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Alam O, Purugganan MD. Domestication and the evolution of crops: variable syndromes, complex genetic architectures, and ecological entanglements. THE PLANT CELL 2024; 36:1227-1241. [PMID: 38243576 PMCID: PMC11062453 DOI: 10.1093/plcell/koae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/01/2023] [Accepted: 12/14/2023] [Indexed: 01/21/2024]
Abstract
Domestication can be considered a specialized mutualism in which a domesticator exerts control over the reproduction or propagation (fitness) of a domesticated species to gain resources or services. The evolution of crops by human-associated selection provides a powerful set of models to study recent evolutionary adaptations and their genetic bases. Moreover, the domestication and dispersal of crops such as rice, maize, and wheat during the Holocene transformed human social and political organization by serving as the key mechanism by which human societies fed themselves. Here we review major themes and identify emerging questions in three fundamental areas of crop domestication research: domestication phenotypes and syndromes, genetic architecture underlying crop evolution, and the ecology of domestication. Current insights on the domestication syndrome in crops largely come from research on cereal crops such as rice and maize, and recent work indicates distinct domestication phenotypes can arise from different domestication histories. While early studies on the genetics of domestication often identified single large-effect loci underlying major domestication traits, emerging evidence supports polygenic bases for many canonical traits such as shattering and plant architecture. Adaptation in human-constructed environments also influenced ecological traits in domesticates such as resource acquisition rates and interactions with other organisms such as root mycorrhizal fungi and pollinators. Understanding the ecological context of domestication will be key to developing resource-efficient crops and implementing more sustainable land management and cultivation practices.
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Affiliation(s)
- Ornob Alam
- Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA
| | - Michael D Purugganan
- Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA
- Center for Genomics and Systems Biology, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Institute for the Study of the Ancient World, New York University, New York, NY, 10028, USA
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2
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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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Ndlovu N, Kachapur RM, Beyene Y, Das B, Ogugo V, Makumbi D, Spillane C, McKeown PC, Prasanna BM, Gowda M. Linkage mapping and genomic prediction of grain quality traits in tropical maize ( Zea mays L.). Front Genet 2024; 15:1353289. [PMID: 38456017 PMCID: PMC10918846 DOI: 10.3389/fgene.2024.1353289] [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: 12/10/2023] [Accepted: 02/07/2024] [Indexed: 03/09/2024] Open
Abstract
The suboptimal productivity of maize systems in sub-Saharan Africa (SSA) is a pressing issue, with far-reaching implications for food security, nutrition, and livelihood sustainability within the affected smallholder farming communities. Dissecting the genetic basis of grain protein, starch and oil content can increase our understanding of the governing genetic systems, improve the efficacy of future breeding schemes and optimize the end-use quality of tropical maize. Here, four bi-parental maize populations were evaluated in field trials in Kenya and genotyped with mid-density single nucleotide polymorphism (SNP) markers. Genotypic (G), environmental (E) and G×E variations were found to be significant for all grain quality traits. Broad sense heritabilities exhibited substantial variation (0.18-0.68). Linkage mapping identified multiple quantitative trait loci (QTLs) for the studied grain quality traits: 13, 7, 33, 8 and 2 QTLs for oil content, protein content, starch content, grain texture and kernel weight, respectively. The co-localization of QTLs identified in our research suggests the presence of shared genetic factors or pleiotropic effects, implying that specific genomic regions influence the expression of multiple grain quality traits simultaneously. Genomic prediction accuracies were moderate to high for the studied traits. Our findings highlight the polygenic nature of grain quality traits and demonstrate the potential of genomic selection to enhance genetic gains in maize breeding. Furthermore, the identified genomic regions and single nucleotide polymorphism markers can serve as the groundwork for investigating candidate genes that regulate grain quality traits in tropical maize. This, in turn, can facilitate the implementation of marker-assisted selection (MAS) in breeding programs focused on improving grain nutrient levels.
