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Lee AMJ, Foong MYM, Song BK, Chew FT. Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2024; 44:60. [PMID: 39267903 PMCID: PMC11391014 DOI: 10.1007/s11032-024-01497-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 09/01/2024] [Indexed: 09/15/2024]
Abstract
To ensure the nutritional needs of an expanding global population, it is crucial to optimize the growing capabilities and breeding values of fruit and vegetable crops. While genomic selection, initially implemented in animal breeding, holds tremendous potential, its utilization in fruit and vegetable crops remains underexplored. In this systematic review, we reviewed 63 articles covering genomic selection and its applications across 25 different types of fruit and vegetable crops over the last decade. The traits examined were directly related to the edible parts of the crops and carried significant economic importance. Comparative analysis with WHO/FAO data identified potential economic drivers underlying the study focus of some crops and highlighted crops with potential for further genomic selection research and application. Factors affecting genomic selection accuracy in fruit and vegetable studies are discussed and suggestions made to assist in their implementation into plant breeding schemes. Genetic gain in fruits and vegetables can be improved by utilizing genomic selection to improve selection intensity, accuracy, and integration of genetic variation. However, the reduction of breeding cycle times may not be beneficial in crops with shorter life cycles such as leafy greens as compared to fruit trees. There is an urgent need to integrate genomic selection methods into ongoing breeding programs and assess the actual genomic estimated breeding values of progeny resulting from these breeding programs against the prediction models. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-024-01497-2.
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Affiliation(s)
- Adrian Ming Jern Lee
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543 Republic of Singapore
- NUS Agritech Centre, National University of Singapore, 85 Science Park Dr, #01-03, Singapore, 118258 Republic of Singapore
| | - Melissa Yuin Mern Foong
- School of Science, Monash University Malaysia, Bandar Sunway, 47500 Subang Jaya, Selangor Darul Ehsan Malaysia
| | - Beng Kah Song
- School of Science, Monash University Malaysia, Bandar Sunway, 47500 Subang Jaya, Selangor Darul Ehsan Malaysia
| | - Fook Tim Chew
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543 Republic of Singapore
- NUS Agritech Centre, National University of Singapore, 85 Science Park Dr, #01-03, Singapore, 118258 Republic of Singapore
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Kim S, Chu SH, Park YJ, Lee CY. Stacked generalization as a computational method for the genomic selection. Front Genet 2024; 15:1401470. [PMID: 39050246 PMCID: PMC11266134 DOI: 10.3389/fgene.2024.1401470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 05/30/2024] [Indexed: 07/27/2024] Open
Abstract
As genomic selection emerges as a promising breeding method for both plants and animals, numerous methods have been introduced and applied to various real and simulated data sets. Research suggests that no single method is universally better than others; rather, performance is highly dependent on the characteristics of the data and the nature of the prediction task. This implies that each method has its strengths and weaknesses. In this study, we exploit this notion and propose a different approach. Rather than comparing multiple methods to determine the best one for a particular study, we advocate combining multiple methods to achieve better performance than each method in isolation. In pursuit of this goal, we introduce and develop a computational method of the stacked generalization within ensemble methods. In this method, the meta-model merges predictions from multiple base models to achieve improved performance. We applied this method to plant and animal data and compared its performance with currently available methods using standard performance metrics. We found that the proposed method yielded a lower or comparable mean squared error in predicting phenotypes compared to the current methods. In addition, the proposed method showed greater resistance to overfitting compared to the current methods. Further analysis included statistical hypothesis testing, which showed that the proposed method outperformed or matched the current methods. In summary, the proposed stacked generalization integrates currently available methods to achieve stable and better performance. In this context, our study provides general recommendations for effective practices in genomic selection.
