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Larue F, Rouan L, Pot D, Rami JF, Luquet D, Beurier G. Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits. FRONTIERS IN PLANT SCIENCE 2024; 15:1393965. [PMID: 39139722 PMCID: PMC11319263 DOI: 10.3389/fpls.2024.1393965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 07/04/2024] [Indexed: 08/15/2024]
Abstract
Introduction Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. Methods In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. Results The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. Discussion These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.
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Affiliation(s)
- Florian Larue
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Lauriane Rouan
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - David Pot
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Jean-François Rami
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Delphine Luquet
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Grégory Beurier
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
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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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3
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Meyenberg C, Braun V, Longin CFH, Thorwarth P. Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:188. [PMID: 39037501 PMCID: PMC11263437 DOI: 10.1007/s00122-024-04695-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 07/10/2024] [Indexed: 07/23/2024]
Abstract
KEY MESSAGE Optimized phenomic selection in durum wheat uses near-infrared spectra, feature engineering and parameter tuning. Our study reports improvements in predictive ability and emphasizes customized preprocessing for different traits and models. The success of plant breeding programs depends on efficient selection decisions. Phenomic selection has been proposed as a tool to predict phenotype performance based on near-infrared spectra (NIRS) to support selection decisions. In this study, we test the performance of phenomic selection in multi-environmental trials from our durum wheat breeding program for three breeding scenarios and use feature engineering as well as parameter tuning to improve the phenomic prediction ability. In addition, we investigate the influence of genotype and environment on the phenomic prediction ability for agronomic and quality traits. Preprocessing, based on a grid search over the Savitzky-Golay filter parameters based on 756,000 genotype best linear unbiased estimate (BLUE) computations, improved the phenomic prediction ability by up to 1500% (0.02-0.3). Furthermore, we show that preprocessing should be optimized depending on the dataset, trait, and model used for prediction. The phenomic prediction scenarios in our durum breeding program resulted in low-to-moderate prediction abilities with the highest and most stable prediction results when predicting new genotypes in the same environment as used for model training. This is consistent with the finding that NIRS capture both the genotype and genotype-by-environment ( G × E ) interaction variance.
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Affiliation(s)
- Carina Meyenberg
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany
| | - Vincent Braun
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany
| | | | - Patrick Thorwarth
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany.
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Laurençon M, Legrix J, Wagner MH, Demilly D, Baron C, Rolland S, Ducournau S, Laperche A, Nesi N. Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:156. [PMID: 38858297 PMCID: PMC11164772 DOI: 10.1007/s00122-024-04659-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/25/2024] [Indexed: 06/12/2024]
Abstract
KEY MESSAGE Phenomic prediction implemented on a large diversity set can efficiently predict seed germination, capture low-effect favorable alleles that are not revealed by GWAS and identify promising genetic resources. Oilseed rape faces many challenges, especially at the beginning of its developmental cycle. Achieving rapid and uniform seed germination could help to ensure a successful establishment and therefore enabling the crop to compete with weeds and tolerate stresses during the earliest developmental stages. The polygenic nature of seed germination was highlighted in several studies, and more knowledge is needed about low- to moderate-effect underlying loci in order to enhance seed germination effectively by improving the genetic background and incorporating favorable alleles. A total of 17 QTL were detected for seed germination-related traits, for which the favorable alleles often corresponded to the most frequent alleles in the panel. Genomic and phenomic predictions methods provided moderate-to-high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination. This study also showed that phenomic prediction estimated phenotypic values closer to phenotypic values than GEBV. Finally, as the predictive ability of phenomic prediction was less influenced by the genetic structure of the panel, it is worth using this prediction method to characterize genetic resources, particularly with a view to design prebreeding populations.
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Affiliation(s)
- Marianne Laurençon
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Julie Legrix
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Marie-Hélène Wagner
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Didier Demilly
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Cécile Baron
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Sophie Rolland
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Sylvie Ducournau
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Anne Laperche
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France.
| | - Nathalie Nesi
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
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DeSalvio AJ, Adak A, Murray SC, Jarquín D, Winans ND, Crozier D, Rooney WL. Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments. THE PLANT GENOME 2024; 17:e20454. [PMID: 38715204 DOI: 10.1002/tpg2.20454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/12/2024] [Accepted: 04/01/2024] [Indexed: 07/02/2024]
Abstract
For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across-environment sparse genomic prediction models. One phenomic data modality is whole grain near-infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500-kernel weight (KW) across 2 years (2011-2012) and two management conditions (water-stressed and well-watered) were conducted using combinations of reflectance data obtained from high-throughput, F2 whole-kernel scans and genomic data obtained from genotyping-by-sequencing within four different cross-validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689 ± 0.024-genomic vs. 0.612 ± 0.045-phenomic), but phenomic data were better than genomic data for KW (0.535 ± 0.034-genomic vs. 0.617 ± 0.145-phenomic). Multi-kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single-kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single-kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00.
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Affiliation(s)
- Aaron J DeSalvio
- Interdisciplinary Graduate Program in Genetics and Genomics (Department of Biochemistry and Biophysics), Texas A&M University, College Station, Texas, USA
| | - Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
| | - Diego Jarquín
- Department of Agronomy, University of Florida, Gainesville, Florida, USA
| | - Noah D Winans
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
| | - Daniel Crozier
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
| | - William L Rooney
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, 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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Maggiorelli A, Baig N, Prigge V, Bruckmüller J, Stich B. Using drone-retrieved multispectral data for phenomic selection in potato breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:70. [PMID: 38446220 PMCID: PMC10917832 DOI: 10.1007/s00122-024-04567-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/30/2024] [Indexed: 03/07/2024]
Abstract
Predictive breeding approaches, like phenomic or genomic selection, have the potential to increase the selection gain for potato breeding programs which are characterized by very large numbers of entries in early stages and the availability of very few tubers per entry in these stages. The objectives of this study were to (i) explore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding by testing different prediction scenarios on a diverse panel of tetraploid potato material from all market segments and considering a broad range of traits, (ii) compare the performance of phenomic and genomic predictions, and (iii) assess the predictive power of mixed relationship matrices utilizing weighted SNP array and multispectral reflectance data. Predictive abilities of phenomic prediction scenarios varied greatly within a range of - 0.15 and 0.88 and were strongly dependent on the environment, predicted trait, and considered prediction scenario. We observed high predictive abilities with phenomic prediction for yield (0.45), maturity (0.88), foliage development (0.73), and emergence (0.73), while all other traits achieved higher predictive ability with genomic compared to phenomic prediction. When a mixed relationship matrix was used for prediction, higher predictive abilities were observed for 20 out of 22 traits, showcasing that phenomic and genomic data contained complementary information. We see the main application of phenomic selection in potato breeding programs to allow for the use of the principle of predictive breeding in the pot seedling or single hill stage where genotyping is not recommended due to high costs.
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Affiliation(s)
- Alessio Maggiorelli
- Institute of Quantitative Genetics and Genomics of Plants (QGGP), Heinrich-Heine-University, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Nadia Baig
- Institute of Quantitative Genetics and Genomics of Plants (QGGP), Heinrich-Heine-University, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Vanessa Prigge
- SaKa Pflanzenzucht GmbH & Co. KG, Eichenallee 9, 24340, Windeby, Germany
| | - Julien Bruckmüller
- SaKa Pflanzenzucht GmbH & Co. KG, Eichenallee 9, 24340, Windeby, Germany
| | - Benjamin Stich
- Institute of Quantitative Genetics and Genomics of Plants (QGGP), Heinrich-Heine-University, Universitätsstraße 1, 40225, Düsseldorf, Germany.
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich-Heine-University, Universitätsstraße 1, 40225, Düsseldorf, Germany.
- Julius Kühn-Institut (JKI), Institute for Breeding Research on Agricultural Crops, Rudolf-Schick-Platz 3a, 18190, Sanitz, Germany.
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Tuggle CK, Clarke JL, Murdoch BM, Lyons E, Scott NM, Beneš B, Campbell JD, Chung H, Daigle CL, Das Choudhury S, Dekkers JCM, Dórea JRR, Ertl DS, Feldman M, Fragomeni BO, Fulton JE, Guadagno CR, Hagen DE, Hess AS, Kramer LM, Lawrence-Dill CJ, Lipka AE, Lübberstedt T, McCarthy FM, McKay SD, Murray SC, Riggs PK, Rowan TN, Sheehan MJ, Steibel JP, Thompson AM, Thornton KJ, Van Tassell CP, Schnable PS. Current challenges and future of agricultural genomes to phenomes in the USA. Genome Biol 2024; 25:8. [PMID: 38172911 PMCID: PMC10763150 DOI: 10.1186/s13059-023-03155-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (phenomics). Identifying the most critical opportunities and challenges in genome to phenome (G2P) research is the focus of this paper. Previously (Genome Biol, 23(1):1-11, 2022), we laid out how Agricultural Genome to Phenome Initiative (AG2PI) will coordinate activities with USA federal government agencies expand public-private partnerships, and engage with external stakeholders to achieve a shared vision of future the AG2PI. Acting on this latter step, AG2PI organized the "Thinking Big: Visualizing the Future of AG2PI" two-day workshop held September 9-10, 2022, in Ames, Iowa, co-hosted with the United State Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA). During the meeting, attendees were asked to use their experience and curiosity to review the current status of agricultural genome to phenome (AG2P) work and envision the future of the AG2P field. The topic summaries composing this paper are distilled from two 1.5-h small group discussions. Challenges and solutions identified across multiple topics at the workshop were explored. We end our discussion with a vision for the future of agricultural progress, identifying two areas of innovation needed: (1) innovate in genetic improvement methods development and evaluation and (2) innovate in agricultural research processes to solve societal problems. To address these needs, we then provide six specific goals that we recommend be implemented immediately in support of advancing AG2P research.
