Statistical and Evolutionary Genetics (SEG)

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.