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Mishra DC, Budhlakoti N, Juliana P, Kumar S. Editorial: Accelerating genetic gain for key traits using genome-wide association studies and genomic selection: promising breeding tools for sustainable agriculture. Front Genet 2023; 14:1351870. [PMID: 38188500 PMCID: PMC10770247 DOI: 10.3389/fgene.2023.1351870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 12/08/2023] [Indexed: 01/09/2024] Open
Affiliation(s)
| | - Neeraj Budhlakoti
- 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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Liu Y, Ao M, Lu M, Zheng S, Zhu F, Ruan Y, Guan Y, Zhang A, Cui Z. Genomic selection to improve husk tightness based on genomic molecular markers in maize. Front Plant Sci 2023; 14:1252298. [PMID: 37828926 PMCID: PMC10566295 DOI: 10.3389/fpls.2023.1252298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/04/2023] [Indexed: 10/14/2023]
Abstract
Introduction The husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models. Methods An association panel of maize inbred lines was grown across three sites over two years, divided into four subgroups. GS analysis for HTI prediction was performed using marker data from three sequencing platforms and six marker densities with six statistical methods. Results The findings indicate that a loosely attached husk can aid in the dissipation of water from kernels in temperate maize germplasms across most environments but not nessarily for tropical-origin maize. Considering the balance between GS prediction accuracy and breeding cost, the optimal prediction strategy is the rrBLUP model, the 50K sequencing platform, a 30% proportion of the test population, and a marker density of r2=0.1. Additionally, selecting a specific SS subgroup for sampling the testing set significantly enhances the predictive capacity for husk tightness. Discussion The determination of the optimal GS prediction strategy for HTI provides an economically feasible reference for the practice of molecular breeding. It also serves as a reference method for GS breeding of other agronomic traits.
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Affiliation(s)
- Yuncan Liu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Man Ao
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Ming Lu
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, China
| | - Shubo Zheng
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, China
| | - Fangbo Zhu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Yanye Ruan
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Yixin Guan
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Ao Zhang
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Zhenhai Cui
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
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Garcia-Abadillo J, Morales L, Buerstmayr H, Michel S, Lillemo M, Holzapfel J, Hartl L, Akdemir D, Carvalho HF, Isidro-Sánchez J. Alternative scoring methods of fusarium head blight resistance for genomic assisted breeding. Front Plant Sci 2023; 13:1057914. [PMID: 36714712 PMCID: PMC9876611 DOI: 10.3389/fpls.2022.1057914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/24/2022] [Indexed: 06/18/2023]
Abstract
Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection.
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Affiliation(s)
- J. Garcia-Abadillo
- Department of Biotechnology and Plant Biology - Centre for Biotechnology and Plant Genomics (CBGP) - Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - L. Morales
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna (BOKU), Tulln an der Donau, Austria
| | - H. Buerstmayr
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna (BOKU), Tulln an der Donau, Austria
| | - S. Michel
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna (BOKU), Tulln an der Donau, Austria
| | - M. Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | | | - L. Hartl
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany
| | - D. Akdemir
- CIBMTR (Center for International Blood and Marrow Transplant Research), National Marrow Donor Program/Be The Match, Minneapolis, MN, United States
| | - H. F. Carvalho
- Department of Biotechnology and Plant Biology - Centre for Biotechnology and Plant Genomics (CBGP) - Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - J. Isidro-Sánchez
- Department of Biotechnology and Plant Biology - Centre for Biotechnology and Plant Genomics (CBGP) - Universidad Politécnica de Madrid (UPM), Madrid, Spain