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Affiliation(s)
- Sunhee Kim
- The Department of Industrial Engineering, Kongju National University, Cheonan, Republic of Korea
| | - Sang-Ho Chu
- The Department of Plant Resources, Kongju National University, Yesan, Republic of Korea
| | - Yong-Jin Park
- The Department of Plant Resources, Kongju National University, Yesan, Republic of Korea
| | - Chang-Yong Lee
- The Department of Industrial Engineering, Kongju National University, Cheonan, Republic of Korea
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Wang J, Yang Q, Chen Y, Liu K, Zhang Z, Xiong Y, Yu H, Yu Y, Wang J, Song J, Qiu L. QTL mapping and genomic selection of stem and branch diameter in soybean ( Glycine max L.). FRONTIERS IN PLANT SCIENCE 2024; 15:1388365. [PMID: 38882575 PMCID: PMC11176531 DOI: 10.3389/fpls.2024.1388365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 05/03/2024] [Indexed: 06/18/2024]
Abstract
Introduction Soybean stem diameter (SD) and branch diameter (BD) are closely related traits, and genetic clarification of SD and BD is crucial for soybean breeding. Methods SD and BD were genetically analyzed by a population of 363 RIL derived from the cross between Zhongdou41 (ZD41) and ZYD02878 using restricted two-stage multi-locus genome-wide association, inclusive composite interval mapping, and three-variance component multi-locus random SNP effect mixed linear modeling. Then candidate genes of major QTLs were selected and genetic selection model of SD and BD were constructed respectively. Results and discussion The results showed that SD and BD were significantly correlated (r = 0.74, P < 0.001). A total of 93 and 84 unique quantitative trait loci (QTL) were detected for SD and BD, respectively by three different methods. There were two and ten major QTLs for SD and BD, respectively, with phenotypic variance explained (PVE) by more than 10%. Within these loci, seven genes involved in the regulation of phytohormones (IAA and GA) and cell proliferation and showing extensive expression of shoot apical meristematic genes were selected as candidate genes. Genomic selection (GS) analysis showed that the trait-associated markers identified in this study reached 0.47-0.73 in terms of prediction accuracy, which was enhanced by 6.56-23.69% compared with genome-wide markers. These results clarify the genetic basis of SD and BD, which laid solid foundation in regulation gene cloning, and GS models constructed could be potentially applied in future breeding programs.
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Affiliation(s)
- Jing Wang
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Qichao Yang
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Yijie Chen
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Kanglin Liu
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China
| | - Zhiqing Zhang
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China
| | - Yajun Xiong
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China
| | - Huan Yu
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China
| | - Yingdong Yu
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China
| | - Jun Wang
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Jian Song
- MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China
| | - Lijuan Qiu
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- National Key Facility for Gene Resources and Genetic Improvement, Key Laboratory of Crop Germplasm Utilization, Ministry of Agriculture and Rural Affairs, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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Yeon J, Le NT, Heo J, Sim SC. Low-density SNP markers with high prediction accuracy of genomic selection for bacterial wilt resistance in tomato. FRONTIERS IN PLANT SCIENCE 2024; 15:1402693. [PMID: 38872894 PMCID: PMC11169939 DOI: 10.3389/fpls.2024.1402693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 05/07/2024] [Indexed: 06/15/2024]
Abstract
Bacterial wilt (BW) is a soil-borne disease that leads to severe damage in tomato. Host resistance against BW is considered polygenic and effective in controlling this destructive disease. In this study, genomic selection (GS), which is a promising breeding strategy to improve quantitative traits, was investigated for BW resistance. Two tomato collections, TGC1 (n = 162) and TGC2 (n = 191), were used as training populations. Disease severity was assessed using three seedling assays in each population, and the best linear unbiased prediction (BLUP) values were obtained. The 31,142 SNP data were generated using the 51K Axiom array™ in the training populations. With these data, six GS models were trained to predict genomic estimated breeding values (GEBVs) in three populations (TGC1, TGC2, and combined). The parametric models Bayesian LASSO and RR-BLUP resulted in higher levels of prediction accuracy compared with all the non-parametric models (RKHS, SVM, and random forest) in two training populations. To identify low-density markers, two subsets of 1,557 SNPs were filtered based on marker effects (Bayesian LASSO) and variable importance values (random forest) in the combined population. An additional subset was generated using 1,357 SNPs from a genome-wide association study. These subsets showed prediction accuracies of 0.699 to 0.756 in Bayesian LASSO and 0.670 to 0.682 in random forest, which were higher relative to the 31,142 SNPs (0.625 and 0.614). Moreover, high prediction accuracies (0.743 and 0.702) were found with a common set of 135 SNPs derived from the three subsets. The resulting low-density SNPs will be useful to develop a cost-effective GS strategy for BW resistance in tomato breeding programs.