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Affiliation(s)
- Noel Ndlovu
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Rajashekar M. Kachapur
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
- University of Agricultural Sciences, Dharwad, Karnataka, India
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Veronica Ogugo
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Charles Spillane
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | - Peter C. McKeown
- 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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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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Petroli CD, Subbarao GV, Burgueño JA, Yoshihashi T, Li H, Franco Duran J, Pixley KV. Genetic variation among elite inbred lines suggests potential to breed for BNI-capacity in maize. Sci Rep 2023; 13:13422. [PMID: 37591891 PMCID: PMC10435450 DOI: 10.1038/s41598-023-39720-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 07/29/2023] [Indexed: 08/19/2023] Open
Abstract
Biological nitrification inhibition (BNI) is a plant function where root systems release antibiotic compounds (BNIs) specifically aimed at suppressing nitrifiers to limit soil-nitrate formation in the root zone. Little is known about BNI-activity in maize (Zea mays L.), the most important food, feed, and energy crop. Two categories of BNIs are released from maize roots; hydrophobic and hydrophilic BNIs, that determine BNI-capacity in root systems. Zeanone is a recently discovered hydrophobic compound with BNI-activity, released from maize roots. The objectives of this study were to understand/quantify the relationship between zeanone activity and hydrophobic BNI-capacity. We assessed genetic variability among 250 CIMMYT maize lines (CMLs) characterized for hydrophobic BNI-capacity and zeanone activity, towards developing genetic markers linked to this trait in maize. CMLs with high BNI-capacity and ability to release zeanone from roots were identified. GWAS was performed using 27,085 SNPs (with unique positions on the B73v.4 reference genome, and false discovery rate = 10), and phenotypic information for BNI-capacity and zeanone production from root systems. Eighteen significant markers were identified; three associated with specific BNI-activity (SBNI), four with BNI-activity per plant (BNIPP), another ten were common between SBNI and BNIPP, and one with zeanone release. Further, 30 annotated genes were associated with the significant SNPs; most of these genes are involved in pathways of "biological process", and one (AMT5) in ammonium regulation in maize roots. Although the inbred lines in this study were not developed for BNI-traits, the identification of markers associated with BNI-capacity suggests the possibility of using these genomic tools in marker-assisted selection to improve hydrophobic BNI-capacity in maize.
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Affiliation(s)
- César D Petroli
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Km. 45, El Batán, Texcoco, C.P. 56237, Mexico.
| | - Guntur V Subbarao
- Japan International Research Center for Agricultural Science, 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
| | - Juan A Burgueño
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Km. 45, El Batán, Texcoco, C.P. 56237, Mexico
| | - Tadashi Yoshihashi
- Japan International Research Center for Agricultural Science, 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
| | - Huihui Li
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Km. 45, El Batán, Texcoco, C.P. 56237, Mexico
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), No 12 Zhongguancun South Street, Beijing, 10081, China
| | - Jorge Franco Duran
- Departamento de Biometría y Estadística, Facultad de Agronomía, Universidad de la República, Ruta 3, Km 363, C.P. 60000, Paysandú, Uruguay
| | - Kevin V Pixley
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Km. 45, El Batán, Texcoco, C.P. 56237, Mexico
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Crop germplasm: Current challenges, physiological-molecular perspective, and advance strategies towards development of climate-resilient crops. Heliyon 2023; 9:e12973. [PMID: 36711267 PMCID: PMC9880400 DOI: 10.1016/j.heliyon.2023.e12973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 01/01/2023] [Accepted: 01/10/2023] [Indexed: 01/18/2023] Open
Abstract
Germplasm is a long-term resource management mission and investment for civilization. An estimated ∼7.4 million accessions are held in 1750 plant germplasm centres around the world; yet, only 2% of these assets have been utilized as plant genetic resources (PGRs). According to recent studies, the current food yield trajectory will be insufficient to feed the world's population in 2050. Additionally, possible negative effects in terms of crop failure because of climate change are already being experienced across the world. Therefore, it is necessary to reconciliation of research advancement and innovation of practices for further exploration of the potential of crop germplasm especially for the complex traits associated with yield such as water- and nitrogen use efficiency. In this review, we tried to address current challenges, research gaps, physiological and molecular aspects of two broad spectrum complex traits such as water- and nitrogen-use efficiency, and advanced integrated strategies that could provide a platform for combined stress management for climate-smart crop development. Additionally, recent development in technologies that are directly related to germplasm characterization was highlighted for further molecular utilization towards the development of elite varieties.
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7
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Maize Breeding for Low Nitrogen Inputs in Agriculture: Mechanisms Underlying the Tolerance to the Abiotic Stress. STRESSES 2023. [DOI: 10.3390/stresses3010011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Nitrogen (N) is essential for sustaining life on Earth and plays a vital role in plant growth and thus agricultural production. The excessive use of N fertilizers not only harms the economy, but also the environment. In the context of the environmental impacts caused by agriculture, global maize improvement programs aim to develop cultivars with high N-use efficiency (NUE) to reduce the use of N fertilizers. Since N is highly mobile in plants, NUE is related to numerous little-known morphophysiological and molecular mechanisms. In this review paper we present an overview of the morpho-physiological adaptations of shoot and root, molecular mechanisms involved in plant response to low nitrogen environment, and the genetic effects involved in the control of key traits for NUE. Some studies show that the efficiency of cultivars growing under low N is related to deep root architecture, more lateral roots (LR), and sparser branching of LR, resulting in lower metabolic costs. The NUE cultivars also exhibit more efficient photosynthesis, which affects plant growth under suboptimal nitrogen conditions. In this sense, obtaining superior genotypes for NUE can be achieved with the exploitation of heterosis, as non-additive effects are more important in the expression of traits associated with NUE.
