Estimating evolutionary and demographic parameters via ARG-derived IBD

• DOI: 10.1371/journal.pgen.1011537
• Publication Date: 2025-01-08T19:36:42Z
• Authors: Zhendong Huang; Jerome Kelleher; Yao-ban Chan; David Balding
• Keywords: Approximate Bayesian Computation

Inference of evolutionary and demographic parameters from a sample genome sequences often proceeds by first inferring identical-by-descent (IBD) segments. By exploiting efficient data encoding based on the ancestral recombination graph (ARG), we obtain three major advantages over current approaches: (i) no need to impose length threshold IBD segments, (ii) can be defined without hard-to-verify requirement recombination, (iii) computation time reduced with little loss statistical efficiency using only segments set sequence pairs that scales linearly size. We demonstrate powerful inferences when true information is available simulated data. For inferred real data, propose an approximate Bayesian inference algorithm use it show even poorly-inferred short improve estimation. Our mutation-rate estimator achieves precision similar previously-published method despite 4 000-fold reduction in used for inference, identify significant differences between human populations. Computational cost limits model complexity our approach, but are able incorporate unknown nuisance misspecification, still finding improved parameter inference.