scholarly journals Mapping QTLs for binary traits in backcross and F2 populations

1996 ◽  
Vol 68 (1) ◽  
pp. 55-63 ◽  
Author(s):  
P. M. Visscher ◽  
C. S. Haley ◽  
S. A. Knott
Keyword(s):  
Quantitative Trait Loci ◽  
Linear Regression ◽  
Quantitative Trait ◽  
Generalized Linear Model ◽  
Model Parameters ◽  
Qtl Detection ◽  
Binary Traits ◽  
Standard Linear ◽  
Marker Spacing ◽  
Qtl Effects

SummaryMapping quantitative trait loci (QTLs) for binary traits in backcross and F2 populations was investigated using stochastic stimulation. Data were analysed using either linear regression or a generalized linear model. Parameters which were varied in the simulations were the population size (200 and 500), heritability in the backcross or F2 population (0·01, 0·05, 0·10), marker spacing (10 and 20 cM) and the incidence of the trait (0·50, 0·25, 0·10). The methods gave very similar results in terms of estimates of the QTL location and QTL effects and power of QTL detection, and it was concluded that in practice treating the zero-one data as continuous and using standard linear regression was efficient.

scholarly journals Bayesian Mapping of Quantitative Trait Loci for Complex Binary Traits

Genetics ◽  
2000 ◽  
Vol 155 (3) ◽  
pp. 1391-1403
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Bayesian Statistics ◽  
Quantitative Trait ◽  
Data Augmentation ◽  
Threshold Model ◽  
Binary Trait ◽  
Monte Carlo Algorithm ◽  
Binary Traits ◽  
Trait Loci ◽  
Bayesian Mapping

Abstract A complex binary trait is a character that has a dichotomous expression but with a polygenic genetic background. Mapping quantitative trait loci (QTL) for such traits is difficult because of the discrete nature and the reduced variation in the phenotypic distribution. Bayesian statistics are proved to be a powerful tool for solving complicated genetic problems, such as multiple QTL with nonadditive effects, and have been successfully applied to QTL mapping for continuous traits. In this study, we show that Bayesian statistics are particularly useful for mapping QTL for complex binary traits. We model the binary trait under the classical threshold model of quantitative genetics. The Bayesian mapping statistics are developed on the basis of the idea of data augmentation. This treatment allows an easy way to generate the value of a hypothetical underlying variable (called the liability) and a threshold, which in turn allow the use of existing Bayesian statistics. The reversible jump Markov chain Monte Carlo algorithm is used to simulate the posterior samples of all unknowns, including the number of QTL, the locations and effects of identified QTL, genotypes of each individual at both the QTL and markers, and eventually the liability of each individual. The Bayesian mapping ends with an estimation of the joint posterior distribution of the number of QTL and the locations and effects of the identified QTL. Utilities of the method are demonstrated using a simulated outbred full-sib family. A computer program written in FORTRAN language is freely available on request.

Genetic and Nongenetic Bases for the L-Shaped Distribution of Quantitative Trait Loci Effects

Genetics ◽  
2001 ◽  
Vol 157 (4) ◽  
pp. 1773-1787 ◽  
Author(s):  
Bruno Bost ◽  
Dominique de Vienne ◽  
Frédéric Hospital ◽  
Laurence Moreau ◽  
Christine Dillmann
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Population Size ◽  
Quantitative Trait ◽  
Metabolic Flux ◽  
Genetic Model ◽  
Small Population ◽  
Additive Genetic Model ◽  
Metabolic Mechanism ◽  
Qtl Effects

Abstract The L-Shaped distribution of estimated QTL effects (R2) has long been reported. We recently showed that a metabolic mechanism could account for this phenomenon. But other nonexclusive genetic or nongenetic causes may contribute to generate such a distribution. Using analysis and simulations of an additive genetic model, we show that linkage disequilibrium between QTL, low heritability, and small population size may also be involved, regardless of the gene effect distribution. In addition, a comparison of the additive and metabolic genetic models revealed that estimates of the QTL effects for traits proportional to metabolic flux are far less robust than for additive traits. However, in both models the highest R2's repeatedly correspond to the same set of QTL.

scholarly journals Mapping quantitative trait loci in a selectively genotyped outbred population using a mixture model approach

