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Bioinformatics Advance Access published online on February 22, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti342
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received December 20, 2004
Revised January 25, 2005
Accepted February 17, 2005

Article

Mapping genome-genome epistasis: a high-dimensional model

Yuehua Cui 1 and Rongling Wu 1*

1 Department of Statistics, University of Florida, Gainesville, FL 32611

* To whom correspondence should be addressed.
Rongling Wu, E-mail: rwu{at}stat.ufl.edu


  Abstract

Motivation: Proper development of any organ or tissue requires coordinated expression of its underlying genes that can be located on different genomes contained in a single organism. For example, each step in seed development for a single higher plant is the consequence of gene interactions from the maternal, embryo and endosperm genomes.

Results: We present a multivariate statistical model for mapping quantitative trait loci (QTL) by incorporate two important aspects of seed development in plants - QTL interactions derived from different genomes, the maternal, embryo and endosperm, and genetic correlations among phenotypic traits expressed in different genome-specific tissues. This model, which has high dimensionality, is constructed within the maximum likelihood context based on a finite mixture model. The implementation of the EM algorithm allows for the efficient estimation of QTL positions, their action and interaction effects and pleiotropic effects. The application of this high-dimensional model to a real rice dataset has validated its usefulness.

Conclusions: Our model was derived for self-pollinated plants, but it can be extended to cross-pollinated plants and animals. With the burgeoning of genetic and genomic data, this high-dimensional model will have many implications for agricultural and evolutionary genetic research.


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