Statistical and Evolutionary Genetics (SEG)
The SEG group works on developing and applying new computational methods in population and evolutionary genomics. The aim is to understand basic evolutionary processes such as natural selection, speciation, and migration, to facilitate ecological analyses, and to understand the map between phenotype and genotype.
Our work focuses on advanced computational and statistical methods for leveraging DNA sequences to understand ecology, evolution, and anthropology. Some of the past and current research includes:
- Developing and applying methods for detecting natural selection from DNA sequences. Using these methods we have, for example, described selection acting in humans for high altitude adaptation in Tibet, environmental adaption of the Inuit in Greenland, and adaption to a diving lifestyle of the Bajau people in Indonesia.
- Developing and applying methods for inferring demographic processes such as migration, population divergence, and introgression, from DNA sequence data.
- Development and application of computational methods for analyses of ancient DNA (with Eske Willerslev and others).
- Development of new methods for analysing and assigning environmental DNA sequences.
- Analyses of the evolution of viral DNA/RNA including SARS-CoV-2.
Development of methods for leveraging phylogenies for understanding processes such as the evolution of expression levels and protein evolution
Genetic mapping of adaptive traits in non-model organisms such as frogs and lizards.
Development of methods for inferring genetic models (dominance, pleiotropy, epistasis) using large data sets with both phenotypes and genotypes.
Much of our work has focused on the bioinformatical challenge of accommodating the errors and biases invariably observed in real data, including various ascertainment biases and sequencing errors. We aim to study biology but we end up studying noise.
- Stern, A.J. 2019. An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data. PLoS genetics 15: e1008384.
- Nielsen, R., et al. 2017 Tracing the peopling of the world through genomics. Nature 541: 302-310.
- Fumagalli, M., et al. 2015. Greenlandic Inuit show genetic signatures of diet and climate adaptation. Science: 349: 1343-1347.
- Huerta-Sánchez, E., et al. 2014. Altitude adaptation in Tibetans caused by introgression of Denisovan-like DNA. Nature 512: 194-197
- Korneliussen, T. S., et al. 2014. ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinformatics DOI:10.1186/s12859-014-0356-4.
See Google Scholar for full publication record.
Independent Research Fund Denmark
National Science Foundation
Foundation for the National Institutes of Health