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Li J, Zhao P, Zhao L, Chen Q, Nong S, Li Q, Wang L. Integrated VIS/NIR Spectrum and Genome-Wide Association Study for Genetic Dissection of Cellulose Crystallinity in Wheat Stems. Int J Mol Sci 2024; 25:3028. [PMID: 38474272 DOI: 10.3390/ijms25053028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 02/19/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
Cellulose crystallinity is a crucial factor influencing stem strength and, consequently, wheat lodging. However, the genetic dissection of cellulose crystallinity is less reported due to the difficulty of its measurement. In this study, VIS/NIR spectra and cellulose crystallinity were measured for a wheat accession panel with diverse genetic backgrounds. We developed a reliable VIS/NIR model for cellulose crystallinity with a high determination coefficient (R2) (0.95) and residual prediction deviation (RPD) (4.04), enabling the rapid screening of wheat samples. A GWAS of the cellulose crystallinity in 326 wheat accessions revealed 14 significant SNPs and 13 QTLs. Two candidate genes, TraesCS4B03G0029800 and TraesCS5B03G1085500, were identified. In summary, this study establishes an efficient method for the measurement of cellulose crystallinity in wheat stems and provides a genetic basis for enhancing lodging resistance in wheat.
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Affiliation(s)
- Jianguo Li
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
- State Key Laboratory for Conservation & Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530004, China
| | - Peimin Zhao
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Liyan Zhao
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
- State Key Laboratory for Conservation & Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530004, China
| | - Qiang Chen
- State Key Laboratory for Conservation & Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530004, China
| | - Shikun Nong
- State Key Laboratory for Conservation & Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530004, China
| | - Qiang Li
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Lingqiang Wang
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
- State Key Laboratory for Conservation & Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530004, China
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Esposito S, Vitale P, Taranto F, Saia S, Pecorella I, D'Agostino N, Rodriguez M, Natoli V, De Vita P. Simultaneous improvement of grain yield and grain protein concentration in durum wheat by using association tests and weighted GBLUP. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:242. [PMID: 37947927 DOI: 10.1007/s00122-023-04487-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 10/16/2023] [Indexed: 11/12/2023]
Abstract
KEY MESSAGE Simultaneous improvement for GY and GPC by using GWAS and GBLUP suggested a significant application in durum wheat breeding. Despite the importance of grain protein concentration (GPC) in determining wheat quality, its negative correlation with grain yield (GY) is still one of the major challenges for breeders. Here, a durum wheat panel of 200 genotypes was evaluated for GY, GPC, and their derived indices (GPD and GYD), under eight different agronomic conditions. The plant material was genotyped with the Illumina 25 k iSelect array, and a genome-wide association study was performed. Two statistical models revealed dozens of marker-trait associations (MTAs), each explaining up to 30%. phenotypic variance. Two markers on chromosomes 2A and 6B were consistently identified by both models and were found to be significantly associated with GY and GPC. MTAs identified for phenological traits co-mapped to well-known genes (i.e., Ppd-1, Vrn-1). The significance values (p-values) that measure the strength of the association of each single nucleotide polymorphism marker with the target traits were used to perform genomic prediction by using a weighted genomic best linear unbiased prediction model. The trained models were ultimately used to predict the agronomic performances of an independent durum wheat panel, confirming the utility of genomic prediction, although environmental conditions and genetic backgrounds may still be a challenge to overcome. The results generated through our study confirmed the utility of GPD and GYD to mitigate the inverse GY and GPC relationship in wheat, provided novel markers for marker-assisted selection and opened new ways to develop cultivars through genomic prediction approaches.
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Affiliation(s)
- Salvatore Esposito
- Research Centre for Cereal and Industrial Crops (CREA-CI), CREA - Council for Agricultural Research and Economics, SS 673 Meters 25200, 71122, Foggia, Italy
| | - Paolo Vitale
- Research Centre for Cereal and Industrial Crops (CREA-CI), CREA - Council for Agricultural Research and Economics, SS 673 Meters 25200, 71122, Foggia, Italy
- Department of Agriculture, Food, Natural Science, Engineering, University of Foggia, Via Napoli 25, 71122, Foggia, Italy
| | - Francesca Taranto
- Institute of Biosciences and Bioresources (CNR-IBBR), Via Amendola 165/A, 70126, Bari, Italy
| | - Sergio Saia
- Department of Veterinary Sciences, University of Pisa, 56129, Pisa, Italy
| | - Ivano Pecorella
- Research Centre for Cereal and Industrial Crops (CREA-CI), CREA - Council for Agricultural Research and Economics, SS 673 Meters 25200, 71122, Foggia, Italy
| | - Nunzio D'Agostino
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
| | - Monica Rodriguez
- Department of Agriculture, University of Sassari, Viale Italia, 39, 07100, Sassari, Italy
| | - Vincenzo Natoli
- Genetic Services SRL, Contrada Catenaccio, snc, 71026, Deliceto, FG, Italy
| | - Pasquale De Vita
- Research Centre for Cereal and Industrial Crops (CREA-CI), CREA - Council for Agricultural Research and Economics, SS 673 Meters 25200, 71122, Foggia, Italy.
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Kiseleva AA, Leonova IN, Ageeva EV, Likhenko IE, Salina EA. Identification of genetic loci for early maturity in spring bread wheat using the association analysis and gene dissection. PeerJ 2023; 11:e16109. [PMID: 37842052 PMCID: PMC10569184 DOI: 10.7717/peerj.16109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/25/2023] [Indexed: 10/17/2023] Open
Abstract
Background Early maturity in spring bread wheat is highly desirable in the regions where it enables the plants to evade high temperatures and plant pathogens at the end of the growing season. Methods To reveal the genetic loci responsible for the maturity time association analysis was carried out based on phenotyping for an 11-year period and high-throughput SNP genotyping of a panel of the varieties contrasting for this trait. The expression of candidate genes was verified using qPCR. The association between the SNP markers and the trait was validated using the biparental F2:3 population. Results Our data showed that under long-day conditions, the period from seedling to maturity is mostly influenced by the time from heading to maturity, rather than the heading time. The QTLs associated with the trait were located on 2A, 3B, 4A, 5B, 7A and 7B chromosomes with the 7BL locus being the most significant and promising for its SNPs accelerated the maturity time by about 9 days. Gene dissection in this locus detected a number of candidates, the best being TraesCS7B02G391800 (bZIP9) and TraesCS7B02G412200 (photosystem II reaction center). The two genes are predominantly expressed in the flag leaf while flowering. The effect of the SNPs was verified in F2:3 population and confirmed the association of the 4A, 5B and 7BL loci with the maturity time.
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Affiliation(s)
- Antonina A. Kiseleva
- Laboratory of Plant Molecular Genetics and Cytogenetics, The Federal State Budgetary Institution of Science Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | - Irina N. Leonova
- Laboratory of Plant Molecular Genetics and Cytogenetics, The Federal State Budgetary Institution of Science Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Elena V. Ageeva
- Laboratory of Field Crop Breeding and Seed Industry, Siberian Research Institute of Plant Production and Breeding, Branch of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Ivan E. Likhenko
- Laboratory of Field Crop Breeding and Seed Industry, Siberian Research Institute of Plant Production and Breeding, Branch of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Elena A. Salina
- Laboratory of Plant Molecular Genetics and Cytogenetics, The Federal State Budgetary Institution of Science Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
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Qin R, Ma T, Cai Y, Shi X, Cheng J, Dong J, Wang C, Li S, Pan G, Guan Y, Zhang L, Yang S, Xu H, Zhao C, Sun H, Li X, Wu Y, Li J, Cui F. Characterization and fine mapping analysis of a major stable QTL qKnps-4A for kernel number per spike in wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:211. [PMID: 37737910 DOI: 10.1007/s00122-023-04456-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/28/2023] [Indexed: 09/23/2023]
Abstract
KEY MESSAGE A major stable QTL for kernel number per spike was narrowed down to a 2.19-Mb region containing two potential candidate genes, and its effects on yield-related traits were characterized. Kernel number per spike (KNPS) in wheat is a key yield component. Dissection and characterization of major stable quantitative trait loci (QTLs) for KNPS would be of considerable value for the genetic improvement of yield potential using molecular breeding technology. We had previously reported a major stable QTL controlling KNPS, qKnps-4A. In the current study, primary fine-mapping analysis, based on the primary mapping population, located qKnps-4A to an interval of approximately 6.8-Mb from 649.0 to 655.8 Mb on chromosome 4A refering to 'Kenong 9204' genome. Further fine-mapping analysis based on a secondary mapping population narrowed qKnps-4A to an approximately 2.19-Mb interval from 653.72 to 655.91 Mb. Transcriptome sequencing, gene function annotation analysis and homologous gene related reports showed that TraesKN4A01HG38570 and TraesKN4A01HG38590 were most likely to be candidate genes of qKnps-4A. Phenotypic analysis based on paired near-isogenic lines in the target region showed that qKnps-4A increased KNPS mainly by increasing the number of central florets per spike. We also evaluated the effects of qKnps-4A on other yield-related traits. Moreover, we dissected the QTL cluster of qKnps-4A and qTkw-4A and proved that the phenotypic effects were probably due to close linkage of two or more genes rather than pleiotropic effects of a single gene. This study provides molecular marker resource for wheat molecular breeding designed to improve yield potential, and lay the foundation for gene functional analysis of qKnps-4A.
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Affiliation(s)
- Ran Qin
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Tianhang Ma
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Yibiao Cai
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Xinyao Shi
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Jiajia Cheng
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Jizi Dong
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Chenyang Wang
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Shihui Li
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Guoqing Pan
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Yuxiang Guan
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Lei Zhang
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Shuang Yang
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Huiyuan Xu
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Chunhua Zhao
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Han Sun
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
| | - Ximei Li
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China
- Shandong Key Laboratory of Dryland Farming Technology, Shandong Engineering Research Center of Germplasm Innovation and Utilization of Salt-Tolerant Crops, College of Agronomy, Qingdao Agricultural University, Qingdao, 266109, China
| | - Yongzhen Wu
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China.
| | - Junming Li
- Ministry of Education Key Laboratory of Molecular and Cellular Biology, Hebei Key Laboratory of Molecular and Cellular Biology, College of Life Sciences, Hebei Normal University, Hebei Collaboration Innovation Center for Cell Signaling, Shijiazhuang, 050024, China.
| | - Fa Cui
- Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai, 264025, China.
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Liu Y, Chen J, Yin C, Wang Z, Wu H, Shen K, Zhang Z, Kang L, Xu S, Bi A, Zhao X, Xu D, He Z, Zhang X, Hao C, Wu J, Gong Y, Yu X, Sun Z, Ye B, Liu D, Zhang L, Shen L, Hao Y, Ma Y, Lu F, Guo Z. A high-resolution genotype-phenotype map identifies the TaSPL17 controlling grain number and size in wheat. Genome Biol 2023; 24:196. [PMID: 37641093 PMCID: PMC10463835 DOI: 10.1186/s13059-023-03044-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Large-scale genotype-phenotype association studies of crop germplasm are important for identifying alleles associated with favorable traits. The limited number of single-nucleotide polymorphisms (SNPs) in most wheat genome-wide association studies (GWASs) restricts their power to detect marker-trait associations. Additionally, only a few genes regulating grain number per spikelet have been reported due to sensitivity of this trait to variable environments. RESULTS We perform a large-scale GWAS using approximately 40 million filtered SNPs for 27 spike morphology traits. We detect 132,086 significant marker-trait associations and the associated SNP markers are located within 590 associated peaks. We detect additional and stronger peaks by dividing spike morphology into sub-traits relative to GWAS results of spike morphology traits. We propose that the genetic dissection of spike morphology is a powerful strategy to detect signals for grain yield traits in wheat. The GWAS results reveal that TaSPL17 positively controls grain size and number by regulating spikelet and floret meristem development, which in turn leads to enhanced grain yield per plant. The haplotypes at TaSPL17 indicate geographical differentiation, domestication effects, and breeding selection. CONCLUSION Our study provides valuable resources for genetic improvement of spike morphology and a fast-forward genetic solution for candidate gene detection and cloning in wheat.