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Fenstemaker S, Cho J, McCoy JE, Mercer KL, Francis DM. Selection strategies to introgress water deficit tolerance derived from Solanum galapagense accession LA1141 into cultivated tomato. Front Plant Sci 2022; 13:947538. [PMID: 35968091 PMCID: PMC9366722 DOI: 10.3389/fpls.2022.947538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/27/2022] [Indexed: 06/15/2023]
Abstract
Crop wild relatives have been used as a source of genetic diversity for over one hundred years. The wild tomato relative Solanum galapagense accession LA1141 demonstrates the ability to tolerate deficit irrigation, making it a potential resource for crop improvement. Accessing traits from LA1141 through introgression may improve the response of cultivated tomatoes grown in water-limited environments. Canopy temperature is a proxy for physiological traits which are challenging to measure efficiently and may be related to water deficit tolerance. We optimized phenotypic evaluation based on variance partitioning and further show that objective phenotyping methods coupled with genomic prediction lead to gain under selection for water deficit tolerance. The objectives of this work were to improve phenotyping workflows for measuring canopy temperature, mapping quantitative trait loci (QTLs) from LA1141 that contribute to water deficit tolerance and comparing selection strategies. The phenotypic variance attributed to genetic causes for canopy temperature was higher when estimated from thermal images relative to estimates based on an infrared thermometer. Composite interval mapping using BC2S3 families, genotyped with single nucleotide polymorphisms, suggested that accession LA1141 contributed alleles that lower canopy temperature and increase plant turgor under water deficit. QTLs for lower canopy temperature were mapped to chromosomes 1 and 6 and explained between 6.6 and 9.5% of the total phenotypic variance. QTLs for higher leaf turgor were detected on chromosomes 5 and 7 and explained between 6.8 and 9.1% of the variance. We advanced tolerant BC2S3 families to the BC2S5 generation using selection indices based on phenotypic values and genomic estimated breeding values (GEBVs). Phenotypic, genomic, and combined selection strategies demonstrated gain under selection and improved performance compared to randomly advanced BC2S5 progenies. Leaf turgor, canopy temperature, stomatal conductance, and vapor pressure deficit (VPD) were evaluated and compared in BC2S5 progenies grown under deficit irrigation. Progenies co-selected for phenotypic values and GEBVs wilted less, had significantly lower canopy temperature, higher stomatal conductance, and lower VPD than randomly advanced lines. The fruit size of water deficit tolerant selections was small compared to the recurrent parent. However, lines with acceptable yield, canopy width, and quality parameters were recovered. These results suggest that we can create selection indices to improve water deficit tolerance in a recurrent parent background, and additional crossing and evaluation are warranted.
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Affiliation(s)
- Sean Fenstemaker
- Department of Horticulture and Crop Science, The Ohio State University, Wooster, OH, United States
| | - Jin Cho
- Department of Horticulture and Crop Science, The Ohio State University, Wooster, OH, United States
| | - Jack E. McCoy
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, United States
| | - Kristin L. Mercer
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, United States
| | - David M. Francis
- Department of Horticulture and Crop Science, The Ohio State University, Wooster, OH, United States
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Martini JWR, Hearne SJ, Gardunia B, Wimmer V, Toledo FH. Editorial: Genomic Selection: Lessons Learned and Perspectives. Front Plant Sci 2022; 13:890434. [PMID: 35693181 PMCID: PMC9186467 DOI: 10.3389/fpls.2022.890434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 04/08/2022] [Indexed: 06/15/2023]
Affiliation(s)
| | - Sarah J. Hearne
- International Maize and Wheat Improvement Center, Texcoco, Mexico
- Excellence in Breeding Platform, Texcoco, Mexico
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Conaty WC, Broughton KJ, Egan LM, Li X, Li Z, Liu S, Llewellyn DJ, MacMillan CP, Moncuquet P, Rolland V, Ross B, Sargent D, Zhu QH, Pettolino FA, Stiller WN. Cotton Breeding in Australia: Meeting the Challenges of the 21st Century. Front Plant Sci 2022; 13:904131. [PMID: 35646011 PMCID: PMC9136452 DOI: 10.3389/fpls.2022.904131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/08/2022] [Indexed: 06/15/2023]
Abstract
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program's commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.
