An ancestral recombination graph of human, Neanderthal, and Denisovan genomes

Many humans carry genes from Neanderthals, a legacy of past admixture. Existing methods detect this archaic hominin ancestry within human genomes using patterns of linkage disequilibrium or direct comparison to Neanderthal genomes. Each of these methods is limited in sensitivity and scalability. We describe a new ancestral recombination graph inference algorithm that scales to large genome-wide datasets and demonstrate its accuracy on real and simulated data. We then generate a genome-wide ancestral recombination graph including human and archaic hominin genomes. From this, we generate a map within human genomes of archaic ancestry and of genomic regions not shared with archaic hominins either by admixture or incomplete lineage sorting. We find that only 1.5 to 7% of the modern human genome is uniquely human. We also find evidence of multiple bursts of adaptive changes specific to modern humans within the past 600,000 years involving genes related to brain development and function.

Advancing admixture graph estimation via maximum likelihood network orientation

MotivationAdmixture, 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. While enabling a more faithful representation of evolutionary history, admixture graphs present formidable inferential challenges. A key challenge is the need for admixture graph inference algorithms that are accurate while being completely automated and computationally efficient. Given the challenge of exhaustively evaluating all topologies, search heuristics have been developed to enable efficient inference. One heuristic, implemented in the popular method TreeMix, consists of adding admixture edges to an initial tree while optimizing a suitable objective function.ResultsHere, we 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 the incorrect network topology. To address this issue, we propose a new search strategy based on reorientating the admixture graph that we term the maximum likelihood network orientation (MLNO) problem. We augment TreeMix with an exhaustive search for MLNO, referred to as OrientAGraph. In evaluations using previously published admixture graphs, OrientAGraph outperforms TreeMix on 4/8 models (there are no differences in the other cases). Overall, OrientAGraph finds graphs with higher likelihood scores and topological accuracy while remaining computationally efficient. Lastly, our study reveals important directions for improving maximum likelihood admixture graph estimation.AvailabilityOrientAGraph is available on Github (https://github.coin/ekinolloy/OrientAGraph) under the GNU General Public License v3.0.