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Roscher-Ehrig L, Weber SE, Abbadi A, Malenica M, Abel S, Hemker R, Snowdon RJ, Wittkop B, Stahl A. Phenomic Selection for Hybrid Rapeseed Breeding. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0215. [PMID: 39049840 PMCID: PMC11268845 DOI: 10.34133/plantphenomics.0215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/19/2024] [Indexed: 07/27/2024]
Abstract
Phenomic selection is a recent approach suggested as a low-cost, high-throughput alternative to genomic selection. Instead of using genetic markers, it employs spectral data to predict complex traits using equivalent statistical models. Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted. However, for hybrid breeding, the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation. Here, we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding. We performed predictions for various traits in a structured population of 410 test hybrids, grown in multiple environments, using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models. We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height, even when spectral data was collected at single locations, while being less affected by population structure. Furthermore, we demonstrate that phenomic prediction across generations is feasible, and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection. We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.
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Affiliation(s)
| | - Sven E. Weber
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | | | | | | | | | - Rod J. Snowdon
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | - Benjamin Wittkop
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | - Andreas Stahl
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants,
Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
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Thapa S, Gill HS, Halder J, Rana A, Ali S, Maimaitijiang M, Gill U, Bernardo A, St Amand P, Bai G, Sehgal SK. Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat. THE PLANT GENOME 2024:e20470. [PMID: 38853339 DOI: 10.1002/tpg2.20470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/07/2024] [Accepted: 04/14/2024] [Indexed: 06/11/2024]
Abstract
Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI-based FDK (FDK_QVIS/FDK_QNIR) showed a two-fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI-based FDK was evaluated along with other traits in multi-trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single-trait GS model. We next used hyperspectral imaging of FHB-infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single-trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB-infected wheat kernels and observed an accuracy (R2 = 0.45) comparable to best-performing MT GS models. This study demonstrates the potential application of AI and vision-based platforms to improve PA for FHB-related traits using genomic and phenomic selection.
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Affiliation(s)
- Subash Thapa
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Harsimardeep S Gill
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Jyotirmoy Halder
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Anshul Rana
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Shaukat Ali
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Maitiniyazi Maimaitijiang
- Department of Geography & Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, South Dakota, USA
| | - Upinder Gill
- Department of Plant Pathology, North Dakota State University, Fargo, North Dakota, USA
| | - Amy Bernardo
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Paul St Amand
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Guihua Bai
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Sunish K Sehgal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
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Kaushal S, Gill HS, Billah MM, Khan SN, Halder J, Bernardo A, Amand PS, Bai G, Glover K, Maimaitijiang M, Sehgal SK. Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning. FRONTIERS IN PLANT SCIENCE 2024; 15:1410249. [PMID: 38872880 PMCID: PMC11169824 DOI: 10.3389/fpls.2024.1410249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 05/06/2024] [Indexed: 06/15/2024]
Abstract
Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.
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Affiliation(s)
- Swas Kaushal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Harsimardeep S. Gill
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Mohammad Maruf Billah
- Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States
| | - Shahid Nawaz Khan
- Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States
| | - Jyotirmoy Halder
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Amy Bernardo
- Hard Winter Wheat Genetics Research Unit, USDA-ARS, Manhattan, KS, United States
| | - Paul St. Amand
- Hard Winter Wheat Genetics Research Unit, USDA-ARS, Manhattan, KS, United States
| | - Guihua Bai
- Hard Winter Wheat Genetics Research Unit, USDA-ARS, Manhattan, KS, United States
| | - Karl Glover
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Maitiniyazi Maimaitijiang
- Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States
| | - Sunish K. Sehgal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
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Magon G, De Rosa V, Martina M, Falchi R, Acquadro A, Barcaccia G, Portis E, Vannozzi A, De Paoli E. Boosting grapevine breeding for climate-smart viticulture: from genetic resources to predictive genomics. FRONTIERS IN PLANT SCIENCE 2023; 14:1293186. [PMID: 38148866 PMCID: PMC10750425 DOI: 10.3389/fpls.2023.1293186] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/27/2023] [Indexed: 12/28/2023]
Abstract
The multifaceted nature of climate change is increasing the urgency to select resilient grapevine varieties, or generate new, fitter cultivars, to withstand a multitude of new challenging conditions. The attainment of this goal is hindered by the limiting pace of traditional breeding approaches, which require decades to result in new selections. On the other hand, marker-assisted breeding has proved useful when it comes to traits governed by one or few genes with great effects on the phenotype, but its efficacy is still restricted for complex traits controlled by many loci. On these premises, innovative strategies are emerging which could help guide selection, taking advantage of the genetic diversity within the Vitis genus in its entirety. Multiple germplasm collections are also available as a source of genetic material for the introgression of alleles of interest via adapted and pioneering transformation protocols, which present themselves as promising tools for future applications on a notably recalcitrant species such as grapevine. Genome editing intersects both these strategies, not only by being an alternative to obtain focused changes in a relatively rapid way, but also by supporting a fine-tuning of new genotypes developed with other methods. A review on the state of the art concerning the available genetic resources and the possibilities of use of innovative techniques in aid of selection is presented here to support the production of climate-smart grapevine genotypes.
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Affiliation(s)
- Gabriele Magon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padova, Agripolis, Viale dell’Università 16, Legnaro, Italy
| | - Valeria De Rosa
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Via delle Scienze, 206, Udine, Italy
| | - Matteo Martina
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Largo P. Braccini 2, Grugliasco, Italy
| | - Rachele Falchi
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Via delle Scienze, 206, Udine, Italy
| | - Alberto Acquadro
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Largo P. Braccini 2, Grugliasco, Italy
| | - Gianni Barcaccia
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padova, Agripolis, Viale dell’Università 16, Legnaro, Italy
| | - Ezio Portis
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Largo P. Braccini 2, Grugliasco, Italy
| | - Alessandro Vannozzi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padova, Agripolis, Viale dell’Università 16, Legnaro, Italy
| | - Emanuele De Paoli
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Via delle Scienze, 206, Udine, Italy
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