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Genomic Prediction Strategies for Dry-Down-Related Traits in Maize
For efficient mechanical harvesting, low grain moisture content at harvest time is essential. Dry-down rate (DR), which refers to the reduction in grain moisture content after the plants enter physiological maturity, is one of the main factors affecting the amount of moisture in the kernels. Dry-dow...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9280646/ https://www.ncbi.nlm.nih.gov/pubmed/35845649 http://dx.doi.org/10.3389/fpls.2022.930429 |
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author | Ni, Pengzun Anche, Mahlet Teka Ruan, Yanye Dang, Dongdong Morales, Nicolas Li, Lingyue Liu, Meiling Wang, Shu Robbins, Kelly R. |
author_facet | Ni, Pengzun Anche, Mahlet Teka Ruan, Yanye Dang, Dongdong Morales, Nicolas Li, Lingyue Liu, Meiling Wang, Shu Robbins, Kelly R. |
author_sort | Ni, Pengzun |
collection | PubMed |
description | For efficient mechanical harvesting, low grain moisture content at harvest time is essential. Dry-down rate (DR), which refers to the reduction in grain moisture content after the plants enter physiological maturity, is one of the main factors affecting the amount of moisture in the kernels. Dry-down rate is estimated using kernel moisture content at physiological maturity and at harvest time; however, measuring kernel water content at physiological maturity, which is sometimes referred as kernel water content at black layer formation (BWC), is time-consuming and resource-demanding. Therefore, inferring BWC from other correlated and easier to measure traits could improve the efficiency of breeding efforts for dry-down-related traits. In this study, multi-trait genomic prediction models were used to estimate genetic correlations between BWC and water content at harvest time (HWC) and flowering time (FT). The results show there is moderate-to-high genetic correlation between the traits (0.24–0.66), which supports the use of multi-trait genomic prediction models. To investigate genomic prediction strategies, several cross-validation scenarios representing possible implementations of genomic prediction were evaluated. The results indicate that, in most scenarios, the use of multi-trait genomic prediction models substantially increases prediction accuracy. Furthermore, the inclusion of historical records for correlated traits can improve prediction accuracy, even when the target trait is not measured on all the plots in the training set. |
format | Online Article Text |
id | pubmed-9280646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92806462022-07-15 Genomic Prediction Strategies for Dry-Down-Related Traits in Maize Ni, Pengzun Anche, Mahlet Teka Ruan, Yanye Dang, Dongdong Morales, Nicolas Li, Lingyue Liu, Meiling Wang, Shu Robbins, Kelly R. Front Plant Sci Plant Science For efficient mechanical harvesting, low grain moisture content at harvest time is essential. Dry-down rate (DR), which refers to the reduction in grain moisture content after the plants enter physiological maturity, is one of the main factors affecting the amount of moisture in the kernels. Dry-down rate is estimated using kernel moisture content at physiological maturity and at harvest time; however, measuring kernel water content at physiological maturity, which is sometimes referred as kernel water content at black layer formation (BWC), is time-consuming and resource-demanding. Therefore, inferring BWC from other correlated and easier to measure traits could improve the efficiency of breeding efforts for dry-down-related traits. In this study, multi-trait genomic prediction models were used to estimate genetic correlations between BWC and water content at harvest time (HWC) and flowering time (FT). The results show there is moderate-to-high genetic correlation between the traits (0.24–0.66), which supports the use of multi-trait genomic prediction models. To investigate genomic prediction strategies, several cross-validation scenarios representing possible implementations of genomic prediction were evaluated. The results indicate that, in most scenarios, the use of multi-trait genomic prediction models substantially increases prediction accuracy. Furthermore, the inclusion of historical records for correlated traits can improve prediction accuracy, even when the target trait is not measured on all the plots in the training set. Frontiers Media S.A. 2022-06-30 /pmc/articles/PMC9280646/ /pubmed/35845649 http://dx.doi.org/10.3389/fpls.2022.930429 Text en Copyright © 2022 Ni, Anche, Ruan, Dang, Morales, Li, Liu, Wang and Robbins. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Ni, Pengzun Anche, Mahlet Teka Ruan, Yanye Dang, Dongdong Morales, Nicolas Li, Lingyue Liu, Meiling Wang, Shu Robbins, Kelly R. Genomic Prediction Strategies for Dry-Down-Related Traits in Maize |
title | Genomic Prediction Strategies for Dry-Down-Related Traits in Maize |
title_full | Genomic Prediction Strategies for Dry-Down-Related Traits in Maize |
title_fullStr | Genomic Prediction Strategies for Dry-Down-Related Traits in Maize |
title_full_unstemmed | Genomic Prediction Strategies for Dry-Down-Related Traits in Maize |
title_short | Genomic Prediction Strategies for Dry-Down-Related Traits in Maize |
title_sort | genomic prediction strategies for dry-down-related traits in maize |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9280646/ https://www.ncbi.nlm.nih.gov/pubmed/35845649 http://dx.doi.org/10.3389/fpls.2022.930429 |
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