TRIAD; T32 PI: Dr. Linda Van Eldik)] in 2022, the University of Kentucky Center for Clinical and Translational Science TL-1 Fellowship [TL1TR001998] from 2019-2021, RF1AG082339 from 2019-2023, and R56AG057191 in 2023.
Recommended Citation
Shade, Lincoln M. P., "STATISTICAL ANALYSES TO IDENTIFY GENETIC VARIANTS ASSOCIATED WITH NEUROPATHOLOGICAL AND MRI-BASED ENDOPHENOTYPES OF ALZHEIMER’S DISEASE" (2023). Theses and Dissertations--Epidemiology and Biostatistics. 43.
https://uknowledge.uky.edu/epb_etds/43
NPE Harmonization Across Data Sets
Supplementary_table_2.csv (13 kB)
Discovery and Replication Results
Supplementary_table_3.csv (67 kB)
Associations between NPE and AD Loci
Supplementary_table_4.csv (57 kB)
QTL Colocalization Results
