Phyloseminar

Bayesian inference of Ancestral Recombination Graphs: progress and challenges
Ancestral Recombination Graphs (ARG), or sometimes known as Genome-wide Genealogies, describe the full genealogical history of the genomes and are richly informative about the evolutionary history. Recent years we have witnessed great progress in scalable inference of ARG on thousands or more genomes. However, many of them lack accuracy and can be sensitive to model mis-specification from demographic histories or selection. Moreover, they reconstruct only a single ARG topology and cannot quantify the considerable estimation uncertainty in ARG inference. To address these challenges, we introduce SINGER, a novel method which accelerates posterior sampling of ARG by highly optimized MCMC for at least hundreds of genomes. In this talk I will demonstrate the enhanced accuracy and robustness to model mis-specification of SINGER, and give examples of applications to real data. These examples include various aspects of evolutionary biology, such as demography, positive selection, balancing selection, and introgression, etc. Last but not least, I will discuss possible directions of pushing Bayesian inference of ARGs even further.