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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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Sadeh R, Ben-David R, Herrmann I, Peleg Z. Spectral-genomic chain-model approach enhances the wheat yield component prediction under the Mediterranean climate. PHYSIOLOGIA PLANTARUM 2024; 176:e14480. [PMID: 39187437 DOI: 10.1111/ppl.14480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 08/28/2024]
Abstract
In light of the changing climate that jeopardizes future food security, genomic selection is emerging as a valuable tool for breeders to enhance genetic gains and introduce high-yielding varieties. However, predicting grain yield is challenging due to the genetic and physiological complexities involved and the effect of genetic-by-environment interactions on prediction accuracy. We utilized a chained model approach to address these challenges, breaking down the complex prediction task into simpler steps. A diversity panel with a narrow phenological range was phenotyped across three Mediterranean environments for various morpho-physiological and yield-related traits. The results indicated that a multi-environment model outperformed a single-environment model in prediction accuracy for most traits. However, prediction accuracy for grain yield was not improved. Thus, in an attempt to ameliorate the grain yield prediction accuracy, we integrated a spectral estimation of spike number, being a major wheat yield component, with genomic data. A machine learning approach was used for spike number estimation from canopy hyperspectral reflectance captured by an unmanned aerial vehicle. The spectral-based estimated spike number was utilized as a secondary trait in a multi-trait genomic selection, significantly improving grain yield prediction accuracy. Moreover, the ability to predict the spike number based on data from previous seasons implies that it could be applied to new trials at various scales, even in small plot sizes. Overall, we demonstrate here that incorporating a novel spectral-genomic chain-model workflow, which utilizes spectral-based phenotypes as a secondary trait, improves the predictive accuracy of wheat grain yield.
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Affiliation(s)
- Roy Sadeh
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Roi Ben-David
- Institute of Plant Sciences, Agriculture Research Organization (ARO)-Volcani Institute, Rishon LeZion, Israel
| | - Ittai Herrmann
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Zvi Peleg
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [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: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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Önder H, Sitskowska B, Kurnaz B, Piwczyński D, Kolenda M, Şen U, Tırınk C, Çanga Boğa D. Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population. Animals (Basel) 2023; 13:3070. [PMID: 37835676 PMCID: PMC10572056 DOI: 10.3390/ani13193070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model that accounts for interactions between marker effects to estimate heritability and genetic correlations of traits including 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein Friesian cow population. For this aim, 14,742 SNP genotype records for 586 Polish Holstein Friesian dairy cows from Poland were used. Single-Trait-ssGBLUP (ST) and Multi-Trait-ssGBLUP (MT) methods were used for estimation. We examined 305-day milk yield (MY, kg), milk fat percentage (MF, %), milk protein percentage (MP, %), milk lactose percentage (ML, %), and milk dry matter percentage (MDM, %). The results showed that the highest marker effect rank correlation was found between milk fat percentage and milk dry matter. The weakest marker effect rank correlation was found between ML and all other traits. Obtained accuracies of this study were between 0.770 and 0.882, and 0.773 and 0.876 for MT and ST, respectively, which were acceptable values. All estimated bias values were positive, which is proof of underestimation. The highest heritability value was obtained for MP (0.3029) and the lowest heritability value was calculated for ML (0.2171). Estimated heritability values were low for milk yield and milk composition as expected. The strongest genetic correlation was estimated between MDM and MF (0.4990) and the weakest genetic correlation was estimated between MY and ML (0.001). The genetic relations with milk yield were negative and can be ignored as they were not significant. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction.
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Affiliation(s)
- Hasan Önder
- Department of Animal Science, Ondokuz Mayis University, Samsun 55139, Türkiye;
| | - Beata Sitskowska
- Department of Animal Biotechnology and Genetic, Faculty of Animal Breeding and Biology, Bydgoszcz University of Science and Technology, 85084 Bydgoszcz, Poland; (B.S.); (D.P.); (M.K.)
| | - Burcu Kurnaz
- Department of Animal Science, Ondokuz Mayis University, Samsun 55139, Türkiye;
| | - Dariusz Piwczyński
- Department of Animal Biotechnology and Genetic, Faculty of Animal Breeding and Biology, Bydgoszcz University of Science and Technology, 85084 Bydgoszcz, Poland; (B.S.); (D.P.); (M.K.)
