Joint Mapping of Quantitative Trait Loci for Multiple Binary Characters

Xu, Chenwu; Li, Zhikang; Xu, Shizhong

doi:10.1534/genetics.103.019406

Cited by 57 publications

(38 citation statements)

References 35 publications

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“…Some of them use a maximum-likelihood-based approach ( Jiang and Zeng 1995;Jackson et al 1999; Williams et al 1999a,b;Vieira et al 2000;Huang and Jiang 2003;Lund et al 2003;Xu et al 2005) or a least-squares approach (Knott and Haley 2000;Hackett et al 2001). Most of these methods involve a single-QTL model or at most very few QTL.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Quantitative Trait Loci Mapping for Multiple Traits

2008

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Most quantitative trait loci (QTL) mapping experiments typically collect phenotypic data on multiple correlated complex traits. However, there is a lack of a comprehensive genomewide mapping strategy for correlated traits in the literature. We develop Bayesian multiple-QTL mapping methods for correlated continuous traits using two multivariate models: one that assumes the same genetic model for all traits, the traditional multivariate model, and the other known as the seemingly unrelated regression (SUR) model that allows different genetic models for different traits. We develop computationally efficient Markov chain Monte Carlo (MCMC) algorithms for performing joint analysis. We conduct extensive simulation studies to assess the performance of the proposed methods and to compare with the conventional single-trait model. Our methods have been implemented in the freely available package R/qtlbim (http:/ /www.qtlbim.org), which greatly facilitates the general usage of the Bayesian methodology for unraveling the genetic architecture of complex traits. C OMPLEX traits involve effects of a multitude of genes in an interacting network. Mapping quantitative trait loci (QTL) means inferring the genetic architecture (number of genes, their positions, and their effects) underlying these complex traits. The QTL mapping problem has several salient features: first, the predictor variables in the regression (the genotypes of QTL) are not observed; second, it is really a model selection problem as there are typically thousands of loci to choose from; and third, the genomic loci on the same chromosome are correlated. Much has been done in this regard, especially in the univariate case (e.g., Lander and Botstein 1989;Jiang and Zeng 1997;Broman and Speed 2002). Bayesian methods have been very successful in the QTL mapping framework (Satagopan and Yandell 1996;Yi and Xu 2002;Yi et al. 2003Yi et al. , 2005Yi et al. , 2007Yi 2004); see a recent review by Yi and Shriner (2008).Most of these methods are applicable to mapping QTL for a single trait. However, in QTL experiments typically data on more than one trait are collected and, more often than not, they are correlated. It seems natural to jointly analyze these correlated traits. There are two distinct advantages for jointly analyzing correlated traits: including information from all traits can increase the power to detect QTL and the precision of the estimated QTL effects. Biologically, it is imperative to jointly analyze correlated traits to answer questions like pleiotropy (one gene influencing more than one trait) and/or close linkage (different QTL physically close to each other influencing the traits). Testing these hypotheses is key to understanding the underlying biochemical pathways causing complex traits, which is the ultimate goal of QTL mapping.Several methods have been developed to jointly analyze multiple correlated traits. Some of them use a maximum-likelihood-based approach ( Jiang and Zeng 1995;Jackson et al. 1999; Williams et al. 1999a,b;Vieira et al. 2000...

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Section: Introductionmentioning

confidence: 99%

Bayesian Quantitative Trait Loci Mapping for Multiple Traits

2008

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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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“…However, the conventional method of segregation analysis usually has Jiang and Zeng (1995) carried out seminal work in extending composite interval mapping to a multivariate method. Xu et al (2005) made the procedure applicable to multiple discrete traits. Methods have also been developed to handle longitudinal data by fitting them to a particular growth trajectory (Wu et al, 2004;Yang et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

“…After transformation, existing single-trait methods can be used directly. However, the results of super-trait analyses are often difficult to interpret in a biological setting (Hackett etal., 2001;Xu et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Multivariate segregation analysis for quantitative traits in line crosses

Xiao

Wang

et al. 2007

Heredity

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Segregation analysis is a method of detecting major genes for quantitative traits without using marker information. It serves as an important tool in helping investigators to plan further studies such as quantitative trait loci mapping or more sophisticated genomic analyses. However, current methods of segregation analysis for a single trait typically have low statistical power. We propose a multivariate segregation analysis (MSA) that takes advantage of the correlation structure of multiple quantitative traits to detect major genes. This method not only increases the statistical power, but allows dissection of the genetic architecture underlying the trait complex. In MSA the observed phenotypes of multiple correlated traits are fitted to a multivariate Gaussian mixture model. Model parameters are estimated under the maximum likelihood framework via the expectationmaximization algorithm. The presence of major genes is tested using likelihood ratio test statistics. Pleiotropy is distinguished from close linkage by comparing three possible models using the Bayesian information criterion. Two simulation experiments were performed based on the F 2 mating design. In the first, the statistical properties of MSA under varying heritabilities and sample sizes were investigated and the results compared with those obtained from single-trait analysis. In the second simulation the efficacy of MSA in separating pleiotropy from close linkage was demonstrated. Finally, the new method was applied to real data and detected a major gene responsible for both plant height and tiller number in rice.

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Joint Mapping of Quantitative Trait Loci for Multiple Binary Characters

Cited by 57 publications

References 35 publications

Bayesian Quantitative Trait Loci Mapping for Multiple Traits

Bayesian Quantitative Trait Loci Mapping for Multiple Traits

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

Multivariate segregation analysis for quantitative traits in line crosses

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