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Affiliation(s)
- Jeyun Yeon
- Department of Bioindustry and Bioresource Engineering, Sejong University, Seoul, Republic of Korea
| | - Ngoc Thi Le
- Department of Bioindustry and Bioresource Engineering, Sejong University, Seoul, Republic of Korea
| | - Jaehun Heo
- Department of Bioindustry and Bioresource Engineering, Sejong University, Seoul, Republic of Korea
| | - Sung-Chur Sim
- Department of Bioindustry and Bioresource Engineering, Sejong University, Seoul, Republic of Korea
- Plant Engineering Research Institute, Sejong University, Seoul, Republic of Korea
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Martina M, De Rosa V, Magon G, Acquadro A, Barchi L, Barcaccia G, De Paoli E, Vannozzi A, Portis E. Revitalizing agriculture: next-generation genotyping and -omics technologies enabling molecular prediction of resilient traits in the Solanaceae family. FRONTIERS IN PLANT SCIENCE 2024; 15:1278760. [PMID: 38375087 PMCID: PMC10875072 DOI: 10.3389/fpls.2024.1278760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024]
Abstract
This review highlights -omics research in Solanaceae family, with a particular focus on resilient traits. Extensive research has enriched our understanding of Solanaceae genomics and genetics, with historical varietal development mainly focusing on disease resistance and cultivar improvement but shifting the emphasis towards unveiling resilience mechanisms in genebank-preserved germplasm is nowadays crucial. Collecting such information, might help researchers and breeders developing new experimental design, providing an overview of the state of the art of the most advanced approaches for the identification of the genetic elements laying behind resilience. Building this starting point, we aim at providing a useful tool for tackling the global agricultural resilience goals in these crops.
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Affiliation(s)
- Matteo Martina
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
| | - Valeria De Rosa
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Udine, Italy
| | - Gabriele Magon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padua, Legnaro, Italy
| | - Alberto Acquadro
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
| | - Lorenzo Barchi
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
| | - Gianni Barcaccia
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padua, Legnaro, Italy
| | - Emanuele De Paoli
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Udine, Italy
| | - Alessandro Vannozzi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padua, Legnaro, Italy
| | - Ezio Portis
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
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Lozada DN, Sandhu KS, Bhatta M. Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers. BMC Genom Data 2023; 24:80. [PMID: 38110866 PMCID: PMC10726521 DOI: 10.1186/s12863-023-01179-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/05/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA. RESULTS Accuracy of prediction differed across different models under ten-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multilayer perceptron (MLP) was the most superior for flowering time (0.76) and plant height (0.73), whereas the genomic BLUP model had the highest accuracy for plant width (0.62). Using a subset of 7,690 SNP loci resulting from grouping markers based on linkage disequilibrium coefficients resulted in improved accuracy for first pod date, ten pod weight, and total yield per plant, even under a relatively small training population size for MLP and random forest models. Genomic and ridge regression BLUP models were sufficient for optimal prediction accuracies for small training population size. Combining phenotypic selection and genomewide selection resulted in improved selection response for yield-related traits, indicating that integrated approaches can result in improved gains achieved through selection. CONCLUSIONS Accuracy values for ridge regression and deep learning prediction models demonstrate the potential of implementing genomewide selection for genetic improvement in chile pepper breeding programs. Ultimately, a large training data is relevant for improved genomic selection accuracy for the deep learning models.
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Affiliation(s)
- Dennis N Lozada
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA.
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, 88003, USA.
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Meher PK, Gupta A, Rustgi S, Mir RR, Kumar A, Kumar J, Balyan HS, Gupta PK. Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.). THE PLANT GENOME 2023; 16:e20332. [PMID: 37122189 DOI: 10.1002/tpg2.20332] [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: 10/21/2022] [Revised: 02/21/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
In wheat, genomic prediction accuracy (GPA) was assessed for three micronutrient traits (grain iron, grain zinc, and β-carotenoid concentrations) using eight Bayesian regression models. For this purpose, data on 246 accessions, each genotyped with 17,937 DArT markers, were utilized. The phenotypic data on traits were available for 2013-2014 from Powerkheda (Madhya Pradesh) and for 2014-2015 from Meerut (Uttar Pradesh), India. The accuracy of the models was measured in terms of reliability, which was computed following a repeated cross-validation approach. The predictions were obtained independently for each of the two environments after adjusting for the local effects and across environments after adjusting for the environmental effects. The Bayes ridge regression (BayesRR) model outperformed the other seven models, whereas BayesLASSO (BayesL) was the least efficient. The GPA increased with an increase in the size of the training set as well as with an increase in marker density. The GPA values differed for the three traits and were higher for the best linear unbiased estimate (BLUE) (obtained after adjusting for the environmental effects) relative to those for the two environments. The GPA also remained unaffected after accounting for the population structure. The results of the present study suggest that only the best model should be used for the estimations of genomic estimated breeding values (GEBVs) before their use for genomic selection to improve the grain micronutrient contents.
