1
|
Mukaro R, Chaingeni D, Sneller C, Cairns JE, Musundire L, Prasanna BM, Mavankeni BO, Das B, Mulanya M, Chivasa W, Mhike X, Ndhlela T, Matongera N, Matova PM, Muungani D, Mutimaamba C, Wegary D, Zaman-Allah M, Magorokosho C, Chingwara V, Kutywayo D. Genetic trends in the Zimbabwe's national maize breeding program over two decades. FRONTIERS IN PLANT SCIENCE 2024; 15:1391926. [PMID: 38988630 PMCID: PMC11234322 DOI: 10.3389/fpls.2024.1391926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 05/16/2024] [Indexed: 07/12/2024]
Abstract
Monitoring genetic gains within breeding programs is a critical component for continuous improvement. While several national breeding programs in Africa have assessed genetic gain using era studies, this study is the first to use two decades of historical data to estimate genetic trends within a national breeding program. The objective of this study was to assess genetic trends within the final two stages of Zimbabwe's Department of Research & Specialist Services maize breeding pipeline between 2002 and 2021. Data from 107 intermediate and 162 advanced variety trials, comprising of 716 and 398 entries, respectively, was analyzed. Trials were conducted under optimal, managed drought stress, low nitrogen stress, low pH, random stress, and disease pressure (maize streak virus (MSV), grey leaf spot (GLS), and turcicum leaf blight under artificial inoculation. There were positive and significant genetic gains for grain yield across management conditions (28-35 kg ha-1 yr-1), under high-yield potential environments (17-61 kg ha-1 yr-1), and under low-yield potential environments (0-16 kg ha-1 yr-1). No significant changes were observed in plant and ear height over the study period. Stalk and root lodging, as well as susceptibility to MSV and GLS, significantly decreased over the study period. New breeding technologies need to be incorporated into the program to further increase the rate of genetic gain in the maize breeding programs and to effectively meet future needs.
Collapse
Affiliation(s)
- Ronica Mukaro
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
| | - Davison Chaingeni
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
| | - Clay Sneller
- Department of Horticulture and Crop Science, The Ohio State University College of Food, Agriculture and Environmental Science, Columbus, OH, United States
| | - Jill E Cairns
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe
| | - Lennin Musundire
- Accelerated Breeding Initiative (ABI)-Transform, International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
| | - Boddupalli M Prasanna
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
| | - Busiso Olga Mavankeni
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
| | - Biswanath Das
- Accelerated Breeding Initiative (ABI)-Transform, International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
| | - Mable Mulanya
- Integrated Breeding Platform (IBP), International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
| | - Walter Chivasa
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
| | - Xavier Mhike
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe
| | - Thokozile Ndhlela
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe
| | - Nakai Matongera
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
- Scientific and Industrial Research and Development Center (SIRDC), Harare, Zimbabwe
| | - Prince Muchapondwa Matova
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
- Scientific and Industrial Research and Development Center (SIRDC), Harare, Zimbabwe
| | - Dean Muungani
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
- Mukushi Seeds (Pvt) Ltd, Harare, Zimbabwe
| | - Charles Mutimaamba
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
- Research for Development (R4D), International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Dagne Wegary
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe
| | - Mainassara Zaman-Allah
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe
| | - Cosmos Magorokosho
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe
| | - Victor Chingwara
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
| | - Dumisani Kutywayo
- Crop Breeding Institute, Department of Research & Specialist Services, Harare, Zimbabwe
| |
Collapse
|
2
|
Kabade PG, Dixit S, Singh UM, Alam S, Bhosale S, Kumar S, Singh SK, Badri J, Varma NRG, Chetia S, Singh R, Pradhan SK, Banerjee S, Deshmukh R, Singh SP, Kalia S, Sharma TR, Singh S, Bhardwaj H, Kohli A, Kumar A, Sinha P, Singh VK. SpeedFlower: a comprehensive speed breeding protocol for indica and japonica rice. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:1051-1066. [PMID: 38070179 PMCID: PMC11022788 DOI: 10.1111/pbi.14245] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/10/2023] [Accepted: 11/13/2023] [Indexed: 04/18/2024]
Abstract
To increase rice yields and feed billions of people, it is essential to enhance genetic gains. However, the development of new varieties is hindered by longer generation times and seasonal constraints. To address these limitations, a speed breeding facility has been established and a robust speed breeding protocol, SpeedFlower is developed that allows growing 4-5 generations of indica and/or japonica rice in a year. Our findings reveal that a high red-to-blue (2R > 1B) spectrum ratio, followed by green, yellow and far-red (FR) light, along with a 24-h long day (LD) photoperiod for the initial 15 days of the vegetative phase, facilitated early flowering. This is further enhanced by 10-h short day (SD) photoperiod in the later stage and day and night temperatures of 32/30 °C, along with 65% humidity facilitated early flowering ranging from 52 to 60 days at high light intensity (800 μmol m-2 s-1). Additionally, the use of prematurely harvested seeds and gibberellic acid treatment reduced the maturity duration by 50%. Further, SpeedFlower was validated on a diverse subset of 198 rice accessions from 3K RGP panel encompassing all 12 distinct groups of Oryza sativa L. classes. Our results confirmed that using SpeedFlower one generation can be achieved within 58-71 days resulting in 5.1-6.3 generations per year across the 12 sub-groups. This breakthrough enables us to enhance genetic gain, which could feed half of the world's population dependent on rice.