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Affiliation(s)
- Yangyang Liu
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jun Chen
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
| | - Changbin Yin
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 10011, China
| | - Ziying Wang
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - He Wu
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kuocheng Shen
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhiliang Zhang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 10011, China
| | - Lipeng Kang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 10011, China
| | - Song Xu
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 10011, China
| | - Aoyue Bi
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 10011, China
| | - Xuebo Zhao
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 10011, China
| | - Daxing Xu
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 10011, China
| | - Zhonghu He
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
- International Maize and Wheat Improvement Center (CIMMYT) China Office, c/o CAAS, Beijing, 100081, China
| | - Xueyong Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
| | - Chenyang Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
| | - Jianhui Wu
- State Key Laboratory of Crop Stress Biology for Arid Areas, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Yan Gong
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
| | - Xuchang Yu
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhiwen Sun
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Botao Ye
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Danni Liu
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lili Zhang
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Liping Shen
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Yuanfeng Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
| | - Youzhi Ma
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
| | - Fei Lu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 10011, China.
- CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100093, China.
| | - Zifeng Guo
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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Li W, Lin M, Li J, Liu D, Tan W, Yin X, Zhai Y, Zhou Y, Xing W. Genome-wide association study of drought tolerance traits in sugar beet germplasms at the seedling stage. Front Genet 2023; 14:1198600. [PMID: 37547461 PMCID: PMC10401439 DOI: 10.3389/fgene.2023.1198600] [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: 04/01/2023] [Accepted: 06/30/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction: Sugar beets are an important crop for global sugar production. Intense drought and the increasing lack of water resources pose a great threat to sugar beet cultivation. It is a priority to investigate favourable germplasms and functional genes to improve the breeding of drought tolerant plants. Methods: Thus, in this study, 328 sugar beet germplasms were used in a genome-wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers and candidate genes associated with drought tolerance. Results: The results showed that under drought stress (9% PEG-6000), there were 11 significantly associated loci on chromosomes 2, 3, 5, 7, and 9 from the 108946 SNPs filtered using a mixed linear model (MLM). Genome-wide association analysis combined with qRT-PCR identified 13 genes that were significantly differentially expressed in drought-tolerant extreme materials. Discussion: These candidate genes mainly exhibited functions such as regulating sugar metabolism, maintaining internal environmental stability and participating in photosystem repair. This study provides valuable information for exploring the molecular mechanisms of drought tolerance and improvement in sugar beet.
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Affiliation(s)
- Wangsheng Li
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Ming Lin
- Xinjiang Academy of Agricultural Sciences, Urumqi, China
| | - Jiajia Li
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Dali Liu
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Wenbo Tan
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Xilong Yin
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Yan Zhai
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Yuanhang Zhou
- Xinjiang Academy of Agricultural Sciences, Urumqi, China
| | - Wang Xing
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
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Liu B, Li L, Fu C, Zhang Y, Bai B, Du J, Zeng J, Bian Y, Liu S, Song J, Luo X, Xie L, Sun M, Xu X, Xia X, Cao S. Genetic dissection of grain morphology and yield components in a wheat line with defective grain filling. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:165. [PMID: 37392240 DOI: 10.1007/s00122-023-04410-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/22/2023] [Indexed: 07/03/2023]
Abstract
KEY MESSAGE We identified stable QTL for grain morphology and yield component traits in a wheat defective grain filling line and validated genetic effects in a panel of cultivars using breeding-relevant markers. Grain filling capacity is essential for grain yield and appearance quality in cereal crops. Identification of genetic loci for grain filling is important for wheat improvement. However, there are few genetic studies on grain filling in wheat. Here, a defective grain filling (DGF) line wdgf1 characterized by shrunken grains was identified in a population derived from multi-round crosses involving nine parents and a recombinant inbreed line (RIL) population was generated from the cross between wdgf1 and a sister line with normal grains. We constructed a genetic map of the RIL population using the wheat 15K single nucleotide polymorphism chip and detected 25 stable quantitative trait loci (QTL) for grain morphology and yield components, including three for DGF, eleven for grain size, six for thousand grain weight, three for grain number per spike and two for spike number per m2. Among them, QDGF.caas-7A is co-located with QTGW.caas-7A and can explain 39.4-64.6% of the phenotypic variances, indicating that this QTL is a major locus controlling DGF. Sequencing and linkage mapping showed that TaSus2-2B and Rht-B1 were candidate genes for QTGW.caas-2B and the QTL cluster (QTGW.caas-4B, QGNS.caas-4B, and QSN.caas-4B), respectively. We developed kompetitive allele-specific PCR markers tightly linked to the stable QTL without corresponding to known yield-related genes, and validated their genetic effects in a diverse panel of wheat cultivars. These findings not only lay a solid foundation for genetic dissection underlying grain filling and yield formation, but also provide useful tools for marker-assisted breeding.
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Affiliation(s)
- Bingyan Liu
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Lingli Li
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Chao Fu
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Yingjun Zhang
- Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Laboratory of Crop Genetics and Breeding of Hebei, Shijiazhuang, China
| | - Bin Bai
- Wheat Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, China
| | - Jiuyuan Du
- Wheat Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, China
| | - Jianqi Zeng
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Yingjie Bian
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Siyang Liu
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Jie Song
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Xumei Luo
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Lina Xie
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Mengjing Sun
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Xiaowan Xu
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Xianchun Xia
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Shuanghe Cao
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
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8
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Liu Y, Shen K, Yin C, Xu X, Yu X, Ye B, Sun Z, Dong J, Bi A, Zhao X, Xu D, He Z, Zhang X, Hao C, Wu J, Wang Z, Wu H, Liu D, Zhang L, Shen L, Hao Y, Lu F, Guo Z. Genetic basis of geographical differentiation and breeding selection for wheat plant architecture traits. Genome Biol 2023; 24:114. [PMID: 37173729 PMCID: PMC10176713 DOI: 10.1186/s13059-023-02932-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 04/10/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND Plant architecture associated with increased grain yield and adaptation to the local environments is selected during wheat (Triticum aestivum) breeding. The internode length of individual stems and tiller length of individual plants are important for the determination of plant architecture. However, few studies have explored the genetic basis of these traits. RESULTS Here, we conduct a genome-wide association study (GWAS) to dissect the genetic basis of geographical differentiation of these traits in 306 worldwide wheat accessions including both landraces and traditional varieties. We determine the changes of haplotypes for the associated genomic regions in frequency in 831 wheat accessions that are either introduced from other countries or developed in China from last two decades. We identify 83 loci that are associated with one trait, while the remaining 247 loci are pleiotropic. We also find 163 associated loci are under strong selective sweep. GWAS results demonstrate independent regulation of internode length of individual stems and consistent regulation of tiller length of individual plants. This makes it possible to obtain ideal haplotype combinations of the length of four internodes. We also find that the geographical distribution of the haplotypes explains the observed differences in internode length among the worldwide wheat accessions. CONCLUSION This study provides insights into the genetic basis of plant architecture. It will facilitate gene functional analysis and molecular design of plant architecture for breeding.
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Affiliation(s)
- Yangyang Liu
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Kuocheng Shen
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Changbin Yin
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100010, China
| | - Xiaowan Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
| | - Xuchang Yu
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Botao Ye
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Zhiwen Sun
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Jiayu Dong
- University of Chinese Academy of Sciences, 100049, Beijing, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100010, China
| | - Aoyue Bi
- University of Chinese Academy of Sciences, 100049, Beijing, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100010, China
| | - Xuebo Zhao
- University of Chinese Academy of Sciences, 100049, Beijing, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100010, China
| | - Daxing Xu
- University of Chinese Academy of Sciences, 100049, Beijing, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100010, China
| | - Zhonghu He
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
- International Maize and Wheat Improvement Center (CIMMYT) China Office, c/o CAAS, Beijing, 100081, China
| | - Xueyong Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
| | - Chenyang Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
| | - Jianhui Wu
- State Key Laboratory of Crop Stress Biology for Arid Areas, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Ziying Wang
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - He Wu
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Danni Liu
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Lili Zhang
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Liping Shen
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Yuanfeng Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
| | - Fei Lu
- University of Chinese Academy of Sciences, 100049, Beijing, China.
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100010, China.
- CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.
| | - Zifeng Guo
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
- University of Chinese Academy of Sciences, 100049, Beijing, China.
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9
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Zhao Y, Islam S, Alhabbar Z, Zhang J, O'Hara G, Anwar M, Ma W. Current Progress and Future Prospect of Wheat Genetics Research towards an Enhanced Nitrogen Use Efficiency. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12091753. [PMID: 37176811 PMCID: PMC10180859 DOI: 10.3390/plants12091753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 05/15/2023]
Abstract
To improve the yield and quality of wheat is of great importance for food security worldwide. One of the most effective and significant approaches to achieve this goal is to enhance the nitrogen use efficiency (NUE) in wheat. In this review, a comprehensive understanding of the factors involved in the process of the wheat nitrogen uptake, assimilation and remobilization of nitrogen in wheat were introduced. An appropriate definition of NUE is vital prior to its precise evaluation for the following gene identification and breeding process. Apart from grain yield (GY) and grain protein content (GPC), the commonly recognized major indicators of NUE, grain protein deviation (GPD) could also be considered as a potential trait for NUE evaluation. As a complex quantitative trait, NUE is affected by transporter proteins, kinases, transcription factors (TFs) and micro RNAs (miRNAs), which participate in the nitrogen uptake process, as well as key enzymes, circadian regulators, cross-talks between carbon metabolism, which are associated with nitrogen assimilation and remobilization. A series of quantitative genetic loci (QTLs) and linking markers were compiled in the hope to help discover more efficient and useful genetic resources for breeding program. For future NUE improvement, an exploration for other criteria during selection process that incorporates morphological, physiological and biochemical traits is needed. Applying new technologies from phenomics will allow high-throughput NUE phenotyping and accelerate the breeding process. A combination of multi-omics techniques and the previously verified QTLs and molecular markers will facilitate the NUE QTL-mapping and novel gene identification.
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Affiliation(s)
- Yun Zhao
- Food Futures Institute & College of Science, Health, Engineering and Education, Murdoch University, Perth 6150, Australia
- Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Laboratory of Crop Genetics and Breeding of Hebei, Shijiazhuang 050035, China
| | - Shahidul Islam
- Food Futures Institute & College of Science, Health, Engineering and Education, Murdoch University, Perth 6150, Australia
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58108, USA
| | - Zaid Alhabbar
- Department of Field Crops, College of Agriculture and Forestry, University of Mosul, Mosul 41002, Iraq
| | - Jingjuan Zhang
- Food Futures Institute & College of Science, Health, Engineering and Education, Murdoch University, Perth 6150, Australia
| | - Graham O'Hara
- Food Futures Institute & College of Science, Health, Engineering and Education, Murdoch University, Perth 6150, Australia
| | - Masood Anwar
- Food Futures Institute & College of Science, Health, Engineering and Education, Murdoch University, Perth 6150, Australia
| | - Wujun Ma
- Food Futures Institute & College of Science, Health, Engineering and Education, Murdoch University, Perth 6150, Australia
- College of Agronomy, Qingdao Agriculture University, Qingdao 266109, China
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10
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Zeng Z, Zhao D, Wang C, Yan X, Song J, Chen P, Lan C, Singh RP. QTL cluster analysis and marker development for kernel traits based on DArT markers in spring bread wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2023; 14:1072233. [PMID: 36844075 PMCID: PMC9951491 DOI: 10.3389/fpls.2023.1072233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Genetic dissection of yield component traits including kernel characteristics is essential for the continuous improvement in wheat yield. In the present study, one recombinant inbred line (RIL) F6 population derived from a cross between Avocet and Chilero was used to evaluate the phenotypes of kernel traits of thousand-kernel weight (TKW), kernel length (KL), and kernel width (KW) in four environments at three experimental stations during the 2018-2020 wheat growing seasons. The high-density genetic linkage map was constructed with the diversity arrays technology (DArT) markers and the inclusive composite interval mapping (ICIM) method to identify the quantitative trait loci (QTLs) for TKW, KL, and KW. A total of 48 QTLs for three traits were identified in the RIL population on the 21 chromosomes besides 2A, 4D, and 5B, accounting for 3.00%-33.85% of the phenotypic variances. Based on the physical positions of each QTL, nine stable QTL clusters were identified in the RILs, and among these QTL clusters, TaTKW-1A was tightly linked to the DArT marker interval 3950546-1213099, explaining 10.31%-33.85% of the phenotypic variances. A total of 347 high-confidence genes were identified in a 34.74-Mb physical interval. TraesCS1A02G045300 and TraesCS1A02G058400 were among the putative candidate genes associated with kernel traits, and they were expressed during grain development. Moreover, we also developed high-throughput kompetitive allele-specific PCR (KASP) markers of TaTKW-1A, validated in a natural population of 114 wheat varieties. The study provides a basis for cloning the functional genes underlying the QTL for kernel traits and a practical and accurate marker for molecular breeding.