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Affiliation(s)
| | | | - Lucy M. Egan
- CSIRO Agriculture and Food, Narrabri, NSW, Australia
| | - Xiaoqing Li
- CSIRO Agriculture and Food, Canberra, ACT, Australia
| | - Zitong Li
- CSIRO Agriculture and Food, Canberra, ACT, Australia
| | - Shiming Liu
- CSIRO Agriculture and Food, Narrabri, NSW, Australia
| | | | | | | | | | - Brett Ross
- Cotton Seed Distributors Ltd., Wee Waa, NSW, Australia
| | - Demi Sargent
- CSIRO Agriculture and Food, Narrabri, NSW, Australia
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
| | - Qian-Hao Zhu
- CSIRO Agriculture and Food, Canberra, ACT, Australia
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Hoyos-Villegas V, Chen J, Mastrangelo AM, Raman H. Editorial: Advances in Breeding for Quantitative Disease Resistance. Front Plant Sci 2022; 13:890002. [PMID: 35498649 PMCID: PMC9043843 DOI: 10.3389/fpls.2022.890002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 03/23/2022] [Indexed: 06/14/2023]
Affiliation(s)
| | - Jianjun Chen
- Mid-Florida Research & Education Center, University of Florida, Apopka, FL, United States
| | - Anna Maria Mastrangelo
- Research Centre for Cereal and Industrial Crops, Council for Agricultural and Economics Research (CREA), Foggia, Italy
| | - Harsh Raman
- New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia
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8
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Juliana P, He X, Marza F, Islam R, Anwar B, Poland J, Shrestha S, Singh GP, Chawade A, Joshi AK, Singh RP, Singh PK. Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel. Front Plant Sci 2022; 12:745379. [PMID: 35069614 PMCID: PMC8782147 DOI: 10.3389/fpls.2021.745379] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/09/2021] [Indexed: 06/14/2023]
Abstract
Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical.
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Affiliation(s)
| | - Xinyao He
- International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico
| | - Felix Marza
- Instituto Nacional de Innovación Agropecuaria y Forestal (INIAF), La Paz, Bolivia
| | - Rabiul Islam
- Bangladesh Wheat and Maize Research Institute (BWMRI), Dinajpur, Bangladesh
| | - Babul Anwar
- Bangladesh Wheat and Maize Research Institute (BWMRI), Dinajpur, Bangladesh
| | - Jesse Poland
- Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, United States
| | - Sandesh Shrestha
- Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, United States
| | - Gyanendra P. Singh
- Indian Council of Agricultural Research (ICAR)-Indian Institute of Wheat and Barley Research, Karnal, India
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Arun K. Joshi
- Borlaug Institute for South Asia (BISA), Ludhiana, India
- CIMMYT-India, New Delhi, India
| | - Ravi P. Singh
- International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico
| | - Pawan K. Singh
- International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico
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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.). Front Plant Sci 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Widener S, Graef G, Lipka AE, Jarquin D. An Assessment of the Factors Influencing the Prediction Accuracy of Genomic Prediction Models Across Multiple Environments. Front Genet 2021; 12:689319. [PMID: 34367248 PMCID: PMC8343134 DOI: 10.3389/fgene.2021.689319] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022] Open
Abstract
The effects of climate change create formidable challenges for breeders striving to produce sufficient food quantities in rapidly changing environments. It is therefore critical to investigate the ability of multi-environment genomic prediction (GP) models to predict genomic estimated breeding values (GEBVs) in extreme environments. Exploration of the impact of training set composition on the accuracy of such GEBVs is also essential. Accordingly, we examined the influence of the number of training environments and the use of environmental covariates (ECs) in GS models on four subsets of n = 500 lines of the soybean nested association mapping (SoyNAM) panel grown in nine environments in the US-North Central Region. The ensuing analyses provided insights into the influence of both of these factors for predicting grain yield in the most and the least extreme of these environments. We found that only a subset of the available environments was needed to obtain the highest observed prediction accuracies. The inclusion of ECs in the GP model did not substantially increase prediction accuracies relative to competing models, and instead more often resulted in negative prediction accuracies. Combined with the overall low prediction accuracies for grain yield in the most extreme environment, our findings highlight weaknesses in current GP approaches for prediction in extreme environments, and point to specific areas on which to focus future research efforts.