1999 ◽  
Vol 73 (1) ◽  
pp. 75-83 ◽  
Author(s):  
DAVID L. JOHNSON ◽  
RITSERT C. JANSEN ◽  
JOHAN A. M. VAN ARENDONK
Keyword(s):  
Quantitative Trait Loci ◽  
Mixture Model ◽  
Quantitative Trait ◽  
Model Parameters ◽  
Outbred Population ◽  
Bivariate Model ◽  
Mixture Model Approach ◽  
Trait Loci ◽  
Unbiased Estimates ◽  
Model Approach

A mixture model approach is employed for the mapping of quantitative trait loci (QTL) for the situation where individuals, in an outbred population, are selectively genotyped. Maximum likelihood estimation of model parameters is obtained from an Expectation-Maximization (EM) algorithm facilitated by Monte Carlo sampling using a Gibbs sampler. All individuals with phenotypes, whether genotyped or not, are included in the analysis where both putative QTLs and missing marker genotypes are sampled conditional on known marker information and phenotype. A simulation of a half-sib family structure demonstrates that this mixture model approach will yield unbiased estimates of the allelic effects of a QTL affecting the trait on which selective genotyping is based. Unbiased estimates were also obtained for the QTL effect on a correlated trait provided both traits were analysed jointly in a bivariate model. The procedure is also compared with a standard linear model approach. The application of these methods is demonstrated for bovine chromosome six, the data arising from two Holstein–Friesian families selectively genotyped for protein yield in a daughter design.

scholarly journals Mapping quantitative trait loci with epistatic effects

2002 ◽  
Vol 79 (2) ◽  
pp. 185-198 ◽  
Author(s):  
NENGJUN YI ◽  
SHIZHONG XU
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Quantitative Traits ◽  
Threshold Model ◽  
Monte Carlo Algorithm ◽  
Epistatic Effects ◽  
Trait Loci ◽  
Robust Statistical Methods ◽  
Qtl Effects

Epistatic variance can be an important source of variation for complex traits. However, detecting epistatic effects is difficult primarily due to insufficient sample sizes and lack of robust statistical methods. In this paper, we develop a Bayesian method to map multiple quantitative trait loci (QTLs) with epistatic effects. The method can map QTLs in complicated mating designs derived from the cross of two inbred lines. In addition to mapping QTLs for quantitative traits, the proposed method can even map genes underlying binary traits such as disease susceptibility using the threshold model. The parameters of interest are various QTL effects, including additive, dominance and epistatic effects of QTLs, the locations of identified QTLs and even the number of QTLs. When the number of QTLs is treated as an unknown parameter, the dimension of the model becomes a variable. This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data.

QTL Detection in the UK Suffolk and Texel Sheep Sire Referencing Schemes

2002 ◽  
Vol 2002 ◽  
pp. 22-22 ◽  
Author(s):  
G.A. Walling ◽  
A.D. Wilson ◽  
B.L. McTeir ◽  
P.M. Visscher ◽  
G. Simm ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Genomic Research ◽  
Qtl Detection ◽  
Genetic Progress ◽  
Sheep Breeds ◽  
Performance Traits ◽  
Commercially Important ◽  
The Uk ◽  
Terminal Sire

Genomic research and the detection of quantitative trait loci (QTL) provide tools to enhance genetic progress and improve understanding of the biology of commercially important traits. The large sire reference schemes in UK terminal sire sheep breeds provide a unique opportunity to investigate QTL segregation within commercial populations. This study aims to identify QTL for performance traits in commercial Suffolk and Texel sheep.

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 Stochastic Search Variable Selection for Identifying Multiple Quantitative Trait Loci

Genetics ◽  
2003 ◽  
Vol 164 (3) ◽  
pp. 1129-1138 ◽  
Author(s):  
Nengjun Yi ◽  
Varghese George ◽  
David B Allison
Keyword(s):  
Quantitative Trait Loci ◽  
Variable Selection ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Stochastic Search ◽  
Model Parameters ◽  
Data Set ◽  
Stochastic Search Variable Selection ◽  
Trait Loci ◽  
Search Variable

AbstractIn this article, we utilize stochastic search variable selection methodology to develop a Bayesian method for identifying multiple quantitative trait loci (QTL) for complex traits in experimental designs. The proposed procedure entails embedding multiple regression in a hierarchical normal mixture model, where latent indicators for all markers are used to identify the multiple markers. The markers with significant effects can be identified as those with higher posterior probability included in the model. A simple and easy-to-use Gibbs sampler is employed to generate samples from the joint posterior distribution of all unknowns including the latent indicators, genetic effects for all markers, and other model parameters. The proposed method was evaluated using simulated data and illustrated using a real data set. The results demonstrate that the proposed method works well under typical situations of most QTL studies in terms of number of markers and marker density.

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