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Ndlovu N, Spillane C, McKeown PC, Cairns JE, Das B, Gowda M. Genome-wide association studies of grain yield and quality traits under optimum and low-nitrogen stress in tropical maize (Zea mays L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:4351-4370. [PMID: 36131140 PMCID: PMC9734216 DOI: 10.1007/s00122-022-04224-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 09/14/2022] [Indexed: 06/15/2023]
Abstract
Genome-wide association study (GWAS) demonstrated that multiple genomic regions influence grain quality traits under nitrogen-starved soils. Using genomic prediction, genetic gains can be improved through selection for grain quality traits. Soils in sub-Saharan Africa are nitrogen deficient due to low fertilizer use and inadequate soil fertility management practices. This has resulted in a significant yield gap for the major staple crop maize, which is undermining nutritional security and livelihood sustainability across the region. Dissecting the genetic basis of grain protein, starch and oil content under nitrogen-starved soils can increase our understanding of the governing genetic systems and improve the efficacy of future breeding schemes. An association mapping panel of 410 inbred lines and four bi-parental populations were evaluated in field trials in Kenya and South Africa under optimum and low nitrogen conditions and genotyped with 259,798 SNP markers. Genetic correlations demonstrated that these populations may be utilized to select higher performing lines under low nitrogen stress. Furthermore, genotypic, environmental and GxE variations in nitrogen-starved soils were found to be significant for oil content. Broad sense heritabilities ranged from moderate (0.18) to high (0.86). Under low nitrogen stress, GWAS identified 42 SNPs linked to grain quality traits. These significant SNPs were associated with 51 putative candidate genes. Linkage mapping identified multiple QTLs for the grain quality traits. Under low nitrogen conditions, average prediction accuracies across the studied genotypes were higher for oil content (0.78) and lower for grain yield (0.08). Our findings indicate that grain quality traits are polygenic and that using genomic selection in maize breeding can improve genetic gain. Furthermore, the identified genomic regions and SNP markers can be utilized for selection to improve maize grain quality traits.
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Affiliation(s)
- Noel Ndlovu
- Plant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland
| | - Charles Spillane
- Plant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland.
| | - Peter C McKeown
- Plant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland
| | - Jill E Cairns
- International Maize and Wheat Improvement Center (CIMMYT), P.O. Box MP163, Harare, Zimbabwe
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), P.O. Box 1041-00621, Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), P.O. Box 1041-00621, Nairobi, Kenya.
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Genetic trends in CIMMYT's tropical maize breeding pipelines. Sci Rep 2022; 12:20110. [PMID: 36418412 PMCID: PMC9684471 DOI: 10.1038/s41598-022-24536-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Fostering a culture of continuous improvement through regular monitoring of genetic trends in breeding pipelines is essential to improve efficiency and increase accountability. This is the first global study to estimate genetic trends across the International Maize and Wheat Improvement Center (CIMMYT) tropical maize breeding pipelines in eastern and southern Africa (ESA), South Asia, and Latin America over the past decade. Data from a total of 4152 advanced breeding trials and 34,813 entries, conducted at 1331 locations in 28 countries globally, were used for this study. Genetic trends for grain yield reached up to 138 kg ha-1 yr-1 in ESA, 118 kg ha-1 yr-1 South Asia and 143 kg ha-1 yr-1 in Latin America. Genetic trend was, in part, related to the extent of deployment of new breeding tools in each pipeline, strength of an extensive phenotyping network, and funding stability. Over the past decade, CIMMYT's breeding pipelines have significantly evolved, incorporating new tools/technologies to increase selection accuracy and intensity, while reducing cycle time. The first pipeline, Eastern Africa Product Profile 1a (EA-PP1a), to implement marker-assisted forward-breeding for resistance to key diseases, coupled with rapid-cycle genomic selection for drought, recorded a genetic trend of 2.46% per year highlighting the potential for deploying new tools/technologies to increase genetic gain.