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Affiliation(s)
- Sarah Widener
- Department of Crop Sciences, University of Illinois, Urbana, IL, United States
| | - George Graef
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana, IL, United States
| | - Diego Jarquin
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States
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Odilbekov F, Armoniené R, Koc A, Svensson J, Chawade A. GWAS-Assisted Genomic Prediction to Predict Resistance to Septoria Tritici Blotch in Nordic Winter Wheat at Seedling Stage. Front Genet 2019; 10:1224. [PMID: 31850073 PMCID: PMC6901976 DOI: 10.3389/fgene.2019.01224] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 11/05/2019] [Indexed: 02/01/2023] Open
Abstract
Septoria tritici blotch (STB) disease caused by Zymoseptoria tritici is one of the most damaging diseases of wheat causing significant yield losses worldwide. Identification and employment of resistant germplasm is the most cost-effective method to control STB. In this study, we characterized seedling stage resistance to STB in 175 winter wheat landraces and old cultivars of Nordic origin. The study revealed significant (p < 0.05) phenotypic differences in STB severity in the germplasm. Genome-wide association analysis (GWAS) using five different algorithms identified ten significant markers on five chromosomes. Six markers were localized within a region of 2 cM that contained seven candidate genes on chromosome 1B. Genomic prediction (GP) analysis resulted in a model with an accuracy of 0.47. To further improve the prediction efficiency, significant markers identified by GWAS were included as fixed effects in the GP model. Depending on the number of fixed effect markers, the prediction accuracy improved from 0.47 (without fixed effects) to 0.62 (all non-redundant GWAS markers as fixed effects), respectively. The resistant genotypes and single-nucleotide polymorphism (SNP) markers identified in the present study will serve as a valuable resource for future breeding for STB resistance in wheat. The results also highlight the benefits of integrating GWAS with GP to further improve the accuracy of GP.
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Affiliation(s)
- Firuz Odilbekov
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Rita Armoniené
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.,Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry (LAMMC), Akademija, Lithuania
| | - Alexander Koc
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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Sun Q, Wang P, Li W, Li W, Lu S, Yu Y, Zhao M, Meng Z. Genomic selection on shelling percentage and other traits for maize. Breed Sci 2019; 69:266-271. [PMID: 31481835 PMCID: PMC6711738 DOI: 10.1270/jsbbs.18141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 01/25/2019] [Indexed: 05/26/2023]
Abstract
Genomic selection (GS) is the one of the new method for molecular marker-assisted selection (MAS) that can improve selection efficiency and thereby accelerate selective breeding progress. In the present study, we used the exotic germplasm LK1 to improve the shelling percentage of Qi319 by GS. Genome-wide marker effects for each trait were estimated based on the performance of the testcross and SNP data for F2 progenies in the training population. The accuracy of genomic predictions was estimated as the correlation between marker-predicted genotypic values and phenotypic values of the testcrosses for each trait in the validation population. Our study result indicated that selection response for shell percentage was 33.7%, which is greater than those for grain yield, kernel number per ear, or grain moisture at harvest. Selection response for tassel branch number and weight per 100 kernels was greater than 60%. The Higher trait heritability resulted in better prediction efficiency; Prediction accuracy increased with the training population size; Prediction efficiency did not differ significantly between SNP densities of 1000 bp and 55,000 bp. The results of the present research project will provide a basis for genome-wide selection technology in maize breeding, and lay the groundwork for the application of GS to germplasms that are useful in China.
