1
|
Liu X, Wang M, Qin J, Liu Y, Wang S, Wu S, Zhang M, Zhong J, Wang J. GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction. BMC Genomics 2024; 25:386. [PMID: 38641604 PMCID: PMC11027269 DOI: 10.1186/s12864-024-10310-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 04/15/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS). RESULTS Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability. CONCLUSIONS Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).
Collapse
Affiliation(s)
- Xinrui Liu
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China
- Nanchong Academy of Agricultural Sciences, Nanchong, 637000, China
| | - Mingxiu Wang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China
| | - Jie Qin
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China
| | - Yaxin Liu
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China
| | - Shikai Wang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China
| | - Shiyu Wu
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China
| | - Ming Zhang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China
| | - Jincheng Zhong
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China
| | - Jiabo Wang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China.
| |
Collapse
|
2
|
Joshi B, Singh S, Tiwari GJ, Kumar H, Boopathi NM, Jaiswal S, Adhikari D, Kumar D, Sawant SV, Iquebal MA, Jena SN. Genome-wide association study of fiber yield-related traits uncovers the novel genomic regions and candidate genes in Indian upland cotton ( Gossypium hirsutum L.). FRONTIERS IN PLANT SCIENCE 2023; 14:1252746. [PMID: 37941674 PMCID: PMC10630025 DOI: 10.3389/fpls.2023.1252746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/11/2023] [Indexed: 11/10/2023]
Abstract
Upland cotton (Gossypium hirsutum L.) is a major fiber crop that is cultivated worldwide and has significant economic importance. India harbors the largest area for cotton cultivation, but its fiber yield is still compromised and ranks 22nd in terms of productivity. Genetic improvement of cotton fiber yield traits is one of the major goals of cotton breeding, but the understanding of the genetic architecture underlying cotton fiber yield traits remains limited and unclear. To better decipher the genetic variation associated with fiber yield traits, we conducted a comprehensive genome-wide association mapping study using 117 Indian cotton germplasm for six yield-related traits. To accomplish this, we generated 2,41,086 high-quality single nucleotide polymorphism (SNP) markers using genotyping-by-sequencing (GBS) methods. Population structure, PCA, kinship, and phylogenetic analyses divided the germplasm into two sub-populations, showing weak relatedness among the germplasms. Through association analysis, 205 SNPs and 134 QTLs were identified to be significantly associated with the six fiber yield traits. In total, 39 novel QTLs were identified in the current study, whereas 95 QTLs overlapped with existing public domain data in a comparative analysis. Eight QTLs, qGhBN_SCY_D6-1, qGhBN_SCY_D6-2, qGhBN_SCY_D6-3, qGhSI_LI_A5, qGhLI_SI_A13, qGhLI_SI_D9, qGhBW_SCY_A10, and qGhLP_BN_A8 were identified. Gene annotation of these fiber yield QTLs revealed 2,509 unique genes. These genes were predominantly enriched for different biological processes, such as plant cell wall synthesis, nutrient metabolism, and vegetative growth development in the gene ontology (GO) enrichment study. Furthermore, gene expression analysis using RNAseq data from 12 diverse cotton tissues identified 40 candidate genes (23 stable and 17 novel genes) to be transcriptionally active in different stages of fiber, ovule, and seed development. These findings have revealed a rich tapestry of genetic elements, including SNPs, QTLs, and candidate genes, and may have a high potential for improving fiber yield in future breeding programs for Indian cotton.
Collapse
Affiliation(s)
- Babita Joshi
- Plant Genetic Resources and Improvement, CSIR-National Botanical Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Sanjay Singh
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Gopal Ji Tiwari
- Plant Genetic Resources and Improvement, CSIR-National Botanical Research Institute, Lucknow, India
| | - Harish Kumar
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Regional Research Station, Faridkot, Punjab, India
| | - Narayanan Manikanda Boopathi
- Department of Plant Biotechnology, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
| | - Sarika Jaiswal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Dibyendu Adhikari
- Plant Ecology and Climate Change Science, CSIR-National Botanical Research Institute, Lucknow, India
| | - Dinesh Kumar
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Samir V. Sawant
- Molecular Biology & Biotechnology, CSIR-National Botanical Research Institute, Lucknow, India
| | - Mir Asif Iquebal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Satya Narayan Jena
- Plant Genetic Resources and Improvement, CSIR-National Botanical Research Institute, Lucknow, India
| |
Collapse
|
3
|
Shimono H, Abe A, Kim CH, Sato C, Iwata H. Upcycling rice yield trial data using a weather-driven crop growth model. Commun Biol 2023; 6:764. [PMID: 37479731 PMCID: PMC10362053 DOI: 10.1038/s42003-023-05145-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 07/13/2023] [Indexed: 07/23/2023] Open
Abstract
Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Yp) calculated by a weather-driven crop growth model, called as the "YpCGM method". We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world's changing climate.
Collapse
Affiliation(s)
- Hiroyuki Shimono
- Faculty of Agriculture, Iwate University, Morioka, Iwate, 020-8550, Japan.
- Agri-Innovation Center, Iwate University, Morioka, Iwate, 020-8550, Japan.
| | - Akira Abe
- Iwate Biotechnology Research Center, Kitakami, Iwate, 024-0003, Japan
| | - Chyon Hae Kim
- Faculty of Science and Engineering, Iwate University, Morioka, Iwate, 020-8550, Japan
| | - Chikashi Sato
- Ifuu Rinrin, 77-9, Rikuzentakata, Iwate, 029-2205, Japan
| | - Hiroyoshi Iwata
- Laboratory of Biometry and Bioinformatics, University of Tokyo, Bunkyo-ku, Tokyo, 113-8657, Japan
| |
Collapse
|
4
|
A divide-and-conquer approach for genomic prediction in rubber tree using machine learning. Sci Rep 2022; 12:18023. [PMID: 36289298 PMCID: PMC9605989 DOI: 10.1038/s41598-022-20416-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/13/2022] [Indexed: 01/20/2023] Open
Abstract
Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.
Collapse
|
5
|
Manthena V, Jarquín D, Varshney RK, Roorkiwal M, Dixit GP, Bharadwaj C, Howard R. Evaluating dimensionality reduction for genomic prediction. Front Genet 2022; 13:958780. [PMID: 36313472 PMCID: PMC9614092 DOI: 10.3389/fgene.2022.958780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the main effects of line, environment, marker, and the genotype by environment interactions. The methods were applied on a real data set containing 315 lines phenotyped in nine environments with 26,817 markers each. Regardless of the DR method and prediction model used, only a fraction of features was sufficient to achieve maximum correlation. Our results underline the usefulness of DR methods as a key pre-processing step in GS models to improve computational efficiency in the face of ever-increasing size of genomic data.
Collapse
Affiliation(s)
- Vamsi Manthena
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Diego Jarquín
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Rajeev K. Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Murdoch, WA, Australia
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Manish Roorkiwal
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Khalifa Center for Genetic Engineering and Biotechnology, United Arab Emirates University, Al-Ain, United Arab Emirates
| | - Girish Prasad Dixit
- ICAR- All India Coordinated Research Project (AICRP)- Chickpea, ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, Uttar Pradesh, India
| | | | - Reka Howard
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, United States
- *Correspondence: Reka Howard,
| |
Collapse
|
6
|
Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction. PLANTS 2022; 11:plants11131736. [PMID: 35807690 PMCID: PMC9269065 DOI: 10.3390/plants11131736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 11/17/2022]
Abstract
Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from −0.11 to −0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.
Collapse
|
7
|
Li Y, Kaur S, Pembleton LW, Valipour-Kahrood H, Rosewarne GM, Daetwyler HD. Strategies of preserving genetic diversity while maximizing genetic response from implementing genomic selection in pulse breeding programs. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1813-1828. [PMID: 35316351 PMCID: PMC9205836 DOI: 10.1007/s00122-022-04071-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/26/2022] [Indexed: 06/14/2023]
Abstract
Genomic selection maximizes genetic gain by recycling parents to germplasm pool earlier and preserves genetic diversity by restricting the number of fixed alleles and the relationship in pulse breeding programs. Using a stochastic computer simulation, we investigated the benefit of optimization strategies in the context of genomic selection (GS) for pulse breeding programs. We simulated GS for moderately complex to highly complex traits such as disease resistance, grain weight and grain yield in multiple environments with a high level of genotype-by-environment interaction for grain yield. GS led to higher genetic gain per unit of time and higher genetic diversity loss than phenotypic selection by shortening the breeding cycle time. The genetic gain obtained from selecting the segregating parents early in the breeding cycle (at F1 or F2 stages) was substantially higher than selecting at later stages even though prediction accuracy was moderate. Increasing the number of F1 intercross (F1i) families and keeping the total number of progeny of F1i families constant, we observed a decrease in genetic gain and increase in genetic diversity, whereas increasing the number of progeny per F1i family while keeping a constant number of F1i families increased the rate of genetic gain and had higher genetic diversity loss per unit of time. Adding 50 F2 family phenotypes to the training population increased the accuracy of genomic breeding values (GEBVs) and genetic gain per year and decreased the rate of genetic diversity loss. Genetic diversity could be preserved by applying a strategy that restricted both the percentage of alleles fixed and the average relationship of the group of selected parents to preserve long-term genetic improvement in the pulse breeding program.
