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Thudi M, Samineni S, Li W, Boer MP, Roorkiwal M, Yang Z, Ladejobi F, Zheng C, Chitikineni A, Nayak S, He Z, Valluri V, Bajaj P, Khan AW, Gaur PM, van Eeuwijk F, Mott R, Xin L, Varshney RK. Whole genome resequencing and phenotyping of MAGIC population for high resolution mapping of drought tolerance in chickpea. THE PLANT GENOME 2024; 17:e20333. [PMID: 37122200 DOI: 10.1002/tpg2.20333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/17/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Terminal drought is one of the major constraints to crop production in chickpea (Cicer arietinum L.). In order to map drought tolerance related traits at high resolution, we sequenced multi-parent advanced generation intercross (MAGIC) population using whole genome resequencing approach and phenotyped it under drought stress environments for two consecutive years (2013-14 and 2014-15). A total of 52.02 billion clean reads containing 4.67 TB clean data were generated on the 1136 MAGIC lines and eight parental lines. Alignment of clean data on to the reference genome enabled identification of a total, 932,172 of SNPs, 35,973 insertions, and 35,726 deletions among the parental lines. A high-density genetic map was constructed using 57,180 SNPs spanning a map distance of 1606.69 cM. Using compressed mixed linear model, genome-wide association study (GWAS) enabled us to identify 737 markers significantly associated with days to 50% flowering, days to maturity, plant height, 100 seed weight, biomass, and harvest index. In addition to the GWAS approach, an identity-by-descent (IBD)-based mixed model approach was used to map quantitative trait loci (QTLs). The IBD-based mixed model approach detected major QTLs that were comparable to those from the GWAS analysis as well as some exclusive QTLs with smaller effects. The candidate genes like FRIGIDA and CaTIFY4b can be used for enhancing drought tolerance in chickpea. The genomic resources, genetic map, marker-trait associations, and QTLs identified in the study are valuable resources for the chickpea community for developing climate resilient chickpeas.
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Affiliation(s)
- Mahendar Thudi
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
- Department of Agricultural Biotechnology and Molecular Biology, Dr. Rajendra Prasad Central Agricultural University (RPCAU), Pusa, India
| | - Srinivasan Samineni
- Crop Improvement Program-Asia, ICRISAT, Patancheru, India
- International Center for Biosaline Agriculture, Dubai, United Arab Emirates
| | - Wenhao Li
- Wageningen University and Research, Wageningen, The Netherlands
| | - Martin P Boer
- Wageningen University and Research, Wageningen, The Netherlands
| | - Manish Roorkiwal
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
- Khalifa Center for Genetic Engineering and Biotechnology (KCGEB), United Arab Emirates University, Al Ain, United Arab Emirates
| | | | - Funmi Ladejobi
- Department of Genetics, Evolution and Environment, Genetics Institute, University College London, London, UK
| | - Chaozhi Zheng
- Wageningen University and Research, Wageningen, The Netherlands
- BGI-Shenzhen, Shenzhen, China
| | - Annapurna Chitikineni
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | - Sourav Nayak
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | | | - Vinod Valluri
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | - Prasad Bajaj
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | - Aamir W Khan
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | - Pooran M Gaur
- Department of Agricultural Biotechnology and Molecular Biology, Dr. Rajendra Prasad Central Agricultural University (RPCAU), Pusa, India
- The UWA Institute of Agriculture, University of Western Australia, Perth, Western Australia, Australia
| | | | - Richard Mott
- Department of Genetics, Evolution and Environment, Genetics Institute, University College London, London, UK
| | - Liu Xin
- BGI-Shenzhen, Shenzhen, China
| | - Rajeev K Varshney
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
- Centre for Crop & Food Innovation, WA State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia
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Kaur R, Vasistha NK, Ravat VK, Mishra VK, Sharma S, Joshi AK, Dhariwal R. Genome-Wide Association Study Reveals Novel Powdery Mildew Resistance Loci in Bread Wheat. PLANTS (BASEL, SWITZERLAND) 2023; 12:3864. [PMID: 38005757 PMCID: PMC10675159 DOI: 10.3390/plants12223864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 11/26/2023]
Abstract
Powdery mildew (PM), caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt), significantly threatens global bread wheat production. Although the use of resistant cultivars is an effective strategy for managing PM, currently available wheat cultivars lack sufficient levels of resistance. To tackle this challenge, we conducted a comprehensive genome-wide association study (GWAS) using a diverse panel of 286 bread wheat genotypes. Over three consecutive years (2020-2021, 2021-2022, and 2022-2023), these genotypes were extensively evaluated for PM severity under field conditions following inoculation with virulent Bgt isolates. The panel was previously genotyped using the Illumina 90K Infinium iSelect assay to obtain genome-wide single-nucleotide polymorphism (SNP) marker coverage. By applying FarmCPU, a multilocus mixed model, we identified a total of 113 marker-trait associations (MTAs) located on chromosomes 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, and 7B at a significance level of p ≤ 0.001. Notably, four novel MTAs on chromosome 6B were consistently detected in 2020-2021 and 2021-2022. Furthermore, within the confidence intervals of the identified SNPs, we identified 96 candidate genes belonging to different proteins including 12 disease resistance/host-pathogen interaction-related protein families. Among these, protein kinases, leucine-rich repeats, and zinc finger proteins were of particular interest due to their potential roles in PM resistance. These identified loci can serve as targets for breeding programs aimed at developing disease-resistant wheat cultivars.
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Affiliation(s)
- Ramandeep Kaur
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Sigh Gill Akal College of Agriculture, Eternal University, Baru Sahib, Sirmour 173101, India
| | - Neeraj Kumar Vasistha
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Sigh Gill Akal College of Agriculture, Eternal University, Baru Sahib, Sirmour 173101, India
- Department of Genetics and Plant Breeding, Rajiv Gandhi University, Rono Hills, Itanagar 791112, India
| | - Vikas Kumar Ravat
- Department of Plant Pathology, Rajiv Gandhi University, Rono Hills, Itanagar 791112, India
| | - Vinod Kumar Mishra
- Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Sandeep Sharma
- Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Arun Kumar Joshi
- Borlaug Institute for South Asia (BISA), NASC Complex, DPS Marg, New Delhi 110012, India
- International Maize and Wheat Improvement Center (CIMMYT) Regional Office, NASC Complex, DPS Marg, New Delhi 110012, India
| | - Raman Dhariwal
- Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, 5403 1 Avenue South, Lethbridge, AB T1J 4B1, Canada
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Dowell JA, Mason C. Candidate pathway association and genome-wide association approaches reveal alternative genetic architectures of carotenoid content in cultivated sunflower ( Helianthus annuus). APPLICATIONS IN PLANT SCIENCES 2023; 11:e11558. [PMID: 38106540 PMCID: PMC10719882 DOI: 10.1002/aps3.11558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 05/10/2023] [Accepted: 05/19/2023] [Indexed: 12/19/2023]
Abstract
Premise The explosion of available genomic data poses significant opportunities and challenges for genome-wide association studies. Current approaches via linear mixed models (LMM) are straightforward but prevent flexible assumptions of an a priori genomic architecture, while Bayesian sparse LMMs (BSLMMs) allow this flexibility. Complex traits, such as specialized metabolites, are subject to various hierarchical effects, including gene regulation, enzyme efficiency, and the availability of reactants. Methods To identify alternative genetic architectures, we examined the genetic architecture underlying the carotenoid content of an association mapping panel of Helianthus annuus individuals using multiple BSLMM and LMM frameworks. Results The LMMs of genome-wide single-nucleotide polymorphisms (SNPs) identified a single transcription factor responsible for the observed variations in the carotenoid content; however, a BSLMM of the SNPs with the bottom 1% of effect sizes from the results of the LMM identified multiple biologically relevant quantitative trait loci (QTLs) for carotenoid content external to the known (annotated) carotenoid pathway. A candidate pathway analysis (CPA) suggested a β-carotene isomerase to be the enzyme with the highest impact on the observed carotenoid content within the carotenoid pathway. Discussion While traditional LMM approaches suggested a single unknown transcription factor associated with carotenoid content variation in sunflower petals, BSLMM proposed several QTLs with interpretable biological relevance to this trait. In addition, the CPA allowed for the dissection of the regulatory vs. biosynthetic genetic architectures underlying this metabolic trait.
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Affiliation(s)
- Jordan A. Dowell
- Department of Plant SciencesUniversity of CaliforniaDavisCalifornia95616USA
- Present address:
Department of Biological SciencesLouisiana State UniversityBaton RougeLouisiana70803USA
| | - Chase Mason
- Department of BiologyUniversity of Central FloridaOrlandoFlorida32816USA
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Kumar N, Boatwright JL, Sapkota S, Brenton ZW, Ballén-Taborda C, Myers MT, Cox WA, Jordan KE, Kresovich S, Boyles RE. Discovering useful genetic variation in the seed parent gene pool for sorghum improvement. Front Genet 2023; 14:1221148. [PMID: 37790706 PMCID: PMC10544336 DOI: 10.3389/fgene.2023.1221148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/04/2023] [Indexed: 10/05/2023] Open
Abstract
Multi-parent populations contain valuable genetic material for dissecting complex, quantitative traits and provide a unique opportunity to capture multi-allelic variation compared to the biparental populations. A multi-parent advanced generation inter-cross (MAGIC) B-line (MBL) population composed of 708 F6 recombinant inbred lines (RILs), was recently developed from four diverse founders. These selected founders strategically represented the four most prevalent botanical races (kafir, guinea, durra, and caudatum) to capture a significant source of genetic variation to study the quantitative traits in grain sorghum [Sorghum bicolor (L.) Moench]. MBL was phenotyped at two field locations for seven yield-influencing traits: panicle type (PT), days to anthesis (DTA), plant height (PH), grain yield (GY), 1000-grain weight (TGW), tiller number per meter (TN) and yield per panicle (YPP). High phenotypic variation was observed for all the quantitative traits, with broad-sense heritabilities ranging from 0.34 (TN) to 0.84 (PH). The entire population was genotyped using Diversity Arrays Technology (DArTseq), and 8,800 single nucleotide polymorphisms (SNPs) were generated. A set of polymorphic, quality-filtered markers (3,751 SNPs) and phenotypic data were used for genome-wide association studies (GWAS). We identified 52 marker-trait associations (MTAs) for the seven traits using BLUPs generated from replicated plots in two locations. We also identified desirable allelic combinations based on the plant height loci (Dw1, Dw2, and Dw3), which influences yield related traits. Additionally, two novel MTAs were identified each on Chr1 and Chr7 for yield traits independent of dwarfing genes. We further performed a multi-variate adaptive shrinkage analysis and 15 MTAs with pleiotropic effect were identified. The five best performing MBL progenies were selected carrying desirable allelic combinations. Since the MBL population was designed to capture significant diversity for maintainer line (B-line) accessions, these progenies can serve as valuable resources to develop superior sorghum hybrids after validation of their general combining abilities via crossing with elite pollinators. Further, newly identified desirable allelic combinations can be used to enrich the maintainer germplasm lines through marker-assisted backcross breeding.
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Affiliation(s)
- Neeraj Kumar
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - J. Lucas Boatwright
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Sirjan Sapkota
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
| | - Zachary W. Brenton
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- Carolina Seed Systems, Darlington, SC, United States
| | - Carolina Ballén-Taborda
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
| | - Matthew T. Myers
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - William A. Cox
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Kathleen E. Jordan
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Stephen Kresovich
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Feed the Future Innovation Lab for Crop Improvement, Cornell University, Ithaca, NY, United States
| | - Richard E. Boyles
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
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5
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Chawla R, Poonia A, Samantara K, Mohapatra SR, Naik SB, Ashwath MN, Djalovic IG, Prasad PVV. Green revolution to genome revolution: driving better resilient crops against environmental instability. Front Genet 2023; 14:1204585. [PMID: 37719711 PMCID: PMC10500607 DOI: 10.3389/fgene.2023.1204585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 08/11/2023] [Indexed: 09/19/2023] Open
Abstract
Crop improvement programmes began with traditional breeding practices since the inception of agriculture. Farmers and plant breeders continue to use these strategies for crop improvement due to their broad application in modifying crop genetic compositions. Nonetheless, conventional breeding has significant downsides in regard to effort and time. Crop productivity seems to be hitting a plateau as a consequence of environmental issues and the scarcity of agricultural land. Therefore, continuous pursuit of advancement in crop improvement is essential. Recent technical innovations have resulted in a revolutionary shift in the pattern of breeding methods, leaning further towards molecular approaches. Among the promising approaches, marker-assisted selection, QTL mapping, omics-assisted breeding, genome-wide association studies and genome editing have lately gained prominence. Several governments have progressively relaxed their restrictions relating to genome editing. The present review highlights the evolutionary and revolutionary approaches that have been utilized for crop improvement in a bid to produce climate-resilient crops observing the consequence of climate change. Additionally, it will contribute to the comprehension of plant breeding succession so far. Investing in advanced sequencing technologies and bioinformatics will deepen our understanding of genetic variations and their functional implications, contributing to breakthroughs in crop improvement and biodiversity conservation.
