*EASTBIO* Evolutionary genomic investigations into healthy human ageing

Supervisors: Dr Jacob Moorad ([Email Address Removed]), Dr Jim Wilson ([Email Address Removed]), Dr Peter Joshi

Ageing and senescence are understood to affect wild populations of plants and animals as well as an ever-growing fraction of modern human populations. While evolutionary theory can explain senescence broadly as a result of age-related declines in the strength of natural selection to preserve physiological function, a detailed understanding of the genomic underpinnings of proximate causes of ageing is important to understanding how evolution shapes late-age health and longevity.
Until recently lifespan genetic research has been hampered by the size of data available, but new datasets available to us and global collaborations led by us with leading research teams now make such problems tractable. This project will apply quantitative genetic approaches to explore the heritable bases of age-specific mortality and chronic and killer-diseases using large-scale databases of historical and contemporary human populations. Evolutionary predictions will motivate specific lines of enquiry, such as investigations into the role that fitness trade-offs might play in increased susceptibility with age to particular diseases, or in the sex-specificity of mortality causes, with the aim to identify the forces that have shaped the genome and specific genetic variants that shorten or lengthen life or lead to healthy and unhealthy ageing, leading to prognostic tools and therapies.
This is an interdisciplinary project to be supervisor by staff from the Institute of Evolutionary Biology and the Usher Institute of Population Health Sciences and Informatics. The successful applicant will acquire over the course of this project an impressive breadth of computational and statistical skills necessary to link demography, evolutionary biology, epidemiology, and human genetics. These may include survival analysis, phenotypic selection analysis, genome-wide association studies, and mixed model analysis.