Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition
Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the pot...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/10/9/1221 |
| _version_ | 1797557096330297344 |
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| author | Sirjan Sapkota J. Lucas Boatwright Kathleen Jordan Richard Boyles Stephen Kresovich |
| author_facet | Sirjan Sapkota J. Lucas Boatwright Kathleen Jordan Richard Boyles Stephen Kresovich |
| author_sort | Sirjan Sapkota |
| collection | DOAJ |
| description | Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits—amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain. |
| first_indexed | 2024-03-10T17:12:21Z |
| format | Article |
| id | doaj.art-b0e8ece6590b4aeaa0f59d6a7009417a |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T17:12:21Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-b0e8ece6590b4aeaa0f59d6a7009417a2023-11-20T10:37:14ZengMDPI AGAgronomy2073-43952020-08-01109122110.3390/agronomy10091221Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain CompositionSirjan Sapkota0J. Lucas Boatwright1Kathleen Jordan2Richard Boyles3Stephen Kresovich4Advanced Plant Technology Program, Clemson University, Clemson, SC 29634, USAAdvanced Plant Technology Program, Clemson University, Clemson, SC 29634, USAAdvanced Plant Technology Program, Clemson University, Clemson, SC 29634, USADepartment of Plant and Environmental Sciences, Clemson University, Clemson, SC 29634, USAAdvanced Plant Technology Program, Clemson University, Clemson, SC 29634, USAGenomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits—amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.https://www.mdpi.com/2073-4395/10/9/1221genomicsgenomic selectiongenomic predictionmarker-assisted selectionwhole genome regressiongrain quality |
| spellingShingle | Sirjan Sapkota J. Lucas Boatwright Kathleen Jordan Richard Boyles Stephen Kresovich Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition Agronomy genomics genomic selection genomic prediction marker-assisted selection whole genome regression grain quality |
| title | Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition |
| title_full | Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition |
| title_fullStr | Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition |
| title_full_unstemmed | Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition |
| title_short | Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition |
| title_sort | multi trait regressor stacking increased genomic prediction accuracy of sorghum grain composition |
| topic | genomics genomic selection genomic prediction marker-assisted selection whole genome regression grain quality |
| url | https://www.mdpi.com/2073-4395/10/9/1221 |
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