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Estimation of Genetic Effects and Genotype-Phenotype Maps

Determining the genetic architecture of complex traits is a necessary step to understand phenotypic changes in natural, experimental and domestic populations. However, this is still a major challenge for modern genetics, since the estimation of genetic effects tends to be complicated by genetic inte...

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Detalles Bibliográficos
Autores principales: Le Rouzic, Arnaud, Álvarez-Castro, José M.
Formato: Texto
Lenguaje:English
Publicado: Libertas Academica 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2614198/
https://www.ncbi.nlm.nih.gov/pubmed/19204820
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author Le Rouzic, Arnaud
Álvarez-Castro, José M.
author_facet Le Rouzic, Arnaud
Álvarez-Castro, José M.
author_sort Le Rouzic, Arnaud
collection PubMed
description Determining the genetic architecture of complex traits is a necessary step to understand phenotypic changes in natural, experimental and domestic populations. However, this is still a major challenge for modern genetics, since the estimation of genetic effects tends to be complicated by genetic interactions, which lead to changes in the effect of allelic substitutions depending on the genetic background. Recent progress in statistical tools aiming to describe and quantify genetic effects meaningfully improves the efficiency and the availability of genotype-to-phenotype mapping methods. In this contribution, we facilitate the practical use of the recently published ‘NOIA’ quantitative framework by providing an implementation of linear and multilinear regressions, change of reference operation and genotype-to-phenotype mapping in a package (‘noia’) for the software R, and we discuss theoretical and practical benefits evolutionary and quantitative geneticists may find in using proper modeling strategies to quantify the effects of genes.
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spelling pubmed-26141982009-02-09 Estimation of Genetic Effects and Genotype-Phenotype Maps Le Rouzic, Arnaud Álvarez-Castro, José M. Evol Bioinform Online Original Research Determining the genetic architecture of complex traits is a necessary step to understand phenotypic changes in natural, experimental and domestic populations. However, this is still a major challenge for modern genetics, since the estimation of genetic effects tends to be complicated by genetic interactions, which lead to changes in the effect of allelic substitutions depending on the genetic background. Recent progress in statistical tools aiming to describe and quantify genetic effects meaningfully improves the efficiency and the availability of genotype-to-phenotype mapping methods. In this contribution, we facilitate the practical use of the recently published ‘NOIA’ quantitative framework by providing an implementation of linear and multilinear regressions, change of reference operation and genotype-to-phenotype mapping in a package (‘noia’) for the software R, and we discuss theoretical and practical benefits evolutionary and quantitative geneticists may find in using proper modeling strategies to quantify the effects of genes. Libertas Academica 2008-06-28 /pmc/articles/PMC2614198/ /pubmed/19204820 Text en Copyright © 2008 The authors. http://creativecommons.org/licenses/by/3.0 This article is published under the Creative Commons Attribution By licence. For further information go to: http://creativecommons.org/licenses/by/3.0. (http://creativecommons.org/licenses/by/3.0)
spellingShingle Original Research
Le Rouzic, Arnaud
Álvarez-Castro, José M.
Estimation of Genetic Effects and Genotype-Phenotype Maps
title Estimation of Genetic Effects and Genotype-Phenotype Maps
title_full Estimation of Genetic Effects and Genotype-Phenotype Maps
title_fullStr Estimation of Genetic Effects and Genotype-Phenotype Maps
title_full_unstemmed Estimation of Genetic Effects and Genotype-Phenotype Maps
title_short Estimation of Genetic Effects and Genotype-Phenotype Maps
title_sort estimation of genetic effects and genotype-phenotype maps
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2614198/
https://www.ncbi.nlm.nih.gov/pubmed/19204820
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