Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes
The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (<i>Triticum aestivum</i> L.) lines was evaluated to compare si...
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MDPI AG
2020-10-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/11/11/1270 |
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| author | Jia Guo Jahangir Khan Sumit Pradhan Dipendra Shahi Naeem Khan Muhsin Avci Jordan Mcbreen Stephen Harrison Gina Brown-Guedira Joseph Paul Murphy Jerry Johnson Mohamed Mergoum Richanrd Esten Mason Amir M. H. Ibrahim Russel Sutton Carl Griffey Md Ali Babar |
| author_facet | Jia Guo Jahangir Khan Sumit Pradhan Dipendra Shahi Naeem Khan Muhsin Avci Jordan Mcbreen Stephen Harrison Gina Brown-Guedira Joseph Paul Murphy Jerry Johnson Mohamed Mergoum Richanrd Esten Mason Amir M. H. Ibrahim Russel Sutton Carl Griffey Md Ali Babar |
| author_sort | Jia Guo |
| collection | DOAJ |
| description | The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (<i>Triticum aestivum</i> L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat. |
| first_indexed | 2024-03-10T15:17:19Z |
| format | Article |
| id | doaj.art-e763587658dd4caab422802a34b64801 |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-10T15:17:19Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-e763587658dd4caab422802a34b648012023-11-20T18:52:53ZengMDPI AGGenes2073-44252020-10-011111127010.3390/genes11111270Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water RegimesJia Guo0Jahangir Khan1Sumit Pradhan2Dipendra Shahi3Naeem Khan4Muhsin Avci5Jordan Mcbreen6Stephen Harrison7Gina Brown-Guedira8Joseph Paul Murphy9Jerry Johnson10Mohamed Mergoum11Richanrd Esten Mason12Amir M. H. Ibrahim13Russel Sutton14Carl Griffey15Md Ali Babar16Department of Agronomy, University of Florida, Gainesville, FL 32611, USADepartment of Agronomy, University of Florida, Gainesville, FL 32611, USADepartment of Agronomy, University of Florida, Gainesville, FL 32611, USADepartment of Agronomy, University of Florida, Gainesville, FL 32611, USADepartment of Agronomy, University of Florida, Gainesville, FL 32611, USADepartment of Agronomy, University of Florida, Gainesville, FL 32611, USADepartment of Agronomy, University of Florida, Gainesville, FL 32611, USASchool of Plant Environment and Soil Sciences, Louisiana State University, Baton Rouge, LA 70803, USAUSDA-ARS, North Carolina State University, Raleigh, NC 27607, USADepartment of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27607, USADepartment of Crop and Soil Sciences, University of Georgia, Griffin, GA 32223, USADepartment of Crop and Soil Sciences, University of Georgia, Griffin, GA 32223, USADepartment of Crop Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USADepartment of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USADepartment of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USASchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USADepartment of Agronomy, University of Florida, Gainesville, FL 32611, USAThe performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (<i>Triticum aestivum</i> L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.https://www.mdpi.com/2073-4425/11/11/1270genomic predictionmulti-trait modelmulti-environment genomic best linear unbiased predictorBayesian multi-trait multi-environment modelBayesian multi-output regressor stacking modeldeep learning multi-trait multi-environment model |
| spellingShingle | Jia Guo Jahangir Khan Sumit Pradhan Dipendra Shahi Naeem Khan Muhsin Avci Jordan Mcbreen Stephen Harrison Gina Brown-Guedira Joseph Paul Murphy Jerry Johnson Mohamed Mergoum Richanrd Esten Mason Amir M. H. Ibrahim Russel Sutton Carl Griffey Md Ali Babar Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes Genes genomic prediction multi-trait model multi-environment genomic best linear unbiased predictor Bayesian multi-trait multi-environment model Bayesian multi-output regressor stacking model deep learning multi-trait multi-environment model |
| title | Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes |
| title_full | Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes |
| title_fullStr | Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes |
| title_full_unstemmed | Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes |
| title_short | Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes |
| title_sort | multi trait genomic prediction of yield related traits in us soft wheat under variable water regimes |
| topic | genomic prediction multi-trait model multi-environment genomic best linear unbiased predictor Bayesian multi-trait multi-environment model Bayesian multi-output regressor stacking model deep learning multi-trait multi-environment model |
| url | https://www.mdpi.com/2073-4425/11/11/1270 |
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