scholarly journals A Simulation Study of the Effects of Assignment of Prior Identity-by-Descent Probabilities to Unselected Sib Pairs, in Covariance-Structure Modeling of a Quantitative-Trait Locus

1999 ◽  
Vol 64 (1) ◽  
pp. 268-280 ◽  
Author(s):  
Conor V. Dolan ◽  
Dorret I. Boomsma ◽  
Michael C. Neale
Keyword(s):  
Quantitative Trait Locus ◽  
Simulation Study ◽  
Quantitative Trait ◽  
Covariance Structure ◽  
Structure Modeling ◽  
Identity By Descent ◽  
Trait Locus ◽  
Sib Pairs

scholarly journals Optimal population size to detect quantitative trait locus in Korean native chicken: a simulation study

2021 ◽  
Author(s):  
Chiemela Peter Nwogwugwu ◽  
Yeongkuk Kim ◽  
Sunghyun Cho ◽  
Hee-Jong Roh ◽  
Jihye Cha ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Population Size ◽  
Simulation Study ◽  
Quantitative Trait ◽  
Native Chicken ◽  
Optimal Population ◽  
Trait Locus ◽  
Korean Native Chicken

scholarly journals Markov chain Monte Carlo for mapping a quantitative trait locus in outbred populations

2000 ◽  
Vol 75 (2) ◽  
pp. 231-241 ◽  
Author(s):  
M. C. A. M. BINK ◽  
L. L. G. JANSS ◽  
R. L. QUAAS
Keyword(s):  
Quantitative Trait Locus ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Quantitative Trait ◽  
Covariance Structure ◽  
Simulated Data ◽  
Simulated Tempering ◽  
Variance Ratios ◽  
Trait Locus

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.

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