scholarly journals Bayesian Mapping of Genomewide Interacting Quantitative Trait Loci for Ordinal Traits

Genetics ◽  
2007 ◽  
Vol 176 (3) ◽  
pp. 1855-1864 ◽  
Author(s):  
Nengjun Yi ◽  
Samprit Banerjee ◽  
Daniel Pomp ◽  
Brian S. Yandell
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Ordinal Traits ◽  
Trait Loci ◽  
Bayesian Mapping

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.

Mapping Multiple Quantitative Trait Loci for Ordinal Traits

2004 ◽  
Vol 34 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu ◽  
Varghese George ◽  
David B. Allison
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Ordinal Traits ◽  
Trait Loci

Robust Bayesian mapping of quantitative trait loci using Student-t distribution for residual

2008 ◽  
Vol 118 (3) ◽  
pp. 609-617 ◽  
Author(s):  
Xin Wang ◽  
Zhongze Piao ◽  
Biye Wang ◽  
Runqing Yang ◽  
Zhixiang Luo
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Student T Distribution ◽  
Trait Loci ◽  
Bayesian Mapping ◽  
T Distribution

Bayesian Mapping of Quantitative Trait Loci Under Complicated Mating Designs

Genetics ◽  
2001 ◽  
Vol 157 (4) ◽  
pp. 1759-1771 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Mixed Model ◽  
Inbred Lines ◽  
Likelihood Method ◽  
Mcmc Algorithm ◽  
Model Framework ◽  
Multiple Alleles ◽  
Trait Loci ◽  
Bayesian Mapping

AbstractQuantitative trait loci (QTL) are easily studied in a biallelic system. Such a system requires the cross of two inbred lines presumably fixed for alternative alleles of the QTL. However, development of inbred lines can be time consuming and cost ineffective for species with long generation intervals and severe inbreeding depression. In addition, restriction of the investigation to a biallelic system can sometimes be misleading because many potentially important allelic interactions do not have a chance to express and thus fail to be detected. A complicated mating design involving multiple alleles mimics the actual breeding system. However, it is difficult to develop the statistical model and algorithm using the classical maximum-likelihood method. In this study, we investigate the application of a Bayesian method implemented via the Markov chain Monte Carlo (MCMC) algorithm to QTL mapping under arbitrarily complicated mating designs. We develop the method under a mixed-model framework where the genetic values of founder alleles are treated as random and the nongenetic effects are treated as fixed. With the MCMC algorithm, we first draw the gene flows from the founders to the descendants for each QTL and then draw samples of the genetic parameters. Finally, we are able to simultaneously infer the posterior distribution of the number, the additive and dominance variances, and the chromosomal locations of all identified QTL.

scholarly journals Bayesian Mapping of Quantitative Trait Loci for Multiple Complex Traits with the Use of Variance Components

2007 ◽  
Vol 81 (2) ◽  
pp. 304-320 ◽  
Author(s):  
Jianfeng Liu ◽  
Yongjun Liu ◽  
Xiaogang Liu ◽  
Hong-Wen Deng
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Variance Components ◽  
Trait Loci ◽  
Bayesian Mapping

Bayesian mapping of quantitative trait loci (QTL) controlling soybean cyst nematode resistant

Euphytica ◽  
2012 ◽  
Vol 186 (3) ◽  
pp. 907-917 ◽  
Author(s):  
Osvin Arriagada ◽  
Freddy Mora ◽  
Joaquín C. Dellarossa ◽  
Marcia F. S. Ferreira ◽  
Gerardo D. L. Cervigni ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Soybean Cyst Nematode ◽  
Cyst Nematode ◽  
Trait Loci ◽  
Bayesian Mapping

scholarly journals Bayesian Mapping of Quantitative Trait Loci Under the Identity-by-Descent-Based Variance Component Model

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 411-422 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Posterior Distribution ◽  
Quantitative Trait ◽  
Variance Component ◽  
Threshold Model ◽  
Variance Component Model ◽  
Identity By Descent ◽  
Mapping Procedure ◽  
Trait Loci ◽  
Bayesian Mapping

AbstractVariance component analysis of quantitative trait loci (QTL) is an important strategy of genetic mapping for complex traits in humans. The method is robust because it can handle an arbitrary number of alleles with arbitrary modes of gene actions. The variance component method is usually implemented using the proportion of alleles with identity-by-descent (IBD) shared by relatives. As a result, information about marker linkage phases in the parents is not required. The method has been studied extensively under either the maximum-likelihood framework or the sib-pair regression paradigm. However, virtually all investigations are limited to normally distributed traits under a single QTL model. In this study, we develop a Bayes method to map multiple QTL. We also extend the Bayesian mapping procedure to identify QTL responsible for the variation of complex binary diseases in humans under a threshold model. The method can also treat the number of QTL as a parameter and infer its posterior distribution. We use the reversible jump Markov chain Monte Carlo method to infer the posterior distributions of parameters of interest. The Bayesian mapping procedure ends with an estimation of the joint posterior distribution of the number of QTL and the locations and variances of the identified QTL. Utilities of the method are demonstrated using a simulated population consisting of multiple full-sib families.

Identification of cis-regulated genes in quantitative trait loci driving liver fibrosis

2012 ◽  
Vol 50 (08) ◽  
Author(s):  
R Hall ◽  
R Müllenbach ◽  
S Huss ◽  
R Alberts ◽  
K Schughart ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Liver Fibrosis ◽  
Quantitative Trait ◽  
Trait Loci

Identification of NASH Quantitative Trait Loci (QTLs) using in silico mapping analysis in inbred mice

2004 ◽  
Vol 42 (08) ◽  
Author(s):  
F Lammert ◽  
S Hillebrandt ◽  
S Matern ◽  
K Spatz ◽  
RM Green
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
In Silico ◽  
Inbred Mice ◽  
In Silico Mapping ◽  
Mapping Analysis ◽  
Trait Loci
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