Finding Diamonds in the Rough: Uncovering Genetic Variants, Transcripts, and Biological Processes Associated with Resilience to Alzheimer's Disease

Abstract

Late-onset Alzheimer's disease (LOAD) is a multifactorial disease with a strong genetic component. The growing understanding of the genetic basis and molecular mechanisms underlying LOAD risk presents an opportunity to uncover the factors that counter the risk and protect individuals from developing LOAD. The phenomenon wherein individuals demonstrate adaptability to the burden of disease risk can be referred to as "resilience". In this dissertation, I presented three studies that focused on the resilience to LOAD. Because resilience depends on and interacts with risk, we employed a risk-informed strategy to uncover resilience factors. This approach leveraged the current best-estimated LOAD risk to identify resilient individuals who, despite facing the highest LOAD risk, exhibit no dementia symptoms in old age. In Chapter 1, we demonstrated that a large number of risk-independent common genetic variants could reduce the penetrance of heightened genetic risk burden in LOAD. This study provided insights into the genetic architecture of resilience to LOAD, addressing a significant knowledge gap that requires attention. In addition, this study yielded a polygenic resilience score, enabling the assessment of the relative genetic resilience levels among individuals. In Chapters 2 and 3, we explored resilience to LOAD at the transcriptomic level. The study in Chapter 2 meta-analyzed all publicly available blood and brain transcriptomic studies of AD. This study laid the groundwork for investigating the resilience-conferring genes and pathways by establishing the best-estimated transcriptomic risk features in LOAD. In Chapter 3, we capitalized on the transcriptomic risk defined in Chapter 2 and examined the risk-residual genes that might confer resilience to increased transcriptomic risk of LOAD. This study implicated a couple of interesting pathways in resilience to LOAD and suggested that resilience and risk may operate in the same biological pathways. Taken together, our findings corroborated the idea that resilience in LOAD has a polygenic basis and highlighted the need to gain a deeper understanding of the genetic components, biological mechanisms, and phenotypic characteristics of resilience to LOAD risk. The dissertation contextualized these findings with the existing literature and suggested potential future directions to help further address the gaps in understanding resilience in LOAD.