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Affiliation(s)
- Rukoo Chawla
- Department of Genetics and Plant Breeding, Maharana Pratap University of Agriculture and Technology, Udaipur, Rajasthan, India
| | - Atman Poonia
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh Haryana Agricultural University, Bawal, Haryana, India
| | - Kajal Samantara
- Institute of Technology, University of Tartu, Tartu, Estonia
| | - Sourav Ranjan Mohapatra
- Department of Forest Biology and Tree Improvement, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India
| | - S. Balaji Naik
- Institute of Integrative Biology and Systems, University of Laval, Quebec City, QC, Canada
| | - M. N. Ashwath
- Department of Forest Biology and Tree Improvement, Kerala Agricultural University, Thrissur, Kerala, India
| | - Ivica G. Djalovic
- Institute of Field and Vegetable Crops, National Institute of the Republic of Serbia, Novi Sad, Serbia
| | - P. V. Vara Prasad
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
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Li Y, Si Z, Wang G, Shi Z, Chen J, Qi G, Jin S, Han Z, Gao W, Tian Y, Mao Y, Fang L, Hu Y, Chen H, Zhu X, Zhang T. Genomic insights into the genetic basis of cotton breeding in China. MOLECULAR PLANT 2023; 16:662-677. [PMID: 36738104 DOI: 10.1016/j.molp.2023.01.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/08/2022] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
The excellent Upland cotton (Gossypium hirsutum) cultivars developed since 1949 have made a huge contribution to cotton production in China, the world's largest producer and consumer of cotton. However, the genetic and genomic basis for the improvements of these cotton cultivars remains largely unclear. In this study, we selected 16 Upland cotton cultivars with important historical status in Chinese cotton breeding and constructed a multiparent, advanced generation, intercross (MAGIC) population comprising 920 recombinant inbred lines. A genome-wide association study using the MAGIC population identified 54 genomic loci associated with lint yield and fiber quality. Of them, 25 (46.30%) pleiotropic genomic loci cause simultaneous changes of lint yield and/or fiber quality traits, revealing complex trade-offs and linkage drags in Upland cotton agronomic traits. Deep sequencing data of 11 introduced ancestor cultivars and publicly available resequencing datasets of 839 cultivars developed in China during the past 70 years were integrated to explore the historical distribution and origin of the elite or selected alleles. Interestingly, 85% of these elite alleles were selected and fixed from different American ancestors, consistent with cotton breeding practices in China. However, seven elite alleles of native origin that are responsible for Fusarium wilt resistance, early maturing, good-quality fiber, and other characteristics were not found in American ancestors but have greatly contributed to Chinese cotton breeding and wide cultivation. Taken together, these results provide a genetic basis for further improving cotton cultivars and reveal that the genetic composition of Chinese cotton cultivars is narrow and mainly derived from early introduced American varieties.
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Affiliation(s)
- Yiqian Li
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Zhanfeng Si
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Guoping Wang
- Xinjiang Production and Construction Corps Seventh Division Agricultural Research Institute, Kuintun, China
| | - Zhuolin Shi
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Jinwen Chen
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Guoan Qi
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Shangkun Jin
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Zegang Han
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Wenhao Gao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Yue Tian
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China; State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China; College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, China
| | - Yun Mao
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Lei Fang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Yan Hu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Hong Chen
- Key Laboratory of China Northwestern Inland Region, Ministry of Agriculture, Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Xiefei Zhu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Tianzhen Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, the Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.
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Takanashi H. Genetic control of morphological traits useful for improving sorghum. BREEDING SCIENCE 2023; 73:57-69. [PMID: 37168813 PMCID: PMC10165342 DOI: 10.1270/jsbbs.22069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/14/2022] [Indexed: 05/13/2023]
Abstract
Global climate change and global warming, coupled with the growing population, have raised concerns about sustainable food supply and bioenergy demand. Sorghum [Sorghum bicolor (L.) Moench] ranks fifth among cereals produced worldwide; it is a C4 crop with a higher stress tolerance than other major cereals and has a wide range of uses, such as grains, forage, and biomass. Therefore, sorghum has attracted attention as a promising crop for achieving sustainable development goals (SDGs). In addition, sorghum is a suitable genetic model for C4 grasses because of its high morphological diversity and relatively small genome size compared to other C4 grasses. Although sorghum breeding and genetic studies have lagged compared to other crops such as rice and maize, recent advances in research have identified several genes and many quantitative trait loci (QTLs) that control important agronomic traits in sorghum. This review outlines traits and genetic information with a focus on morphogenetic aspects that may be useful in sorghum breeding for grain and biomass utilization.
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Affiliation(s)
- Hideki Takanashi
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
- Corresponding author (e-mail: )
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8
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Cheng L, Li M, Wang Y, Han Q, Hao Y, Qiao Z, Zhang W, Qiu L, Gong A, Zhang Z, Li T, Luo S, Tang L, Liu D, Yin H, Lu S, Balbuena TS, Zhao Y. Transcriptome-based variations effectively untangling the intraspecific relationships and selection signals in Xinyang Maojian tea population. FRONTIERS IN PLANT SCIENCE 2023; 14:1114284. [PMID: 36890899 PMCID: PMC9986275 DOI: 10.3389/fpls.2023.1114284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
As one of the world's top three popular non-alcoholic beverages, tea is economically and culturally valuable. Xinyang Maojian, this elegant green tea, is one of the top ten famous tea in China and has gained prominence for thousands of years. However, the cultivation history of Xinyang Maojian tea population and selection signals of differentiation from the other major variety Camellia sinensis var. assamica (CSA) remain unclear. We newly generated 94 Camellia sinensis (C. sinensis) transcriptomes including 59 samples in the Xinyang area and 35 samples collected from 13 other major tea planting provinces in China. Comparing the very low resolution of phylogeny inferred from 1785 low-copy nuclear genes with 94 C. sinensis samples, we successfully resolved the phylogeny of C. sinensis samples by 99,115 high-quality SNPs from the coding region. The sources of tea planted in the Xinyang area were extensive and complex. Specifically, Shihe District and Gushi County were the two earliest tea planting areas in Xinyang, reflecting a long history of tea planting. Furthermore, we identified numerous selection sweeps during the differentiation of CSA and CSS and these positive selection genes are involved in many aspects such as regulation of secondary metabolites synthesis, amino acid metabolism, photosynthesis, etc. Numerous specific selective sweeps of modern cultivars were annotated with functions in various different aspects, indicating the CSS and CSA populations possibly underwent independent specific domestication processes. Our study indicated that transcriptome-based SNP-calling is an efficient and cost-effective method in untangling intraspecific phylogenetic relationships. This study provides a significant understanding of the cultivation history of the famous Chinese tea Xinyang Maojian and unravels the genetic basis of physiological and ecological differences between the two major tea subspecies.
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Affiliation(s)
- Lin Cheng
- Henan International Joint Laboratory of Tea-oil tree Biology and High Value Utilization, Xinyang Normal University, Xinyang, Henan, China
- College of Life Sciences, Xinyang Normal University, Xinyang, Henan, China
| | - Mengge Li
- Henan International Joint Laboratory of Tea-oil tree Biology and High Value Utilization, Xinyang Normal University, Xinyang, Henan, China
- College of Life Sciences, Xinyang Normal University, Xinyang, Henan, China
| | - Yachao Wang
- Laboratory of Systematic Evolution and Biogeography of Woody Plants, School of Ecology and Nature Conservation, Beijing Forestry University, Beijing, China
| | - Qunwei Han
- Henan International Joint Laboratory of Tea-oil tree Biology and High Value Utilization, Xinyang Normal University, Xinyang, Henan, China
- College of Life Sciences, Xinyang Normal University, Xinyang, Henan, China
| | - Yanlin Hao
- Henan International Joint Laboratory of Tea-oil tree Biology and High Value Utilization, Xinyang Normal University, Xinyang, Henan, China
- College of Life Sciences, Xinyang Normal University, Xinyang, Henan, China
| | - Zhen Qiao
- Henan International Joint Laboratory of Tea-oil tree Biology and High Value Utilization, Xinyang Normal University, Xinyang, Henan, China
- College of Life Sciences, Xinyang Normal University, Xinyang, Henan, China
| | - Wei Zhang
- College of Life Sciences, Xinyang Normal University, Xinyang, Henan, China
| | - Lin Qiu
- Institute of Forestry Science, Xinyang Forestry Bureau, Xinyang, Henan, China
| | - Andong Gong
- Henan International Joint Laboratory of Tea-oil tree Biology and High Value Utilization, Xinyang Normal University, Xinyang, Henan, China
- College of Life Sciences, Xinyang Normal University, Xinyang, Henan, China
| | - Zhihan Zhang
- College of Engineering and Technology, Northeast Forestry University, Harbin, China
| | - Tao Li
- College of Agriculture, Guizhou University, Guiyang, China
| | - Shanshan Luo
- College of Agriculture, Guizhou University, Guiyang, China
| | - Linshuang Tang
- College of Agriculture, Guizhou University, Guiyang, China
| | - Daliang Liu
- College of Agriculture, Guizhou University, Guiyang, China
| | - Hao Yin
- College of Agriculture, Guizhou University, Guiyang, China
| | - Song Lu
- College of Agriculture, Guizhou University, Guiyang, China
| | - Tiago Santana Balbuena
- Department of Agricultural, Livestock and Environmental Biotechnology, Sao Paulo State University, Jaboticabal, Brazil
| | - Yiyong Zhao
- College of Agriculture, Guizhou University, Guiyang, China
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Yuan G, Sun K, Yu W, Jiang Z, Jiang C, Liu D, Wen L, Si H, Wu F, Meng H, Cheng L, Yang A, Wang Y. Development of a MAGIC population and high-resolution quantitative trait mapping for nicotine content in tobacco. FRONTIERS IN PLANT SCIENCE 2023; 13:1086950. [PMID: 36704165 PMCID: PMC9871594 DOI: 10.3389/fpls.2022.1086950] [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: 11/01/2022] [Accepted: 12/02/2022] [Indexed: 06/18/2023]
Abstract
Multiparent Advanced Generation Inter-Cross (MAGIC) population is an ideal genetic and breeding material for quantitative trait locus (QTL) mapping and molecular breeding. In this study, a MAGIC population derived from eight tobacco parents was developed. Eight parents and 560 homozygous lines were genotyped by a 430K single-nucleotide polymorphism (SNP) chip assay and phenotyped for nicotine content under different conditions. Four QTLs associated with nicotine content were detected by genome-wide association mapping (GWAS), and one major QTL, named qNIC7-1, was mapped repeatedly under different conditions. Furthermore, by combining forward mapping, bioinformatics analysis and gene editing, we identified an ethylene response factor (ERF) transcription factor as a candidate gene underlying the major QTL qNIC7-1 for nicotine content in tobacco. A presence/absence variation (PAV) at qNIC7-1 confers changes in nicotine content. Overall, the large size of this MAGIC population, diverse genetic composition, balanced parental contributions and high levels of recombination all contribute to its value as a genetic and breeding resource. The application of the tobacco MAGIC population for QTL mapping and detecting rare allelic variation was demonstrated using nicotine content as a proof of principle.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Lirui Cheng
- *Correspondence: Lirui Cheng, ; Aiguo Yang, ; Yuanying Wang,
| | - Aiguo Yang
- *Correspondence: Lirui Cheng, ; Aiguo Yang, ; Yuanying Wang,
| | - Yuanying Wang
- *Correspondence: Lirui Cheng, ; Aiguo Yang, ; Yuanying Wang,
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10
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Fang DD, Thyssen GN, Wang M, Jenkins JN, McCarty JC, Jones DC. Genomic confirmation of Gossypium barbadense introgression into G. hirsutum and a subsequent MAGIC population. Mol Genet Genomics 2023; 298:143-152. [PMID: 36346467 DOI: 10.1007/s00438-022-01974-3] [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: 08/26/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
Introgression of superior fiber traits from Pima cotton (Gossypium barbadense, GB) into high yield Upland cotton (G. hirsutum) has been a breeding objective for many years in a few breeding programs in the world. However, progress has been very slow due to introgression barriers resulting from whole genome hybridization between the two species. To minimize such barriers, chromosome substitution lines (CS-B) from Pima cotton 3-79 in an Upland cotton cultivar TM-1 were developed. A multiparent advanced generation inter-cross (MAGIC) population consisting of 180 recombinant inbred lines (RILs) was subsequently made using the 18 CS-B lines and three Upland cotton cultivars as parents. In this research, we sequenced the whole genomes of the 21 parents and 180 RILs to examine the G. barbadense introgression. Of the 18 CS-B lines, 11 contained the target GB chromosome or chromosome segment, two contained more than two GB chromosomes, and five did not have the expected introgression. Residual introgression in non-target chromosomes was prevalent in all CS-B lines. A clear structure existed in the MAGIC population and the 180 RILs were distributed into three groups, i.e., high, moderate, and low GB introgression. Large blocks of GB chromosome introgression were still present in some RILs after five cycles of random-mating, an indication of recombination suppression or other unknown reasons present in the population. Identity by descent analysis revealed that the MAGIC RILs contained less introgression than expected. This research presents an insight on understanding the complex problems of introgression between cotton species.
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Affiliation(s)
- David D Fang
- Cotton Fiber Bioscience Research Unit, USDA-ARS, Southern Regional Research Center, New Orleans, LA, 70124, USA.
| | - Gregory N Thyssen
- Cotton Fiber Bioscience Research Unit, USDA-ARS, Southern Regional Research Center, New Orleans, LA, 70124, USA
| | - Maojun Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Johnie N Jenkins
- Genetics and Sustainable Agriculture Research Unit, USDA-ARS, Mississippi State, MS, 39762, USA
| | - Jack C McCarty
- Genetics and Sustainable Agriculture Research Unit, USDA-ARS, Mississippi State, MS, 39762, USA
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11
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Flame resistant cotton lines generated by synergistic epistasis in a MAGIC population. PLoS One 2023; 18:e0278696. [PMID: 36652412 PMCID: PMC9847967 DOI: 10.1371/journal.pone.0278696] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 11/22/2022] [Indexed: 01/19/2023] Open
Abstract
Textiles made from cotton fibers are flammable and thus often include flame retardant additives for consumer safety. Transgressive segregation in multi-parent populations facilitates new combinations of alleles of genes and can result in traits that are superior to those of any of the parents. A screen of 257 recombinant inbred lines from a multi-parent advanced generation intercross (MAGIC) population for naturally enhance flame retardance (FR) was conducted. All eleven parents, like all conventional white fiber cotton cultivars produce flammable fabric. MAGIC recombinant inbred lines (RILs) that produced fibers with significantly lower heat release capacities (HRC) as measured by microscale combustion calorimetry (MCC) were identified and the stability of the phenotypes of the outliers were confirmed when the RILs were grown at an additional location. Of the textiles fabricated from the five superior RILs, four exhibited the novel characteristic of inherent flame resistance. When exposed to open flame by standard 45° incline flammability testing, these four fabrics self-extinguished. To determine the genetic architecture of this novel trait, linkage, epistatic and multi-locus genome wide association studies (GWAS) were conducted with 473k SNPs identified by whole genome sequencing (WGS). Transcriptomes of developing fiber cells from select RILs were sequenced (RNAseq). Together, these data provide insight into the genetic mechanism of the unexpected emergence of flame-resistant cotton by transgressive segregation in a breeding program. The incorporation of this trait into global cotton germplasm by breeding has the potential to greatly reduce the costs and impacts of flame-retardant chemicals.
