bioRxivEvolutionary Biology
SpectralTDF: transition densities of diffusion processes with time-varying selection parameters, mutation rates, and effective population sizes
Abstract
In the Wright-Fisher diffusion, the transition density function (TDF) describes the time-evolution of the population-wide frequency of an allele. This function has several practical applications in population genetics, and computing it for biologically realistic scenarios with selection and demography is an important problem. We develop an efficient method for finding a spectral representation of the TDF for a general model where the effective population size, selection coefficients, and mutation parameters vary over time in a piecewise constant manner. The method, called spectralTDF, is available at https://sourceforge.net/projects/spectraltdf/.
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Transition densities and sample frequency spectra of diffusion processes with selection and variable population size
Daniel Zivkovic, Matthias Steinrücken, … Wolfgang Stephan·bioRxiv·Feb 2, 2015
- EWF 2.0: Exact sampling of allele trajectories using the Wright--Fisher diffusion with time-varying demography
Jaromir Sant, Paul A. Jenkins, … Dario Spanò·BMC BioinformaticsPeer-reviewed·Sep 8, 2026
- Inferring the Joint Demographic History of Multiple Populations: Beyond the Diffusion Approximation
J. Jouganous, W. Long, S. Gravel·bioRxiv·Jan 25, 2017
- Joint effect of changing selection and demography on the site frequency spectrum
K. Jain, S. Kaushik·bioRxiv·Feb 19, 2022
- Numerical simulation of the two-locus Wright-Fisher stochastic differential equation with application to approximating transition probability densities
Zhangyi He, Mark Beaumont, Feng Yu·bioRxiv·Jul 21, 2020
- Scaling the Discrete-time Wright Fisher model to biobank-scale datasets
J. P. Spence, T. Zeng, … J. K. Pritchard·bioRxiv·May 19, 2023