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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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10
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Stejskal J, Čepl J, Neuwirthová E, Akinyemi OO, Chuchlík J, Provazník D, Keinänen M, Campbell P, Albrechtová J, Lstibůrek M, Lhotáková Z. Making the Genotypic Variation Visible: Hyperspectral Phenotyping in Scots Pine Seedlings. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0111. [PMID: 38026471 PMCID: PMC10644830 DOI: 10.34133/plantphenomics.0111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023]
Abstract
Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant's physiological status. Therefore, using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments. We evaluated the use of 2 nondestructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350- to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic. Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view. The proximal canopy measurements were collected under natural solar light, using the same spectroradiometer with fiber optical cable to collect data on individual seedlings' hemispherical conical reflectance factor. The latter method was highly susceptible to changes in incoming radiation. Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range. Moreover, using random forest and support vector machine learning algorithms, the proximal data obtained from the top of the seedlings offered up to 83% accuracy in predicting 3 different Scots pine populations. We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.
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Affiliation(s)
- Jan Stejskal
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Jaroslav Čepl
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Eva Neuwirthová
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
| | - Olusegun Olaitan Akinyemi
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
- Department of Environmental and Biological Sciences,
University of Eastern Finland, Joensuu, Finland
| | - Jiří Chuchlík
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Daniel Provazník
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Markku Keinänen
- Department of Environmental and Biological Sciences,
University of Eastern Finland, Joensuu, Finland
- Center for Photonic Sciences,
University of Eastern Finland, Joensuu, Finland
| | - Petya Campbell
- Department of Geography and Environmental Sciences,
University of Maryland Baltimore County, Baltimore, MD, USA
- Biospheric Sciences Laboratory,
NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Jana Albrechtová
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
| | - Milan Lstibůrek
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Zuzana Lhotáková
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
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11
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Dallinger HG, Löschenberger F, Bistrich H, Ametz C, Hetzendorfer H, Morales L, Michel S, Buerstmayr H. Predictor bias in genomic and phenomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:235. [PMID: 37878079 PMCID: PMC10600307 DOI: 10.1007/s00122-023-04479-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/08/2023] [Indexed: 10/26/2023]
Abstract
KEY MESSAGE NIRS of wheat grains as phenomic predictors for grain yield show inflated prediction ability and are biased toward grain protein content. Estimating the breeding value of individuals using genome-wide marker data (genomic prediction) is currently one of the most important drivers of breeding progress in major crops. Recently, phenomic technologies, including remote sensing and aerial hyperspectral imaging of plant canopies, have made it feasible to predict the breeding value of individuals in the absence of genetic marker data. This is commonly referred to as phenomic prediction. Hyperspectral measurements in the form of near-infrared spectroscopy have been used since the 1980 s to predict compositional parameters of harvest products. Moreover, in recent studies NIRS from grains was used to predict grain yield. The same studies showed that phenomic prediction can outperform genomic prediction for grain yield. The genome is static and not environment dependent, thereby limiting genomic prediction ability. Gene expression is tissue specific and differs under environmental influences, leading to a tissue- and environment-specific phenome, potentially explaining the higher predictive ability of phenomic prediction. Here, we compare genomic prediction and phenomic prediction from hyperspectral measurements of wheat grains for the prediction of a variety of traits including grain yield. We show that phenomic predictions outperform genomic prediction for some traits. However, phenomic predictions are biased toward the information present in the predictor. Future studies on this topic should investigate whether population parameters are retained in phenomic prediction as they are in genomic prediction. Furthermore, we find that unbiased phenomic prediction abilities are considerably lower than previously reported and recommend a method to circumvent this issue.
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Affiliation(s)
- Hermann Gregor Dallinger
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria.
- Saatzucht Donau GesmbH & Co KG, Saatzuchtstrasse 11, 2301, Probstdorf, Austria.
| | | | - Herbert Bistrich
- Saatzucht Donau GesmbH & Co KG, Saatzuchtstrasse 11, 2301, Probstdorf, Austria
| | - Christian Ametz
- Saatzucht Donau GesmbH & Co KG, Saatzuchtstrasse 11, 2301, Probstdorf, Austria
| | | | - Laura Morales
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
| | - Sebastian Michel
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
| | - Hermann Buerstmayr
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
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12
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Ferrão LFV, Dhakal R, Dias R, Tieman D, Whitaker V, Gore MA, Messina C, Resende MFR. Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics. Curr Opin Biotechnol 2023; 83:102968. [PMID: 37515935 DOI: 10.1016/j.copbio.2023.102968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/31/2023]
Abstract
Over the last decades, significant strides were made in understanding the biochemical factors influencing the nutritional content and flavor profile of fruits and vegetables. Product differentiation in the produce aisle is the natural consequence of increasing consumer power in the food industry. Cotton-candy grapes, specialty tomatoes, and pineapple-flavored white strawberries provide a few examples. Given the increased demand for flavorful varieties, and pressing need to reduce micronutrient malnutrition, we expect breeding to increase its prioritization toward these traits. Reaching this goal will, in part, necessitate knowledge of the genetic architecture controlling these traits, as well as the development of breeding methods that maximize their genetic gain. Can artificial intelligence (AI) help predict flavor preferences, and can such insights be leveraged by breeding programs? In this Perspective, we outline both the opportunities and challenges for the development of more flavorful and nutritious crops, and how AI can support these breeding initiatives.
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Affiliation(s)
- Luís Felipe V Ferrão
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Rakshya Dhakal
- Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States
| | - Raquel Dias
- Microbiology and Cell Science Department, University of Florida, Gainesville, FL, United States
| | - Denise Tieman
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Vance Whitaker
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States; Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Carlos Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States; Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States
| | - Márcio F R Resende
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States; Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States.
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13
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Adak A, Kang M, Anderson SL, Murray SC, Jarquin D, Wong RKW, Katzfuß M. Phenomic data-driven biological prediction of maize through field-based high-throughput phenotyping integration with genomic data. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5307-5326. [PMID: 37279568 DOI: 10.1093/jxb/erad216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/02/2023] [Indexed: 06/08/2023]
Abstract
High-throughput phenotyping (HTP) has expanded the dimensionality of data in plant research; however, HTP has resulted in few novel biological discoveries to date. Field-based HTP (FHTP), using small unoccupied aerial vehicles (UAVs) equipped with imaging sensors, can be deployed routinely to monitor segregating plant population interactions with the environment under biologically meaningful conditions. Here, flowering dates and plant height, important phenological fitness traits, were collected on 520 segregating maize recombinant inbred lines (RILs) in both irrigated and drought stress trials in 2018. Using UAV phenomic, single nucleotide polymorphism (SNP) genomic, as well as combined data, flowering times were predicted using several scenarios. Untested genotypes were predicted with 0.58, 0.59, and 0.41 prediction ability for anthesis, silking, and terminal plant height, respectively, using genomic data, but prediction ability increased to 0.77, 0.76, and 0.58 when phenomic and genomic data were used together. Using the phenomic data in a genome-wide association study, a heat-related candidate gene (GRMZM2G083810; hsp18f) was discovered using temporal reflectance phenotypes belonging to flowering times (both irrigated and drought) trials where heat stress also peaked. Thus, a relationship between plants and abiotic stresses belonging to a specific time of growth was revealed only through use of temporal phenomic data. Overall, this study showed that (i) it is possible to predict complex traits using high dimensional phenomic data between different environments, and (ii) temporal phenomic data can reveal a time-dependent association between genotypes and abiotic stresses, which can help understand mechanisms to develop resilient plants.
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Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Myeongjong Kang
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | | | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL 32611, USA
| | - Raymond K W Wong
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - Matthias Katzfuß
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
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14
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Li Y, Yang X, Tong L, Wang L, Xue L, Luan Q, Jiang J. Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery. FRONTIERS IN PLANT SCIENCE 2023; 14:1156430. [PMID: 37670863 PMCID: PMC10475579 DOI: 10.3389/fpls.2023.1156430] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/02/2023] [Indexed: 09/07/2023]
Abstract
Genomic selection (GS) is an option for plant domestication that offers high efficiency in improving genetics. However, GS is often not feasible for long-lived tree species with large and complex genomes. In this paper, we investigated UAV multispectral imagery in time series to evaluate genetic variation in tree growth and developed a new predictive approach that is independent of sequencing or pedigrees based on multispectral imagery plus vegetation indices (VIs) for slash pine. Results show that temporal factors have a strong influence on the h2 of tree growth traits. High genetic correlations were found in most months, and genetic gain also showed a slight influence on the time series. Using a consistent ranking of family breeding values, optimal slash pine families were selected, obtaining a promising and reliable predictive ability based on multispectral+VIs (MV) alone or on the combination of pedigree and MV. The highest predictive value, ranging from 0.52 to 0.56, was found in July. The methods described in this paper provide new approaches for phenotypic selection (PS) using high-throughput multispectral unmanned aerial vehicle (UAV) technology, which could potentially be used to reduce the generation time for conifer species and increase the genetic granularity independent of sequencing or pedigrees.
