ArticleGenetics2022
Evaluation of methods for estimating coalescence times using ancestral recombination graphs.
Article in Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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50 citing papers in PubMed.
- Comparing ARG Inference Methods Under Transmission of Reproductive Success: Tree Imbalance Matters.Genome biology and evolution · 2026Article
- Recovering signatures of archaic introgression using ancestral recombination graphs.bioRxiv : the preprint server for biology · 2026Article
- Estimating Waiting Distances between Genealogy Changes under a Multi-Species Extension of the Sequentially Markov Coalescent.Systematic biology · 2026Article
- Summary statistics and approximate bayesian computation are comparable to convolutional neural networks for inferring times to fixation.bioRxiv : the preprint server for biology · 2026Article
- Polarising SNPs Without Outgroup.Molecular ecology resources · 2026Article
- Tracing the evolutionary histories of ultra-rare variants using variational dating of large ancestral recombination graphs.bioRxiv : the preprint server for biology · 2026Article
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- Not Just Ne Ne-More: New Applications for SMC from Ecology to Phylogenies.Genome biology and evolution · 2026Review
- A general framework for branch length estimation in Ancestral Recombination Graphs.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Genealogical Analysis of Replicate Flower Colour Hybrid Zones in Antirrhinum.Molecular ecology · 2025Article
- Documenting homoploid hybrid speciation.Molecular ecology · 2025Review
- A General Framework for Branch Length Estimation in Ancestral Recombination Graphs.bioRxiv : the preprint server for biology · 2025Article
- GHIST 2024: The First Genomic History Inference Strategies Tournament.Molecular biology and evolution · 2025Article
- Inference of complex demographic history using composite likelihood based on whole-genome genealogies.bioRxiv : the preprint server for biology · 2025Article
- Accelerated Bayesian inference of population size history from recombining sequence data.Nature genetics · 2025Article
- Recent Statistical Innovations in Human Genetics.Annals of human genetics · 2025Review
- The Length of Haplotype Blocks and Signals of Structural Variation in Reconstructed Genealogies.Molecular biology and evolution · 2025Article
- Robust and accurate Bayesian inference of genome-wide genealogies for hundreds of genomes.Nature genetics · 2025Article
- A genealogy-based approach for revealing ancestry-specific structures in admixed populations.American journal of human genetics · 2025Article
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5 authors.
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Abstract
The ancestral recombination graph is a structure that describes the joint genealogies of sampled DNA sequences along the genome. Recent computational methods have made impressive progress toward scalably estimating whole-genome genealogies. In addition to inferring the ancestral recombination graph, some of these methods can also provide ancestral recombination graphs sampled from a defined posterior distribution. Obtaining good samples of ancestral recombination graphs is crucial for quantifying statistical uncertainty and for estimating population genetic parameters such as effective population size, mutation rate, and allele age. Here, we use standard neutral coalescent simulations to benchmark the estimates of pairwise coalescence times from 3 popular ancestral recombination graph inference programs: ARGweaver, Relate, and tsinfer+tsdate. We compare (1) the true coalescence times to the inferred times at each locus; (2) the distribution of coalescence times across all loci to the expected exponential distribution; (3) whether the sampled coalescence times have the properties expected of a valid posterior distribution. We find that inferred coalescence times at each locus are most accurate in ARGweaver, and often more accurate in Relate than in tsinfer+tsdate. However, all 3 methods tend to overestimate small coalescence times and underestimate large ones. Lastly, the posterior distribution of ARGweaver is closer to the expected posterior distribution than Relate's, but this higher accuracy comes at a substantial trade-off in scalability. The best choice of method will depend on the number and length of input sequences and on the goal of downstream analyses, and we provide guidelines for the best practices.
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