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Affiliation(s)
- Prabina Kumar Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Ajit Gupta
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sachin Rustgi
- Department of Plant and Environmental Sciences, Pee Dee Research and Education Centre, Clemson University, Florence, South Carolina, USA
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, SKUAST-Kashmir, Kashmir, India
| | - Anuj Kumar
- Department of Microbiology and Immunology, Dalhousie University, Halifax, Nova Scotia, Canada
- Laboratory of Immunity, Shantou University Medical College, Shantou, People's Republic of China
| | - Jitendra Kumar
- National Agri-Food Biotechnology Institute (NABI), Ajitgarh, India
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
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Zhang Y, Zhang M, Ye J, Xu Q, Feng Y, Xu S, Hu D, Wei X, Hu P, Yang Y. Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice ( Oryza sativa L.). MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:81. [PMID: 37965378 PMCID: PMC10641074 DOI: 10.1007/s11032-023-01423-y] [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: 07/11/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023]
Abstract
Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01423-y.
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Affiliation(s)
- Yuanyuan Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Mengchen Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Junhua Ye
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Qun Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yue Feng
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Siliang Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Dongxiu Hu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Xinghua Wei
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Peisong Hu
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yaolong Yang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
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Choi H, Back S, Kim GW, Lee K, Venkatesh J, Lee HB, Kwon JK, Kang BC. Development of a speed breeding protocol with flowering gene investigation in pepper ( Capsicum annuum). FRONTIERS IN PLANT SCIENCE 2023; 14:1151765. [PMID: 37841628 PMCID: PMC10569693 DOI: 10.3389/fpls.2023.1151765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 08/14/2023] [Indexed: 10/17/2023]
Abstract
Pepper (Capsicum spp.) is a vegetable and spice crop in the Solanaceae family with many nutritional benefits for human health. During several decades, horticultural traits, including disease resistance, yield, and fruit quality, have been improved through conventional breeding methods. Nevertheless, cultivar development is a time-consuming process because of the long generation time of pepper. Recently, speed breeding has been introduced as a solution for shorting the breeding cycle in long-day or day-neutral field crops, but there have been only a few studies on speed breeding in vegetable crops. In this study, a speed breeding protocol for pepper was developed by controlling the photoperiod and light quality. Under the condition of a low red (R) to far-red (FR) ratio of 0.3 with an extended photoperiod (Epp) of 20 h (95 ± 0 DAT), the time to first harvest was shortened by 75 days after transplant (DAT) compared to that of the control treatment (170 ± 2 DAT), suggesting that Epp with FR light is an essential factor for flowering in pepper. In addition, we established the speed breeding system in a greenhouse with a 20 h photoperiod and a 3.8 R:FR ratio and promoted the breeding cycle of C. annuum for 110 days from seed to seed. To explain the accelerated flowering response to the Epp and supplemented FR light, genome-wide association study (GWAS) and gene expression analysis were performed. As a result of the GWAS, we identified a new flowering gene locus for pepper and suggested four candidate genes for flowering (APETALA2 (AP2), WUSCHEL-RELATED HOMEOBOX4 (WOX4), FLOWERING LOCUS T (FT), and GIGANTEA (GI)). Through expression analysis with the candidate genes, it appeared that Epp and FR induced flowering by up-regulating the flowering-promoting gene GI and down-regulating FT. The results demonstrate the effect of a combination of Epp and FR light by genetic analysis of flowering gene expression. This is the first study that verifies gene expression patterns associated with the flowering responses of pepper in a speed breeding system. Overall, this study demonstrates that speed breeding can shorten the breeding cycle and accelerate genetic research in pepper through reduced generation time.
