ArticleGenetics2015
Estimating effective population size from temporally spaced samples with a novel, efficient maximum-likelihood algorithm.
Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- MaxTemp: A Method to Maximise Precision of the Temporal Method for Estimating NMolecular ecology resources · 2025Article
- Maximising the Potential of TemporalMolecular ecology resources · 2025Article
- Dealing With the Complexity of Effective Population Size in Conservation Practice.Evolutionary applications · 2024Article
- Self-contained Beta-with-Spikes approximation for inference under a Wright-Fisher model.Genetics · 2023Article
- Whole-genome sequencing of cryopreserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.Genetics, selection, evolution : GSE · 2023Article
- A brief history and popularity of methods and tools used to estimate micro-evolutionary forces.Ecology and evolution · 2021Review
- Contemporary NMolecular ecology resources · 2021Article
- Gene Banks as Reservoirs to Detect Recent Selection: The Example of the Asturiana de los Valles Bovine Breed.Frontiers in genetics · 2021Article
- The Limits to Estimating Population-Genetic Parameters with Temporal Data.Genome biology and evolution · 2020Article
- Inference of Selection from Genetic Time Series Using Various Parametric Approximations to the Wright-Fisher Model.G3 (Bethesda, Md.) · 2019Article
- Article
- Cytonuclear discordance in the Florida Everglades invasive Burmese python (Ecology and evolution · 2018Article
- Inferring sex-specific demographic history from SNP data.PLoS genetics · 2018Article
- Statistical Inference in the Wright-Fisher Model Using Allele Frequency Data.Systematic biology · 2017Review
- Prediction and estimation of effective population size.Heredity · 2016Review
- Estimating the Effective Population Size from Temporal Allele Frequency Changes in Experimental Evolution.Genetics · 2016Article
- Genomic Signatures of North American Soybean Improvement Inform Diversity Enrichment Strategies and Clarify the Impact of Hybridization.G3 (Bethesda, Md.) · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The effective population size [Formula: see text] is a key parameter in population genetics and evolutionary biology, as it quantifies the expected distribution of changes in allele frequency due to genetic drift. Several methods of estimating [Formula: see text] have been described, the most direct of which uses allele frequencies measured at two or more time points. A new likelihood-based estimator [Formula: see text] for contemporary effective population size using temporal data is developed in this article. The existing likelihood methods are computationally intensive and unable to handle the case when the underlying [Formula: see text] is large. This article tries to work around this problem by using a hidden Markov algorithm and applying continuous approximations to allele frequencies and transition probabilities. Extensive simulations are run to evaluate the performance of the proposed estimator [Formula: see text], and the results show that it is more accurate and has lower variance than previous methods. The new estimator also reduces the computational time by at least 1000-fold and relaxes the upper bound of [Formula: see text] to several million, hence allowing the estimation of larger [Formula: see text]. Finally, we demonstrate how this algorithm can cope with nonconstant [Formula: see text] scenarios and be used as a likelihood-ratio test to test for the equality of [Formula: see text] throughout the sampling horizon. An R package "NB" is now available for download to implement the method described in this article.
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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.