1
|
Jamil S, Ahmad S, Shahzad R, Umer N, Kanwal S, Rehman HM, Rana IA, Atif RM. Leveraging Multiomics Insights and Exploiting Wild Relatives' Potential for Drought and Heat Tolerance in Maize. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024. [PMID: 38980762 DOI: 10.1021/acs.jafc.4c01375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Climate change, particularly drought and heat stress, may slash agricultural productivity by 25.7% by 2080, with maize being the hardest hit. Therefore, unraveling the molecular nature of plant responses to these stressors is vital for the development of climate-smart maize. This manuscript's primary objective was to examine how maize plants respond to these stresses, both individually and in combination. Additionally, the paper delved into harnessing the potential of maize wild relatives as a valuable genetic resource and leveraging AI-based technologies to boost maize resilience. The role of multiomics approaches particularly genomics and transcriptomics in dissecting the genetic basis of stress tolerance was also highlighted. The way forward was proposed to utilize a bunch of information obtained through omics technologies by an interdisciplinary state-of-the-art forward-looking big-data, cyberagriculture system, and AI-based approach to orchestrate the development of climate resilient maize genotypes.
Collapse
Affiliation(s)
- Shakra Jamil
- Agricultural Biotechnology Research Institute, Ayub Agricultural Research Institute, Faisalabad 38000, Pakistan
| | - Shakeel Ahmad
- Seed Centre and Plant Genetic Resources Bank Ministry of Environment, Water and Agriculture, Riyadh 14712, Saudi Arabia
| | - Rahil Shahzad
- Agricultural Biotechnology Research Institute, Ayub Agricultural Research Institute, Faisalabad 38000, Pakistan
| | - Noroza Umer
- Dr. Ikram ul Haq - Institute of Industrial Biotechnology, Government College University, Lahore 54590, Pakistan
| | - Shamsa Kanwal
- Agricultural Biotechnology Research Institute, Ayub Agricultural Research Institute, Faisalabad 38000, Pakistan
| | - Hafiz Mamoon Rehman
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38000, Pakistan
| | - Iqrar Ahmad Rana
- Centre for Advanced Studies in Agriculture and Food Security, University of Agriculture, Faisalabad 38000, Pakistan
| | - Rana Muhammad Atif
- Department of Plant Sciences, University of California Davis, California 95616, United States
- Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad 38000, Pakistan
- Precision Agriculture and Analytics Lab, Centre for Advanced Studies in Agriculture and Food Security, National Centre in Big Data and Cloud Computing, University of Agriculture, Faisalabad 38000, Pakistan
| |
Collapse
|
2
|
Fernández-González J, Haquin B, Combes E, Bernard K, Allard A, Isidro Y Sánchez J. Maximizing efficiency in sunflower breeding through historical data optimization. PLANT METHODS 2024; 20:42. [PMID: 38493115 PMCID: PMC10943787 DOI: 10.1186/s13007-024-01151-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 01/30/2024] [Indexed: 03/18/2024]
Abstract
Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.
Collapse
Affiliation(s)
- Javier Fernández-González
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, Pozuelo de Alarcón, Madrid, 28223, Spain.
| | | | | | | | | | - Julio Isidro Y Sánchez
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, Pozuelo de Alarcón, Madrid, 28223, Spain.
