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Saminadane T, Geddam S, Krishnaswamy P, Jothiganapathy K, Tamilselvan A, Ramadoss BR, Sri Hari Reddy P, Singh US, Singh RK, Platten JD, Gregorio GB, Singh NK, Bisht DS, Kota S, Ponnuvel S, Guntupalli P. Development of early maturing salt-tolerant rice variety KKL(R) 3 using a combination of conventional and molecular breeding approaches. Front Genet 2024; 14:1332691. [PMID: 38371308 PMCID: PMC10869446 DOI: 10.3389/fgene.2023.1332691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 12/27/2023] [Indexed: 02/20/2024] Open
Abstract
Introduction: Soil salinity poses a severe threat to rice production, resulting in stunted growth, leaf damage, and substantial yield losses. This study focuses on developing an early maturing seedling stage salinity tolerant rice variety by integrating conventional breeding methods with marker assisted breeding (MAB) approaches. Methods: Seedling-stage salinity tolerance Quantitative Trait Locus (QTL) "Saltol" from the salt-tolerant parent FL478 was introduced into the high-yielding but salt-sensitive rice variety ADT 45. This was achieved through a combination of conventional breeding and MAB. The breeding process involved rigorous selection, screening, and physiological parameter assessments. Results: KKL(R) 3 (KR 15066) identified as the top performing Recombinant Inbred Line (RIL), consistently demonstrating maximum mean grain yields under both salinity (3435.6 kg/ha) and normal (6421.8 kg/ha) conditions. In comparison to the early maturing, salt-tolerant national check variety CSR 10, KKL(R) 3 exhibited a substantial yield increase over 50%. Discussion: The notable improvement observed in KKL(R) 3 positions it as a promising variety for release, offering a reliable solution to maximize yields, ensure food security, and promote agricultural sustainability in both saline and non-saline environments. The study highlights the effectiveness of MAB in developing salt-tolerant rice varieties and emphasizes the significance of the Saltol QTL in enhancing seedling stage salinity tolerance. The potential release of KKL(R) 3 has the capacity to revolutionize rice production in salt affected regions, providing farmers with a reliable solution to maximize yields and contribute to food security while ensuring agricultural sustainability.
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Affiliation(s)
- Thirumeni Saminadane
- Department of Plant Breeding and Genetics, Pandit Jawaharlal Nehru College of Agriculture and Research Institute, Karaikal, Puducherry, India
| | - Sathyadevi Geddam
- Department of Plant Breeding and Genetics, Pandit Jawaharlal Nehru College of Agriculture and Research Institute, Karaikal, Puducherry, India
| | - Paramasivam Krishnaswamy
- Department of Plant Breeding and Genetics, Pandit Jawaharlal Nehru College of Agriculture and Research Institute, Karaikal, Puducherry, India
| | - Karthick Jothiganapathy
- Department of Plant Breeding and Genetics, Pandit Jawaharlal Nehru College of Agriculture and Research Institute, Karaikal, Puducherry, India
| | - Anandhan Tamilselvan
- Department of Plant Breeding and Genetics, Pandit Jawaharlal Nehru College of Agriculture and Research Institute, Karaikal, Puducherry, India
- Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore, Tamilnadu, India
| | - Bharathi Raja Ramadoss
- Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore, Tamilnadu, India
| | - Patil Sri Hari Reddy
- Department of Plant Breeding and Genetics, Pandit Jawaharlal Nehru College of Agriculture and Research Institute, Karaikal, Puducherry, India
| | - Uma Shankar Singh
- International Rice Research Institute, Manila, Metro Manila, Philippines
| | - Rakesh Kumar Singh
- International Rice Research Institute, Manila, Metro Manila, Philippines
| | | | - Glenn B. Gregorio
- College of Agriculture and Food Science, University of the Philippines Los Banos (UPLB), Los Baños, Laguna, Philippines
| | - Nagendra Kumar Singh
- Genomics Laboratory, Indian Council of Agricultural Research (ICAR) - National Institute for Plant Biotechnology, New Delhi, India
| | - Deepak Singh Bisht
- Genomics Laboratory, Indian Council of Agricultural Research (ICAR) - National Institute for Plant Biotechnology, New Delhi, India
| | - Suneetha Kota
- ICAR - Indian Institute of Rice Research, Hyderabad, Telangana, India
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Zhang Y, Zhang M, Ye J, Xu Q, Feng Y, Xu S, Hu D, Wei X, Hu P, Yang Y. Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice ( Oryza sativa L.). MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:81. [PMID: 37965378 PMCID: PMC10641074 DOI: 10.1007/s11032-023-01423-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023]
Abstract
Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01423-y.
