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Simulations of rate of genetic gain in dry bean breeding programs

KEY MESSAGE: A reference study for breeders aiming at maximizing genetic gain in common bean. Depending on trait heritability and genetic architecture, conventional approaches may provide an advantage over other frameworks. ABSTRACT: Dry beans (Phaseolus vulgaris L.) are a nutrient dense legume that...

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Autores principales: Lin, Jennifer, Arief, Vivi, Jahufer, Zulfi, Osorno, Juan, McClean, Phil, Jarquin, Diego, Hoyos-Villegas, Valerio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9859924/
https://www.ncbi.nlm.nih.gov/pubmed/36662255
http://dx.doi.org/10.1007/s00122-023-04244-x
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author Lin, Jennifer
Arief, Vivi
Jahufer, Zulfi
Osorno, Juan
McClean, Phil
Jarquin, Diego
Hoyos-Villegas, Valerio
author_facet Lin, Jennifer
Arief, Vivi
Jahufer, Zulfi
Osorno, Juan
McClean, Phil
Jarquin, Diego
Hoyos-Villegas, Valerio
author_sort Lin, Jennifer
collection PubMed
description KEY MESSAGE: A reference study for breeders aiming at maximizing genetic gain in common bean. Depending on trait heritability and genetic architecture, conventional approaches may provide an advantage over other frameworks. ABSTRACT: Dry beans (Phaseolus vulgaris L.) are a nutrient dense legume that is consumed by developed and developing nations around the world. The progress to improve this crop has been quite steady. However, with the continued rise in global populations, there are demands to expedite genetic gains. Plant breeders have been at the forefront at increasing yields in the common bean. As breeding programs are both time-consuming and resource intensive, resource allocation must be carefully considered. To assist plant breeders, computer simulations can provide useful information that may then be applied to the real world. This study evaluated multiple breeding scenarios in the common bean and involved five selection strategies, three breeding frameworks, and four different parental population sizes. In addition, the breeding scenarios were implemented in three different traits: days to flowering, white mold tolerance, and seed yield. Results from the study reflect the complexity of breeding programs, with the optimal breeding scenario varying based on trait being selected. Relative genetic gains per cycle of up to 8.69% for seed yield could be obtained under the use of the optimal breeding scenario. Principal component analyses revealed similarity between strategies, where single seed descent and the modified pedigree method would often aggregate. As well, clusters in the direction of the Hamming distance eigenvector are a good indicator of poor performance in a strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00122-023-04244-x.
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spelling pubmed-98599242023-01-22 Simulations of rate of genetic gain in dry bean breeding programs Lin, Jennifer Arief, Vivi Jahufer, Zulfi Osorno, Juan McClean, Phil Jarquin, Diego Hoyos-Villegas, Valerio Theor Appl Genet Original Article KEY MESSAGE: A reference study for breeders aiming at maximizing genetic gain in common bean. Depending on trait heritability and genetic architecture, conventional approaches may provide an advantage over other frameworks. ABSTRACT: Dry beans (Phaseolus vulgaris L.) are a nutrient dense legume that is consumed by developed and developing nations around the world. The progress to improve this crop has been quite steady. However, with the continued rise in global populations, there are demands to expedite genetic gains. Plant breeders have been at the forefront at increasing yields in the common bean. As breeding programs are both time-consuming and resource intensive, resource allocation must be carefully considered. To assist plant breeders, computer simulations can provide useful information that may then be applied to the real world. This study evaluated multiple breeding scenarios in the common bean and involved five selection strategies, three breeding frameworks, and four different parental population sizes. In addition, the breeding scenarios were implemented in three different traits: days to flowering, white mold tolerance, and seed yield. Results from the study reflect the complexity of breeding programs, with the optimal breeding scenario varying based on trait being selected. Relative genetic gains per cycle of up to 8.69% for seed yield could be obtained under the use of the optimal breeding scenario. Principal component analyses revealed similarity between strategies, where single seed descent and the modified pedigree method would often aggregate. As well, clusters in the direction of the Hamming distance eigenvector are a good indicator of poor performance in a strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00122-023-04244-x. Springer Berlin Heidelberg 2023-01-20 2023 /pmc/articles/PMC9859924/ /pubmed/36662255 http://dx.doi.org/10.1007/s00122-023-04244-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Lin, Jennifer
Arief, Vivi
Jahufer, Zulfi
Osorno, Juan
McClean, Phil
Jarquin, Diego
Hoyos-Villegas, Valerio
Simulations of rate of genetic gain in dry bean breeding programs
title Simulations of rate of genetic gain in dry bean breeding programs
title_full Simulations of rate of genetic gain in dry bean breeding programs
title_fullStr Simulations of rate of genetic gain in dry bean breeding programs
title_full_unstemmed Simulations of rate of genetic gain in dry bean breeding programs
title_short Simulations of rate of genetic gain in dry bean breeding programs
title_sort simulations of rate of genetic gain in dry bean breeding programs
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9859924/
https://www.ncbi.nlm.nih.gov/pubmed/36662255
http://dx.doi.org/10.1007/s00122-023-04244-x
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