Collapse
Affiliation(s)
- Pramod Gorakhanath Kabade
- International Rice Research InstituteLos BanosPhilippines
- IRRI South Asia Regional CentreVaranasiIndia
- Banaras Hindu University (BHU)VaranasiUttar PradeshIndia
| | - Shilpi Dixit
- International Rice Research InstituteLos BanosPhilippines
- IRRI South Asia Regional CentreVaranasiIndia
| | - Uma Maheshwar Singh
- International Rice Research InstituteLos BanosPhilippines
- IRRI South Asia Regional CentreVaranasiIndia
| | - Shamshad Alam
- International Rice Research InstituteLos BanosPhilippines
- IRRI South Asia HubHyderabadIndia
| | | | - Sanjay Kumar
- Banaras Hindu University (BHU)VaranasiUttar PradeshIndia
| | | | - Jyothi Badri
- Indian Institute of Rice Research (IIRR)HyderabadTelanganaIndia
| | | | - Sanjay Chetia
- Assam Agricultural University (AAU)TitabarAssamIndia
| | - Rakesh Singh
- National Bureau of Plant Genetic Resources (NBPGR)New DelhiIndia
| | | | - Shubha Banerjee
- Indira Gandhi Krishi Vishwavidyalaya (IGKV)RaipurChhattisgarhIndia
| | - Rupesh Deshmukh
- National Agri‐Food Biotechnology Institute (NABI)Mohali, ChandigarhIndia
- Present address:
Central University of Haryana (CUH)MahendragarhHaryanaIndia
| | | | - Sanjay Kalia
- Department of Biotechnology (DBT)CGO ComplexNew DelhiIndia
| | | | - Sudhanshu Singh
- International Rice Research InstituteLos BanosPhilippines
- IRRI South Asia Regional CentreVaranasiIndia
| | - Hans Bhardwaj
- International Rice Research InstituteLos BanosPhilippines
| | - Ajay Kohli
- International Rice Research InstituteLos BanosPhilippines
| | - Arvind Kumar
- International Rice Research InstituteLos BanosPhilippines
- IRRI South Asia Regional CentreVaranasiIndia
- Present address:
International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)PatancheruTelanganaIndia
| | - Pallavi Sinha
- International Rice Research InstituteLos BanosPhilippines
- IRRI South Asia HubHyderabadIndia
| | - Vikas Kumar Singh
- International Rice Research InstituteLos BanosPhilippines
- IRRI South Asia Regional CentreVaranasiIndia
| |
Collapse
|
3
|
Matsushita K, Onogi A, Yonemaru JI. NARO historical phenotype dataset from rice breeding. BREEDING SCIENCE 2024; 74:114-123. [PMID: 39355631 PMCID: PMC11442108 DOI: 10.1270/jsbbs.23040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 12/10/2023] [Indexed: 10/03/2024]
Abstract
Data from breeding, including phenotypic information, may improve the efficiency of breeding. Historical data from breeding trials accumulated over a long time are also useful. Here, by organizing data accumulated in the National Agriculture and Food Research Organization (NARO) rice breeding program, we developed a historical phenotype dataset, which includes 6052 records obtained for 667 varieties in yield trials in 1991-2018 at six NARO research stations. The best linear unbiased predictions (BLUPs) and principal component analysis (PCA) were used to determine the relationships with various factors, including the year of cultivar release, for 15 traits, including yield. Yield-related traits such as the number of grains per panicle, plant weight, grain yield, and thousand-grain weight increased significantly with time, whereas the number of panicles decreased significantly. Ripening time significantly increased, whereas the lodging degree and protein content of brown rice significantly decreased. These results suggest that panicle-weight-type high-yielding varieties with excellent lodging resistance have been selected. These trends differed slightly among breeding locations, indicating that the main breeding objectives may differ among them. PCA revealed a higher diversity of traits in newer varieties.