Collapse
Affiliation(s)
- Yongjun Li
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, 3083, Australia.
| | - Sukhjiwan Kaur
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, 3083, Australia
| | - Luke W Pembleton
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, 3083, Australia
| | | | - Garry M Rosewarne
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC, 3400, Australia
| | - Hans D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
| |
Collapse
|
8
|
Mohd Saad NS, Neik TX, Thomas WJW, Amas JC, Cantila AY, Craig RJ, Edwards D, Batley J. Advancing designer crops for climate resilience through an integrated genomics approach. CURRENT OPINION IN PLANT BIOLOGY 2022; 67:102220. [PMID: 35489163 DOI: 10.1016/j.pbi.2022.102220] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 03/15/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
Climate change and exponential population growth are exposing an immediate need for developing future crops that are highly resilient and adaptable to changing environments to maintain global food security in the next decade. Rigorous selection from long domestication history has rendered cultivated crops genetically disadvantaged, raising concerns in their ability to adapt to these new challenges and limiting their usefulness in breeding programmes. As a result, future crop improvement efforts must rely on integrating various genomic strategies ranging from high-throughput sequencing to machine learning, in order to exploit germplasm diversity and overcome bottlenecks created by domestication, expansive multi-dimensional phenotypes, arduous breeding processes, complex traits and big data.
Collapse
Affiliation(s)
- Nur Shuhadah Mohd Saad
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Ting Xiang Neik
- Sunway College Kuala Lumpur, Bandar Sunway, 47500, Selangor, Malaysia
| | - William J W Thomas
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Junrey C Amas
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Aldrin Y Cantila
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Ryan J Craig
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - David Edwards
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Jacqueline Batley
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia.
| |
Collapse
|
9
|
Semagn K, Iqbal M, Jarquin D, Crossa J, Howard R, Ciechanowska I, Henriquez MA, Randhawa H, Aboukhaddour R, McCallum BD, Brûlé-Babel AL, Navabi A, N'Diaye A, Pozniak C, Spaner D. Genomic Predictions for Common Bunt, FHB, Stripe Rust, Leaf Rust, and Leaf Spotting Resistance in Spring Wheat. Genes (Basel) 2022; 13:565. [PMID: 35456370 PMCID: PMC9032109 DOI: 10.3390/genes13040565] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/15/2022] [Accepted: 03/21/2022] [Indexed: 02/04/2023] Open
Abstract
Some studies have investigated the potential of genomic selection (GS) on stripe rust, leaf rust, Fusarium head blight (FHB), and leaf spot in wheat, but none of them have assessed the effect of the reaction norm model that incorporated GE interactions. In addition, the prediction accuracy on common bunt has not previously been studied. Here, we investigated within-population prediction accuracies using the baseline M1 model and two reaction norm models (M2 and M3) with three random cross-validation (CV1, CV2, and CV0) schemes. Three Canadian spring wheat populations were evaluated in up to eight field environments and genotyped with 3158, 5732, and 23,795 polymorphic markers. The M3 model that incorporated GE interactions reduced residual variance by an average of 10.2% as compared with the main effect M2 model and increased prediction accuracies on average by 2-6%. In some traits, the M3 model increased prediction accuracies up to 54% as compared with the M2 model. The average prediction accuracies of the M3 model with CV1, CV2, and CV0 schemes varied from 0.02 to 0.48, from 0.25 to 0.84, and from 0.14 to 0.87, respectively. In both CV2 and CV0 schemes, stripe rust in all three populations, common bunt and leaf rust in two populations, as well as FHB severity, FHB index, and leaf spot in one population had high to very high (0.54-0.87) prediction accuracies. This is the first comprehensive genomic selection study on five major diseases in spring wheat.
Collapse
Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Muhammad Iqbal
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL 32611, USA
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico 06600, Mexico
| | - Reka Howard
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Izabela Ciechanowska
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Maria Antonia Henriquez
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, 101 Route 100, Morden, MB R6M 1Y5, Canada
| | - Harpinder Randhawa
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB T1J 4B1, Canada
| | - Reem Aboukhaddour
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB T1J 4B1, Canada
| | - Brent D McCallum
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, 101 Route 100, Morden, MB R6M 1Y5, Canada
| | - Anita L Brûlé-Babel
- Department of Plant Science, University of Manitoba, 66 Dafoe Road, Winnipeg, MB R3T 2N2, Canada
| | - Alireza Navabi
- Department of Plant Agriculture, Crop Science Building, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Amidou N'Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
| | - Dean Spaner
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| |
Collapse
|
10
|
Rocchetti L, Gioia T, Logozzo G, Brezeanu C, Pereira LG, la Rosa LD, Marzario S, Pieri A, Fernie AR, Alseekh S, Susek K, Cook DR, Varshney RK, Agrawal SK, Hamwieh A, Bitocchi E, Papa R. Towards the Development, Maintenance and Standardized Phenotypic Characterization of Single-Seed-Descent Genetic Resources for Chickpea. Curr Protoc 2022; 2:e371. [PMID: 35179832 DOI: 10.1002/cpz1.371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Here we present the approach used to develop the INCREASE "Intelligent Chickpea" Collections, from analysis of the information on the life history and population structure of chickpea germplasm, the availability of genomic and genetic resources, the identification of key phenotypic traits and methodologies to characterize chickpea. We present two phenotypic protocols within H2O20 Project INCREASE to characterize, develop, and maintain chickpea single-seed-descent (SSD) line collections. Such protocols and related genetic resource data from the project will be available for the legume community to apply the standardized approaches to develop Chickpea Intelligent Collections further or for multiplication/seed-increase purposes. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Characterization of chickpea seeds for seed-trait descriptors Basic Protocol 2: Characterization of chickpea lines for plant-trait descriptors specific for primary seed increase.
Collapse
Affiliation(s)
- Lorenzo Rocchetti
- Department of Agricultural, Food, and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Tania Gioia
- School of Agriculture, Forestry, Food, and Environmental Sciences, University of Basilicata, Potenza, Italy
| | - Giuseppina Logozzo
- School of Agriculture, Forestry, Food, and Environmental Sciences, University of Basilicata, Potenza, Italy
| | - Creola Brezeanu
- Staţiunea de Cercetare Dezvoltare Pentru Legumicultură, Bacău, Romania
| | - Luis Guasch Pereira
- Spanish Plant Genetic Resources National Center, National Institute for Agricultural and Food Research and Technology (CRF-INIA-CSIC), Alcalá de Henares, Madrid, Spain
| | - Lucía De la Rosa
- Spanish Plant Genetic Resources National Center, National Institute for Agricultural and Food Research and Technology (CRF-INIA-CSIC), Alcalá de Henares, Madrid, Spain
| | - Stefania Marzario
- School of Agriculture, Forestry, Food, and Environmental Sciences, University of Basilicata, Potenza, Italy
| | - Alice Pieri
- Department of Agricultural, Food, and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Saleh Alseekh
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.,Center for Plant Systems Biology, Plovdiv, Bulgaria
| | - Karolina Susek
- Legume Genomics Team, Institute of Plant Genetics, Polish Academy of Sciences, Poznan, Poland
| | - Douglas R Cook
- Department of Plant Pathology, University of California Davis, Davis, California
| | - Rajeev K Varshney
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, Telangana, India
| | - Shiv Kumar Agrawal
- Genetic Resources Section, International Center for Agricultural Research in Dry Areas (ICARDA), Agdal Rabat, Morocco
| | - Aladdin Hamwieh
- Genetic Resources Section, International Center for Agricultural Research in Dry Areas (ICARDA), Agdal Rabat, Morocco
| | - Elena Bitocchi
- Department of Agricultural, Food, and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Roberto Papa
- Department of Agricultural, Food, and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| |
Collapse
|
11
|
Semagn K, Iqbal M, Crossa J, Jarquin D, Howard R, Chen H, Bemister DH, Beres BL, Randhawa H, N'Diaye A, Pozniak C, Spaner D. Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:537-552. [PMID: 34724078 DOI: 10.1007/s00122-021-03982-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/19/2021] [Indexed: 06/13/2023]
Abstract
Using phenotype data of three spring wheat populations evaluated at 6-15 environments under two management systems, we found moderate to very high prediction accuracies across seven traits. The phenotype data collected under an organic management system effectively predicted the performance of lines in the conventional management and vice versa. There is growing interest in developing wheat cultivars specifically for organic agriculture, but we are not aware of the effect of organic management on the predictive ability of genomic selection (GS). Here, we evaluated within populations prediction accuracies of four GS models, four combinations of training and testing sets, three reaction norm models, and three random cross-validations (CV) schemes in three populations phenotyped under organic and conventional management systems. Our study was based on a total of 578 recombinant inbred lines and varieties from three spring wheat populations, which were evaluated for seven traits at 3-9 conventionally and 3-6 organically managed field environments and genotyped either with the wheat 90 K SNP array or DArTseq. We predicted the management systems (CV0M) or environments (CV0), a subset of lines that have been evaluated in either management (CV2M) or some environments (CV2), and the performance of newly developed lines in either management (CV1M) or environments (CV1). The average prediction accuracies of the model that incorporated genotype × environment interactions with CV0 and CV2 schemes varied from 0.69 to 0.97. In the CV1 and CV1M schemes, prediction accuracies ranged from - 0.12 to 0.77 depending on the reaction norm models, the traits, and populations. In most cases, grain protein showed the highest prediction accuracies. The phenotype data collected under the organic management effectively predicted the performance of lines under conventional management and vice versa. This is the first comprehensive GS study that investigated the effect of the organic management system in wheat.
