Statistical Methods for Phylogenetic Networks: Advances and Open Problems
I will present advances to infer the structure of phylogenetic networks from genome-wide sequence data. These networks are directed acyclic graphs, where each node represents an ancestral species or ancestral population. Genetic data are available at the leaves only, which makes inference difficult. The history of each small gene fragment is a tree, but different genes can have different trees because of population dynamics. When individuals migrate between populations or when species hybridize, a network is necessary to represent population dynamics, with individual gene trees embedded in this network. I will explore probability models for these biological processes, computational complexity, current solutions to statistical inference, and open problems to scale analyses to larger data sets.
Cécile Ané did her undergraduate in France (Lyon, Ecole Normale Superieure) and went for a PhD in probability at the University of Toulouse (France). She got interested in statistical methods applied to evolutionary biology, as Assistant Professor in Paris. She joined the faculty at the University of Wisconsin - Madison in 2004, with a joint appointment in Statistics and in Botany. She teaches applied statistics, provides statistical consulting at UW-Madison, serves as president for the Society for Systematic Biology, and was recently named H. I. Romnes Faculty Fellow.