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Activity Number:
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520
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2008 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #301682 |
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Title:
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Bayesian Mixture Structural Equation Modeling in Multiple-Trait QTL Mapping
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Author(s):
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Xiaojuan Mi*+ and Kent M. Eskridge and Dong Wang
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Companies:
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University of Nebraska and University of Nebraska and University of Nebraska-Lincoln
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Address:
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Department of Statistics, 340 Hardin Hall North,, Lincoln, 68583-0963,
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Keywords:
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Bayesian ; QTL mapping ; mixture SEM ; multiple-trait
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Abstract:
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We develop a structural equation model (SEM) for multiple traits QTL mapping using a Bayesian approach in a recombinant inbred lines (RIL) population. Under the Bayesian framework, parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study. Compared with single trait Bayesian analysis, our proposed method not only improved statistical power of QTL detection and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more complex and biologically realistic model.
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