Our group works in the field of population genetics and bioinformatics. With regard to population genetics, our goals are to study the mechanism of recent adaptation due to positive selection in living organisms, such as humans, Drosophila and pathogens. Our research activities can help us to understand the function of genes and the interaction between genes and environment. We are studying how natural selection has helped humans to adapt to environmental changes and especially how natural selection has accelerated the emergence of consciousness and language. Moreover, our study of evolutionary adaptation between host and pathogen is helpful in the more efficient design of vaccines.

Positive selection is an evolutionary process that increases the frequency of mutations that confer a fitness advantage to individuals carrying those mutations. As an important evolutionary force, positive selection plays an essential role in natural processes as well as in domestication processes, from rice to dogs and pigs. Notably, these selective events usually leave their footprints in the genomes of these living organisms; thus, we can detect these recent adaptations and map causal mutations.

However, when detecting recent positive selection, common neutrality tests suffer severely from a high false positive rate because of the confounding impact of demography (i.e., varying population size and population structure). The risk of false positives could be so high (up to 80 or 90%) that testing for recent positive selection would be unreliable. Over the past 20 years, the search for methods to detect selection that are not influenced by demography has been a major goal of population genetics. Unfortunately, this goal has not been (completely) achieved until very recently. In 2010, we developed a novel approach for detecting recent positive selection. We proved mathematically that our statistical test is free from the confounding impacts of demography, meaning that the false positive rate of the statistical test remains less than the significance level under arbitrary demographic scenarios. Notably, we are the first to achieve this goal in population genetics, and, in doing so, the first to confirm that this goal is achievable.

In the field of bioinformatics, we develop comprehensive software to meet various needs in genetic data analysis. We are also interested in developing methods to predict amino acid sites that are modified in the post-translational process, forecast nucleotide sites for determining RNA splicing and so on.

We are currently hiring postdocs and students/interns. Salaries are competitive.

Group of Evolutionary Genomics, PICB