A Restricted Model Space Approach for the Detection of Epistasis in Quantitative Trait Loci Using Markov Chain Monte Carlo Model Composition

2013 ◽  
pp. 101-114
Author(s):  
Edward L. Boone ◽  
Susan J. Simmons ◽  
Karl Ricanek
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Monte Carlo Model ◽  
Model Space ◽  
Model Composition ◽  
Trait Loci ◽  
Space Approach

scholarly journals Mapping-Linked Quantitative Trait Loci Using Bayesian Analysis and Markov Chain Monte Carlo Algorithms

Genetics ◽  
1997 ◽  
Vol 146 (2) ◽  
pp. 735-743 ◽  
Author(s):  
Pekka Uimari ◽  
Ina Hoeschele
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Allele Frequencies ◽  
Mcmc Methods ◽  
Substitution Effects ◽  
Indicator Variable ◽  
Trait Loci

A Bayesian method for mapping linked quantitative trait loci (QTL) using multiple linked genetic markers is presented. Parameter estimation and hypothesis testing was implemented via Markov chain Monte Carlo (MCMC) algorithms. Parameters included were allele frequencies and substitution effects for two biallelic QTL, map positions of the QTL and markers, allele frequencies of the markers, and polygenic and residual variances. Missing data were polygenic effects and multi-locus marker-QTL genotypes. Three different MCMC schemes for testing the presence of a single or two linked QTL on the chromosome were compared. The first approach includes a model indicator variable representing two unlinked QTL affecting the trait, one linked and one unlinked QTL, or both QTL linked with the markers. The second approach incorporates an indicator variable for each QTL into the model for phenotype, allowing or not allowing for a substitution effect of a QTL on phenotype, and the third approach is based on model determination by reversible jump MCMC. Methods were evaluated empirically by analyzing simulated granddaughter designs. All methods identified correctly a second, linked QTL and did not reject the one-QTL model when there was only a single QTL and no additional or an unlinked QTL.

Study on mapping Quantitative Trait Loci for animal complex binary traits using Bayesian-Markov chain Monte Carlo approach

2006 ◽  
Vol 49 (6) ◽  
pp. 552-559 ◽  
Author(s):  
Jianfeng Liu ◽  
Yuan Zhang ◽  
Qin Zhang ◽  
Lixian Wang ◽  
Jigang Zhang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Monte Carlo Approach ◽  
Binary Traits ◽  
Trait Loci

scholarly journals A Bayesian Approach to Detect Quantitative Trait Loci Using Markov Chain Monte Carlo

Genetics ◽  
1996 ◽  
Vol 144 (2) ◽  
pp. 805-816 ◽  
Author(s):  
Jaya M Satagopan ◽  
Brian S Yandell ◽  
Michael A Newton ◽  
Thomas C Osborn
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Quantitative Trait Loci ◽  
Bayesian Approach ◽  
Quantitative Trait ◽  
Confidence Regions ◽  
Phenotypic Trait ◽  
Parameter Estimates ◽  
Trait Loci

Abstract Markov chain Monte Carlo (MCMC) techniques are applied to simultaneously identify multiple quantitative trait loci (QTL) and the magnitude of their effects. Using a Bayesian approach a multi-locus model is fit to quantitative trait and molecular marker data, instead of fitting one locus at a time. The phenotypic trait is modeled as a linear function of the additive and dominance effects of the unknown QTL genotypes. Inference summaries for the locations of the QTL and their effects are derived from the corresponding marginal posterior densities obtained by integrating the likelihood, rather than by optimizing the joint likelihood surface. This is done using MCMC by treating the unknown QTL genotypes, and any missing marker genotypes, as augmented data and then by including these unknowns in the Markov chain cycle along with the unknown parameters. Parameter estimates are obtained as means of the corresponding marginal posterior densities. High posterior density regions of the marginal densities are obtained as confidence regions. We examine flowering time data from double haploid progeny of Brassica napus to illustrate the proposed method.

scholarly journals Model-averaged ℓ1regularization using Markov chain Monte Carlo model composition

2013 ◽  
Vol 85 (6) ◽  
pp. 1090-1101 ◽  
Author(s):  
Chris Fraley ◽  
Daniel Percival
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Model ◽  
Model Composition
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