ArticleGenetics2013
Complete numerical solution of the diffusion equation of random genetic drift.
Article in Genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Competition and evolutionary selection among core regulatory motifs in gene expression control.Nature communications · 2023Article
- Article
- Inference of Selection from Genetic Time Series Using Various Parametric Approximations to the Wright-Fisher Model.G3 (Bethesda, Md.) · 2019Article
- Allele Age Under Non-Classical Assumptions is Clarified by an Exact Computational Markov Chain Approach.Scientific reports · 2017Article
- Wright-Fisher exact solver (WFES): scalable analysis of population genetic models without simulation or diffusion theory.Bioinformatics (Oxford, England) · 2017Article
- Statistical Inference in the Wright-Fisher Model Using Allele Frequency Data.Systematic biology · 2017Review
- Article
- Transition Densities and Sample Frequency Spectra of Diffusion Processes with Selection and Variable Population Size.Genetics · 2015Article
- A path integral formulation of the Wright-Fisher process with genic selection.Theoretical population biology · 2014Article
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Authors and funding
3 authors.
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Abstract
A numerical method is presented to solve the diffusion equation for the random genetic drift that occurs at a single unlinked locus with two alleles. The method was designed to conserve probability, and the resulting numerical solution represents a probability distribution whose total probability is unity. We describe solutions of the diffusion equation whose total probability is unity as complete. Thus the numerical method introduced in this work produces complete solutions, and such solutions have the property that whenever fixation and loss can occur, they are automatically included within the solution. This feature demonstrates that the diffusion approximation can describe not only internal allele frequencies, but also the boundary frequencies zero and one. The numerical approach presented here constitutes a single inclusive framework from which to perform calculations for random genetic drift. It has a straightforward implementation, allowing it to be applied to a wide variety of problems, including those with time-dependent parameters, such as changing population sizes. As tests and illustrations of the numerical method, it is used to determine: (i) the probability density and time-dependent probability of fixation for a neutral locus in a population of constant size; (ii) the probability of fixation in the presence of selection; and (iii) the probability of fixation in the presence of selection and demographic change, the latter in the form of a changing population size.
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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.