Collapse
Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Muhammad Iqbal
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico
| | - Diego Jarquin
- University of Nebraska - Lincoln, Lincoln, NE, 68583, USA
| | - Reka Howard
- University of Nebraska - Lincoln, Lincoln, NE, 68583, USA
| | - Hua Chen
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
- Department of Agronomy, School of Life Science and Engineering, Southwest University of Science and Technology, 59 Qinglong Road, Mianyang, 621010, Sichuan, China
| | - Darcy H Bemister
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Brian L Beres
- Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Harpinder Randhawa
- Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Amidou N'Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Dean Spaner
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
| |
Collapse
|
12
|
Sandhu KS, Merrick LF, Sankaran S, Zhang Z, Carter AH. Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs. Front Genet 2022. [PMCID: PMC8814369 DOI: 10.3389/fgene.2021.829131] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.
Collapse
Affiliation(s)
- Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
- *Correspondence: Karansher S. Sandhu,
| | - Lance F. Merrick
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Sindhuja Sankaran
- Department of Biological System Engineering, Washington State University, Pullman, WA, United States
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| |
Collapse
|
13
|
Morota G, Jarquin D, Campbell MT, Iwata H. Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data. Methods Mol Biol 2022; 2539:269-296. [PMID: 35895210 DOI: 10.1007/978-1-0716-2537-8_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
Collapse
Affiliation(s)
- Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL, USA
| | - Malachy T Campbell
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
14
|
Montesinos-López OA, Montesinos-López A, Mosqueda-González BA, Bentley AR, Lillemo M, Varshney RK, Crossa J. A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits. Front Genet 2021; 12:798840. [PMID: 34976026 PMCID: PMC8718701 DOI: 10.3389/fgene.2021.798840] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 11/18/2021] [Indexed: 11/13/2022] Open
Abstract
Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.
Collapse
Affiliation(s)
| | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Mexico
- *Correspondence: Abelardo Montesinos-López, ; Rajeev K. Varshney, ; José Crossa,
| | - Brandon A. Mosqueda-González
- Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Esq. Miguel Othón de Mendizábal, Mexico city, Mexico
| | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Morten Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences, IHA/CIGENE, As, Norway
| | - Rajeev K. Varshney
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Perth, WA, Australia
- *Correspondence: Abelardo Montesinos-López, ; Rajeev K. Varshney, ; José Crossa,
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Colegio de Postgraduados, Montecillo, Mexico
- *Correspondence: Abelardo Montesinos-López, ; Rajeev K. Varshney, ; José Crossa,
| |
Collapse
|
15
|
Saradadevi GP, Das D, Mangrauthia SK, Mohapatra S, Chikkaputtaiah C, Roorkiwal M, Solanki M, Sundaram RM, Chirravuri NN, Sakhare AS, Kota S, Varshney RK, Mohannath G. Genetic, Epigenetic, Genomic and Microbial Approaches to Enhance Salt Tolerance of Plants: A Comprehensive Review. BIOLOGY 2021; 10:biology10121255. [PMID: 34943170 PMCID: PMC8698797 DOI: 10.3390/biology10121255] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 12/17/2022]
Abstract
Simple Summary Globally, soil salinity, which refers to salt-affected soils, is increasing due to various environmental factors and human activities. Soil salinity poses one of the most serious challenges in the field of agriculture as it significantly reduces the growth and yield of crop plants, both quantitatively and qualitatively. Over the last few decades, several studies have been carried out to understand plant biology in response to soil salinity stress with a major emphasis on genetic and other hereditary components. Based on the outcome of these studies, several approaches are being followed to enhance plants’ ability to tolerate salt stress while still maintaining reasonable levels of crop yields. In this manuscript, we comprehensively list and discuss various biological approaches being followed and, based on the recent advances in the field of molecular biology, we propose some new approaches to improve salinity tolerance of crop plants. The global scientific community can make use of this information for the betterment of crop plants. This review also highlights the importance of maintaining global soil health to prevent several crop plant losses. Abstract Globally, soil salinity has been on the rise owing to various factors that are both human and environmental. The abiotic stress caused by soil salinity has become one of the most damaging abiotic stresses faced by crop plants, resulting in significant yield losses. Salt stress induces physiological and morphological modifications in plants as a result of significant changes in gene expression patterns and signal transduction cascades. In this comprehensive review, with a major focus on recent advances in the field of plant molecular biology, we discuss several approaches to enhance salinity tolerance in plants comprising various classical and advanced genetic and genetic engineering approaches, genomics and genome editing technologies, and plant growth-promoting rhizobacteria (PGPR)-based approaches. Furthermore, based on recent advances in the field of epigenetics, we propose novel approaches to create and exploit heritable genome-wide epigenetic variation in crop plants to enhance salinity tolerance. Specifically, we describe the concepts and the underlying principles of epigenetic recombinant inbred lines (epiRILs) and other epigenetic variants and methods to generate them. The proposed epigenetic approaches also have the potential to create additional genetic variation by modulating meiotic crossover frequency.
Collapse
Affiliation(s)
- Gargi Prasad Saradadevi
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
| | - Debajit Das
- Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat 785006, India; (D.D.); (C.C.)
| | - Satendra K. Mangrauthia
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Sridev Mohapatra
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
| | - Channakeshavaiah Chikkaputtaiah
- Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat 785006, India; (D.D.); (C.C.)
| | - Manish Roorkiwal
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India;
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Manish Solanki
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Raman Meenakshi Sundaram
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Neeraja N. Chirravuri
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Akshay S. Sakhare
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Suneetha Kota
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India;
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA 6150, Australia
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| | - Gireesha Mohannath
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| |
Collapse
|
16
|
Bhat JA, Yu D, Bohra A, Ganie SA, Varshney RK. Features and applications of haplotypes in crop breeding. Commun Biol 2021; 4:1266. [PMID: 34737387 PMCID: PMC8568931 DOI: 10.1038/s42003-021-02782-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 10/09/2021] [Indexed: 12/17/2022] Open
Abstract
Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.
Collapse
Affiliation(s)
- Javaid Akhter Bhat
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Deyue Yu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Abhishek Bohra
- Crop Improvement Division, ICAR- Indian Institute of Pulses Research (ICAR- IIPR), Kanpur, India
| | - Showkat Ahmad Ganie
- Department of Biotechnology, Visva-Bharati, Santiniketan, 731235, WB, India.
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India.
- State Agricultural Biotechnology Centre, Centre for Crop & Food Research Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, Australia.
| |
Collapse
|
17
|
Tomar V, Singh D, Dhillon GS, Chung YS, Poland J, Singh RP, Joshi AK, Gautam Y, Tiwari BS, Kumar U. Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2021; 12:720123. [PMID: 34691100 PMCID: PMC8531512 DOI: 10.3389/fpls.2021.720123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2-3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.