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Affiliation(s)
- Qi Sun
- Maize Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture, P.R. China,
No. 202 North of Industry Road, Licheng District, Jinan, Shandong Province 250100,
China
| | - Ping Wang
- Tai’an Academy of Agricultural Science,
No. 16 Tailai Road, Tai’an, Shandong Province, 271000,
China
| | - Wenlan Li
- Maize Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture, P.R. China,
No. 202 North of Industry Road, Licheng District, Jinan, Shandong Province 250100,
China
| | - Wencai Li
- Maize Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture, P.R. China,
No. 202 North of Industry Road, Licheng District, Jinan, Shandong Province 250100,
China
| | - Shouping Lu
- Maize Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture, P.R. China,
No. 202 North of Industry Road, Licheng District, Jinan, Shandong Province 250100,
China
| | - Yanli Yu
- Maize Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture, P.R. China,
No. 202 North of Industry Road, Licheng District, Jinan, Shandong Province 250100,
China
| | - Meng Zhao
- Maize Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture, P.R. China,
No. 202 North of Industry Road, Licheng District, Jinan, Shandong Province 250100,
China
| | - Zhaodong Meng
- Maize Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture, P.R. China,
No. 202 North of Industry Road, Licheng District, Jinan, Shandong Province 250100,
China
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Ikeogu UN, Akdemir D, Wolfe MD, Okeke UG, Chinedozi A, Jannink JL, Egesi CN. Genetic Correlation, Genome-Wide Association and Genomic Prediction of Portable NIRS Predicted Carotenoids in Cassava Roots. Front Plant Sci 2019; 10:1570. [PMID: 31867030 PMCID: PMC6904298 DOI: 10.3389/fpls.2019.01570] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Accepted: 11/08/2019] [Indexed: 05/21/2023]
Abstract
Random forests (RF) was used to correlate spectral responses to known wet chemistry carotenoid concentrations including total carotenoid content (TCC), all-trans β-carotene (ATBC), violaxanthin (VIO), lutein (LUT), 15-cis beta-carotene (15CBC), 13-cis beta-carotene (13CBC), alpha-carotene (AC), 9-cis beta-carotene (9CBC), and phytoene (PHY) from laboratory analysis of 173 cassava root samples in Columbia. The cross-validated correlations between the actual and estimated carotenoid values using RF ranged from 0.62 in PHY to 0.97 in ATBC. The developed models were used to evaluate the carotenoids of 594 cassava clones with spectral information collected across three locations in a national breeding program (NRCRI, Umudike), Nigeria. Both populations contained cassava clones characterized as white and yellow. The NRCRI evaluated phenotypes were used to assess the genetic correlations, conduct genome-wide association studies (GWAS), and genomic predictions. Estimates of genetic correlation showed various levels of the relationship among the carotenoids. The associations between TCC and the individual carotenoids were all significant (P < 0.001) with high positive values (r > 0.75, except in LUT and PHY where r < 0.3). The GWAS revealed significant genomic regions on chromosomes 1, 2, 4, 13, 14, and 15 associated with variation in at least one of the carotenoids. One of the identified candidate genes, phytoene synthase (PSY) has been widely reported for variation in TCC in cassava. On average, genomic prediction accuracies from the single-trait genomic best linear unbiased prediction (GBLUP) and RF as well as from a multiple-trait GBLUP model ranged from ∼0.2 in LUT and PHY to 0.52 in TCC. The multiple-trait GBLUP model gave slightly higher accuracies than the single trait GBLUP and RF models. This study is one of the initial attempts in understanding the genetic basis of individual carotenoids and demonstrates the usefulness of NIRS in cassava improvement.
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Affiliation(s)
- Ugochukwu N. Ikeogu
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
- Biotechnology Department, National Root Crops Research Institute, Umudike, Nigeria
- *Correspondence: Ugochukwu N. Ikeogu,
| | - Deniz Akdemir
- Cornell University Statistical Consulting Unit (CSCU), Cornell University, Ithaca, NY, United States
| | - Marnin D. Wolfe
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
| | - Uche G. Okeke
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
| | - Amaefula Chinedozi
- Biotechnology Department, National Root Crops Research Institute, Umudike, Nigeria
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
- Plant, Soil and Nutrition Research, Robert W. Holley Center for Agriculture & Health, Agricultural Research Service, United States Department of Agriculture (USDA), Ithaca, NY, United States
| | - Chiedozie N. Egesi
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
- Biotechnology Department, National Root Crops Research Institute, Umudike, Nigeria
- Cassava Breeding Department, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
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14
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Grattapaglia D, Silva-Junior OB, Resende RT, Cappa EP, Müller BSF, Tan B, Isik F, Ratcliffe B, El-Kassaby YA. Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding. Front Plant Sci 2018; 9:1693. [PMID: 30524463 PMCID: PMC6262028 DOI: 10.3389/fpls.2018.01693] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 10/31/2018] [Indexed: 05/18/2023]
Abstract
Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.