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Affiliation(s)
- Zhankui Zeng
- College of Agronomy, Henan University of Science and Technology, Luoyang, Henan, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Dehui Zhao
- College of Agronomy, Henan University of Science and Technology, Luoyang, Henan, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Chunping Wang
- College of Agronomy, Henan University of Science and Technology, Luoyang, Henan, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Xuefang Yan
- College of Agronomy, Henan University of Science and Technology, Luoyang, Henan, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Junqiao Song
- College of Agronomy, Henan University of Science and Technology, Luoyang, Henan, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Peng Chen
- College of Agronomy, Henan University of Science and Technology, Luoyang, Henan, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Caixia Lan
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Ravi P. Singh
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico
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11
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Subedi M, Ghimire B, Bagwell JW, Buck JW, Mergoum M. Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities. Front Genet 2023; 13:1032601. [PMID: 36685944 PMCID: PMC9849398 DOI: 10.3389/fgene.2022.1032601] [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: 08/31/2022] [Accepted: 12/20/2022] [Indexed: 01/06/2023] Open
Abstract
Wheat is the most important source of food, feed, and nutrition for humans and livestock around the world. The expanding population has increasing demands for various wheat products with different quality attributes requiring the development of wheat cultivars that fulfills specific demands of end-users including millers and bakers in the international market. Therefore, wheat breeding programs continually strive to meet these quality standards by screening their improved breeding lines every year. However, the direct measurement of various end-use quality traits such as milling and baking qualities requires a large quantity of grain, traits-specific expensive instruments, time, and an expert workforce which limits the screening process. With the advancement of sequencing technologies, the study of the entire plant genome is possible, and genetic mapping techniques such as quantitative trait locus mapping and genome-wide association studies have enabled researchers to identify loci/genes associated with various end-use quality traits in wheat. Modern breeding techniques such as marker-assisted selection and genomic selection allow the utilization of these genomic resources for the prediction of quality attributes with high accuracy and efficiency which speeds up crop improvement and cultivar development endeavors. In addition, the candidate gene approach through functional as well as comparative genomics has facilitated the translation of the genomic information from several crop species including wild relatives to wheat. This review discusses the various end-use quality traits of wheat, their genetic control mechanisms, the use of genetics and genomics approaches for their improvement, and future challenges and opportunities for wheat breeding.
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Affiliation(s)
- Madhav Subedi
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - Bikash Ghimire
- Department of Plant Pathology, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - John White Bagwell
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - James W. Buck
- Department of Plant Pathology, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - Mohamed Mergoum
- Department of Crop and Soil Sciences, University of Georgia, Griffin Campus, Griffin, GA, United States,*Correspondence: Mohamed Mergoum,
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12
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Chang S, Chen Q, Yang T, Li B, Xin M, Su Z, Du J, Guo W, Hu Z, Liu J, Peng H, Ni Z, Sun Q, Yao Y. Pinb-D1p is an elite allele for improving end-use quality in wheat (Triticum aestivum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:4469-4481. [PMID: 36175525 PMCID: PMC9734229 DOI: 10.1007/s00122-022-04232-7] [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: 07/18/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
We identified ten QTLs controlling SDS-SV trait in a RIL population derived from ND3331 and Zang1817. Pinb-D1p is an elite allele from Tibetan semi‑wild wheat for good end-use quality. Gluten strength is an important factor for wheat processing and end-product quality and is commonly characterized using the sodium dodecyl sulfate-sedimentation volume (SDS-SV) test. The objective of this study was to identify quantitative trait loci (QTLs) associated with wheat SDS-SV traits using a recombinant inbred line (RIL) population derived from common wheat line NongDa3331 (ND3331) and Tibetan semi-wild wheat accession Zang1817. We detected 10 QTLs controlling SDS-SV on chromosomes 1A, 1B, 3A, 4A, 4B, 5A, 5D, 6B and 7A, with individual QTLs explaining 2.02% to 15.53% of the phenotypic variation. They included four major QTLs, Qsdss-1A, Qsdss-1B.1, Qsdss-1B.2, and Qsdss-5D, whose effects on SDS-SV were due to the Glu-A1 locus encoding the high-molecular-weight glutenin subunit 1Ax1, the 1B/1R translocation, 1Bx7 + 1By8 at the Glu-B1 locus, and the hardness-controlling loci Pina-D1 and Pinb-D1, respectively. We developed KASP markers for the Glu-A1, Glu-B1, and Pinb-D1 loci. Importantly, we showed for the first time that the hardness allele Pinb-D1p positively affects SDS-SV, making it a good candidate for wheat quality improvement. These results broaden our understanding of the genetic characterization of SDS-SV, and the QTLs identified are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.
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Affiliation(s)
- Siyuan Chang
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Qian Chen
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Tao Yang
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Binyong Li
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Mingming Xin
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Zhenqi Su
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Jinkun Du
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Weilong Guo
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Zhaorong Hu
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Jie Liu
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Huiru Peng
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Zhongfu Ni
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Qixin Sun
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Yingyin Yao
- State Key Laboratory for Agrobiotechnology, Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization (MOE), and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
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13
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Sayed MRI, Alshallash KS, Safhi FA, Alatawi A, ALshamrani SM, Dessoky ES, Althobaiti AT, Althaqafi MM, Gharib HS, Shafie WWM, Awad-Allah MMA, Sultan FM. Genetic Diversity, Analysis of Some Agro-Morphological and Quality Traits and Utilization of Plant Resources of Alfalfa. Genes (Basel) 2022; 13:1521. [PMID: 36140688 PMCID: PMC9498742 DOI: 10.3390/genes13091521] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/18/2022] [Accepted: 08/19/2022] [Indexed: 11/25/2022] Open
Abstract
Alfalfa (Medicago sativa L.) is one of the most important perennial forage crops to build effective diets for livestock producers. Forage crop improvement depends largely on the availability of diverse germplasms and their efficient utilization. The present investigation was conducted at Ismailia Agricultural Research Station to assess twenty-one alfalfa genotypes for yield components, forage yield and quality traits during 2019/2020 and 2020/2021. The genotypes were evaluated in field experiments with three replicates and a randomized complete block design, using analysis of variance, estimate of genetic variability, estimate of broad sense heritability (hb2) and cluster analysis to identify the inter relationships among the studied genotypes as well as principal component analysis (PCA) to explain the majority of the total variation. Significant differences were found among genotypes for all studied traits. The general mean of the studied traits was higher in the second year than the first year. Moreover, the combined analysis showed highly significant differences between the two years, genotypes and the year × gen. interaction for the traits studied. The genotype F18 recorded the highest values for plant height, number of tiller/m2, total fresh yield and total dry yield, while, the genotype F49 ranked first for leaf/stem ratio. The results showed highly significant variation among the studied genotypes for crude protein %, crude fiber % and ash %. Data revealed that the genotypes P13 and P5 showed the highest values for crude protein %, whereas, the genotype F18 recorded the highest values for crude fiber % and ash content. The results revealed high estimates of genotypic coefficient and phenotypic coefficient of variation (GCV% and PCV%) with high hb2, indicating the presence of genetic variability and effective potential selection for these traits. The cluster analysis exhibited considerable genetic diversity among the genotypes, which classified the twenty one genotypes of alfalfa into five sub-clusters. The genotypes F18, F49, K75, S35, P20, P5 and P13 recorded the highest values for all studied traits compared with other clusters. Furthermore, the PC analysis grouped the studied genotypes into groups and remained scattered in all four quadrants based on all studied traits. Ultimately, superior genotypes were identified can be utilized for crop improvement in future breeding schemes.
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Affiliation(s)
- Mervat R. I. Sayed
- Forage Crop Research, Section, Field Crops Research Institute, Agricultural Research Center, Giza 12619, Egypt
| | - Khalid S. Alshallash
- College of Science and Humanities-Huraymila, Imam Mohammed Bin Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Fatmah Ahmed Safhi
- Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Aishah Alatawi
- Biology Department, Faculty of Science, University of Tabuk, Tabuk 71421, Saudi Arabia
| | | | - Eldessoky S. Dessoky
- Department of Biology, College of Science, Taif University, Taif 21944, Saudi Arabia
| | - Ashwaq T. Althobaiti
- Department of Biology, College of Science, Taif University, Taif 21944, Saudi Arabia
| | - Mohammed M. Althaqafi
- Department of Biotechnology, Faculty of Science, Taif University, Taif 21974, Saudi Arabia
| | - Hany S. Gharib
- Department of Agronomy, Faculty of Agriculture, University of Kafrelsheikh, Kafrelsheikh 33516, Egypt
| | - Wafaa W. M. Shafie
- Central Lab. For Design and Stat. Analysis Agricultural Research Center, Giza 12619, Egypt
| | - Mamdouh M. A. Awad-Allah
- Rice Research Department, Field Crops Research Institute, Agricultural Research Center, Giza 12619, Egypt
| | - Fadia M. Sultan
- Forage Crop Research, Section, Field Crops Research Institute, Agricultural Research Center, Giza 12619, Egypt
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14
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Aoun M, Carter AH, Morris CF, Kiszonas AM. Genetic architecture of end-use quality traits in soft white winter wheat. BMC Genomics 2022; 23:440. [PMID: 35701755 PMCID: PMC9195237 DOI: 10.1186/s12864-022-08676-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 06/01/2022] [Indexed: 11/30/2022] Open
Abstract
Background Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States. Results This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end-use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection. Conclusions In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08676-5.
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Affiliation(s)
- Meriem Aoun
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA.,Currently Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Arron H Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Craig F Morris
- USDA-ARS Western Wheat & Pulse Quality Laboratory, Washington State University, E-202 Food Quality Building, Pullman, WA, 99164, USA
| | - Alecia M Kiszonas
- USDA-ARS Western Wheat & Pulse Quality Laboratory, Washington State University, E-202 Food Quality Building, Pullman, WA, 99164, USA.
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15
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Telfer P, Edwards J, Taylor J, Able JA, Kuchel H. A multi-environment framework to evaluate the adaptation of wheat (Triticum aestivum) to heat stress. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1191-1208. [PMID: 35050395 PMCID: PMC9033731 DOI: 10.1007/s00122-021-04024-5] [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: 09/12/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
Assessing adaptation to abiotic stresses such as high temperature conditions across multiple environments presents opportunities for breeders to target selection for broad adaptation and specific adaptation. Adaptation of wheat to heat stress is an important component of adaptation in variable climates such as the cereal producing areas of Australia. However, in variable climates stress conditions may not be present in every season or are present to varying degrees, at different times during the season. Such conditions complicate plant breeders' ability to select for adaptation to abiotic stress. This study presents a framework for the assessment of the genetic basis of adaptation to heat stress conditions with improved relevance to breeders' selection objectives. The framework was applied here with the evaluation of 1225 doubled haploid lines from five populations across six environments (three environments selected for contrasting temperature stress conditions during anthesis and grain fill periods, over two consecutive seasons), using regionally best practice planting times to evaluate the role of heat stress conditions in genotype adaptation. Temperature co-variates were determined for each genotype, in each environment, for the anthesis and grain fill periods. Genome-wide QTL analysis identified performance QTL for stable effects across all environments, and QTL that illustrated responsiveness to heat stress conditions across the sampled environments. A total of 199 QTL were identified, including 60 performance QTL, and 139 responsiveness QTL. Of the identified QTL, 99 occurred independent of the 21 anthesis date QTL identified. Assessing adaptation to heat stress conditions as the combination of performance and responsiveness offers breeders opportunities to select for grain yield stability across a range of environments, as well as genotypes with higher relative yield in stress conditions.
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Affiliation(s)
- Paul Telfer
- Australian Grain Technologies, 20 Leitch Road, Roseworthy, SA, 5371, Australia.
