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Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast

Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that co...

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Detalles Bibliográficos
Autores principales: Forsberg, Simon K. G., Bloom, Joshua S., Sadhu, Meru J., Kruglyak, Leonid, Carlborg, Örjan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5459553/
https://www.ncbi.nlm.nih.gov/pubmed/28250458
http://dx.doi.org/10.1038/ng.3800
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author Forsberg, Simon K. G.
Bloom, Joshua S.
Sadhu, Meru J.
Kruglyak, Leonid
Carlborg, Örjan
author_facet Forsberg, Simon K. G.
Bloom, Joshua S.
Sadhu, Meru J.
Kruglyak, Leonid
Carlborg, Örjan
author_sort Forsberg, Simon K. G.
collection PubMed
description Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multi-locus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection.
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spelling pubmed-54595532017-08-27 Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast Forsberg, Simon K. G. Bloom, Joshua S. Sadhu, Meru J. Kruglyak, Leonid Carlborg, Örjan Nat Genet Article Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multi-locus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection. 2017-02-27 2017-04 /pmc/articles/PMC5459553/ /pubmed/28250458 http://dx.doi.org/10.1038/ng.3800 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Forsberg, Simon K. G.
Bloom, Joshua S.
Sadhu, Meru J.
Kruglyak, Leonid
Carlborg, Örjan
Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast
title Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast
title_full Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast
title_fullStr Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast
title_full_unstemmed Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast
title_short Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast
title_sort accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5459553/
https://www.ncbi.nlm.nih.gov/pubmed/28250458
http://dx.doi.org/10.1038/ng.3800
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