Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes

Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with h...
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Autor*in:	Shiva Azizinia [verfasserIn] Daniel Mullan [verfasserIn] Allan Rattey [verfasserIn] Jayfred Godoy [verfasserIn] Hannah Robinson [verfasserIn] David Moody [verfasserIn] Kerrie Forrest [verfasserIn] Gabriel Keeble-Gagnere [verfasserIn] Matthew J. Hayden [verfasserIn] Josquin FG. Tibbits [verfasserIn] Hans D. Daetwyler [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	genomic prediction multi-trait model wheat breeding genomic best linear unbiased prediction NIR-predictor forward-prediction

Übergeordnetes Werk:	In: Frontiers in Plant Science - Frontiers Media S.A., 2011, 14(2023)
Übergeordnetes Werk:	volume:14 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fpls.2023.1167221

Katalog-ID:	DOAJ090590600

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allfields	10.3389/fpls.2023.1167221 doi (DE-627)DOAJ090590600 (DE-599)DOAJ0d48e16aa0d04cc0a5d7b61769c9ad48 DE-627 ger DE-627 rakwb eng SB1-1110 Shiva Azizinia verfasserin aut Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle. genomic prediction multi-trait model wheat breeding genomic best linear unbiased prediction NIR-predictor forward-prediction Plant culture Daniel Mullan verfasserin aut Allan Rattey verfasserin aut Jayfred Godoy verfasserin aut Hannah Robinson verfasserin aut David Moody verfasserin aut Kerrie Forrest verfasserin aut Gabriel Keeble-Gagnere verfasserin aut Matthew J. Hayden verfasserin aut Matthew J. Hayden verfasserin aut Josquin FG. Tibbits verfasserin aut Hans D. Daetwyler verfasserin aut Hans D. Daetwyler verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 14(2023) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:14 year:2023 https://doi.org/10.3389/fpls.2023.1167221 kostenfrei https://doaj.org/article/0d48e16aa0d04cc0a5d7b61769c9ad48 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2023.1167221/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
spelling	10.3389/fpls.2023.1167221 doi (DE-627)DOAJ090590600 (DE-599)DOAJ0d48e16aa0d04cc0a5d7b61769c9ad48 DE-627 ger DE-627 rakwb eng SB1-1110 Shiva Azizinia verfasserin aut Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle. genomic prediction multi-trait model wheat breeding genomic best linear unbiased prediction NIR-predictor forward-prediction Plant culture Daniel Mullan verfasserin aut Allan Rattey verfasserin aut Jayfred Godoy verfasserin aut Hannah Robinson verfasserin aut David Moody verfasserin aut Kerrie Forrest verfasserin aut Gabriel Keeble-Gagnere verfasserin aut Matthew J. Hayden verfasserin aut Matthew J. Hayden verfasserin aut Josquin FG. Tibbits verfasserin aut Hans D. Daetwyler verfasserin aut Hans D. Daetwyler verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 14(2023) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:14 year:2023 https://doi.org/10.3389/fpls.2023.1167221 kostenfrei https://doaj.org/article/0d48e16aa0d04cc0a5d7b61769c9ad48 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2023.1167221/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
allfields_unstemmed	10.3389/fpls.2023.1167221 doi (DE-627)DOAJ090590600 (DE-599)DOAJ0d48e16aa0d04cc0a5d7b61769c9ad48 DE-627 ger DE-627 rakwb eng SB1-1110 Shiva Azizinia verfasserin aut Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle. genomic prediction multi-trait model wheat breeding genomic best linear unbiased prediction NIR-predictor forward-prediction Plant culture Daniel Mullan verfasserin aut Allan Rattey verfasserin aut Jayfred Godoy verfasserin aut Hannah Robinson verfasserin aut David Moody verfasserin aut Kerrie Forrest verfasserin aut Gabriel Keeble-Gagnere verfasserin aut Matthew J. Hayden verfasserin aut Matthew J. Hayden verfasserin aut Josquin FG. Tibbits verfasserin aut Hans D. Daetwyler verfasserin aut Hans D. Daetwyler verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 14(2023) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:14 year:2023 https://doi.org/10.3389/fpls.2023.1167221 kostenfrei https://doaj.org/article/0d48e16aa0d04cc0a5d7b61769c9ad48 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2023.1167221/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
allfieldsGer	10.3389/fpls.2023.1167221 doi (DE-627)DOAJ090590600 (DE-599)DOAJ0d48e16aa0d04cc0a5d7b61769c9ad48 DE-627 ger DE-627 rakwb eng SB1-1110 Shiva Azizinia verfasserin aut Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle. genomic prediction multi-trait model wheat breeding genomic best linear unbiased prediction NIR-predictor forward-prediction Plant culture Daniel Mullan verfasserin aut Allan Rattey verfasserin aut Jayfred Godoy verfasserin aut Hannah Robinson verfasserin aut David Moody verfasserin aut Kerrie Forrest verfasserin aut Gabriel Keeble-Gagnere verfasserin aut Matthew J. Hayden verfasserin aut Matthew J. Hayden verfasserin aut Josquin FG. Tibbits verfasserin aut Hans D. Daetwyler verfasserin aut Hans D. Daetwyler verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 14(2023) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:14 year:2023 https://doi.org/10.3389/fpls.2023.1167221 kostenfrei https://doaj.org/article/0d48e16aa0d04cc0a5d7b61769c9ad48 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2023.1167221/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
