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Temme AA, Kerr KL, Nolting KM, Dittmar EL, Masalia RR, Bucksch AK, Burke JM, Donovan LA. The genomic basis of nitrogen utilization efficiency and trait plasticity to improve nutrient stress tolerance in cultivated sunflower. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:2527-2544. [PMID: 38270266 DOI: 10.1093/jxb/erae025] [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: 12/12/2022] [Accepted: 01/23/2024] [Indexed: 01/26/2024]
Abstract
Maintaining crop productivity is challenging as population growth, climate change, and increasing fertilizer costs necessitate expanding crop production to poorer lands whilst reducing inputs. Enhancing crops' nutrient use efficiency is thus an important goal, but requires a better understanding of related traits and their genetic basis. We investigated variation in low nutrient stress tolerance in a diverse panel of cultivated sunflower genotypes grown under high and low nutrient conditions, assessing relative growth rate (RGR) as performance. We assessed variation in traits related to nitrogen utilization efficiency (NUtE), mass allocation, and leaf elemental content. Across genotypes, nutrient limitation generally reduced RGR. Moreover, there was a negative correlation between vigor (RGR in control) and decline in RGR in response to stress. Given this trade-off, we focused on nutrient stress tolerance independent of vigor. This tolerance metric correlated with the change in NUtE, plasticity for a suite of morphological traits, and leaf element content. Genome-wide associations revealed regions associated with variation and plasticity in multiple traits, including two regions with seemingly additive effects on NUtE change. Our results demonstrate potential avenues for improving sunflower nutrient stress tolerance independent of vigor, and highlight specific traits and genomic regions that could play a role in enhancing tolerance.
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Affiliation(s)
- Andries A Temme
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
- Department of Plant Breeding, Wageningen University & Research, 6700 HB Wageningen, The Netherlands
| | - Kelly L Kerr
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Kristen M Nolting
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Emily L Dittmar
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Rishi R Masalia
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | | | - John M Burke
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Lisa A Donovan
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
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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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Kimutai C, Ndlovu N, Chaikam V, Ertiro BT, Das B, Beyene Y, Kiplagat O, Spillane C, Prasanna BM, Gowda M. Discovery of genomic regions associated with grain yield and agronomic traits in Bi-parental populations of maize ( Zea mays. L) Under optimum and low nitrogen conditions. Front Genet 2023; 14:1266402. [PMID: 37964777 PMCID: PMC10641019 DOI: 10.3389/fgene.2023.1266402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/12/2023] [Indexed: 11/16/2023] Open
Abstract
Low soil nitrogen levels, compounded by the high costs associated with nitrogen supplementation through fertilizers, significantly contribute to food insecurity, malnutrition, and rural poverty in maize-dependent smallholder communities of sub-Saharan Africa (SSA). The discovery of genomic regions associated with low nitrogen tolerance in maize can enhance selection efficiency and facilitate the development of improved varieties. To elucidate the genetic architecture of grain yield (GY) and its associated traits (anthesis-silking interval (ASI), anthesis date (AD), plant height (PH), ear position (EPO), and ear height (EH)) under different soil nitrogen regimes, four F3 maize populations were evaluated in Kenya and Zimbabwe. GY and all the traits evaluated showed significant genotypic variance and moderate heritability under both optimum and low nitrogen stress conditions. A total of 91 quantitative trait loci (QTL) related to GY (11) and other secondary traits (AD (26), PH (19), EH (24), EPO (7) and ASI (4)) were detected. Under low soil nitrogen conditions, PH and ASI had the highest number of QTLs. Furthermore, some common QTLs were identified between secondary traits under both nitrogen regimes. These QTLs are of significant value for further validation and possible rapid introgression into maize populations using marker-assisted selection. Identification of many QTL with minor effects indicates genomic selection (GS) is more appropriate for their improvement. Genomic prediction within each population revealed low to moderately high accuracy under optimum and low soil N stress management. However, the accuracies were higher for GY, PH and EH under optimum compared to low soil N stress. Our findings indicate that genetic gain can be improved in maize breeding for low N stress tolerance by using GS.
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Affiliation(s)
- Collins Kimutai
- Seed, Crop and Horticultural Sciences, School of Agriculture and Biotechnology, University of Eldoret, Eldoret, Kenya
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Noel Ndlovu
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | - Vijay Chaikam
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Oliver Kiplagat
- Seed, Crop and Horticultural Sciences, School of Agriculture and Biotechnology, University of Eldoret, Eldoret, Kenya
| | - Charles Spillane
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | | | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
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Li D, Liu J, Guo H, Zong J, Li J, Wang J, Li L, Chen J. Effects of low nitrogen supply on nitrogen uptake, assimilation and remobilization in wild bermudagrass. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2022; 191:34-41. [PMID: 36179517 DOI: 10.1016/j.plaphy.2022.09.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/30/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
The natural mechanism of underlying the low nitrogen (N) tolerance of wild bermudagrass (Cynodon dactylon (L.) Pers.) germplasm was important for reducing N fertilizer input to turf while also maintaining acceptable turf quality. The growth, N uptake, assimilation and remobilization of two wild bermudagrass accessions (C291, low N tolerant and C716, low N sensitive) were determined under low N (0.5 mM) and control N (5 mM) levels. C291 exhibited lower reduction in shoot and plant dry weight than C716. Furthermore, C291 presented a lower decrease in 15NO3- influx compared with C716, maintained its root dry weight and root surface and showed obviously enhanced CyNRT2.2 and CyNRT2.3 expression resulting in higher shoot NO3--N content than the control. Moreover, in C291, nitrate reductase (NR) activity had no significant difference with control, and cytosolic glutamine synthetase (GS1) protein content, glutamate synthetase (GOGAT) activity and glutamate dehydrogenase (GDH) activity higher than control, result in the soluble protein and free amino acid contents in the shoots did not differ compared with that in the control under low N conditions. Overall, the low N tolerant wild bermudagrass accessions adopted a low N supply based on improved root N uptake ability to achieve more nitrate to kept shoot N assimilation, and meanwhile increased N remobilization in the shoots, thereby maintaining a better N status in bermudagrass. The findings may help elucidate the low N tolerance mechanisms in bermudagrass and therefore facilitate genetic improvement of N use efficiency aiming to promote low-input turfgrass management.
