ArticleGenetics2016
An Approximate Markov Model for the Wright-Fisher Diffusion and Its Application to Time Series Data.
Article in Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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44 citing papers in PubMed.
- Direct estimation of genotype fitness from time series.bioRxiv : the preprint server for biology · 2026Article
- Assessing Ancient DNA Sampling Strategies for Natural Selection Inference in Humans Using Allele Frequency Time Series Data.Genome biology and evolution · 2026Article
- Linkage-aware inference of fitness from short-read time-series genomic data.bioRxiv : the preprint server for biology · 2026Article
- Linkage-aware inference of fitness from short-read time-series genomic data.Virus evolution · 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
- Article
- Estimating the Distribution of Fitness Effects in Aye-Ayes (Daubentonia madagascariensis), Accounting for Population History as Well as Mutation and Recombination Rate Heterogeneity.American journal of primatology · 2025Article
- Taylor's Power Law rules the dynamics of allele frequencies during viral evolution in response to host changes.Journal of the Royal Society, Interface · 2025Article
- 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
- Article
- Global diversity, recurrent evolution, and recent selection on amylase structural haplotypes in humans.bioRxiv : the preprint server for biology · 2024Article
- Timesweeper: accurately identifying selective sweeps using population genomic time series.Genetics · 2023Article
- Estimating Temporally Variable Selection Intensity from Ancient DNA Data.Molecular biology and evolution · 2023Article
- Bézier interpolation improves the inference of dynamical models from data.Physical review. E · 2023Article
- Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection.PLoS genetics · 2022Article
- Inferring Epistasis from Genetic Time-series Data.Molecular biology and evolution · 2022Article
- Recommendations for improving statistical inference in population genomics.PLoS biology · 2022Article
- Inferring the distribution of fitness effects in patient-sampled and experimental virus populations: two case studies.Heredity · 2022Article
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4 authors.
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Abstract
The joint and accurate inference of selection and demography from genetic data is considered a particularly challenging question in population genetics, since both process may lead to very similar patterns of genetic diversity. However, additional information for disentangling these effects may be obtained by observing changes in allele frequencies over multiple time points. Such data are common in experimental evolution studies, as well as in the comparison of ancient and contemporary samples. Leveraging this information, however, has been computationally challenging, particularly when considering multilocus data sets. To overcome these issues, we introduce a novel, discrete approximation for diffusion processes, termed mean transition time approximation, which preserves the long-term behavior of the underlying continuous diffusion process. We then derive this approximation for the particular case of inferring selection and demography from time series data under the classic Wright-Fisher model and demonstrate that our approximation is well suited to describe allele trajectories through time, even when only a few states are used. We then develop a Bayesian inference approach to jointly infer the population size and locus-specific selection coefficients with high accuracy and further extend this model to also infer the rates of sequencing errors and mutations. We finally apply our approach to recent experimental data on the evolution of drug resistance in influenza virus, identifying likely targets of selection and finding evidence for much larger viral population sizes than previously reported.
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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.