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A method to optimize selection on multiple identified quantitative trait loci

A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multipl...

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Detalles Bibliográficos
Autores principales: Chakraborty, Reena, Moreau, Laurence, Dekkers, Jack CM
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2002
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2705425/
https://www.ncbi.nlm.nih.gov/pubmed/12081805
http://dx.doi.org/10.1186/1297-9686-34-2-145
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author Chakraborty, Reena
Moreau, Laurence
Dekkers, Jack CM
author_facet Chakraborty, Reena
Moreau, Laurence
Dekkers, Jack CM
author_sort Chakraborty, Reena
collection PubMed
description A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models.
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spelling pubmed-27054252009-07-03 A method to optimize selection on multiple identified quantitative trait loci Chakraborty, Reena Moreau, Laurence Dekkers, Jack CM Genet Sel Evol Research A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models. BioMed Central 2002-03-15 /pmc/articles/PMC2705425/ /pubmed/12081805 http://dx.doi.org/10.1186/1297-9686-34-2-145 Text en Copyright © 2002 INRA, EDP Sciences
spellingShingle Research
Chakraborty, Reena
Moreau, Laurence
Dekkers, Jack CM
A method to optimize selection on multiple identified quantitative trait loci
title A method to optimize selection on multiple identified quantitative trait loci
title_full A method to optimize selection on multiple identified quantitative trait loci
title_fullStr A method to optimize selection on multiple identified quantitative trait loci
title_full_unstemmed A method to optimize selection on multiple identified quantitative trait loci
title_short A method to optimize selection on multiple identified quantitative trait loci
title_sort method to optimize selection on multiple identified quantitative trait loci
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2705425/
https://www.ncbi.nlm.nih.gov/pubmed/12081805
http://dx.doi.org/10.1186/1297-9686-34-2-145
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