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Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program
KEY MESSAGE: Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. ABSTRACT: The current strategy for large-scale implementation of genomic...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813723/ https://www.ncbi.nlm.nih.gov/pubmed/33037897 http://dx.doi.org/10.1007/s00122-020-03696-9 |
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author | Atanda, Sikiru Adeniyi Olsen, Michael Burgueño, Juan Crossa, Jose Dzidzienyo, Daniel Beyene, Yoseph Gowda, Manje Dreher, Kate Zhang, Xuecai Prasanna, Boddupalli M. Tongoona, Pangirayi Danquah, Eric Yirenkyi Olaoye, Gbadebo Robbins, Kelly R. |
author_facet | Atanda, Sikiru Adeniyi Olsen, Michael Burgueño, Juan Crossa, Jose Dzidzienyo, Daniel Beyene, Yoseph Gowda, Manje Dreher, Kate Zhang, Xuecai Prasanna, Boddupalli M. Tongoona, Pangirayi Danquah, Eric Yirenkyi Olaoye, Gbadebo Robbins, Kelly R. |
author_sort | Atanda, Sikiru Adeniyi |
collection | PubMed |
description | KEY MESSAGE: Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. ABSTRACT: The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00122-020-03696-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7813723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-78137232021-01-25 Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program Atanda, Sikiru Adeniyi Olsen, Michael Burgueño, Juan Crossa, Jose Dzidzienyo, Daniel Beyene, Yoseph Gowda, Manje Dreher, Kate Zhang, Xuecai Prasanna, Boddupalli M. Tongoona, Pangirayi Danquah, Eric Yirenkyi Olaoye, Gbadebo Robbins, Kelly R. Theor Appl Genet Original Article KEY MESSAGE: Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. ABSTRACT: The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00122-020-03696-9) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-10-10 2021 /pmc/articles/PMC7813723/ /pubmed/33037897 http://dx.doi.org/10.1007/s00122-020-03696-9 Text en © The Author(s) 2020 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/. |
spellingShingle | Original Article Atanda, Sikiru Adeniyi Olsen, Michael Burgueño, Juan Crossa, Jose Dzidzienyo, Daniel Beyene, Yoseph Gowda, Manje Dreher, Kate Zhang, Xuecai Prasanna, Boddupalli M. Tongoona, Pangirayi Danquah, Eric Yirenkyi Olaoye, Gbadebo Robbins, Kelly R. Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program |
title | Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program |
title_full | Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program |
title_fullStr | Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program |
title_full_unstemmed | Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program |
title_short | Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program |
title_sort | maximizing efficiency of genomic selection in cimmyt’s tropical maize breeding program |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813723/ https://www.ncbi.nlm.nih.gov/pubmed/33037897 http://dx.doi.org/10.1007/s00122-020-03696-9 |
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