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Zuffo LT, DeLima RO, Lübberstedt T. Combining datasets for maize root seedling traits increases the power of GWAS and genomic prediction accuracies. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5460-5473. [PMID: 35608947 PMCID: PMC9467658 DOI: 10.1093/jxb/erac236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 06/06/2022] [Indexed: 05/13/2023]
Abstract
The identification of genomic regions associated with root traits and the genomic prediction of untested genotypes can increase the rate of genetic gain in maize breeding programs targeting roots traits. Here, we combined two maize association panels with different genetic backgrounds to identify single nucleotide polymorphisms (SNPs) associated with root traits, and used a genome-wide association study (GWAS) and to assess the potential of genomic prediction for these traits in maize. For this, we evaluated 377 lines from the Ames panel and 302 from the Backcrossed Germplasm Enhancement of Maize (BGEM) panel in a combined panel of 679 lines. The lines were genotyped with 232 460 SNPs, and four root traits were collected from 14-day-old seedlings. We identified 30 SNPs significantly associated with root traits in the combined panel, whereas only two and six SNPs were detected in the Ames and BGEM panels, respectively. Those 38 SNPs were in linkage disequilibrium with 35 candidate genes. In addition, we found higher prediction accuracy in the combined panel than in the Ames or BGEM panel. We conclude that combining association panels appears to be a useful strategy to identify candidate genes associated with root traits in maize and improve the efficiency of genomic prediction.
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Affiliation(s)
- Leandro Tonello Zuffo
- Corteva Agriscience, Rio Verde, GO, Brazil
- Department of Agronomy, Universidade Federal de Viçosa, Viçosa, MG, Brazil
- Department of Agronomy, Iowa State University, Ames, IA, USA
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Bhadmus OA, Badu-Apraku B, Adeyemo OA, Agre PA, Queen ON, Ogunkanmi AL. Genome-Wide Association Analysis Reveals Genetic Architecture and Candidate Genes Associated with Grain Yield and Other Traits under Low Soil Nitrogen in Early-Maturing White Quality Protein Maize Inbred Lines. Genes (Basel) 2022; 13:genes13050826. [PMID: 35627211 PMCID: PMC9141126 DOI: 10.3390/genes13050826] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 02/01/2023] Open
Abstract
Maize production in the savannas of sub-Saharan Africa (SSA) is constrained by the low nitrogen in the soils. The identification of quantitative trait loci (QTL) conferring tolerance to low soil nitrogen (low-N) is crucial for the successful breeding of high-yielding QPM maize genotypes under low-N conditions. The objective of this study was to identify QTLs significantly associated with grain yield and other low-N tolerance-related traits under low-N. The phenotypic data of 140 early-maturing white quality protein maize (QPM) inbred lines were evaluated under low-N. The inbred lines were genotyped using 49,185 DArTseq markers, from which 7599 markers were filtered for population structure analysis and genome-wide association study (GWAS). The inbred lines were grouped into two major clusters based on the population structure analysis. The GWAS identified 24, 3, 10, and 3 significant SNPs respectively associated with grain yield, stay-green characteristic, and plant and ear aspects, under low-N. Sixteen SNP markers were physically located in proximity to 32 putative genes associated with grain yield, stay-green characteristic, and plant and ear aspects. The putative genes GRMZM2G127139, GRMZM5G848945, GRMZM2G031331, GRMZM2G003493, GRMZM2G067964, GRMZM2G180254, on chromosomes 1, 2, 8, and 10 were involved in cellular nitrogen assimilation and biosynthesis, normal plant growth and development, nitrogen assimilation, and disease resistance. Following the validation of the markers, the putative candidate genes and SNPs could be used as genomic markers for marker-assisted selection, to facilitate genetic gains for low-N tolerance in maize production.
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Affiliation(s)
- Olatunde A. Bhadmus
- Department of Cell Biology and Genetics, University of Lagos, Lagos 101017, Nigeria; (O.A.B.); (O.A.A.); (A.L.O.)
- International Institute of Tropical Agriculture, IITA, PMB 5320 Oyo Road, Ibadan 200285, Nigeria; (P.A.A.); (O.N.Q.)
| | - Baffour Badu-Apraku
- International Institute of Tropical Agriculture, IITA, PMB 5320 Oyo Road, Ibadan 200285, Nigeria; (P.A.A.); (O.N.Q.)
