From classical genetics to quantitative genetics to systems biology: modeling epistasis

Gene expression data has been used in lieu of phenotype in both classical and quantitative genetic settings. These two disciplines have separate approaches to measuring and interpreting epistasis, which is the interaction between alleles at different loci. We propose a framework for estimating and i...

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Bibliographic Details
Published in:PLoS genetics 2008-03, Vol.4 (3), p.e1000029-e1000029
Main Authors: Aylor, David L, Zeng, Zhao-Bang
Format: Article
Language:English
Subjects:
Alleles
Animals
Biology
Computational Biology/Systems Biology
Cyclic AMP-Dependent Protein Kinases - genetics
Cyclic AMP-Dependent Protein Kinases - metabolism
Dictyostelium - enzymology
Dictyostelium - genetics
Epistasis, Genetic
Experiments
Gene Expression
Gene Expression Profiling
Genetics and Genomics/Complex Traits
Genetics and Genomics/Gene Expression
Models, Genetic
Mutation
Oligonucleotide Array Sequence Analysis
Phenotype
Quantitative genetics
Regression Analysis
Systems Biology
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Summary:Gene expression data has been used in lieu of phenotype in both classical and quantitative genetic settings. These two disciplines have separate approaches to measuring and interpreting epistasis, which is the interaction between alleles at different loci. We propose a framework for estimating and interpreting epistasis from a classical experiment that combines the strengths of each approach. A regression analysis step accommodates the quantitative nature of expression measurements by estimating the effect of gene deletions plus any interaction. Effects are selected by significance such that a reduced model describes each expression trait. We show how the resulting models correspond to specific hierarchical relationships between two regulator genes and a target gene. These relationships are the basic units of genetic pathways and genomic system diagrams. Our approach can be extended to analyze data from a variety of experiments, multiple loci, and multiple environments.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1000029