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Affiliation(s)
- Yanjie Li
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Xinyu Yang
- Soybean Research Institute, National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Long Tong
- Chongqing Academy of Forestry, Chongqing, China
| | - Lingling Wang
- Forestry and Water Conservancy Bureau of Fuyang District in Hangzhou, Hangzhou, China
| | - Liang Xue
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Qifu Luan
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Jingmin Jiang
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
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15
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Roth L, Fossati D, Krähenbühl P, Walter A, Hund A. Image-based phenomic prediction can provide valuable decision support in wheat breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:162. [PMID: 37368140 DOI: 10.1007/s00122-023-04395-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 05/29/2023] [Indexed: 06/28/2023]
Abstract
KEY MESSAGE Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.
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Affiliation(s)
- Lukas Roth
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland.
| | | | - Patrick Krähenbühl
- Delley Samen und Pflanzen AG, Route de Portalban 40, 1567, Delley, Switzerland
| | - Achim Walter
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
| | - Andreas Hund
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
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16
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Dossa EN, Shimelis H, Mrema E, Shayanowako ATI, Laing M. Genetic resources and breeding of maize for Striga resistance: a review. FRONTIERS IN PLANT SCIENCE 2023; 14:1163785. [PMID: 37235028 PMCID: PMC10206272 DOI: 10.3389/fpls.2023.1163785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 04/07/2023] [Indexed: 05/28/2023]
Abstract
The potential yield of maize (Zea mays L.) and other major crops is curtailed by several biotic, abiotic, and socio-economic constraints. Parasitic weeds, Striga spp., are major constraints to cereal and legume crop production in sub-Saharan Africa (SSA). Yield losses reaching 100% are reported in maize under severe Striga infestation. Breeding for Striga resistance has been shown to be the most economical, feasible, and sustainable approach for resource-poor farmers and for being environmentally friendly. Knowledge of the genetic and genomic resources and components of Striga resistance is vital to guide genetic analysis and precision breeding of maize varieties with desirable product profiles under Striga infestation. This review aims to present the genetic and genomic resources, research progress, and opportunities in the genetic analysis of Striga resistance and yield components in maize for breeding. The paper outlines the vital genetic resources of maize for Striga resistance, including landraces, wild relatives, mutants, and synthetic varieties, followed by breeding technologies and genomic resources. Integrating conventional breeding, mutation breeding, and genomic-assisted breeding [i.e., marker-assisted selection, quantitative trait loci (QTL) analysis, next-generation sequencing, and genome editing] will enhance genetic gains in Striga resistance breeding programs. This review may guide new variety designs for Striga-resistance and desirable product profiles in maize.
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Affiliation(s)
- Emeline Nanou Dossa
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Hussein Shimelis
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Emmanuel Mrema
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
- Tanzania Agricultural Research Institute, Tumbi Center, Tabora, Tanzania
| | | | - Mark Laing
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
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17
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Fichtl L, Hofmann M, Kahlen K, Voss-Fels KP, Cast CS, Ollat N, Vivin P, Loose S, Nsibi M, Schmid J, Strack T, Schultz HR, Smith J, Friedel M. Towards grapevine root architectural models to adapt viticulture to drought. FRONTIERS IN PLANT SCIENCE 2023; 14:1162506. [PMID: 36998680 PMCID: PMC10043487 DOI: 10.3389/fpls.2023.1162506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/27/2023] [Indexed: 05/31/2023]
Abstract
To sustainably adapt viticultural production to drought, the planting of rootstock genotypes adapted to a changing climate is a promising means. Rootstocks contribute to the regulation of scion vigor and water consumption, modulate scion phenological development and determine resource availability by root system architecture development. There is, however, a lack of knowledge on spatio-temporal root system development of rootstock genotypes and its interactions with environment and management that prevents efficient knowledge transfer into practice. Hence, winegrowers take only limited advantage of the large variability of existing rootstock genotypes. Models of vineyard water balance combined with root architectural models, using both static and dynamic representations of the root system, seem promising tools to match rootstock genotypes to frequently occurring future drought stress scenarios and address scientific knowledge gaps. In this perspective, we discuss how current developments in vineyard water balance modeling may provide the background for a better understanding of the interplay of rootstock genotypes, environment and management. We argue that root architecture traits are key drivers of this interplay, but our knowledge on rootstock architectures in the field remains limited both qualitatively and quantitatively. We propose phenotyping methods to help close current knowledge gaps and discuss approaches to integrate phenotyping data into different models to advance our understanding of rootstock x environment x management interactions and predict rootstock genotype performance in a changing climate. This could also provide a valuable basis for optimizing breeding efforts to develop new grapevine rootstock cultivars with optimal trait configurations for future growing conditions.
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Affiliation(s)
- Lukas Fichtl
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Marco Hofmann
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Katrin Kahlen
- Department of Modeling and Systems Analysis, Hochschule Geisenheim University, Geisenheim, Germany
| | - Kai P. Voss-Fels
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Clément Saint Cast
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Nathalie Ollat
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Philippe Vivin
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Simone Loose
- Department of Wine and Beverage Business, Hochschule Geisenheim University, Geisenheim, Germany
| | - Mariem Nsibi
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Joachim Schmid
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Timo Strack
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Hans Reiner Schultz
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Jason Smith
- Gulbali Institute for Agriculture, Water and Environment, Charles Sturt University, Orange, NSW, Australia
| | - Matthias Friedel
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
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18
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Tilhou NW, Poudel HP, Lovell J, Mamidi S, Schmutz J, Daum C, Zane M, Yoshinaga Y, Lipzen A, Casler MD. Genomic prediction of switchgrass winter survivorship across diverse lowland populations. G3 (BETHESDA, MD.) 2023; 13:jkad014. [PMID: 36648238 PMCID: PMC9997553 DOI: 10.1093/g3journal/jkad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/15/2022] [Accepted: 12/16/2022] [Indexed: 01/18/2023]
Abstract
In the North-Central United States, lowland ecotype switchgrass can increase yield by up to 50% compared with locally adapted but early flowering cultivars. However, lowland ecotypes are not winter tolerant. The mechanism for winter damage is unknown but previously has been associated with late flowering time. This study investigated heading date (measured for two years) and winter survivorship (measured for three years) in a multi-generation population generated from two winter-hardy lowland individuals and diverse southern lowland populations. Sequencing data (311,776 markers) from 1,306 individuals were used to evaluate genome-wide trait prediction through cross-validation and progeny prediction (n = 52). Genetic variance for heading date and winter survivorship was additive with high narrow-sense heritability (0.64 and 0.71, respectively) and reliability (0.68 and 0.76, respectively). The initial negative correlation between winter survivorship and heading date degraded across generations (F1r = -0.43, pseudo-F2r = -0.28, pseudo-F2 progeny r = -0.15). Within-family predictive ability was moderately high for heading date and winter survivorship (0.53 and 0.52, respectively). A multi-trait model did not improve predictive ability for either trait. Progeny predictive ability was 0.71 for winter survivorship and 0.53 for heading date. These results suggest that lowland ecotype populations can obtain sufficient survival rates in the northern United States with two or three cycles of effective selection. Despite accurate genomic prediction, naturally occurring winter mortality successfully isolated winter tolerant genotypes and appears to be an efficient method to develop high-yielding, cold-tolerant switchgrass cultivars.
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Affiliation(s)
- Neal W Tilhou
- Department of Agronomy, University of Wisconsin, 1575 Linden Dr, Madison, WI 53706, USA
| | - Hari P Poudel
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, AB, T1J 4B1 Canada
| | - John Lovell
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Sujan Mamidi
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Jeremy Schmutz
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Christopher Daum
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Lawrence, CA 94704, USA
| | - Matthew Zane
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Lawrence, CA 94704, USA
| | - Yuko Yoshinaga
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Lawrence, CA 94704, USA
| | - Anna Lipzen
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Lawrence, CA 94704, USA
| | - Michael D Casler
- Department of Agronomy, University of Wisconsin, 1575 Linden Dr, Madison, WI 53706, USA
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19
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Gonzalez EM, Zarei A, Hendler N, Simmons T, Zarei A, Demieville J, Strand R, Rozzi B, Calleja S, Ellingson H, Cosi M, Davey S, Lavelle DO, Truco MJ, Swetnam TL, Merchant N, Michelmore RW, Lyons E, Pauli D. PhytoOracle: Scalable, modular phenomics data processing pipelines. FRONTIERS IN PLANT SCIENCE 2023; 14:1112973. [PMID: 36950362 PMCID: PMC10025408 DOI: 10.3389/fpls.2023.1112973] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).
