%0 Journal Article %1 Aylor2008From %A Aylor, David L. %A Zeng, Zhao-Bang %C Bioinformatics Research Center and Program in Bioinformatics, North Carolina State University, Raleigh, North Carolina, United States of America. %D 2008 %I Public Library of Science %J PLoS Genet %K epistasis genetics %N 3 %P e1000029+ %R 10.1371/journal.pgen.1000029 %T From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis %U http://dx.doi.org/10.1371/journal.pgen.1000029 %V 4 %X 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. Epistasis has long had two slightly different meanings depending on the context in which it is discussed. The classical definition describes an allele at one locus completely masking the effect of an allele at a second locus. Such relationships can be interpreted as hierarchical, and they can be combined to infer genetic pathways. In quantitative genetics, epistasis encompasses a wide range of interactions and can be extended to more than two loci. These two definitions coexist because they are typically applied to different types of study populations and different types of traits. The current trend is to treat gene expression as a trait in a variety of genetic backgrounds. This provides reason to revisit epistasis in this new context. We accommodate the continuous nature of gene expression using ideas from quantitative genetics, but retain the hierarchical interpretation of the classical experiment. These hierarchical relationships are the building blocks of systems diagrams and genetic pathways. This framework can serve as a foundation for future epistasis analyses based on genomic data.

@article{Aylor2008From, abstract = {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. Epistasis has long had two slightly different meanings depending on the context in which it is discussed. The classical definition describes an allele at one locus completely masking the effect of an allele at a second locus. Such relationships can be interpreted as hierarchical, and they can be combined to infer genetic pathways. In quantitative genetics, epistasis encompasses a wide range of interactions and can be extended to more than two loci. These two definitions coexist because they are typically applied to different types of study populations and different types of traits. The current trend is to treat gene expression as a trait in a variety of genetic backgrounds. This provides reason to revisit epistasis in this new context. We accommodate the continuous nature of gene expression using ideas from quantitative genetics, but retain the hierarchical interpretation of the classical experiment. These hierarchical relationships are the building blocks of systems diagrams and genetic pathways. This framework can serve as a foundation for future epistasis analyses based on genomic data.}, added-at = {2018-12-02T16:09:07.000+0100}, address = {Bioinformatics Research Center and Program in Bioinformatics, North Carolina State University, Raleigh, North Carolina, United States of America.}, author = {Aylor, David L. and Zeng, Zhao-Bang}, biburl = {https://www.bibsonomy.org/bibtex/2dabae1f210fb3c0bb7dbc9f66a5bf27c/karthikraman}, citeulike-article-id = {2681648}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pgen.1000029}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18369448}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18369448}, day = 14, doi = {10.1371/journal.pgen.1000029}, interhash = {d6fa63c8d96863491f0d4843697d76c7}, intrahash = {dabae1f210fb3c0bb7dbc9f66a5bf27c}, issn = {1553-7404}, journal = {PLoS Genet}, keywords = {epistasis genetics}, month = mar, number = 3, pages = {e1000029+}, pmid = {18369448}, posted-at = {2010-06-21 08:13:12}, priority = {2}, publisher = {Public Library of Science}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis}, url = {http://dx.doi.org/10.1371/journal.pgen.1000029}, volume = 4, year = 2008 }