- Correspondence:
| | - Oyenike A. Adeyemo
- Department of Cell Biology and Genetics, University of Lagos, Lagos 101017, Nigeria; (O.A.B.); (O.A.A.); (A.L.O.)
| | - Paterne A. Agre
- International Institute of Tropical Agriculture, IITA, PMB 5320 Oyo Road, Ibadan 200285, Nigeria; (P.A.A.); (O.N.Q.)
| | - Offornedo N. Queen
- International Institute of Tropical Agriculture, IITA, PMB 5320 Oyo Road, Ibadan 200285, Nigeria; (P.A.A.); (O.N.Q.)
| | - Adebayo L. Ogunkanmi
- Department of Cell Biology and Genetics, University of Lagos, Lagos 101017, Nigeria; (O.A.B.); (O.A.A.); (A.L.O.)
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Sadessa K, Beyene Y, Ifie BE, Suresh LM, Olsen MS, Ogugo V, Wegary D, Tongoona P, Danquah E, Offei SK, Prasanna BM, Gowda M. Identification of Genomic Regions Associated with Agronomic and Disease Resistance Traits in a Large Set of Multiple DH Populations. Genes (Basel) 2022; 13:genes13020351. [PMID: 35205395 PMCID: PMC8872035 DOI: 10.3390/genes13020351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/08/2022] [Accepted: 02/08/2022] [Indexed: 11/17/2022] Open
Abstract
Breeding maize lines with the improved level of desired agronomic traits under optimum and drought conditions as well as increased levels of resistance to several diseases such as maize lethal necrosis (MLN) is one of the most sustainable approaches for the sub-Saharan African region. In this study, 879 doubled haploid (DH) lines derived from 26 biparental populations were evaluated under artificial inoculation of MLN, as well as under well-watered (WW) and water-stressed (WS) conditions for grain yield and other agronomic traits. All DH lines were used for analyses of genotypic variability, association studies, and genomic predictions for the grain yield and other yield-related traits. Genome-wide association study (GWAS) using a mixed linear FarmCPU model identified SNPs associated with the studied traits i.e., about seven and eight SNPs for the grain yield; 16 and 12 for anthesis date; seven and eight for anthesis silking interval; 14 and 5 for both ear and plant height; and 15 and 5 for moisture under both WW and WS environments, respectively. Similarly, about 13 and 11 SNPs associated with gray leaf spot and turcicum leaf blight were identified. Eleven SNPs associated with senescence under WS management that had depicted drought-stress-tolerant QTLs were identified. Under MLN artificial inoculation, a total of 12 and 10 SNPs associated with MLN disease severity and AUDPC traits, respectively, were identified. Genomic prediction under WW, WS, and MLN disease artificial inoculation revealed moderate-to-high prediction accuracy. The findings of this study provide useful information on understanding the genetic basis for the MLN resistance, grain yield, and other agronomic traits under MLN artificial inoculation, WW, and WS conditions. Therefore, the obtained information can be used for further validation and developing functional molecular markers for marker-assisted selection and for implementing genomic prediction to develop superior elite lines.
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Affiliation(s)
- Kassahun Sadessa
- Ethiopian Institute of Agricultural Research (EIAR), Ambo Agricultural Research Center, Ambo P.O. Box 37, West Shoa, Ethiopia;
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
- International Maize and Wheat Improvement Center (CIMMYT), 12.5 KM Peg, Harare P.O. Box MP163, Zimbabwe;
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG23, Accra 00233, Ghana; (B.E.I.); (P.T.); (E.D.); (S.K.O.)
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
| | - Beatrice E. Ifie
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG23, Accra 00233, Ghana; (B.E.I.); (P.T.); (E.D.); (S.K.O.)
| | - L. M. Suresh
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
| | - Michael S. Olsen
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
| | - Veronica Ogugo
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
| | - Dagne Wegary
- International Maize and Wheat Improvement Center (CIMMYT), 12.5 KM Peg, Harare P.O. Box MP163, Zimbabwe;
| | - Pangirayi Tongoona
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG23, Accra 00233, Ghana; (B.E.I.); (P.T.); (E.D.); (S.K.O.)
| | - Eric Danquah
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG23, Accra 00233, Ghana; (B.E.I.); (P.T.); (E.D.); (S.K.O.)
| | - Samuel Kwame Offei
- West Africa Centre for Crop Improvement (WACCI), College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG23, Accra 00233, Ghana; (B.E.I.); (P.T.); (E.D.); (S.K.O.)
| | - Boddupalli M. Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041-00621, Nairobi 00100, Kenya; (Y.B.); (L.M.S.); (M.S.O.); (V.O.); (B.M.P.)