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Affiliation(s)
- Dandan Li
- The National Forestry and Grassland Administration Engineering Research Center for Germplasm Innovation and Utilization of Warm-season Turfgrasses, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014, Jiangsu, China
| | - Jianxiu Liu
- The National Forestry and Grassland Administration Engineering Research Center for Germplasm Innovation and Utilization of Warm-season Turfgrasses, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014, Jiangsu, China
| | - Hailin Guo
- The National Forestry and Grassland Administration Engineering Research Center for Germplasm Innovation and Utilization of Warm-season Turfgrasses, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014, Jiangsu, China
| | - Junqin Zong
- The National Forestry and Grassland Administration Engineering Research Center for Germplasm Innovation and Utilization of Warm-season Turfgrasses, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014, Jiangsu, China
| | - Jianjian Li
- The National Forestry and Grassland Administration Engineering Research Center for Germplasm Innovation and Utilization of Warm-season Turfgrasses, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014, Jiangsu, China
| | - Jingjing Wang
- The National Forestry and Grassland Administration Engineering Research Center for Germplasm Innovation and Utilization of Warm-season Turfgrasses, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014, Jiangsu, China
| | - Ling Li
- The National Forestry and Grassland Administration Engineering Research Center for Germplasm Innovation and Utilization of Warm-season Turfgrasses, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014, Jiangsu, China
| | - Jingbo Chen
- The National Forestry and Grassland Administration Engineering Research Center for Germplasm Innovation and Utilization of Warm-season Turfgrasses, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014, Jiangsu, China.
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Wang Q, Yan T, Long Z, Huang LY, Zhu Y, Xu Y, Chen X, Pak H, Li J, Wu D, Xu Y, Hua S, Jiang L. Prediction of heterosis in the recent rapeseed (Brassica napus) polyploid by pairing parental nucleotide sequences. PLoS Genet 2021; 17:e1009879. [PMID: 34735437 PMCID: PMC8608326 DOI: 10.1371/journal.pgen.1009879] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/22/2021] [Accepted: 10/15/2021] [Indexed: 11/19/2022] Open
Abstract
The utilization of heterosis is a successful strategy in increasing yield for many crops. However, it consumes tremendous manpower to test the combining ability of the parents in fields. Here, we applied the genomic-selection (GS) strategy and developed models that significantly increase the predictability of heterosis by introducing the concept of a regional parental genetic-similarity index (PGSI) and reducing dimension in the calculation matrix in a machine-learning approach. Overall, PGSI negatively affected grain yield and several other traits but positively influenced the thousand-seed weight of the hybrids. It was found that the C subgenome of rapeseed had a greater impact on heterosis than the A subgenome. We drew maps with overviews of quantitative-trait loci that were responsible for the heterosis (h-QTLs) of various agronomic traits. Identifications and annotations of genes underlying high impacting h-QTLs were provided. Using models that we elaborated, combining abilities between an Ogu-CMS-pool member and a potential restorer can be simulated in silico, sidestepping laborious work, such as testing crosses in fields. The achievements here provide a case of heterosis prediction in polyploid genomes with relatively large genome sizes. Oilseed rape (Brassica napus) is of significant economic interest worldwide, providing high-quality oil with excellent health-promoting properties. It represents an excellent model of a successful recent polyploid that rapidly became an important crop worldwide. The utilization of hybridization, leading to hybrid vigor, or heterosis, is a successful strategy in increasing yield and vigor for many field crops including rapeseed (Brassica napus). However, the procedure of using classical breeding methods remains slow and laborious, illustrating the need for predictive and innovative methods. Here, we have achieved a significant breakthrough by using genome selection and significantly advanced models to predict the heterosis by pairing genome-wide nucleotides of parents. We provided maps with overviews of quantitative trait loci that were responsible for the heterosis of various agronomic traits. The research used deep resequencing (>30x) data of the entire polyploidy rapeseed genome, providing a successful case for the prediction of heterosis in polyploid genomes with relatively large genome sizes. Moreover, we provided the genetic information (SNPs) of 1007 core accessions of this species in the public domain for testing combinations with high heterosis using our predicting model for rapeseed breeders all over the world.
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Affiliation(s)
- Qian Wang
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Tao Yan
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Zhengbiao Long
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Luna Yue Huang
- Department of Agricultural and Resource Economics, University of California, Berkeley, California, United States of America
| | - Yang Zhu
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Ying Xu
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Xiaoyang Chen
- Institute of Crop Science, Jinhua Academy of Agricultural Sciences, Jinhua, China
| | - Haksong Pak
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Jiqiang Li
- Institute of Crop Science, Zhangye Academy of Agricultural Sciences, Zhangye, China
| | - Dezhi Wu
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Yang Xu
- Agricultural College, Yangzhou University, Yangzhou, China
- * E-mail: (YX); (SH); (LJ)
| | - Shuijin Hua
- Institute of Crop and Nuclear Agricultural Sciences, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
- * E-mail: (YX); (SH); (LJ)
| | - Lixi Jiang
- Institute of Crop Science, Zhejiang University, Hangzhou, China
- * E-mail: (YX); (SH); (LJ)
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