Inference from the stationary distribution of allele frequencies in a family of Wright–Fisher models with two levels of genetic variability
| Autor: | Jake M. Ferguson, Erkan Ozge Buzbas |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Genotype Inference Balancing selection 01 natural sciences Article 010104 statistics & probability 03 medical and health sciences Gene Frequency Statistics Humans Quantitative Biology::Populations and Evolution Computer Simulation Genetic variability Selection Genetic 0101 mathematics Allele Allele frequency Alleles Ecology Evolution Behavior and Systematics Mathematics Stationary distribution Models Genetic Genetic Drift Bayes Theorem Biological Evolution Quantitative Biology::Genomics Genetics Population 030104 developmental biology Conditional independence Mutation Approximate Bayesian computation Algorithms |
| Zdroj: | Theoretical Population Biology. 122:78-87 |
| ISSN: | 0040-5809 |
| DOI: | 10.1016/j.tpb.2018.03.004 |
| Popis: | The distribution of allele frequencies obtained from diffusion approximations to Wright-Fisher models are useful in developing intuition about the population level effects of evolutionary processes. The statistical properties of the stationary distributions of K-allele models have been extensively studied under neutrality or under selection. Here, we introduce a new family of Wright-Fisher models in which there are two hierarchical levels of genetic variability. The genotypes composed of alleles differing from each other at the selected level have fitness differences with respect to each other and evolve under selection. The genotypes composed of alleles differing from each other only at the neutral level have the same fitness and evolve under neutrality. We show that with an appropriate scaling of the mutation parameter with respect to the number of alleles at each level, the frequencies of alleles at the selected and the neutral level are conditionally independent of each other, conditional on knowing the number of alleles at all levels. This conditional independence allows us to simulate from the the joint stationary distribution of the allele frequencies. We use these simulated frequencies to perform inference on parameters of the model with two levels of genetic variability using Approximate Bayesian Computation. |
| Databáze: | OpenAIRE |
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