Date of Award

2018

Degree Type

Thesis

Degree Name

Doctor of Philosophy

School

School of Medical and Health Sciences

First Advisor

Associate Professor Simon Laws

Second Advisor

Associate Professor Giuseppe Verdile

Third Advisor

Dr Samantha Burham

Fourth Advisor

Associate Professor David Groth

Field of Research Code

1109, 1103, 0604

Abstract

In 2015 the number of people worldwide living with Dementia was 46.8 million, with approximately 50-75% of these cases being clinically defined as Alzheimer’s disease (AD). Despite extensive efforts, clinical trials have so far failed to yield a treatment that successfully addresses the underlying cause of AD. This lack of treatment has been suggested, in part, to be a result of late stage of intervention in current clinical trial design. For this reason, greater focus has been placed on preclinical trials and in turn both the identification of individuals at-risk for AD and, amongst these, those that are expected to decline over the course of a trial. While brain imaging to determine Aβ- amyloid burden has utility in identifying individuals with preclinical AD, further work needs to be conducted to determine what influences rates of change during these early disease stages. Of particular focus is the rate of decline in cognitive performance, as it is the primary outcome measure of efficacy in clinical trials. A number of genetic variants have been associated with cognitive performance, however additional research needs to be conducted to accurately understand the influence that genetic variation has on cognition in preclinical AD.

Aims

Initially the aim of this thesis was to assess the combined genetic influence of established AD risk genetic variants on preclinical cognitive performance, specifically using AD-risk effect-size weighted polygenic risk scores (PRSs) (Chapter 2). It was then aimed to evaluate the effects on cognitive rates of change in preclinical AD of genes with a priori evidence for association with cognition, both individually (Chapter 3) and then when combined (Chapter 4). The results of the preceding chapters informed the final aim which was to determine a novel method of weighting individual variants in genes associated with AD-risk and/or cognition, for use in a genetic risk score that would improve the prediction of preclinical cognitive rates of change (Chapter 5).

Methods

All studies presented in this thesis utilised data from the highly characterised Australian Imaging, Biomarkers and Lifestyle Study of Aging (AIBL). The AIBL study is a longitudinal cohort study collecting data at 18-monthly intervals, currently consisting of 7.5 years of follow up. Individuals investigated in this thesis had been Positron Emission Tomography (PET) imaged to determine neocortical amyloid burden. Further, all individuals were classified as Αβhigh or Αβlow based on tracer specific cut offs. In addition, a subset of these samples underwent lumbar puncture for CSF collection at the study baseline, and Aβ42, total-tau and phospho-tau were quantified. Finally, based on the AIBL neuropsychological test battery, three cognitive composites previously developed were calculated for all participants. The cognitive composites investigated were; verbal episodic memory, a statistically driven global cognition composite, and the Pre-Alzheimer’s Cognitive Composite.

The AD-risk weighted PRS (Chapter 2) consisted of 22 genetic variants associated with AD classification, and was calculated by weighting individual variants based on their previously published associations with risk for AD. A statistically derived Cognitive Genetic Risk Profile (Cog-GRP), specifically driven by verbal episodic memory, was developed using a decision tree analysis (Chapter 4). Finally, a 27 genetic variant cognition weighted PRS (cwPRS), was developed and tested in a preclinical AD sample (Chapter 5). For the cwPRS, effect sizes for decline in a verbal episodic memory were determined individually for all variants in a reference sample. The resulting effect sizes were then used to calculate the cwPRS for each participant in a test sample (Chapter 5). For both the AD-risk weighted PRS (Chapter 2) and the cwPRS (Chapter 5), PRS calculations were conducted with both the inclusion and exclusion of the major genetic risk factor for, Apolipoprotein E (APOE).

In all studies, linear mixed models were used to investigate associations between genetic factors, independent or in combination, and longitudinal rates of cognitive performance.

Results

In CN older adults the AD-risk weighted PRS, both including and excluding APOE, was positively correlated with brain and blood biomarkers, specifically; brain Aβ burden, CSF total-tau and phospho-tau (Chapter 2). When investigating cognitive performance, specifically in CN Αβhigh participants, significant associations with baseline and longitudinal cognition were only observed in the AD-risk weighted PRS with APOE (Chapter 2).

When investigating gene variants previously reported to influence cognition, in CN Αβhigh participants, no independent associations were observed for any variant (Chapter 3). However, in the same sample, after interaction with APOE e4, significant associations were observed for variants in the Kidney Brain Expressed Protein (KIBRA) and Spondin-1 (SPON1) genes (Chapter 3). The combination of variants investigated in Chapter 3, with additional variants, resulted in the development of the Cog-GRP (Chapter 4). The Cog-GRP was able to delineate four groups: APOE ε4+ Risk, APOE ε4+ Resilient, APOE ε4- Risk, APOE ε4- Resilient, with the ε4+ Risk group reporting significantly faster decline in cognition than all other groups (Chapter 4).

Finally, a PRS encompassing a combination of AD-risk genes (Chapter 2) and cognitive-risk genes (Chapters 3 and 4), weighted by episodic memory (cwPRS), was reported to be associated with preclinical longitudinal cognitive performance (Chapter 5). Further, these associations were observed irrespective of the presence or absence of APOE in the calculation of the cwPRS (Chapter 5).

Conclusions

The work presented in this thesis provides an in depth investigation of genetic influences in preclinical AD, particularly on cognitive performance. Importantly, it supports the hypothesis that there is are differences between the genetic architectures of AD-risk and AD progression. The results presented here support the use of combinatory approaches when investigating genetic influence. Finally, reported here is a novel method for PRS weighting, with the ability to predict preclinical cognitive performance in the presence and absence of APOE. Further investigation is required in cohorts with comparable data to the AIBL study, to validate the methods explored in this thesis, allowing for their eventual use in a clinical setting.

Access Note

Please note that this online copy of the thesis does not contain the complete version of Chapters 1, 2, 3, and 5.

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