ArticlePLoS computational biology2022
Bayesian inference of ancestral recombination graphs.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 37 citations in OpenAlex.
- Tracing the evolutionary histories of ultra-rare variants using variational dating of large ancestral recombination graphs.bioRxiv : the preprint server for biology · 2026Article
- Likelihoods for a general class of ARGs under the SMC.Genetics · 2026Article
- Not Just Ne Ne-More: New Applications for SMC from Ecology to Phylogenies.Genome biology and evolution · 2026Review
- Human ancestries simulation and inference: a review of ancestral recombination graph-based approaches.Frontiers in bioinformatics · 2026Review
- Moonshine.jl: a Julia package for genome-scale model-based ancestral recombination graph inference.Frontiers in genetics · 2026Article
- A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.bioRxiv : the preprint server for biology · 2025Article
- Recent Statistical Innovations in Human Genetics.Annals of human genetics · 2025Review
- Robust and accurate Bayesian inference of genome-wide genealogies for hundreds of genomes.Nature genetics · 2025Article
- Tsbrowse: an interactive browser for ancestral recombination graphs.Bioinformatics (Oxford, England) · 2025Article
- Likelihoods for a general class of ARGs under the SMC.bioRxiv : the preprint server for biology · 2025Article
- Inference and applications of ancestral recombination graphs.Nature reviews. Genetics · 2025Review
- Estimating evolutionary and demographic parameters via ARG-derived IBD.PLoS genetics · 2025Article
- Constructing ancestral recombination graphs through reinforcement learning.Frontiers in genetics · 2025Article
- Tree Sequences as a General-Purpose Tool for Population Genetic Inference.Molecular biology and evolution · 2024Article
- Tree sequences as a general-purpose tool for population genetic inference.bioRxiv : the preprint server for biology · 2024Article
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- Fast and Accurate Estimation of Selection Coefficients and Allele Histories from Ancient and Modern DNA.Molecular biology and evolution · 2024Article
- A general and efficient representation of ancestral recombination graphs.bioRxiv : the preprint server for biology · 2024Article
- Estimating evolutionary and demographic parameters via ARG-derived IBD.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel algorithm, implemented in the software ARGinfer, for probabilistic inference of the Ancestral Recombination Graph under the Coalescent with Recombination. Our Markov Chain Monte Carlo algorithm takes advantage of the Succinct Tree Sequence data structure that has allowed great advances in simulation and point estimation, but not yet probabilistic inference. Unlike previous methods, which employ the Sequentially Markov Coalescent approximation, ARGinfer uses the Coalescent with Recombination, allowing more accurate inference of key evolutionary parameters. We show using simulations that ARGinfer can accurately estimate many properties of the evolutionary history of the sample, including the topology and branch lengths of the genealogical tree at each sequence site, and the times and locations of mutation and recombination events. ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. ARGinfer is currently limited to tens of DNA sequences of several hundreds of kilobases, but has scope for further computational improvements to increase its applicability.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.