Robust and Accurate Bayesian Inference of Genome-Wide Genealogies for Large Samples

Abstract

The Ancestral Recombination Graph (ARG), which describes the full genealogical history of a sample of genomes, is a vital tool in population genomics and biomedical research. Recent advancements have increased ARG reconstruction scalability to tens or hundreds of thousands of genomes, but these methods rely on heuristics, which can reduce accuracy, particularly in the presence of model misspecification. Moreover, they reconstruct only a single ARG topology and cannot quantify the considerable uncertainty associated with ARG inferences. To address these challenges, we here introduce SINGER, a novel method that accelerates ARG sampling from the posterior distribution by two orders of magnitude, enabling accurate inference and uncertainty quantification for large samples. Through extensive simulations, we demonstrate SINGERs enhanced accuracy and robustness to model misspecification compared to existing methods. We illustrate the utility of SINGER by applying it to African populations within the 1000 Genomes Project, identifying signals of local adaptation and archaic introgression, as well as strong support of trans-species polymorphism and balancing selection in HLA regions.