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Activity Number:
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473
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305661 |
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Title:
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Hierarchical Generalized Linear Models for Multiple QTL Mapping
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Author(s):
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Nengjun Yi*+
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Companies:
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The University of Alabama at Birmingham
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Address:
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Department of Biostatistics, Birmingham, AL, 35294,
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Keywords:
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Bayesian methods ; Generalized linear models ; Interactions ; Quantitative trait loci ; Shrinkage
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Abstract:
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We develop hierarchical generalized linear models and computationally efficient algorithms for genome-wide analysis of quantitative trait loci (QTL) for various types of phenotypes in experimental crosses. The proposed models can fit a large number of effects, including covariates, main effects of numerous loci, gene-gene (epistasis) and gene-environment (G×E) interactions. The key to the approach is the use of continuous prior distribution on coefficients that favors sparseness in the fitted model and facilitates computation. We develop a fast expectation-maximization (EM) algorithm to fit models by estimating posterior modes of coefficients. We incorporate our algorithm into the iteratively weighted least squares for classical generalized linear models as implemented in the package R. We propose a model search strategy to build a parsimonious model.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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