Collapse
Affiliation(s)
- Vipin Tomar
- Borlaug Institute for South Asia, Ludhiana, India
- Department of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
- International Maize and Wheat Improvement Center, New Delhi, India
| | - Daljit Singh
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Guriqbal Singh Dhillon
- Department of Biotechnology, Thapar Institute of Engineering & Technology, Patiala, India
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju-si, South Korea
| | - Jesse Poland
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Ravi Prakash Singh
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - Arun Kumar Joshi
- Borlaug Institute for South Asia, Ludhiana, India
- International Maize and Wheat Improvement Center, New Delhi, India
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | | | - Budhi Sagar Tiwari
- Department of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
| | - Uttam Kumar
- Borlaug Institute for South Asia, Ludhiana, India
- International Maize and Wheat Improvement Center, New Delhi, India
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| |
Collapse
|
18
|
Mahadevaiah C, Appunu C, Aitken K, Suresha GS, Vignesh P, Mahadeva Swamy HK, Valarmathi R, Hemaprabha G, Alagarasan G, Ram B. Genomic Selection in Sugarcane: Current Status and Future Prospects. FRONTIERS IN PLANT SCIENCE 2021; 12:708233. [PMID: 34646284 PMCID: PMC8502939 DOI: 10.3389/fpls.2021.708233] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 08/24/2021] [Indexed: 05/18/2023]
Abstract
Sugarcane is a C4 and agro-industry-based crop with a high potential for biomass production. It serves as raw material for the production of sugar, ethanol, and electricity. Modern sugarcane varieties are derived from the interspecific and intergeneric hybridization between Saccharum officinarum, Saccharum spontaneum, and other wild relatives. Sugarcane breeding programmes are broadly categorized into germplasm collection and characterization, pre-breeding and genetic base-broadening, and varietal development programmes. The varietal identification through the classic breeding programme requires a minimum of 12-14 years. The precise phenotyping in sugarcane is extremely tedious due to the high propensity of lodging and suckering owing to the influence of environmental factors and crop management practices. This kind of phenotyping requires data from both plant crop and ratoon experiments conducted over locations and seasons. In this review, we explored the feasibility of genomic selection schemes for various breeding programmes in sugarcane. The genetic diversity analysis using genome-wide markers helps in the formation of core set germplasm representing the total genomic diversity present in the Saccharum gene bank. The genome-wide association studies and genomic prediction in the Saccharum gene bank are helpful to identify the complete genomic resources for cane yield, commercial cane sugar, tolerances to biotic and abiotic stresses, and other agronomic traits. The implementation of genomic selection in pre-breeding, genetic base-broadening programmes assist in precise introgression of specific genes and recurrent selection schemes enhance the higher frequency of favorable alleles in the population with a considerable reduction in breeding cycles and population size. The integration of environmental covariates and genomic prediction in multi-environment trials assists in the prediction of varietal performance for different agro-climatic zones. This review also directed its focus on enhancing the genetic gain over time, cost, and resource allocation at various stages of breeding programmes.
Collapse
Affiliation(s)
| | - Chinnaswamy Appunu
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Karen Aitken
- CSIRO (Commonwealth Scientific and Industrial Research Organization), St. Lucia, QLD, Australia
| | | | - Palanisamy Vignesh
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | | | | | - Govind Hemaprabha
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Ganesh Alagarasan
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Bakshi Ram
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| |
Collapse
|
19
|
Clouse KM, Wagner MR. Plant Genetics as a Tool for Manipulating Crop Microbiomes: Opportunities and Challenges. Front Bioeng Biotechnol 2021; 9:567548. [PMID: 34136470 PMCID: PMC8201784 DOI: 10.3389/fbioe.2021.567548] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 05/05/2021] [Indexed: 11/22/2022] Open
Abstract
Growing human population size and the ongoing climate crisis create an urgent need for new tools for sustainable agriculture. Because microbiomes have profound effects on host health, interest in methods of manipulating agricultural microbiomes is growing rapidly. Currently, the most common method of microbiome manipulation is inoculation of beneficial organisms or engineered communities; however, these methods have been met with limited success due to the difficulty of establishment in complex farm environments. Here we propose genetic manipulation of the host plant as another avenue through which microbiomes could be manipulated. We discuss how domestication and modern breeding have shaped crop microbiomes, as well as the potential for improving plant-microbiome interactions through conventional breeding or genetic engineering. We summarize the current state of knowledge on host genetic control of plant microbiomes, as well as the key challenges that remain.
Collapse
Affiliation(s)
- Kayla M. Clouse
- Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, United States
| | - Maggie R. Wagner
- Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, United States
- Kansas Biological Survey, University of Kansas, Lawrence, KS, United States
| |
Collapse
|
20
|
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.
Collapse
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
| |
Collapse
|
21
|
Krishnappa G, Savadi S, Tyagi BS, Singh SK, Mamrutha HM, Kumar S, Mishra CN, Khan H, Gangadhara K, Uday G, Singh G, Singh GP. Integrated genomic selection for rapid improvement of crops. Genomics 2021; 113:1070-1086. [PMID: 33610797 DOI: 10.1016/j.ygeno.2021.02.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/08/2020] [Accepted: 02/15/2021] [Indexed: 11/15/2022]
Abstract
An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.
Collapse
Affiliation(s)
| | | | | | | | | | - Satish Kumar
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Hanif Khan
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | | | - Gyanendra Singh
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | |
Collapse
|
22
|
Doležalová A, Sládeková L, Šimoníková D, Holušová K, Karafiátová M, Varshney RK, Doležel J, Hřibová E. Karyotype Differentiation in Cultivated Chickpea Revealed by Oligopainting Fluorescence in situ Hybridization. FRONTIERS IN PLANT SCIENCE 2021; 12:791303. [PMID: 35145533 PMCID: PMC8822127 DOI: 10.3389/fpls.2021.791303] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 12/29/2021] [Indexed: 05/22/2023]
Abstract
Chickpea (Cicer arietinum L.) is one of the main sources of plant proteins in the Indian subcontinent and West Asia, where two different morphotypes, desi and kabuli, are grown. Despite the progress in genome mapping and sequencing, the knowledge of the chickpea genome at the chromosomal level, including the long-range molecular chromosome organization, is limited. Earlier cytogenetic studies in chickpea suffered from a limited number of cytogenetic landmarks and did not permit to identify individual chromosomes in the metaphase spreads or to anchor pseudomolecules to chromosomes in situ. In this study, we developed a system for fast molecular karyotyping for both morphotypes of cultivated chickpea. We demonstrate that even draft genome sequences are adequate to develop oligo-fluorescence in situ hybridization (FISH) barcodes for the identification of chromosomes and comparative analysis among closely related chickpea genotypes. Our results show the potential of oligo-FISH barcoding for the identification of structural changes in chromosomes, which accompanied genome diversification among chickpea cultivars. Moreover, oligo-FISH barcoding in chickpea pointed out some problematic, most probably wrongly assembled regions of the pseudomolecules of both kabuli and desi reference genomes. Thus, oligo-FISH appears as a powerful tool not only for comparative karyotyping but also for the validation of genome assemblies.
Collapse
Affiliation(s)
- Alžběta Doležalová
- Centre of the Region Hana for Biotechnological and Agricultural Research, Institute of Experimental Botany of the Czech Academy of Sciences, Olomouc, Czechia
| | - Lucia Sládeková
- Centre of the Region Hana for Biotechnological and Agricultural Research, Institute of Experimental Botany of the Czech Academy of Sciences, Olomouc, Czechia
- Department of Cell Biology and Genetics, Faculty of Science, Palacký University, Olomouc, Czechia
| | - Denisa Šimoníková
- Centre of the Region Hana for Biotechnological and Agricultural Research, Institute of Experimental Botany of the Czech Academy of Sciences, Olomouc, Czechia
| | - Kateřina Holušová
- Centre of the Region Hana for Biotechnological and Agricultural Research, Institute of Experimental Botany of the Czech Academy of Sciences, Olomouc, Czechia
| | - Miroslava Karafiátová
- Centre of the Region Hana for Biotechnological and Agricultural Research, Institute of Experimental Botany of the Czech Academy of Sciences, Olomouc, Czechia
| | - Rajeev K. Varshney
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Murdoch, WA, Australia
| | - Jaroslav Doležel
- Centre of the Region Hana for Biotechnological and Agricultural Research, Institute of Experimental Botany of the Czech Academy of Sciences, Olomouc, Czechia
| | - Eva Hřibová
- Centre of the Region Hana for Biotechnological and Agricultural Research, Institute of Experimental Botany of the Czech Academy of Sciences, Olomouc, Czechia
- *Correspondence: Eva Hřibová,
| |
Collapse
|
23
|
Badji A, Machida L, Kwemoi DB, Kumi F, Okii D, Mwila N, Agbahoungba S, Ibanda A, Bararyenya A, Nghituwamhata SN, Odong T, Wasswa P, Otim M, Ochwo-Ssemakula M, Talwana H, Asea G, Kyamanywa S, Rubaihayo P. Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils. PLANTS 2020; 10:plants10010029. [PMID: 33374402 PMCID: PMC7823878 DOI: 10.3390/plants10010029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/11/2020] [Accepted: 09/14/2020] [Indexed: 12/23/2022]
Abstract
Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.