Collapse
Affiliation(s)
- Kei Matsushita
- Research Center for Agricultural Information Technology (RCAIT), National Agriculture and Food Research Organization (NARO), 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8517, Japan
- Institute of Crop Science (NICS), NARO, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan
| | - Akio Onogi
- Research Center for Agricultural Information Technology (RCAIT), National Agriculture and Food Research Organization (NARO), 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8517, Japan
- Faculty of Agriculture, Ryukoku University, 1-5 Yokotani, Seta Oe-cho, Otsu, Shiga 520-2194, Japan
| | - Jun-Ichi Yonemaru
- Research Center for Agricultural Information Technology (RCAIT), National Agriculture and Food Research Organization (NARO), 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8517, Japan
- Institute of Crop Science (NICS), NARO, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan
| |
Collapse
|
4
|
Khanna A, Anumalla M, Ramos J, Cruz MTS, Catolos M, Sajise AG, Gregorio G, Dixit S, Ali J, Islam MR, Singh VK, Rahman MA, Khatun H, Pisano DJ, Bhosale S, Hussain W. Genetic gains in IRRI's rice salinity breeding and elite panel development as a future breeding resource. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:37. [PMID: 38294550 PMCID: PMC10830834 DOI: 10.1007/s00122-024-04545-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024]
Abstract
KEY MESSAGE Estimating genetic gains and formulating a future salinity elite breeding panel for rice pave the way for developing better high-yielding salinity tolerant lines with enhanced genetic gains. Genetic gain is a crucial parameter to check the breeding program's success and help optimize future breeding strategies for enhanced genetic gains. To estimate the genetic gains in IRRI's salinity breeding program and identify the best genotypes based on high breeding values for grain yield (kg/ha), we analyzed the historical data from the trials conducted in the IRRI, Philippines and Bangladesh. A two-stage mixed-model approach accounting for experimental design factors and a relationship matrix was fitted to obtain the breeding values for grain yield and estimate genetic trends. A positive genetic trend of 0.1% per annum with a yield advantage of 1.52 kg/ha was observed in IRRI, Philippines. In Bangladesh, we observed a genetic gain of 0.31% per annum with a yield advantage of 14.02 kg/ha. In the released varieties, we observed a genetic gain of 0.12% per annum with a 2.2 kg/ha/year yield advantage in the IRRI, Philippines. For the Bangladesh dataset, a genetic gain of 0.14% per annum with a yield advantage of 5.9 kg/ha/year was observed in the released varieties. Based on breeding values for grain yield, a core set of the top 145 genotypes with higher breeding values of > 2400 kg/ha in the IRRI, Philippines, and > 3500 kg/ha in Bangladesh with a reliability of > 0.4 were selected to develop the elite breeding panel. Conclusively, a recurrent selection breeding strategy integrated with novel technologies like genomic selection and speed breeding is highly required to achieve higher genetic gains in IRRI's salinity breeding programs.
Collapse
Affiliation(s)
- Apurva Khanna
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Mahender Anumalla
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Joie Ramos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Ma Teresa Sta Cruz
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Margaret Catolos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Andres Godwin Sajise
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Glenn Gregorio
- Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA) and University of Philippines, 4031, Los Baños, Laguna, Philippines
| | - Shalabh Dixit
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Jauhar Ali
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Md Rafiqul Islam
- IRRI South Asia Regional Center (IRRI-SA Hub), Hyderabad, Telangana, 502324, India
| | - Vikas Kumar Singh
- IRRI South Asia Regional Center (IRRI-SA Hub), Hyderabad, Telangana, 502324, India
| | - Md Akhlasur Rahman
- Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, 1701, Bangladesh
| | - Hasina Khatun
- Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, 1701, Bangladesh
| | - Daniel Joseph Pisano
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Sankalp Bhosale
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Waseem Hussain
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines.