- Correspondence: ; Tel.: +254-727019454
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Hyten DL. Genotyping Platforms for Genome-Wide Association Studies: Options and Practical Considerations. Methods Mol Biol 2022; 2481:29-42. [PMID: 35641757 DOI: 10.1007/978-1-0716-2237-7_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Genome-wide association studies (GWAS) in crops requires genotyping platforms that are capable of producing accurate high density genotyping data on hundreds of plants in a cost-effective manner. Currently there are multiple commercial platforms available that are being effectively used across crops. These platforms include genotyping arrays such as the Illumina Infinium arrays and the Applied Biosystems Axiom Arrays along with a variety of resequencing methods. These methods are being used to genotype tens of thousands of markers up to millions of markers on GWAS panels. They are being used on crops with simple genomes to crops with very complex, large, polyploid genomes. Depending on the crop and the goal of the GWAS, there are several options and practical considerations to take into account when selecting a genotyping technology to ensure that the right coverage, accuracy, and cost for the study is achieved.
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Affiliation(s)
- David L Hyten
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
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Shikha K, Shahi JP, Vinayan MT, Zaidi PH, Singh AK, Sinha B. Genome-wide association mapping in maize: status and prospects. 3 Biotech 2021; 11:244. [PMID: 33968587 PMCID: PMC8085158 DOI: 10.1007/s13205-021-02799-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 04/19/2021] [Indexed: 12/11/2022] Open
Abstract
Genome-wide association study (GWAS) provides a robust and potent tool to retrieve complex phenotypic traits back to their underlying genetics. Maize is an excellent crop for performing GWAS due to diverse genetic variability, rapid decay of linkage disequilibrium, availability of distinct sub-populations and abundant SNP information. The application of GWAS in maize has resulted in successful identification of thousands of genomic regions associated with many abiotic and biotic stresses. Many agronomic and quality traits of maize are severely affected by such stresses and, significantly affecting its growth and productivity. To improve productivity of maize crop in countries like India which contribute only 2% to the world's total production in 2019-2020, it is essential to understand genetic complexity of underlying traits. Various DNA markers and trait associations have been revealed using conventional linkage mapping methods. However, it has achieved limited success in improving polygenic complex traits due to lower resolution of trait mapping. The present review explores the prospects of GWAS in improving yield, quality and stress tolerance in maize besides, strengths and challenges of using GWAS for molecular breeding and genomic selection. The information gathered will facilitate elucidation of genetic mechanisms of complex traits and improve efficiency of marker-assisted selection in maize breeding. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s13205-021-02799-4.
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Affiliation(s)
- Kumari Shikha
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
| | - J. P. Shahi
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
| | - M. T. Vinayan
- International Maize and Wheat Improvement Centre (CIMMYT)-Asia, ICRISAT Campus, Patancheru, Hyderabad, Telangana India
| | - P. H. Zaidi
- International Maize and Wheat Improvement Centre (CIMMYT)-Asia, ICRISAT Campus, Patancheru, Hyderabad, Telangana India
| | - A. K. Singh
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
| | - B. Sinha
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
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Gowda M, Makumbi D, Das B, Nyaga C, Kosgei T, Crossa J, Beyene Y, Montesinos-López OA, Olsen MS, Prasanna BM. Genetic dissection of Striga hermonthica (Del.) Benth. resistance via genome-wide association and genomic prediction in tropical maize germplasm. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:941-958. [PMID: 33388884 PMCID: PMC7925482 DOI: 10.1007/s00122-020-03744-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 12/02/2020] [Indexed: 06/01/2023]
Abstract
KEY MESSAGE Genome-wide association revealed that resistance to Striga hermonthica is influenced by multiple genomic regions with moderate effects. It is possible to increase genetic gains from selection for Striga resistance using genomic prediction. Striga hermonthica (Del.) Benth., commonly known as the purple witchweed or giant witchweed, is a serious problem for maize-dependent smallholder farmers in sub-Saharan Africa. Breeding for Striga resistance in maize is complicated due to limited genetic variation, complexity of resistance and challenges with phenotyping. This study was conducted to (i) evaluate a set of diverse tropical maize lines for their responses to Striga under artificial infestation in three environments in Kenya; (ii) detect quantitative trait loci associated with Striga resistance through genome-wide association study (GWAS); and (iii) evaluate the effectiveness of genomic prediction (GP) of Striga-related traits. An association mapping panel of 380 inbred lines was evaluated in three environments under artificial Striga infestation in replicated trials and genotyped with 278,810 single-nucleotide polymorphism (SNP) markers. Genotypic and genotype x environment variations were significant for measured traits associated with Striga resistance. Heritability estimates were moderate (0.42) to high (0.92) for measured traits. GWAS revealed 57 SNPs significantly associated with Striga resistance indicator traits and grain yield (GY) under artificial Striga infestation with low to moderate effect. A set of 32 candidate genes physically near the significant SNPs with roles in plant defense against biotic stresses were identified. GP with different cross-validations revealed that prediction of performance of lines in new environments is better than prediction of performance of new lines for all traits. Predictions across environments revealed high accuracy for all the traits, while inclusion of GWAS-detected SNPs led to slight increase in the accuracy. The item-based collaborative filtering approach that incorporates related traits evaluated in different environments to predict GY and Striga-related traits outperformed GP for Striga resistance indicator traits. The results demonstrated the polygenic nature of resistance to S. hermonthica, and that implementation of GP in Striga resistance breeding could potentially aid in increasing genetic gain for this important trait.