Collapse
Affiliation(s)
- Arfang Badji
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Lewis Machida
- Alliance Bioversity-CIAT, Africa-Office, Kampala P.O. Box 24384, Uganda
| | | | - Frank Kumi
- Department of Crop Science, University of Cape Coast, Cape Coast PMB, Ghana
| | - Dennis Okii
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Natasha Mwila
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | | | - Angele Ibanda
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Astere Bararyenya
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | | | - Thomas Odong
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Peter Wasswa
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Michael Otim
- National Crops Resource Research Institute, Kampala P.O. Box 7084, Uganda
| | | | - Herbert Talwana
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Godfrey Asea
- National Crops Resource Research Institute, Kampala P.O. Box 7084, Uganda
| | - Samuel Kyamanywa
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| | - Patrick Rubaihayo
- Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
| |
Collapse
|
24
|
Bohra A, Chand Jha U, Godwin ID, Kumar Varshney R. Genomic interventions for sustainable agriculture. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:2388-2405. [PMID: 32875704 PMCID: PMC7680532 DOI: 10.1111/pbi.13472] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/21/2020] [Accepted: 08/16/2020] [Indexed: 05/05/2023]
Abstract
Agricultural production faces a Herculean challenge to feed the increasing global population. Food production systems need to deliver more with finite land and water resources while exerting the least negative influence on the ecosystem. The unpredictability of climate change and consequent changes in pests/pathogens dynamics aggravate the enormity of the challenge. Crop improvement has made significant contributions towards food security, and breeding climate-smart cultivars are considered the most sustainable way to accelerate food production. However, a fundamental change is needed in the conventional breeding framework in order to respond adequately to the growing food demands. Progress in genomics has provided new concepts and tools that hold promise to make plant breeding procedures more precise and efficient. For instance, reference genome assemblies in combination with germplasm sequencing delineate breeding targets that could contribute to securing future food supply. In this review, we highlight key breakthroughs in plant genome sequencing and explain how the presence of these genome resources in combination with gene editing techniques has revolutionized the procedures of trait discovery and manipulation. Adoption of new approaches such as speed breeding, genomic selection and haplotype-based breeding could overcome several limitations of conventional breeding. We advocate that strengthening varietal release and seed distribution systems will play a more determining role in delivering genetic gains at farmer's field. A holistic approach outlined here would be crucial to deliver steady stream of climate-smart crop cultivars for sustainable agriculture.
Collapse
Affiliation(s)
- Abhishek Bohra
- ICAR‐Indian Institute of Pulses Research (IIPR)KanpurIndia
| | - Uday Chand Jha
- ICAR‐Indian Institute of Pulses Research (IIPR)KanpurIndia
| | - Ian D. Godwin
- Centre for Crop ScienceQueensland Alliance for Agriculture and Food Innovation (QAAFI)The University of QueenslandBrisbaneQldAustralia
| | - Rajeev Kumar Varshney
- International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
- The UWA Institute of AgricultureThe University of Western AustraliaPerthAustralia
| |
Collapse
|
25
|
Pandey MK, Chaudhari S, Jarquin D, Janila P, Crossa J, Patil SC, Sundravadana S, Khare D, Bhat RS, Radhakrishnan T, Hickey JM, Varshney RK. Genome-based trait prediction in multi- environment breeding trials in groundnut. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:3101-3117. [PMID: 32809035 PMCID: PMC7547976 DOI: 10.1007/s00122-020-03658-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 08/03/2020] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE Comparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits. Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K 'Axiom_Arachis' SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400-0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.
Collapse
Affiliation(s)
- Manish K Pandey
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
| | - Sunil Chaudhari
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Diego Jarquin
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Pasupuleti Janila
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Sudam C Patil
- Mahatma Phule Krishi Vidyapeeth (MPKV), Jalgaon, India
| | | | - Dhirendra Khare
- Jawaharlal Nehru Krishi Vishwa Vidyalaya (JNKVV), Jabalpur, India
| | - Ramesh S Bhat
- University of Agricultural Sciences (UAS)-Dharwad, Dharwad, India
| | | | - John M Hickey
- The Roslin Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Rajeev K Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
| |
Collapse
|
26
|
Kumar J, Sen Gupta D. Prospects of next generation sequencing in lentil breeding. Mol Biol Rep 2020; 47:9043-9053. [PMID: 33037962 DOI: 10.1007/s11033-020-05891-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 10/03/2020] [Indexed: 11/28/2022]
Abstract
Lentil is an important food legume crop that has large and complex genome. During past years, considerable attention has been given on the use of next generation sequencing for enriching the genomic resources including identification of SSR and SNP markers, development of unigenes, transcripts, and identification of candidate genes for biotic and abiotic stresses, analysis of genetic diversity and identification of genes/ QTLs for agronomically important traits. However, in other crops including pulses, next generation sequencing has revolutionized the genomic research and helped in genomic assisted breeding rapidly and cost effectively. The present review discuss current status and future prospects of the use NGS based breeding in lentil.
Collapse
Affiliation(s)
- Jitendra Kumar
- Division of Crop Improvement, ICAR-Indian Institute of Pulses Research, Kalyanpur, Kanpur, 208024, India.
| | - Debjyoti Sen Gupta
- Division of Crop Improvement, ICAR-Indian Institute of Pulses Research, Kalyanpur, Kanpur, 208024, India
| |
Collapse
|
27
|
Mageto EK, Crossa J, Pérez-Rodríguez P, Dhliwayo T, Palacios-Rojas N, Lee M, Guo R, San Vicente F, Zhang X, Hindu V. Genomic Prediction with Genotype by Environment Interaction Analysis for Kernel Zinc Concentration in Tropical Maize Germplasm. G3 (BETHESDA, MD.) 2020; 10:2629-2639. [PMID: 32482728 PMCID: PMC7407456 DOI: 10.1534/g3.120.401172] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 05/28/2020] [Indexed: 01/25/2023]
Abstract
Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world's population. To study the potential of genomic selection (GS) for maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments. Three genomic prediction models, M (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) incorporating main effects (lines and genomic) and the interaction between genomic and environment (G x E) were assessed to estimate the prediction ability (rMP ) for each model. Two distinct cross-validation (CV) schemes simulating two genomic prediction breeding scenarios were used. CV1 predicts the performance of newly developed lines, whereas CV2 predicts the performance of lines tested in sparse multi-location trials. Predictions for Zn in CV1 ranged from -0.01 to 0.56 for DH1, 0.04 to 0.50 for DH2 and -0.001 to 0.47 for the association panel. For CV2, rMP values ranged from 0.67 to 0.71 for DH1, 0.40 to 0.56 for DH2 and 0.64 to 0.72 for the association panel. The genomic prediction model which included G x E had the highest average rMP for both CV1 (0.39 and 0.44) and CV2 (0.71 and 0.51) for the association panel and DH2 population, respectively. These results suggest that GS has potential to accelerate breeding for enhanced kernel Zn concentration by facilitating selection of superior genotypes.
