Detecting hybridization despite incomplete lineage sorting We have shown, through extensive coalescent-based simulations of multilocus data sets on phylogenetic networks, how divergence times before and after hybridization events can result in incomplete lineage sorting with gene tree incongruence signatures identical to those exhibited by hybridization. Evolutionary analysis of such data under the assumption of a species tree model can miss all hybridization events, whereas analysis under the assumption of a species network model would grossly overestimate hybridization events. These issues necessitate a paradigm shift in evolutionary analysis under these scenarios, from a model that assumes a priori a single source of gene tree incongruence to one that integrates multiple sources in a unifying framework. We propose a framework of coalescence within the branches of a phylogenetic network and show how this framework can be used to detect hybridization despite incomplete lineage sorting. We apply the model to simulated data and show that the signature of hybridization can be revealed as long as the interval between the divergence times of the species involved in hybridization is not too small.

Project participants: Yun Yu, Cuong Than, Luay Nakhleh.

Publication: Yu et al. (2010)

Improvements to a class of distance matrix methods for inferring species trees. Among the methods currently available for inferring species trees from gene trees, the GLASS method of Mossel and Roch (2010), the Shallowest Divergence (SD) method of Maddison and Knowles (2006), the STEAC method of Liu et al. (2009), and a related method that we call Minimum Average Coalescence (MAC) are computationally efficient and provide branch length estimates. Further, GLASS and STEAC have been shown to be consistent estimators of tree topology under a multispecies coalescent model. However, divergence time estimates obtained with these methods are all systematically biased. We have derived the biases of SD, STEAC, and MAC, and we propose improved analogues of these methods that we call iSD, iSTEAC, and iMAC.

Project participants: Laura Helmkamp, Ethan Jewett, Noah Rosenberg.

Publication: Helmkamp et al. (submitted)

iGLASS The GLASS method of Mossel and Roch (2010) is a distance matrix method for estimating speciest trees from gene trees. We have derived distributions that allow us to reduce the bias in the GLASS method's estimates of species divergence times. We call the method with reduced bias iGLASS for "improved GLASS." Our correction makes use of a deterministic approximation to the coalescent model described by Maruvka et al. (2010). Our results demonstrate that Maruvka's deterministic approximation can be used to obtain accurate approximations to coalescent distributions that are computationally efficient even when many lineages are sampled.

Project participants: Ethan Jewett,Noah Rosenberg.

Publication: Jewett and Rosenberg (2012)