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Affiliation(s)
| | - Ariyan Zarei
- Department of Computer Science, University of Arizona, Tucson, AZ, United States
| | - Nathanial Hendler
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Travis Simmons
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Arman Zarei
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Jeffrey Demieville
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Robert Strand
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Bruno Rozzi
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Sebastian Calleja
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Holly Ellingson
- Data Science Institute, University of Arizona, Tucson, AZ, United States
| | - Michele Cosi
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Sean Davey
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, United States
| | - Dean O. Lavelle
- The Genome and Biomedical Sciences Facility, University of California, Davis, Davis, CA, United States
| | - Maria José Truco
- The Genome and Biomedical Sciences Facility, University of California, Davis, Davis, CA, United States
| | - Tyson L. Swetnam
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, United States
| | - Nirav Merchant
- Data Science Institute, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Richard W. Michelmore
- The Genome and Biomedical Sciences Facility, University of California, Davis, Davis, CA, United States
- Department of Plant Sciences, University of California, Davis, Davis, CA, United States
| | - Eric Lyons
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
- Data Science Institute, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Duke Pauli
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
- Data Science Institute, University of Arizona, Tucson, AZ, United States
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20
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Ballesta P, Maldonado C, Mora-Poblete F, Mieres-Castro D, del Pozo A, Lobos GA. Spectral-Based Classification of Genetically Differentiated Groups in Spring Wheat Grown under Contrasting Environments. PLANTS (BASEL, SWITZERLAND) 2023; 12:440. [PMID: 36771526 PMCID: PMC9920124 DOI: 10.3390/plants12030440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/06/2023] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
The global concern about the gap between food production and consumption has intensified the research on the genetics, ecophysiology, and breeding of cereal crops. In this sense, several genetic studies have been conducted to assess the effectiveness and sustainability of collections of germplasm accessions of major crops. In this study, a spectral-based classification approach for the assignment of wheat cultivars to genetically differentiated subpopulations (genetic structure) was carried out using a panel of 316 spring bread cultivars grown in two environments with different water regimes (rainfed and fully irrigated). For that, different machine-learning models were trained with foliar spectral and genetic information to assign the wheat cultivars to subpopulations. The results revealed that, in general, the hyperparameters ReLU (as the activation function), adam (as the optimizer), and a size batch of 10 give neural network models better accuracy. Genetically differentiated groups showed smaller differences in mean wavelengths under rainfed than under full irrigation, which coincided with a reduction in clustering accuracy in neural network models. The comparison of models indicated that the Convolutional Neural Network (CNN) was significantly more accurate in classifying individuals into their respective subpopulations, with 92 and 93% of correct individual assignments in water-limited and fully irrigated environments, respectively, whereas 92% (full irrigation) and 78% (rainfed) of cultivars were correctly assigned to their respective classes by the multilayer perceptron method and partial least squares discriminant analysis, respectively. Notably, CNN did not show significant differences between both environments, which indicates stability in the prediction independent of the different water regimes. It is concluded that foliar spectral variation can be used to accurately infer the belonging of a cultivar to its respective genetically differentiated group, even considering radically different environments, which is highly desirable in the context of crop genetic resources management.
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Affiliation(s)
- Paulina Ballesta
- Instituto de Nutrición y Tecnología de Los Alimentos, Universidad de Chile, Santiago 7830490, Chile
| | - Carlos Maldonado
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago 8580745, Chile
| | | | | | - Alejandro del Pozo
- Plant Breeding and Phenomic Center, Faculty of Agricultural Sciences, University of Talca, Talca 3460000, Chile
| | - Gustavo A. Lobos
- Plant Breeding and Phenomic Center, Faculty of Agricultural Sciences, University of Talca, Talca 3460000, Chile
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21
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Puppala N, Nayak SN, Sanz-Saez A, Chen C, Devi MJ, Nivedita N, Bao Y, He G, Traore SM, Wright DA, Pandey MK, Sharma V. Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance. Front Genet 2023; 14:1121462. [PMID: 36968584 PMCID: PMC10030941 DOI: 10.3389/fgene.2023.1121462] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 02/06/2023] [Indexed: 03/29/2023] Open
Abstract
Climate change is significantly impacting agricultural production worldwide. Peanuts provide food and nutritional security to millions of people across the globe because of its high nutritive values. Drought and heat stress alone or in combination cause substantial yield losses to peanut production. The stress, in addition, adversely impact nutritional quality. Peanuts exposed to drought stress at reproductive stage are prone to aflatoxin contamination, which imposes a restriction on use of peanuts as health food and also adversely impact peanut trade. A comprehensive understanding of the impact of drought and heat stress at physiological and molecular levels may accelerate the development of stress tolerant productive peanut cultivars adapted to a given production system. Significant progress has been achieved towards the characterization of germplasm for drought and heat stress tolerance, unlocking the physiological and molecular basis of stress tolerance, identifying significant marker-trait associations as well major QTLs and candidate genes associated with drought tolerance, which after validation may be deployed to initiate marker-assisted breeding for abiotic stress adaptation in peanut. The proof of concept about the use of transgenic technology to add value to peanuts has been demonstrated. Advances in phenomics and artificial intelligence to accelerate the timely and cost-effective collection of phenotyping data in large germplasm/breeding populations have also been discussed. Greater focus is needed to accelerate research on heat stress tolerance in peanut. A suits of technological innovations are now available in the breeders toolbox to enhance productivity and nutritional quality of peanuts in harsh environments. A holistic breeding approach that considers drought and heat-tolerant traits to simultaneously address both stresses could be a successful strategy to produce climate-resilient peanut genotypes with improved nutritional quality.
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Affiliation(s)
- Naveen Puppala
- Agricultural Science Center at Clovis, New Mexico State University, Las Cruces, NM, United States
- *Correspondence: Naveen Puppala,
| | - Spurthi N. Nayak
- Department of Biotechnology, University of Agricultural Sciences, Dharwad, India
| | - Alvaro Sanz-Saez
- Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Charles Chen
- Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Mura Jyostna Devi
- USDA-ARS Vegetable Crops Research, Madison, WI, United States
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, United States
| | - Nivedita Nivedita
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, United States
| | - Yin Bao
- Biosystems Engineering Department, Auburn University, Auburn, AL, United States
| | - Guohao He
- Department of Plant and Soil Sciences, Tuskegee University, Tuskegee, AL, United States
| | - Sy M. Traore
- Department of Plant and Soil Sciences, Tuskegee University, Tuskegee, AL, United States
| | - David A. Wright
- Department of Biotechnology, Iowa State University, Ames, IA, United States
| | - Manish K. Pandey
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India
| | - Vinay Sharma
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India
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22
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Legarra A, Christensen O. Genomic evaluation methods to include intermediate correlated features such as high-throughput or omics phenotypes. JDS COMMUNICATIONS 2022; 4:55-60. [PMID: 36713125 PMCID: PMC9873823 DOI: 10.3168/jdsc.2022-0276] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/26/2022] [Indexed: 12/05/2022]
Abstract
Gene expression is supposed to be an intermediate between DNA and the phenotype, and it can be measured. Thus, for a trait, we may have intermediate measures, which are in fact a series of genetically controlled traits. Similarly, several traits may be measured or predicted using infrared spectra, accelerometers, and similar high-throughput measures that we will call "omics." Although these measurements have errors, many of them are heritable, and they may be more accurate or easier to record than the trait of interest. It is therefore important to develop methods to use intermediate measurements in selection. Here, we present methods and perspectives for selection based on massively recorded intermediate traits (omics). Recent developments allow a hierarchical integrated framework for prediction, in which a trait is partially controlled by omics. In addition, the omics measures are themselves partly controlled by genetics ("mediated breeding values") and partly by environment or residual factors. Thus, a part of the genetic determinism of a trait is mediated by omics, whereas the remaining part is not mediated, which results in "residual breeding values." In such a framework, genetic evaluations consist of 2 nested genomic BLUP-based models. In the first, the effect of omics on the trait (which can be seen as an improved estimate of the phenotype) and the residual breeding values are estimated. The second model extracts the mediated breeding values from the improved estimate of the phenotype, considering that omics themselves are heritable. The whole procedure is called GOBLUP (genomics omics BLUP) and it allows measures in only some individuals; that is, it is a "single-step"-like method. In this model, heritability is split into "mediated" and "not mediated" parts. This decomposition allows us to predict how accurate the omics measure of the trait would be compared with the direct measure. The ideal omics measure is heritable and explains a large part of the phenotypic variation of the trait. Ideally, this could be the case for some traits with low heritability. However, even if the omics measure explains only a small part of the phenotypic variation, when omics measurement themselves are heritable, the use of such a model would lead to more accurate selection. Expressions for upper bounds of reliability given omics measurements are also presented. More studies are needed to confirm the usefulness of omics or high-throughput prediction. Usefulness of the technology likely needs to be checked on a case-by-case basis.
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Affiliation(s)
- A. Legarra
- GenPhySE (Genetique, Physiologie et Systemes d'Elevage), INRA, 31326 Castanet-Tolosan, France,Corresponding author
| | - O.F. Christensen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
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23
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Adak A, Murray SC, Anderson SL. Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions. G3 (BETHESDA, MD.) 2022; 13:6851143. [PMID: 36445027 PMCID: PMC9836347 DOI: 10.1093/g3journal/jkac294] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 10/21/2022] [Indexed: 11/30/2022]
Abstract
A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade, genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over 15 growth time points and multispectral (RGB, red-edge and near infrared) bands over 12 time points were compared across 280 unique maize hybrids. Through cross-validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP), outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in 3 other cross-validation scenarios. Genome-wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5% of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51% of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, TPP appeared to work successfully on unrelated individuals unlike GP.
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Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Seth C Murray
- Corresponding author: Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA.
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24
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Robert P, Goudemand E, Auzanneau J, Oury FX, Rolland B, Heumez E, Bouchet S, Caillebotte A, Mary-Huard T, Le Gouis J, Rincent R. Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3337-3356. [PMID: 35939074 DOI: 10.1007/s00122-022-04170-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.
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Affiliation(s)
- Pauline Robert
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Ellen Goudemand
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Jérôme Auzanneau
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
| | - François-Xavier Oury
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Bernard Rolland
- INRAE-Agrocampus Ouest-Université Rennes 1, UMR1349, IGEPP, Domaine de la Motte, 35653, Le Rheu, France
| | - Emmanuel Heumez
- INRAE, UE 972, Grandes Cultures Innovation Environnement, 2 Chaussée Brunehaut, 80200, Estrées-Mons, France
| | - Sophie Bouchet
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Antoine Caillebotte
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- MIA, INRAE, AgroParisTech, Université Paris-Saclay, 75005, Paris, France
| | - Jacques Le Gouis
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Renaud Rincent
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France.
