ArticleG3 (Bethesda, Md.)2019
Inference of Selection from Genetic Time Series Using Various Parametric Approximations to the Wright-Fisher Model.
Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Evolution of virulence of a plant RNA virus in developmental stage-structured host populations.Molecular biology and evolution · 2026Article
- Optimal control and the dynamics of ancestral lineages.Genetics · 2026Article
- Direct estimation of genotype fitness from time series.bioRxiv : the preprint server for biology · 2026Article
- Unsupervised detection and fitness estimation of emerging SARS-CoV-2 variants: Application to wastewater samples (ANRS0160).PLoS computational biology · 2025Article
- Selection Estimation from Genetic Time-Series Data: Effects of Limited Sampling and Genetic Drift.Molecular biology and evolution · 2025Article
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- Article
- A novel expectation-maximization approach to infer general diploid selection from time-series genetic data.PLoS genetics · 2025Article
- A novel expectation-maximization approach to infer general diploid selection from time-series genetic data.bioRxiv : the preprint server for biology · 2025Article
- Microevolutionary change in wild stickleback: Using integrative time-series data to infer responses to selection.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Fast and Accurate Estimation of Selection Coefficients and Allele Histories from Ancient and Modern DNA.Molecular biology and evolution · 2024Article
- Self-contained Beta-with-Spikes approximation for inference under a Wright-Fisher model.Genetics · 2023Article
- Estimating Temporally Variable Selection Intensity from Ancient DNA Data.Molecular biology and evolution · 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
- Identification of genomic regions affecting production traits in pigs divergently selected for feed efficiency.Genetics, selection, evolution : GSE · 2021Article
- Gene Banks as Reservoirs to Detect Recent Selection: The Example of the Asturiana de los Valles Bovine Breed.Frontiers in genetics · 2021Article
- Article
- Direct detection of natural selection in Bronze Age Britain.Genome researchArticle
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3 authors.
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Abstract
Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they model the transition probabilities of the Markov chain. Using the Wright-Fisher model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this model based on parametric distributions have been proposed. Here, we compared the performance of some of these approximations with respect to their power to detect selection and their estimation of the selection coefficient. We developped a new generic Hidden Markov Model likelihood calculator and applied it on genetic time series simulated under various evolutionary scenarios. The Beta with spikes approximation, which combines discrete fixation probabilities with a continuous Beta distribution, was found to perform consistently better than the others. This distribution provides an almost perfect fit to the Wright-Fisher model in terms of selection inference, for a computational cost that does not increase with population size. We further evaluated this model for population sizes not accessible to the Wright-Fisher model and illustrated its performance on a dataset of two divergently selected chicken populations.
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