Project Reference: TRG27-GEN-AS1
Project title: Demography and population genetics in ancient and historical landscapes
Project outline
Genetic studies typically consider abstract populations, but real populations evolve on landscapes in two (or even three) dimensions. The effects of spatial dynamics on demography and ancestry can now be investigated using efficient computational simulation tools, and the high-dimensional parameter spaces involved can be explored with statistical learning methods. Furthermore, a growing quantity of ancient genomic and archaeological datasets are available for ancient and historical populations, particularly from northern, western and central Europe. These developments provide an opportunity to investigate the detailed demographic, social and cultural factors in ancient and historical populations in these regions.
This project will develop a simulation and inference framework for spatial population demography and apply it to data from prehistoric and medieval Britain. It will investigate the effects of spatial demographic processes and parameters such as migration rates, individual range, mate choice and landscape features on genealogies ancestry distributions. These will be compared with observed genetic and archaeological data from the Iron Age and medieval periods, including measures of genetic kinship such as identity by descent. It will address scientific questions, such as:
- Given present-day and ancient genetic data, with what resolution can we infer spatial aspects of past populations, such as their distributions of ancestry and the movements of individuals and groups?
- To what extent can we use archaeogenetic evidence to infer social and cultural factors such as population stratification, sex biases in mating and movement, past mortality from epidemics, and life expectancy or reproductive behaviour?
The methods and approaches developed will also have application to other species, as data become available for them, and indeed to other high-dimensional and data-driven inference problems.
Principal Supervisor
Dr Aylwyn Scally
Email address: aos21@cam.ac.uk
Department / Partner Institute: Genetics
Website: https://www.gen.cam.ac.uk/people/aylwyn-scally
BBSRC strategic theme
Understanding the rules of life