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, SA, 5064, Australia.
| | - James Edwards
- Australian Grain Technologies, 20 Leitch Road, Roseworthy, SA, 5371, Australia
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, SA, 5064, Australia
| | - Julian Taylor
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, SA, 5064, Australia
| | - Jason A Able
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, SA, 5064, Australia
| | - Haydn Kuchel
- Australian Grain Technologies, 20 Leitch Road, Roseworthy, SA, 5371, Australia
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, SA, 5064, Australia
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16
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Qiao L, Li H, Wang J, Zhao J, Zheng X, Wu B, Du W, Wang J, Zheng J. Analysis of Genetic Regions Related to Field Grain Number per Spike From Chinese Wheat Founder Parent Linfen 5064. FRONTIERS IN PLANT SCIENCE 2022; 12:808136. [PMID: 35069666 PMCID: PMC8769526 DOI: 10.3389/fpls.2021.808136] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 11/29/2021] [Indexed: 06/14/2023]
Abstract
Wheat founder parents have been important in the development of new wheat cultivars. Understanding the effects of specific genome regions on yield-related traits in founder variety derivatives can enable more efficient use of these genetic resources through molecular breeding. In this study, the genetic regions related to field grain number per spike (GNS) from the founder parent Linfen 5064 were analyzed using a doubled haploid (DH) population developed from a cross between Linfen 5064 and Nongda 3338. Quantitative trait loci (QTL) for five spike-related traits over nine experimental locations/years were identified, namely, total spikelet number per spike (TSS), base sterile spikelet number per spike (BSSS), top sterile spikelet number per spike (TSSS), fertile spikelet number per spike (FSS), and GNS. A total of 13 stable QTL explaining 3.91-19.51% of the phenotypic variation were found. The effect of six of these QTL, Qtss.saw-2B.1, Qtss.saw-2B.2, Qtss.saw-3B, Qfss.saw-2B.2, Qbsss.saw-5A.1, and Qgns.saw-1A, were verified by another DH population (Linfen 5064/Jinmai 47), which showed extreme significance (P < 0.05) in more than three environments. No homologs of reported grain number-related from grass species were found in the physical regions of Qtss.saw-2B.1 and Qtss.saw-3B, that indicating both of them are novel QTL, or possess novel-related genes. The positive alleles of Qtss.saw-2B.2 from Linfen 5064 have the larger effect on TSS (3.30%, 0.62) and have 66.89% in Chinese cultivars under long-term artificial selection. This study revealed three key regions for GNS in Linfen 5064 and provides insights into molecular marker-assisted breeding.
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Affiliation(s)
- Ling Qiao
- College of Agronomy, State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), Shanxi Agricultural University, Jinzhong, China
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Hanlin Li
- College of Agronomy, State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), Shanxi Agricultural University, Jinzhong, China
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Jie Wang
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Jiajia Zhao
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Xingwei Zheng
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Bangbang Wu
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Weijun Du
- College of Agronomy, State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), Shanxi Agricultural University, Jinzhong, China
| | - Juanling Wang
- College of Agronomy, State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), Shanxi Agricultural University, Jinzhong, China
| | - Jun Zheng
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
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17
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Yu H, An Y, Wang A, Guan X, Tian J, Ning T, Fan K, Li H, Liu Q, Wang D, Chen J. Genetic Dissection of the Mixing Properties of Wheat Flour ( Triticum aestivum L.) Using Unconditional and Conditional QTL Mapping. J Genomics 2022; 10:8-15. [PMID: 34976226 PMCID: PMC8709693 DOI: 10.7150/jgen.67253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 11/17/2021] [Indexed: 11/05/2022] Open
Abstract
Wheat (Triticum aestivum L.) flour mixing properties are essential quality parameters in the dough development process. Limited research on superior alleles for mixing properties has restricted their molecular improvement, and other factors related to the complex traits have been ignored. A molecular map of 9576 polymorphic markers in the RIL population (F8:9) (Shannong01-35/Gaocheng9411) was constructed to evaluate mixing property effects in three environments. The parents were selected with markedly distinct high-molecular-weight glutenin subunits (HMW-GS). This study not only evaluated mixing properties using conventional unconditional QTL mapping but also evaluated the relationships between protein-related traits using conditional QTL mapping. The analyses identified most additive QTLs for major mixing properties on chromosomes 1A, 1B, and 1D. Two major loci (1A.1-15 and 1D-1) associated with mixing properties have confirmed the important contributions of Glu-A1 and Glu-D1 to wheat quality at the QTL level, which were mainly affected by the gluten index. Another important locus, 1B.1-24 (associated with midline peak value and midline peak width, with high phenotypic variations explained), might represent a new variation distinct from Glu-B1. The favored alleles came from Gaocheng9411. Several mixing properties shared the same QTLs (1B.1-6 and 1A.1-15), indicating tight linkage or pleiotropism. Genotype-by-environment (G×E) interactions were also investigated in the present study. The QTL results in our study may improve our understanding of the genetic interrelationships between mixing properties and protein-related traits.
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Affiliation(s)
- Haixia Yu
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, Shandong Agricultural University, Tai'an 271018, P.R. China
| | - Yuling An
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, Shandong Agricultural University, Tai'an 271018, P.R. China
| | - Aiping Wang
- Dezhou Agricultural Protection and Technological Extension Center, Dezhou, 253000, P.R. China
| | - Xin Guan
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, Shandong Agricultural University, Tai'an 271018, P.R. China
| | - Jichun Tian
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, Shandong Agricultural University, Tai'an 271018, P.R. China
| | - Tangyuan Ning
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, Shandong Agricultural University, Tai'an 271018, P.R. China
| | - Kexin Fan
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, Shandong Agricultural University, Tai'an 271018, P.R. China
| | - Hao Li
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, Shandong Agricultural University, Tai'an 271018, P.R. China
| | - Qianqian Liu
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, Shandong Agricultural University, Tai'an 271018, P.R. China
| | - Dongxue Wang
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, Shandong Agricultural University, Tai'an 271018, P.R. China
| | - Jiansheng Chen
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, Shandong Agricultural University, Tai'an 271018, P.R. China
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18
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Miao Y, Jing F, Ma J, Liu Y, Zhang P, Chen T, Che Z, Yang D. Major Genomic Regions for Wheat Grain Weight as Revealed by QTL Linkage Mapping and Meta-Analysis. FRONTIERS IN PLANT SCIENCE 2022; 13:802310. [PMID: 35222467 PMCID: PMC8866663 DOI: 10.3389/fpls.2022.802310] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/06/2022] [Indexed: 05/21/2023]
Abstract
Grain weight is a key determinant for grain yield potential in wheat, which is highly governed by a type of quantitative genetic basis. The identification of major quantitative trait locus (QTL) and functional genes are urgently required for molecular improvements in wheat grain yield. In this study, major genomic regions and putative candidate genes for thousand grain weight (TGW) were revealed by integrative approaches with QTL linkage mapping, meta-analysis and transcriptome evaluation. Forty-five TGW QTLs were detected using a set of recombinant inbred lines, explaining 1.76-12.87% of the phenotypic variation. Of these, ten stable QTLs were identified across more than four environments. Meta-QTL (MQTL) analysis were performed on 394 initial TGW QTLs available from previous studies and the present study, where 274 loci were finally refined into 67 MQTLs. The average confidence interval of these MQTLs was 3.73-fold less than that of initial QTLs. A total of 134 putative candidate genes were mined within MQTL regions by combined analysis of transcriptomic and omics data. Some key putative candidate genes similar to those reported early for grain development and grain weight formation were further discussed. This finding will provide a better understanding of the genetic determinants of TGW and will be useful for marker-assisted selection of high yield in wheat breeding.
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Affiliation(s)
- Yongping Miao
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
| | - Fanli Jing
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
| | - Jingfu Ma
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
| | - Yuan Liu
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
| | - Peipei Zhang
- State Key Laboratory of Aridland Crop Science, Gansu, China
| | - Tao Chen
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
| | - Zhuo Che
- Plant Seed Master Station of Gansu Province, Gansu, China
| | - Delong Yang
- State Key Laboratory of Aridland Crop Science, Gansu, China
- College of Life Science and Technology, Gansu Agricultural University, Gansu, China
- *Correspondence: Delong Yang,
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19
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Tian S, Zhang M, Li J, Wen S, Bi C, Zhao H, Wei C, Chen Z, Yu J, Shi X, Liang R, Xie C, Li B, Sun Q, Zhang Y, You M. Identification and Validation of Stable Quantitative Trait Loci for SDS-Sedimentation Volume in Common Wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2021; 12:747775. [PMID: 34950162 PMCID: PMC8688774 DOI: 10.3389/fpls.2021.747775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/08/2021] [Indexed: 06/02/2023]
Abstract
Sodium dodecyl sulfate-sedimentation volume is an important index to evaluate the gluten strength of common wheat and is closely related to baking quality. In this study, a total of 15 quantitative trait locus (QTL) for sodium dodecyl sulfate (SDS)-sedimentation volume (SSV) were identified by using a high-density genetic map including 2,474 single-nucleotide polymorphism (SNP) markers, which was constructed with a doubled haploid (DH) population derived from the cross between Non-gda3753 (ND3753) and Liangxing99 (LX99). Importantly, four environmentally stable QTLs were detected on chromosomes 1A, 2D, and 5D, respectively. Among them, the one with the largest effect was identified on chromosome 1A (designated as QSsv.cau-1A.1) explaining up to 39.67% of the phenotypic variance. Subsequently, QSsv.cau-1A.1 was dissected into two QTLs named as QSsv.cau-1A.1.1 and QSsv.cau-1A.1.2 by saturating the genetic linkage map of the chromosome 1A. Interestedly, favorable alleles of these two loci were from different parents. Due to the favorable allele of QSsv.cau-1A.1.1 was from the high-value parents ND3753 and revealed higher genetic effect, which explained 25.07% of the phenotypic variation, mapping of this locus was conducted by using BC3F1 and BC3F2 populations. By comparing the CS reference sequence, the physical interval of QSsv.cau-1A.1.1 was delimited into 14.9 Mb, with 89 putative high-confidence annotated genes. SSVs of different recombinants between QSsv.cau-1A.1.1 and QSsv.cau-1A.1 detected from DH and BC3F2 populations showed that these two loci had an obvious additive effect, of which the combination of two favorable loci had the high SSV, whereas recombinants with unfavorable loci had the lowest. These results provide further insight into the genetic basis of SSV and QSsv.cau-1A.1.1 will be an ideal target for positional cloning and wheat breeding programs.
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Affiliation(s)
- Shuai Tian
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Minghu Zhang
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Jinghui Li
- Wheat Center, Henan Institute of Science and Technology, Henan Provincial Key Laboratory of Hybrid Wheat, Xinxiang, China
| | - Shaozhe Wen
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Chan Bi
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Huanhuan Zhao
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Chaoxiong Wei
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Zelin Chen
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Jiazheng Yu
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Xintian Shi
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Rongqi Liang
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Chaojie Xie
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Baoyun Li
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Qixin Sun
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
- National Plant Gene Research Centre, Beijing, China
| | - Yufeng Zhang
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
| | - Mingshan You
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education, Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China
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20
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Schierenbeck M, Alqudah AM, Lohwasser U, Tarawneh RA, Simón MR, Börner A. Genetic dissection of grain architecture-related traits in a winter wheat population. BMC PLANT BIOLOGY 2021; 21:417. [PMID: 34507551 PMCID: PMC8431894 DOI: 10.1186/s12870-021-03183-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 08/20/2021] [Indexed: 05/08/2023]
Abstract
BACKGROUND The future productivity of wheat (T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated. RESULTS Genome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589 (602,710,399 bp) found to be inside TraesCS6A01G383800 (602,699,767-602,711,726 bp). Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture. CONCLUSIONS These results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.
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Affiliation(s)
- Matías Schierenbeck
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr 3, D-06466, Seeland, Germany.
- Cereals, Faculty of Agricultural Sciences and Forestry, National University of La Plata, La Plata, Argentina.
- CONICET CCT La Plata. La Plata, Buenos Aires, Argentina.
| | - Ahmad M Alqudah
- Department of Agroecology, Aarhus University at Flakkebjerg, Forsøgsvej 1, 4200, Slagelse, Denmark.
| | - Ulrike Lohwasser
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr 3, D-06466, Seeland, Germany
| | - Rasha A Tarawneh
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr 3, D-06466, Seeland, Germany
| | - María Rosa Simón
- Cereals, Faculty of Agricultural Sciences and Forestry, National University of La Plata, La Plata, Argentina
- CONICET CCT La Plata. La Plata, Buenos Aires, Argentina
| | - Andreas Börner
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr 3, D-06466, Seeland, Germany
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21
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Hargreaves W, N'Daiye A, Walkowiak S, Pozniak CJ, Wiebe K, Enns J, Lukens L. The effects of crop attributes, selection, and recombination on Canadian bread wheat molecular variation. THE PLANT GENOME 2021; 14:e20099. [PMID: 34009734 DOI: 10.1002/tpg2.20099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 03/09/2021] [Indexed: 06/12/2023]
Abstract
Cultivated germplasm provides an opportunity to investigate how crop agronomic traits, selection for major genes, and differences in crossing-over rates drive patterns of allelic variation. To identify how these factors correlated with allelic variation within a collection of cultivated bread wheat (Triticum aestivum L.), we generated genotypes for 388 accessions grown in Canada over the past 170 yr using filtered single nucleotide polymorphism (SNP) calls from an Illumina Wheat iSelect 90K SNP-array. Entries' breeding program, era of release, grain texture, kernel color, and growth habit contributed to allelic differentiation. Allelic diversity and linkage disequilibrium (LD) of markers flanking some major loci known to affect traits such as gluten strength, growth habit, and grain color were consistent with selective sweeps. Nonetheless, some flanking markers of major loci had low LD and high allelic diversity. Positive selection may have acted upon homoeologous genes that had significant enrichment for the gene ontology terms 'response-to-auxin' and 'response-to-wounding.' Long regions of LD, spanning approximately one-third the length of entire chromosomes, were associated with many pericentromeric regions. These regions were also characterized by low diversity. Enhancing recombination across these regions could generate novel allele combinations to accelerate Canadian wheat improvement.