allfieldsSound	10.3389/fpls.2023.1167221 doi (DE-627)DOAJ090590600 (DE-599)DOAJ0d48e16aa0d04cc0a5d7b61769c9ad48 DE-627 ger DE-627 rakwb eng SB1-1110 Shiva Azizinia verfasserin aut Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle. genomic prediction multi-trait model wheat breeding genomic best linear unbiased prediction NIR-predictor forward-prediction Plant culture Daniel Mullan verfasserin aut Allan Rattey verfasserin aut Jayfred Godoy verfasserin aut Hannah Robinson verfasserin aut David Moody verfasserin aut Kerrie Forrest verfasserin aut Gabriel Keeble-Gagnere verfasserin aut Matthew J. Hayden verfasserin aut Matthew J. Hayden verfasserin aut Josquin FG. Tibbits verfasserin aut Hans D. Daetwyler verfasserin aut Hans D. Daetwyler verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 14(2023) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:14 year:2023 https://doi.org/10.3389/fpls.2023.1167221 kostenfrei https://doaj.org/article/0d48e16aa0d04cc0a5d7b61769c9ad48 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2023.1167221/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
language	English
source	In Frontiers in Plant Science 14(2023) volume:14 year:2023
sourceStr	In Frontiers in Plant Science 14(2023) volume:14 year:2023
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	genomic prediction multi-trait model wheat breeding genomic best linear unbiased prediction NIR-predictor forward-prediction Plant culture
isfreeaccess_bool	true
container_title	Frontiers in Plant Science
authorswithroles_txt_mv	Shiva Azizinia @@aut@@ Daniel Mullan @@aut@@ Allan Rattey @@aut@@ Jayfred Godoy @@aut@@ Hannah Robinson @@aut@@ David Moody @@aut@@ Kerrie Forrest @@aut@@ Gabriel Keeble-Gagnere @@aut@@ Matthew J. Hayden @@aut@@ Josquin FG. Tibbits @@aut@@ Hans D. Daetwyler @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	662359240
id	DOAJ090590600
language_de	englisch
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callnumber-first	S - Agriculture
author	Shiva Azizinia
spellingShingle	Shiva Azizinia misc SB1-1110 misc genomic prediction misc multi-trait model misc wheat breeding misc genomic best linear unbiased prediction misc NIR-predictor misc forward-prediction misc Plant culture Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes
authorStr	Shiva Azizinia
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topic_title	SB1-1110 Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes genomic prediction multi-trait model wheat breeding genomic best linear unbiased prediction NIR-predictor forward-prediction
topic	misc SB1-1110 misc genomic prediction misc multi-trait model misc wheat breeding misc genomic best linear unbiased prediction misc NIR-predictor misc forward-prediction misc Plant culture
topic_unstemmed	misc SB1-1110 misc genomic prediction misc multi-trait model misc wheat breeding misc genomic best linear unbiased prediction misc NIR-predictor misc forward-prediction misc Plant culture
topic_browse	misc SB1-1110 misc genomic prediction misc multi-trait model misc wheat breeding misc genomic best linear unbiased prediction misc NIR-predictor misc forward-prediction misc Plant culture
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title	Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes
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title_full	Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes
author_sort	Shiva Azizinia
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author_browse	Shiva Azizinia Daniel Mullan Allan Rattey Jayfred Godoy Hannah Robinson David Moody Kerrie Forrest Gabriel Keeble-Gagnere Matthew J. Hayden Josquin FG. Tibbits Hans D. Daetwyler
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title_sort	improved multi-trait prediction of wheat end-product quality traits by integrating nir-predicted phenotypes
callnumber	SB1-1110
title_auth	Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes
abstract	Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.
abstractGer	Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.
abstract_unstemmed	Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.
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title_short	Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes
url	https://doi.org/10.3389/fpls.2023.1167221 https://doaj.org/article/0d48e16aa0d04cc0a5d7b61769c9ad48 https://www.frontiersin.org/articles/10.3389/fpls.2023.1167221/full https://doaj.org/toc/1664-462X
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author2	Daniel Mullan Allan Rattey Jayfred Godoy Hannah Robinson David Moody Kerrie Forrest Gabriel Keeble-Gagnere Matthew J. Hayden Josquin FG. Tibbits Hans D. Daetwyler
author2Str	Daniel Mullan Allan Rattey Jayfred Godoy Hannah Robinson David Moody Kerrie Forrest Gabriel Keeble-Gagnere Matthew J. Hayden Josquin FG. Tibbits Hans D. Daetwyler
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up_date	2024-07-03T15:39:47.939Z
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