Identification of genetic influences in late-onset Alzheimer’s disease (LOAD)

Abstract

Late-onset Alzheimer’s disease (LOAD) is the most common form of dementia, with an incidence of up to 50% in western populations over the age of 85 and a high heritability (up to 80%). The identification of risk factors for the development of LOAD is imperative for improving our understanding of this disease and for identifying therapeutic targets for treatment or prevention. Currently, the major known risk factors for the development of LOAD are age and the ApoE ε4 genotype. Previous studies have implicated plasma levels of the amyloid beta (Aß) peptide as a LOAD-associated quantitative trait and identification of loci influencing this trait could provide new insights into LOAD. In this thesis, plasma levels of the Aß peptides Aß40 and Aß42 have been measured in two isolated populations and genome-wide linkage and association analyses were performed. The genome-wide association analyses identified a number of promising quantitative trait loci; highlighting both novel and previously reported LOAD genes for further study, whilst also providing an excellent resource for genetic convergence studies with other LOAD related traits. Several studies have reported an association between levels of oxidative stress and levels of Aß such that increasing levels of Aß appear to increase markers for oxidative stress and vice versa. The role of oxidative stress in LOAD and aging was therefore also investigated through analysis of mitochondrial mutational burden and DNA damage respectively, using DNA isolated from both blood and the brain and by carrying out a candidate gene association study of loci involved in mitochondrial function in LOAD cases and controls. Approaches to the investigation of mitochondrial genetics for the study of LOAD are comprehensively reviewed, adapted and tested and the results indicate a need for additional research in this aspect of the disease. This thesis therefore presents a focus on two aspects of genetic research into LOAD, a complex disease with multiple environmental and genetic influences which aims to advance our understanding of the disease and bring us closer to treatment and prevention strategies.