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12
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Lisker A, Maurer A, Schmutzer T, Kazman E, Cöster H, Holzapfel J, Ebmeyer E, Alqudah AM, Sannemann W, Pillen K. A Haplotype-Based GWAS Identified Trait-Improving QTL Alleles Controlling Agronomic Traits under Contrasting Nitrogen Fertilization Treatments in the MAGIC Wheat Population WM-800. PLANTS (BASEL, SWITZERLAND) 2022; 11:3508. [PMID: 36559621 PMCID: PMC9784842 DOI: 10.3390/plants11243508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/27/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
The multi-parent-advanced-generation-intercross (MAGIC) population WM-800 was developed by intercrossing eight modern winter wheat cultivars to enhance the genetic diversity present in breeding populations. We cultivated WM-800 during two seasons in seven environments under two contrasting nitrogen fertilization treatments. WM-800 lines exhibited highly significant differences between treatments, as well as high heritabilities among the seven agronomic traits studied. The highest-yielding WM-line achieved an average yield increase of 4.40 dt/ha (5.2%) compared to the best founder cultivar Tobak. The subsequent genome-wide-association-study (GWAS), which was based on haplotypes, located QTL for seven agronomic traits including grain yield. In total, 40, 51, and 46 QTL were detected under low, high, and across nitrogen treatments, respectively. For example, the effect of QYLD_3A could be associated with the haplotype allele of cultivar Julius increasing yield by an average of 4.47 dt/ha (5.2%). A novel QTL on chromosome 2B exhibited pleiotropic effects, acting simultaneously on three-grain yield components (ears-per-square-meter, grains-per-ear, and thousand-grain-weight) and plant-height. These effects may be explained by a member of the nitrate-transporter-1 (NRT1)/peptide-family, TaNPF5.34, located 1.05 Mb apart. The WM-800 lines and favorable QTL haplotypes, associated with yield improvements, are currently implemented in wheat breeding programs to develop advanced nitrogen-use efficient wheat cultivars.
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Affiliation(s)
- Antonia Lisker
- Institute of Agricultural and Nutritional Sciences, Martin-Luther-University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Andreas Maurer
- Institute of Agricultural and Nutritional Sciences, Martin-Luther-University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Thomas Schmutzer
- Institute of Agricultural and Nutritional Sciences, Martin-Luther-University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Ebrahim Kazman
- Syngenta Seeds GmbH, Kroppenstedter Str. 4, 39387 Oschersleben, Germany
| | | | - Josef Holzapfel
- Secobra Saatzucht GmbH, Feldkirchen 3, 85368 Moosburg an der Isar, Germany
| | - Erhard Ebmeyer
- KWS Lochow GMBH, Ferdinand-Lochow-Str. 5, 29303 Bergen, Germany
| | - Ahmad M. Alqudah
- Biological Science Program, Department of Biological and Environmental Sciences, College of Art and Science, Qatar University, Doha P.O. Box 2713, Qatar
| | - Wiebke Sannemann
- Institute of Agricultural and Nutritional Sciences, Martin-Luther-University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Klaus Pillen
- Institute of Agricultural and Nutritional Sciences, Martin-Luther-University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany
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13
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Wu Q, Han Y, Wu X, Wang Y, Su Q, Shen Y, Guan K, Michal JJ, Jiang Z, Liu B, Zhou X. Integrated time-series transcriptomic and metabolomic analyses reveal different inflammatory and adaptive immune responses contributing to host resistance to PRRSV. Front Immunol 2022; 13:960709. [PMID: 36341362 PMCID: PMC9631489 DOI: 10.3389/fimmu.2022.960709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 10/05/2022] [Indexed: 11/20/2022] Open
Abstract
Porcine reproductive and respiratory syndrome virus (PRRSV) is a highly contagious disease that affects the global pig industry. To understand mechanisms of susceptibility/resistance to PRRSV, this study profiled the time-serial white blood cells transcriptomic and serum metabolomic responses to PRRSV in piglets from a crossbred population of PRRSV-resistant Tongcheng pigs and PRRSV-susceptible Large White pigs. Gene set enrichment analysis (GSEA) illustrated that PRRSV infection up-regulated the expression levels of marker genes of dendritic cells, monocytes and neutrophils and inflammatory response, but down-regulated T cells, B cells and NK cells markers. CIBERSORT analysis confirmed the higher T cells proportion in resistant pigs during PRRSV infection. Resistant pigs showed a significantly higher level of T cell activation and lower expression levels of monocyte surface signatures post infection than susceptible pigs, corresponding to more severe suppression of T cell immunity and inflammatory response in susceptible pigs. Differentially expressed genes between resistant/susceptible pigs during the course of infection were significantly enriched in oxidative stress, innate immunity and humoral immunity, cell cycle, biotic stimulated cellular response, wounding response and behavior related pathways. Fourteen of these genes were distributed in 5 different QTL regions associated with PRRSV-related traits. Chemokine CXCL10 levels post PRRSV infection were differentially expressed between resistant pigs and susceptible pigs and can be a promising marker for susceptibility/resistance to PRRSV. Furthermore, the metabolomics dataset indicated differences in amino acid pathways and lipid metabolism between pre-infection/post-infection and resistant/susceptible pigs. The majority of metabolites levels were also down-regulated after PRRSV infection and were significantly positively correlated to the expression levels of marker genes in adaptive immune response. The integration of transcriptome and metabolome revealed concerted molecular events triggered by the infection, notably involving inflammatory response, adaptive immunity and G protein-coupled receptor downstream signaling. This study has increased our knowledge of the immune response differences induced by PRRSV infection and susceptibility differences at the transcriptomic and metabolomic levels, providing the basis for the PRRSV resistance mechanism and effective PRRS control.
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Affiliation(s)
- Qingqing Wu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
| | - Yu Han
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
| | - Xianmeng Wu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
| | - Yuan Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
| | - Qiuju Su
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
| | - Yang Shen
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
| | - Kaifeng Guan
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
| | - Jennifer J. Michal
- Department of Animal Sciences and Center for Reproductive Biology, Washington State University, Pullman, WA, United States
| | - Zhihua Jiang
- Department of Animal Sciences and Center for Reproductive Biology, Washington State University, Pullman, WA, United States
| | - Bang Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
- The Engineering Technology Research Center of Hubei Province Local Pig Breed Improvement, Huazhong Agricultural University, Wuhan, China
- *Correspondence: Xiang Zhou, ; Bang Liu,
| | - Xiang Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
- The Engineering Technology Research Center of Hubei Province Local Pig Breed Improvement, Huazhong Agricultural University, Wuhan, China
- *Correspondence: Xiang Zhou, ; Bang Liu,
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14
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Li W, Boer MP, van Rossum BJ, Zheng C, Joosen RVL, van Eeuwijk FA. statgenMPP: an R package implementing an IBD-based mixed model approach for QTL mapping in a wide range of multi-parent populations. Bioinformatics 2022; 38:5134-5136. [PMID: 36193999 PMCID: PMC9665859 DOI: 10.1093/bioinformatics/btac662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/23/2022] [Accepted: 10/03/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Multi-parent populations (MPPs) are popular for QTL mapping because they combine wide genetic diversity in parents with easy control of population structure, but a limited number of software tools for QTL mapping are specifically developed for general MPP designs. RESULTS We developed an R package called statgenMPP, adopting a unified identity-by-descent (IBD)-based mixed model approach for QTL analysis in MPPs. The package offers easy-to-use functionalities of IBD calculations, mixed model solutions and visualizations for QTL mapping in a wide range of MPP designs, including diallele, nested-association mapping populations, multi-parent advanced genetic inter-cross populations and other complicated MPPs with known crossing schemes. AVAILABILITY AND IMPLEMENTATION The R package statgenMPP is open-source and freely available on CRAN at https://CRAN.R-project.org/package=statgenMPP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wenhao Li
- Biometris, Wageningen University and Research Center, Wageningen, 6700 AC, The Netherlands
| | | | - Bart-Jan van Rossum
- Biometris, Wageningen University and Research Center, Wageningen, 6700 AC, The Netherlands
| | - Chaozhi Zheng
- Biometris, Wageningen University and Research Center, Wageningen, 6700 AC, The Netherlands
| | | | - Fred A van Eeuwijk
- Biometris, Wageningen University and Research Center, Wageningen, 6700 AC, The Netherlands
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15
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Dang VH, Hill CB, Zhang XQ, Angessa TT, McFawn LA, Li C. Multi-locus genome-wide association studies reveal novel alleles for flowering time under vernalisation and extended photoperiod in a barley MAGIC population. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3087-3102. [PMID: 35879467 PMCID: PMC9482607 DOI: 10.1007/s00122-022-04169-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 06/30/2022] [Indexed: 06/15/2023]
Abstract
Key genes controlling flowering and interactions of different photoperiod alleles with various environments were identified in a barley MAGIC population. A new candidate gene for vernalisation requirements was also detected. Optimal flowering time has a major impact on grain yield in crop species, including the globally important temperate cereal crop barley (Hordeum vulgare L.). Understanding the genetics of flowering is a key avenue to enhancing yield potential. Although bi-parental populations were used intensively to map genes controlling flowering, their lack of genetic diversity requires additional work to obtain desired gene combinations in the selected lines, especially when the two parental cultivars did not carry the genes. Multi-parent mapping populations, which use a combination of four or eight parental cultivars, have higher genetic and phenotypic diversity and can provide novel genetic combinations that cannot be achieved using bi-parental populations. This study uses a Multi-parent advanced generation intercross (MAGIC) population from four commercial barley cultivars to identify genes controlling flowering time in different environmental conditions. Genome-wide association studies (GWAS) were performed using 5,112 high-quality markers from Diversity Arrays Technology sequencing (DArT-seq), and Kompetitive allele-specific polymerase chain reaction (KASP) genetic markers were developed. Phenotypic data were collected from fifteen different field trials for three consecutive years. Planting was conducted at various sowing times, and plants were grown with/without additional vernalisation and extended photoperiod treatments. This study detected fourteen stable regions associated with flowering time across multiple environments. GWAS combined with pangenome data highlighted the role of CEN gene in flowering and enabled the prediction of different CEN alleles from parental lines. As the founder lines of the multi-parental population are elite germplasm, the favourable alleles identified in this study are directly relevant to breeding, increasing the efficiency of subsequent breeding strategies and offering better grain yield and adaptation to growing conditions.
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Affiliation(s)
- Viet Hoang Dang
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Perth, WA, Australia
- Department of Primary Industries and Regional Development, Perth, WA, Australia
| | - Camilla Beate Hill
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Perth, WA, Australia
| | - Xiao-Qi Zhang
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Perth, WA, Australia
| | - Tefera Tolera Angessa
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Perth, WA, Australia
| | - Lee-Anne McFawn
- Department of Primary Industries and Regional Development, Perth, WA, Australia
| | - Chengdao Li
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Perth, WA, Australia.
- Department of Primary Industries and Regional Development, Perth, WA, Australia.
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16
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Singh RK, Singh C, Chandana BS, Mahto RK, Patial R, Gupta A, Gahlaut V, Hamwieh A, Upadhyaya HD, Kumar R. Exploring Chickpea Germplasm Diversity for Broadening the Genetic Base Utilizing Genomic Resourses. Front Genet 2022; 13:905771. [PMID: 36035111 PMCID: PMC9416867 DOI: 10.3389/fgene.2022.905771] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 06/24/2022] [Indexed: 12/01/2022] Open
Abstract
Legume crops provide significant nutrition to humans as a source of protein, omega-3 fatty acids as well as specific macro and micronutrients. Additionally, legumes improve the cropping environment by replenishing the soil nitrogen content. Chickpeas are the second most significant staple legume food crop worldwide behind dry bean which contains 17%–24% protein, 41%–51% carbohydrate, and other important essential minerals, vitamins, dietary fiber, folate, β-carotene, anti-oxidants, micronutrients (phosphorus, calcium, magnesium, iron, and zinc) as well as linoleic and oleic unsaturated fatty acids. Despite these advantages, legumes are far behind cereals in terms of genetic improvement mainly due to far less effort, the bottlenecks of the narrow genetic base, and several biotic and abiotic factors in the scenario of changing climatic conditions. Measures are now called for beyond conventional breeding practices to strategically broadening of narrow genetic base utilizing chickpea wild relatives and improvement of cultivars through advanced breeding approaches with a focus on high yield productivity, biotic and abiotic stresses including climate resilience, and enhanced nutritional values. Desirable donors having such multiple traits have been identified using core and mini core collections from the cultivated gene pool and wild relatives of Chickpea. Several methods have been developed to address cross-species fertilization obstacles and to aid in inter-specific hybridization and introgression of the target gene sequences from wild Cicer species. Additionally, recent advances in “Omics” sciences along with high-throughput and precise phenotyping tools have made it easier to identify genes that regulate traits of interest. Next-generation sequencing technologies, whole-genome sequencing, transcriptomics, and differential genes expression profiling along with a plethora of novel techniques like single nucleotide polymorphism exploiting high-density genotyping by sequencing assays, simple sequence repeat markers, diversity array technology platform, and whole-genome re-sequencing technique led to the identification and development of QTLs and high-density trait mapping of the global chickpea germplasm. These altogether have helped in broadening the narrow genetic base of chickpeas.