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Affiliation(s)
- Yuanyuan Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Mengchen Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Junhua Ye
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Qun Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yue Feng
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Siliang Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Dongxiu Hu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Xinghua Wei
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Peisong Hu
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yaolong Yang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
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Hafeez A, Ali B, Javed MA, Saleem A, Fatima M, Fathi A, Afridi MS, Aydin V, Oral MA, Soudy FA. Plant breeding for harmony between sustainable agriculture, the environment, and global food security: an era of genomics-assisted breeding. PLANTA 2023; 258:97. [PMID: 37823963 DOI: 10.1007/s00425-023-04252-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/22/2023] [Indexed: 10/13/2023]
Abstract
MAIN CONCLUSION Genomics-assisted breeding represents a crucial frontier in enhancing the balance between sustainable agriculture, environmental preservation, and global food security. Its precision and efficiency hold the promise of developing resilient crops, reducing resource utilization, and safeguarding biodiversity, ultimately fostering a more sustainable and secure food production system. Agriculture has been seriously threatened over the last 40 years by climate changes that menace global nutrition and food security. Changes in environmental factors like drought, salt concentration, heavy rainfalls, and extremely low or high temperatures can have a detrimental effects on plant development, growth, and yield. Extreme poverty and increasing food demand necessitate the need to break the existing production barriers in several crops. The first decade of twenty-first century marks the rapid development in the discovery of new plant breeding technologies. In contrast, in the second decade, the focus turned to extracting information from massive genomic frameworks, speculating gene-to-phenotype associations, and producing resilient crops. In this review, we will encompass the causes, effects of abiotic stresses and how they can be addressed using plant breeding technologies. Both conventional and modern breeding technologies will be highlighted. Moreover, the challenges like the commercialization of biotechnological products faced by proponents and developers will also be accentuated. The crux of this review is to mention the available breeding technologies that can deliver crops with high nutrition and climate resilience for sustainable agriculture.
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Affiliation(s)
- Aqsa Hafeez
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Baber Ali
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
| | - Muhammad Ammar Javed
- Institute of Industrial Biotechnology, Government College University, Lahore, 54000, Pakistan
| | - Aroona Saleem
- Institute of Industrial Biotechnology, Government College University, Lahore, 54000, Pakistan
| | - Mahreen Fatima
- Faculty of Biosciences, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, 63100, Pakistan
| | - Amin Fathi
- Department of Agronomy, Ayatollah Amoli Branch, Islamic Azad University, Amol, 46151, Iran
| | - Muhammad Siddique Afridi
- Department of Plant Pathology, Federal University of Lavras (UFLA), Lavras, MG, 37200-900, Brazil
| | - Veysel Aydin
- Sason Vocational School, Department of Plant and Animal Production, Batman University, Batman, 72060, Turkey
| | - Mükerrem Atalay Oral
- Elmalı Vocational School of Higher Education, Akdeniz University, Antalya, 07058, Turkey
| | - Fathia A Soudy
- Genetics and Genetic Engineering Department, Faculty of Agriculture, Benha University, Moshtohor, 13736, Egypt
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de Verdal H, Baertschi C, Frouin J, Quintero C, Ospina Y, Alvarez MF, Cao TV, Bartholomé J, Grenier C. Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population. RICE (NEW YORK, N.Y.) 2023; 16:43. [PMID: 37758969 PMCID: PMC10533757 DOI: 10.1186/s12284-023-00661-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023]
Abstract
Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program.
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Affiliation(s)
- Hugues de Verdal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France.
| | - Cédric Baertschi
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France
| | - Julien Frouin
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France
| | - Constanza Quintero
- Alliance Bioversity-CIAT, A.A.6713, Km 17 Recta Palmira Cali, Cali, Colombia
| | - Yolima Ospina
- Alliance Bioversity-CIAT, A.A.6713, Km 17 Recta Palmira Cali, Cali, Colombia
| | | | - Tuong-Vi Cao
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France
| | - Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France
- Alliance Bioversity-CIAT, A.A.6713, Km 17 Recta Palmira Cali, Cali, Colombia
| | - Cécile Grenier
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France.
