ArticleBMC genomics2022
Beta-PSMC: uncovering more detailed population history using beta distribution.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Genomic insights into recurrent demographic collapses and recovery dynamics in Tibetan antelopes (Pantholops hodgsoni).Science China. Life sciences · 2026Article
- Holotype genome of the lesula provides insights into demography and evolution of a threatened primate lineage.Genome biology · 2025Article
- Genomic Signatures of Island Colonisation in Highly Diverse Primates.Molecular ecology · 2025Article
- Were bed bugs the first urban pest insect? Genome-wide patterns of bed bug demography mirror global human expansion.Biology letters · 2025Article
- Parameterizing Pantherinae: De Novo Mutation Rate Estimates from Panthera and Neofelis Pedigrees.Genome biology and evolution · 2025Article
- Domestication history and genetic changes for the newly evolved flower color in the ornamental plantHorticulture research · 2025Article
- Comparative genomic analyses provide new insights into evolutionary history and conservation genomics of gorillas.BMC ecology and evolution · 2024Review
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Authors and funding
3 authors.
Funding
Abstract
backgroundInferring the demographic history of a population is essential in population genetic studies. Though the inference methods based on the sequentially Markov coalescent can present the population history in detail, these methods assume that the population size remains unchanged in each time interval during discretizing the hidden state in the hidden Markov model. Therefore, these methods fail to uncover the detailed population history in each time interval.
resultsWe present a new method called Beta-PSMC, which introduces the probability density function of a beta distribution with a broad variety of shapes into the Pairwise Sequentially Markovian Coalescent (PSMC) model to refine the population history in each discretized time interval in place of the assumption that the population size is unchanged. Using simulation, we demonstrate that Beta-PSMC can uncover more detailed population history, and improve the accuracy and resolution of the recent population history inference. We also apply Beta-PSMC to infer the population history of Adélie penguin and find that the fluctuation in population size is contrary to the temperature change 15-27 thousand years ago.
conclusionsBeta-PSMC extends PSMC by allowing more detailed fluctuation of population size in each discretized time interval with the probability density function of beta distribution and will serve as a useful tool for population genetics.
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