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Affiliation(s)
- William Hargreaves
- Department of Plant Agriculture, University of Guelph, Crop Science Building, 50 Stone Road E, Guelph, ON, N1G 2W1, Canada
| | - Amidou N'Daiye
- Department of Plant Sciences and Crop Development Centre, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Sean Walkowiak
- Grain Research Laboratory, Canadian Grain Commission, 196 Innovation Drive, Winnipeg, MB, R3T 6C5, Canada
| | - Curtis J Pozniak
- Department of Plant Sciences and Crop Development Centre, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Krystalee Wiebe
- Department of Plant Sciences and Crop Development Centre, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Jennifer Enns
- Department of Plant Sciences and Crop Development Centre, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Lewis Lukens
- Department of Plant Agriculture, University of Guelph, Crop Science Building, 50 Stone Road E, Guelph, ON, N1G 2W1, Canada
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22
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Rabbi SMHA, Kumar A, Mohajeri Naraghi S, Simsek S, Sapkota S, Solanki S, Alamri MS, Elias EM, Kianian S, Missaoui A, Mergoum M. Genome-Wide Association Mapping for Yield and Related Traits Under Drought Stressed and Non-stressed Environments in Wheat. Front Genet 2021; 12:649988. [PMID: 34239537 PMCID: PMC8258415 DOI: 10.3389/fgene.2021.649988] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 04/28/2021] [Indexed: 12/02/2022] Open
Abstract
Understanding the genetics of drought tolerance in hard red spring wheat (HRSW) in northern USA is a prerequisite for developing drought-tolerant cultivars for this region. An association mapping (AM) study for drought tolerance in spring wheat in northern USA was undertaken using 361 wheat genotypes and Infinium 90K single-nucleotide polymorphism (SNP) assay. The genotypes were evaluated in nine different locations of North Dakota (ND) for plant height (PH), days to heading (DH), yield (YLD), test weight (TW), and thousand kernel weight (TKW) under rain-fed conditions. Rainfall data and soil type of the locations were used to assess drought conditions. A mixed linear model (MLM), which accounts for population structure and kinship (PC+K), was used for marker–trait association. A total of 69 consistent QTL involved with drought tolerance-related traits were identified, with p ≤ 0.001. Chromosomes 1A, 3A, 3B, 4B, 4D, 5B, 6A, and 6B were identified to harbor major QTL for drought tolerance. Six potential novel QTL were identified on chromosomes 3D, 4A, 5B, 7A, and 7B. The novel QTL were identified for DH, PH, and TKW. The findings of this study can be used in marker-assisted selection (MAS) for drought-tolerance breeding in spring wheat.
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Affiliation(s)
- S M Hisam A Rabbi
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Ajay Kumar
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | | | - Senay Simsek
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Suraj Sapkota
- Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Griffin, GA, United States
| | - Shyam Solanki
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Mohammed S Alamri
- Department of Food Sciences and Nutrition, King Saud University, Riyadh, Saudi Arabia
| | - Elias M Elias
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Shahryar Kianian
- United States Department of Agriculture-The Agricultural Research Service (USDA-ARS) Cereal Disease Laboratory, University of Minnesota, St. Paul, MN, United States
| | - Ali Missaoui
- Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Griffin, GA, United States.,Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
| | - Mohamed Mergoum
- Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Griffin, GA, United States.,Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
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23
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Rabbi SMHA, Kumar A, Mohajeri Naraghi S, Sapkota S, Alamri MS, Elias EM, Kianian S, Seetan R, Missaoui A, Solanki S, Mergoum M. Identification of Main-Effect and Environmental Interaction QTL and Their Candidate Genes for Drought Tolerance in a Wheat RIL Population Between Two Elite Spring Cultivars. Front Genet 2021; 12:656037. [PMID: 34220939 PMCID: PMC8249774 DOI: 10.3389/fgene.2021.656037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/13/2021] [Indexed: 01/22/2023] Open
Abstract
Understanding the genetics of drought tolerance can expedite the development of drought-tolerant cultivars in wheat. In this study, we dissected the genetics of drought tolerance in spring wheat using a recombinant inbred line (RIL) population derived from a cross between a drought-tolerant cultivar, ‘Reeder’ (PI613586), and a high-yielding but drought-susceptible cultivar, ‘Albany.’ The RIL population was evaluated for grain yield (YLD), grain volume weight (GVW), thousand kernel weight (TKW), plant height (PH), and days to heading (DH) at nine different environments. The Infinium 90 k-based high-density genetic map was generated using 10,657 polymorphic SNP markers representing 2,057 unique loci. Quantitative trait loci (QTL) analysis detected a total of 11 consistent QTL for drought tolerance-related traits. Of these, six QTL were exclusively identified in drought-prone environments, and five were constitutive QTL (identified under both drought and normal conditions). One major QTL on chromosome 7B was identified exclusively under drought environments and explained 13.6% of the phenotypic variation (PV) for YLD. Two other major QTL were detected, one each on chromosomes 7B and 2B under drought-prone environments, and explained 14.86 and 13.94% of phenotypic variation for GVW and YLD, respectively. One novel QTL for drought tolerance was identified on chromosome 2D. In silico expression analysis of candidate genes underlaying the exclusive QTLs associated with drought stress identified the enrichment of ribosomal and chloroplast photosynthesis-associated proteins showing the most expression variability, thus possibly contributing to stress response by modulating the glycosyltransferase (TraesCS6A01G116400) and hexosyltransferase (TraesCS7B01G013300) unique genes present in QTL 21 and 24, respectively. While both parents contributed favorable alleles to these QTL, unexpectedly, the high-yielding and less drought-tolerant parent contributed desirable alleles for drought tolerance at four out of six loci. Regardless of the origin, all QTL with significant drought tolerance could assist significantly in the development of drought-tolerant wheat cultivars, using genomics-assisted breeding approaches.
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Affiliation(s)
- S M Hisam Al Rabbi
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Ajay Kumar
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | | | - Suraj Sapkota
- Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Griffin, GA, United States
| | - Mohammed S Alamri
- Department of Food Science and Nutrition, King Saud University, Riyadh, Saudi Arabia
| | - Elias M Elias
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Shahryar Kianian
- USDA-ARS Cereal Disease Laboratory, University of Minnesota, St. Paul, MN, United States
| | - Raed Seetan
- Department of Computer Science, Slippery Rock University, Slippery Rock, PA, United States
| | - Ali Missaoui
- Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Griffin, GA, United States.,Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
| | - Shyam Solanki
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Mohamed Mergoum
- Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Griffin, GA, United States.,Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
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24
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Utilization of a Wheat50K SNP Microarray-Derived High-Density Genetic Map for QTL Mapping of Plant Height and Grain Traits in Wheat. PLANTS 2021; 10:plants10061167. [PMID: 34201388 PMCID: PMC8229693 DOI: 10.3390/plants10061167] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/18/2021] [Accepted: 05/26/2021] [Indexed: 11/22/2022]
Abstract
Plant height is significantly correlated with grain traits, which is a component of wheat yield. The purpose of this study is to investigate the main quantitative trait loci (QTLs) that control plant height and grain-related traits in multiple environments. In this study, we constructed a high-density genetic linkage map using the Wheat50K SNP Array to map QTLs for these traits in 198 recombinant inbred lines (RILs). The two ends of the chromosome were identified as recombination-rich areas in all chromosomes except chromosome 1B. Both the genetic map and the physical map showed a significant correlation, with a correlation coefficient between 0.63 and 0.99. However, there was almost no recombination between 1RS and 1BS. In terms of plant height, 1RS contributed to the reduction of plant height by 3.43 cm. In terms of grain length, 1RS contributed to the elongation of grain by 0.11 mm. A total of 43 QTLs were identified, including eight QTLs for plant height (PH), 11 QTLs for thousand grain weight (TGW), 15 QTLs for grain length (GL), and nine QTLs for grain width (GW), which explained 1.36–33.08% of the phenotypic variation. Seven were environment-stable QTLs, including two loci (Qph.nwafu-4B and Qph.nwafu-4D) that determined plant height. The explanation rates of phenotypic variation were 7.39–12.26% and 20.11–27.08%, respectively. One QTL, Qtgw.nwafu-4B, which influenced TGW, showed an explanation rate of 3.43–6.85% for phenotypic variation. Two co-segregating KASP markers were developed, and the physical locations corresponding to KASP_AX-109316968 and KASP_AX-109519968 were 25.888344 MB and 25.847691 MB, respectively. Qph.nwafu-4B, controlling plant height, and Qtgw.nwafu-4B, controlling TGW, had an obvious linkage relationship, with a distance of 7–8 cM. Breeding is based on molecular markers that control plant height and thousand-grain weight by selecting strains with low plant height and large grain weight. Another QTL, Qgw.nwafu-4D, which determined grain width, had an explanation rate of 3.43–6.85%. Three loci that affected grain length were Qgl.nwafu-5A, Qgl.nwafu-5D.2, and Qgl.nwafu-6B, illustrating the explanation rates of phenotypic variation as 6.72–9.59%, 5.62–7.75%, and 6.68–10.73%, respectively. Two QTL clusters were identified on chromosomes 4B and 4D.
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25
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Telfer P, Edwards J, Norman A, Bennett D, Smith A, Able JA, Kuchel H. Genetic analysis of wheat (Triticum aestivum) adaptation to heat stress. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1387-1407. [PMID: 33675373 DOI: 10.1007/s00122-021-03778-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 01/19/2021] [Indexed: 06/12/2023]
Abstract
Adaptation to abiotic stresses such as high-temperature conditions should be considered as its independent components of total performance and responsiveness. Understanding and identifying improved adaptation to abiotic stresses such as heat stress has been the focus of a number of studies in recent decades. However, confusing and potentially misleading terminology has made progress difficult and hard to apply within breeding programs selecting for improved adaption to heat stress conditions. This study proposes that adaption to heat stress (and other abiotic stresses) be considered as the combination of total performance and responsiveness to heat stress. In this study, 1413 doubled haploid lines from seven populations were screened through a controlled environment assay, subjecting plants to three consecutive eight hour days of an air temperature of 36 °C and a wind speed of 40 km h-1, 10 days after the end of anthesis. QTL mapping identified a total of 96 QTL for grain yield determining traits and anthesis date with nine correlating to responsiveness, 72 for total performance and 15 for anthesis date. Responsiveness QTL were found both collocated with other performance QTL as well as independently. A sound understanding of genomic regions associated with total performance and responsiveness will be important for breeders. Genomic regions of total performance, those that show higher performance that is stable under both stressed and non-stressed conditions, potentially offer significant opportunities to breeders. We propose this as a definition and selection target that has not previously been defined for heat stress adaptation.
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Affiliation(s)
- Paul Telfer
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, SA, 5064, Australia.