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Affiliation(s)
- Dario Grattapaglia
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Programa de Ciências Genômicas e BiotecnologiaUniversidade Católica de Brasília, Brasília, Brazil
- Departamento de Biologia CelularUniversidade de Brasília, Brasília, Brazil
- Department of Forestry and Environmental Resources, North Carolina State UniversityRaleigh, NC, United States
| | - Orzenil B. Silva-Junior
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Programa de Ciências Genômicas e BiotecnologiaUniversidade Católica de Brasília, Brasília, Brazil
| | | | - Eduardo P. Cappa
- Centro de Investigación de Recursos Naturales, Instituto de Recursos BiológicosINTA, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y TécnicasBuenos Aires, Argentina
| | - Bárbara S. F. Müller
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Departamento de Biologia CelularUniversidade de Brasília, Brasília, Brazil
| | - Biyue Tan
- Biomaterials DivisionStora Enso AB, Stockholm, Sweden
| | - Fikret Isik
- Department of Forestry and Environmental Resources, North Carolina State UniversityRaleigh, NC, United States
| | - Blaise Ratcliffe
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British ColumbiaVancouver, BC, Canada
| | - Yousry A. El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British ColumbiaVancouver, BC, Canada
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15
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Galiano-Carneiro AL, Miedaner T. Genetics of Resistance and Pathogenicity in the Maize/ Setosphaeria turcica Pathosystem and Implications for Breeding. Front Plant Sci 2017; 8:1490. [PMID: 28900437 PMCID: PMC5581881 DOI: 10.3389/fpls.2017.01490] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Accepted: 08/11/2017] [Indexed: 05/24/2023]
Abstract
Northern corn leaf blight (NCLB), the most devastating leaf pathogen in maize (Zea mays L.), is caused by the heterothallic ascomycete Setosphaeria turcica. The pathogen population shows an extremely high genetic diversity in tropical and subtropical regions. Varietal resistance is the most efficient technique to control NCLB. Host resistance can be qualitative based on race-specific Ht genes or quantitative controlled by many genes with small effects. Quantitative resistance is moderately to highly effective and should be more durable combatting all races of the pathogen. Quantitative resistance must, however, be analyzed in many environments (= location × year combinations) to select stable resistances. In the tropical and subtropical environments, quantitative resistance is the preferred option to manage NCLB epidemics. Resistance level can be increased in practical breeding programs by several recurrent selection cycles based on disease severity rating and/or by genomic selection. This review aims to address two important aspects of the NCLB pathosystem: the genetics of the fungus S. turcica and the modes of inheritance of the host plant maize, including successful breeding strategies regarding NCLB resistance. Both drivers of this pathosystem, pathogen, and host, must be taken into account to result in more durable resistance.
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Affiliation(s)
- Ana L. Galiano-Carneiro
- State Plant Breeding Institute, University of HohenheimStuttgart, Germany
- Kleinwanzlebener Saatzucht (KWS) SAAT SEEinbeck, Germany
| | - Thomas Miedaner
- State Plant Breeding Institute, University of HohenheimStuttgart, Germany
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16
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Iwata H, Minamikawa MF, Kajiya-Kanegae H, Ishimori M, Hayashi T. Genomics-assisted breeding in fruit trees. Breed Sci 2016; 66:100-15. [PMID: 27069395 PMCID: PMC4780794 DOI: 10.1270/jsbbs.66.100] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Accepted: 01/12/2016] [Indexed: 05/03/2023]
Abstract
Recent advancements in genomic analysis technologies have opened up new avenues to promote the efficiency of plant breeding. Novel genomics-based approaches for plant breeding and genetics research, such as genome-wide association studies (GWAS) and genomic selection (GS), are useful, especially in fruit tree breeding. The breeding of fruit trees is hindered by their long generation time, large plant size, long juvenile phase, and the necessity to wait for the physiological maturity of the plant to assess the marketable product (fruit). In this article, we describe the potential of genomics-assisted breeding, which uses these novel genomics-based approaches, to break through these barriers in conventional fruit tree breeding. We first introduce the molecular marker systems and whole-genome sequence data that are available for fruit tree breeding. Next we introduce the statistical methods for biparental linkage and quantitative trait locus (QTL) mapping as well as GWAS and GS. We then review QTL mapping, GWAS, and GS studies conducted on fruit trees. We also review novel technologies for rapid generation advancement. Finally, we note the future prospects of genomics-assisted fruit tree breeding and problems that need to be overcome in the breeding.
