An investigation of the genetic contribution to cognitive resilience in healthy ageing
Fitzgerald, Joan
Fitzgerald, Joan
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Identifiers
http://hdl.handle.net/10379/17021
https://doi.org/10.13025/16864
https://doi.org/10.13025/16864
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Publication Date
2021-07
Type
Thesis
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Abstract
Cognitive decline is one of the most feared aspects of ageing leading to major health and social issues. Non-pathological or age-related cognitive decline leads to increased challenges in completing tasks that require information processing and memory, which in turn leads to a deleterious effect on an individual’s enjoyment of and participation in life events. Cognitive resilience is our ability to withstand negative effects of stress and maintain cognitive functioning. Understanding the factors that contribute to resilience is becoming increasingly important given the ageing demographics of the world’s population. There is a growing knowledge of how non-genetic factors such as cardiovascular health and social participation contribute to cognitive resilience; however, an understanding of the genetic contribution has been hampered by the lack of large datasets with genetic data and suitable longitudinal data on cognition. Chapter 1 explores the current understanding of cognitive genetics leading to our current knowledge of what constitutes cognitive resilience. In chapter 2, I discuss the various bioinformatic tools and methods employed to create a cognitive resilience phenotype and to explore genetic variation associated with cognitive resilience within the UK Biobank (UKB). In Chapter 3, I discuss how in the absence of direct measurements of cognitive ability at distal timepoints we employed proxy phenotypes. We used number of years in education (education years (EY)) as a proxy phenotype for cognitive performance in early adulthood, following several previous studies. Current cognitive performance was determined based on reaction time (RT) as a measure of processing speed. This approach captured an average time span of 40 years between past and current cognitive performance in 330,097 individuals. A confounding factor in my analysis is that EY is highly polygenic and masked the genetics of resilience. To overcome this, I employed Genomics Structural Equation Modelling (GenomicSEM) to perform a GWAS-by-subtraction using two GWAS, one GWAS of EY and resilience and a second GWAS of EY but not resilience. Subtracting one from the other generated a GWAS of Resilience. Replication of this approach was shown using independent discovery and replication samples within UKB. Chapter 4 outlines the results of functional analysis on the full UKB GWAS which show significant genetic correlation with a GWAS of cognitive change in the independent Health and Retirement Study (N=9,526; P=1.5x10-3). We found 13 independent genetic loci for Resilience. Functional analyses showed enrichment in several brain regions and involvement of specific cell types, including GABAergic neurons (P=6.59x10-8) and glutamatergic neurons (P=6.98x10-6) in the cortex. Gene-set analyses implicated the biological process “neuron differentiation” (P=9.7x10-7) and the cellular component “synaptic part” (P=2.14x10-6). The cellular component “wnt signalosome” had a strong effect size (Beta=1.22, P=4.75x10-6). The role of Mendelian randomization analysis showed a causative effect of white matter volume on cognitive resilience. In chapter 5, I discuss ad hoc testing to show that the genetic correlation between Resilience and RT is strong because this is an RT-based resilience phenotype. However, there are differences in the associated genes being detected. This phenotype enabled the identification of genetic differences between those individuals in the UKB who preserved or maintained their capability to process information and respond over a 40-year time period compared to individuals who showed diminishing processing speed. This chapter also explores the effect of the gene rich locus on chromosome 3 showing that is does not unduly influence the functional analysis. Ad hoc testing also shows limited overlap with a GWAS of declining cognitive ability in the Health and Retirement Study. The discussion in chapter 6 summaries the findings of this thesis and highlights that this research is the first of its kind to explore the genetics of cognitive resilience in large data and opens the way for future investigations in the area to enhance the neurobiological understanding of resilience. It also proposes ways to advance the knowledge of the genetics of cognitive resilience going forward with the ultimate goal of discovering interventions and therapeutic compounds that will combat cognitive decline and improve quality of life for an ageing population.
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NUI Galway