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Affiliation(s)
| | | | | | | | | | | | | | - Byoung-Cheorl Kang
- Department of Agriculture, Forestry and Bioresources, Research Institute of Agriculture and Life Sciences, Plant Genomics and Breeding Institute, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
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Das P, Chandra T, Negi A, Jaiswal S, Iquebal MA, Rai A, Kumar D. A comprehensive review on genomic resources in medicinally and industrially important major spices for future breeding programs: Status, utility and challenges. Curr Res Food Sci 2023; 7:100579. [PMID: 37701635 PMCID: PMC10494321 DOI: 10.1016/j.crfs.2023.100579] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/21/2023] [Accepted: 08/26/2023] [Indexed: 09/14/2023] Open
Abstract
In the global market, spices possess a high-value but low-volume commodities of commerce. The food industry depends largely on spices for taste, flavor, and therapeutic properties in replacement of cheap synthetic ones. The estimated growth rate for spices demand in the world is ∼3.19%. Since spices grow in limited geographical regions, India is one of the leading producer of spices, contributing 25-30 percent of total world trade. Hitherto, there has been no comprehensive review of the genomic resources of industrially important major medicinal spices to overcome major impediments in varietal improvement and management. This review focuses on currently available genomic resources of 24 commercially significant spices, namely, Ajwain, Allspice, Asafoetida, Black pepper, Cardamom large, Cardamom small, Celery, Chillies, Cinnamon, Clove, Coriander, Cumin, Curry leaf, Dill seed, Fennel, Fenugreek, Garlic, Ginger, Mint, Nutmeg, Saffron, Tamarind, Turmeric and Vanilla. The advent of low-cost sequencing machines has contributed immensely to the voluminous data generation of these spices, cracking the complex genomic architecture, marker discovery, and understanding comparative and functional genomics. This review of spice genomics resources concludes the perspective and way forward to provide footprints by uncovering genome assemblies, sequencing and re-sequencing projects, transcriptome-based studies, non-coding RNA-mediated regulation, organelles-based resources, developed molecular markers, web resources, databases and AI-directed resources in candidate spices for enhanced breeding potential in them. Further, their integration with molecular breeding could be of immense use in formulating a strategy to protect and expand the production of the spices due to increased global demand.
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Affiliation(s)
- Parinita Das
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Tilak Chandra
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Ankita Negi
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sarika Jaiswal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Mir Asif Iquebal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anil Rai
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Dinesh Kumar
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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Kang Y, Choi C, Kim JY, Min KD, Kim C. Optimizing genomic selection of agricultural traits using K-wheat core collection. FRONTIERS IN PLANT SCIENCE 2023; 14:1112297. [PMID: 37389296 PMCID: PMC10303932 DOI: 10.3389/fpls.2023.1112297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/02/2023] [Indexed: 07/01/2023]
Abstract
The agricultural traits that constitute basic plant breeding information are usually quantitative or complex in nature. This quantitative and complex combination of traits complicates the process of selection in breeding. This study examined the potential of genome-wide association studies (GWAS) and genomewide selection (GS) for breeding ten agricultural traits by using genome-wide SNPs. As a first step, a trait-associated candidate marker was identified by GWAS using a genetically diverse 567 Korean (K)-wheat core collection. The accessions were genotyped using an Axiom® 35K wheat DNA chip, and ten agricultural traits were determined (awn color, awn length, culm color, culm length, ear color, ear length, days to heading, days to maturity, leaf length, and leaf width). It is essential to sustain global wheat production by utilizing accessions in wheat breeding. Among the traits associated with awn color and ear color that showed a high positive correlation, a SNP located on chr1B was significantly associated with both traits. Next, GS evaluated the prediction accuracy using six predictive models (G-BLUP, LASSO, BayseA, reproducing kernel Hilbert space, support vector machine (SVM), and random forest) and various training populations (TPs). With the exception of the SVM, all statistical models demonstrated a prediction accuracy of 0.4 or better. For the optimization of the TP, the number of TPs was randomly selected (10%, 30%, 50% and 70%) or divided into three subgroups (CC-sub 1, CC-sub 2 and CC-sub 3) based on the subpopulation structure. Based on subgroup-based TPs, better prediction accuracy was found for awn color, culm color, culm length, ear color, ear length, and leaf width. A variety of Korean wheat cultivars were used for validation to evaluate the prediction ability of populations. Seven out of ten cultivars showed phenotype-consistent results based on genomics-evaluated breeding values (GEBVs) calculated by the reproducing kernel Hilbert space (RKHS) predictive model. Our research provides a basis for improving complex traits in wheat breeding programs through genomics assisted breeding. The results of our research can be used as a basis for improving wheat breeding programs by using genomics-assisted breeding.
