Markov chain Monte Carlo for mapping a quantitative trait locus in outbred populations
| Autor: | Marco C. A. M. Bink, L. L. G. Janss, R. L. Quaas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2000 |
| Předmět: |
Bayesian probability
Posterior probability Multivariate normal distribution Quantitative trait locus Animal Breeding and Genomics Bioinformatics symbols.namesake Quantitative Trait Heritable Inclusive composite interval mapping Genetics Animals Humans Life Science Fokkerij en Genomica Instituut voor Dierhouderij en Diergezondheid Mathematics Models Genetic Markov chain ID-Lelystad Chromosome Mapping Markov chain Monte Carlo General Medicine Covariance Markov Chains ID Lelystad ID-Lelystad Instituut voor Dierhouderij en Diergezondheid ID Lelystad Institute for Animal Science and Health symbols WIAS Monte Carlo Method Algorithm Institute for Animal Science and Health |
| Zdroj: | Genetical Research 75 (2000) 2 Genetical Research, 75(2), 231-241 |
| ISSN: | 0016-6723 |
| DOI: | 10.1017/s0016672399004310 |
| Popis: | A Bayesian approach is presented for mapping a quantitative trait locus (QTL) using the ‘Fernando and Grossman’ multivariate Normal approximation to QTL inheritance. For this model, a Bayesian implementation that includes QTL position is problematic because standard Markov chain Monte Carlo (MCMC) algorithms do not mix, i.e. the QTL position gets stuck in one marker interval. This is because of the dependence of the covariance structure for the QTL effects on the adjacent markers and may be typical of the ‘Fernando and Grossman’ model. A relatively new MCMC technique, simulated tempering, allows mixing and so makes possible inferences about QTL position based on marginal posterior probabilities. The model was implemented for estimating variance ratios and QTL position using a continuous grid of allowed positions and was applied to simulated data of a standard granddaughter design. The results showed a smooth mixing of QTL position after implementation of the simulated tempering sampler. In this implementation, map distance between QTL and its flanking markers was artificially stretched to reduce the dependence of markers and covariance. The method generalizes easily to more complicated applications and can ultimately contribute to QTL mapping in complex, heterogeneous, human, animal or plant populations. |
| Databáze: | OpenAIRE |
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