ArticleJournal of the American Statistical Association2024
Exact Decoding of a Sequentially Markov Coalescent Model in Genetics.
Article in Journal of the American Statistical Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Not Just Ne Ne-More: New Applications for SMC from Ecology to Phylogenies.Genome biology and evolution · 2026Review
- Accelerated Bayesian inference of population size history from recombining sequence data.Nature genetics · 2025Article
- Article
- The solution surface of the Li-Stephens haplotype copying model.Algorithms for molecular biology : AMB · 2023Article
- Ultrafast genome-wide inference of pairwise coalescence times.Genome research · 2023Article
- Robust inference of population size histories from genomic sequencing data.PLoS computational biology · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
In statistical genetics, the sequentially Markov coalescent (SMC) is an important family of models for approximating the distribution of genetic variation data under complex evolutionary models. Methods based on SMC are widely used in genetics and evolutionary biology, with significant applications to genotype phasing and imputation, recombination rate estimation, and inferring population history. SMC allows for likelihood-based inference using hidden Markov models (HMMs), where the latent variable represents a genealogy. Because genealogies are continuous, while HMMs are discrete, SMC requires discretizing the space of trees in a way that is awkward and creates bias. In this work, we propose a method that circumvents this requirement, enabling SMC-based inference to be performed in the natural setting of a continuous state space. We derive fast, exact procedures for frequentist and Bayesian inference using SMC. Compared to existing methods, ours requires minimal user intervention or parameter tuning, no numerical optimization or E-M, and is faster and more accurate.
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Registered trials
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