Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design
Abstract Genomic prediction is a modern approach that uses genome‐wide markers to predict the genetic merit of unphenotyped individuals. With the potential to reduce the breeding cycles and increase the selection accuracy, this tool has been designed to rank genotypes and maximize genetic gains. Des...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-09-01
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| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20488 |
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| _version_ | 1849221253734334464 |
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| author | Paul Adunola Luis Felipe V. Ferrão Juliana Benevenuto Camila F. Azevedo Patricio R. Munoz |
| author_facet | Paul Adunola Luis Felipe V. Ferrão Juliana Benevenuto Camila F. Azevedo Patricio R. Munoz |
| author_sort | Paul Adunola |
| collection | DOAJ |
| description | Abstract Genomic prediction is a modern approach that uses genome‐wide markers to predict the genetic merit of unphenotyped individuals. With the potential to reduce the breeding cycles and increase the selection accuracy, this tool has been designed to rank genotypes and maximize genetic gains. Despite this importance, its practical implementation in breeding programs requires critical allocation of resources for its application in a predictive framework. In this study, we integrated genetic and data‐driven methods to allocate resources for phenotyping and genotyping tailored to genomic prediction. To this end, we used a historical blueberry (Vaccinium corymbosun L.) breeding dataset containing more than 3000 individuals, genotyped using probe‐based target sequencing and phenotyped for three fruit quality traits over several years. Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. Altogether, we provided a data‐oriented decision‐making approach for breeders by demonstrating that critical breeding decisions associated with resource allocation for genomic prediction can be tackled through a combination of statistics and genetic methods. |
| format | Article |
| id | doaj-art-9dbb331d91eb4acdbf38ef25f7e7c948 |
| institution | Kabale University |
| issn | 1940-3372 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Plant Genome |
| spelling | doaj-art-9dbb331d91eb4acdbf38ef25f7e7c9482024-11-17T09:46:07ZengWileyThe Plant Genome1940-33722024-09-01173n/an/a10.1002/tpg2.20488Genomic selection optimization in blueberry: Data‐driven methods for marker and training population designPaul Adunola0Luis Felipe V. Ferrão1Juliana Benevenuto2Camila F. Azevedo3Patricio R. Munoz4Blueberry Breeding and Genomics Lab, Horticultural Sciences Department University of Florida Gainesville Florida USABlueberry Breeding and Genomics Lab, Horticultural Sciences Department University of Florida Gainesville Florida USABlueberry Breeding and Genomics Lab, Horticultural Sciences Department University of Florida Gainesville Florida USABlueberry Breeding and Genomics Lab, Horticultural Sciences Department University of Florida Gainesville Florida USABlueberry Breeding and Genomics Lab, Horticultural Sciences Department University of Florida Gainesville Florida USAAbstract Genomic prediction is a modern approach that uses genome‐wide markers to predict the genetic merit of unphenotyped individuals. With the potential to reduce the breeding cycles and increase the selection accuracy, this tool has been designed to rank genotypes and maximize genetic gains. Despite this importance, its practical implementation in breeding programs requires critical allocation of resources for its application in a predictive framework. In this study, we integrated genetic and data‐driven methods to allocate resources for phenotyping and genotyping tailored to genomic prediction. To this end, we used a historical blueberry (Vaccinium corymbosun L.) breeding dataset containing more than 3000 individuals, genotyped using probe‐based target sequencing and phenotyped for three fruit quality traits over several years. Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. Altogether, we provided a data‐oriented decision‐making approach for breeders by demonstrating that critical breeding decisions associated with resource allocation for genomic prediction can be tackled through a combination of statistics and genetic methods.https://doi.org/10.1002/tpg2.20488 |
| spellingShingle | Paul Adunola Luis Felipe V. Ferrão Juliana Benevenuto Camila F. Azevedo Patricio R. Munoz Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design The Plant Genome |
| title | Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design |
| title_full | Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design |
| title_fullStr | Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design |
| title_full_unstemmed | Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design |
| title_short | Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design |
| title_sort | genomic selection optimization in blueberry data driven methods for marker and training population design |
| url | https://doi.org/10.1002/tpg2.20488 |
| work_keys_str_mv | AT pauladunola genomicselectionoptimizationinblueberrydatadrivenmethodsformarkerandtrainingpopulationdesign AT luisfelipevferrao genomicselectionoptimizationinblueberrydatadrivenmethodsformarkerandtrainingpopulationdesign AT julianabenevenuto genomicselectionoptimizationinblueberrydatadrivenmethodsformarkerandtrainingpopulationdesign AT camilafazevedo genomicselectionoptimizationinblueberrydatadrivenmethodsformarkerandtrainingpopulationdesign AT patriciormunoz genomicselectionoptimizationinblueberrydatadrivenmethodsformarkerandtrainingpopulationdesign |