| | - Magdalena Kolenda
- Department of Animal Biotechnology and Genetic, Faculty of Animal Breeding and Biology, Bydgoszcz University of Science and Technology, 85084 Bydgoszcz, Poland; (B.S.); (D.P.); (M.K.)
| | - Uğur Şen
- Department of Agricultural Biotechnology, Ondokuz Mayis University, Samsun 55139, Türkiye;
| | - Cem Tırınk
- Department of Animal Science, Iğdır University, Iğdır 76000, Türkiye;
| | - Demet Çanga Boğa
- Department of Chemistry and Chemical Processing, Osmaniye Korkut Ata University, Osmaniye 80050, Türkiye;
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Alves AAC, Fernandes AFA, Lopes FB, Breen V, Hawken R, Gianola D, Rosa GJDM. (Quasi) multitask support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits: a case study with carcass traits in broilers. G3 (BETHESDA, MD.) 2023; 13:jkad109. [PMID: 37216670 PMCID: PMC10411556 DOI: 10.1093/g3journal/jkad109] [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: 03/13/2023] [Revised: 03/13/2023] [Accepted: 04/24/2023] [Indexed: 05/24/2023]
Abstract
This study investigates nonlinear kernels for multitrait (MT) genomic prediction using support vector regression (SVR) models. We assessed the predictive ability delivered by single-trait (ST) and MT models for 2 carcass traits (CT1 and CT2) measured in purebred broiler chickens. The MT models also included information on indicator traits measured in vivo [Growth and feed efficiency trait (FE)]. We proposed an approach termed (quasi) multitask SVR (QMTSVR), with hyperparameter optimization performed via genetic algorithm. ST and MT Bayesian shrinkage and variable selection models [genomic best linear unbiased predictor (GBLUP), BayesC (BC), and reproducing kernel Hilbert space (RKHS) regression] were employed as benchmarks. MT models were trained using 2 validation designs (CV1 and CV2), which differ if the information on secondary traits is available in the testing set. Models' predictive ability was assessed with prediction accuracy (ACC; i.e. the correlation between predicted and observed values, divided by the square root of phenotype accuracy), standardized root-mean-squared error (RMSE*), and inflation factor (b). To account for potential bias in CV2-style predictions, we also computed a parametric estimate of accuracy (ACCpar). Predictive ability metrics varied according to trait, model, and validation design (CV1 or CV2), ranging from 0.71 to 0.84 for ACC, 0.78 to 0.92 for RMSE*, and between 0.82 and 1.34 for b. The highest ACC and smallest RMSE* were achieved with QMTSVR-CV2 in both traits. We observed that for CT1, model/validation design selection was sensitive to the choice of accuracy metric (ACC or ACCpar). Nonetheless, the higher predictive accuracy of QMTSVR over MTGBLUP and MTBC was replicated across accuracy metrics, besides the similar performance between the proposed method and the MTRKHS model. Results showed that the proposed approach is competitive with conventional MT Bayesian regression models using either Gaussian or spike-slab multivariate priors.
