Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information
Abstract Enhancing predictive modeling accuracy in wheat (Triticum aestivum) breeding through the integration of high‐throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. In this study, spanning four locations in the southeastern United States over 3 yea...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20532 |
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| author | Jordan McBreen Md. Ali Babar Diego Jarquin Naeem Khan Steve Harrison Noah DeWitt Mohamed Mergoum Ben Lopez Richard Boyles Jeanette Lyerly J. Paul Murphy Ehsan Shakiba Russel Sutton Amir Ibrahim Kimberly Howell Jared H. Smith Gina Brown‐Guedira Vijay Tiwari Nicholas Santantonio David A. Van Sanford |
| author_facet | Jordan McBreen Md. Ali Babar Diego Jarquin Naeem Khan Steve Harrison Noah DeWitt Mohamed Mergoum Ben Lopez Richard Boyles Jeanette Lyerly J. Paul Murphy Ehsan Shakiba Russel Sutton Amir Ibrahim Kimberly Howell Jared H. Smith Gina Brown‐Guedira Vijay Tiwari Nicholas Santantonio David A. Van Sanford |
| author_sort | Jordan McBreen |
| collection | DOAJ |
| description | Abstract Enhancing predictive modeling accuracy in wheat (Triticum aestivum) breeding through the integration of high‐throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. In this study, spanning four locations in the southeastern United States over 3 years, models to predict grain yield (GY) were investigated through different cross‐validation approaches. The results demonstrate the superiority of multivariate comprehensive models that incorporate both genomic and HTP data, particularly in accurately predicting GY across diverse locations and years. These HTP‐incorporating models achieve prediction accuracies ranging from 0.59 to 0.68, compared to 0.40–0.54 for genomic‐only models when tested under different prediction scenarios both across years and locations. The comprehensive models exhibit superior generalization to new environments and achieve the highest accuracy when trained on diverse datasets. Predictive accuracy improves as models incorporate data from multiple years, highlighting the importance of considering temporal dynamics in modeling approaches. The study reveals that multivariate prediction outperformed genomic prediction methods in predicting lines across years and locations. The percentage of top 25% lines selected based on multivariate prediction was higher compared to genomic‐only models, indicated by higher specificity, which is the proportion of correctly identified top‐yielding lines that matched the observed top 25% performance across different sites and years. Additionally, the study addresses the prediction of untested locations based on other locations within the same year and in new years at previously tested locations. Findings show the comprehensive models effectively extrapolate to new environments, highlighting their potential for guiding breeding strategies. |
| format | Article |
| id | doaj-art-7c1ba9c47f3f400cb114eacfbfca9b09 |
| institution | Kabale University |
| issn | 1940-3372 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Plant Genome |
| spelling | doaj-art-7c1ba9c47f3f400cb114eacfbfca9b092025-08-20T03:43:57ZengWileyThe Plant Genome1940-33722025-03-01181n/an/a10.1002/tpg2.20532Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal informationJordan McBreen0Md. Ali Babar1Diego Jarquin2Naeem Khan3Steve Harrison4Noah DeWitt5Mohamed Mergoum6Ben Lopez7Richard Boyles8Jeanette Lyerly9J. Paul Murphy10Ehsan Shakiba11Russel Sutton12Amir Ibrahim13Kimberly Howell14Jared H. Smith15Gina Brown‐Guedira16Vijay Tiwari17Nicholas Santantonio18David A. Van Sanford19Department of Agronomy University of Florida Gainesville Florida USADepartment of Agronomy University of Florida Gainesville Florida USADepartment of Agronomy University of Florida Gainesville Florida USADepartment of Agronomy University of Florida Gainesville Florida USASchool of Plant, Environmental and Soil Sciences Louisiana State University Baton Rouge Louisiana USASchool of Plant, Environmental and Soil Sciences Louisiana State University Baton Rouge Louisiana USACrop and Soil Sciences Department/Institute of Plant Breeding, Genetics and Genomics University of Georgia Athens Georgia USACrop and Soil Sciences Department/Institute of Plant Breeding, Genetics and Genomics University of Georgia Athens Georgia USAPee Dee Research and Education Center Clemson University Florence South Carolina USADepartment of Crop and Soil Sciences North Carolina State University Raleigh North Carolina USADepartment of Crop and Soil Sciences North Carolina State University Raleigh North Carolina USADepartment of Crop, Soil, and Environmental Sciences University of Arkansas Fayetteville Arkansas USAAgriLife Research Texas A&M University College Station Texas USAAgriLife Research Texas A&M University College Station Texas USAUSDA‐ARS SEA, Plant Science Research Raleigh North Carolina USAUSDA‐ARS SEA, Plant Science Research Raleigh North Carolina USAUSDA‐ARS SEA, Plant Science Research Raleigh North Carolina USADepartment of Plant Science and Landscape Architecture University of Maryland College Park Maryland USADepartment Of Crop and Soil Environmental Sciences Virginia Polytechnic Institute and State University Blacksburg Virginia USADepartment of Plant and Soil Sciences University of Kentucky Lexington Kentucky USAAbstract Enhancing predictive modeling accuracy in wheat (Triticum aestivum) breeding through the integration of high‐throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. In this study, spanning four locations in the southeastern United States over 3 years, models to predict grain yield (GY) were investigated through different cross‐validation approaches. The results demonstrate the superiority of multivariate comprehensive models that incorporate both genomic and HTP data, particularly in accurately predicting GY across diverse locations and years. These HTP‐incorporating models achieve prediction accuracies ranging from 0.59 to 0.68, compared to 0.40–0.54 for genomic‐only models when tested under different prediction scenarios both across years and locations. The comprehensive models exhibit superior generalization to new environments and achieve the highest accuracy when trained on diverse datasets. Predictive accuracy improves as models incorporate data from multiple years, highlighting the importance of considering temporal dynamics in modeling approaches. The study reveals that multivariate prediction outperformed genomic prediction methods in predicting lines across years and locations. The percentage of top 25% lines selected based on multivariate prediction was higher compared to genomic‐only models, indicated by higher specificity, which is the proportion of correctly identified top‐yielding lines that matched the observed top 25% performance across different sites and years. Additionally, the study addresses the prediction of untested locations based on other locations within the same year and in new years at previously tested locations. Findings show the comprehensive models effectively extrapolate to new environments, highlighting their potential for guiding breeding strategies.https://doi.org/10.1002/tpg2.20532 |
| spellingShingle | Jordan McBreen Md. Ali Babar Diego Jarquin Naeem Khan Steve Harrison Noah DeWitt Mohamed Mergoum Ben Lopez Richard Boyles Jeanette Lyerly J. Paul Murphy Ehsan Shakiba Russel Sutton Amir Ibrahim Kimberly Howell Jared H. Smith Gina Brown‐Guedira Vijay Tiwari Nicholas Santantonio David A. Van Sanford Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information The Plant Genome |
| title | Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information |
| title_full | Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information |
| title_fullStr | Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information |
| title_full_unstemmed | Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information |
| title_short | Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information |
| title_sort | enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern united states through multivariate and multi environment genomic prediction models incorporating spectral and thermal information |
| url | https://doi.org/10.1002/tpg2.20532 |
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