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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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Detalles Bibliográficos
Autores principales: Aylor, David L., Zeng, Zhao-Bang
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2265472/
https://www.ncbi.nlm.nih.gov/pubmed/18369448
http://dx.doi.org/10.1371/journal.pgen.1000029
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author Aylor, David L.
Zeng, Zhao-Bang
author_facet Aylor, David L.
Zeng, Zhao-Bang
author_sort Aylor, David L.
collection PubMed
description 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.
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spelling pubmed-22654722008-03-14 From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis Aylor, David L. Zeng, Zhao-Bang PLoS Genet Research Article 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. Public Library of Science 2008-03-14 /pmc/articles/PMC2265472/ /pubmed/18369448 http://dx.doi.org/10.1371/journal.pgen.1000029 Text en Aylor and Zeng. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Aylor, David L.
Zeng, Zhao-Bang
From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis
title From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis
title_full From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis
title_fullStr From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis
title_full_unstemmed From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis
title_short From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis
title_sort from classical genetics to quantitative genetics to systems biology: modeling epistasis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2265472/
https://www.ncbi.nlm.nih.gov/pubmed/18369448
http://dx.doi.org/10.1371/journal.pgen.1000029
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