Collapse
Affiliation(s)
- Edna K Mageto
- Department of Agronomy, Iowa State University, Ames, IA 50011
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco CP 56237, Mexico
| | - Paulino Pérez-Rodríguez
- Colegio de Postgraduados, Department of Statistics and Computer Sciences, Montecillos, Edo. De México 56230, México
| | - Thanda Dhliwayo
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco CP 56237, Mexico
| | - Natalia Palacios-Rojas
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco CP 56237, Mexico
| | - Michael Lee
- Department of Agronomy, Iowa State University, Ames, IA 50011,
| | - Rui Guo
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning 110866, China, and
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco CP 56237, Mexico
| | - Félix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco CP 56237, Mexico
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco CP 56237, Mexico
| | - Vemuri Hindu
- Asia Regional Maize Program, International Maize and Wheat Improvement Center (CIMMYT), ICRISAT Campus, Patancheru, Hyderabad, Telangana 502324, India
| |
Collapse
|
28
|
Keller B, Ariza-Suarez D, de la Hoz J, Aparicio JS, Portilla-Benavides AE, Buendia HF, Mayor VM, Studer B, Raatz B. Genomic Prediction of Agronomic Traits in Common Bean ( Phaseolus vulgaris L.) Under Environmental Stress. FRONTIERS IN PLANT SCIENCE 2020; 11:1001. [PMID: 32774338 PMCID: PMC7381332 DOI: 10.3389/fpls.2020.01001] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 06/18/2020] [Indexed: 05/19/2023]
Abstract
In plant and animal breeding, genomic prediction models are established to select new lines based on genomic data, without the need for laborious phenotyping. Prediction models can be trained on recent or historic phenotypic data and increasingly available genotypic data. This enables the adoption of genomic selection also in under-used legume crops such as common bean. Beans are an important staple food in the tropics and mainly grown by smallholders under limiting environmental conditions such as drought or low soil fertility. Therefore, genotype-by-environment interactions (G × E) are an important consideration when developing new bean varieties. However, G × E are often not considered in genomic prediction models nor are these models implemented in current bean breeding programs. Here we show the prediction abilities of four agronomic traits in common bean under various environmental stresses based on twelve field trials. The dataset includes 481 elite breeding lines characterized by 5,820 SNP markers. Prediction abilities over all twelve trials ranged between 0.6 and 0.8 for yield and days to maturity, respectively, predicting new lines into new seasons. In all four evaluated traits, the prediction abilities reached about 50-80% of the maximum accuracies given by phenotypic correlations and heritability. Predictions under drought and low phosphorus stress were up to 10 and 20% improved when G × E were included in the model, respectively. Our results demonstrate the potential of genomic selection to increase the genetic gain in common bean breeding. Prediction abilities improved when more phenotypic data was available and G × E could be accounted for. Furthermore, the developed models allowed us to predict genotypic performance under different environmental stresses. This will be a key factor in the development of common bean varieties adapted to future challenging conditions.
Collapse
Affiliation(s)
- Beat Keller
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Daniel Ariza-Suarez
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Juan de la Hoz
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Johan Steven Aparicio
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | | | - Hector Fabio Buendia
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Victor Manuel Mayor
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Bruno Studer
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Bodo Raatz
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| |
Collapse
|
29
|
Genomes of major fishes in world fisheries and aquaculture: Status, application and perspective. AQUACULTURE AND FISHERIES 2020. [DOI: 10.1016/j.aaf.2020.05.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
30
|
Palit P, Kudapa H, Zougmore R, Kholova J, Whitbread A, Sharma M, Varshney RK. An integrated research framework combining genomics, systems biology, physiology, modelling and breeding for legume improvement in response to elevated CO 2 under climate change scenario. CURRENT PLANT BIOLOGY 2020; 22:100149. [PMID: 32494569 PMCID: PMC7233140 DOI: 10.1016/j.cpb.2020.100149] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 04/03/2020] [Accepted: 04/06/2020] [Indexed: 05/24/2023]
Abstract
How unprecedented changes in climatic conditions will impact yield and productivity of some crops and their response to existing stresses, abiotic and biotic interactions is a key global concern. Climate change can also alter natural species' abundance and distribution or favor invasive species, which in turn can modify ecosystem dynamics and the provisioning of ecosystem services. Basic anatomical differences in C3 and C4 plants lead to their varied responses to climate variations. In plants having a C3 pathway of photosynthesis, increased atmospheric carbon dioxide (CO2) positively regulates photosynthetic carbon (C) assimilation and depresses photorespiration. Legumes being C3 plants, they may be in a favorable position to increase biomass and yield through various strategies. This paper comprehensively presents recent progress made in the physiological and molecular attributes in plants with special emphasis on legumes under elevated CO2 conditions in a climate change scenario. A strategic research framework for future action integrating genomics, systems biology, physiology and crop modelling approaches to cope with changing climate is also discussed. Advances in sequencing and phenotyping methodologies make it possible to use vast genetic and genomic resources by deploying high resolution phenotyping coupled with high throughput multi-omics approaches for trait improvement. Integrated crop modelling studies focusing on farming systems design and management, prediction of climate impacts and disease forecasting may also help in planning adaptation. Hence, an integrated research framework combining genomics, plant molecular physiology, crop breeding, systems biology and integrated crop-soil-climate modelling will be very effective to cope with climate change.
Collapse
Affiliation(s)
- Paramita Palit
- Research Program- Genetic Gains, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | - Himabindu Kudapa
- Research Program- Genetic Gains, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | - Robert Zougmore
- CGIAR Research Program on Climate Change, Agriculture and Food Security program (CCAFS), Bamako, Mali
- Research Program- West & Central Africa, ICRISAT, Bamako, Mali
| | - Jana Kholova
- Research Program- Innovation System for Drylands, ICRISAT, Patancheru, India
| | - Anthony Whitbread
- Research Program- Innovation System for Drylands, ICRISAT, Patancheru, India
| | - Mamta Sharma
- Research Program- Asia, ICRISAT, Patancheru, India
| | - Rajeev K Varshney
- Research Program- Genetic Gains, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| |
Collapse
|
31
|
de Oliveira AA, Resende MFR, Ferrão LFV, Amadeu RR, Guimarães LJM, Guimarães CT, Pastina MM, Margarido GRA. Genomic prediction applied to multiple traits and environments in second season maize hybrids. Heredity (Edinb) 2020; 125:60-72. [PMID: 32472060 DOI: 10.1038/s41437-020-0321-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 05/05/2020] [Accepted: 05/08/2020] [Indexed: 02/06/2023] Open
Abstract
Genomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these datasets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in 4 years of second season field trials for the traits grain yield, number of ears, and grain moisture. Genotypes of these hybrids were inferred in silico based on their parental inbred lines using single nucleotide polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the H matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to predict the performance of hybrids that had not been evaluated in any environment. However, the computational requirements of this kind of model could represent a limitation to its practical implementation and further investigation is necessary.
Collapse
Affiliation(s)
- Amanda Avelar de Oliveira
- Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of Sao Paulo, Piracicaba, SP, 13418-900, Brazil.,Horticultural Sciences Department, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, 32611, USA
| | - Marcio F R Resende
- Horticultural Sciences Department, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, 32611, USA
| | - Luís Felipe Ventorim Ferrão
- Horticultural Sciences Department, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, 32611, USA
| | - Rodrigo Rampazo Amadeu
- Horticultural Sciences Department, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, 32611, USA
| | | | | | | | | |
Collapse
|
32
|
Bohra A, Saxena KB, Varshney RK, Saxena RK. Genomics-assisted breeding for pigeonpea improvement. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:1721-1737. [PMID: 32062675 DOI: 10.1007/s00122-020-03563-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 02/08/2020] [Indexed: 05/25/2023]
Abstract
The review outlines advances in pigeonpea genomics, breeding and seed delivery systems to achieve yield gains at farmers' field. Pigeonpea is a nutritious and stress-tolerant grain legume crop of tropical and subtropical regions. Decades of breeding efforts in pigeonpea have resulted in development of a number of high-yielding cultivars. Of late, the development of CMS-based hybrid technology has allowed the exploitation of heterosis for yield enhancement in this crop. Despite these positive developments, the actual on-farm yield of pigeonpea is still well below its potential productivity. Growing needs for high and sustainable pigeonpea yields motivate scientists to improve the breeding efficiency to deliver a steady stream of cultivars that will provide yield benefits under both ideal and stressed environments. To achieve this objective in the shortest possible time, it is imperative that various crop breeding activities are integrated with appropriate new genomics technologies. In this context, the last decade has seen a remarkable rise in the generation of important genomic resources such as genome-wide markers, high-throughput genotyping assays, saturated genome maps, marker/gene-trait associations, whole-genome sequence and germplasm resequencing data. In some cases, marker/gene-trait associations are being employed in pigeonpea breeding programs to improve the valuable yield and market-preferred traits. Embracing new breeding tools like genomic selection and speed breeding is likely to improve genetic gains. Breeding high-yielding pigeonpea cultivars with key adaptation traits also calls for a renewed focus on systematic selection and utilization of targeted genetic resources. Of equal importance is to overcome the difficulties being faced by seed industry to take the new cultivars to the doorstep of farmers.