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Affiliation(s)
- Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo,
1-1-1 Yayoi, Bunkyo, Tokyo 113-8657,
Japan
- Corresponding author (e-mail: )
| | - Mai F. Minamikawa
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo,
1-1-1 Yayoi, Bunkyo, Tokyo 113-8657,
Japan
| | - Hiromi Kajiya-Kanegae
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo,
1-1-1 Yayoi, Bunkyo, Tokyo 113-8657,
Japan
| | - Motoyuki Ishimori
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo,
1-1-1 Yayoi, Bunkyo, Tokyo 113-8657,
Japan
| | - Takeshi Hayashi
- Agroinfomatics Division, NARO Agricultural Research Center (NARC),
3-1-1 Kannondai, Tsukuba, Ibaraki 305-8666,
Japan
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17
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He J, Zhao X, Laroche A, Lu ZX, Liu H, Li Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 2014; 5:484. [PMID: 25324846 DOI: 10.3389/fpls.2014.00484/full] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 09/02/2014] [Indexed: 05/23/2023]
Abstract
Marker-assisted selection (MAS) refers to the use of molecular markers to assist phenotypic selections in crop improvement. Several types of molecular markers, such as single nucleotide polymorphism (SNP), have been identified and effectively used in plant breeding. The application of next-generation sequencing (NGS) technologies has led to remarkable advances in whole genome sequencing, which provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To further broaden NGS usages to large crop genomes such as maize and wheat, genotyping-by-sequencing (GBS) has been developed and applied in sequencing multiplexed samples that combine molecular marker discovery and genotyping. GBS is a novel application of NGS protocols for discovering and genotyping SNPs in crop genomes and populations. The GBS approach includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane of flow cells. Bioinformatic pipelines are needed to analyze and interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS has been successfully used in implementing genome-wide association study (GWAS), genomic diversity study, genetic linkage analysis, molecular marker discovery and genomic selection under a large scale of plant breeding programs.
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Affiliation(s)
- Jiangfeng He
- Inner Mongolia Academy of Agriculture and Husbandry Science Hohhot, China ; Lethbridge Research Centre, Agriculture and Agri-Food Canada Lethbridge, AB, Canada
| | - Xiaoqing Zhao
- Inner Mongolia Academy of Agriculture and Husbandry Science Hohhot, China
| | - André Laroche
- Lethbridge Research Centre, Agriculture and Agri-Food Canada Lethbridge, AB, Canada
| | - Zhen-Xiang Lu
- Lethbridge Research Centre, Agriculture and Agri-Food Canada Lethbridge, AB, Canada
| | - HongKui Liu
- Inner Mongolia Academy of Agriculture and Husbandry Science Hohhot, China
| | - Ziqin Li
- Inner Mongolia Academy of Agriculture and Husbandry Science Hohhot, China
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18
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He J, Zhao X, Laroche A, Lu ZX, Liu H, Li Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 2014; 5:484. [PMID: 25324846 PMCID: PMC4179701 DOI: 10.3389/fpls.2014.00484] [Citation(s) in RCA: 257] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 09/02/2014] [Indexed: 05/05/2023]
Abstract
Marker-assisted selection (MAS) refers to the use of molecular markers to assist phenotypic selections in crop improvement. Several types of molecular markers, such as single nucleotide polymorphism (SNP), have been identified and effectively used in plant breeding. The application of next-generation sequencing (NGS) technologies has led to remarkable advances in whole genome sequencing, which provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To further broaden NGS usages to large crop genomes such as maize and wheat, genotyping-by-sequencing (GBS) has been developed and applied in sequencing multiplexed samples that combine molecular marker discovery and genotyping. GBS is a novel application of NGS protocols for discovering and genotyping SNPs in crop genomes and populations. The GBS approach includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane of flow cells. Bioinformatic pipelines are needed to analyze and interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS has been successfully used in implementing genome-wide association study (GWAS), genomic diversity study, genetic linkage analysis, molecular marker discovery and genomic selection under a large scale of plant breeding programs.
