We study the evolutionary processes that generated the complex networks that comprise life. To do so, we integrate computational population genomics, bioinformatics, systems biology, and molecular evolution.
We prepare group members for fulfilling professional lives. Our group is interdisciplinary and collaborative, with an atmosphere that promotes creativity. To learn more about the scientific and professional aspirations and expectations of the group, see our handbook.
Polyploid inference preprint posted
We've recently added the ability to model polyploidy and infer rates of exchange between subgenomes to dadi. A preprint describing and applying the method is now posted. To avoid some bioinformatic biases, in our approach we collapse the data from across the subgenomes into a single site frequency spectrum. In this collapsed spectrum, fixed differences between the subgenomes create a spike at 50% frequency. Allelic exchange between the subgenomes generates distinctive shoulders to the spike. Consequently, we can precisely infer model parameters describing the events after the formation of the polyploid. Justin Conover will be building on this work when he joins us to model DFEs in polyploids.
Justin Conover has been awarded and NSF Postdoctoral Research Fellowship in Biology to work together with our group and Mike Barker's group. Justin's research will focus on the effects of polyploidy on distributions of fitness effects. He will testing whether DFEs differ between an allotetraploid and its diploid progenitors, and he will also extend our joint DFE approach to test the correlation of fitness effects between subgenomes.