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Affiliation(s)
- Yuna Kang
- Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Changhyun Choi
- Wheat Research Team, National Institution Crop Sciences, Wanju-gun, Republic of Korea
| | - Jae Yoon Kim
- Department of Plant Resources, Kongju National University, Yesan, Republic of Korea
| | - Kyeong Do Min
- Department of Plant Resources, Kongju National University, Yesan, Republic of Korea
| | - Changsoo Kim
- Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
- Department of Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
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12
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Jang S, Kim GW, Han K, Kim YM, Jo J, Lee SY, Kwon JK, Kang BC. Investigation of genetic factors regulating chlorophyll and carotenoid biosynthesis in red pepper fruit. FRONTIERS IN PLANT SCIENCE 2022; 13:922963. [PMID: 36186014 PMCID: PMC9521427 DOI: 10.3389/fpls.2022.922963] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 08/22/2022] [Indexed: 06/16/2023]
Abstract
Chlorophylls and carotenoids are synthesized in the chloroplast and chromoplast, respectively. Even though the two pigments are generated from the same precursor, the genetic correlation between chlorophyll and carotenoid biosynthesis has not yet been fully understood. We investigated the genetic correlation of chlorophyll and carotenoid biosynthesis during fruit ripening. Two recombinant inbred lines populations, "Long Sweet" × "AC2212" ("LA") RILs derived from a cross between Capsicum annuum "Long Sweet" with light-green and light-red fruit and C. annuum "AC2212" with dark-green and brown-fruit and "3501 (F)" × "3509 (C)" ("FC") RILs from C. annuum "3501" with dark-green and dark-red fruit and C. annuum "3509" with intermediate green and light-red fruit, were used. As the fruit ripened, three accessions produced high levels of xanthophyll. The dark-green immature fruit accumulated more total carotenoids than the light-green fruit. This trend corresponded to the expression pattern of 1-deoxy-d-xylulose 5-phosphate synthase (DXS) and CaGLK2 genes during fruit development. The expression levels of DXS and CaGLK2 in the dark-green accession "3501" were significantly higher than those of "3509" and "Long Sweet" during the early stages of fruit development. Furthermore, the genotype analysis of the transcription factor controlling chloroplast development (CaGLK2) in LA RILs revealed that CaGLK2 expression affected both carotenoid and chlorophyll contents. The single nucleotide polymorphism (SNP) linkage maps were constructed using genotyping-by-sequencing (GBS) for the two populations, and QTL analysis was performed for green fruit color intensity and carotenoid content. The QTL (LA_BG-CST10) for capsanthin content in LA RILs located at 24.4 to 100.4 Mbp on chromosome 10 was overlapped with the QTL (FC15-Cap10) for capsanthin content in FC RILs. Three QTLs for capsanthin content, American spice trade association (ASTA) value, and immature green fruit color intensity were also overlapped from 178.2 to 204 Mbp on chromosome 10. At the location, 151.6 to 165 Mbp on chromosome 8, QTLs (FC15-tcar8, FC17-ASTA8.1, and FC17-ASTA8.2) for total carotenoid content and ASTA value were discovered, and this region contained 2-C-methyl-d-erythritol 4-phosphate cytidylyltransferase (MCT), which is involved in the MEP pathway. This result is the first report to show the correlation between carotenoid and chlorophyll biosynthesis in pepper. This research will expand our understanding of the mechanism of the chloroplast-to-chromoplast transition and the development of high pigment pepper varieties.