| |
Collapse
|
5
|
Tarekegne A, Wegary D, Cairns JE, Zaman-Allah M, Beyene Y, Negera D, Teklewold A, Tesfaye K, Jumbo MB, Das B, Nhamucho EJ, Simpasa K, Kaonga KKE, Mashingaidze K, Thokozile N, Mhike X, Prasanna BM. Genetic gains in early maturing maize hybrids developed by the International Maize and Wheat Improvement Center in Southern Africa during 2000-2018. FRONTIERS IN PLANT SCIENCE 2024; 14:1321308. [PMID: 38293626 PMCID: PMC10825029 DOI: 10.3389/fpls.2023.1321308] [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: 10/13/2023] [Accepted: 12/07/2023] [Indexed: 02/01/2024]
Abstract
Genetic gain estimation in a breeding program provides an opportunity to monitor breeding efficiency and genetic progress over a specific period. The present study was conducted to (i) assess the genetic gains in grain yield of the early maturing maize hybrids developed by the International Maize and Wheat Improvement Center (CIMMYT) Southern African breeding program during the period 2000-2018 and (ii) identify key agronomic traits contributing to the yield gains under various management conditions. Seventy-two early maturing hybrids developed by CIMMYT and three commercial checks were assessed under stress and non-stress conditions across 68 environments in seven eastern and southern African countries through the regional on-station trials. Genetic gain was estimated as the slope of the regression of grain yield and other traits against the year of first testing of the hybrid in the regional trial. The results showed highly significant (p< 0.01) annual grain yield gains of 118, 63, 46, and 61 kg ha-1 year-1 under optimum, low N, managed drought, and random stress conditions, respectively. The gains in grain yield realized in this study under both stress and non-stress conditions were associated with improvements in certain agronomic traits and resistance to major maize diseases. The findings of this study clearly demonstrate the significant progress made in developing productive and multiple stress-tolerant maize hybrids together with other desirable agronomic attributes in CIMMYT's hybrid breeding program.
Collapse
Affiliation(s)
- Amsal Tarekegne
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Dagne Wegary
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Jill E. Cairns
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Mainassara Zaman-Allah
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Yoseph Beyene
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
| | - Demewoz Negera
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Addis Ababa, Ethiopia
| | - Adefris Teklewold
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Addis Ababa, Ethiopia
| | - Kindie Tesfaye
- Sustianable Agrifood Systems Program, International Maize and Wheat Improvement Centre (CIMMYT), Addis Ababa, Ethiopia
| | - MacDonald B. Jumbo
- Crop Improvement Program, International Crops Research Institute for Semi-Arid Tropics, Bamako, Mali
| | - Biswanath Das
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
| | - Egas J. Nhamucho
- Instituto de Investigação Agrária de Moçambique (IIAM), Chokwe, Mozambique
| | | | | | | | - Ndhlela Thokozile
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Xavier Mhike
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Boddupalli M. Prasanna
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
| |
Collapse
|
6
|
Seck F, Covarrubias-Pazaran G, Gueye T, Bartholomé J. Realized Genetic Gain in Rice: Achievements from Breeding Programs. RICE (NEW YORK, N.Y.) 2023; 16:61. [PMID: 38099942 PMCID: PMC10724102 DOI: 10.1186/s12284-023-00677-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/10/2023] [Indexed: 12/18/2023]
Abstract
Genetic improvement is crucial for ensuring food security globally. Indeed, plant breeding has contributed significantly to increasing the productivity of major crops, including rice, over the last century. Evaluating the efficiency of breeding strategies necessitates a quantification of this progress. One approach involves assessing the genetic gain achieved through breeding programs based on quantitative traits. This study aims to provide a theoretical understanding of genetic gain, summarize the major results of genetic gain studies in rice breeding, and suggest ways of improving breeding program strategies and future studies on genetic gain. To achieve this, we present the concept of genetic gain and the essential aspects of its estimation. We also provide an extensive literature review of genetic gain studies in rice (Oryza sativa L.) breeding programs to understand the advances made to date. We reviewed 29 studies conducted between 1999 and 2023, covering different regions, traits, periods, and estimation methods. The genetic gain for grain yield, in particular, showed significant variation, ranging from 1.5 to 167.6 kg/ha/year, with a mean value of 36.3 kg/ha/year. This translated into a rate of genetic gain for grain yield ranging from 0.1% to over 3.0%. The impact of multi-trait selection on grain yield was clarified by studies that reported genetic gains for other traits, such as plant height, days to flowering, and grain quality. These findings reveal that while breeding programs have achieved significant gains, further improvements are necessary to meet the growing demand for rice. We also highlight the limitations of these studies, which hinder accurate estimations of genetic gain. In conclusion, we offer suggestions for improving the estimation of genetic gain based on quantitative genetic principles and computer simulations to optimize rice breeding strategies.
