ArticleGenetics2015
The SMC' is a highly accurate approximation to the ancestral recombination graph.
Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 56 citations in OpenAlex.
- Estimating Waiting Distances between Genealogy Changes under a Multi-Species Extension of the Sequentially Markov Coalescent.Systematic biology · 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
- High-quality genome assembly of Chironomus riparius and its population history in European populations.G3 (Bethesda, Md.) · 2025Article
- The Length of Haplotype Blocks and Signals of Structural Variation in Reconstructed Genealogies.Molecular biology and evolution · 2025Article
- Inference and applications of ancestral recombination graphs.Nature reviews. Genetics · 2025Review
- Article
- A general and efficient representation of ancestral recombination graphs.bioRxiv : the preprint server for biology · 2024Article
- TRAILS: Tree reconstruction of ancestry using incomplete lineage sorting.PLoS genetics · 2024Article
- Espalier: Efficient Tree Reconciliation and Ancestral Recombination Graphs Reconstruction Using Maximum Agreement Forests.Systematic biology · 2023Article
- Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks.Molecular biology and evolution · 2023Article
- Recombination-aware phylogeographic inference using the structured coalescent with ancestral recombination.PLoS computational biology · 2022Article
- Evaluation of methods for estimating coalescence times using ancestral recombination graphs.Genetics · 2022Article
- Efficient ancestry and mutation simulation with msprime 1.0.Genetics · 2022Article
- The distribution of waiting distances in ancestral recombination graphs.Theoretical population biology · 2021Article
- Contemporary Demographic Reconstruction Methods Are Robust to Genome Assembly Quality: A Case Study in Tasmanian Devils.Molecular biology and evolution · 2019Article
- Inference of recombination maps from a single pair of genomes and its application to ancient samples.PLoS genetics · 2019Article
- Bridging trees for posterior inference on ancestral recombination graphs.Proceedings. Mathematical, physical, and engineering sciences · 2018Article
- Inference of population history using coalescent HMMs: review and outlook.Current opinion in genetics & development · 2018Review
- Efficient pedigree recording for fast population genetics simulation.PLoS computational biology · 2018Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Two sequentially Markov coalescent models (SMC and SMC') are available as tractable approximations to the ancestral recombination graph (ARG). We present a Markov process describing coalescence at two fixed points along a pair of sequences evolving under the SMC'. Using our Markov process, we derive a number of new quantities related to the pairwise SMC', thereby analytically quantifying for the first time the similarity between the SMC' and the ARG. We use our process to show that the joint distribution of pairwise coalescence times at recombination sites under the SMC' is the same as it is marginally under the ARG, which demonstrates that the SMC' is, in a particular well-defined, intuitive sense, the most appropriate first-order sequentially Markov approximation to the ARG. Finally, we use these results to show that population size estimates under the pairwise SMC are asymptotically biased, while under the pairwise SMC' they are approximately asymptotically unbiased.
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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.