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Affiliation(s)
| | | | | | | | | | | | | | - Byoung-Cheorl Kang
- Department of Agriculture, Forestry and Bioresources, Research Institute of Agriculture and Life Sciences, Plant Genomics and Breeding Institute, College of Agriculture and Life Sciences, Seoul National University, Seoul, South Korea
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13
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Kim GW, Hong JP, Lee HY, Kwon JK, Kim DA, Kang BC. Genomic selection with fixed-effect markers improves the prediction accuracy for Capsaicinoid contents in Capsicum annuum. HORTICULTURE RESEARCH 2022; 9:uhac204. [PMID: 36467271 PMCID: PMC9714256 DOI: 10.1093/hr/uhac204] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/05/2022] [Indexed: 06/17/2023]
Abstract
Capsaicinoids provide chili peppers (Capsicum spp.) with their characteristic pungency. Several structural and transcription factor genes are known to control capsaicinoid contents in pepper. However, many other genes also regulating capsaicinoid contents remain unknown, making it difficult to develop pepper cultivars with different levels of capsaicinoids. Genomic selection (GS) uses genome-wide random markers (including many in undiscovered genes) for a trait to improve selection efficiency. In this study, we predicted the capsaicinoid contents of pepper breeding lines using several GS models trained with genotypic and phenotypic data from a training population. We used a core collection of 351 Capsicum accessions and 96 breeding lines as training and testing populations, respectively. To obtain the optimal number of single nucleotide polymorphism (SNP) markers for GS, we tested various numbers of genome-wide SNP markers based on linkage disequilibrium. We obtained the highest mean prediction accuracy (0.550) for different models using 3294 SNP markers. Using this marker set, we conducted GWAS and selected 25 markers that were associated with capsaicinoid biosynthesis genes and quantitative trait loci for capsaicinoid contents. Finally, to develop more accurate prediction models, we obtained SNP markers from GWAS as fixed-effect markers for GS, where 3294 genome-wide SNPs were employed. When four to five fixed-effect markers from GWAS were used as fixed effects, the RKHS and RR-BLUP models showed accuracies of 0.696 and 0.689, respectively. Our results lay the foundation for developing pepper cultivars with various capsaicinoid levels using GS for capsaicinoid contents.
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Affiliation(s)
- Geon Woo Kim
- Department of Agriculture, Forestry and Bioresources, Research Institute of Agriculture and Life Sciences, Plant Genomics Breeding Institute, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Ju-Pyo Hong
- Department of Agriculture, Forestry and Bioresources, Research Institute of Agriculture and Life Sciences, Plant Genomics Breeding Institute, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Hea-Young Lee
- Department of Agriculture, Forestry and Bioresources, Research Institute of Agriculture and Life Sciences, Plant Genomics Breeding Institute, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Jin-Kyung Kwon
- Department of Agriculture, Forestry and Bioresources, Research Institute of Agriculture and Life Sciences, Plant Genomics Breeding Institute, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Dong-Am Kim
- R&D Center, Hana Seed Co., Ltd., Anseong 17601, Republic of Korea
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Meher PK, Rustgi S, Kumar A. Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results. Heredity (Edinb) 2022; 128:519-530. [PMID: 35508540 DOI: 10.1038/s41437-022-00539-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 11/09/2022] Open
Abstract
We evaluated the performances of three BLUP and five Bayesian methods for genomic prediction by using nine actual and 54 simulated datasets. The genomic prediction accuracy was measured using Pearson's correlation coefficient between the genomic estimated breeding value (GEBV) and the observed phenotypic data using a fivefold cross-validation approach with 100 replications. The Bayesian alphabets performed better for the traits governed by a few genes/QTLs with relatively larger effects. On the contrary, the BLUP alphabets (GBLUP and CBLUP) exhibited higher genomic prediction accuracy for the traits controlled by several small-effect QTLs. Additionally, Bayesian methods performed better for the highly heritable traits and, for other traits, performed at par with the BLUP methods. Further, genomic BLUP (GBLUP) was identified as the least biased method for the GEBV estimation. Among the Bayesian methods, the Bayesian ridge regression and Bayesian LASSO were less biased than other Bayesian alphabets. Nonetheless, genomic prediction accuracy increased with an increase in trait heritability, irrespective of the sample size, marker density, and the QTL type (major/minor effect). In sum, this study provides valuable information regarding the choice of the selection method for genomic prediction in different breeding programs.