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Affiliation(s)
- Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya.
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Christine Nyaga
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Titus Kosgei
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
- Moi University, P. O. Box 3900-30100, Eldoret, Kenya
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo, Postal 6-641, 06600, Mexico, D.F, Mexico
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | | | - Michael S Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
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Kibe M, Nair SK, Das B, Bright JM, Makumbi D, Kinyua J, Suresh LM, Beyene Y, Olsen MS, Prasanna BM, Gowda M. Genetic Dissection of Resistance to Gray Leaf Spot by Combining Genome-Wide Association, Linkage Mapping, and Genomic Prediction in Tropical Maize Germplasm. FRONTIERS IN PLANT SCIENCE 2020; 11:572027. [PMID: 33224163 PMCID: PMC7667048 DOI: 10.3389/fpls.2020.572027] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/29/2020] [Indexed: 05/05/2023]
Abstract
Gray leaf spot (GLS) is one of the major maize foliar diseases in sub-Saharan Africa. Resistance to GLS is controlled by multiple genes with additive effect and is influenced by both genotype and environment. The objectives of the study were to dissect the genetic architecture of GLS resistance through linkage mapping and genome-wide association study (GWAS) and assessing the potential of genomic prediction (GP). We used both biparental populations and an association mapping panel of 410 diverse tropical/subtropical inbred lines that were genotyped using genotype by sequencing. Phenotypic evaluation in two to four environments revealed significant genotypic variation and moderate to high heritability estimates ranging from 0.43 to 0.69. GLS was negatively and significantly correlated with grain yield, anthesis date, and plant height. Linkage mapping in five populations revealed 22 quantitative trait loci (QTLs) for GLS resistance. A QTL on chromosome 7 (qGLS7-105) is a major-effect QTL that explained 28.2% of phenotypic variance. Together, all the detected QTLs explained 10.50, 49.70, 23.67, 18.05, and 28.71% of phenotypic variance in doubled haploid (DH) populations 1, 2, 3, and F3 populations 4 and 5, respectively. Joint linkage association mapping across three DH populations detected 14 QTLs that individually explained 0.10-15.7% of phenotypic variance. GWAS revealed 10 significantly (p < 9.5 × 10-6) associated SNPs distributed on chromosomes 1, 2, 6, 7, and 8, which individually explained 6-8% of phenotypic variance. A set of nine candidate genes co-located or in physical proximity to the significant SNPs with roles in plant defense against pathogens were identified. GP revealed low to moderate prediction correlations of 0.39, 0.37, 0.56, 0.30, 0.29, and 0.38 for within IMAS association panel, DH pop1, DH pop2, DH pop3, F3 pop4, and F3 po5, respectively, and accuracy was increased substantially to 0.84 for prediction across three DH populations. When the diversity panel was used as training set to predict the accuracy of GLS resistance in biparental population, there was 20-50% reduction compared to prediction within populations. Overall, the study revealed that resistance to GLS is quantitative in nature and is controlled by many loci with a few major and many minor effects. The SNPs/QTLs identified by GWAS and linkage mapping can be potential targets in improving GLS resistance in breeding programs, while GP further consolidates the development of high GLS-resistant lines by incorporating most of the major- and minor-effect genes.
