2025/09/01 by Yun Deng, Rasmus Nielsen, Yun S. Song · 1 voice · 18 citations
Biochemistry, Genetics and Molecular Biology · #Bayes' theorem #Bayesian inference #Bayesian probability #Genetic diversity and population structure #Genome #Genomics #Genomics and Phylogenetic Studies #Identity by descent #Inference #Introgression #Population #RNA and protein synthesis mechanisms #Robustness (evolution)
paper · pdf · doi:10.1038/s41588-025-02317-9
published in Nature Genetics 57(9), 2124-2135 (Nature Portfolio)
openalex publication_date 2025/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The Ancestral Recombination Graph (ARG), which describes the genealogical history of a sample of genomes, is a vital tool in population genomics and biomedical research. Recent advancements have substantially increased ARG reconstruction scalability, but they rely on approximations that can reduce accuracy, especially under model misspecification. Moreover, they reconstruct only a single ARG topology and cannot quantify the considerable uncertainty associated with ARG inferences. Here, to address these challenges, we introduce SINGER (sampling and inferring of genealogies with recombination), a method that accelerates ARG sampling from the posterior distribution by two orders of magnitude, enabling accurate inference and uncertainty quantification for hundreds of whole-genome sequences. Through extensive simulations, we demonstrate SINGER's enhanced accuracy and robustness to model misspecification compared to existing methods. We demonstrate the utility of SINGER by applying it to individuals of British and African descent within the 1000 Genomes Project, identifying signals of population differentiation, archaic introgression and strong support for ancient polymorphism in the human leukocyte antigen region shared across primates.