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Affiliation(s)
| | - Charul Singh
- University School of Biotechnology, Guru Gobind Singh Indraprastha University, New Delhi, India
| | - B S Chandana
- Indian Agricultural Research Institute (ICAR), New Delhi, India
| | - Rohit K Mahto
- Indian Agricultural Research Institute (ICAR), New Delhi, India
| | - Ranjana Patial
- Department of Agricultural Sciences, Chandigarh University, Mohali, India
| | - Astha Gupta
- School of Agricultural Sciences, Sharda University, Greater Noida, India
| | - Vijay Gahlaut
- Institute of Himalayan Bioresource Technology (CSIR), Pālampur, India
| | - Aladdin Hamwieh
- International Center for Agriculture Research in the Dry Areas (ICARDA), Giza, Egypt
| | - H D Upadhyaya
- Department of Entomology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Plant Genome Mapping Laboratory, University of Georgia, Athens, GA, United States
| | - Rajendra Kumar
- Indian Agricultural Research Institute (ICAR), New Delhi, India
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Xu X, Wang Z, Xu S, Xu M, He L, Zhang J, Luo Z, Xie X, Wu M, Yang J. Identifying loci controlling total starch content of leaf in Nicotiana tabacum through genome-wide association study. Funct Integr Genomics 2022; 22:537-552. [PMID: 35404023 DOI: 10.1007/s10142-022-00851-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/07/2022] [Accepted: 03/22/2022] [Indexed: 11/04/2022]
Abstract
Starch is an important primary metabolite in plants, which can provide bioenergy for fuel ethanol production. There are many studies focusing on starch metabolism in Arabidopsis, maize, and rice, but few reports have been made on the starch content of tobacco leaves. Hence, to identify the marker-trait associations and isolate the candidate genes related to starch content of tobacco leaf, the genome-wide association study (GWAS) was performed using a multiparent advanced generation intercross (MAGIC) population consisting of 276 accessions genotyped by a 430 K SNP array. In this study, we detected the leaf starch content of tobacco plants cultivated in two places (Zhucheng and Chenzhou), which showed a wide variation of starch content in the population. A total of 28 and 45 significant single-nucleotide polymorphism (SNP) loci associated with leaf starch content were identified by single-locus and multi-locus GWAS models, respectively, and the phenotypic variance explained by these loci varied from 1.80 to - 14.73%. Furthermore, among these quantitative trait loci (QTLs), one SNP, AX-106011713 located on chromosome 19, was detected repeatedly in multiple models and two environments, which was selected for linkage disequilibrium (LD) analysis to obtain the target candidate region. Through gene annotation, haplotype, and gene expression analysis, two candidate genes encoding E3 ubiquitin-protein ligase (Ntab0823160) and fructose-bisphosphate aldolase (Ntab0375050) were obtained. Results showed that the variety carrying the beneficial alleles of the two candidate genes had higher gene expression level and leaf starch content, suggesting the potential role of candidate genes in enhancing the level of tobacco leaf starch content. Furthermore, silencing of Ntab0823160 in tobacco leaves reduced the content of total starch to 39.41-69.75% of that in the wide type plants. Taken together, our results provide useful resources for further investigation of the starch metabolic pathway and are also beneficial for the creation of eco-friendly cultivars with increased accumulation of leaf starch content.
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Affiliation(s)
- Xin Xu
- China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Zhong Wang
- China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Shixiao Xu
- Henan Agricultural University, Zhengzhou, 450002, China
| | - Min Xu
- Henan Tobacco Company of CNTC, Zhengzhou, 450018, China
| | - Lei He
- Henan Tobacco Company of CNTC, Zhengzhou, 450018, China
| | - Jianfeng Zhang
- China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Zhaopeng Luo
- China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Xiaodong Xie
- China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Mingzhu Wu
- China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Jun Yang
- China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China.
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18
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Hashemi SM, Perry G, Rajcan I, Eskandari M. SoyMAGIC: An Unprecedented Platform for Genetic Studies and Breeding Activities in Soybean. FRONTIERS IN PLANT SCIENCE 2022; 13:945471. [PMID: 35874009 PMCID: PMC9301248 DOI: 10.3389/fpls.2022.945471] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
Multi-Parent Advanced Generation Inter-Cross (MAGIC) populations are emerging genetic platforms for high-resolution and fine mapping of quantitative traits, such as agronomic and seed composition traits in soybean (Glycine max L.). We have established an eight-parent MAGIC population, comprising 721 recombinant inbred lines (RILs), through conical inter-mating of eight soybean lines. The parental lines were genetically diverse elite cultivars carrying different agronomic and seed composition characteristics, including amino acids and fatty acids, as well as oil and protein concentrations. This study aimed to introduce soybean MAGIC (SoyMAGIC) population as an unprecedented platform for genotypic and phenotypic investigation of agronomic and seed quality traits in soybean. The RILs were evaluated for important seed composition traits using replicated field trials during 2020 and 2021. To measure the seed composition traits, near-infrared reflectance (NIR) was employed. The RILs were genotyped using genotyping-by-sequencing (GBS) method to decipher the genome and discover single-nucleotide polymorphic (SNP) markers among the RILs. A high-density linkage map was constructed through inclusive composite interval mapping (ICIM). The linkage map was 3,770.75 cM in length and contained 12,007 SNP markers. Chromosomes 11 and 18 were recorded as the shortest and longest linkage groups with 71.01 and 341.15 cM in length, respectively. Observed transgressive segregation of the selected traits and higher recombination frequency across the genome confirmed the capability of MAGIC population in reshuffling the diversity in the soybean genome among the RILs. The assessment of haplotype blocks indicated an uneven distribution of the parents' genomes in RILs, suggesting cryptic influence against or in favor of certain parental genomes. The SoyMAGIC population is a recombined genetic material that will accelerate further genomic studies and the development of soybean cultivars with improved seed quality traits through the development and implementation of reliable molecular-based toolkits.
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Arriagada O, Cacciuttolo F, Cabeza RA, Carrasco B, Schwember AR. A Comprehensive Review on Chickpea ( Cicer arietinum L.) Breeding for Abiotic Stress Tolerance and Climate Change Resilience. Int J Mol Sci 2022; 23:ijms23126794. [PMID: 35743237 PMCID: PMC9223724 DOI: 10.3390/ijms23126794] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 02/05/2023] Open
Abstract
Chickpea is one of the most important pulse crops worldwide, being an excellent source of protein. It is grown under rain-fed conditions averaging yields of 1 t/ha, far from its potential of 6 t/ha under optimum conditions. The combined effects of heat, cold, drought, and salinity affect species productivity. In this regard, several physiological, biochemical, and molecular mechanisms are reviewed to confer tolerance to abiotic stress. A large collection of nearly 100,000 chickpea accessions is the basis of breeding programs, and important advances have been achieved through conventional breeding, such as germplasm introduction, gene/allele introgression, and mutagenesis. In parallel, advances in molecular biology and high-throughput sequencing have allowed the development of specific molecular markers for the genus Cicer, facilitating marker-assisted selection for yield components and abiotic tolerance. Further, transcriptomics, proteomics, and metabolomics have permitted the identification of specific genes, proteins, and metabolites associated with tolerance to abiotic stress of chickpea. Furthermore, some promising results have been obtained in studies with transgenic plants and with the use of gene editing to obtain drought-tolerant chickpea. Finally, we propose some future lines of research that may be useful to obtain chickpea genotypes tolerant to abiotic stress in a scenario of climate change.
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Affiliation(s)
- Osvin Arriagada
- Departamento de Ciencias Vegetales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile; (O.A.); (F.C.)
| | - Felipe Cacciuttolo
- Departamento de Ciencias Vegetales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile; (O.A.); (F.C.)
| | - Ricardo A. Cabeza
- Departamento de Producción Agrícola, Facultad de Ciencias Agrarias, Universidad de Talca, Talca 3460000, Chile;
| | - Basilio Carrasco
- Centro de Estudios en Alimentos Procesados (CEAP), Av. Lircay s/n, Talca 3480094, Chile;
| | - Andrés R. Schwember
- Departamento de Ciencias Vegetales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile; (O.A.); (F.C.)
- Correspondence:
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Simpson CJC, Reeves G, Tripathi A, Singh P, Hibberd JM. Using breeding and quantitative genetics to understand the C4 pathway. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3072-3084. [PMID: 34747993 PMCID: PMC9126733 DOI: 10.1093/jxb/erab486] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/03/2021] [Indexed: 05/09/2023]
Abstract
Reducing photorespiration in C3 crops could significantly increase rates of photosynthesis and yield. One method to achieve this would be to integrate C4 photosynthesis into C3 species. This objective is challenging as it involves engineering incompletely understood traits into C3 leaves, including complex changes to their biochemistry, cell biology, and anatomy. Quantitative genetics and selective breeding offer underexplored routes to identify regulators of these processes. We first review examples of natural intraspecific variation in C4 photosynthesis as well as the potential for hybridization between C3 and C4 species. We then discuss how quantitative genetic approaches including artificial selection and genome-wide association could be used to better understand the C4 syndrome and in so doing guide the engineering of the C4 pathway into C3 crops.
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Affiliation(s)
- Conor J C Simpson
- Department of Plant Sciences, University of Cambridge, Cambridge, UK
| | - Gregory Reeves
- Department of Plant Sciences, University of Cambridge, Cambridge, UK
| | - Anoop Tripathi
- Department of Plant Sciences, University of Cambridge, Cambridge, UK
| | - Pallavi Singh
- Department of Plant Sciences, University of Cambridge, Cambridge, UK
| | - Julian M Hibberd
- Department of Plant Sciences, University of Cambridge, Cambridge, UK
- Correspondence:
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21
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Liang F, Zhan W, Hu G, Liu H, Xing Y, Li Z, Han Z. Five plants per RIL for phenotyping traits of high or moderate heritability ensure the power of QTL mapping in a rice MAGIC population. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:28. [PMID: 37309531 PMCID: PMC10248629 DOI: 10.1007/s11032-022-01299-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/20/2022] [Indexed: 06/14/2023]
Abstract
Currently, the power of QTL mapping is mainly dependent on the quality of phenotypic data in a given population, regardless of the statistical method, as the quality of genotypic data is easily guaranteed in the laboratory. Increasing the sample size per line used for phenotyping is a good way to improve the quality of phenotypic data. However, accommodating a large-scale mapping population takes a large area of rice field, which frequently results in high costs and extra environmental noises. To acquire a reasonable small sample size without a penalty in mapping power, we conducted three experiments with a 4-way MAGIC population and measured phenotypes of 5, 10, and 20 plants per RIL. Three traits including heading date, plant height, and tillers per plant were focused. With SNP- and bin-based QTL mapping, 3 major and 3 minor QTLs for heading date with high heritability and 2 major QTLs for plant height with moderate heritability were commonly detected across the three experiments, but no QTL for tillers per plant with low heritability were commonly identified. In addition, bin-based QTL mapping was more powerful than SNP-based mapping and able to rank the genetic effects of parental alleles. Thus, 5 plants per RIL for phenotyping ensure the power of QTL mapping for traits of high or moderate heritability, and bin-based QTL mapping is recommended for multiparent populations.
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Affiliation(s)
- Famao Liang
- College of Agriculture, Yangtze University, Jingzhou, 434000 China
| | - Wei Zhan
- Hubei Provincial Key Laboratory for Protection and Application of Special Plant Germplasm in Wuling Area of China, South-Central University for Nationalities, Wuhan, 430074 China
| | - Gang Hu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070 China
| | - Hua Liu
- College of Agriculture, Yangtze University, Jingzhou, 434000 China
| | - Yongzhong Xing
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070 China
| | - Zhixin Li
- College of Agriculture, Yangtze University, Jingzhou, 434000 China
| | - Zhongmin Han
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, 150081 Harbin, China
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070 China
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22
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Abberton M, Paliwal R, Faloye B, Marimagne T, Moriam A, Oyatomi O. Indigenous African Orphan Legumes: Potential for Food and Nutrition Security in SSA. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2022. [DOI: 10.3389/fsufs.2022.708124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
In Sub-Saharan Africa (SSA), both crop production and the hidden hunger index (HHI, a combination of zinc, iron, and vitamin A deficiency), continue to be worse than the rest of the world. Currently, 31 out of 36 countries of SSA show the highest HHI. At the same time, several studies show climate change as a major constraint to agriculture productivity and a significant threat to SSA food security without significant action regarding adaptation. The food security of SSA is dependent on a few major crops, with many of them providing largely only an energy source in the diet. To address this, crop diversification and climate-resilient crops that have adaptation to climate change can be used and one route toward this is promoting the cultivation of African orphan (neglected or underutilized) crops. These crops, particularly legumes, have the potential to improve food and nutrition security in SSA due to their cultural linkage with the regional food habits of the communities, nutritionally rich food, untapped genetic diversity, and adaptation to harsh climate conditions and poor marginal soils. Despite the wide distribution of orphan legumes across the landscape of SSA, these important crop species are characterized by low yield and decreasing utilization due in part to a lack of improved varieties and a lack of adequate research attention. Genomic-assisted breeding (GAB) can contribute to developing improved varieties that yield more, have improved resilience, and high nutritional value. The availability of large and diverse collections of germplasm is an essential resource for crop improvement. In the Genetic Resources Center of the International Institute of Tropical Agriculture, the collections of orphan legumes, particularly the Bambara groundnut, African yambean, and Kersting's groundnut, have been characterized and evaluated for their key traits, and new collections are being undertaken to fill gaps and to widen the genetic diversity available to underpin breeding that can be further utilized with GAB tools to develop faster and cost-effective climate-resilient cultivars with a high nutrition value for SSA farmers. However, a greater investment of resources is required for applying modern breeding to orphan legume crops if their full potential is to be realized.