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Affiliation(s)
- Prabina Kumar Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-12, India.
| | - Sachin Rustgi
- Department of Plant and Environmental Sciences, Clemson University Pee Dee Research and Education Center, Darlington, SC, USA.
| | - Anuj Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-12, India
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Lozada DN, Bosland PW, Barchenger DW, Haghshenas-Jaryani M, Sanogo S, Walker S. Chile Pepper ( Capsicum) Breeding and Improvement in the "Multi-Omics" Era. FRONTIERS IN PLANT SCIENCE 2022; 13:879182. [PMID: 35592583 PMCID: PMC9113053 DOI: 10.3389/fpls.2022.879182] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/12/2022] [Indexed: 06/15/2023]
Abstract
Chile pepper (Capsicum spp.) is a major culinary, medicinal, and economic crop in most areas of the world. For more than hundreds of years, chile peppers have "defined" the state of New Mexico, USA. The official state question, "Red or Green?" refers to the preference for either red or the green stage of chile pepper, respectively, reflects the value of these important commodities. The presence of major diseases, low yields, decreased acreages, and costs associated with manual labor limit production in all growing regions of the world. The New Mexico State University (NMSU) Chile Pepper Breeding Program continues to serve as a key player in the development of improved chile pepper varieties for growers and in discoveries that assist plant breeders worldwide. Among the traits of interest for genetic improvement include yield, disease resistance, flavor, and mechanical harvestability. While progress has been made, the use of conventional breeding approaches has yet to fully address producer and consumer demand for these traits in available cultivars. Recent developments in "multi-omics," that is, the simultaneous application of multiple omics approaches to study biological systems, have allowed the genetic dissection of important phenotypes. Given the current needs and production constraints, and the availability of multi-omics tools, it would be relevant to examine the application of these approaches in chile pepper breeding and improvement. In this review, we summarize the major developments in chile pepper breeding and present novel tools that can be implemented to facilitate genetic improvement. In the future, chile pepper improvement is anticipated to be more data and multi-omics driven as more advanced genetics, breeding, and phenotyping tools are developed.
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Affiliation(s)
- Dennis N. Lozada
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, United States
| | - Paul W. Bosland
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, United States
| | | | - Mahdi Haghshenas-Jaryani
- Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM, United States
| | - Soumaila Sanogo
- Department of Entomology, Plant Pathology and Weed Science, New Mexico State University, Las Cruces, NM, United States
| | - Stephanie Walker
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, United States
- Department of Extension Plant Sciences, New Mexico State University, Las Cruces, NM, United States
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16
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Tong H, Nankar AN, Liu J, Todorova V, Ganeva D, Grozeva S, Tringovska I, Pasev G, Radeva-Ivanova V, Gechev T, Kostova D, Nikoloski Z. Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits. HORTICULTURE RESEARCH 2022; 9:uhac072. [PMID: 35669711 PMCID: PMC9157653 DOI: 10.1093/hr/uhac072] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 03/16/2022] [Indexed: 06/15/2023]
Abstract
Selection of high-performance lines with respect to traits of interest is a key step in plant breeding. Genomic prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers, e.g. single-nucleotide polymorphisms (SNPs), and machine learning approaches, which can therefore shorten breeding cycles, referring to genomic selection (GS). Here, we applied GS approaches in two populations of Solanaceous crops, i.e. tomato and pepper, to predict morphometric and colorimetric traits. The traits were measured by using scoring-based conventional descriptors (CDs) as well as by Tomato Analyzer (TA) tool using the longitudinally and latitudinally cut fruit images. The GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits, respectively. The results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous fruits. The highest predictability of 0.89 was achieved for fruit width in pepper, with an average predictability of 0.69 over all traits. The multi-trait GS models are of slightly better predictability than single-trait models for some colorimetric traits in pepper. While model validation performs poorly on wild tomato accessions, the usage as many as one accession per wild species in the training set can increase the transferability of models to unseen populations for some traits (e.g. fruit shape for which predictability in unseen scenario increased from zero to 0.6). Overall, GS approaches can assist the selection of high-performance Solanaceous fruits in crop breeding.
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Affiliation(s)
- Hao Tong
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, 14476, Germany
| | - Amol N Nankar
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Jintao Liu
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
| | | | - Daniela Ganeva
- Maritsa Vegetable Crops Research Institute, Plovdiv, 4003, Bulgaria
| | | | | | - Gancho Pasev
- Maritsa Vegetable Crops Research Institute, Plovdiv, 4003, Bulgaria
| | | | - Tsanko Gechev
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Dimitrina Kostova
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
- Maritsa Vegetable Crops Research Institute, Plovdiv, 4003, Bulgaria
| | - Zoran Nikoloski
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, 14476, Germany
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