- Australian Grain Technologies, 20 Leitch Rd, Roseworthy, SA, 5371, Australia.
| | - James Edwards
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, SA, 5064, Australia
- Australian Grain Technologies, 20 Leitch Rd, Roseworthy, SA, 5371, Australia
| | - Adam Norman
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, SA, 5064, Australia
- Australian Grain Technologies, 20 Leitch Rd, Roseworthy, SA, 5371, Australia
| | - Dion Bennett
- Australian Grain Technologies, 100 Byfield St, Northam, WA, 6401, Australia
| | - Alison Smith
- Centre for Bioinformatics and Biometrics, National Institute for Applied Statistics Research Australia, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, 2522, Australia
| | - Jason A Able
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, SA, 5064, Australia
| | - Haydn Kuchel
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, SA, 5064, Australia
- Australian Grain Technologies, 20 Leitch Rd, Roseworthy, SA, 5371, Australia
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26
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Kang L, Dai S, Song Z, Xiang Q, Zuo Y, Bao T, Chen G, Wei Y, Zheng Y, Liu G, Li J, Yan Z. Production and characterization of a disomic 1M/1D Triticum aestivum- Aegilops comosa substitution line. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:16. [PMID: 37309475 PMCID: PMC10236073 DOI: 10.1007/s11032-021-01207-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 01/25/2021] [Indexed: 06/14/2023]
Abstract
PI 554419, formerly designated as Ae. uniaristata, showed significant difference with other Ae. uniaristata and Ae. comosa accessions in morphological traits at the seedling stage and its leaf color, length, and width behaved as an intermediate type. In this study, we reclassified PI 554419 as Ae. comosa subsp. comosa by comparing the fluorescence in situ hybridization (FISH) signals and the patterns of PCR-based landmark unique gene (PLUG) markers and conserved orthologous set (COS) markers of PI 554419 with other Ae. uniaristata and Ae. comosa accessions as well as the taxonomic character of spike morphology. A disomic 1M/1D substitution line NB 4-8-5-9 derived from PI 554419 was identified from a distant hybridization of Ae. comosa with common wheat (STM 10/CSph1b//CM 39///13 P2-6) by the molecular cytological method. Furthermore, the agronomic and seed morphological traits, as well as the flour processing quality properties of NB 4-8-5-9, were compared with those of its three common wheat parents in two different locations during the 2017-2018 growing seasons. The agronomical traits of NB 4-8-5-9 were similar to or even better than its parents. The seed size-related traits of NB 4-8-5-9 were better than those of all three parents, and the 1000-grain weight and grain width were close to those of Chuanmai 39 (CM 39) and 13 P2-6 and larger than those of CSph1b. The processing quality properties of NB 4-8-5-9 were more similar to those of 13 P2-6 and CSph1b but less similar to those of CM 39. The 1M/1D substitution line NB 4-8-5-9 could further be used for developing translocation lines with 1M segment. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01207-2.
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Affiliation(s)
- Liangzhu Kang
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Shoufen Dai
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
- State Key laboratory of Crop Gene Exploration and Utilization in Southwest China, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Zhongping Song
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Qin Xiang
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Yuanyuan Zuo
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Tingyu Bao
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Guoyue Chen
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
- State Key laboratory of Crop Gene Exploration and Utilization in Southwest China, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Yuming Wei
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
- State Key laboratory of Crop Gene Exploration and Utilization in Southwest China, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Youliang Zheng
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
- State Key laboratory of Crop Gene Exploration and Utilization in Southwest China, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Gang Liu
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Jian Li
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
| | - Zehong Yan
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
- State Key laboratory of Crop Gene Exploration and Utilization in Southwest China, Wenjiang, Chengdu, 611130 Sichuan People’s Republic of China
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27
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Luo Q, Zheng Q, Hu P, Liu L, Yang G, Li H, Li B, Li Z. Mapping QTL for agronomic traits under two levels of salt stress in a new constructed RIL wheat population. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:171-189. [PMID: 32995899 DOI: 10.1007/s00122-020-03689-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 09/16/2020] [Indexed: 06/11/2023]
Abstract
QTL for 15 agronomic traits under two levels of salt stress in dry salinity field were mapped in a new constructed RIL population utilizing a Wheat55K SNP array. Furthermore, eight QTL were validated in a collected natural population. Soil salinity is one of the major abiotic stresses causing serious impact on crop growth, development and yield. As one of the three most important crops in the world, bread wheat (Triticum aestivum L.) is severely affected by salinity, too. In this study, an F7 recombinant inbred line (RIL) population derived from a cross between high-yield wheat cultivar Zhongmai 175 and salt-tolerant cultivar Xiaoyan 60 was constructed. The adult stage performances of the RIL population and their parent lines under low and high levels of salt stress were evaluated for three consecutive growing seasons. Utilizing a Wheat55K SNP array, a high-density genetic linkage map spinning 3250.71 cM was constructed. QTL mapping showed that 90 stable QTL for 15 traits were detected, and they were distributed on all wheat chromosomes except 4D, 6B and 7D. These QTL individually explained 2.34-32.43% of the phenotypic variation with LOD values ranging from 2.68 to 47.15. It was found that four QTL clusters were located on chromosomes 2D, 3D, 4B and 6A, respectively. Notably, eight QTL from the QTL clusters were validated in a collected natural population. Among them, QPh-4B was deduced to be an allele of Rht-B1. In addition, three kompetitive allele-specific PCR (KASP) markers derived from SNPs were successfully designed for three QTL clusters. This study provides an important base for salt-tolerant QTL (gene) cloning in wheat, and the markers, especially the KASP markers, will be useful for marker-assisted selection in salt-tolerant wheat breeding.
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Affiliation(s)
- Qiaoling Luo
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qi Zheng
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Pan Hu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Liqin Liu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Guotang Yang
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hongwei Li
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Bin Li
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Zhensheng Li
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
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28
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Qu X, Liu J, Xie X, Xu Q, Tang H, Mu Y, Pu Z, Li Y, Ma J, Gao Y, Jiang Q, Liu Y, Chen G, Wang J, Qi P, Habib A, Wei Y, Zheng Y, Lan X, Ma J. Genetic Mapping and Validation of Loci for Kernel-Related Traits in Wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2021; 12:667493. [PMID: 34163507 PMCID: PMC8215603 DOI: 10.3389/fpls.2021.667493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 04/22/2021] [Indexed: 05/11/2023]
Abstract
Kernel size (KS) and kernel weight play a key role in wheat yield. Phenotypic data from six environments and a Wheat55K single-nucleotide polymorphism array-based constructed genetic linkage map from a recombinant inbred line population derived from the cross between the wheat line 20828 and the line SY95-71 were used to identify quantitative trait locus (QTL) for kernel length (KL), kernel width (KW), kernel thickness (KT), thousand-kernel weight (TKW), kernel length-width ratio (LWR), KS, and factor form density (FFD). The results showed that 65 QTLs associated with kernel traits were detected, of which the major QTLs QKL.sicau-2SY-1B, QKW.sicau-2SY-6D, QKT.sicau-2SY-2D, and QTKW.sicau-2SY-2D, QLWR.sicau-2SY-6D, QKS.sicau-2SY-1B/2D/6D, and QFFD.sicau-2SY-2D controlling KL, KW, KT, TKW, LWR, KS, and FFD, and identified in multiple environments, respectively. They were located on chromosomes 1BL, 2DL, and 6DS and formed three QTL clusters. Comparison of genetic and physical interval suggested that only QKL.sicau-2SY-1B located on chromosome 1BL was likely a novel QTL. A Kompetitive Allele Specific Polymerase chain reaction (KASP) marker, KASP-AX-109379070, closely linked to this novel QTL was developed and used to successfully confirm its effect in two different genetic populations and three variety panels consisting of 272 Chinese wheat landraces, 300 Chinese wheat cultivars most from the Yellow and Huai River Valley wheat region, and 165 Sichuan wheat cultivars. The relationships between kernel traits and other agronomic traits were detected and discussed. A few predicted genes involved in regulation of kernel growth and development were identified in the intervals of these identified major QTL. Taken together, these stable and major QTLs provide valuable information for understanding the genetic composition of kernel yield and provide the basis for molecular marker-assisted breeding.
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Affiliation(s)
- Xiangru Qu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Jiajun Liu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Xinlin Xie
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Qiang Xu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Huaping Tang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yang Mu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Zhien Pu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
| | - Yang Li
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
| | - Jun Ma
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Yutian Gao
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Qiantao Jiang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yaxi Liu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guoyue Chen
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Jirui Wang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Pengfei Qi
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Ahsan Habib
- Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna, Bangladesh
| | - Yuming Wei
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Youliang Zheng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Xiujin Lan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
- *Correspondence: Xiujin Lan,
| | - Jian Ma
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
- Jian Ma, ;
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29
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Cao J, Shang Y, Xu D, Xu K, Cheng X, Pan X, Liu X, Liu M, Gao C, Yan S, Yao H, Gao W, Lu J, Zhang H, Chang C, Xia X, Xiao S, Ma C. Identification and Validation of New Stable QTLs for Grain Weight and Size by Multiple Mapping Models in Common Wheat. Front Genet 2020; 11:584859. [PMID: 33262789 PMCID: PMC7686802 DOI: 10.3389/fgene.2020.584859] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 09/21/2020] [Indexed: 11/13/2022] Open
Abstract
Improvement of grain weight and size is an important objective for high-yield wheat breeding. In this study, 174 recombinant inbred lines (RILs) derived from the cross between Jing 411 and Hongmangchun 21 were used to construct a high-density genetic map by specific locus amplified fragment sequencing (SLAF-seq). Three mapping methods, including inclusive composite interval mapping (ICIM), genome-wide composite interval mapping (GCIM), and a mixed linear model performed with forward-backward stepwise (NWIM), were used to identify QTLs for thousand grain weight (TGW), grain width (GW), and grain length (GL). In total, we identified 30, 15, and 18 putative QTLs for TGW, GW, and GL that explain 1.1-33.9%, 3.1%-34.2%, and 1.7%-22.8% of the phenotypic variances, respectively. Among these, 19 (63.3%) QTLs for TGW, 10 (66.7%) for GW, and 7 (38.9%) for GL were consistent with those identified by genome-wide association analysis in 192 wheat varieties. Five new stable QTLs, including 3 for TGW (Qtgw.ahau-1B.1, Qtgw.ahau-4B.1, and Qtgw.ahau-4B.2) and 2 for GL (Qgl.ahau-2A.1 and Qgl.ahau-7A.2), were detected by the three aforementioned mapping methods across environments. Subsequently, five cleaved amplified polymorphic sequence (CAPS) markers corresponding to these QTLs were developed and validated in 180 Chinese mini-core wheat accessions. In addition, 19 potential candidate genes for Qtgw.ahau-4B.2 in a 0.31-Mb physical interval were further annotated, of which TraesCS4B02G376400 and TraesCS4B02G376800 encode a plasma membrane H+-ATPase and a serine/threonine-protein kinase, respectively. These new QTLs and CAPS markers will be useful for further marker-assisted selection and map-based cloning of target genes.
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Affiliation(s)
- Jiajia Cao
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Yaoyao Shang
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Dongmei Xu
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Kangle Xu
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Xinran Cheng
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Xu Pan
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Xue Liu
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Mingli Liu
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Chang Gao
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Shengnan Yan
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Hui Yao
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Wei Gao
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Jie Lu
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Haiping Zhang
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Cheng Chang
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Xianchun Xia
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shihe Xiao
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chuanxi Ma
- KeyLaboratory of Wheat Biology and Genetic Improvement on Southern Yellow and Huai River Valley, Ministry of Agriculture and Rural Affairs, College of Agronomy, Anhui Agricultural University, Hefei, China
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30
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Zheng X, Liu C, Qiao L, Zhao J, Han R, Wang X, Ge C, Zhang W, Zhang S, Qiao L, Zheng J, Hao C. The MYB transcription factor TaPHR3-A1 is involved in phosphate signaling and governs yield-related traits in bread wheat. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:5808-5822. [PMID: 32725154 DOI: 10.1093/jxb/eraa355] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 07/23/2020] [Indexed: 06/11/2023]
Abstract
Improved inorganic phosphate (Pi) use efficiency in crops will be important for sustainable agriculture. Exploring molecular mechanisms that regulate Pi uptake could provide useful information for breeding wheat with improved Pi use efficiency. Here, a TaPHR3-A1 (Gene ID: TraesCS7A02G415800) ortholog of rice OsPHR3 that functions in transcriptional regulation of Pi signaling was cloned from wheat chromosome 7A. Ectopic expression of TaPHR3-A1 in Arabidopsis and rice produced enhanced vegetative growth and more seeds. Overexpression in transgenic rice led to increased biomass, grain number, and primary panicle branching by 61.23, 42.12, and 36.34% compared with the wild type. Transgenic wheat lines with down-regulation of TaPHR3-A1 exhibited retarded growth and root hair development at the seedling stage, and showed yield-related effects at the adult stage when grown in both low- and sufficient Pi conditions, indicating that TaPHR3-A1 positively regulated tolerance to low Pi. Introgression lines further confirmed the effect of TaPHR3-A1 in improving grain number. The Chinese wheat mini core collection and a recombinant inbred line analysis demonstrated that the favorable allele TaPHR3-A1-A associated with higher grain number was positively selected in breeding. A TaPHR3-A1-derived cleaved amplified polymorphic sequence marker effectively identified haplotype TaPHR3-A1-A. Our results suggested that TaPHR3-A1 was a functional regulatory factor for Pi uptake and provided useful information for marker-assisted selection for high yield in wheat.
