ArticleGenetics2015
Inference Under a Wright-Fisher Model Using an Accurate Beta Approximation.
Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Genetic drift acts strongly on influenza virus populations within acute human infections but is obscured by other factors within acutely infected swine.Virus evolution · 2026Article
- Selection Estimation from Genetic Time-Series Data: Effects of Limited Sampling and Genetic Drift.Molecular biology and evolution · 2025Article
- MalKinID: A classification model for identifying malaria parasite genealogical relationships using identity-by-descent.Genetics · 2025Article
- Article
- MalKinID: A Likelihood-Based Model for Identifying Malaria Parasite Genealogical Relationships Using Identity-by-Descent.bioRxiv : the preprint server for biology · 2024Article
- A Path Integral Approach for Allele Frequency Dynamics Under Polygenic Selection.bioRxiv : the preprint server for biology · 2024Article
- A mechanistic model of gossip, reputations, and cooperation.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Self-contained Beta-with-Spikes approximation for inference under a Wright-Fisher model.Genetics · 2023Article
- Inferring Epistasis from Genetic Time-series Data.Molecular biology and evolution · 2022Article
- MPL resolves genetic linkage in fitness inference from complex evolutionary histories.Nature biotechnology · 2021Article
- Impact of incentive and selection strength on green technology innovation in Moran process.PloS one · 2020Article
- Inference of Selection from Genetic Time Series Using Various Parametric Approximations to the Wright-Fisher Model.G3 (Bethesda, Md.) · 2019Article
- A general framework for moment-based analysis of genetic data.Journal of mathematical biology · 2019Article
- Article
- Evaluating genetic drift in time-series evolutionary analysis.Journal of theoretical biology · 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
- Quantifying evolutionary dynamics from variant-frequency time series.Scientific reports · 2016Article
- Direct detection of natural selection in Bronze Age Britain.Genome researchArticle
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The large amount and high quality of genomic data available today enable, in principle, accurate inference of evolutionary histories of observed populations. The Wright-Fisher model is one of the most widely used models for this purpose. It describes the stochastic behavior in time of allele frequencies and the influence of evolutionary pressures, such as mutation and selection. Despite its simple mathematical formulation, exact results for the distribution of allele frequency (DAF) as a function of time are not available in closed analytical form. Existing approximations build on the computationally intensive diffusion limit or rely on matching moments of the DAF. One of the moment-based approximations relies on the beta distribution, which can accurately describe the DAF when the allele frequency is not close to the boundaries (0 and 1). Nonetheless, under a Wright-Fisher model, the probability of being on the boundary can be positive, corresponding to the allele being either lost or fixed. Here we introduce the beta with spikes, an extension of the beta approximation that explicitly models the loss and fixation probabilities as two spikes at the boundaries. We show that the addition of spikes greatly improves the quality of the approximation. We additionally illustrate, using both simulated and real data, how the beta with spikes can be used for inference of divergence times between populations with comparable performance to an existing state-of-the-art method.
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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.