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25
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Brault C, Lazerges J, Doligez A, Thomas M, Ecarnot M, Roumet P, Bertrand Y, Berger G, Pons T, François P, Le Cunff L, This P, Segura V. Interest of phenomic prediction as an alternative to genomic prediction in grapevine. PLANT METHODS 2022; 18:108. [PMID: 36064570 PMCID: PMC9442960 DOI: 10.1186/s13007-022-00940-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/24/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. RESULTS For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions. CONCLUSION NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.
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Affiliation(s)
- Charlotte Brault
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
- Institut Français de la vigne et du vin, 34398, Montpellier, France
| | - Juliette Lazerges
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Agnès Doligez
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Miguel Thomas
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Martin Ecarnot
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
| | - Pierre Roumet
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
| | - Yves Bertrand
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Gilles Berger
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Thierry Pons
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Pierre François
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Loïc Le Cunff
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
- Institut Français de la vigne et du vin, 34398, Montpellier, France
| | - Patrice This
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Vincent Segura
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France.
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France.
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26
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Mbebi AJ, Breitler JC, Bordeaux M, Sulpice R, McHale M, Tong H, Toniutti L, Castillo JA, Bertrand B, Nikoloski Z. A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids. G3 GENES|GENOMES|GENETICS 2022; 12:6632664. [PMID: 35792875 PMCID: PMC9434219 DOI: 10.1093/g3journal/jkac170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/14/2022] [Indexed: 11/14/2022]
Abstract
Abstract
Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.
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Affiliation(s)
- Alain J Mbebi
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam , Potsdam-Golm 14476, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm 14476, Germany
| | - Jean-Christophe Breitler
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier 34398, France
| | - Mélanie Bordeaux
- Fundación Nicafrance , Finca La Cumplida Km. 147 Carretera Matagalpa - La Dalia, 3 Km al Noreste, Matagalpa, Nicaragua
| | - Ronan Sulpice
- National University Ireland Galway, Plant Systems Biology Laboratory, Ryan Institute, School of Natural Sciences , Galway H91 TK33, Ireland
| | - Marcus McHale
- National University Ireland Galway, Plant Systems Biology Laboratory, Ryan Institute, School of Natural Sciences , Galway H91 TK33, Ireland
| | - Hao Tong
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam , Potsdam-Golm 14476, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm 14476, Germany
- Center for Plant Systems Biology and Biotechnology , Plovdiv 4000, Bulgaria
| | - Lucile Toniutti
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier 34398, France
| | - Jonny Alonso Castillo
- Fundación Nicafrance , Finca La Cumplida Km. 147 Carretera Matagalpa - La Dalia, 3 Km al Noreste, Matagalpa, Nicaragua
| | - Benoît Bertrand
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier 34398, France
| | - Zoran Nikoloski
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam , Potsdam-Golm 14476, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm 14476, Germany
- Center for Plant Systems Biology and Biotechnology , Plovdiv 4000, Bulgaria
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A Neural Network-Based Spectral Approach for the Assignment of Individual Trees to Genetically Differentiated Subpopulations. REMOTE SENSING 2022. [DOI: 10.3390/rs14122898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Studying population structure has made an essential contribution to understanding evolutionary processes and demographic history in forest ecology research. This inference process basically involves the identification of common genetic variants among individuals, then grouping the similar individuals into subpopulations. In this study, a spectral-based classification of genetically differentiated groups was carried out using a provenance–progeny trial of Eucalyptus cladocalyx. First, the genetic structure was inferred through a Bayesian analysis using single-nucleotide polymorphisms (SNPs). Then, different machine learning models were trained with foliar spectral information to assign individual trees to subpopulations. The results revealed that spectral-based classification using the multilayer perceptron method was very successful at classifying individuals into their respective subpopulations (with an average of 87% of correct individual assignments), whereas 85% and 81% of individuals were assigned to their respective classes correctly by convolutional neural network and partial least squares discriminant analysis, respectively. Notably, 93% of individual trees were assigned correctly to the class with the smallest size using the spectral data-based multi-layer perceptron classification method. In conclusion, spectral data, along with neural network models, are able to discriminate and assign individuals to a given subpopulation, which could facilitate the implementation and application of population structure studies on a large scale.
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Vasseur F, Cornet D, Beurier G, Messier J, Rouan L, Bresson J, Ecarnot M, Stahl M, Heumos S, Gérard M, Reijnen H, Tillard P, Lacombe B, Emanuel A, Floret J, Estarague A, Przybylska S, Sartori K, Gillespie LM, Baron E, Kazakou E, Vile D, Violle C. A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 13:836488. [PMID: 35668791 PMCID: PMC9163986 DOI: 10.3389/fpls.2022.836488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/09/2022] [Indexed: 05/31/2023]
Abstract
The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.
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Affiliation(s)
| | - Denis Cornet
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Grégory Beurier
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Julie Messier
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Lauriane Rouan
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Justine Bresson
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Martin Ecarnot
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Mark Stahl
- Center for Plant Molecular Biology (ZMBP), University of Tübingen, Tübingen, Germany
| | - Simon Heumos
- Quantitative Biology Center (QBiC), University of Tübingen, Quantitative Biology Center (QBiC), University of Tübingen, Germany
- Biomedical Data Science, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Marianne Gérard
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Hans Reijnen
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Pascal Tillard
- BPMP, Univ Montpellier, CNRS, INRAE, Montpellier, France
| | - Benoît Lacombe
- BPMP, Univ Montpellier, CNRS, INRAE, Montpellier, France
| | - Amélie Emanuel
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
- BPMP, Univ Montpellier, CNRS, INRAE, Montpellier, France
| | - Justine Floret
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | - Kevin Sartori
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | | | - Etienne Baron
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Elena Kazakou
- CEFE, Univ Montpellier, CNRS, EPHE, Institut Agro, IRD, Montpellier, France
| | - Denis Vile
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Cyrille Violle
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
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Hudzenko VM, Polishchuk TP, Lysenko AA, Fedorenko IV, Fedorenko MV, Khudolii LV, Ishchenko VA, Kozelets HM, Babenko AI, Tanchyk SP, Mandrovska SM. Elucidation of gene action and combining ability for productive tillering in spring barley. REGULATORY MECHANISMS IN BIOSYSTEMS 2022. [DOI: 10.15421/022225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
The purpose of the present study is to identify breeding and genetic peculiarities for productive tillering in spring barley genotypes of different origin, purposes of usage and botanical affiliation, as well as to identify effective genetic sources to further improving of the trait. There were created two complete (6 × 6) diallel crossing schemes. Into the Scheme I elite Ukrainian (MIP Tytul and Avhur) and Western European (Datcha, Quench, Gladys, and Beatrix) malting spring barley varieties were involved. Scheme II included awnless covered barley varieties Kozyr and Vitrazh bred at the Plant Production Institute named after V. Y. Yuriev of NAAS of Ukraine, naked barley varieties Condor and CDC Rattan from Canada, as well as awned feed barley variety MIP Myroslav created at MIW and malting barley variety Sebastian from Denmark. For more reliable and informative characterization of barley varieties and their progeny for productive tillering in terms of inheritance, parameters of genetic variation and general combining ability (GCA) statistical analyses of experimental data from different (2019 and 2020) growing seasons were conducted. Accordingly to the indicator of phenotypic dominance all possible modes of inheritance were detected, except for negative dominance in the Scheme I in 2020. The degree of phenotypic dominance significantly varied depending on both varieties involved in crossing schemes and conditions of the years of trials. There was overdominance in loci in both schemes in both years. The other parameters of genetic variation showed significant differences in gene action for productive tillering between crossing Schemes. In Scheme I in both years the dominance was mainly unidirectional and due to dominant effects. In the Scheme II in both years there was multidirectional dominance. In Scheme I compliance with the additive-dominant system was revealed in 2019, but in 2020 there was a strong epistasis. In Scheme II in both years non-allelic interaction was identified. In general, the mode of gene action showed a very complex gene action for productive tillering in barley and a significant role of non-genetic factors in phenotypic manifestation of the trait. Despite this, the level of heritability in the narrow sense in both Schemes pointed to the possibility of the successful selection of individuals with genetically determined increased productive tillering in the splitting generations. In Scheme I the final selection for productive tillering will be more effective in later generations, when dominant alleles become homozygous. In Scheme II it is theoretically possible to select plants with high productive tillering on both recessive and dominant basis. In both schemes the non-allelic interaction should be taken into consideration. Spring barley varieties Beatrix, Datcha, MIP Myroslav and Kozyr can be used as effective genetic sources for involvement in crossings aimed at improving the productive tillering. The results of present study contribute to further development of studies devoted to evaluation of gene action for yield-related traits in spring barley, as well as identification of new genetic sources for plant improvement.
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Phenomic Selection: A New and Efficient Alternative to Genomic Selection. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:397-420. [PMID: 35451784 DOI: 10.1007/978-1-0716-2205-6_14] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Recently, it has been proposed to switch molecular markers to near-infrared (NIR) spectra for inferring relationships between individuals and further performing phenomic selection (PS), analogous to genomic selection (GS). The PS concept is similar to genomic-like omics-based (GLOB) selection, in which molecular markers are replaced by endophenotypes, such as metabolites or transcript levels, except that the phenomic information obtained for instance by near-infrared spectroscopy (NIRS ) has usually a much lower cost than other omics. Though NIRS has been routinely used in breeding for several decades, especially to deal with end-product quality traits, its use to predict other traits of interest and further make selections is new. Since the seminal paper on PS , several publications have advocated the use of spectral acquisition (including NIRS and hyperspectral imaging) in plant breeding towards PS , potentially providing a scope of what is possible. In the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding. We further provide a review of the selected literature concerning the type of technology used, the preprocessing of the spectra, and the statistical modeling to make predictions. We discuss the factors that likely affect the efficiency of PS and compare it to GS in terms of predictive ability. Finally, we propose several prospects for future work and application of PS in the context of plant breeding.