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Affiliation(s)
- Jiangfeng He
- Inner Mongolia Academy of Agriculture and Husbandry ScienceHohhot, China
- Lethbridge Research Centre, Agriculture and Agri-Food CanadaLethbridge, AB, Canada
| | - Xiaoqing Zhao
- Inner Mongolia Academy of Agriculture and Husbandry ScienceHohhot, China
| | - André Laroche
- Lethbridge Research Centre, Agriculture and Agri-Food CanadaLethbridge, AB, Canada
| | - Zhen-Xiang Lu
- Lethbridge Research Centre, Agriculture and Agri-Food CanadaLethbridge, AB, Canada
| | - HongKui Liu
- Inner Mongolia Academy of Agriculture and Husbandry ScienceHohhot, China
- *Correspondence: Hongkui Liu and Ziqin Li, Inner Mongolia Academy of Agriculture and Husbandry Science, Zhaojun Road 22, Hohhot, Inner Mongolia 010031, China e-mail: ;
| | - Ziqin Li
- Inner Mongolia Academy of Agriculture and Husbandry ScienceHohhot, China
- *Correspondence: Hongkui Liu and Ziqin Li, Inner Mongolia Academy of Agriculture and Husbandry Science, Zhaojun Road 22, Hohhot, Inner Mongolia 010031, China e-mail: ;
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Iwata H, Hayashi T, Terakami S, Takada N, Sawamura Y, Yamamoto T. Potential assessment of genome-wide association study and genomic selection in Japanese pear Pyrus pyrifolia. Breed Sci 2013; 63:125-40. [PMID: 23641189 PMCID: PMC3621438 DOI: 10.1270/jsbbs.63.125] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2012] [Accepted: 12/09/2012] [Indexed: 05/03/2023]
Abstract
Although the potential of marker-assisted selection (MAS) in fruit tree breeding has been reported, bi-parental QTL mapping before MAS has hindered the introduction of MAS to fruit tree breeding programs. Genome-wide association studies (GWAS) are an alternative to bi-parental QTL mapping in long-lived perennials. Selection based on genomic predictions of breeding values (genomic selection: GS) is another alternative for MAS. This study examined the potential of GWAS and GS in pear breeding with 76 Japanese pear cultivars to detect significant associations of 162 markers with nine agronomic traits. We applied multilocus Bayesian models accounting for ordinal categorical phenotypes for GWAS and GS model training. Significant associations were detected at harvest time, black spot resistance and the number of spurs and two of the associations were closely linked to known loci. Genome-wide predictions for GS were accurate at the highest level (0.75) in harvest time, at medium levels (0.38-0.61) in resistance to black spot, firmness of flesh, fruit shape in longitudinal section, fruit size, acid content and number of spurs and at low levels (<0.2) in all soluble solid content and vigor of tree. Results suggest the potential of GWAS and GS for use in future breeding programs in Japanese pear.
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Affiliation(s)
- Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan
- Corresponding author (e-mail: )
| | - Takeshi Hayashi
- National Agricultural Research Center, National Agriculture and Food Research Organization, 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8666, Japan
| | - Shingo Terakami
- National Institute of Fruit Tree Science, National Agriculture and Food Research Organization, 2-1 Fujimoto, Tsukuba, Ibaraki 305-8605, Japan
| | - Norio Takada
- National Institute of Fruit Tree Science, National Agriculture and Food Research Organization, 2-1 Fujimoto, Tsukuba, Ibaraki 305-8605, Japan
| | - Yutaka Sawamura
- National Institute of Fruit Tree Science, National Agriculture and Food Research Organization, 2-1 Fujimoto, Tsukuba, Ibaraki 305-8605, Japan
| | - Toshiya Yamamoto
- National Institute of Fruit Tree Science, National Agriculture and Food Research Organization, 2-1 Fujimoto, Tsukuba, Ibaraki 305-8605, Japan
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