Collapse
Affiliation(s)
- Abhishek Bohra
- ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, 208024, India.
| | - K B Saxena
- , 17, NMC Housing, Al Ain, Abu Dhabi, United Arab Emirates
| | - Rajeev K Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India
| | - Rachit K Saxena
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India.
| |
Collapse
|
33
|
Roorkiwal M, Bharadwaj C, Barmukh R, Dixit GP, Thudi M, Gaur PM, Chaturvedi SK, Fikre A, Hamwieh A, Kumar S, Sachdeva S, Ojiewo CO, Tar'an B, Wordofa NG, Singh NP, Siddique KHM, Varshney RK. Integrating genomics for chickpea improvement: achievements and opportunities. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:1703-1720. [PMID: 32253478 PMCID: PMC7214385 DOI: 10.1007/s00122-020-03584-2] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 03/18/2020] [Indexed: 05/19/2023]
Abstract
Integration of genomic technologies with breeding efforts have been used in recent years for chickpea improvement. Modern breeding along with low cost genotyping platforms have potential to further accelerate chickpea improvement efforts. The implementation of novel breeding technologies is expected to contribute substantial improvements in crop productivity. While conventional breeding methods have led to development of more than 200 improved chickpea varieties in the past, still there is ample scope to increase productivity. It is predicted that integration of modern genomic resources with conventional breeding efforts will help in the delivery of climate-resilient chickpea varieties in comparatively less time. Recent advances in genomics tools and technologies have facilitated the generation of large-scale sequencing and genotyping data sets in chickpea. Combined analysis of high-resolution phenotypic and genetic data is paving the way for identifying genes and biological pathways associated with breeding-related traits. Genomics technologies have been used to develop diagnostic markers for use in marker-assisted backcrossing programmes, which have yielded several molecular breeding products in chickpea. We anticipate that a sequence-based holistic breeding approach, including the integration of functional omics, parental selection, forward breeding and genome-wide selection, will bring a paradigm shift in development of superior chickpea varieties. There is a need to integrate the knowledge generated by modern genomics technologies with molecular breeding efforts to bridge the genome-to-phenome gap. Here, we review recent advances that have led to new possibilities for developing and screening breeding populations, and provide strategies for enhancing the selection efficiency and accelerating the rate of genetic gain in chickpea.
Collapse
Affiliation(s)
- Manish Roorkiwal
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Australia.
| | | | - Rutwik Barmukh
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Department of Genetics, Osmania University, Hyderabad, India
| | - Girish P Dixit
- ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, India
| | - Mahendar Thudi
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Pooran M Gaur
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Asnake Fikre
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Addis Ababa, Ethiopia
| | - Aladdin Hamwieh
- International Center for Agriculture Research in the Dry Areas (ICARDA), Cairo, Egypt
| | - Shiv Kumar
- International Center for Agriculture Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Supriya Sachdeva
- ICAR-Indian Agricultural Research Institute (IARI), Delhi, India
| | - Chris O Ojiewo
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi, Kenya
| | - Bunyamin Tar'an
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, Canada
| | | | | | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Australia
| | - Rajeev K Varshney
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Australia.
| |
Collapse
|
34
|
Rani A, Devi P, Jha UC, Sharma KD, Siddique KHM, Nayyar H. Developing Climate-Resilient Chickpea Involving Physiological and Molecular Approaches With a Focus on Temperature and Drought Stresses. FRONTIERS IN PLANT SCIENCE 2020; 10:1759. [PMID: 32161601 PMCID: PMC7052492 DOI: 10.3389/fpls.2019.01759] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 12/16/2019] [Indexed: 05/19/2023]
Abstract
Chickpea is one of the most economically important food legumes, and a significant source of proteins. It is cultivated in more than 50 countries across Asia, Africa, Europe, Australia, North America, and South America. Chickpea production is limited by various abiotic stresses (cold, heat, drought, salt, etc.). Being a winter-season crop in northern south Asia and some parts of the Australia, chickpea faces low-temperature stress (0-15°C) during the reproductive stage that causes substantial loss of flowers, and thus pods, to inhibit its yield potential by 30-40%. The winter-sown chickpea in the Mediterranean, however, faces cold stress at vegetative stage. In late-sown environments, chickpea faces high-temperature stress during reproductive and pod filling stages, causing considerable yield losses. Both the low and the high temperatures reduce pollen viability, pollen germination on the stigma, and pollen tube growth resulting in poor pod set. Chickpea also experiences drought stress at various growth stages; terminal drought, along with heat stress at flowering and seed filling can reduce yields by 40-45%. In southern Australia and northern regions of south Asia, lack of chilling tolerance in cultivars delays flowering and pod set, and the crop is usually exposed to terminal drought. The incidences of temperature extremes (cold and heat) as well as inconsistent rainfall patterns are expected to increase in near future owing to climate change thereby necessitating the development of stress-tolerant and climate-resilient chickpea cultivars having region specific traits, which perform well under drought, heat, and/or low-temperature stress. Different approaches, such as genetic variability, genomic selection, molecular markers involving quantitative trait loci (QTLs), whole genome sequencing, and transcriptomics analysis have been exploited to improve chickpea production in extreme environments. Biotechnological tools have broadened our understanding of genetic basis as well as plants' responses to abiotic stresses in chickpea, and have opened opportunities to develop stress tolerant chickpea.
Collapse
Affiliation(s)
- Anju Rani
- Department of Botany, Panjab University, Chandigarh, India
| | - Poonam Devi
- Department of Botany, Panjab University, Chandigarh, India
| | - Uday Chand Jha
- Department of Crop Improvement Division, Indian Institute of Pulses Research, Kanpur, India
| | - Kamal Dev Sharma
- Department of Agricultural Biotechnology, Himachal Pradesh Agricultural University, Palampur, India
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia
| | - Harsh Nayyar
- Department of Botany, Panjab University, Chandigarh, India
| |
Collapse
|
35
|
Santantonio N, Atanda SA, Beyene Y, Varshney RK, Olsen M, Jones E, Roorkiwal M, Gowda M, Bharadwaj C, Gaur PM, Zhang X, Dreher K, Ayala-Hernández C, Crossa J, Pérez-Rodríguez P, Rathore A, Gao SY, McCouch S, Robbins KR. Strategies for Effective Use of Genomic Information in Crop Breeding Programs Serving Africa and South Asia. FRONTIERS IN PLANT SCIENCE 2020; 11:353. [PMID: 32292411 PMCID: PMC7119190 DOI: 10.3389/fpls.2020.00353] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 03/10/2020] [Indexed: 05/20/2023]
Abstract
Much of the world's population growth will occur in regions where food insecurity is prevalent, with large increases in food demand projected in regions of Africa and South Asia. While improving food security in these regions will require a multi-faceted approach, improved performance of crop varieties in these regions will play a critical role. Current rates of genetic gain in breeding programs serving Africa and South Asia fall below rates achieved in other regions of the world. Given resource constraints, increased genetic gain in these regions cannot be achieved by simply expanding the size of breeding programs. New approaches to breeding are required. The Genomic Open-source Breeding informatics initiative (GOBii) and Excellence in Breeding Platform (EiB) are working with public sector breeding programs to build capacity, develop breeding strategies, and build breeding informatics capabilities to enable routine use of new technologies that can improve the efficiency of breeding programs and increase genetic gains. Simulations evaluating breeding strategies indicate cost-effective implementations of genomic selection (GS) are feasible using relatively small training sets, and proof-of-concept implementations have been validated in the International Maize and Wheat Improvement Center (CIMMYT) maize breeding program. Progress on GOBii, EiB, and implementation of GS in CIMMYT and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) breeding programs are discussed, as well as strategies for routine implementation of GS in breeding programs serving Africa and South Asia.