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Ballesta P, Ahmar S, Lobos GA, Mieres-Castro D, Jiménez-Aspee F, Mora-Poblete F. Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus. FRONTIERS IN PLANT SCIENCE 2022; 13:871943. [PMID: 35432412 PMCID: PMC9008590 DOI: 10.3389/fpls.2022.871943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
Plants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability ( h g 2 =0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 ( h g 2 =0.58), followed by the anthocyanin reflectance index and the Browning reflectance index ( h g 2 =0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.
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Affiliation(s)
- Paulina Ballesta
- The National Fund for Scientific and Technological Development, Talca, Chile
| | - Sunny Ahmar
- The National Fund for Scientific and Technological Development, Talca, Chile
| | - Gustavo A. Lobos
- Plant Breeding and Phenomic Center, Faculty of Agricultural Sciences, Universidad de Talca, Talca, Chile
| | | | - Felipe Jiménez-Aspee
- Department of Food Biofunctionality, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
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32
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Colombo M, Roumet P, Salon C, Jeudy C, Lamboeuf M, Lafarge S, Dumas AV, Dubreuil P, Ngo W, Derepas B, Beauchêne K, Allard V, Le Gouis J, Rincent R. Genetic Analysis of Platform-Phenotyped Root System Architecture of Bread and Durum Wheat in Relation to Agronomic Traits. FRONTIERS IN PLANT SCIENCE 2022; 13:853601. [PMID: 35401645 PMCID: PMC8992431 DOI: 10.3389/fpls.2022.853601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Roots are essential for water and nutrient uptake but are rarely the direct target of breeding efforts. To characterize the genetic variability of wheat root architecture, the root and shoot traits of 200 durum and 715 bread wheat varieties were measured at a young stage on a high-throughput phenotyping platform. Heritability of platform traits ranged from 0.40 for root biomass in durum wheat to 0.82 for the number of tillers. Field phenotyping data for yield components and SNP genotyping were already available for all the genotypes. Taking differences in earliness into account, several significant correlations between root traits and field agronomic performances were found, suggesting that plants investing more resources in roots in some stressed environments favored water and nutrient uptake, with improved wheat yield. We identified 100 quantitative trait locus (QTLs) of root traits in the bread wheat panels and 34 in the durum wheat panel. Most colocalized with QTLs of traits measured in field conditions, including yield components and earliness for bread wheat, but only in a few environments. Stress and climatic indicators explained the differential effect of some platform QTLs on yield, which was positive, null, or negative depending on the environmental conditions. Modern breeding has led to deeper rooting but fewer seminal roots in bread wheat. The number of tillers has been increased in bread wheat, but decreased in durum wheat, and while the root-shoot ratio for bread wheat has remained stable, for durum wheat it has been increased. Breeding for root traits or designing ideotypes might help to maintain current yield while adapting to specific drought scenarios.
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Affiliation(s)
- Michel Colombo
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Pierre Roumet
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Christophe Salon
- Univ. Bourgogne, Agroecol Lab, Univ. Bourgogne Franche Comte, AgroSup Dijon, INRAE, Dijon, France
| | - Christian Jeudy
- Univ. Bourgogne, Agroecol Lab, Univ. Bourgogne Franche Comte, AgroSup Dijon, INRAE, Dijon, France
| | - Mickael Lamboeuf
- Univ. Bourgogne, Agroecol Lab, Univ. Bourgogne Franche Comte, AgroSup Dijon, INRAE, Dijon, France
| | | | | | | | - Wa Ngo
- INRAE-Universite Clermont-Auvergne, UMR 1095, GDEC, Clermont-Ferrand, France
| | - Brice Derepas
- INRAE-Universite Clermont-Auvergne, UMR 1095, GDEC, Clermont-Ferrand, France
| | | | - Vincent Allard
- INRAE-Universite Clermont-Auvergne, UMR 1095, GDEC, Clermont-Ferrand, France
| | - Jacques Le Gouis
- INRAE-Universite Clermont-Auvergne, UMR 1095, GDEC, Clermont-Ferrand, France
| | - Renaud Rincent
- INRAE-Universite Clermont-Auvergne, UMR 1095, GDEC, Clermont-Ferrand, France
- GQE-Le Moulon, INRAE, Univ. Paris-Sud, CNRS, AgroParisTech, Universite Paris-Saclay, Gif-sur-Yvette, France
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33
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Nagel‐Held J, Kaiser L, H. Longin CF, Hitzmann B. Prediction of Wheat Quality Parameters Combining Raman, Fluorescence and Near‐Infrared Spectroscopy (NIRS). Cereal Chem 2022. [DOI: 10.1002/cche.10540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Johannes Nagel‐Held
- Department of Process Analytics and Cereal Science University of Hohenheim, Garbenstraße 23 Stuttgart Germany
| | - Leonie Kaiser
- Department of Process Analytics and Cereal Science University of Hohenheim, Garbenstraße 23 Stuttgart Germany
| | - C. Friedrich H. Longin
- State Plant Breeding Institute University of Hohenheim Fruwirthstraße 21 70599 Stuttgart Germany
| | - Bernd Hitzmann
- Department of Process Analytics and Cereal Science University of Hohenheim, Garbenstraße 23 Stuttgart Germany
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Weiß TM, Zhu X, Leiser WL, Li D, Liu W, Schipprack W, Melchinger AE, Hahn V, Würschum T. Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.). G3 (BETHESDA, MD.) 2022; 12:6509517. [PMID: 35100379 PMCID: PMC8895988 DOI: 10.1093/g3journal/jkab445] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/16/2021] [Indexed: 12/19/2022]
Abstract
Genomic selection is a well-investigated approach that facilitates and supports selection decisions for complex traits and has meanwhile become a standard tool in modern plant breeding. Phenomic selection has only recently been suggested and uses the same statistical procedures to predict the targeted traits but replaces marker data with near-infrared spectroscopy data. It may represent an attractive low-cost, high-throughput alternative but has not been sufficiently studied until now. Here, we used 400 genotypes of maize (Zea mays L.) comprising elite lines of the Flint and Dent heterotic pools as well as 6 Flint landraces, which were phenotyped in multienvironment trials for anthesis-silking-interval, early vigor, final plant height, grain dry matter content, grain yield, and phosphorus concentration in the maize kernels, to compare the predictive abilities of genomic as well as phenomic prediction under different scenarios. We found that both approaches generally achieved comparable predictive abilities within material groups. However, phenomic prediction was less affected by population structure and performed better than its genomic counterpart for predictions among diverse groups of breeding material. We therefore conclude that phenomic prediction is a promising tool for practical breeding, for instance when working with unknown and rather diverse germplasm. Moreover, it may make the highly monopolized sector of plant breeding more accessible also for low-tech institutions by combining well established, widely available, and cost-efficient spectral phenotyping with the statistical procedures elaborated for genomic prediction - while achieving similar or even better results than with marker data.
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Affiliation(s)
- Thea Mi Weiß
- State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany.,Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany
| | - Xintian Zhu
- State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany.,Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany
| | - Willmar L Leiser
- State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany
| | - Dongdong Li
- Key Laboratory of Crop Heterosis and Utilization, Ministry of Education, Key Laboratory of Crop Genetic Improvement, Beijing Municipality, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
| | - Wenxin Liu
- Key Laboratory of Crop Heterosis and Utilization, Ministry of Education, Key Laboratory of Crop Genetic Improvement, Beijing Municipality, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
| | - Wolfgang Schipprack
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany
| | - Volker Hahn
- State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany
| | - Tobias Würschum
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany
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35
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Sakurai N. Recent applications of metabolomics in plant breeding. BREEDING SCIENCE 2022; 72:56-65. [PMID: 36045891 PMCID: PMC8987846 DOI: 10.1270/jsbbs.21065] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 12/19/2021] [Indexed: 05/27/2023]
Abstract
Metabolites play a central role in maintaining organismal life and in defining crop phenotypes, such as nutritional value, fragrance, color, and stress resistance. Among the 'omes' in biology, the metabolome is the closest to the phenotype. Consequently, metabolomics has been applied to crop improvement as method for monitoring changes in chemical compositions, clarifying the mechanisms underlying cellular functions, discovering markers and diagnostics, and phenotyping for mQTL, mGWAS, and metabolite-genome predictions. In this review, 359 reports of the most recent applications of metabolomics to plant breeding-related studies were examined. In addition to the major crops, more than 160 other crops including rare medicinal plants were considered. One bottleneck associated with using metabolomics is the wide array of instruments that are used to obtain data and the ambiguity associated with metabolite identification and quantification. To further the application of metabolomics to plant breeding, the features and perspectives of the technology are discussed.