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De-la-Cruz IM, Batsleer F, Bonte D, Diller C, Hytönen T, Muola A, Osorio S, Posé D, Vandegehuchte ML, Stenberg JA. Evolutionary Ecology of Plant-Arthropod Interactions in Light of the "Omics" Sciences: A Broad Guide. FRONTIERS IN PLANT SCIENCE 2022; 13:808427. [PMID: 35548276 PMCID: PMC9084618 DOI: 10.3389/fpls.2022.808427] [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: 11/03/2021] [Accepted: 04/01/2022] [Indexed: 06/15/2023]
Abstract
Aboveground plant-arthropod interactions are typically complex, involving herbivores, predators, pollinators, and various other guilds that can strongly affect plant fitness, directly or indirectly, and individually, synergistically, or antagonistically. However, little is known about how ongoing natural selection by these interacting guilds shapes the evolution of plants, i.e., how they affect the differential survival and reproduction of genotypes due to differences in phenotypes in an environment. Recent technological advances, including next-generation sequencing, metabolomics, and gene-editing technologies along with traditional experimental approaches (e.g., quantitative genetics experiments), have enabled far more comprehensive exploration of the genes and traits involved in complex ecological interactions. Connecting different levels of biological organization (genes to communities) will enhance the understanding of evolutionary interactions in complex communities, but this requires a multidisciplinary approach. Here, we review traditional and modern methods and concepts, then highlight future avenues for studying the evolution of plant-arthropod interactions (e.g., plant-herbivore-pollinator interactions). Besides promoting a fundamental understanding of plant-associated arthropod communities' genetic background and evolution, such knowledge can also help address many current global environmental challenges.
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Affiliation(s)
- Ivan M. De-la-Cruz
- Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Femke Batsleer
- Terrestrial Ecology Unit, Department of Biology, Ghent University, Ghent, Belgium
| | - Dries Bonte
- Terrestrial Ecology Unit, Department of Biology, Ghent University, Ghent, Belgium
| | - Carolina Diller
- Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Timo Hytönen
- Department of Agricultural Sciences, Viikki Plant Science Centre, University of Helsinki, Helsinki, Finland
- NIAB EMR, West Malling, United Kingdom
| | - Anne Muola
- Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden
- Biodiversity Unit, University of Turku, Finland
| | - Sonia Osorio
- Departamento de Biología Molecular y Bioquímica, Instituto de Hortofruticultura Subtropical y Mediterránea “La Mayora”, Universidad de Málaga-Consejo Superior de Investigaciones Científicas, Campus de Teatinos, Málaga, Spain
| | - David Posé
- Departamento de Biología Molecular y Bioquímica, Instituto de Hortofruticultura Subtropical y Mediterránea “La Mayora”, Universidad de Málaga-Consejo Superior de Investigaciones Científicas, Campus de Teatinos, Málaga, Spain
| | - Martijn L. Vandegehuchte
- Terrestrial Ecology Unit, Department of Biology, Ghent University, Ghent, Belgium
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Johan A. Stenberg
- Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden
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24
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Trade-offs in the genetic control of functional and nutritional quality traits in UK winter wheat. Heredity (Edinb) 2022; 128:420-433. [PMID: 35393550 PMCID: PMC9178040 DOI: 10.1038/s41437-022-00503-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 01/18/2022] [Accepted: 01/18/2022] [Indexed: 12/15/2022] Open
Abstract
A complex network of trade-offs exists between wheat quality and nutritional traits. We investigated the correlated relationships among several milling and baking traits as well as mineral density in refined white and whole grain flour. Our aim was to determine their pleiotropic genetic control in a multi-parent population over two trial years with direct application to practical breeding. Co-location of major quantitative trait loci (QTL) and principal component based multi-trait QTL mapping increased the power to detect QTL and revealed pleiotropic effects explaining many complementary and antagonistic trait relationships. High molecular weight glutenin subunit genes explained much of the heritable variation in important dough rheology traits, although additional QTL were detected. Several QTL, including one linked to the TaGW2 gene, controlled grain size and increased flour extraction rate. The semi-dwarf Rht-D1b allele had a positive effect on Hagberg falling number, but reduced grain size, specific weight, grain protein content and flour water absorption. Mineral nutrient concentrations were lower in Rht-D1b lines for many elements, in wholemeal and white flour, but potassium concentration was higher in Rht-D1b lines. The presence of awns increased calcium content without decreasing extraction rate, despite the negative correlation between these traits. QTL were also found that affect the relative concentrations of key mineral nutrients compared to phosphorus which may help increase bioavailability without associated anti-nutritional effects of phytic acid. Taken together these results demonstrate the potential for marker-based selection to optimise trait trade-offs and enhance wheat nutritional value by considering pleiotropic genetic effects across multiple traits.
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25
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Pflugfelder D, Kochs J, Koller R, Jahnke S, Mohl C, Pariyar S, Fassbender H, Nagel KA, Watt M, van Dusschoten D. The root system architecture of wheat establishing in soil is associated with varying elongation rates of seminal roots: quantification using 4D magnetic resonance imaging. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:2050-2060. [PMID: 34918078 DOI: 10.1093/jxb/erab551] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
Seedling establishment is the first stage of crop productivity, and root phenotypes at seed emergence are critical to a successful start of shoot growth as well as for water and nutrient uptake. In this study, we investigate seedling establishment in winter wheat utilizing a newly developed workflow based on magnetic resonance imaging (MRI). Using the eight parents of the MAGIC (multi-parent advanced generation inter-cross) population we analysed the 4D root architecture of 288 individual seedlings grown in natural soils with plant neighbors over 3 d of development. Time of root and shoot emergence, total length, angle, and depth of the axile roots varied significantly among these genotypes. The temporal data resolved rates of elongation of primary roots and first and second seminal root pairs. Genotypes with slowly elongating primary roots had rapidly elongating first and second seminal root pairs and vice versa, resulting in variation in root system architecture mediated not only by root angle but also by initiation and relative elongation of axile roots. We demonstrated that our novel MRI workflow with a unique planting design and automated measurements allowed medium throughput phenotyping of wheat roots in 4D and could give new insights into regulation of root system architecture.
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Affiliation(s)
- Daniel Pflugfelder
- Forschungszentrum Jülich GmbH, IBG-2: Plant Sciences, 52425 Jülich, Germany
| | - Johannes Kochs
- Forschungszentrum Jülich GmbH, IBG-2: Plant Sciences, 52425 Jülich, Germany
| | - Robert Koller
- Forschungszentrum Jülich GmbH, IBG-2: Plant Sciences, 52425 Jülich, Germany
| | - Siegfried Jahnke
- Forschungszentrum Jülich GmbH, IBG-2: Plant Sciences, 52425 Jülich, Germany
- University of Duisburg-Essen, Biodiversity, Universitätsstr. 5, 45141 Essen, Germany
| | - Carola Mohl
- Forschungszentrum Jülich GmbH, IBG-2: Plant Sciences, 52425 Jülich, Germany
| | - Shree Pariyar
- Forschungszentrum Jülich GmbH, IBG-2: Plant Sciences, 52425 Jülich, Germany
| | - Heike Fassbender
- Forschungszentrum Jülich GmbH, IBG-2: Plant Sciences, 52425 Jülich, Germany
| | - Kerstin A Nagel
- Forschungszentrum Jülich GmbH, IBG-2: Plant Sciences, 52425 Jülich, Germany
| | - Michelle Watt
- Forschungszentrum Jülich GmbH, IBG-2: Plant Sciences, 52425 Jülich, Germany
- School of BioSciences, Faculty of Science, University of Melbourne, Parkville, Victoria, 3010Australia
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26
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Mangino G, Arrones A, Plazas M, Pook T, Prohens J, Gramazio P, Vilanova S. Newly Developed MAGIC Population Allows Identification of Strong Associations and Candidate Genes for Anthocyanin Pigmentation in Eggplant. FRONTIERS IN PLANT SCIENCE 2022; 13:847789. [PMID: 35330873 PMCID: PMC8940277 DOI: 10.3389/fpls.2022.847789] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 01/20/2022] [Indexed: 05/17/2023]
Abstract
Multi-parent advanced generation inter-cross (MAGIC) populations facilitate the genetic dissection of complex quantitative traits in plants and are valuable breeding materials. We report the development of the first eggplant MAGIC population (S3 Magic EGGplant InCanum, S3MEGGIC; 8-way), constituted by the 420 S3 individuals developed from the intercrossing of seven cultivated eggplant (Solanum melongena) and one wild relative (S. incanum) parents. The S3MEGGIC recombinant population was genotyped with the eggplant 5k probes SPET platform and phenotyped for anthocyanin presence in vegetative plant tissues (PA) and fruit epidermis (FA), and for the light-insensitive anthocyanic pigmentation under the calyx (PUC). The 7,724 filtered high-confidence single-nucleotide polymorphisms (SNPs) confirmed a low residual heterozygosity (6.87%), a lack of genetic structure in the S3MEGGIC population, and no differentiation among subpopulations carrying a cultivated or wild cytoplasm. Inference of haplotype blocks of the nuclear genome revealed an unbalanced representation of the founder genomes, suggesting a cryptic selection in favour or against specific parental genomes. Genome-wide association study (GWAS) analysis for PA, FA, and PUC detected strong associations with two myeloblastosis (MYB) genes similar to MYB113 involved in the anthocyanin biosynthesis pathway, and with a COP1 gene which encodes for a photo-regulatory protein and may be responsible for the PUC trait. Evidence was found of a duplication of an ancestral MYB113 gene with a translocation from chromosome 10 to chromosome 1 compared with the tomato genome. Parental genotypes for the three genes were in agreement with the identification of the candidate genes performed in the S3MEGGIC population. Our new eggplant MAGIC population is the largest recombinant population in eggplant and is a powerful tool for eggplant genetics and breeding studies.
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Affiliation(s)
- Giulio Mangino
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Valencia, Spain
| | - Andrea Arrones
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Valencia, Spain
| | - Mariola Plazas
- Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas-Universitat Politècnica de València, Valencia, Spain
| | - Torsten Pook
- Animal Breeding and Genetics Group, Department of Animal Sciences, Center for Integrated Breeding Research, University of Göttingen, Göttingin, Germany
| | - Jaime Prohens
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Valencia, Spain
| | - Pietro Gramazio
- Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas-Universitat Politècnica de València, Valencia, Spain
| | - Santiago Vilanova
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Valencia, Spain
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Odell SG, Hudson AI, Praud S, Dubreuil P, Tixier MH, Ross-Ibarra J, Runcie DE. Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci. G3 (BETHESDA, MD.) 2022; 12:6509518. [PMID: 35100382 PMCID: PMC8895984 DOI: 10.1093/g3journal/jkac011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/27/2021] [Indexed: 12/02/2022]
Abstract
The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.
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Affiliation(s)
- Sarah G Odell
- Department of Plant Sciences, University of California, Davis, CA 95616, USA.,Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | - Asher I Hudson
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA.,Center for Population Biology, University of California, Davis, CA 95616, USA
| | - Sébastien Praud
- Limagrain, Centre de Recherche de Chappes, Chappes 63720, France
| | - Pierre Dubreuil
- Limagrain, Centre de Recherche de Chappes, Chappes 63720, France
| | | | - Jeffrey Ross-Ibarra
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA.,Center for Population Biology, University of California, Davis, CA 95616, USA.,Genome Center, University of California, Davis, CA 95616, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California, Davis, CA 95616, USA
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28
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Back to the wild: mining maize (Zea mays L.) disease resistance using advanced breeding tools. Mol Biol Rep 2022; 49:5787-5803. [PMID: 35064401 DOI: 10.1007/s11033-021-06815-x] [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: 05/28/2021] [Accepted: 10/06/2021] [Indexed: 10/19/2022]
Abstract
Cultivated modern maize (Zea mays L.) originated through the continuous process of domestication from its wild progenitors. Today, maize is considered as the most important cereal crop which is extensively cultivated in all parts of the world. Maize shows remarkable genotypic and phenotypic diversity which makes it an ideal model species for crop genetic research. However, intensive breeding and artificial selection of desired agronomic traits greatly narrow down the genetic bases of maize. This reduction in genetic diversity among cultivated maize led to increase the chance of more attack of biotic stress as climate changes hampering the maize grain production globally. Maize germplasm requires to integrate both durable multiple-diseases and multiple insect-pathogen resistance through tapping the unexplored resources of maize landraces. Revisiting the landraces seed banks will provide effective opportunities to transfer the resistant genes into the modern cultivars. Here, we describe the maize domestication process and discuss the unique genes from wild progenitors which potentially can be utilized for disease resistant in maize. We also focus on the genetics and disease resistance mechanism of various genes against maize biotic stresses and then considered the different molecular breeding tools for gene transfer and advanced high resolution mapping for gene pyramiding in maize lines. At last, we provide an insight for targeting identified key genes through CRISPR/Cas9 genome editing system to enhance the maize resilience towards biotic stress.