- Alliance Bioversity-CIAT, A.A.6713, Km 17 Recta Palmira Cali, Cali, Colombia.
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Bartholomé J, Frouin J, Brottier L, Cao TV, Boisnard A, Ahmadi N, Courtois B. Genomic selection for salinity tolerance in japonica rice. PLoS One 2023; 18:e0291833. [PMID: 37756295 PMCID: PMC10530037 DOI: 10.1371/journal.pone.0291833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
Improving plant performance in salinity-prone conditions is a significant challenge in breeding programs. Genomic selection is currently integrated into many plant breeding programs as a tool for increasing selection intensity and precision for complex traits and for reducing breeding cycle length. A rice reference panel (RP) of 241 Oryza sativa L. japonica accessions genotyped with 20,255 SNPs grown in control and mild salinity stress conditions was evaluated at the vegetative stage for eight morphological traits and ion mass fractions (Na and K). Weak to strong genotype-by-condition interactions were found for the traits considered. Cross-validation showed that the predictive ability of genomic prediction methods ranged from 0.25 to 0.64 for multi-environment models with morphological traits and from 0.05 to 0.40 for indices of stress response and ion mass fractions. The performances of a breeding population (BP) comprising 393 japonica accessions were predicted with models trained on the RP. For validation of the predictive performances of the models, a subset of 41 accessions was selected from the BP and phenotyped under the same experimental conditions as the RP. The predictive abilities estimated on this subset ranged from 0.00 to 0.66 for the multi-environment models, depending on the traits, and were strongly correlated with the predictive abilities on cross-validation in the RP in salt condition (r = 0.69). We show here that genomic selection is efficient for predicting the salt stress tolerance of breeding lines. Genomic selection could improve the efficiency of rice breeding strategies for salinity-prone environments.
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Affiliation(s)
- Jérôme Bartholomé
- UMR AGAP Institut, CIRAD, Cali, Colombia
- UMR AGAP Institut, Institut Agro, Univ Montpellier, CIRAD, INRAE, Montpellier, France
- Alliance Bioversity-CIAT, Recta Palmira Cali, Colombia
| | - Julien Frouin
- UMR AGAP Institut, Institut Agro, Univ Montpellier, CIRAD, INRAE, Montpellier, France
- CIRAD, UMR AGAP Institut, Montpellier, France
| | - Laurent Brottier
- UMR AGAP Institut, Institut Agro, Univ Montpellier, CIRAD, INRAE, Montpellier, France
- CIRAD, UMR AGAP Institut, Montpellier, France
| | - Tuong-Vi Cao
- UMR AGAP Institut, Institut Agro, Univ Montpellier, CIRAD, INRAE, Montpellier, France
- CIRAD, UMR AGAP Institut, Montpellier, France
| | | | - Nourollah Ahmadi
- UMR AGAP Institut, Institut Agro, Univ Montpellier, CIRAD, INRAE, Montpellier, France
- CIRAD, UMR AGAP Institut, Montpellier, France
| | - Brigitte Courtois
- UMR AGAP Institut, Institut Agro, Univ Montpellier, CIRAD, INRAE, Montpellier, France
- CIRAD, UMR AGAP Institut, Montpellier, France
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Sinha D, Maurya AK, Abdi G, Majeed M, Agarwal R, Mukherjee R, Ganguly S, Aziz R, Bhatia M, Majgaonkar A, Seal S, Das M, Banerjee S, Chowdhury S, Adeyemi SB, Chen JT. Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals. Genes (Basel) 2023; 14:1484. [PMID: 37510388 PMCID: PMC10380062 DOI: 10.3390/genes14071484] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.