Collapse
Affiliation(s)
- Nicholas Santantonio
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | - Sikiru Adeniyi Atanda
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Elizabeth Jones
- Genomic Open-Source Breeding Informatics Initiative (GOBii) Project, Institute of Biotechnology, Cornell University, Ithaca, NY, United States
| | - Manish Roorkiwal
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Chellapilla Bharadwaj
- Division of Genetics, Indian Agriculture Research Institute (ICAR), New Delhi, India
| | - Pooran M. Gaur
- Research Program - Asia, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kate Dreher
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Abhishek Rathore
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Star Yanxin Gao
- Genomic Open-Source Breeding Informatics Initiative (GOBii) Project, Institute of Biotechnology, Cornell University, Ithaca, NY, United States
| | - Susan McCouch
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | - Kelly R. Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
- *Correspondence: Kelly R. Robbins,
| |
Collapse
|
36
|
Millet EJ, Kruijer W, Coupel-Ledru A, Alvarez Prado S, Cabrera-Bosquet L, Lacube S, Charcosset A, Welcker C, van Eeuwijk F, Tardieu F. Genomic prediction of maize yield across European environmental conditions. Nat Genet 2019; 51:952-956. [PMID: 31110353 DOI: 10.1038/s41588-019-0414-y] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 04/08/2019] [Indexed: 11/10/2022]
Abstract
The development of germplasm adapted to changing climate is required to ensure food security1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios3-7 (genotype × environment interaction), in spite of promising results for flowering time8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields9,10. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype-specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.
Collapse
Affiliation(s)
- Emilie J Millet
- Biometris, WUR, Wageningen, the Netherlands.,LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France.,Biometris, WUR, Wageningen, the Netherlands
| | | | - Aude Coupel-Ledru
- LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France.,University of Bristol, School of Biological Sciences, Bristol, UK
| | - Santiago Alvarez Prado
- LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France.,IFEVA and CONICET, Buenos Aires, Argentina
| | | | - Sébastien Lacube
- LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France
| | - Alain Charcosset
- GQE-Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Claude Welcker
- LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France
| | | | - François Tardieu
- LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France.
| |
Collapse
|
37
|
Jain A, Roorkiwal M, Kale S, Garg V, Yadala R, Varshney RK. InDel markers: An extended marker resource for molecular breeding in chickpea. PLoS One 2019; 14:e0213999. [PMID: 30883592 PMCID: PMC6422259 DOI: 10.1371/journal.pone.0213999] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 03/05/2019] [Indexed: 12/03/2022] Open
Abstract
Chickpea is one of the most important food legumes that holds the key to meet rising global food and nutritional demand. In order to deploy molecular breeding approaches in crop improvement programs, user friendly and cost effective marker resources remain prerequisite. The advent of next generation sequencing (NGS) technology has resulted in the generation of several thousands of markers as part of several large scale genome sequencing and re-sequencing initiatives. Very recently, PCR based Insertion-deletions (InDels) are becoming a popular gel based genotyping solution because of their co-dominant, inexpensive, and highly polymorphic nature. With an objective to expand marker resources for genomics assisted breeding (GAB) in chickpea, whole genome re-sequencing data generated on five parental lines of one interspecific (ICC 4958 × PI 489777) and two intra-specific (ICC 283 × ICC 8261 and ICC 4958 × ICC 1882) mapping populations, were used for identification of InDels. A total of 231,658 InDels were identified using Dindel software with default parameters. Further, a total of 8,307 InDels with ≥20 bp size were selected for development of gel based markers, of which primers could be designed for 7,523 (90.56%) markers. On average, markers appeared at a frequency of 1,038 InDels/LG with a maximum number of markers on CaLG04 (1,952 InDels) and minimum on CaLG08 (360 InDels). In order to validate these InDels, a total of 423 primer pairs were randomly selected and tested on the selected parental lines. A high amplification rate of 80% was observed ranging from 46.06 to 58.01% polymorphism rate across parents on 3% agarose gel. This study clearly reflects the usefulness of available sequence data for the development of genome-wide InDels in chickpea that can further contribute and accelerate a wide range of genetic and molecular breeding activities in chickpea.
Collapse
Affiliation(s)
- Ankit Jain
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Manish Roorkiwal
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- * E-mail: (RKV); (MR)
| | - Sandip Kale
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung (IPK), Gatersleben, Germany
| | - Vanika Garg
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Ramakrishna Yadala
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rajeev K. Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- * E-mail: (RKV); (MR)
| |
Collapse
|
38
|
Varshney RK, Pandey MK, Bohra A, Singh VK, Thudi M, Saxena RK. Toward the sequence-based breeding in legumes in the post-genome sequencing era. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:797-816. [PMID: 30560464 PMCID: PMC6439141 DOI: 10.1007/s00122-018-3252-x] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 11/27/2018] [Indexed: 05/19/2023]
Abstract
Efficiency of breeding programs of legume crops such as chickpea, pigeonpea and groundnut has been considerably improved over the past decade through deployment of modern genomic tools and technologies. For instance, next-generation sequencing technologies have facilitated availability of genome sequence assemblies, re-sequencing of several hundred lines, development of HapMaps, high-density genetic maps, a range of marker genotyping platforms and identification of markers associated with a number of agronomic traits in these legume crops. Although marker-assisted backcrossing and marker-assisted selection approaches have been used to develop superior lines in several cases, it is the need of the hour for continuous population improvement after every breeding cycle to accelerate genetic gain in the breeding programs. In this context, we propose a sequence-based breeding approach which includes use of independent or combination of parental selection, enhancing genetic diversity of breeding programs, forward breeding for early generation selection, and genomic selection using sequencing/genotyping technologies. Also, adoption of speed breeding technology by generating 4-6 generations per year will be contributing to accelerate genetic gain. While we see a huge potential of the sequence-based breeding to revolutionize crop improvement programs in these legumes, we anticipate several challenges especially associated with high-quality and precise phenotyping at affordable costs, data analysis and management related to improving breeding operation efficiency. Finally, integration of improved seed systems and better agronomic packages with the development of improved varieties by using sequence-based breeding will ensure higher genetic gains in farmers' fields.
Collapse
Affiliation(s)
- Rajeev K Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India.
| | - Manish K Pandey
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India
| | - Abhishek Bohra
- ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, 208024, India
| | - Vikas K Singh
- International Rice Research Institute (IRRI), IRRI South Asia Hub, ICRISAT, Hyderabad, 502324, India
| | - Mahendar Thudi
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India
| | - Rachit K Saxena
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India
| |
Collapse
|
39
|
Cobb JN, Juma RU, Biswas PS, Arbelaez JD, Rutkoski J, Atlin G, Hagen T, Quinn M, Ng EH. Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder's equation. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:627-645. [PMID: 30824972 PMCID: PMC6439161 DOI: 10.1007/s00122-019-03317-0] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Accepted: 02/21/2019] [Indexed: 05/20/2023]
Abstract
The integration of new technologies into public plant breeding programs can make a powerful step change in agricultural productivity when aligned with principles of quantitative and Mendelian genetics. The breeder's equation is the foundational application of quantitative genetics to crop improvement. Guided by the variables that describe response to selection, emerging breeding technologies can make a powerful step change in the effectiveness of public breeding programs. The most promising innovations for increasing the rate of genetic gain without greatly increasing program size appear to be related to reducing breeding cycle time, which is likely to require the implementation of parent selection on non-inbred progeny, rapid generation advance, and genomic selection. These are complex processes and will require breeding organizations to adopt a culture of continuous optimization and improvement. To enable this, research managers will need to consider and proactively manage the, accountability, strategy, and resource allocations of breeding teams. This must be combined with thoughtful management of elite genetic variation and a clear separation between the parental selection process and product development and advancement process. With an abundance of new technologies available, breeding teams need to evaluate carefully the impact of any new technology on selection intensity, selection accuracy, and breeding cycle length relative to its cost of deployment. Finally breeding data management systems need to be well designed to support selection decisions and novel approaches to accelerate breeding cycles need to be routinely evaluated and deployed.
Collapse
Affiliation(s)
- Joshua N Cobb
- International Rice Research Institute, Los Banos, Laguna, Philippines.
| | - Roselyne U Juma
- International Rice Research Institute, Los Banos, Laguna, Philippines
- Kenya Agricultural and Livestock Research Organization, Nairobi, Kenya
| | - Partha S Biswas
- International Rice Research Institute, Los Banos, Laguna, Philippines
- Bangladesh Rice Research Institute, Gazipur, Bangladesh
| | - Juan D Arbelaez
- International Rice Research Institute, Los Banos, Laguna, Philippines
| | - Jessica Rutkoski
- International Rice Research Institute, Los Banos, Laguna, Philippines
| | - Gary Atlin
- Bill and Melinda Gates Foundation, Seattle, WA, USA
| | - Tom Hagen
- CGIAR Excellence in Breeding Platform (EiB), El Batan, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Michael Quinn
- CGIAR Excellence in Breeding Platform (EiB), El Batan, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Eng Hwa Ng
- CGIAR Excellence in Breeding Platform (EiB), El Batan, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| |
Collapse
|