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Affiliation(s)
- Nozomu Sakurai
- Bioinformation and DDBJ Center, National Institute of Genetics, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
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Griffiths M, Delory BM, Jawahir V, Wong KM, Bagnall GC, Dowd TG, Nusinow DA, Miller AJ, Topp CN. Optimisation of root traits to provide enhanced ecosystem services in agricultural systems: A focus on cover crops. PLANT, CELL & ENVIRONMENT 2022; 45:751-770. [PMID: 34914117 PMCID: PMC9306666 DOI: 10.1111/pce.14247] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 11/05/2021] [Accepted: 12/01/2021] [Indexed: 05/26/2023]
Abstract
Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical and biotic properties of soil. However, cover crops, a group of plants that provide ecosystem services, can be utilised during fallow periods or used as an intercrop to restore soil health. The effectiveness of ecosystem services provided by cover crops varies widely as very little breeding has occurred in these species. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. In this study, we provide an overview of the range of belowground ecosystem services provided by cover crop roots: (1) soil structural remediation, (2) capture of soil resources and (3) maintenance of the rhizosphere and building of organic matter content. Based on the ecosystem services described, we outline current and promising phenotyping technologies and breeding strategies in cover crops that can enhance agricultural sustainability through improvement of root traits.
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Affiliation(s)
| | | | | | - Kong M. Wong
- Donald Danforth Plant Science CenterSt. LouisMissouriUSA
| | | | - Tyler G. Dowd
- Donald Danforth Plant Science CenterSt. LouisMissouriUSA
| | | | - Allison J. Miller
- Donald Danforth Plant Science CenterSt. LouisMissouriUSA
- Department of BiologySaint Louis UniversitySt. LouisMissouriUSA
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37
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Robert P, Auzanneau J, Goudemand E, Oury FX, Rolland B, Heumez E, Bouchet S, Le Gouis J, Rincent R. Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:895-914. [PMID: 34988629 DOI: 10.1007/s00122-021-04005-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/23/2021] [Indexed: 05/15/2023]
Abstract
Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of grain yield and heading date of wheat breeding lines. Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs.
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Affiliation(s)
- Pauline Robert
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Jérôme Auzanneau
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
| | - Ellen Goudemand
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - François-Xavier Oury
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Bernard Rolland
- INRAE-Agrocampus Ouest-Université Rennes 1, UMR1349, IGEPP, Domaine de la Motte, 35653, Le Rheu, France
| | - Emmanuel Heumez
- INRAE, UE 972, Grandes Cultures Innovation Environnement, 2 Chaussée Brunehaut, 80200, EstréesMons, France
| | - Sophie Bouchet
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Jacques Le Gouis
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France.
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France.
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Phenotypic Traits Extraction and Genetic Characteristics Assessment of Eucalyptus Trials Based on UAV-Borne LiDAR and RGB Images. REMOTE SENSING 2022. [DOI: 10.3390/rs14030765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Phenotype describes the physical, physiological and biochemical characteristics of organisms that are determined or influenced by genes and environment. Accurate extraction of phenotypic data is a prerequisite for comprehensive forest phenotyping in order to improve the growth and development of forest plantations. Combined with the assessments of genetic characteristics, forest phenotyping will help to accelerate the breeding process, improve stress resistance and enhance the quality of the planted forest. In this study, we disposed our study in Eucalyptus trials within the Gaofeng forest farm (a typical Eucalyptus plantation site in southern China) for a high-throughput phenotypic traits extraction and genetic characteristics analysis based on high-density point clouds (acquired by a UAV-borne LiDAR sensor) and high-resolution RGB images (acquired by a UAV-borne camera), aiming at developing a high-resolution and high-throughput UAV-based phenotyping approach for tree breeding. First, we compared the effect of CHM-based Marker-Controlled Watershed Segmentation (MWS) and Point Cloud-based Cluster Segmentation (PCS) for extracting individual trees; Then, the phenotypic traits (i.e., tree height, diameter at breast height, crown width), the structural metrics (n = 19) and spectral indices (n = 9) of individual trees were extracted and assessed; Finally, a genetic characteristics analysis was carried out based on the above results, and we compared the differences between high-throughput phenotyping by UAV-based data and on manual measurements. Results showed that: in the relatively low stem density site of the trial (760 n/ha), the overall accuracy of MWS and PCS was similar, while in the higher stem density sites (982 n/ha, 1239 n/ha), the overall accuracy of MWS (F(2) = 0.93, F(3) = 0.86) was higher than PCS (F(2) = 0.84, F(3) = 0.74); With the increase of stem density, the difference between the overall accuracy of MWS and PCS gradually expanded. Both UAV–LiDAR extracted phenotypic traits and manual measurements were significantly different across the Eucalyptus clones (P < 0.05), as were most of the structural metrics (47/57) and spectral indices (26/27), revealing the genetic divergence between the clones. The rank of clones demonstrated that the pure clones (of E. urophylla), the hybrid clones (of E. urophylla as the female parent) and the hybrid clones (of E. wetarensis and E. grandis) have a higher fineness of growth. This study proved that UAV-based fine-resolution remote sensing could be an efficient, accurate and precise technology in phenotyping (used in genetic analysis) for tree breeding.
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Zhu X, Maurer HP, Jenz M, Hahn V, Ruckelshausen A, Leiser WL, Würschum T. The performance of phenomic selection depends on the genetic architecture of the target trait. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:653-665. [PMID: 34807268 PMCID: PMC8866387 DOI: 10.1007/s00122-021-03997-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/08/2021] [Indexed: 06/13/2023]
Abstract
The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.
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Affiliation(s)
- Xintian Zhu
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | - Hans Peter Maurer
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | - Mario Jenz
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
- Hochschule Osnabrück, Sedanstr. 26, 49076, Osnabrück, Germany
| | - Volker Hahn
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | | | - Willmar L Leiser
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | - Tobias Würschum
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany.
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Sandhu KS, Merrick LF, Sankaran S, Zhang Z, Carter AH. Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs. Front Genet 2022. [PMCID: PMC8814369 DOI: 10.3389/fgene.2021.829131] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.
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Affiliation(s)
- Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
- *Correspondence: Karansher S. Sandhu,
| | - Lance F. Merrick
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Sindhuja Sankaran
- Department of Biological System Engineering, Washington State University, Pullman, WA, United States
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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Paux E, Lafarge S, Balfourier F, Derory J, Charmet G, Alaux M, Perchet G, Bondoux M, Baret F, Barillot R, Ravel C, Sourdille P, Le Gouis J. Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection. BIOLOGY 2022; 11:149. [PMID: 35053148 PMCID: PMC8773325 DOI: 10.3390/biology11010149] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 12/21/2022]
Abstract
There is currently a strong societal demand for sustainability, quality, and safety in bread wheat production. To address these challenges, new and innovative knowledge, resources, tools, and methods to facilitate breeding are needed. This starts with the development of high throughput genomic tools including single nucleotide polymorphism (SNP) arrays, high density molecular marker maps, and full genome sequences. Such powerful tools are essential to perform genome-wide association studies (GWAS), to implement genomic and phenomic selection, and to characterize the worldwide diversity. This is also useful to breeders to broaden the genetic basis of elite varieties through the introduction of novel sources of genetic diversity. Improvement in varieties particularly relies on the detection of genomic regions involved in agronomical traits including tolerance to biotic (diseases and pests) and abiotic (drought, nutrient deficiency, high temperature) stresses. When enough resolution is achieved, this can result in the identification of candidate genes that could further be characterized to identify relevant alleles. Breeding must also now be approached through in silico modeling to simulate plant development, investigate genotype × environment interactions, and introduce marker-trait linkage information in the models to better implement genomic selection. Breeders must be aware of new developments and the information must be made available to the world wheat community to develop new high-yielding varieties that can meet the challenge of higher wheat production in a sustainable and fluctuating agricultural context. In this review, we compiled all knowledge and tools produced during the BREEDWHEAT project to show how they may contribute to face this challenge in the coming years.
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Affiliation(s)
- Etienne Paux
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
| | - Stéphane Lafarge
- Limagrain, Chappes Research Center, Route d’Ennezat, 63720 Chappes, France; (S.L.); (J.D.)
| | - François Balfourier
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
| | - Jérémy Derory
- Limagrain, Chappes Research Center, Route d’Ennezat, 63720 Chappes, France; (S.L.); (J.D.)
| | - Gilles Charmet
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
| | - Michael Alaux
- Université Paris-Saclay—INRAE, URGI, 78026 Versailles, France;
- Université Paris-Saclay—INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026 Versailles, France
| | - Geoffrey Perchet
- Vegepolys Valley, Maison du Végétal, 26 Rue Jean Dixmeras, 49066 Angers, France;
| | - Marion Bondoux
- INRAE—Transfert, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France;
| | - Frédéric Baret
- UMR EMMAH, INRAE—Université d’Avignon et des Pays de Vaucluse, 84914 Avignon, France;
| | | | - Catherine Ravel
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
| | - Pierre Sourdille
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
| | - Jacques Le Gouis
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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Abstract
To date, genomic prediction has been conducted in about 20 aquaculture species, with a preference for intra-family genomic selection (GS). For every trait under GS, the increase in accuracy obtained by genomic estimated breeding values instead of classical pedigree-based estimation of breeding values is very important in aquaculture species ranging from 15% to 89% for growth traits, and from 0% to 567% for disease resistance. Although the implementation of GS in aquaculture is of little additional investment in breeding programs already implementing sib testing on pedigree, the deployment of GS remains sparse, but could be boosted by adaptation of cost-effective imputation from low-density panels. Moreover, GS could help to anticipate the effect of climate change by improving sustainability-related traits such as production yield (e.g., carcass or fillet yields), feed efficiency or disease resistance, and by improving resistance to environmental variation (tolerance to temperature or salinity variation). This chapter synthesized the literature in applications of GS in finfish, crustaceans and molluscs aquaculture in the present and future breeding programs.