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Affiliation(s)
- Dwaipayan Sinha
- Department of Botany, Government General Degree College, Mohanpur 721436, India
| | - Arun Kumar Maurya
- Department of Botany, Multanimal Modi College, Modinagar, Ghaziabad 201204, India
| | - Gholamreza Abdi
- Department of Biotechnology, Persian Gulf Research Institute, Persian Gulf University, Bushehr 75169, Iran
| | - Muhammad Majeed
- Department of Botany, University of Gujrat, Punjab 50700, Pakistan
| | - Rachna Agarwal
- Applied Genomics Section, Bhabha Atomic Research Centre, Mumbai 400085, India
| | - Rashmi Mukherjee
- Research Center for Natural and Applied Sciences, Department of Botany (UG & PG), Raja Narendralal Khan Women's College, Gope Palace, Midnapur 721102, India
| | - Sharmistha Ganguly
- Department of Dravyaguna, Institute of Post Graduate Ayurvedic Education and Research, Kolkata 700009, India
| | - Robina Aziz
- Department of Botany, Government, College Women University, Sialkot 51310, Pakistan
| | - Manika Bhatia
- TERI School of Advanced Studies, New Delhi 110070, India
| | - Aqsa Majgaonkar
- Department of Botany, St. Xavier's College (Autonomous), Mumbai 400001, India
| | - Sanchita Seal
- Department of Botany, Polba Mahavidyalaya, Polba 712148, India
| | - Moumita Das
- V. Sivaram Research Foundation, Bangalore 560040, India
| | - Swastika Banerjee
- Department of Botany, Kairali College of +3 Science, Champua, Keonjhar 758041, India
| | - Shahana Chowdhury
- Department of Biotechnology, Faculty of Engineering Sciences, German University Bangladesh, TNT Road, Telipara, Chandona Chowrasta, Gazipur 1702, Bangladesh
| | - Sherif Babatunde Adeyemi
- Ethnobotany/Phytomedicine Laboratory, Department of Plant Biology, Faculty of Life Sciences, University of Ilorin, Ilorin P.M.B 1515, Nigeria
| | - Jen-Tsung Chen
- Department of Life Sciences, National University of Kaohsiung, Kaohsiung 811, Taiwan
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Nguyen VH, Morantte RIZ, Lopena V, Verdeprado H, Murori R, Ndayiragije A, Katiyar SK, Islam MR, Juma RU, Flandez-Galvez H, Glaszmann JC, Cobb JN, Bartholomé J. Multi-environment Genomic Selection in Rice Elite Breeding Lines. RICE (NEW YORK, N.Y.) 2023; 16:7. [PMID: 36752880 PMCID: PMC9908796 DOI: 10.1186/s12284-023-00623-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. RESULTS The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia's and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25-0.88 for plant height, and - 0.29-0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset. CONCLUSION Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability.
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Affiliation(s)
- Van Hieu Nguyen
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
- Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Rose Imee Zhella Morantte
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Vitaliano Lopena
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Holden Verdeprado
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Rosemary Murori
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Alexis Ndayiragije
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Sanjay Kumar Katiyar
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Md Rafiqul Islam
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Roselyne Uside Juma
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Hayde Flandez-Galvez
- Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Jean-Christophe Glaszmann
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Joshua N Cobb
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
- RiceTec. Inc, PO Box 1305, Alvin, TX, 77512, USA
| | - Jérôme Bartholomé
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
- CIRAD, UMR AGAP Institut, Cali, Colombia.
- Alliance Bioversity-CIAT, Cali, Colombia.
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Reyes VP, Kitony JK, Nishiuchi S, Makihara D, Doi K. Utilization of Genotyping-by-Sequencing (GBS) for Rice Pre-Breeding and Improvement: A Review. Life (Basel) 2022; 12:1752. [PMID: 36362909 PMCID: PMC9694628 DOI: 10.3390/life12111752] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 09/29/2023] Open
Abstract
Molecular markers play a crucial role in the improvement of rice. To benefit from these markers, genotyping is carried out to identify the differences at a specific position in the genome of individuals. The advances in sequencing technologies have led to the development of different genotyping techniques such as genotyping-by-sequencing. Unlike PCR-fragment-based genotyping, genotyping-by-sequencing has enabled the parallel sequencing and genotyping of hundreds of samples in a single run, making it more cost-effective. Currently, GBS is being used in several pre-breeding programs of rice to identify beneficial genes and QTL from different rice genetic resources. In this review, we present the current advances in the utilization of genotyping-by-sequencing for the development of rice pre-breeding materials and the improvement of existing rice cultivars. The challenges and perspectives of using this approach are also highlighted.
