Estimation of Selection Intensity under Overdominance by Bayesian Methods
| Autor: | Zaid Abdo, Paul Joyce, Erkan Ozge Buzbas |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Statistics and Probability
Models Genetic Bayesian probability Posterior probability Population genetics Bayes Theorem Locus (genetics) Overdominance Biology Bayesian inference Article Computational Mathematics Bayes' theorem Genetics Population Gene Frequency HLA Antigens Statistics Genetics Humans Computer Simulation Selection Genetic Molecular Biology Allele frequency |
| Zdroj: | Statistical Applications in Genetics and Molecular Biology. 8:1-22 |
| ISSN: | 1544-6115 |
| DOI: | 10.2202/1544-6115.1466 |
| Popis: | A balanced pattern in the allele frequencies of polymorphic loci is a potential sign of selection, particularly of overdominance. Although this type of selection is of some interest in population genetics, there exists no likelihood based approaches specifically tailored to make inference on selection intensity. To fill this gap, we present Bayesian methods to estimate selection intensity under k-allele models with overdominance. Our model allows for an arbitrary number of loci and alleles within a locus. The neutral and selected variability within each locus are modeled with corresponding k-allele models. To estimate the posterior distribution of the mean selection intensity in a multilocus region, a hierarchical setup between loci is used. The methods are demonstrated with data at the Human Leukocyte Antigen loci from world-wide populations. |
| Databáze: | OpenAIRE |
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