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Affiliation(s)
- Xingwei Zheng
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Cheng Liu
- Crop Research Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory for Wheat & Maize, Jinan, China
| | - Ling Qiao
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Jiajia Zhao
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Ran Han
- Crop Research Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory for Wheat & Maize, Jinan, China
| | - Xiaolu Wang
- Crop Research Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory for Wheat & Maize, Jinan, China
| | - Chuan Ge
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Wenyun Zhang
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Shuwei Zhang
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Linyi Qiao
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Jun Zheng
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Chenyang Hao
- The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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31
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Barakat M, Al-Doss A, Moustafa K, Motawei M, Alamri M, Mergoum M, Sallam M, Al-Ashkar I. QTL analysis of farinograph and mixograph related traits in spring wheat under heat stress conditions. Mol Biol Rep 2020; 47:5477-5486. [PMID: 32632781 DOI: 10.1007/s11033-020-05638-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 06/29/2020] [Indexed: 01/05/2023]
Abstract
Farinograph and mixograph-related parameters are key elements in wheat end-products quality. Understanding the genetic control of these traits and the influence of environmental factors such as heat stress, and their interaction are critical for developing cultivars with improved for those traits. To identify QTL for six farinograph and three mixograph traits, two double haploid (DH) populations (Yecora Rojo × Ksu106 and Klasic × Ksu105) were used in experiments conducted at Riyadh and Al Qassim locations under heat stress. Single nucleotide polymorphism (SNP) markers were used to determine the number of QTLs controlling these parameters. The genetic analysis of farinograph and mixograph-related traits showed considerable variation with transgressive segregation regardless of heat stress conditions in both locations. A total of 108 additive QTLs were detected for the six farinograph and three mixograph traits in the Yecora Rojo × Ksu106 population in both locations under heat treatments. These QTLs were distributed over all 21 wheat chromosomes except 3A. Similarly, in Klassic × Ksu105 population, there were an additional 68 QTLs identified over the two locations and were allocated on all chromosomes except 1D, 2A, 6A, and 6D. In population (Yecora Rojo × Ksu106), the QTL on chromosome 7A (Excalibur_c62415_288) showed significant effects for farinograph and mixograph traits (FDDT, FDST, FBD, M × h8, and M × t) under normal and heat stress condition at both locations. Interestingly, several QTLs that are related to farinograph and mixograph traits, which showed stable expression under both locations, were detected on chromosome 7A in population (Klassic × Ksu105). Results from this study show the quantitative nature of the genetic control of the studied traits and constitute a step toward identifying major QTLs that can be sued molecular-marker assisted breeding to develop new improved quality wheat cultivars.
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Affiliation(s)
- Mohamed Barakat
- Biotechnology Laboratory, Crop Science Department, Faculty of Agriculture, University of Alexandria, Alexandria, Egypt.
| | - Abdullah Al-Doss
- College of Food Sciences and Agriculture, King Saud University, Riyadh, Saudi Arabia
| | - Khaled Moustafa
- College of Food Sciences and Agriculture, King Saud University, Riyadh, Saudi Arabia
| | - Mohamed Motawei
- Faculty of Agriculture and Veterinary Medicine, Al-Qassim University, Buraydah, Saudi Arabia
| | - Mohamed Alamri
- College of Food Sciences and Agriculture, King Saud University, Riyadh, Saudi Arabia
| | - Mohamed Mergoum
- Department of Crop and Soil Sciences, College of Agriculture & Environmental Sciences, University of Georgia, Athens, USA
| | - Mohamed Sallam
- College of Food Sciences and Agriculture, King Saud University, Riyadh, Saudi Arabia
| | - Ibrahim Al-Ashkar
- College of Food Sciences and Agriculture, King Saud University, Riyadh, Saudi Arabia.,Agronomy Department, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt
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32
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Cao S, Xu D, Hanif M, Xia X, He Z. Genetic architecture underpinning yield component traits in wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:1811-1823. [PMID: 32062676 DOI: 10.1007/s00122-020-03562-8] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 02/06/2020] [Indexed: 05/19/2023]
Abstract
Genetic atlas, reliable QTL and candidate genes of yield component traits in wheat were figured out, laying concrete foundations for map-based gene cloning and dissection of regulatory mechanisms underlying yield. Mining genetic loci for yield is challenging due to the polygenic nature, large influence of environment and complex relationship among yield component traits (YCT). Many genetic loci related to wheat yield have been identified, but its genetic architecture and key genetic loci for selection are largely unknown. Wheat yield potential can be determined by three YCT, thousand kernel weight, kernel number per spike and spike number. Here, we summarized the genetic loci underpinning YCT from QTL mapping, association analysis and homology-based gene cloning. The major loci determining yield-associated agronomic traits, such as flowering time and plant height, were also included in comparative analyses with those for YCT. We integrated yield-related genetic loci onto chromosomes based on their physical locations. To identify the major stable loci for YCT, 58 QTL-rich clusters (QRC) were defined based on their distribution on chromosomes. Candidate genes in each QRC were predicted according to gene annotation of the wheat reference genome and previous information on validation of those genes in other species. Finally, a technological route was proposed to take full advantage of the resultant resources for gene cloning, molecular marker-assisted breeding and dissection of molecular regulatory mechanisms underlying wheat yield.
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Affiliation(s)
- Shuanghe Cao
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China.
| | - Dengan Xu
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China
| | - Mamoona Hanif
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China
| | - Xianchun Xia
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China
| | - Zhonghu He
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China.
- International Maize and Wheat Improvement Center (CIMMYT), c/o CAAS, 12 Zhongguancun South Street, Beijing, 100081, China.
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33
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Zaïm M, Kabbaj H, Kehel Z, Gorjanc G, Filali-Maltouf A, Belkadi B, Nachit MM, Bassi FM. Combining QTL Analysis and Genomic Predictions for Four Durum Wheat Populations Under Drought Conditions. Front Genet 2020; 11:316. [PMID: 32435259 PMCID: PMC7218065 DOI: 10.3389/fgene.2020.00316] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 03/16/2020] [Indexed: 11/28/2022] Open
Abstract
Durum wheat is an important crop for the human diet and its consumption is gaining popularity. In order to ensure that durum wheat production maintains the pace with the increase in demand, it is necessary to raise productivity by approximately 1.5% per year. To deliver this level of annual genetic gain the incorporation of molecular strategies has been proposed as a key solution. Here, four RILs populations were used to conduct QTL discovery for grain yield (GY) and 1,000 kernel weight (TKW). A total of 576 individuals were sown at three locations in Morocco and one in Lebanon. These individuals were genotyped by sequencing with 3,202 high-confidence polymorphic markers, to derive a consensus genetic map of 2,705.7 cM, which was used to impute any missing data. Six QTLs were found to be associated with GY and independent from flowering time on chromosomes 2B, 4A, 5B, 7A and 7B, explaining a phenotypic variation (PV) ranging from 4.3 to 13.4%. The same populations were used to train genomic prediction models incorporating the relationship matrix, the genotype by environment interaction, and marker by environment interaction, to reveal significant advantages for models incorporating the marker effect. Using training populations (TP) in full sibs relationships with the validation population (VP) was shown to be the only effective strategy, with accuracies reaching 0.35–0.47 for GY. Reducing the number of markers to 10% of the whole set, and the TP size to 20% resulted in non-significant changes in accuracies. The QTLs identified were also incorporated in the models as fixed effects, showing significant accuracy gain for all four populations. Our results confirm that the prediction accuracy depends considerably on the relatedness between TP and VP, but not on the number of markers and size of TP used. Furthermore, feeding the model with information on markers associated with QTLs increased the overall accuracy.
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Affiliation(s)
- Meryem Zaïm
- Laboratory of Microbiology and Molecular Biology, Faculty of Sciences, Mohammed V University, Rabat, Morocco.,ICARDA, Biodiversity and Integrated Gene Management, Rabat, Morocco
| | - Hafssa Kabbaj
- Laboratory of Microbiology and Molecular Biology, Faculty of Sciences, Mohammed V University, Rabat, Morocco.,ICARDA, Biodiversity and Integrated Gene Management, Rabat, Morocco
| | - Zakaria Kehel
- ICARDA, Biodiversity and Integrated Gene Management, Rabat, Morocco
| | - Gregor Gorjanc
- The Roslin Institute, The University of Edinburgh, Edinburgh, United Kingdom
| | - Abdelkarim Filali-Maltouf
- Laboratory of Microbiology and Molecular Biology, Faculty of Sciences, Mohammed V University, Rabat, Morocco
| | - Bouchra Belkadi
- Laboratory of Microbiology and Molecular Biology, Faculty of Sciences, Mohammed V University, Rabat, Morocco
| | - Miloudi M Nachit
- ICARDA, Biodiversity and Integrated Gene Management, Rabat, Morocco
| | - Filippo M Bassi
- ICARDA, Biodiversity and Integrated Gene Management, Rabat, Morocco
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34
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Gupta PK, Balyan HS, Sharma S, Kumar R. Genetics of yield, abiotic stress tolerance and biofortification in wheat (Triticum aestivum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:1569-1602. [PMID: 32253477 DOI: 10.1007/s00122-020-03583-3] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 03/13/2020] [Indexed: 05/18/2023]
Abstract
A review of the available literature on genetics of yield and its component traits, tolerance to abiotic stresses and biofortification should prove useful for future research in wheat in the genomics era. The work reviewed in this article mainly covers the available information on genetics of some important quantitative traits including yield and its components, tolerance to abiotic stresses (heat, drought, salinity and pre-harvest sprouting = PHS) and biofortification (Fe/Zn and phytate contents with HarvestPlus Program) in wheat. Major emphasis is laid on the recent literature on QTL interval mapping and genome-wide association studies, giving lists of known QTL and marker-trait associations. Candidate genes for different traits and the cloned and characterized genes for yield traits along with the molecular mechanism are also described. For each trait, an account of the present status of marker-assisted selection has also been included. The details of available results have largely been presented in the form of tables; some of these tables are included as supplementary files.
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Affiliation(s)
- Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, 250 004, India.
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, 250 004, India
| | - Shailendra Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, 250 004, India
| | - Rahul Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, 250 004, India
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35
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QTL mapping for quality traits using a high-density genetic map of wheat. PLoS One 2020; 15:e0230601. [PMID: 32208463 PMCID: PMC7092975 DOI: 10.1371/journal.pone.0230601] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 03/03/2020] [Indexed: 01/27/2023] Open
Abstract
Protein- and starch-related quality traits, which are quantitatively inherited and significantly influenced by the environment, are critical determinants of the end-use quality of wheat. We constructed a high-density genetic map containing 10,739 loci (5,399 unique loci) using a set of 184 recombinant inbred lines (RILs) derived from a cross of 'Tainong 18 × Linmai 6' (TL-RILs). In this study, a quantitative trait loci (QTLs) analysis was used to examine the genetic control of grain protein content, sedimentation value, farinograph parameters, falling number and the performance of the starch pasting properties using TL-RILs grown in a field for three years. A total of 106 QTLs for 13 quality traits were detected, distributed on the 21 chromosomes. Of these, 38 and 68 QTLs for protein- and starch-related traits, respectively, were detected in three environments and their average values (AV). Twenty-six relatively high-frequency QTLs (RHF-QTLs) that were detected in more than two environments. Twelve stable QTL clusters containing at least one RHF-QTL were detected and classified into three types: detected only for protein-related traits (type I), detected only for starch-related traits (type II), and detected for both protein- and starch-related traits (type III). A total of 339 markers flanked with 11 QTL clusters (all except C6), were found to be highly homologous with 282 high confidence (HC) and 57 low confidence (LC) candidate genes based on IWGSC RefSeq v 1.0. These stable QTLs and RHF-QTLs, especially those grouped into clusters, are credible and should be given priority for QTL fine-mapping and identification of candidate genes with which to explain the molecular mechanisms of quality development and inform marker-assisted breeding in the future.