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Affiliation(s)
- Vincent Pamugas Reyes
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan
| | | | - Shunsaku Nishiuchi
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan
| | - Daigo Makihara
- International Center for Research and Education in Agriculture, Nagoya University, Nagoya 464-8601, Japan
| | - Kazuyuki Doi
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan
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9
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Hardner CM, Fikere M, Gasic K, da Silva Linge C, Worthington M, Byrne D, Rawandoozi Z, Peace C. Multi-environment genomic prediction for soluble solids content in peach ( Prunus persica). FRONTIERS IN PLANT SCIENCE 2022; 13:960449. [PMID: 36275520 PMCID: PMC9583944 DOI: 10.3389/fpls.2022.960449] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 08/01/2022] [Indexed: 06/16/2023]
Abstract
Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual's narrow-sense and broad-sense heritability for SSC were high (0.57-0.73 and 0.66-0.80, respectively), with 19-32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.
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Affiliation(s)
- Craig M. Hardner
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Mulusew Fikere
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Ksenija Gasic
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Cassia da Silva Linge
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Margaret Worthington
- Faculty Horticulture, University of Arkansas System Division of Agriculture, Fayetteville, AR, United States
| | - David Byrne
- College of Agriculture and Life Sciences, Texas A&M University, College Station, TX, United States
| | - Zena Rawandoozi
- College of Agriculture and Life Sciences, Texas A&M University, College Station, TX, United States
| | - Cameron Peace
- Department of Horticulture, Washington State University, Pullman, WA, United States
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10
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Anilkumar C, Sunitha NC, Devate NB, Ramesh S. Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review. PLANTA 2022; 256:87. [PMID: 36149531 DOI: 10.1007/s00425-022-03996-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.
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Affiliation(s)
- C Anilkumar
- ICAR-National Rice Research Institute, Cuttack, India
| | - N C Sunitha
- University of Agricultural Sciences, Bangalore, India
| | | | - S Ramesh
- University of Agricultural Sciences, Bangalore, India.
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11
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Muvunyi BP, Zou W, Zhan J, He S, Ye G. Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice. Front Genet 2022; 13:883853. [PMID: 35812754 PMCID: PMC9257107 DOI: 10.3389/fgene.2022.883853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/05/2022] [Indexed: 11/13/2022] Open
Abstract
Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p < 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain.
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Affiliation(s)
- Blaise Pascal Muvunyi
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenli Zou
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Junhui Zhan
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Sang He
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- *Correspondence: Sang He, ; Guoyou Ye,
| | - Guoyou Ye
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Rice Breeding Innovations Platform, International Rice Research Institute, Los Baños, Philippines
- *Correspondence: Sang He, ; Guoyou Ye,
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12
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da Silva Júnior AC, Sant’Anna IDC, Silva Siqueira MJ, Cruz CD, Azevedo CF, Nascimento M, Soares PC. Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice. PLoS One 2022; 17:e0259607. [PMID: 35503772 PMCID: PMC9064078 DOI: 10.1371/journal.pone.0259607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 04/15/2022] [Indexed: 11/19/2022] Open
Abstract
The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039-0.80, and 0.02-0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.
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Affiliation(s)
| | | | | | - Cosme Damião Cruz
- Departmento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brasil
| | | | - Moyses Nascimento
- Departmento de Estatística, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brasil
| | - Plínio César Soares
- Empresa de Pesquisa Agropecuária de Minas Gerais–EPAMIG, Viçosa, Minas Gerais, Brazil
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13
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Darwell CT, Wanchana S, Ruanjaichon V, Siangliw M, Thunnom B, Aesomnuk W, Toojinda T. riceExplorer: Uncovering the Hidden Potential of a National Genomic Resource Against a Global Database. FRONTIERS IN PLANT SCIENCE 2022; 13:781153. [PMID: 35574109 PMCID: PMC9100803 DOI: 10.3389/fpls.2022.781153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 04/06/2022] [Indexed: 06/15/2023]
Abstract
Agricultural crop breeding programs, particularly at the national level, typically consist of a core panel of elite breeding cultivars alongside a number of local landrace varieties (or other endemic cultivars) that provide additional sources of phenotypic and genomic variation or contribute as experimental materials (e.g., in GWAS studies). Three issues commonly arise. First, focusing primarily on core development accessions may mean that the potential contributions of landraces or other secondary accessions may be overlooked. Second, elite cultivars may accumulate deleterious alleles away from nontarget loci due to the strong effects of artificial selection. Finally, a tendency to focus solely on SNP-based methods may cause incomplete or erroneous identification of functional variants. In practice, integration of local breeding programs with findings from global database projects may be challenging. First, local GWAS experiments may only indicate useful functional variants according to the diversity of the experimental panel, while other potentially useful loci-identifiable at a global level-may remain undiscovered. Second, large-scale experiments such as GWAS may prove prohibitively costly or logistically challenging for some agencies. Here, we present a fully automated bioinformatics pipeline (riceExplorer) that can easily integrate local breeding program sequence data with international database resources, without relying on any phenotypic experimental procedure. It identifies associated functional haplotypes that may prove more robust in determining the genotypic determinants of desirable crop phenotypes. In brief, riceExplorer evaluates a global crop database (IRRI 3000 Rice Genomes) to identify haplotypes that are associated with extreme phenotypic variation at the global level and recorded in the database. It then examines which potentially useful variants are present in the local crop panel, before distinguishing between those that are already incorporated into the elite breeding accessions and those only found among secondary varieties (e.g., landraces). Results highlight the effectiveness of our pipeline, identifying potentially useful functional haplotypes across the genome that are absent from elite cultivars and found among landraces and other secondary varieties in our breeding program. riceExplorer can automatically conduct a full genome analysis and produces annotated graphical output of chromosomal maps, potential global diversity sources, and summary tables.
