Multitrait analysis of quantitative trait loci using Bayesian composite space approach

Fang, Ming; Jiang, Dan; Pu, Li Jun; Gao, Hao; Wang, Hong Yi; Yang, Ruihuan

doi:10.1186/1471-2156-9-48

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…The doubled-haploid barley data published by Hayes et al (1993) have been analyzed by numerous investigators (Attari et al 1998;Fang et al 2008;Han et al 1995Han et al , 1997Romagosa et al 1996;Xu and Hu 2010). They are good sample data for Q 9 E interactions because the DH lines were evaluated in multiple environments.…”

Section: Introductionmentioning

confidence: 99%

An expectation and maximization algorithm for estimating Q × E interaction effects

Zhao

2012

Theor Appl Genet

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A Markov chain Monte Carlo (MCMC) implemented Bayesian method has been developed to detect quantitative trait loci (QTL) effects and Q x E interaction effects. However, the MCMC algorithm is time consuming due to repeated samplings of QTL parameters. We developed an expectation and maximization (EM) algorithm as an alternative method for detecting QTL and Q x E interaction. Simulation studies and real data analysis showed that the EM algorithm produced comparable result as the Bayesian method, but with a speed many magnitudes faster than the MCMC algorithm. We used the EM algorithm to analyze a well known barley dataset produced by the North American Barley Genome Mapping Project. The dataset contained eight quantitative traits collected from 150 doubled-haploid (DH) lines evaluated in multiple environments. Each line was genotyped for 495 polymorphic markers. The result showed that all eight traits exhibited QTL main effects and Q x E interaction effects. On average, the main effects and Q x E interaction effects contributed 34.56 and 16.23% of the total phenotypic variance, respectively. Furthermore, we found that whether or not a locus shows Q x E interaction does not depend on the presence of main effect.

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Section: Introductionmentioning

confidence: 99%

An expectation and maximization algorithm for estimating Q × E interaction effects

Zhao

2012

Theor Appl Genet

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“…It is also straightforward to extend the proposed method to map QTL for binary trait by treating the continuous liability as a variable and sampling it from truncated normal distribution, which is not difficult to practice. Finally, we can extend the proposed method to multitrait mapping of QTL (Jiang and Zeng 1995;Xu et al 2005;Mangin et al 1998;Mähler et al 2002;Le Roy 2003, 2004;Liu et al 2007;Fang et al 2008b;Banerjee et al 2008). Multitrait analysis can increase QTL detecting power and explain the genetic mechanism that causes the genetic relation.…”

Section: Discussionmentioning

confidence: 94%

Bayesian Composite Model Space Approach for Mapping Quantitative Trait Loci in Variance Component Model

2009

Self Cite

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In this article, we successfully apply the novel model selection method, Bayesian composite model space approach which has been used to map quantitative trait loci (QTL) for allelic substitution model, to map QTL for variance component model. The novel model selection approach has two advantages compared to the reversible jump Markov chain Monte Carlo method. Firstly, it mixes well due to the fixedness of the model dimension; secondly, it can map multiple QTL with higher power especially in genome-wide QTL mapping; finally, in the new method, it is also easy to incorporate our prior information about the variance components, which may bring precise estimate for variance components. A series of simulation experiments were conducted to demonstrate the general characters of the proposed method. The computer program is written in FORTRAN language, which is also built into a software "BayesMapQTL", and they also can be used for real data analysis and are available for request.

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“…On the other hand, association studies tend to involve more than one quantitative traits or complex diseases located in different regions of chromosomes, allowing the investigation of common genetic risk factors underlying multiple traits. Although these traits could be analyzed separately with univariate genetic model, statistical methods and algorithms have been developed for simultaneously analyzing multiple normal traits (Jiang and Zeng, 1995;Fang et al, 2008;Ayroles et al, 2009;Zhu and Zhang, 2009;Stephens, 2010;Nadeau and Dudley, 2011;Shriner, 2012), multiple discrete traits (Lange and Whittaker, 2001;Xu et al, 2005;Yang et al, 2009) and multiple mixed traits of normal and discrete traits (Prentice and Zhao, 1991;Fitzmaurice and Laird, 1997;Liu et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Gao

Jiang

et al. 2014

Heredity

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Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent 'super traits' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle. Heredity (2014) 113, 526-532; doi:10.1038/hdy.2014.57; published online 2 July 2014 INTRODUCTIONWith the advance of high-throughput genotyping technology, the paradigm of mapping quantitative trait locus (QTL) based on the linkage analysis of sparse genetic markers has gradually shifted to genome-wide association studies (GWAS) based on thousands and thousands of single-nucleotide polymorphisms (SNPs). On the other hand, association studies tend to involve more than one quantitative traits or complex diseases located in different regions of chromosomes, allowing the investigation of common genetic risk factors underlying multiple traits. Although these traits could be analyzed separately with univariate genetic model, statistical methods and algorithms have been developed for simultaneously analyzing multiple

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Multitrait analysis of quantitative trait loci using Bayesian composite space approach

Cited by 5 publications

References 24 publications

An expectation and maximization algorithm for estimating Q × E interaction effects

An expectation and maximization algorithm for estimating Q × E interaction effects

Bayesian Composite Model Space Approach for Mapping Quantitative Trait Loci in Variance Component Model

Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

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