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Affiliation(s)
- François Allal
- MARBEC, Université de Montpellier, CNRS, Ifremer, IRD, Palavas-les-Flots, France.
| | - Nguyen Hong Nguyen
- School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia
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Crossa J, Montesinos-López OA, Pérez-Rodríguez P, Costa-Neto G, Fritsche-Neto R, Ortiz R, Martini JWR, Lillemo M, Montesinos-López A, Jarquin D, Breseghello F, Cuevas J, Rincent R. Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction. Methods Mol Biol 2022; 2467:245-283. [PMID: 35451779 DOI: 10.1007/978-1-0716-2205-6_9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.
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Affiliation(s)
- José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Mexico
- Colegio de Postgraduados, Montecillos, Mexico
| | | | | | - Germano Costa-Neto
- Departamento de Genética, Escola Superior de Agricultura "Luiz de Queiroz" (ESALQ/USP), São Paulo, Brazil
| | - Roberto Fritsche-Neto
- Departamento de Genética, Escola Superior de Agricultura "Luiz de Queiroz" (ESALQ/USP), São Paulo, Brazil
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Alnarp, Sweden
| | - Johannes W R Martini
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Mexico
| | - Morten Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences, IHA/CIGENE, Ås, Norway
| | - Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | | | | | - Jaime Cuevas
- Universidad de Quintana Roo, Chetumal, Quintana Roo, Mexico.
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette, France.
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Robb BC, Olsoy PJ, Mitchell JJ, Caughlin TT, Delparte DM, Galla SJ, Fremgen‐Tarantino MR, Nobler JD, Rachlow JL, Shipley LA, Forbey JS. Near‐infrared spectroscopy aids ecological restoration by classifying variation of taxonomy and phenology of a native shrub. Restor Ecol 2021. [DOI: 10.1111/rec.13584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Brecken C. Robb
- Department of Biological Sciences Boise State University 1910 W University Drive Boise ID 83725 U.S.A
| | - Peter J. Olsoy
- Department of Biological Sciences Boise State University 1910 W University Drive Boise ID 83725 U.S.A
| | - Jessica J. Mitchell
- Department of Ecosystem and Conservation Science University of Montana 32 Campus Drive Missoula MT 59812 U.S.A
| | - T. Trevor Caughlin
- Department of Biological Sciences Boise State University 1910 W University Drive Boise ID 83725 U.S.A
| | - Donna M. Delparte
- Department of Geosciences Idaho State University 921 S 8th Avenue Pocatello ID 83209 U.S.A
| | - Stephanie J. Galla
- Department of Biological Sciences Boise State University 1910 W University Drive Boise ID 83725 U.S.A
| | | | - Jordan D. Nobler
- Department of Biological Sciences Boise State University 1910 W University Drive Boise ID 83725 U.S.A
| | - Janet L. Rachlow
- Department of Fish and Wildlife Sciences University of Idaho 875 Perimeter Drive Moscow ID 83844 U.S.A
| | - Lisa A. Shipley
- School of the Environment Washington State University 100 Dairy Road/1228 Webster Pullman WA 99164 U.S.A
| | - Jennifer S. Forbey
- Department of Biological Sciences Boise State University 1910 W University Drive Boise ID 83725 U.S.A
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Christensen OF, Börner V, Varona L, Legarra A. Genetic evaluation including intermediate omics features. Genetics 2021; 219:6345349. [PMID: 34849886 DOI: 10.1093/genetics/iyab130] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/13/2021] [Indexed: 11/14/2022] Open
Abstract
In animal and plant breeding and genetics, there has been an increasing interest in intermediate omics traits, such as metabolomics and transcriptomics, which mediate the effect of genetics on the phenotype of interest. For inclusion of such intermediate traits into a genetic evaluation system, there is a need for a statistical model that integrates phenotypes, genotypes, pedigree, and omics traits, and a need for associated computational methods that provide estimated breeding values. In this paper, a joint model for phenotypes and omics data is presented, and a formula for the breeding values on individuals is derived. For complete omics data, three equivalent methods for best linear unbiased prediction of breeding values are presented. In all three cases, this requires solving two mixed model equation systems. Estimation of parameters using restricted maximum likelihood is also presented. For incomplete omics data, extensions of two of these methods are presented, where in both cases, the extension consists of extending an omics-related similarity matrix to incorporate individuals without omics data. The methods are illustrated using a simulated data set.
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Affiliation(s)
- Ole F Christensen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Vinzent Börner
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Luis Varona
- Departmento de Anatomía, Embriología y Genética Animal, Universidad de Zaragoza, 50013 Saragoza, Spain
| | - Andres Legarra
- GenPhySE (Génétique, Physiologie et Systèmes d'Elevage), INRA, 31326 Castanet-Tolosan, France
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Ahmar S, Ballesta P, Ali M, Mora-Poblete F. Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing. Int J Mol Sci 2021; 22:10583. [PMID: 34638922 PMCID: PMC8508745 DOI: 10.3390/ijms221910583] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 09/26/2021] [Accepted: 09/27/2021] [Indexed: 12/23/2022] Open
Abstract
Forest tree breeding efforts have focused mainly on improving traits of economic importance, selecting trees suited to new environments or generating trees that are more resilient to biotic and abiotic stressors. This review describes various methods of forest tree selection assisted by genomics and the main technological challenges and achievements in research at the genomic level. Due to the long rotation time of a forest plantation and the resulting long generation times necessary to complete a breeding cycle, the use of advanced techniques with traditional breeding have been necessary, allowing the use of more precise methods for determining the genetic architecture of traits of interest, such as genome-wide association studies (GWASs) and genomic selection (GS). In this sense, main factors that determine the accuracy of genomic prediction models are also addressed. In turn, the introduction of genome editing opens the door to new possibilities in forest trees and especially clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9). It is a highly efficient and effective genome editing technique that has been used to effectively implement targetable changes at specific places in the genome of a forest tree. In this sense, forest trees still lack a transformation method and an inefficient number of genotypes for CRISPR/Cas9. This challenge could be addressed with the use of the newly developing technique GRF-GIF with speed breeding.
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Affiliation(s)
- Sunny Ahmar
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile;
| | - Paulina Ballesta
- The National Fund for Scientific and Technological Development, Av. del Agua 3895, Talca 3460000, Chile
| | - Mohsin Ali
- Department of Forestry and Range Management, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan;
| | - Freddy Mora-Poblete
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile;
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Gogolev YV, Ahmar S, Akpinar BA, Budak H, Kiryushkin AS, Gorshkov VY, Hensel G, Demchenko KN, Kovalchuk I, Mora-Poblete F, Muslu T, Tsers ID, Yadav NS, Korzun V. OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security. PLANTS (BASEL, SWITZERLAND) 2021; 10:1423. [PMID: 34371624 PMCID: PMC8309286 DOI: 10.3390/plants10071423] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/30/2021] [Accepted: 07/07/2021] [Indexed: 12/22/2022]
Abstract
The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.
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Affiliation(s)
- Yuri V. Gogolev
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, 420111 Kazan, Russia;
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
| | - Sunny Ahmar
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile; (S.A.); (F.M.-P.)
| | | | - Hikmet Budak
- Montana BioAg Inc., Missoula, MT 59802, USA; (B.A.A.); (H.B.)
| | - Alexey S. Kiryushkin
- Laboratory of Cellular and Molecular Mechanisms of Plant Development, Komarov Botanical Institute of the Russian Academy of Sciences, 197376 Saint Petersburg, Russia; (A.S.K.); (K.N.D.)
| | - Vladimir Y. Gorshkov
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, 420111 Kazan, Russia;
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
| | - Goetz Hensel
- Centre for Plant Genome Engineering, Institute of Plant Biochemistry, Heinrich-Heine-University, 40225 Dusseldorf, Germany;
- Centre of the Region Haná for Biotechnological and Agricultural Research, Czech Advanced Technology and Research Institute, Palacký University Olomouc, 78371 Olomouc, Czech Republic
| | - Kirill N. Demchenko
- Laboratory of Cellular and Molecular Mechanisms of Plant Development, Komarov Botanical Institute of the Russian Academy of Sciences, 197376 Saint Petersburg, Russia; (A.S.K.); (K.N.D.)
| | - Igor Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (I.K.); (N.S.Y.)
| | - Freddy Mora-Poblete
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile; (S.A.); (F.M.-P.)
| | - Tugdem Muslu
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey;
| | - Ivan D. Tsers
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
| | - Narendra Singh Yadav
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (I.K.); (N.S.Y.)
| | - Viktor Korzun
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
- KWS SAAT SE & Co. KGaA, Grimsehlstr. 31, 37555 Einbeck, Germany
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Costa-Neto G, Galli G, Carvalho HF, Crossa J, Fritsche-Neto R. EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3-GENES GENOMES GENETICS 2021; 11:6129777. [PMID: 33835165 PMCID: PMC8049414 DOI: 10.1093/g3journal/jkab040] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 01/21/2021] [Indexed: 11/13/2022]
Abstract
Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.
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Affiliation(s)
- Germano Costa-Neto
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil
| | - Giovanni Galli
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil
| | - Humberto Fanelli Carvalho
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil
| | - José Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera Mexico-Veracruz, El Batan Km. 45, CP 56237 Mexico; Colegio de Postgraduados, Montecillos, Edo. de Mexico, CP 56264, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, 'Luiz de Queiroz' Agriculture College, University of São Paulo, São Paulo, Brazil.,Quantitative Genetics and Biometrics Cluster, International Rice Research Institute (IRRI), Los Baños, Philippines
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Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression. REMOTE SENSING 2021. [DOI: 10.3390/rs13112141] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.
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