| |
Collapse
|
3
|
Chiwina K, Xiong H, Bhattarai G, Dickson RW, Phiri TM, Chen Y, Alatawi I, Dean D, Joshi NK, Chen Y, Riaz A, Gepts P, Brick M, Byrne PF, Schwartz H, Ogg JB, Otto K, Fall A, Gilbert J, Shi A. Genome-Wide Association Study and Genomic Prediction of Fusarium Wilt Resistance in Common Bean Core Collection. Int J Mol Sci 2023; 24:15300. [PMID: 37894980 PMCID: PMC10607830 DOI: 10.3390/ijms242015300] [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: 08/29/2023] [Revised: 09/29/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
The common bean (Phaseolus vulgaris L.) is a globally cultivated leguminous crop. Fusarium wilt (FW), caused by Fusarium oxysporum f. sp. phaseoli (Fop), is a significant disease leading to substantial yield loss in common beans. Disease-resistant cultivars are recommended to counteract this. The objective of this investigation was to identify single nucleotide polymorphism (SNP) markers associated with FW resistance and to pinpoint potential resistant common bean accessions within a core collection, utilizing a panel of 157 accessions through the Genome-wide association study (GWAS) approach with TASSEL 5 and GAPIT 3. Phenotypes for Fop race 1 and race 4 were matched with genotypic data from 4740 SNPs of BARCBean6K_3 Infinium Bea Chips. After ranking the 157-accession panel and revealing 21 Fusarium wilt-resistant accessions, the GWAS pinpointed 16 SNPs on chromosomes Pv04, Pv05, Pv07, Pv8, and Pv09 linked to Fop race 1 resistance, 23 SNPs on chromosomes Pv03, Pv04, Pv05, Pv07, Pv09, Pv10, and Pv11 associated with Fop race 4 resistance, and 7 SNPs on chromosomes Pv04 and Pv09 correlated with both Fop race 1 and race 4 resistances. Furthermore, within a 30 kb flanking region of these associated SNPs, a total of 17 candidate genes were identified. Some of these genes were annotated as classical disease resistance protein/enzymes, including NB-ARC domain proteins, Leucine-rich repeat protein kinase family proteins, zinc finger family proteins, P-loopcontaining nucleoside triphosphate hydrolase superfamily, etc. Genomic prediction (GP) accuracy for Fop race resistances ranged from 0.26 to 0.55. This study advanced common bean genetic enhancement through marker-assisted selection (MAS) and genomic selection (GS) strategies, paving the way for improved Fop resistance.
Collapse
Affiliation(s)
- Kenani Chiwina
- Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
| | - Haizheng Xiong
- Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
| | - Gehendra Bhattarai
- Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
| | - Ryan William Dickson
- Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
| | - Theresa Makawa Phiri
- Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
| | - Yilin Chen
- Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
| | - Ibtisam Alatawi
- Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
| | - Derek Dean
- Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
| | - Neelendra K. Joshi
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701, USA;
| | - Yuyan Chen
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA;
| | - Awais Riaz
- Department of Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA;
| | - Paul Gepts
- Department of Plant Sciences, University of California, 1 Shields Avenue, Davis, CA 95616, USA;
| | - Mark Brick
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA; (M.B.); (P.F.B.); (J.B.O.); (A.F.); (J.G.)
| | - Patrick F. Byrne
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA; (M.B.); (P.F.B.); (J.B.O.); (A.F.); (J.G.)
| | - Howard Schwartz
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO 80523, USA; (H.S.); (K.O.)
| | - James B. Ogg
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA; (M.B.); (P.F.B.); (J.B.O.); (A.F.); (J.G.)
| | - Kristin Otto
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO 80523, USA; (H.S.); (K.O.)
| | - Amy Fall
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA; (M.B.); (P.F.B.); (J.B.O.); (A.F.); (J.G.)
| | - Jeremy Gilbert
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA; (M.B.); (P.F.B.); (J.B.O.); (A.F.); (J.G.)
| | - Ainong Shi
- Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
| |
Collapse
|
4
|
Gesteiro N, Ordás B, Butrón A, de la Fuente M, Jiménez-Galindo JC, Samayoa LF, Cao A, Malvar RA. Genomic versus phenotypic selection to improve corn borer resistance and grain yield in maize. FRONTIERS IN PLANT SCIENCE 2023; 14:1162440. [PMID: 37484478 PMCID: PMC10360656 DOI: 10.3389/fpls.2023.1162440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/22/2023] [Indexed: 07/25/2023]
Abstract
Introduction The study of yield and resistance/tolerance to pest are related traits fundamental for maize breeding programs. Genomic selection (GS), which uses all marker information to calculate genomic breeding values, is presented as an emerging alternative to phenotypic and marker-assisted selections for improving complex traits controlled by many genes with small effects. Therefore, although phenotypic selection (PS) has been effective for increasing resistance and yield under high infestation with maize stem borers, higher genetic gains are expected to be obtained through GS based on the complex architecture of both traits. Our objective was to test whether GS is more effective than PS for improving resistance and/or tolerance to maize stem borers and grain yield. Methods For this, we compared different selection programs based on phenotype and genotypic value for a single trait, resistance or yield, and for both traits together. Results and discussion We obtained that GS achieved the highest genetic gain for yield, meanwhile phenotypic selection for yield was the program that achieved the highest reduction of tunnel length, but was ineffective for increasing yield. However, phenotypic or genomic selection for increased resistance may be more effective in improving both traits together; although the gains per cycle would be small for both traits.
