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 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.
Aylor
David L
DL
Bioinformatics Research Center and Program in Bioinformatics, North Carolina State University, Raleigh, North Carolina, United States of America.
Zeng
Zhao-Bang
ZB
D000483:Alleles; D000818:Animals; D017868:Cyclic AMP-Dependent Protein Kinases / Q000235:genetics / Q000378:metabolism; D004023:Dictyostelium / Q000201:enzymology / Q000235:genetics; D004843:Epistasis, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling; D008957:Models, Genetic; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D012044:Regression Analysis; D049490:Systems Biology
D017868:Cyclic AMP-Dependent Protein Kinases