Natural genetic and phenotypic variation for aging

Abstract

Aging is a complex, highly variable trait influenced by genes, environment, and the interaction between the two. While aging is universal across nearly all species in the animal kingdom, there are many different ways an organism declines in health. As such, in order to fully understand why and how we age, we must explore the variation of aging in populations, both genetically and phenotypically. Historically, two main approaches have been used to try and understand how and why we age, including evolutionary and mechanistic approaches. Evolutionary approaches to studying aging have largely utilized demography, population and quantitative genetics, and mathematical modeling methods to investigate aging from a population perspective. Mechanistic approaches, in contrast, use cellular and molecular methods to drill down to mechanism, usually in the context of highly specific genetic or cellular backgrounds. While these two approaches have each revealed a great deal about both the evolutionary theory underlying why we age, as well as specific cellular pathways that may explain how we age, there is room for integration of these two complimentary approaches in the current landscape of aging research. An integrated approach to studying aging would incorporate the advantages of population and quantitative genetics, mathematical modeling, and molecular techniques to learn more about aging in individuals within a genetically variable population. Recent advancements in high-throughput genetic and molecular tools, as well as the growing popularity of data sharing, have made such approaches more feasible. In this dissertation, I present three unique but complementary perspectives on how we can use systems biology methods and animal models of genetic variation to learn more about the aging process. In the first study, I investigate the genetic and metabolomic architecture underlying lifespan extension using a collection of 178 genetically variable Drosophila lines. In the second study, I introduce a novel model system for human aging and disease research, the companion dog, and demonstrate that dogs, like humans, show increased levels of comorbidity with age. In my final research chapter, I expand on the versatility of the companion dog as a model for aging by using canine epigenomic profiles to build predictive models of chronological age, and interrogate whether or not those models might also predict the overall health of the animals. Taken together, this collection of highly interdisciplinary studies paves the way for future studies of aging that integrate the advantages of both classical quantitative genetics and cellular approaches to gain a more complete understanding of the biology of aging.