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14
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Budhlakoti N, Kushwaha AK, Rai A, Chaturvedi KK, Kumar A, Pradhan AK, Kumar U, Kumar RR, Juliana P, Mishra DC, Kumar S. Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops. Front Genet 2022; 13:832153. [PMID: 35222548 PMCID: PMC8864149 DOI: 10.3389/fgene.2022.832153] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/10/2022] [Indexed: 12/17/2022] Open
Abstract
Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.
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Affiliation(s)
- Neeraj Budhlakoti
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Anil Rai
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - K K Chaturvedi
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, India
| | | | | | - D C Mishra
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sundeep Kumar
- ICAR- National Bureau of Plant Genetic Resources, New Delhi, India
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15
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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: 14] [Impact Index Per Article: 7.0] [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.
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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
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16
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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Baertschi C, Cao TV, Bartholomé J, Ospina Y, Quintero C, Frouin J, Bouvet JM, Grenier C. Impact of early genomic prediction for recurrent selection in an upland rice synthetic population. G3 (BETHESDA, MD.) 2021; 11:jkab320. [PMID: 34498036 PMCID: PMC8664429 DOI: 10.1093/g3journal/jkab320] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 08/16/2021] [Indexed: 11/14/2022]
Abstract
Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice (Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield, and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51-0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment.
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Affiliation(s)
- Cédric Baertschi
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Tuong-Vi Cao
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- Rice Breeding Platform, International Rice Research Institute, Metro Manila, Philippines
| | - Yolima Ospina
- Alliance Bioversity-CIAT, Recta Palmira Cali, Colombia
| | | | - Julien Frouin
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Jean-Marc Bouvet
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- CIRAD, Dispositif de Recherche et d’Enseignement en Partenariat “Forêts et Biodiversité à Madagascar”, Antananarivo, Madagascar
| | - Cécile Grenier
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- Alliance Bioversity-CIAT, Recta Palmira Cali, Colombia
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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.
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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
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19
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Frontini M, Boisnard A, Frouin J, Ouikene M, Morel JB, Ballini E. Genome-wide association of rice response to blast fungus identifies loci for robust resistance under high nitrogen. BMC PLANT BIOLOGY 2021; 21:99. [PMID: 33602120 PMCID: PMC7893971 DOI: 10.1186/s12870-021-02864-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 02/01/2021] [Indexed: 05/19/2023]
Abstract
BACKGROUND Nitrogen fertilization is known to increase disease susceptibility, a phenomenon called Nitrogen-Induced Susceptibility (NIS). In rice, this phenomenon has been observed in infections with the blast fungus Magnaporthe oryzae. A previous classical genetic study revealed a locus (NIS1) that enhances susceptibility to rice blast under high nitrogen fertilization. In order to further address the underlying genetics of plasticity in susceptibility to rice blast after fertilization, we analyzed NIS under greenhouse-controlled conditions in a panel of 139 temperate japonica rice strains. A genome-wide association analysis was conducted to identify loci potentially involved in NIS by comparing susceptibility loci identified under high and low nitrogen conditions, an approach allowing for the identification of loci validated across different nitrogen environments. We also used a novel NIS Index to identify loci potentially contributing to plasticity in susceptibility under different nitrogen fertilization regimes. RESULTS A global NIS effect was observed in the population, with the density of lesions increasing by 8%, on average, under high nitrogen fertilization. Three new QTL, other than NIS1, were identified. A rare allele of the RRobN1 locus on chromosome 6 provides robust resistance in high and low nitrogen environments. A frequent allele of the NIS2 locus, on chromosome 5, exacerbates blast susceptibility under the high nitrogen condition. Finally, an allele of NIS3, on chromosome 10, buffers the increase of susceptibility arising from nitrogen fertilization but increases global levels of susceptibility. This allele is almost fixed in temperate japonicas, as a probable consequence of genetic hitchhiking with a locus involved in cold stress adaptation. CONCLUSIONS Our results extend to an entire rice subspecies the initial finding that nitrogen increases rice blast susceptibility. We demonstrate the usefulness of estimating plasticity for the identification of novel loci involved in the response of rice to the blast fungus under different nitrogen regimes.