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Affiliation(s)
| | | | | | - Vivian Breen
- Cobb-Vantress Inc., Siloam Springs, AR 72761, USA
| | | | - Daniel Gianola
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
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Kumar M, Kumar S, Sandhu KS, Kumar N, Saripalli G, Prakash R, Nambardar A, Sharma H, Gautam T, Balyan HS, Gupta PK. GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:14. [PMID: 37313293 PMCID: PMC10248620 DOI: 10.1007/s11032-023-01357-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/26/2023] [Indexed: 06/15/2023]
Abstract
In wheat, a genome-wide association study (GWAS) and genomic prediction (GP) analysis were conducted for pre-harvest sprouting (PHS) tolerance and two of its related traits. For this purpose, an association panel of 190 accessions was phenotyped for PHS (using sprouting score), falling number, and grain color over two years and genotyped with 9904 DArTseq based SNP markers. GWAS for main-effect quantitative trait nucleotides (M-QTNs) using three different models (CMLM, SUPER, and FarmCPU) and epistatic QTNs (E-QTNs) using PLINK were performed. A total of 171 M-QTNs (CMLM, 47; SUPER, 70; FarmCPU, 54) for all three traits, and 15 E-QTNs involved in 20 first-order epistatic interactions were identified. Some of the above QTNs overlapped the previously reported QTLs, MTAs, and cloned genes, allowing delineating 26 PHS-responsive genomic regions that spread over 16 wheat chromosomes. As many as 20 definitive and stable QTNs were considered important for use in marker-assisted recurrent selection (MARS). The gene, TaPHS1, for PHS tolerance (PHST) associated with one of the QTNs was also validated using the KASP assay. Some of the M-QTNs were shown to have a key role in the abscisic acid pathway involved in PHST. Genomic prediction accuracies (based on the cross-validation approach) using three different models ranged from 0.41 to 0.55, which are comparable to the results of previous studies. In summary, the results of the present study improved our understanding of the genetic architecture of PHST and its related traits in wheat and provided novel genomic resources for wheat breeding based on MARS and GP. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01357-5.
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Affiliation(s)
- Manoj Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Sachin Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | | | - Neeraj Kumar
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC USA
| | - Gautam Saripalli
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD USA
| | - Ram Prakash
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Akash Nambardar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Hemant Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Tinku Gautam
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
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Mir ZA, Chandra T, Saharan A, Budhlakoti N, Mishra DC, Saharan MS, Mir RR, Singh AK, Sharma S, Vikas VK, Kumar S. Recent advances on genome-wide association studies (GWAS) and genomic selection (GS); prospects for Fusarium head blight research in Durum wheat. Mol Biol Rep 2023; 50:3885-3901. [PMID: 36826681 DOI: 10.1007/s11033-023-08309-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 01/26/2023] [Indexed: 02/25/2023]
Abstract
PURPOSE Wheat is an important cereal crop that is cultivated in different parts of the world. The biotic stresses are the major concerns in wheat-growing nations and are responsible for production loss globally. The change in climate dynamics makes the pathogen more virulent in foothills and tropical regions. There is growing concern about FHB in major wheat-growing nations, and until now, there has been no known potential source of resistance identified in wheat germplasm. The plant pathogen interaction activates the cascade of pathways, genes, TFs, and resistance genes. Pathogenesis-related genes' role in disease resistance is functionally validated in different plant systems. Similarly, Genomewide association Studies (GWAS) and Genomic selection (GS) are promising tools and have led to the discovery of resistance genes, genomic regions, and novel markers. Fusarium graminearum produces deoxynivalenol (DON) mycotoxins in wheat kernels, affecting wheat productivity globally. Modern technology now allows for detecting and managing DON toxin to reduce the risk to humans and animals. This review offers a comprehensive overview of the roles played by GWAS and Genomic selection (GS) in the identification of new genes, genetic variants, molecular markers and DON toxin management strategies. METHODS The review offers a comprehensive and in-depth analysis of the function of Fusarium graminearum virulence factors in Durum wheat. The role of GWAS and GS for Fusarium Head Blight (FHB) resistance has been well described. This paper provides a comprehensive description of the various statistical models that are used in GWAS and GS. In this review, we look at how different detection methods have been used to analyze and manage DON toxin exposure. RESULTS This review highlights the role of virulent genes in Fusarium disease establishment. The role of genome-based selection offers the identification of novel QTLs in resistant wheat germplasm. The role of GWAS and GS selection has minimized the use of population development through breeding technology. Here, we also emphasized the function of recent technological developments in minimizing the impact of DON toxins and their implications for food safety.
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Affiliation(s)
- Zahoor Ahmad Mir
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India
| | - Tilak Chandra
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
| | - Anurag Saharan
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Neeraj Budhlakoti
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
| | - D C Mishra
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
| | - M S Saharan
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-Kashmir), Srinagar, Jammu Kashmir, 190025, India
| | - Amit Kumar Singh
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India
| | - Soumya Sharma
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
| | - V K Vikas
- ICAR- Indian Agricultural Research Institute, Regional Station, Wellington, The Nilgiris, Tamilnadu, 643231, India.
| | - Sundeep Kumar
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India.
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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