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29
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Pichot C, Djari A, Tran J, Verdenaud M, Marande W, Huneau C, Gautier V, Latrasse D, Arribat S, Sommard V, Troadec C, Poncet C, Bendahmane M, Szecsi J, Dogimont C, Salse J, Benhamed M, Zouine M, Boualem A, Bendahmane A. Cantaloupe melon genome reveals 3D chromatin features and structural relationship with the ancestral cucurbitaceae karyotype. iScience 2022; 25:103696. [PMID: 35059606 PMCID: PMC8760558 DOI: 10.1016/j.isci.2021.103696] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/01/2021] [Accepted: 12/20/2021] [Indexed: 11/21/2022] Open
Abstract
Cucumis melo displays a large diversity of horticultural groups with cantaloupe melon the most cultivated type. Using a combination of single-molecule sequencing, 10X Genomics link-reads, high-density optical and genetic maps, and chromosome conformation capture (Hi-C), we assembled a chromosome scale C. melo var. cantalupensis Charentais mono genome. Integration of RNA-seq, MeDip-seq, ChIP-seq, and Hi-C data revealed a widespread compartmentalization of the melon genome, segregating constitutive heterochromatin and euchromatin. Genome-wide comparative and evolutionary analysis between melon botanical groups identified Charentais mono genome increasingly more divergent from Harukei-3 (reticulatus), Payzawat (inodorus), and HS (ssp. agrestis) genomes. To assess the paleohistory of the Cucurbitaceae, we reconstructed the ancestral Cucurbitaceae karyotype and compared it to sequenced cucurbit genomes. In contrast to other species that experienced massive chromosome shuffling, melon has retained the ancestral genome structure. We provide comprehensive genomic resources and new insights in the diversity of melon horticultural groups and evolution of cucurbits. We provide a chromosome scale C. melo var. cantalupensis Charentais mono genome Epigenomic analysis revealed a widespread compartmentalization of the melon genome We reconstructed the ancestral Cucurbitaceae karyotype Melon has retained the ancestral Cucurbitaceae genome structure
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Affiliation(s)
- Clement Pichot
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
| | - Anis Djari
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, Toulouse INP, 31320 Auzeville-Tolosane, France
| | - Joseph Tran
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
| | - Marion Verdenaud
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
| | - William Marande
- INRAE, Centre National de Ressources Génomiques Végétales, 31326 Castanet-Tolosan, France
| | - Cecile Huneau
- INRAE-UCA UMR 1095 GDEC, 5 chemin de Beaulieu, 63000 Clermont-Ferrand, France
| | - Veronique Gautier
- INRAE-UCA UMR 1095 GDEC, 5 chemin de Beaulieu, 63000 Clermont-Ferrand, France
| | - David Latrasse
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
| | - Sandrine Arribat
- INRAE, Centre National de Ressources Génomiques Végétales, 31326 Castanet-Tolosan, France
| | - Vivien Sommard
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
| | - Christelle Troadec
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
| | - Charles Poncet
- INRAE-UCA UMR 1095 GDEC, 5 chemin de Beaulieu, 63000 Clermont-Ferrand, France
| | - Mohammed Bendahmane
- Laboratoire Reproduction et Développement des Plantes, INRAE, CNRS, Université Lyon 1-ENSL, Ecole Normale Supérieure de Lyon, 69364 Lyon Cedex 07, France
| | - Judit Szecsi
- Laboratoire Reproduction et Développement des Plantes, INRAE, CNRS, Université Lyon 1-ENSL, Ecole Normale Supérieure de Lyon, 69364 Lyon Cedex 07, France
| | - Catherine Dogimont
- INRAE GAFL, Génétique et Amélioration des Fruits et Légumes, 84143 Montfavet, France
| | - Jerome Salse
- INRAE-UCA UMR 1095 GDEC, 5 chemin de Beaulieu, 63000 Clermont-Ferrand, France
| | - Moussa Benhamed
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
| | - Mohamed Zouine
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, Toulouse INP, 31320 Auzeville-Tolosane, France
| | - Adnane Boualem
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
| | - Abdelhafid Bendahmane
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif sur Yvette, France
- Corresponding author
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Genomic interrogation of a MAGIC population highlights genetic factors controlling fiber quality traits in cotton. Commun Biol 2022; 5:60. [PMID: 35039628 PMCID: PMC8764025 DOI: 10.1038/s42003-022-03022-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 12/21/2021] [Indexed: 02/05/2023] Open
Abstract
Cotton (Gossypium hirsutum L.) fiber is the most important resource of natural and renewable fiber for the textile industry. However, the understanding of genetic components and their genome-wide interactions controlling fiber quality remains fragmentary. Here, we sequenced a multiple-parent advanced-generation inter-cross (MAGIC) population, consisting of 550 individuals created by inter-crossing 11 founders, and established a mosaic genome map through tracing the origin of haplotypes that share identity-by-descent (IBD). We performed two complementary GWAS methods—SNP-based GWAS (sGWAS) and IBD-based haplotype GWAS (hGWAS). A total of 25 sQTLs and 14 hQTLs related to cotton fiber quality were identified, of which 26 were novel QTLs. Two major QTLs detected by both GWAS methods were responsible for fiber strength and length. The gene Ghir_D11G020400 (GhZF14) encoding the MATE efflux family protein was identified as a novel candidate gene for fiber length. Beyond the additive QTLs, we detected prevalent epistatic interactions that contributed to the genetics of fiber quality, pinpointing another layer for trait variance. This study provides new targets for future molecular design breeding of superior fiber quality. Wang and colleagues use a complementary GWAS approach to identify genetic loci associated with cotton fiber quality. Using a multiparent advanced-generation inter-cross population, 26 new QTLs related to cotton fiber quality were found.
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Rollar S, Geyer M, Hartl L, Mohler V, Ordon F, Serfling A. Quantitative Trait Loci Mapping of Adult Plant and Seedling Resistance to Stripe Rust ( Puccinia striiformis Westend.) in a Multiparent Advanced Generation Intercross Wheat Population. FRONTIERS IN PLANT SCIENCE 2021; 12:684671. [PMID: 35003147 PMCID: PMC8733622 DOI: 10.3389/fpls.2021.684671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 11/19/2021] [Indexed: 05/20/2023]
Abstract
Stripe rust caused by the biotrophic fungus Puccinia striiformis Westend. is one of the most important diseases of wheat worldwide, causing high yield and quality losses. Growing resistant cultivars is the most efficient way to control stripe rust, both economically and ecologically. Known resistance genes are already present in numerous cultivars worldwide. However, their effectiveness is limited to certain races within a rust population and the emergence of stripe rust races being virulent against common resistance genes forces the demand for new sources of resistance. Multiparent advanced generation intercross (MAGIC) populations have proven to be a powerful tool to carry out genetic studies on economically important traits. In this study, interval mapping was performed to map quantitative trait loci (QTL) for stripe rust resistance in the Bavarian MAGIC wheat population, comprising 394 F6 : 8 recombinant inbred lines (RILs). Phenotypic evaluation of the RILs was carried out for adult plant resistance in field trials at three locations across three years and for seedling resistance in a growth chamber. In total, 21 QTL for stripe rust resistance corresponding to 13 distinct chromosomal regions were detected, of which two may represent putatively new QTL located on wheat chromosomes 3D and 7D.
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Affiliation(s)
- Sandra Rollar
- Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Manuel Geyer
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany
| | - Lorenz Hartl
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany
| | - Volker Mohler
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany
| | - Frank Ordon
- Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Albrecht Serfling
- Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
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Gesteiro N, Cao A, Santiago R, Malvar RA, Butrón A. Genomics of maize resistance to kernel contamination with fumonisins using a multiparental advanced generation InterCross maize population (MAGIC). BMC PLANT BIOLOGY 2021; 21:596. [PMID: 34915847 PMCID: PMC8675497 DOI: 10.1186/s12870-021-03380-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/17/2021] [Indexed: 06/01/2023]
Abstract
Maize kernel is exposed to several fungal species, most notably Fusarium verticillioides, which can contaminate maize kernels with fumonisins. In an effort to increase genetic gains and avoid the laborious tasks of conventional breeding, the use of marker-assisted selection or genomic selection programs was proposed. To this end, in the present study a Genome Wide Association Study (GWAS) was performed on 339 RILs of a Multiparental Advanced Generation InterCross (MAGIC) population that had previously been used to locate Quantitative Trait Locus (QTL) for resistance to Fusarium Ear Rot (FER). Six QTLs for fumonisin content were detected in the bins 3.08, 4.07, 4.10, 7.03-7.04, 9.04-9.05 and 10.04-10.5. Five of the six QTLs collocate in regions where QTLs for FER were also found. However, the genetic variation for fumonisin content in kernel is conditioned by many other QTLs of small effect that could show QTL x environment interaction effects. Although a genomic selection approach to directly reduce fumonisin content in the kernel could be suitable, improving resistance to fumonisin content by genomic selection for FER would be more advisable.
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Affiliation(s)
- Noemi Gesteiro
- Misión Biológica de Galicia (CSIC), Box 28, 36080, Pontevedra, Spain
| | - Ana Cao
- Misión Biológica de Galicia (CSIC), Box 28, 36080, Pontevedra, Spain
| | - Rogelio Santiago
- Departamento Biología Vegetal y Ciencias del Suelo, Facultad de Biología, Universidad de Vigo, Unidad Asociada Agrobiología Ambiental, Calidad de Suelos y Plantas, As Lagoas Marcosende, 36310 Vigo, Spain
| | - Rosa Ana Malvar
- Misión Biológica de Galicia (CSIC), Box 28, 36080, Pontevedra, Spain
| | - Ana Butrón
- Misión Biológica de Galicia (CSIC), Box 28, 36080, Pontevedra, Spain
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Hautsalo J, Novakazi F, Jalli M, Göransson M, Manninen O, Isolahti M, Reitan L, Bergersen S, Krusell L, Damsgård Robertsen C, Orabi J, Due Jensen J, Jahoor A, Bengtsson T. Pyramiding of scald resistance genes in four spring barley MAGIC populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3829-3843. [PMID: 34350474 PMCID: PMC8580920 DOI: 10.1007/s00122-021-03930-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/27/2021] [Indexed: 06/13/2023]
Abstract
Genome-Wide Association Studies (GWAS) of four Multi-parent Advanced Generation Inter-Cross (MAGIC) populations identified nine regions on chromosomes 1H, 3H, 4H, 5H, 6H and 7H associated with resistance against barley scald disease. Three of these regions are putatively novel resistance Quantitative Trait Loci (QTL). Barley scald is caused by Rhynchosporium commune, one of the most important barley leaf diseases that are prevalent in most barley-growing regions. Up to 40% yield losses can occur in susceptible barley cultivars. Four MAGIC populations were generated in a Nordic Public-Private Pre-breeding of spring barley project (PPP Barley) to introduce resistance to several important diseases. Here, these MAGIC populations consisting of six to eight founders each were tested for scald resistance in field trials in Finland and Iceland. Eight different model covariate combinations were compared for GWAS studies, and the models that deviated the least from the expected p-values were selected. For all QTL, candidate genes were identified that are predicted to be involved in pathogen defence. The MAGIC progenies contained new haplotypes of significant SNP-markers with high resistance levels. The lines with successfully pyramided resistance against scald and mildew and the significant markers are now distributed among Nordic plant breeders and will benefit development of disease-resistant cultivars.
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Affiliation(s)
- Juho Hautsalo
- Natural Resources Institute Finland (Luke), Survontie 9, 40500, Jyväskylä, Finland
| | - Fluturë Novakazi
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, 234 22, Lomma, Sweden
| | - Marja Jalli
- Natural Resources Institute Finland (Luke), Tietotie 4, 31600, Jokioinen, Finland
| | - Magnus Göransson
- Faculty of Land and Animal Resources, The Agricultural University of Iceland, Hvanneyri, 311, Borgarnes, Iceland
| | - Outi Manninen
- Boreal Plant Breeding Ltd., Myllytie 10, 31600, Jokioinen, Norway
| | - Mika Isolahti
- Boreal Plant Breeding Ltd., Myllytie 10, 31600, Jokioinen, Norway
| | - Lars Reitan
- Graminor Ltd. Hommelstadvegen 60, 2322, Ridabu, Norway
| | | | - Lene Krusell
- Sejet Plant Breeding, Nørremarksvej 67, 8700, Horsens, Norway
| | | | - Jihad Orabi
- Nordic Seed A/S, Kornmarken 1, 8464, Galten, Denmark
| | | | - Ahmed Jahoor
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, 234 22, Lomma, Sweden
- Nordic Seed A/S, Kornmarken 1, 8464, Galten, Denmark
| | - Therése Bengtsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, 234 22, Lomma, Sweden.
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Zenda T, Liu S, Dong A, Li J, Wang Y, Liu X, Wang N, Duan H. Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value. FRONTIERS IN PLANT SCIENCE 2021; 12:774994. [PMID: 34925418 PMCID: PMC8672198 DOI: 10.3389/fpls.2021.774994] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/08/2021] [Indexed: 05/17/2023]
Abstract
Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the "omics" technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.
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Affiliation(s)
- Tinashe Zenda
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
- Department of Crop Science, Faculty of Agriculture and Environmental Science, Bindura University of Science Education, Bindura, Zimbabwe
| | - Songtao Liu
- Academy of Agriculture and Forestry Sciences, Hebei North University, Zhangjiakou, China
| | - Anyi Dong
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Jiao Li
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Yafei Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Xinyue Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Nan Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Huijun Duan
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
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Saradadevi GP, Das D, Mangrauthia SK, Mohapatra S, Chikkaputtaiah C, Roorkiwal M, Solanki M, Sundaram RM, Chirravuri NN, Sakhare AS, Kota S, Varshney RK, Mohannath G. Genetic, Epigenetic, Genomic and Microbial Approaches to Enhance Salt Tolerance of Plants: A Comprehensive Review. BIOLOGY 2021; 10:biology10121255. [PMID: 34943170 PMCID: PMC8698797 DOI: 10.3390/biology10121255] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 12/17/2022]
Abstract
Simple Summary Globally, soil salinity, which refers to salt-affected soils, is increasing due to various environmental factors and human activities. Soil salinity poses one of the most serious challenges in the field of agriculture as it significantly reduces the growth and yield of crop plants, both quantitatively and qualitatively. Over the last few decades, several studies have been carried out to understand plant biology in response to soil salinity stress with a major emphasis on genetic and other hereditary components. Based on the outcome of these studies, several approaches are being followed to enhance plants’ ability to tolerate salt stress while still maintaining reasonable levels of crop yields. In this manuscript, we comprehensively list and discuss various biological approaches being followed and, based on the recent advances in the field of molecular biology, we propose some new approaches to improve salinity tolerance of crop plants. The global scientific community can make use of this information for the betterment of crop plants. This review also highlights the importance of maintaining global soil health to prevent several crop plant losses. Abstract Globally, soil salinity has been on the rise owing to various factors that are both human and environmental. The abiotic stress caused by soil salinity has become one of the most damaging abiotic stresses faced by crop plants, resulting in significant yield losses. Salt stress induces physiological and morphological modifications in plants as a result of significant changes in gene expression patterns and signal transduction cascades. In this comprehensive review, with a major focus on recent advances in the field of plant molecular biology, we discuss several approaches to enhance salinity tolerance in plants comprising various classical and advanced genetic and genetic engineering approaches, genomics and genome editing technologies, and plant growth-promoting rhizobacteria (PGPR)-based approaches. Furthermore, based on recent advances in the field of epigenetics, we propose novel approaches to create and exploit heritable genome-wide epigenetic variation in crop plants to enhance salinity tolerance. Specifically, we describe the concepts and the underlying principles of epigenetic recombinant inbred lines (epiRILs) and other epigenetic variants and methods to generate them. The proposed epigenetic approaches also have the potential to create additional genetic variation by modulating meiotic crossover frequency.