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Affiliation(s)
- Mathias Frontini
- BGPI, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | | | - Julien Frouin
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Malika Ouikene
- Groupe de Valorisation des Produits Agricoles (GVAPRO), Alger, Algeria
| | - Jean Benoit Morel
- BGPI, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Elsa Ballini
- BGPI, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
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20
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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.
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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
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21
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Gemenet DC, Lindqvist-Kreuze H, De Boeck B, da Silva Pereira G, Mollinari M, Zeng ZB, Craig Yencho G, Campos H. Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:3345-3363. [PMID: 32876753 PMCID: PMC7567692 DOI: 10.1007/s00122-020-03673-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Accepted: 08/21/2020] [Indexed: 05/06/2023]
Abstract
KEY MESSAGE Polypoid crop breeders can balance resources between density and sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture. The autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene-action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental F1 population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: (i) do polyploid crop breeders need to invest more for additional sequencing depth? (ii) how many markers are required to make selection decisions? (iii) does considering non-additive genetic effects improve predictive ability (PA)? (iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms (SNPs; ≤ 1000) are adequate for prediction in the type of populations we analyzed. We also show that considering dosage information and models considering only additive effects had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations.
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Affiliation(s)
- Dorcus C Gemenet
- International Potato Center, ILRI Campus, P.O. Box 25171-00603, Nairobi, Kenya.
- CGIAR Excellence in Breeding Platform, International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, 1041-00621, Nairobi, Kenya.
| | | | - Bert De Boeck
- International Potato Center, Av. La Molina 1895, Lima, Peru
| | | | | | - Zhao-Bang Zeng
- North Carolina State University, Raleigh, NC, 27695, USA
| | - G Craig Yencho
- North Carolina State University, Raleigh, NC, 27695, USA
| | - Hugo Campos
- International Potato Center, Av. La Molina 1895, Lima, Peru
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22
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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.
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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.
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23
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Bhandari A, Sandhu N, Bartholome J, Cao-Hamadoun TV, Ahmadi N, Kumari N, Kumar A. Genome-Wide Association Study for Yield and Yield Related Traits under Reproductive Stage Drought in a Diverse indica-aus Rice Panel. RICE (NEW YORK, N.Y.) 2020; 13:53. [PMID: 32761553 PMCID: PMC7410978 DOI: 10.1186/s12284-020-00406-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 07/02/2020] [Indexed: 05/08/2023]
Abstract
BACKGROUND Reproductive-stage drought stress is a major impediment to rice production in rainfed areas. Conventional and marker-assisted breeding strategies for developing drought-tolerant rice varieties are being optimized by mining and exploiting adaptive traits, genetic diversity; identifying the alleles, and understanding their interactions with genetic backgrounds for their increased contribution to drought tolerance. Field experiments were conducted in this study to identify marker-trait associations (MTAs) involved in response to yield under reproductive-stage (RS) drought. A diverse set of 280 indica-aus accessions was phenotyped for ten agronomic traits including yield and yield-related traits under normal irrigated condition and under two managed reproductive-stage drought environments. The accessions were genotyped with 215,250 single nucleotide polymorphism markers. RESULTS The study identified a total of 219 significant MTAs for 10 traits and candidate gene analysis within a 200 kb window centred from GWAS identified SNP peaks detected these MTAs within/ in close proximity to 38 genes, 4 earlier reported major grain yield QTLs and 6 novel QTLs for 7 traits out of the 10. The significant MTAs were mainly located on chromosomes 1, 2, 5, 6, 9, 11 and 12 and the percent phenotypic variance captured for these traits ranged from 5 to 88%. The significant positive correlation of grain yield with yield-related and other agronomic traits except for flowering time, observed under different environments point towards their contribution in improving rice yield under drought. Seven promising accessions were identified for use in future genomics-assisted breeding programs targeting grain yield improvement under drought. CONCLUSION These results provide a promising insight into the complex genetic architecture of grain yield under reproductive-stage drought in different environments. Validation of major genomic regions reported in the study will enable their effectiveness to develop drought-tolerant varieties following marker-assisted selection as well as to identify genes and understanding the associated physiological mechanisms.
