Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis

Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.

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Bibliographic Details
Main Authors: Velazco, Julio G., Jordan, David R., Mace, Emma S., Hunt, Colleen H., Malosetti, Marcos, van Eeuwijk, Fred A.
Format: Article/Letter to editor biblioteca
Language:English
Subjects:BLUP, auxiliary trait, blended kinship matrix, genomic prediction, grain yield, multi-trait analysis, sorghum, stay-green,
Online Access:https://research.wur.nl/en/publications/genomic-prediction-of-grain-yield-and-drought-adaptation-capacity
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spelling dig-wur-nl-wurpubs-5541442025-01-16 Velazco, Julio G. Jordan, David R. Mace, Emma S. Hunt, Colleen H. Malosetti, Marcos van Eeuwijk, Fred A. Article/Letter to editor Frontiers in Plant Science 10 (2019) ISSN: 1664-462X Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis 2019 Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum. en application/pdf https://research.wur.nl/en/publications/genomic-prediction-of-grain-yield-and-drought-adaptation-capacity 10.3389/fpls.2019.00997 https://edepot.wur.nl/501530 BLUP auxiliary trait blended kinship matrix genomic prediction grain yield multi-trait analysis sorghum stay-green https://creativecommons.org/licenses/by/4.0/ Wageningen University & Research
institution WUR NL
collection DSpace
country Países bajos
countrycode NL
component Bibliográfico
access En linea
databasecode dig-wur-nl
tag biblioteca
region Europa del Oeste
libraryname WUR Library Netherlands
language English
topic BLUP
auxiliary trait
blended kinship matrix
genomic prediction
grain yield
multi-trait analysis
sorghum
stay-green
BLUP
auxiliary trait
blended kinship matrix
genomic prediction
grain yield
multi-trait analysis
sorghum
stay-green
spellingShingle BLUP
auxiliary trait
blended kinship matrix
genomic prediction
grain yield
multi-trait analysis
sorghum
stay-green
BLUP
auxiliary trait
blended kinship matrix
genomic prediction
grain yield
multi-trait analysis
sorghum
stay-green
Velazco, Julio G.
Jordan, David R.
Mace, Emma S.
Hunt, Colleen H.
Malosetti, Marcos
van Eeuwijk, Fred A.
Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis
description Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.
format Article/Letter to editor
topic_facet BLUP
auxiliary trait
blended kinship matrix
genomic prediction
grain yield
multi-trait analysis
sorghum
stay-green
author Velazco, Julio G.
Jordan, David R.
Mace, Emma S.
Hunt, Colleen H.
Malosetti, Marcos
van Eeuwijk, Fred A.
author_facet Velazco, Julio G.
Jordan, David R.
Mace, Emma S.
Hunt, Colleen H.
Malosetti, Marcos
van Eeuwijk, Fred A.
author_sort Velazco, Julio G.
title Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis
title_short Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis
title_full Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis
title_fullStr Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis
title_full_unstemmed Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis
title_sort genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis
url https://research.wur.nl/en/publications/genomic-prediction-of-grain-yield-and-drought-adaptation-capacity
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