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Systematic design for trait introgression projects
KEY MESSAGE: Using an Operations Research approach, we demonstrate design of optimal trait introgression projects with respect to competing objectives. ABSTRACT: We demonstrate an innovative approach for designing Trait Introgression (TI) projects based on optimization principles from Operations Res...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5606951/ https://www.ncbi.nlm.nih.gov/pubmed/28647895 http://dx.doi.org/10.1007/s00122-017-2938-9 |
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author | Cameron, John N. Han, Ye Wang, Lizhi Beavis, William D. |
author_facet | Cameron, John N. Han, Ye Wang, Lizhi Beavis, William D. |
author_sort | Cameron, John N. |
collection | PubMed |
description | KEY MESSAGE: Using an Operations Research approach, we demonstrate design of optimal trait introgression projects with respect to competing objectives. ABSTRACT: We demonstrate an innovative approach for designing Trait Introgression (TI) projects based on optimization principles from Operations Research. If the designs of TI projects are based on clear and measurable objectives, they can be translated into mathematical models with decision variables and constraints that can be translated into Pareto optimality plots associated with any arbitrary selection strategy. The Pareto plots can be used to make rational decisions concerning the trade-offs between maximizing the probability of success while minimizing costs and time. The systematic rigor associated with a cost, time and probability of success (CTP) framework is well suited to designing TI projects that require dynamic decision making. The CTP framework also revealed that previously identified ‘best’ strategies can be improved to be at least twice as effective without increasing time or expenses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00122-017-2938-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5606951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-56069512017-10-04 Systematic design for trait introgression projects Cameron, John N. Han, Ye Wang, Lizhi Beavis, William D. Theor Appl Genet Original Article KEY MESSAGE: Using an Operations Research approach, we demonstrate design of optimal trait introgression projects with respect to competing objectives. ABSTRACT: We demonstrate an innovative approach for designing Trait Introgression (TI) projects based on optimization principles from Operations Research. If the designs of TI projects are based on clear and measurable objectives, they can be translated into mathematical models with decision variables and constraints that can be translated into Pareto optimality plots associated with any arbitrary selection strategy. The Pareto plots can be used to make rational decisions concerning the trade-offs between maximizing the probability of success while minimizing costs and time. The systematic rigor associated with a cost, time and probability of success (CTP) framework is well suited to designing TI projects that require dynamic decision making. The CTP framework also revealed that previously identified ‘best’ strategies can be improved to be at least twice as effective without increasing time or expenses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00122-017-2938-9) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2017-06-24 2017 /pmc/articles/PMC5606951/ /pubmed/28647895 http://dx.doi.org/10.1007/s00122-017-2938-9 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Cameron, John N. Han, Ye Wang, Lizhi Beavis, William D. Systematic design for trait introgression projects |
title | Systematic design for trait introgression projects |
title_full | Systematic design for trait introgression projects |
title_fullStr | Systematic design for trait introgression projects |
title_full_unstemmed | Systematic design for trait introgression projects |
title_short | Systematic design for trait introgression projects |
title_sort | systematic design for trait introgression projects |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5606951/ https://www.ncbi.nlm.nih.gov/pubmed/28647895 http://dx.doi.org/10.1007/s00122-017-2938-9 |
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