ArticleNature genetics2025
Accelerated Bayesian inference of population size history from recombining sequence data.
Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Genomic signatures of adaptation across a landscape of crickets following the introduction of a parasitoid fly.Science advances · 2026Article
- Article
- Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Tracing the evolutionary histories of ultra-rare variants using variational dating of large ancestral recombination graphs.bioRxiv : the preprint server for biology · 2026Article
- 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
- A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.bioRxiv : the preprint server for biology · 2025Article
- A General Framework for Branch Length Estimation in Ancestral Recombination Graphs.bioRxiv : the preprint server for biology · 2025Article
- Estimating effective population size trajectories from time-series identity-by-descent segments.Genetics · 2025Article
- Insufficient Evidence for a Severe Bottleneck in Humans During the Early to Middle Pleistocene Transition.Molecular biology and evolution · 2025Article
- A previously reported bottleneck in human ancestry 900 kya is likely a statistical artifact.Genetics · 2025Article
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Abstract
This study introduces population history learning by averaging sampled histories (PHLASH), a new method for inferring population history from whole-genome sequence data. It works by drawing random, low-dimensional projections of the coalescent intensity function from the posterior distribution of a pairwise sequentially Markovian coalescent-like model and averaging them together to form an accurate and adaptive estimator. On simulated data, PHLASH tends to be faster and have lower error than several competing methods, including SMC++, MSMC2 and FITCOAL. Moreover, it provides automatic uncertainty quantification and leads to new Bayesian testing procedures for detecting population structure and ancient bottlenecks. The key technical advance is a new algorithm for computing the score function (gradient of the log likelihood) of a coalescent hidden Markov model, which has the same computational cost as evaluating the log likelihood. PHLASH has been released as an easy-to-use Python software package and leverages graphics processing unit acceleration when available.
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Registered trials
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.