A model for estimating joint maternal-offspring effects on seed development in autogamous plants

1 Department of Statistics, University of Florida, Gainesville, Florida 2 Agronomy Department, University of Florida, Gainesville, Florida 3 Department of Plant Sciences, University of Arizona, Tucson, Arizona We present a statistical model for testing and estimating the effects of maternal-offsprin...

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Published in:Physiological genomics 2004-11, Vol.19 (3), p.262-269
Main Authors: Zhang, Li, Yang, Mark C. K, Wang, Xuelu, Larkins, Brian A, Gallo-Meagher, Maria, Wu, Rongling
Format: Article
Language:English
Subjects:
Algorithms
Amino Acids - metabolism
Chromosome Mapping - methods
Chromosome Mapping - statistics & numerical data
Chromosomes, Plant - genetics
Computer Simulation - statistics & numerical data
Genome, Plant
Genotype
Likelihood Functions
Models, Statistical
Monte Carlo Method
Peptide Elongation Factor 1 - genetics
Plant Proteins - genetics
Plants - genetics
Ploidies
Quantitative Trait Loci - genetics
Research Design - statistics & numerical data
Seeds - growth & development
Zea mays
Zea mays - genetics
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Summary:1 Department of Statistics, University of Florida, Gainesville, Florida 2 Agronomy Department, University of Florida, Gainesville, Florida 3 Department of Plant Sciences, University of Arizona, Tucson, Arizona We present a statistical model for testing and estimating the effects of maternal-offspring genome interaction on the embryo and endosperm traits during seed development in autogamous plants. Our model is constructed within the context of maximum likelihood implemented with the EM algorithm. Extensive simulations were performed to investigate the statistical properties of our approach. We have successfully identified a quantitative trait locus that exerts a significant maternal-offspring interaction effect on amino acid contents of the endosperm in maize, demonstrating the power of our approach. This approach will be broadly useful in mapping endosperm traits for many agriculturally important crop plants and also make it possible to study the genetic significance of double fertilization in the evolution of higher plants. autogamous plants; EM algorithm; linkage; maternal-offspring interaction; quantitative trait loci
ISSN:1094-8341
1531-2267
DOI:10.1152/physiolgenomics.00052.2004