Collapse
Affiliation(s)
- Fallou Seck
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO Box7777, Metro Manila, Philippines
- University Iba Der Thiam of Thiès, GrandStanding, Thiès, Senegal
| | - Giovanny Covarrubias-Pazaran
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO Box7777, Metro Manila, Philippines
| | - Tala Gueye
- University Iba Der Thiam of Thiès, GrandStanding, Thiès, Senegal
| | - Jérôme Bartholomé
- CIRAD, UMR AGAP, Cali, Colombia.
- AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France.
- Alliance Bioversity-CIAT, Cali, Colombia.
| |
Collapse
|
7
|
Zhao H, Lin Z, Khansefid M, Tibbits JF, Hayden MJ. Genomic prediction and selection response for grain yield in safflower. Front Genet 2023; 14:1129433. [PMID: 37051598 PMCID: PMC10083426 DOI: 10.3389/fgene.2023.1129433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/13/2023] [Indexed: 03/29/2023] Open
Abstract
In plant breeding programs, multiple traits are recorded in each trial, and the traits are often correlated. Correlated traits can be incorporated into genomic selection models, especially for traits with low heritability, to improve prediction accuracy. In this study, we investigated the genetic correlation between important agronomic traits in safflower. We observed the moderate genetic correlations between grain yield (GY) and plant height (PH, 0.272-0.531), and low correlations between grain yield and days to flowering (DF, -0.157-0.201). A 4%-20% prediction accuracy improvement for grain yield was achieved when plant height was included in both training and validation sets with multivariate models. We further explored the selection responses for grain yield by selecting the top 20% of lines based on different selection indices. Selection responses for grain yield varied across sites. Simultaneous selection for grain yield and seed oil content (OL) showed positive gains across all sites with equal weights for both grain yield and oil content. Combining g×E interaction into genomic selection (GS) led to more balanced selection responses across sites. In conclusion, genomic selection is a valuable breeding tool for breeding high grain yield, oil content, and highly adaptable safflower varieties.
Collapse
Affiliation(s)
- Huanhuan Zhao
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Zibei Lin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Majid Khansefid
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Josquin F. Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Matthew J. Hayden
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| |
Collapse
|
8
|
Eckardt NA, Ainsworth EA, Bahuguna RN, Broadley MR, Busch W, Carpita NC, Castrillo G, Chory J, DeHaan LR, Duarte CM, Henry A, Jagadish SVK, Langdale JA, Leakey ADB, Liao JC, Lu KJ, McCann MC, McKay JK, Odeny DA, Jorge de Oliveira E, Platten JD, Rabbi I, Rim EY, Ronald PC, Salt DE, Shigenaga AM, Wang E, Wolfe M, Zhang X. Climate change challenges, plant science solutions. THE PLANT CELL 2023; 35:24-66. [PMID: 36222573 PMCID: PMC9806663 DOI: 10.1093/plcell/koac303] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Climate change is a defining challenge of the 21st century, and this decade is a critical time for action to mitigate the worst effects on human populations and ecosystems. Plant science can play an important role in developing crops with enhanced resilience to harsh conditions (e.g. heat, drought, salt stress, flooding, disease outbreaks) and engineering efficient carbon-capturing and carbon-sequestering plants. Here, we present examples of research being conducted in these areas and discuss challenges and open questions as a call to action for the plant science community.
