Estimating and disentangling the contribution of different evolutionary processes such as genetic drift, mutation or gene flow is a longstanding and central issue in statistical population genetics. In many studies, the data come from a sample taken at one point in time and little is known about the demographic history of the population(s) of interest. However, the genetic configuration of a population is affected by past demographic events like population shrinkage or growth, fragmentation, admixture or founder events. This leads to the challenge that in order to get sound population genetic estimates, one needs to take history into account, but at the same time history is unknown.

Nick Barton is working with Konrad Lohse to develop methods for estimating population history from whole-genome data. This method has been applied to a wide variety of systems, including Drosophila, gall wasps, Heliconius butterflies, pigs and Neanderthal.

Harald Ringbauer is working on a method to infer population structure from a relatively novel type of genetic data: Pairwise shared long blocks of genome. Such blocks are the direct traces of distant relatedness, which can be detected within nowadays whole genome data sets. Spatial patterns of sharing are informative about the recent demography of a population. For instance, closer sample share on average more and longer blocks than distant samples, and this provides a rich source to robustly infer typical dispersal distances from genetic data.