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Affiliation(s)
- Maguta Kibe
- International Maize and Wheat Improvement Center, Nairobi, Kenya
- Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
| | - Sudha K. Nair
- International Maize and Wheat Improvement Center, Hyderabad, India
| | - Biswanath Das
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | - Jumbo M. Bright
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | - Johnson Kinyua
- Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
| | - L. M. Suresh
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | - Michael S. Olsen
- International Maize and Wheat Improvement Center, Nairobi, Kenya
| | | | - Manje Gowda
- International Maize and Wheat Improvement Center, Nairobi, Kenya
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Combination of Linkage Mapping, GWAS, and GP to Dissect the Genetic Basis of Common Rust Resistance in Tropical Maize Germplasm. Int J Mol Sci 2020; 21:ijms21186518. [PMID: 32899999 PMCID: PMC7555316 DOI: 10.3390/ijms21186518] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/01/2020] [Accepted: 09/04/2020] [Indexed: 12/27/2022] Open
Abstract
Common rust (CR) caused by Puccina sorghi is one of the destructive fungal foliar diseases of maize and has been reported to cause moderate to high yield losses. Providing CR resistant germplasm has the potential to increase yields. To dissect the genetic architecture of CR resistance in maize, association mapping, in conjunction with linkage mapping, joint linkage association mapping (JLAM), and genomic prediction (GP) was conducted on an association-mapping panel and five F3 biparental populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Analysis of variance for the biparental populations and the association panel showed significant genotypic and genotype x environment (GXE) interaction variances except for GXE of Pop4. Heritability (h2) estimates were moderate with 0.37-0.45 for the individual F3 populations, 0.45 across five populations and 0.65 for the association panel. Genome-wide association study (GWAS) analyses revealed 14 significant marker-trait associations which individually explained 6-10% of the total phenotypic variances. Individual population-based linkage analysis revealed 26 QTLs associated with CR resistance and together explained 14-40% of the total phenotypic variances. Linkage mapping revealed seven QTLs in pop1, nine QTL in pop2, four QTL in pop3, five QTL in pop4, and one QTL in pop5, distributed on all chromosomes except chromosome 10. JLAM for the 921 F3 families from five populations detected 18 QTLs distributed in all chromosomes except on chromosome 8. These QTLs individually explained 0.3 to 3.1% and together explained 45% of the total phenotypic variance. Among the 18 QTL detected through JLAM, six QTLs, qCR1-78, qCR1-227, qCR3-172, qCR3-186, qCR4-171, and qCR7-137 were also detected in linkage mapping. GP within population revealed low to moderate correlations with a range from 0.19 to 0.51. Prediction correlation was high with r = 0.78 for combined analysis of the five F3 populations. Prediction of biparental populations by using association panel as training set reveals positive correlations ranging from 0.05 to 0.22, which encourages to develop an independent but related population as a training set which can be used to predict diverse but related populations. The findings of this study provide valuable information on understanding the genetic basis of CR resistance and the obtained information can be used for developing functional molecular markers for marker-assisted selection and for implementing GP to improve CR resistance in tropical maize.
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Lupini A, Preiti G, Badagliacca G, Abenavoli MR, Sunseri F, Monti M, Bacchi M. Nitrogen Use Efficiency in Durum Wheat Under Different Nitrogen and Water Regimes in the Mediterranean Basin. FRONTIERS IN PLANT SCIENCE 2020; 11:607226. [PMID: 33643329 PMCID: PMC7902889 DOI: 10.3389/fpls.2020.607226] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/28/2020] [Indexed: 05/11/2023]
Abstract
Improving nitrogen use efficiency (NUE) represents one of the main goals to reduce N input in maximizing crop yield for sustainable agriculture. A NUE key strategy is the exploitation of genetic variation in available germplasm together with the understanding of molecular mechanisms governing this complex trait. Thus, NUE, its components, nitrogen uptake efficiency (NUpE) and nitrogen utilization efficiency (NUtE), and NUE-related traits heritability were evaluated in ancient (Cappelli, Capeiti, Russello, and Mazzancoio) and modern (Messapia, Tiziana, Svevo, and Normanno) wheat genotypes for tackling nitrogen (N) and/or water limitation in both growth chamber and field experiments. Our results exhibited a reduction of NUE, NUpE, and NUtE under water and combined (nitrogen + water) stress in all the genotypes, as expected. The contribution of genetic variability on phenotypic variation was significant for NUtE, harvest index, post-anthesis nitrogen uptake (PANU), and biomass production traits. Moreover, the stress tolerance indexes, calculated and bi-plotted for N and water stresses, exhibited two distinct clusters for many traits as then confirmed by principal component analysis. Although modern varieties showed higher crop yield and NUE under conventional N and water regimes, ancient varieties exhibited best performances to cope with both stresses, mainly under water limitation. Finally, the usage index, which takes into account total biomass increase, underlined that old genotypes were less affected by both stresses during crop cycle. In particular, these genotypes showed the best performances for NUE and its components under both stresses at stem elongation and milk ripening as shown also by PANU. In addition, at these stages, nitrate and ammonium transporter gene expressions in the root were performed, showing the highest activity in ancient varieties. In conclusion, the identification of NUE traits during a specific crop cycle stage, under both N and water limitation, will help in the breeding of more resilient varieties in Mediterranean sustainable agriculture by reducing N supply.
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