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Affiliation(s)
- Gargi Prasad Saradadevi
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
| | - Debajit Das
- Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat 785006, India; (D.D.); (C.C.)
| | - Satendra K. Mangrauthia
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Sridev Mohapatra
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
| | - Channakeshavaiah Chikkaputtaiah
- Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat 785006, India; (D.D.); (C.C.)
| | - Manish Roorkiwal
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India;
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Manish Solanki
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Raman Meenakshi Sundaram
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Neeraja N. Chirravuri
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Akshay S. Sakhare
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Suneetha Kota
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India;
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA 6150, Australia
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| | - Gireesha Mohannath
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
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Xin Z, Wang M, Cuevas HE, Chen J, Harrison M, Pugh NA, Morris G. Sorghum genetic, genomic, and breeding resources. PLANTA 2021; 254:114. [PMID: 34739592 PMCID: PMC8571242 DOI: 10.1007/s00425-021-03742-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/28/2021] [Indexed: 05/24/2023]
Abstract
Sorghum research has entered an exciting and fruitful era due to the genetic, genomic, and breeding resources that are now available to researchers and plant breeders. As the world faces the challenges of a rising population and a changing global climate, new agricultural solutions will need to be developed to address the food and fiber needs of the future. To that end, sorghum will be an invaluable crop species as it is a stress-resistant C4 plant that is well adapted for semi-arid and arid regions. Sorghum has already remained as a staple food crop in many parts of Africa and Asia and is critically important for animal feed and niche culinary applications in other regions, such as the United States. In addition, sorghum has begun to be developed into a promising feedstock for forage and bioenergy production. Due to this increasing demand for sorghum and its potential to address these needs, the continuous development of powerful community resources is required. These resources include vast collections of sorghum germplasm, high-quality reference genome sequences, sorghum association panels for genome-wide association studies of traits involved in food and bioenergy production, mutant populations for rapid discovery of causative genes for phenotypes relevant to sorghum improvement, gene expression atlas, and online databases that integrate all resources and provide the sorghum community with tools that can be used in breeding and genomic studies. Used in tandem, these valuable resources will ensure that the rate, quality, and collaborative potential of ongoing sorghum improvement efforts is able to rival that of other major crops.
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Affiliation(s)
- Zhanguo Xin
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, TX, 79424, USA.
| | - Mingli Wang
- Plant Genetic Resources Conservation Unit, USDA-ARS, Griffin, GA, 30223, USA
| | - Hugo E Cuevas
- Tropical Agriculture Research Station, USDA-ARS, Mayagüez, 00680, Puerto Rico
| | - Junping Chen
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, TX, 79424, USA
| | - Melanie Harrison
- Plant Genetic Resources Conservation Unit, USDA-ARS, Griffin, GA, 30223, USA
| | - N Ace Pugh
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, TX, 79424, USA
| | - Geoffrey Morris
- Crop Quantitative Genomics, Soil and Crop Sciences, Colorado State University, Plant Sciences Building, Fort Collins, CO, 80523, USA
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Amadeu RR, Muñoz PR, Zheng C, Endelman JB. QTL mapping in outbred tetraploid (and diploid) diallel populations. Genetics 2021; 219:iyab124. [PMID: 34740237 PMCID: PMC8570786 DOI: 10.1093/genetics/iyab124] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 07/24/2021] [Indexed: 11/14/2022] Open
Abstract
Over the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by developing software for quantitative trait locus (QTL) mapping in connected F1 populations derived from a set of shared parents. For QTL discovery, phenotypes are regressed on the dosage of parental haplotypes to estimate additive effects. Statistical properties of the model were explored by simulating half-diallel diploid and tetraploid populations with different population sizes and numbers of parents. Across scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and accuracy of the estimated haplotype effects. Multiallelic QTL with heritability 0.2 were detected with 90% probability at 25 pph and genome-wide significance level 0.05, and the additive haplotype effects were estimated with over 90% accuracy. Following QTL discovery, the software enables a comparison of models with multiple QTL and nonadditive effects. To illustrate, we analyzed potato tuber shape in a half-diallel population with three tetraploid parents. A well-known QTL on chromosome 10 was detected, for which the inclusion of digenic dominance lowered the Deviance Information Criterion (DIC) by 17 points compared to the additive model. The final model also contained a minor QTL on chromosome 1, but higher-order dominance and epistatic effects were excluded based on the DIC. In terms of practical impacts, the software is already being used to select offspring based on the effect and dosage of particular haplotypes in breeding programs.
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Affiliation(s)
- Rodrigo R Amadeu
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Patricio R Muñoz
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Chaozhi Zheng
- Biometris, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
| | - Jeffrey B Endelman
- Department of Horticulture, University of Wisconsin, Madison, WI 53706, USA
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Li W, Boer MP, Zheng C, Joosen RVL, van Eeuwijk FA. An IBD-based mixed model approach for QTL mapping in multiparental populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3643-3660. [PMID: 34342658 PMCID: PMC8519866 DOI: 10.1007/s00122-021-03919-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 07/16/2021] [Indexed: 05/16/2023]
Abstract
The identity-by-descent (IBD)-based mixed model approach introduced in this study can detect quantitative trait loci (QTLs) referring to the parental origin and simultaneously account for multilevel relatedness of individuals within and across families. This unified approach is proved to be a powerful approach for all kinds of multiparental population (MPP) designs. Multiparental populations (MPPs) have become popular for quantitative trait loci (QTL) detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize well to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercross (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach to estimate variance components for multiallelic genetic effects associated with parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the advanced IBD-based multi-QTL mixed model approach incorporating both kinship relations and family-specific residual variances (IBD.MQMkin_F) is robust across a variety of MPP designs and allele segregation patterns in comparison to a widely used benchmark association mapping method, and in most cases, outperformed or behaved at least as well as other tools developed for specific MPP designs in terms of mapping power and resolution. Successful analyses of real data cases confirmed the wide applicability of our IBD-based mixed model methodology.
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Affiliation(s)
- Wenhao Li
- Biometris, Wageningen University and Research Center, P.O Box 100, 6700 AC, Wageningen, The Netherlands
| | - Martin P Boer
- Biometris, Wageningen University and Research Center, P.O Box 100, 6700 AC, Wageningen, The Netherlands
| | - Chaozhi Zheng
- Biometris, Wageningen University and Research Center, P.O Box 100, 6700 AC, Wageningen, The Netherlands
| | - Ronny V L Joosen
- Rijk Zwaan Breeding B.V., P.O Box 40, 2678 ZG, De Lier, The Netherlands
| | - Fred A van Eeuwijk
- Biometris, Wageningen University and Research Center, P.O Box 100, 6700 AC, Wageningen, The Netherlands.
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Yang CJ, Edmondson RN, Piepho HP, Powell W, Mackay I. Crafting for a better MAGIC: systematic design and test for Multiparental Advanced Generation Inter-Cross population. G3 GENES|GENOMES|GENETICS 2021; 11:6354367. [PMID: 34849794 PMCID: PMC8527519 DOI: 10.1093/g3journal/jkab295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/15/2021] [Indexed: 12/04/2022]
Abstract
Multiparental Advanced Generation Inter-Cross (MAGIC) populations are valuable crop resources with a wide array of research uses including genetic mapping of complex traits, management of genetic resources and breeding of new varieties. Multiple founders are crossed to create a rich mosaic of highly recombined founder genomes in the MAGIC recombinant inbred lines (RILs). Many variations of MAGIC population designs exist; however, a large proportion of the currently available populations have been created empirically and based on similar designs. In our evaluations of five MAGIC populations, we found that the choice of designs has a large impact on the recombination landscape in the RILs. The most popular design used in many MAGIC populations has been shown to have a bias in recombinant haplotypes and low level of unique recombinant haplotypes, and therefore is not recommended. To address this problem and provide a remedy for the future, we have developed the “magicdesign” R package for creating and testing any MAGIC population design via simulation. A Shiny app version of the package is available as well. Our “magicdesign” package provides a unifying tool and a framework for creativity and innovation in MAGIC population designs. For example, using this package, we demonstrate that MAGIC population designs can be found which are very effective in creating haplotype diversity without the requirement for very large crossing programs. Furthermore, we show that interspersing cycles of crossing with cycles of selfing is effective in increasing haplotype diversity. These approaches are applicable in species that are hard to cross or in which resources are limited.
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Affiliation(s)
| | | | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart 70593, Germany
| | - Wayne Powell
- Scotland’s Rural College (SRUC), Edinburgh EH9 3JG, UK
| | - Ian Mackay
- Scotland’s Rural College (SRUC), Edinburgh EH9 3JG, UK
- IMplant Consultancy Ltd., Chelmsford, UK
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Zheng C, Amadeu RR, Munoz PR, Endelman JB. Haplotype reconstruction in connected tetraploid F1 populations. Genetics 2021; 219:6330625. [PMID: 34849879 DOI: 10.1093/genetics/iyab106] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/03/2021] [Indexed: 11/12/2022] Open
Abstract
In diploid species, many multiparental populations have been developed to increase genetic diversity and quantitative trait loci (QTL) mapping resolution. In these populations, haplotype reconstruction has been used as a standard practice to increase the power of QTL detection in comparison with the marker-based association analysis. However, such software tools for polyploid species are few and limited to a single biparental F1 population. In this study, a statistical framework for haplotype reconstruction has been developed and implemented in the software PolyOrigin for connected tetraploid F1 populations with shared parents, regardless of the number of parents or mating design. Given a genetic or physical map of markers, PolyOrigin first phases parental genotypes, then refines the input marker map, and finally reconstructs offspring haplotypes. PolyOrigin can utilize single nucleotide polymorphism (SNP) data coming from arrays or from sequence-based genotyping; in the latter case, bi-allelic read counts can be used (and are preferred) as input data to minimize the influence of genotype calling errors at low depth. With extensive simulation we show that PolyOrigin is robust to the errors in the input genotypic data and marker map. It works well for various population designs with ≥30 offspring per parent and for sequences with read depth as low as 10x. PolyOrigin was further evaluated using an autotetraploid potato dataset with a 3 × 3 half-diallel mating design. In conclusion, PolyOrigin opens up exciting new possibilities for haplotype analysis in tetraploid breeding populations.
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Affiliation(s)
- Chaozhi Zheng
- Biometris, Wageningen University and Research, Wageningen 6700AA, The Netherlands
| | - Rodrigo R Amadeu
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Patricio R Munoz
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Jeffrey B Endelman
- Department of Horticulture, University of Wisconsin, Madison, WI 53706, USA
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Varshney RK, Bohra A, Roorkiwal M, Barmukh R, Cowling WA, Chitikineni A, Lam HM, Hickey LT, Croser JS, Bayer PE, Edwards D, Crossa J, Weckwerth W, Millar H, Kumar A, Bevan MW, Siddique KHM. Fast-forward breeding for a food-secure world. Trends Genet 2021; 37:1124-1136. [PMID: 34531040 DOI: 10.1016/j.tig.2021.08.002] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 10/20/2022]
Abstract
Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people.
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Affiliation(s)
- Rajeev K Varshney
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch WA 6150, Western Australia, Australia; The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia.
| | - Abhishek Bohra
- ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, India
| | - Manish Roorkiwal
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Rutwik Barmukh
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | - Wallace A Cowling
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Annapurna Chitikineni
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | - Hon-Ming Lam
- School of Life Sciences and Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, QLD, Australia
| | - Janine S Croser
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Philipp E Bayer
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia
| | - David Edwards
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria
| | - Harvey Millar
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, WA, Australia
| | - Arvind Kumar
- Deputy Director General's Office, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | | | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
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Messina FJ, Lish AM, Gompert Z. Disparate genetic variants associated with distinct components of cowpea resistance to the seed beetle Callosobruchus maculatus. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2749-2766. [PMID: 34117909 DOI: 10.1007/s00122-021-03856-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/08/2021] [Indexed: 06/12/2023]
Abstract
Polygenic genome-wide association mapping identified two regions of the cowpea genome associated with different components of resistance to its major post-harvest pest, the seed beetle Callosobruchus maculatus. Cowpea (Vigna unguiculata) is an important grain and fodder crop in arid and semi-arid regions of Africa, Asia, and South America, where the cowpea seed beetle, Callosobruchus maculatus, is a serious post-harvest pest. Development of cultivars resistant to C. maculatus population growth in storage could increase grain yield and quality and reduce reliance on insecticides. Here, we use a MAGIC (multi-parent, advanced-generation intercross) population of cowpea consisting of 305 recombinant inbred lines (RILs) to identify genetic variants associated with resistance to seed beetles. Because inferences regarding the genetic basis of resistance may depend on the source of the pest or the assay protocol, we used two divergent geographic populations of C. maculatus and two complementary assays to measure several aspects of resistance. Using polygenic genome-wide association mapping models, we found that the cowpea RILs harbor substantial additive-genetic variation for most resistance measures. Variation in several components of resistance, including larval development time and survival, was largely explained by one or several linked loci on chromosome 5. A second region on chromosome 8 explained increased seed resistance via the induction of early-exiting larvae. Neither of these regions contained genes previously associated with resistance to insects that infest grain legumes. We found some evidence of gene-gene interactions affecting resistance, but epistasis did not contribute substantially to resistance variation in this mapping population. The combination of mostly high heritabilities and a relatively consistent and simple genetic architecture increases the feasibility of breeding for enhanced resistance to C. maculatus.