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Affiliation(s)
- Aditi Bhandari
- Rice Breeding Platform, International Rice Research Institute, DAPO Box, 7777, Metro Manila, Philippines
- Banasthali Vidyapith, Banasthali, 304022, India
| | - Nitika Sandhu
- Rice Breeding Platform, International Rice Research Institute, DAPO Box, 7777, Metro Manila, Philippines
- Punjab Agricultural University, Ludhiana, 141004, India
| | - Jérôme Bartholome
- Rice Breeding Platform, International Rice Research Institute, DAPO Box, 7777, Metro Manila, Philippines
- CIRAD, UMR, AGAP, Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montepellier, France
| | - Tuong-Vi Cao-Hamadoun
- CIRAD, UMR, AGAP, Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montepellier, France
| | - Nourollah Ahmadi
- CIRAD, UMR, AGAP, Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montepellier, France
| | | | - Arvind Kumar
- Rice Breeding Platform, International Rice Research Institute, DAPO Box, 7777, Metro Manila, Philippines.
- IRRI South Asia Regional Centre, Varanasi, 221006, India.
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24
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Melandri G, Sikirou M, Arbelaez JD, Shittu A, Semwal VK, Konaté KA, Maji AT, Ngaujah SA, Akintayo I, Govindaraj V, Shi Y, Agosto-Peréz FJ, Greenberg AJ, Atlin G, Ramaiah V, McCouch SR. Multiple Small-Effect Alleles of Indica Origin Enhance High Iron-Associated Stress Tolerance in Rice Under Field Conditions in West Africa. FRONTIERS IN PLANT SCIENCE 2020; 11:604938. [PMID: 33584748 PMCID: PMC7874229 DOI: 10.3389/fpls.2020.604938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/15/2020] [Indexed: 05/03/2023]
Abstract
Understanding the genetics of field-based tolerance to high iron-associated (HIA) stress in rice can accelerate the development of new varieties with enhanced yield performance in West African lowland ecosystems. To date, few field-based studies have been undertaken to rigorously evaluate rice yield performance under HIA stress conditions. In this study, two NERICA × O. sativa bi-parental rice populations and one O.sativa diversity panel consisting of 296 rice accessions were evaluated for grain yield and leaf bronzing symptoms over multiple years in four West African HIA stress and control sites. Mapping of these traits identified a large number of QTLs and single nucleotide polymorphisms (SNPs) associated with stress tolerance in the field. Favorable alleles associated with tolerance to high levels of iron in anaerobic rice soils were rare and almost exclusively derived from the indica subpopulation, including the most favorable alleles identified in NERICA varieties. These findings highlight the complex genetic architecture underlying rice response to HIA stress and suggest that a recurrent selection program focusing on an expanded indica genepool could be productively used in combination with genomic selection to increase the efficiency of selection in breeding programs designed to enhance tolerance to this prevalent abiotic stress in West Africa.
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Affiliation(s)
- Giovanni Melandri
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | - Mouritala Sikirou
- Africa Rice Center, Ibadan, Nigeria
- School of Horticulture and Green Landscaping, Kétou, Bénin
| | - Juan D. Arbelaez
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | | | | | | | | | | | - Inoussa Akintayo
- Central Agricultural Research Institute, Suakoko, Liberia
- Africa Rice Center, Suakoko, Liberia
| | - Vishnu Govindaraj
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | - Yuxin Shi
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | | | | | - Gary Atlin
- Bill & Melinda Gates Foundation, Seattle, WA, United States
| | | | - Susan R. McCouch
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
- Venuprasad Ramaiah,
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