TITLE: “Advancing admixture graph estimation via maximum likelihood network orientation.”
ABSTRACT: Admixture, the interbreeding between previously distinct populations, is a pervasive force in evolution. The evolutionary history of populations in the presence of admixture can be modeled by augmenting phylogenetic trees with additional nodes that represent admixture events. However, these admixture graphs present formidable inferential challenges. Exhaustively evaluating all admixture graphs can be prohibitively expensive, so heuristics have been developed to enable efficient search. One heuristic, implemented in the popular method TreeMix, consists of adding edges to a starting tree while optimizing a suitable objective function. In this talk, we will present a demographic model (with one admixed population incident to a leaf) where TreeMix and any other starting-tree-based maximum likelihood heuristic using its likelihood function is guaranteed to get stuck in a local optimum and return an incorrect network topology. We will then demonstrate how this issue can be addressed using our new search strategy: maximum likelihood network orientation (MLNO).