What do we do?
When observing nature, one is easily impressed by the huge diversity seen at any biological scale. Our primary aim at the Statistical and Computational Evolutionary Biology Group is to better understand the underlying evolutionary and ecological processes that have been shaping this diversity over the course of evolution on our planet. To achieve this, we design and evaluate new statistical and computational approaches to infer complex evolutionary histories. For this we develop and apply machine learning algorithms, with a particular focus on likelihood-free methods. We then apply these approaches to the wealth of data currently being generated, mostly in collaboration with experimental groups. We are further committed to making all our developments available to the scientific communities by releasing easy-to-use software packages.
Through methodological advances, the retrieval of DNA sequences from ancient bones has become an invaluable tool to study the prehistory of humans and other organisms. However, DNA obtained from very old samples show peculiar characteristics referred to as Post Mortem Damage (PMD).
Our group has been particularly interested in understanding how to incorporate PMD into population genetic analysis. For instance, we developed a novel variant caller to infer accurately the genotypes of ancient samples, and found ways to infer accurately the level of genetic diversity from such data – even when the total amount of data is very low.
We applied these methods to learn more about how farming spread across prehistoric Europe, and found that the early farmers from the Aegean region are direct ancestors of the early farmers in western Europe thus prompting the fact that farming spread, predominantly, as farmers colonized Europe. Interestingly, however, the first farmers of the Agean region are genetically distinct from the first farmers in the fertile crescent, the presumed origin of farming, suggesting that farming initially spread as a cultural idea.
Main publications 2016
- Kousathanas A, Leuenberger C, Link V, Sell C, Burger J & Wegmann D (2017). Inferring heterozygosity from ancient and low coverage genomes. Genetics 205: 317-332.
- Broushaki F, Thomas MG, Link V, López S, van Dorp L, Kirsanow K, Hofmanová Z, Diekmann Y, Cassidy LM, Díez-del-Molino D, Kousathanas A, Sell C, Robson HK, Martiniano R, Blöcher J, Scheu A, Kreutzer S, Bollongino R, Bobo D, Davudi H, Munoz O, Currat M, Abdi K, Biglari F, Craig OE, Bradley DG, Shennan S, Veeramah KR, Mashkour M, Wegmann D, Hellenthal G & Burger J (2016). Early neolithic genomes from the eastern Fertile Crescent. Science 353: 499-503.
- Kousathanas A, Leuenberger C, Helfer J, Quninodoz M, Foll M & Wegmann D (2015). Likelihood-free inference in high-dimensional models. Genetics 203(2): .
- Ferrer-Admetlla A, Leuenberger C, Jensen JD & Wegmann D (2016). An approximate Markov model for the Wright-Fisher diffusion and its application to time-series data. Genetics 203(2):.