A simulation and inference toolbox for spatio-temporal genome evolution
The genomes of organisms contain information about their past history: migrations, displacements and expansions of populations can be discerned from the footprints they left in genetic sequences – including our own genomes. Space is thus a crucial dimension of evolution: organisms interact, mate and compete with organisms that are closest to them in their landscape. Yet, tools for analyzing genomes in space are scarce or highly limited in scope.
Which types of genetic patterns are most informative of spatial aspects of the history of a species? And how can we best harness them to better understand the movement and past distribution of those species? To answer these questions, our research program will generate an array of computational tools for simulating, analyzing and modelling genomes on real geographic landscapes. These tools will be applicable to genetic data from both present-day living organisms and from extinct populations, allowing us to better understand population processes with unprecedented detail.
We will then apply our newly developed methods to a specific case-study: ancient epidemics in recent human prehistory. We will infer the spatial distribution and expansion of ancient pathogens and their hosts, using a combination of present-day and ancient genomic data. We will seek to understand how past epidemics have affected human populations over the last 50,000 years, how humans – in turn – have responded to these epidemics, and how future epidemics might unfold over time, as a consequence of climate change and ecological breakdown.