Current Research

We develop new statistical and computational methods to answer biological questions from genetic and other high-throughput data. Our work is motivated by both the wealth of biological data currently being generated, as well as by the observation that dedicated models are often required to answer long-standing questions in biology.

Much of our work focuses on the inference of evolutionary histories, especially of humans. However, we work closely with our collaborators from many areas of biology, and interact equally closely with research groups in statistics and computer science to push our approaches beyond the state-of-the-art in the field.

Inferring Evolutionary Histories

The main evolutionary forces mutation, genetic drift, migration and selection all affected the genetic diversity observed today. We aim at tracing back the interplay and relative importance of these forces in the history of a population or species. Since multiple evolutionary forces can leave very similar signatures, we continue to develop dedicated statistical tools, with which we elucidate the evolutionary history of a diverse set of organisms from chimpanzees to date palms.

Selected publications:

  • Gros-Balthazard et al. (2017). The discovery of wild date palms in Oman reveals a complex domestication history involving centers in the Middle East and Africa. Current Biology.
  • Ferrer-Admetlla et al. (2016). An approximate Markov model for the Wright-Fisher diffusion and its application to time-series data. Genetics 203(2):.
  • Adrion at al. (2014). Drosophila suzukii: the genetic footprint of a recent, world-wide invasion. Mol Biol Evol. 31 (12):3148-3163.
  • Gray et al. (2014). Demographic history of a recent invasion of house mice on the isolated Island of Gough. Mol Ecol 23 (8): 1923–1939.
  • Chu et al. (2013). Inferring the geographic mode of speciation by contrasting autosomal and sex-linked genetic diversity. Mol Biol Evol30: 2519-2530.

Characterizing Human (Pre-)History with ancient DNA

The ability to obtain DNA from fossils presents a unique opportunity to address long-standing question in human history and pre-history such as questions about the origin of peoples and their migration patterns. Ancient DNA, however, is highly fragmented and affected by so-called Post-Mortem-Damag (PMD), which poses statistical challenges when working with such data. We are developing new tools to address these challenges, and use them to, for instance, better understand how the farming life style arrived in Europe.

Selected publications:

  • Link et al. (2017). ATLAS: Analysis Tools for Low-depth and Ancient Samples. bioRxiv
  • Kousathanas et al. (2017). Inferring heterozygosity from ancient and low coverage genomes. Genetics 205: 317-332.
  • Broushaki et al. (2016). Early neolithic genomes from the eastern Fertile Crescent. Science 353: 499-503
  • Hofmanová et al. (2016). Early farmers from across Europe directly descended from Neolithic Aegeans. PNAS 113: 6886-6891.
  • Veeramah et al. (2011). An early divergence of KhoeSan ancestors from those of other modern humans is supported by an ABC-based analysis of autosomal re-sequencing data. Mol Biol Evol 29: 617-630.
  • Ray et al. (2010). A Statistical Evaluation of Models for the Initial Settlement of the American Continent Emphasizes the Importance of Gene Flow with Asia. Mol Biol Evol 27 (2): 337-345.

Low-depth samples

While next-generation sequencing has massively lowered the costs of sequencing, it is prone to relatively high error rates that are usually overcome by sequencing at higher depth. For a fixed budget, this results in a trade off between technical (sequencing) and biological (samples) replicates. In order to shift that trade-off towards including more biological samples, we develop inference methods that do not require accurate information but integrate over genotype uncertainty, which result in higher statistical power.

Selected publications:

  • Link et al. (2017). ATLAS: Analysis Tools for Low-depth and Ancient Samples. bioRxiv
  • Kousathanas et al. (2017). Inferring heterozygosity from ancient and low coverage genomes. Genetics 205: 317-332.

Nature conservation

Biodiversity is vanishing globally at frightening speed, and preserving what is left is of uttermost importance to ensure the long-term stability of ecosystems and ultimately our own survival. However, to be successful, decisions on conservation actions need to be well informed. To aide in that, we develop and apply new tools to better characterize populations of threatened species such as great apes, and to compile biodiversity data for the last pristine ecosystems on our planet. A major focus of our attention is given to the Chinko reserve in Central Africa, one of the last but existing wilderness areas.

Selected publications:

  • Aebischer et al. (2017) First quantitative survey delineates the distribution of Chimpanzees in the Eastern Central African Republic. Biological Conservation.
  • Dussex et al. (2014) Postglacial expansion and not human influence best explains the population structure in the endangered kea (Nestor notabilis). Mol Ecol 23(9): 2193-2209.
  • Thalmann et al. (2011) Historical sampling reveals dramatic demographic changes in western gorilla populations. BMC Evol Biol 11: #85.
  • Wegmann & Excoffier (2010) Bayesian Inference of the Demographic History of Chimpanzees. Mol Biol Evol 27 (6): 1425-1435.

Developing Approximate Bayesian Computation (ABC)

ABC is a flexible approach for parameter inference when analytical likelihood functions are difficult or impossible to obtain. The main idea is to simplify the problem by replacing the data with summery statistics and to approximate the likelihood function using a large number of simulations. We continue to develop such likelihood-free inference algorithms, with a focus of extending their range to high-dimensional problems.

Selected publications:

Human Genetics and Disease

Population genetics can contribute to our understanding of human health by characterizing the genetic makeup of humans, the evolutionary forces that shaped these, and by studying the evolutionary forces acting on human pathogens. We continue to develop and apply new statistical tools in all these areas.

Selected publications:

  • Foll et al. (2014) Influenza Virus Drug Resistance: A Time-Sampled Population Genetics Perspective. Plos Genet. 10(2): e1004185
  • Zawistowski et al. (2014) Analysis of rare variant population structure in Europeans explains differential stratification of gene-based tests. Eur. J. Hum. Genet. 22: 1137-1144.
  • Schaibley et al. (2013) The influence of genomic context on mutation patterns in the human genome inferred from rare variants. Genome Res. 23: 1974-1984.
  • Nelson et al. (2012) Abundance of rare functional variants in 202 drug target genes found sequencing 14002 people. Science 337: 100-104.
  • Stahl et al. (2012) Polygenic modeling of genome-wide association study data reveals hidden heritability of rheumatoid arthritis risk. Nat Genet 44: 483.
  • Wegmann et al. (2011) Recombination rates in admixed individuals identified by ancestry-based inference. Nat Genet 43:9 847-853.

Circadian Rhythms, Evolution of quantitative traits, ...

Thanks to our expertise in statistical inference, we are able to contribute to a large range of biological questions, and we are very open for collaboration with experimentalists from all areas of biology. Some recent example of such fruitful collaborations include the quest for RNA-seq normalization genes, model-based inference of molecular parameters governing circadian rhythms or the inference of episodes of rapid morphological evolution on phylogenetic trees.

Selected publications:

  • Duchen et al. (2017). Inference of evolutionary jumps in large phylogenies using Lévy processes. Systematic Biology.
  • Fonseca Costa et al. (2017). Normalisation against circadian and age-related disturbances enables robust detection of gene expression changes in liver of aged mice. PLoS ONE 12(1): e0169615.
  • Slater et al. (2012). Fitting models of continuous trait evolution to incompletely sampled comparative data using spproximative Bayesian computation. Evolution 66: 752-762.
Dept Biology - Chemin du musée 10 - 1700 Fribourg - Tel +41 26 / 300 8630 - Fax +41 26 / 300 9741
secretary [at] - Swiss University