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Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model
Prediction techniques are important in plant breeding as they provide a tool for selection that is more efficient and economical than traditional phenotypic and pedigree based selection. The conventional genomic prediction models include molecular marker information to predict the phenotype. With th...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6442071/ https://www.ncbi.nlm.nih.gov/pubmed/30956524 http://dx.doi.org/10.1177/1176934319840026 |
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author | Howard, Reka Jarquin, Diego |
author_facet | Howard, Reka Jarquin, Diego |
author_sort | Howard, Reka |
collection | PubMed |
description | Prediction techniques are important in plant breeding as they provide a tool for selection that is more efficient and economical than traditional phenotypic and pedigree based selection. The conventional genomic prediction models include molecular marker information to predict the phenotype. With the development of new phenomics techniques we have the opportunity to collect image data on the plants, and extend the traditional genomic prediction models where we incorporate diverse set of information collected on the plants. In our research, we developed a hybrid matrix model that incorporates molecular marker and canopy coverage information as a weighted linear combination to predict grain yield for the soybean nested association mapping (SoyNAM) panel. To obtain the testing and training sets, we clustered the individuals based on their marker and canopy information using 2 different clustering techniques, and we compared 5 different cross-validation schemes. The results showed that the predictive ability of the models was the highest when both the canopy and marker information was included, and it was the lowest when only the canopy information was included. |
format | Online Article Text |
id | pubmed-6442071 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-64420712019-04-05 Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model Howard, Reka Jarquin, Diego Evol Bioinform Online Algorithm development for evolutionary biology Prediction techniques are important in plant breeding as they provide a tool for selection that is more efficient and economical than traditional phenotypic and pedigree based selection. The conventional genomic prediction models include molecular marker information to predict the phenotype. With the development of new phenomics techniques we have the opportunity to collect image data on the plants, and extend the traditional genomic prediction models where we incorporate diverse set of information collected on the plants. In our research, we developed a hybrid matrix model that incorporates molecular marker and canopy coverage information as a weighted linear combination to predict grain yield for the soybean nested association mapping (SoyNAM) panel. To obtain the testing and training sets, we clustered the individuals based on their marker and canopy information using 2 different clustering techniques, and we compared 5 different cross-validation schemes. The results showed that the predictive ability of the models was the highest when both the canopy and marker information was included, and it was the lowest when only the canopy information was included. SAGE Publications 2019-03-29 /pmc/articles/PMC6442071/ /pubmed/30956524 http://dx.doi.org/10.1177/1176934319840026 Text en © The Author(s) 2019 http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Algorithm development for evolutionary biology Howard, Reka Jarquin, Diego Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model |
title | Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model |
title_full | Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model |
title_fullStr | Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model |
title_full_unstemmed | Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model |
title_short | Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model |
title_sort | genomic prediction using canopy coverage image and genotypic information in soybean via a hybrid model |
topic | Algorithm development for evolutionary biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6442071/ https://www.ncbi.nlm.nih.gov/pubmed/30956524 http://dx.doi.org/10.1177/1176934319840026 |
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