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Data from: Generalized linear mixed models for mapping multiple quantitative trait loci

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Che X, Xu S (2012) Data from: Generalized linear mixed models for mapping multiple quantitative trait loci. Dryad Digital Repository. doi:10.5061/dryad.mn159hq6
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Dryad Package Identifier doi:10.5061/dryad.mn159hq6    74 views  
Abstract Many biological traits are discretely distributed in phenotype but continuously distributed in genetics because they are controlled by multiple genes and environmental variants. Due to the quantitative nature of the genetic background, these multiple genes are called quantitative trait loci (QTL). When the QTL effects are treated as random, they can be estimated in a single generalized linear mixed model (GLMM), even if the number of QTL may be larger than the sample size. The GLMM in its original form cannot be applied to QTL mapping for discrete traits if there are missing genotypes. We examined two alternative missing genotype handling methods: the expectation method and the overdispersion method. Simulation studies show that the two methods are efficient for multiple QTL mapping under the GLMM framework. The overdispersion method showed slight advantages over the expectation method in terms of smaller mean squared errors of the estimated QTL effects. The two met hods of GLMM were applied to multiple QTL mapping for the female fertility trait of wheat. Multiple QTL were detected to control the variation of the number of seeded spikelets.
Keywords QTL mapping, generalized linear mixed model, binomial trait,
Date Deposited 2012-01-26T18:41:08Z
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Data and Programs HDY-11-OR0427R    6 views   0 downloads View File Details
This folder contains many files related to the manuscript number HDY-11-OR0427R. In the Simulation folder, there are two datasets (binary and binomial) and two SAS programs used to analyze these data. In the Wheat folder, there is a dataset collected from a wheat QTL mapping experiment (Dou et al. 2009) and a SAS program that analyzes this dataset. The wheat data can also be downloaded from the original publication's journal website (Supplemental Material).
At the request of the author, this item is embargoed until the associated article is published.
To the extent possible under law, the authors have waived all copyright and related or neighboring rights to this data.  


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