Collapse
Affiliation(s)
| | | | - Ana Butrón
- Mision Biologica de Galicia (CSIC), Pontevedra, Spain
| | | | | | - Luis Fernando Samayoa
- Department of Crop Science, North Carolina State University, Raleigh, NC, United States
| | - Ana Cao
- Mision Biologica de Galicia (CSIC), Pontevedra, Spain
| | | |
Collapse
|
5
|
Cooper M, Messina CD. Breeding crops for drought-affected environments and improved climate resilience. THE PLANT CELL 2023; 35:162-186. [PMID: 36370076 PMCID: PMC9806606 DOI: 10.1093/plcell/koac321] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/01/2022] [Indexed: 05/12/2023]
Abstract
Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.
Collapse
Affiliation(s)
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
| |
Collapse
|
6
|
Melandri G, Monteverde E, Riewe D, AbdElgawad H, McCouch SR, Bouwmeester H. Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought. PLANT PHYSIOLOGY 2022; 189:1139-1152. [PMID: 35166848 PMCID: PMC9157150 DOI: 10.1093/plphys/kiac053] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 01/17/2022] [Indexed: 05/13/2023]
Abstract
The possibility of introducing metabolic/biochemical phenotyping to complement genomics-based predictions in breeding pipelines has been considered for years. Here we examine to what extent and under what environmental conditions metabolic/biochemical traits can effectively contribute to understanding and predicting plant performance. In this study, multivariable statistical models based on flag leaf central metabolism and oxidative stress status were used to predict grain yield (GY) performance for 271 indica rice (Oryza sativa) accessions grown in the field under well-watered and reproductive stage drought conditions. The resulting models displayed significantly higher predictability than multivariable models based on genomic data for the prediction of GY under drought (Q2 = 0.54-0.56 versus 0.35) and for stress-induced GY loss (Q2 = 0.59-0.64 versus 0.03-0.06). Models based on the combined datasets showed predictabilities similar to metabolic/biochemical-based models alone. In contrast to genetic markers, models with enzyme activities and metabolite values also quantitatively integrated the effect of physiological differences such as plant height on GY. The models highlighted antioxidant enzymes of the ascorbate-glutathione cycle and a lipid oxidation stress marker as important predictors of rice GY stability under drought at the reproductive stage, and these stress-related variables were more predictive than leaf central metabolites. These findings provide evidence that metabolic/biochemical traits can integrate dynamic cellular and physiological responses to the environment and can help bridge the gap between the genome and the phenome of crops as predictors of GY performance under drought.
Collapse
Affiliation(s)
- Giovanni Melandri
- Laboratory of Plant Physiology, Wageningen University and Research, Wageningen, the Netherlands
- School of Integrative Plant Sciences, Plant Breeding and Genetics Section, Cornell University, Ithaca, New York, USA
| | - Eliana Monteverde
- School of Integrative Plant Sciences, Plant Breeding and Genetics Section, Cornell University, Ithaca, New York, USA
- Departamento de Biología Vegetal, Facultad de Agronomía, Laboratorio de Evolución y Domesticación de las Plantas, Universidad de La República, Montevideo, Uruguay
| | - David Riewe
- Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, Berlin, Germany
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | - Hamada AbdElgawad
- Laboratory for Integrated Molecular Plant Physiology Research, University of Antwerp, Antwerp, Belgium
- Department of Botany, Faculty of Science, Beni-Suef University, Beni Suef, Egypt
| | - Susan R McCouch
- School of Integrative Plant Sciences, Plant Breeding and Genetics Section, Cornell University, Ithaca, New York, USA
| | - Harro Bouwmeester
- Laboratory of Plant Physiology, Wageningen University and Research, Wageningen, the Netherlands
- Plant Hormone Biology group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands
| |
Collapse
|
7
|
Jackson D, Tian F, Zhang Z. Maize genetics, genomics, and sustainable improvement. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:2. [PMID: 37309482 PMCID: PMC10248613 DOI: 10.1007/s11032-021-01266-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/08/2021] [Indexed: 06/14/2023]
Affiliation(s)
- David Jackson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724 USA
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Feng Tian
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center of China, and Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing, 100193 China
| | - Zuxin Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| |
Collapse
|