Collapse
Affiliation(s)
- Nancy A Eckardt
- Senior Features Editor, The Plant Cell, American Society of Plant Biologists, USA
| | - Elizabeth A Ainsworth
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, Illinois 61801, USA
| | - Rajeev N Bahuguna
- Centre for Advanced Studies on Climate Change, Dr Rajendra Prasad Central Agricultural University, Samastipur 848125, Bihar, India
| | - Martin R Broadley
- School of Biosciences, University of Nottingham, Nottingham, NG7 2RD, UK
- Rothamsted Research, West Common, Harpenden, Hertfordshire, AL5 2JQ, UK
| | - Wolfgang Busch
- Plant Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California 92037, USA
| | - Nicholas C Carpita
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
| | - Gabriel Castrillo
- School of Biosciences, University of Nottingham, Nottingham, NG7 2RD, UK
- Future Food Beacon of Excellence, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Joanne Chory
- Plant Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California 92037, USA
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, California 92037, USA
| | | | - Carlos M Duarte
- Red Sea Research Center (RSRC) and Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Amelia Henry
- International Rice Research Institute, Rice Breeding Innovations Platform, Los Baños, Laguna 4031, Philippines
| | - S V Krishna Jagadish
- Department of Plant and Soil Science, Texas Tech University, Lubbock, Texas 79410, USA
| | - Jane A Langdale
- Department of Biology, University of Oxford, Oxford, OX1 3RB, UK
| | - Andrew D B Leakey
- Department of Plant Biology, Department of Crop Sciences, and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Illinois 61801, USA
| | - James C Liao
- Institute of Biological Chemistry, Academia Sinica, Taipei 11528, Taiwan
| | - Kuan-Jen Lu
- Institute of Biological Chemistry, Academia Sinica, Taipei 11528, Taiwan
| | - Maureen C McCann
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
| | - John K McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, Colorado 80523, USA
| | - Damaris A Odeny
- The International Crops Research Institute for the Semi-Arid Tropics–Eastern and Southern Africa, Gigiri 39063-00623, Nairobi, Kenya
| | | | - J Damien Platten
- International Rice Research Institute, Rice Breeding Innovations Platform, Los Baños, Laguna 4031, Philippines
| | - Ismail Rabbi
- International Institute of Tropical Agriculture (IITA), PMB 5320 Ibadan, Oyo, Nigeria
| | - Ellen Youngsoo Rim
- Department of Plant Pathology and the Genome Center, University of California, Davis, California 95616, USA
| | - Pamela C Ronald
- Department of Plant Pathology and the Genome Center, University of California, Davis, California 95616, USA
- Innovative Genomics Institute, Berkeley, California 94704, USA
| | - David E Salt
- School of Biosciences, University of Nottingham, Nottingham, NG7 2RD, UK
- Future Food Beacon of Excellence, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Alexandra M Shigenaga
- Department of Plant Pathology and the Genome Center, University of California, Davis, California 95616, USA
| | - Ertao Wang
- National Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Marnin Wolfe
- Auburn University, Dept. of Crop Soil and Environmental Sciences, College of Agriculture, Auburn, Alabama 36849, USA
| | - Xiaowei Zhang
- National Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| |
Collapse
|
9
|
Khanna A, Anumalla M, Catolos M, Bhosale S, Jarquin D, Hussain W. Optimizing predictions in IRRI's rice drought breeding program by leveraging 17 years of historical data and pedigree information. FRONTIERS IN PLANT SCIENCE 2022; 13:983818. [PMID: 36204059 PMCID: PMC9530897 DOI: 10.3389/fpls.2022.983818] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/08/2022] [Indexed: 06/16/2023]
Abstract
Prediction models based on pedigree and/or molecular marker information are now an inextricable part of the crop breeding programs and have led to increased genetic gains in many crops. Optimization of IRRI's rice drought breeding program is crucial for better implementation of selections based on predictions. Historical datasets with precise and robust pedigree information have been a great resource to help optimize the prediction models in the breeding programs. Here, we leveraged 17 years of historical drought data along with the pedigree information to predict the new lines or environments and dissect the G × E interactions. Seven models ranging from basic to proposed higher advanced models incorporating interactions, and genotypic specific effects were used. These models were tested with three cross-validation schemes (CV1, CV2, and CV0) to assess the predictive ability of tested and untested lines in already observed environments and tested lines in novel or new environments. In general, the highest prediction abilities were obtained when the model accounting interactions between pedigrees (additive) and environment were included. The CV0 scheme (predicting unobserved or novel environments) reveals very low predictive abilities among the three schemes. CV1 and CV2 schemes that borrow information from the target and correlated environments have much higher predictive abilities. Further, predictive ability was lower when predicting lines in non-stress conditions using drought data as training set and/or vice-versa. When predicting the lines using the data sets under the same conditions (stress or non-stress data sets), much better prediction accuracy was obtained. These results provide conclusive evidence that modeling G × E interactions are important in predictions. Thus, considering G × E interactions would help to build enhanced genomic or pedigree-based prediction models in the rice breeding program. Further, it is crucial to borrow the correlated information from other environments to improve prediction accuracy.
Collapse
Affiliation(s)
- Apurva Khanna
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| | - Mahender Anumalla
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| | - Margaret Catolos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| | - Sankalp Bhosale
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Waseem Hussain
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| |
Collapse
|