scholarly journals The Use of Multiple Markers in a Bayesian Method for Mapping Quantitative Trait Loci

Genetics ◽  
1996 ◽  
Vol 143 (4) ◽  
pp. 1831-1842 ◽  
Author(s):  
Pekka Uimari ◽  
Georg Thaller ◽  
Ina Hoeschele
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Posterior Probability ◽  
Bayesian Method ◽  
Substitution Effect ◽  
Qtl Detection ◽  
Indicator Variable ◽  
Monte Carlo Algorithms ◽  
Trait Loci ◽  
Residual Variances

Abstract Information on multiple linked genetic markers was used in a Bayesian method for the statistical mapping of quantitative trait loci (QTL). Bayesian parameter estimation and hypothesis testing were implemented via Markov chain Monte Carlo algorithms. Variables sampled were the augmented data (marker-QTL genotypes, polygenic effects), an indicator variable for linkage or nonlinkage, and the parameters. The parameter vector included allele frequencies at the markers and the QTL, map distances of the markers and the QTL, QTL substitution effect, and polygenic and residual variances. The criterion for QTL detection was the marginal posterior probability of a QTL being located on the chromosome carrying the markers, The method was evaluated empirically by analyzing simulated granddaughter designs consisting of 2000 sons, 20 related sires, and their ancestors.

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.

scholarly journals Quantitative Trait Loci (QTL) Detection in Multicross Inbred Designs

Genetics ◽  
2004 ◽  
Vol 168 (3) ◽  
pp. 1737-1749 ◽  
Author(s):  
Sébastien Crepieux ◽  
Claude Lebreton ◽  
Bertrand Servin ◽  
Gilles Charmet
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Qtl Detection ◽  
Trait Loci

scholarly journals Bayesian Methods for Quantitative Trait Loci Mapping Based on Model Selection: Approximate Analysis Using the Bayesian Information Criterion

Genetics ◽  
2001 ◽  
Vol 159 (3) ◽  
pp. 1351-1364 ◽  
Author(s):  
Roderick D Ball
Keyword(s):  
Quantitative Trait Loci ◽  
Model Selection ◽  
Bayesian Methods ◽  
Quantitative Trait ◽  
Bayesian Information Criterion ◽  
Posterior Probability ◽  
Missing Values ◽  
Information Criterion ◽  
Trait Loci ◽  
Model Size

Abstract We describe an approximate method for the analysis of quantitative trait loci (QTL) based on model selection from multiple regression models with trait values regressed on marker genotypes, using a modification of the easily calculated Bayesian information criterion to estimate the posterior probability of models with various subsets of markers as variables. The BIC-δ criterion, with the parameter δ increasing the penalty for additional variables in a model, is further modified to incorporate prior information, and missing values are handled by multiple imputation. Marginal probabilities for model sizes are calculated, and the posterior probability of nonzero model size is interpreted as the posterior probability of existence of a QTL linked to one or more markers. The method is demonstrated on analysis of associations between wood density and markers on two linkage groups in Pinus radiata. Selection bias, which is the bias that results from using the same data to both select the variables in a model and estimate the coefficients, is shown to be a problem for commonly used non-Bayesian methods for QTL mapping, which do not average over alternative possible models that are consistent with the data.

Enhanced Efficiency of Quantitative Trait Loci Mapping Analysis Based on Multivariate Complexes of Quantitative Traits

Genetics ◽  
2001 ◽  
Vol 157 (4) ◽  
pp. 1789-1803 ◽  
Author(s):  
Abraham B Korol ◽  
Yefim I Ronin ◽  
Alexander M Itskovich ◽  
Junhua Peng ◽  
Eviatar Nevo
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Broad Sense ◽  
Broad Sense Heritability ◽  
Qtl Detection ◽  
Data Set ◽  
Correlated Traits ◽  
Detection Power ◽  
Specific Calculation ◽  
Trait Loci

AbstractAn approach to increase the efficiency of mapping quantitative trait loci (QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. The power of QTL detection using the log-likelihood ratio (LOD scores) grows proportionally to the broad sense heritability. We found that this relationship holds also for correlated traits, so that an increased bivariate heritability implicates a higher LOD score, higher detection power, and better mapping resolution. However, the increased number of parameters to be estimated complicates the application of this approach when a large number of traits are considered simultaneously. Here we present a multivariate generalization of our previous two-trait QTL analysis. The proposed multivariate analogue of QTL contribution to the broad-sense heritability based on interval-specific calculation of eigenvalues and eigenvectors of the residual covariance matrix allows prediction of the expected QTL detection power and mapping resolution for any subset of the initial multivariate trait complex. Permutation technique allows chromosome-wise testing of significance for the whole trait complex and the significance of the contribution of individual traits owing to: (a) their correlation with other traits, (b) dependence on the chromosome in question, and (c) both a and b. An example of application of the proposed method on a real data set of 11 traits from an experiment performed on an F2/F3 mapping population of tetraploid wheat (Triticum durum × T. dicoccoides) is provided.

scholarly journals Crop management impacts the efficiency of quantitative trait loci (QTL) detection and use: case study of fruit load×QTL interactions

2013 ◽  
Vol 65 (1) ◽  
pp. 11-22 ◽  
Author(s):  
J. Kromdijk ◽  
N. Bertin ◽  
E. Heuvelink ◽  
J. Molenaar ◽  
P. H. B. de Visser ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Crop Management ◽  
Use Case ◽  
Qtl Detection ◽  
Trait Loci ◽  
Fruit Load

Functional mapping for quantitative trait loci governing growth rates: a parametric model

2003 ◽  
Vol 14 (3) ◽  
pp. 241-249 ◽  
Author(s):  
Rongling Wu ◽  
Chang-Xing Ma ◽  
Wei Zhao ◽  
George Casella
Keyword(s):  
Quantitative Trait Loci ◽  
Growth Rates ◽  
Quantitative Trait ◽  
Evolutionary Biology ◽  
Growth Parameters ◽  
Mathematical Functions ◽  
Trait Loci ◽  
Specific Growth Rates ◽  
Residual Variances ◽  
Challenging Question

Are there-specific quantitative trait loci (QTL) governing growth rates in biology? This is emerging as an exciting but challenging question for contemporary developmental biology, evolutionary biology, and plant and animal breeding. In this article, we present a new statistical model for mapping QTL underlying age-specific growth rates. This model is based on the mechanistic relationship between growth rates and ages established by a variety of mathematical functions. A maximum likelihood approach, implemented with the EM algorithm, is developed to provide the estimates of QTL position, growth parameters characterized by QTL effects, and residual variances and covariances. Based on our model, a number of biologically important hypotheses can be formulated concerning the genetic basis of growth. We use forest trees as an example to demonstrate the power of our model, in which a QTL for stem growth diameter growth rates is successfully mapped to a linkage group constructed from polymorphic markers. The implications of the new model are discussed.

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