Multi-omics approach to understand the role of plasma proteins in cognitive ageing and dementia

Abstract

The global burden of age-related cognitive decline and dementia will continue to rise in tandem with our ageing population. This necessitates the discovery of novel biomarkers and candidate drug targets to combat cognitive dysfunction. Blood proteins are important drug targets, and blood samples can be acquired routinely in clinical settings and epidemiological studies. Whereas hundreds of blood proteins are associated with cognitive ability and dementia, we do not understand whether these associations represent correlation or causation. Genome-wide association studies (GWAS) are required to define variants that are associated with blood protein levels. These variants can proxy for candidate disease-markers and assess their causal associations with health outcomes in analysis methods such as Mendelian randomisation. DNA methylation is an epigenetic mechanism that regulates gene expression and is influenced by genetic and environmental factors. Studying the relationship between DNA methylation and protein levels could reveal whether genetic variation or environmental factors likely mediate associations between blood proteins and disease states. The first aim of this thesis is to conduct GWAS and epigenome-wide association studies (EWAS, using DNA methylation) on plasma levels of 422 unique proteins. Using these data, I apply causal inference approaches to determine whether blood proteins are causally associated with Alzheimer’s disease risk. Several strategies have been proposed to estimate biological age by leveraging inter-individual variation in DNA methylation profiles. Epigenetic measures of ageing correlate strongly with chronological age. Recently, a novel epigenetic measure of ageing termed ‘DNAm GrimAge’ was developed to predict one’s risk of mortality. DNAm GrimAge is a composite biomarker that incorporates methylation-based predictors of seven blood protein levels and smoking. The relationship between this biomarker of ageing and cognitive decline or dementia is not known. Therefore, the second aim of this thesis is to examine whether DNAm GrimAge associates with measures of brain health and Alzheimer’s disease. To conduct these aims, I utilise data from two cohort studies: the Lothian Birth Cohort 1936 (n ≤ 906, LBC1936) and Generation Scotland (n ≤ 9,537, GS). In Chapters 1-3, I provide an overview of cognitive ageing and dementia. I describe GWAS and EWAS on blood protein levels and the development of DNAm GrimAge. In Chapter 4, I detail the population cohorts and main methodologies that are used in this thesis. In Chapter 5, I conduct GWAS and EWAS on plasma levels of 92 neurology-related proteins (n ≤ 750, LBC1936). I identified 41 independent genetic and 26 epigenetic loci that associate with 33 and 9 proteins, respectively. I showed that an immune-related protein, poliovirus receptor (PVR), is causally associated with Alzheimer’s disease risk. In Chapter 6, I use a novel Bayesian framework termed BayesR+ to perform an integrated GWAS/EWAS on plasma levels of 70 inflammation-associated proteins (n = 876, LBC1936). Many GWAS and EWAS use linear models, which examine every measured genetic or epigenetic site in isolation. BayesR+ accounts for intercorrelations among genetic and epigenetic sites and the reciprocal influences of these data types. I estimated the contribution of genetic and epigenetic variation towards inter-individual differences in inflammatory protein levels, considered alone and together. There was no evidence for causal associations between blood inflammatory proteins and the risk of Alzheimer’s disease. In Chapter 7, I perform a systematic literature review to identify known blood protein correlates of Alzheimer’s disease. I then use BayesR+ to conduct an integrated GWAS and EWAS on plasma levels of 282 Alzheimer’s disease-associated proteins (n ≤ 1,064, GS). I observed strong evidence for causal associations between two proteins, TBCA and TREM2, and Alzheimer’s disease risk. In Chapter 8, I examine associations between DNAm GrimAge and measures of brain health (n ≤ 709, LBC1936). A higher-than-expected DNAm GrimAge associated with poorer performance on cognitive tasks and neurostructural correlates of dementia at age 73. I observed weak evidence to suggest that DNAm GrimAge assessed at age 70 predicts cognitive decline up to age 79. In Chapter 9, I assess whether DNAm GrimAge and other measures of epigenetic ageing predict the prevalence and incidence of common disease states, including Alzheimer’s disease (n ≤ 9,537, GS). Epigenetic ageing measures did not predict the prevalence or incidence of Alzheimer’s disease. In Chapter 10, I discuss the major findings from this thesis in light of their limitations. The work presented in this thesis helps to detail the molecular regulation of 422 plasma protein levels and their causal associations with Alzheimer’s disease. This work also highlights the performance of DNAm GrimAge in predicting indices of cognitive performance and common disease states. By incorporating genetic, epigenetic and protein data in two large-scale epidemiological studies, my findings inform our understanding of relationships between blood proteins and cognitive ageing and dementia.