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Affiliation(s)
- Frank J Messina
- Department of Biology, Utah State University, Logan, UT, 84322, USA
| | - Alexandra M Lish
- Department of Biology, Utah State University, Logan, UT, 84322, USA
| | - Zachariah Gompert
- Department of Biology, Utah State University, Logan, UT, 84322, USA.
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Huang C, Shen C, Wen T, Gao B, Zhu D, Li D, Lin Z. Genome-wide association mapping for agronomic traits in an 8-way Upland cotton MAGIC population by SLAF-seq. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2459-2468. [PMID: 33912997 DOI: 10.1007/s00122-021-03835-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 04/12/2021] [Indexed: 06/12/2023]
Abstract
One sub-MAGIC population was genotyped using SLAF-seq, and QTLs and candidate genes for agronomic traits were identified in Upland cotton. The agronomic traits of Upland cotton have serious impacts on cotton production, as well as economic benefits. To discover the genetic basis of important agronomic traits in Upland cotton, a subset MAGIC (multi-parent advanced generation inter-cross) population containing 372 lines (SMLs) was selected from an 8-way MAGIC population with 960 lines. The 372 lines and 8 parents were phenotyped in six environments and deeply genotyped by SLAF-seq with 60,495 polymorphic SNPs. The genetic diversity indexes of all SNPs were 0.324 and 0.362 for the parents and MAGIC lines, respectively. The LD decay distance of the SMLs was 600 kb (r2 = 0.1). Genome-wide association mapping was performed using 60,495 SNPs and the phenotypic data of the SMLs, and 177 SNPs were identified to be significantly associated with 9 stable agronomic traits in multiple environments. The identified SNPs were divided into 117 QTLs (quantitative trait loci) by LD decay distance, explaining 5.44% to 31.64% of the phenotypic variation. Among the 117 QTLs, 3 QTLs were stable in multiple environments, and 11 QTL regions were proven to have pleiotropism associated with multiple traits. Within QTL regions, 154 genes were preferentially expressed in correlated tissues, and 8 genes with known functions were identified as priori candidate genes. Two genes, GhACT1 and GhGASL3, reported to have clear functions, were, respectively, located in qFE-A05-4 and qFE-D04-3, two stable QTLs for FE. This study revealed the genetic basis of important agronomic traits of Upland cotton, and the results will facilitate molecular breeding in cotton.
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Affiliation(s)
- Cong Huang
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Chao Shen
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Tianwang Wen
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Bin Gao
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - De Zhu
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Dingguo Li
- Institute of Crop Genetic and Breeding, Yangtze University, Jingzhou, 434025, Hubei, China.
| | - Zhongxu Lin
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China.
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Billings GT, Jones MA, Rustgi S, Hulse-Kemp AM, Campbell BT. Population structure and genetic diversity of the Pee Dee cotton breeding program. G3 (BETHESDA, MD.) 2021; 11:jkab145. [PMID: 33914887 PMCID: PMC8495920 DOI: 10.1093/g3journal/jkab145] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 04/19/2021] [Indexed: 11/12/2022]
Abstract
Accelerated marker-assisted selection and genomic selection breeding systems require genotyping data to select the best parents for combining beneficial traits. Since 1935, the Pee Dee (PD) cotton germplasm enhancement program has developed an important genetic resource for upland cotton (Gossypium hirsutum L.), contributing alleles for improved fiber quality, agronomic performance, and genetic diversity. To date, a detailed genetic survey of the program's eight historical breeding cycles has yet to be undertaken. The objectives of this study were to evaluate genetic diversity across and within-breeding groups, examine population structure, and contextualize these findings relative to the global upland cotton gene pool. The CottonSNP63K array was used to identify 17,441 polymorphic markers in a panel of 114 diverse PD genotypes. A subset of 4597 markers was selected to decrease marker density bias. Identity-by-state pairwise distance varied substantially, ranging from 0.55 to 0.97. Pedigree-based estimates of relatedness were not very predictive of observed genetic similarities. Few rare alleles were present, with 99.1% of SNP alleles appearing within the first four breeding cycles. Population structure analysis with principal component analysis, discriminant analysis of principal components, fastSTRUCTURE, and a phylogenetic approach revealed an admixed population with moderate substructure. A small core collection (n < 20) captured 99% of the program's allelic diversity. Allele frequency analysis indicated potential selection signatures associated with stress resistance and fiber cell growth. The results of this study will steer future utilization of the program's germplasm resources and aid in combining program-specific beneficial alleles and maintaining genetic diversity.
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Affiliation(s)
- Grant T Billings
- Clemson University, Pee Dee Research and Education Center, Florence, SC 29501, USA
- North Carolina State University, Crop Science Department, Raleigh, NC 27695, USA
| | - Michael A Jones
- Clemson University, Pee Dee Research and Education Center, Florence, SC 29501, USA
| | - Sachin Rustgi
- Clemson University, Pee Dee Research and Education Center, Florence, SC 29501, USA
| | - Amanda M Hulse-Kemp
- North Carolina State University, Crop Science Department, Raleigh, NC 27695, USA
- USDA-ARS, Genomics and Bioinformatics Research Unit, Raleigh, NC 27695, USA
| | - B Todd Campbell
- USDA-ARS, Coastal Plains, Soil, Water, and Plant Research Center, Florence, SC 29501, USA
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Liang Y, Liu HJ, Yan J, Tian F. Natural Variation in Crops: Realized Understanding, Continuing Promise. ANNUAL REVIEW OF PLANT BIOLOGY 2021; 72:357-385. [PMID: 33481630 DOI: 10.1146/annurev-arplant-080720-090632] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Crops feed the world's population and shape human civilization. The improvement of crop productivity has been ongoing for almost 10,000 years and has evolved from an experience-based to a knowledge-driven practice over the past three decades. Natural alleles and their reshuffling are long-standing genetic changes that affect how crops respond to various environmental conditions and agricultural practices. Decoding the genetic basis of natural variation is central to understanding crop evolution and, in turn, improving crop breeding. Here, we review current advances in the approaches used to map the causal alleles of natural variation, provide refined insights into the genetics and evolution of natural variation, and outline how this knowledge promises to drive the development of sustainable agriculture under the dome of emerging technologies.
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Affiliation(s)
- Yameng Liang
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China; ,
| | - Hai-Jun Liu
- Gregor Mendel Institute, Austrian Academy of Sciences, Vienna BioCenter, 1030 Vienna, Austria;
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China;
| | - Feng Tian
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China; ,
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46
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Sinha P, Singh VK, Bohra A, Kumar A, Reif JC, Varshney RK. Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1829-1843. [PMID: 34014373 PMCID: PMC8205890 DOI: 10.1007/s00122-021-03847-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 04/29/2021] [Indexed: 05/03/2023]
Abstract
KEY MESSAGE Integrating genomics technologies and breeding methods to tweak core parameters of the breeder's equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers' fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder's equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder's equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.
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Affiliation(s)
- Pallavi Sinha
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- International Rice Research Institute (IRRI), IRRI South Asia Hub, ICRISAT, Hyderabad, India
| | - Vikas K Singh
- International Rice Research Institute (IRRI), IRRI South Asia Hub, ICRISAT, Hyderabad, India
| | - Abhishek Bohra
- ICAR- Indian Institute of Pulses Research (IIPR), Kanpur, India
| | - Arvind Kumar
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Jochen C Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Rajeev K Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, Australia.
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47
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Pereira L, Santo Domingo M, Argyris J, Mayobre C, Valverde L, Martín-Hernández AM, Pujol M, Garcia-Mas J. A novel introgression line collection to unravel the genetics of climacteric ripening and fruit quality in melon. Sci Rep 2021; 11:11364. [PMID: 34059766 PMCID: PMC8166866 DOI: 10.1038/s41598-021-90783-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 05/18/2021] [Indexed: 11/09/2022] Open
Abstract
Introgression lines are valuable germplasm for scientists and breeders, since they ease genetic studies such as QTL interactions and positional cloning as well as the introduction of favorable alleles into elite varieties. We developed a novel introgression line collection in melon using two commercial European varieties with different ripening behavior, the climacteric cantalupensis 'Védrantais' as recurrent parent and the non-climacteric inodorus 'Piel de Sapo' as donor parent. The collection contains 34 introgression lines, covering 99% of the donor genome. The mean introgression size is 18.16 Mb and ~ 3 lines were obtained per chromosome, on average. The high segregation of these lines for multiple fruit quality traits allowed us to identify 27 QTLs that modified sugar content, altered fruit morphology or were involved in climacteric ripening. In addition, we confirmed the genomic location of five major genes previously described, which control mainly fruit appearance, such as mottled rind and external color. Most of the QTLs had been reported before in other populations sharing parental lines, while three QTLs (EAROQP11.3, ECDQP11.2 and FIRQP4.1) were newly detected in our work. These introgression lines would be useful to perform additional genetic studies, as fine mapping and gene pyramiding, especially for important complex traits such as fruit weight and climacteric ripening.
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Affiliation(s)
- Lara Pereira
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain
| | - Miguel Santo Domingo
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain
| | - Jason Argyris
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain.,Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain
| | - Carlos Mayobre
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain
| | - Laura Valverde
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain
| | - Ana Montserrat Martín-Hernández
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain.,Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain
| | - Marta Pujol
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain. .,Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain.
| | - Jordi Garcia-Mas
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain. .,Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Edifici CRAG, Campus UAB, 08193, Bellaterra, Barcelona, Spain.
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48
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Puglisi D, Delbono S, Visioni A, Ozkan H, Kara İ, Casas AM, Igartua E, Valè G, Piero ARL, Cattivelli L, Tondelli A, Fricano A. Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction. FRONTIERS IN PLANT SCIENCE 2021; 12:664148. [PMID: 34108982 PMCID: PMC8183822 DOI: 10.3389/fpls.2021.664148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/26/2021] [Indexed: 06/12/2023]
Abstract
Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.
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Affiliation(s)
- Damiano Puglisi
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania, Catania, Italy
| | - Stefano Delbono
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Andrea Visioni
- Biodiversity and Crop Improvement Program, International Center for Agricultural Research in the Dry Areas, Avenue Hafiane Cherkaoui, Rabat, Morocco
| | - Hakan Ozkan
- Department of Field Crops, Faculty of Agriculture, University of Cukurova, Adana, Turkey
| | - İbrahim Kara
- Bahri Dagdas International Agricultural Research Institute, Konya, Turkey
| | - Ana M. Casas
- Aula Dei Experimental Station (EEAD-CSIC), Spanish Research Council, Zaragoza, Spain
| | - Ernesto Igartua
- Aula Dei Experimental Station (EEAD-CSIC), Spanish Research Council, Zaragoza, Spain
| | - Giampiero Valè
- DiSIT, Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale, Vercelli, Italy
| | - Angela Roberta Lo Piero
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania, Catania, Italy
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Alessandro Tondelli
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Agostino Fricano
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
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49
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Scott MF, Fradgley N, Bentley AR, Brabbs T, Corke F, Gardner KA, Horsnell R, Howell P, Ladejobi O, Mackay IJ, Mott R, Cockram J. Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding. Genome Biol 2021; 22:137. [PMID: 33957956 PMCID: PMC8101041 DOI: 10.1186/s13059-021-02354-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 04/16/2021] [Indexed: 11/25/2022] Open
Abstract
Background Selection has dramatically shaped genetic and phenotypic variation in bread wheat. We can assess the genomic basis of historical phenotypic changes, and the potential for future improvement, using experimental populations that attempt to undo selection through the randomizing effects of recombination. Results We bred the NIAB Diverse MAGIC multi-parent population comprising over 500 recombinant inbred lines, descended from sixteen historical UK bread wheat varieties released between 1935 and 2004. We sequence the founders’ genes and promoters by capture, and the MAGIC population by low-coverage whole-genome sequencing. We impute 1.1 M high-quality SNPs that are over 99% concordant with array genotypes. Imputation accuracy only marginally improves when including the founders’ genomes as a haplotype reference panel. Despite capturing 73% of global wheat genetic polymorphism, 83% of genes cluster into no more than three haplotypes. We phenotype 47 agronomic traits over 2 years and map 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Around half of these overlap known quantitative trait loci. Most traits exhibit extensive polygenicity, as revealed by multi-locus shrinkage modelling. Conclusions Our results are consistent with a gene pool of low haplotypic diversity, containing few novel loci of large effect. Most past, and projected future, phenotypic changes arising from existing variation involve fine-scale shuffling of a few haplotypes to recombine dozens of polygenic alleles of small effect. Moreover, extensive pleiotropy means selection on one trait will have unintended consequences, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break unfavorable trait-trait associations. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02354-7.
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Affiliation(s)
- Michael F Scott
- University College London (UCL) Genetics Institute, Gower St, London, WC1E 6BT, UK.,Current address: School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
| | - Nick Fradgley
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Alison R Bentley
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.,Current address: International Maize and Wheat Improvement Center (CIMMYT), El Batán, Texcoco, Mexico
| | | | - Fiona Corke
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE, UK
| | - Keith A Gardner
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Richard Horsnell
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Phil Howell
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | | | - Ian J Mackay
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.,Current address: SRUC, Peter Wilson Building King's Buildings, W Mains Rd, Edinburgh, EH9 3JG, UK
| | - Richard Mott
- University College London (UCL) Genetics Institute, Gower St, London, WC1E 6BT, UK.
| | - James Cockram
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.
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50
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Characterization of effects of genetic variants via genome-scale metabolic modelling. Cell Mol Life Sci 2021; 78:5123-5138. [PMID: 33950314 PMCID: PMC8254712 DOI: 10.1007/s00018-021-03844-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/15/2021] [Accepted: 04/23/2021] [Indexed: 12/19/2022]
Abstract
Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.
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