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36
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QTL Mapping of Kernel Traits and Validation of a Major QTL for Kernel Length-Width Ratio Using SNP and Bulked Segregant Analysis in Wheat. Sci Rep 2020; 10:25. [PMID: 31913328 PMCID: PMC6949281 DOI: 10.1038/s41598-019-56979-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 12/17/2019] [Indexed: 01/30/2023] Open
Abstract
One RIL population derived from the cross between Dalibao and BYL8 was used to examine the phenotypes of kernel-related traits in four different environments. Six important kernel traits, kernel length (KL), kernel width (KW), kernel perimeter (KP), kernel area (KA), kernel length/width ratio (KLW), and thousand-kernel weight (TKW) were evaluated in Yangling, Shaanxi Province, China (2016 and 2017), Nanyang, Henan Province, China (2017) and Suqian, Jiangsu Province, China (2017). A genetic linkage map was constructed using 205 SSR markers, and a total of 21 significant QTLs for KL, KW, KP, KA, KLW and TKW were located on 10 of the 21 wheat chromosomes, including 1A, 1B, 2A, 2B, 2D, 3D, 4D, 5A, 5B, and 7D, with a single QTL in different environments explaining 3.495–30.130% of the phenotypic variation. There were four loci for KLW, five for KA, five for KL, three for KP, two for KW, and two for TKW among the detected QTLs. We used BSA + 660 K gene chip technology to reveal the positions of major novel QTLs for KLW. A total of 670 out of 5285 polymorphic SNPs were detected on chromosome 2A. The SNPs in 2A are most likely related to the major QTL, and there may be minor QTLs on 5B, 7A, 3A and 4B. SSR markers were developed to verify the chromosome region associated with KLW. A linkage map was constructed with 7 SSR markers, and a major effect QTL was identified within a 21.55 cM interval, corresponding to a physical interval of 10.8 Mb in the Chinese Spring RefSeq v1.0 sequence. This study can provide useful information for subsequent construction of fine mapping and marker-assisted selection breeding.
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37
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Giancaspro A, Giove SL, Zacheo SA, Blanco A, Gadaleta A. Genetic Variation for Protein Content and Yield-Related Traits in a Durum Population Derived From an Inter-Specific Cross Between Hexaploid and Tetraploid Wheat Cultivars. FRONTIERS IN PLANT SCIENCE 2019; 10:1509. [PMID: 31824537 PMCID: PMC6883369 DOI: 10.3389/fpls.2019.01509] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 10/30/2019] [Indexed: 05/18/2023]
Abstract
Wheat grain protein content (GPC) and yield components are complex quantitative traits influenced by a multi-factorial system consisting of both genetic and environmental factors. Although seed storage proteins represent less than 15% of mature kernels, they are crucial in determining end-use properties of wheat, as well as the nutritional value of derived products. Yield and GPC are negatively correlated, and this hampers breeding programs of commercially valuable wheat varieties. The goal of this work was the evaluation of genetic variability for quantity and composition of seed storage proteins, together with yield components [grain yield per spike (GYS) and thousand-kernel weight (TKW)] in a durum wheat population obtained by an inter-specific cross between a common wheat accession and the durum cv. Saragolla. Quantitative trait loci (QTL) analysis was conducted and closely associated markers identified on a genetic map composed of 4,366 SNP markers previously obtained in the same durum population genotyped with the 90K iSelect SNP assay. A total of 22 QTL were detected for traits related to durum wheat quality. Six genomic regions responsible for GPC control were mapped on chromosomes 2B, 3A, 4A, 4B, 5B, and 7B, with major QTL on chromosomes 2B, 4A, and 5B. Nine loci were detected for GYS: two on chromosome 5B and 7A and one on chromosomes 2A, 2B, 4A, 4B, 7B, with the strongest QTL on 2B. Eight QTL were identified for TKW, three of which located on chromosome 3A, two on 1B and one on 4B, 5A, and 5B. Only small overlapping was found among QTL for GYS, TKW, and GPC, and increasing alleles coming from both parents on different chromosomes. Good candidate genes were identified in the QTL confidence intervals for GYS and TKW.
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Affiliation(s)
| | | | | | | | - Agata Gadaleta
- Department of Agricultural and Environmental Sciences (DiSAAT), Research Unit of “Genetics and Plant Biotechnology”, University of Bari Aldo Moro, Bari, Italy
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38
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Wang X, Dong L, Hu J, Pang Y, Hu L, Xiao G, Ma X, Kong X, Jia J, Wang H, Kong L. Dissecting genetic loci affecting grain morphological traits to improve grain weight via nested association mapping. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:3115-3128. [PMID: 31399755 PMCID: PMC6791957 DOI: 10.1007/s00122-019-03410-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 07/29/2019] [Indexed: 05/30/2023]
Abstract
The quantitative trait loci (QTLs) for grain morphological traits were identified via nested association mapping and validated in a natural wheat population via haplotype analysis. Grain weight, one of the three most important components of crop yield, is largely determined by grain morphological traits. Dissecting the genetic bases of grain morphology could facilitate the improvement of grain weight and yield production. In this study, four wheat recombinant inbred line populations constructed by crossing the modern variety Yanzhan 1 with three semi-wild wheat varieties (i.e., Chayazheda, Yutiandaomai, and Yunnanxiaomai from Xinjiang, Tibet, and Yunnan, respectively) and one exotic accession Hussar from Great Britain were investigated for grain weight and eight morphological traits in seven environments. Eighty-eight QTLs for all measured traits were totally identified through nested association mapping utilizing 14,643 high-quality polymorphic single nucleotide polymorphism (SNP) markers generated by 90 K SNP array. Among them, 64 (72.7%) QTLs have the most favorable alleles donated by semi-wild wheat varieties. For 14 QTL clusters affecting at least two grain morphological traits, nine QTL clusters were located in similar position with known genes/QTL, and the other five were novel. Three important novel QTLs (i.e., qTGW-1B.1, qTGW-1B.2, and qTGW-1A.1) were further validated in a natural wheat population via haplotype analysis. The favorable haplotypes for these three QTLs might be used in marker-assisted selection for the improvement of wheat yield by modifying morphological traits.
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Affiliation(s)
- Xiaoqian Wang
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, China
| | - Luhao Dong
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, China
| | - Junmei Hu
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, China
| | - Yunlong Pang
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, China
| | - Liqin Hu
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, China
| | - Guilian Xiao
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, China
| | - Xin Ma
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, China
| | - Xiuying Kong
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture, The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jizeng Jia
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture, The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Hongwei Wang
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, China.
| | - Lingrang Kong
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, China.
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39
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Xu D, Wen W, Fu L, Li F, Li J, Xie L, Xia X, Ni Z, He Z, Cao S. Genetic dissection of a major QTL for kernel weight spanning the Rht-B1 locus in bread wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:3191-3200. [PMID: 31515582 DOI: 10.1007/s00122-019-03418-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 07/28/2019] [Accepted: 08/27/2019] [Indexed: 05/18/2023]
Abstract
Genetic dissection uncovered a major QTL QTKW.caas-4BS corresponding with a 483 kb deletion that included genes ZnF, EamA and Rht-B1. This deletion was associated with increased grain weight and semi-dwarf phenotype. Previous studies identified quantitative trait loci (QTL) for thousand kernel weight (TKW) in the region spanning the Rht-B1 locus in wheat (Triticum aestivum L.). We recently mapped a major QTL QTKW.caas-4BS for TKW spanning the Rht-B1 locus in a recombinant inbred line (RIL) population derived from Doumai/Shi 4185 using the wheat 90K array. The allele from Doumai at QTKW.caas-4BS significantly increased TKW and kernel number per spike, and conferred semi-dwarf trait, which was beneficial to improve grain yield without a penalty to lodging. To further dissect QTKW.caas-4BS, we firstly re-investigated the genotypes and phenotypes of the RILs and confirmed the QTL using cleaved amplified polymorphic sequence (CAPS) markers developed from flanking SNP markers IWA102 and IWB54814. The target sequences of the CAPS markers were used as queries to BLAST the wheat reference genome RefSeq v1.0 and hit an approximate 10.4 Mb genomic region. Based on genomic mining and SNP loci from the wheat 660K SNP array in the above genomic region, we developed eight new markers and narrowed QTKW.caas-4BS to a genetic interval of 1.5 cM. A 483 kb deletion in Doumai corresponded with QTKW.caas-4BS genetically, including three genes ZnF, EamA and Rht-B1. The other 15 genes with either differential expressions and/or sequence variations between parents were also potential candidate genes for QTKW.caas-4BS. The findings not only provide a toolkit for marker-assisted selection of QTKW.caas-4BS but also defined candidate genes for further functional analysis.
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Affiliation(s)
- Dengan Xu
- Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China
- Department of Plant Genetics & Breeding/State Key Laboratory for Agrobiotechnology, China Agricultural University, 2 Yuanmingyuan West Road, Beijing, 100094, China
| | - Weie Wen
- Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China
| | - Luping Fu
- Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China
| | - Faji Li
- Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China
| | - Jihu Li
- Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China
| | - Li Xie
- Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China
| | - Xianchun Xia
- Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China
| | - Zhongfu Ni
- Department of Plant Genetics & Breeding/State Key Laboratory for Agrobiotechnology, China Agricultural University, 2 Yuanmingyuan West Road, Beijing, 100094, China
| | - Zhonghu He
- Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China.
| | - Shuanghe Cao
- Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China.
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40
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Ma J, Zhang H, Li S, Zou Y, Li T, Liu J, Ding P, Mu Y, Tang H, Deng M, Liu Y, Jiang Q, Chen G, Kang H, Li W, Pu Z, Wei Y, Zheng Y, Lan X. Identification of quantitative trait loci for kernel traits in a wheat cultivar Chuannong16. BMC Genet 2019; 20:77. [PMID: 31619163 PMCID: PMC6796374 DOI: 10.1186/s12863-019-0782-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 09/26/2019] [Indexed: 12/01/2022] Open
Abstract
Background Kernel length (KL), kernel width (KW) and thousand-kernel weight (TKW) are key agronomic traits in wheat breeding. Chuannong16 (‘CN16’) is a commercial cultivar with significantly longer kernels than the line ‘20828’. To identify and characterize potential alleles from CN16 controlling KL, the previously developed recombinant inbred line (RIL) population derived from the cross ‘20828’ × ‘CN16’ and the genetic map constructed by the Wheat55K SNP array and SSR markers were used to perform quantitative trait locus/loci (QTL) analyses for kernel traits. Results A total of 11 putative QTL associated with kernel traits were identified and they were located on chromosomes 1A (2 QTL), 2B (2 QTL), 2D (3 QTL), 3D, 4A, 6A, and 7A, respectively. Among them, three major QTL, QKL.sicau-2D, QKW.sicau-2D and QTKW.sicau-2D, controlling KL, KW and TKW, respectively, were detected in three different environments. Respectively, they explained 10.88–18.85%, 17.21–21.49% and 10.01–23.20% of the phenotypic variance. Further, they were genetically mapped in the same interval on chromosome 2DS. A previously developed kompetitive allele-specific PCR (KASP) marker KASP-AX-94721936 was integrated in the genetic map and QTL re-mapping finally located the three major QTL in a 1- cM region flanked by AX-111096297 and KASP-AX-94721936. Another two co-located QTL intervals for KL and TKW were also identified. A few predicted genes involved in regulation of kernel growth and development were identified in the intervals of these identified QTL. Significant relationships between kernel traits and spikelet number per spike and anthesis date were detected and discussed. Conclusions Three major and stably expressed QTL associated with KL, KW, and TKW were identified. A KASP marker tightly linked to these three major QTL was integrated. These findings provide information for subsequent fine mapping and cloning the three co-localized major QTL for kernel traits.
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Affiliation(s)
- Jian Ma
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China. .,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China.
| | - Han Zhang
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Shuiqin Li
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yaya Zou
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Ting Li
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Jiajun Liu
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Puyang Ding
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yang Mu
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Huaping Tang
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Mei Deng
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yaxi Liu
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Qiantao Jiang
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Guoyue Chen
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Houyang Kang
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Wei Li
- College of Agronomy, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Zhien Pu
- College of Agronomy, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Yuming Wei
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Youliang Zheng
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Xiujin Lan
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China. .,China State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China.
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Li T, Ma J, Zou Y, Chen G, Ding P, Zhang H, Yang C, Mu Y, Tang H, Liu Y, Jiang Q, Chen G, Qi P, Wei Y, Zheng Y, Lan X. Quantitative trait loci for seeding root traits and the relationships between root and agronomic traits in common wheat. Genome 2019; 63:27-36. [PMID: 31580743 DOI: 10.1139/gen-2019-0116] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
A completely developed and vigorous root system can provide a stable platform for aboveground plant organs. To identify loci controlling root traits that could be used in wheat (Triticum aestivum L.) breeding, 199 recombinant inbred lines were used to measure and analyze eight root traits. A total of 18 quantitative trait loci (QTL) located on chromosomes 1A, 2A, 2B, 2D, 4B, 4D, 6A, 7A, and 7B were identified. The phenotypic variation explained by these 18 QTL ranged from 3.27% to 11.75%, and the logarithm of odds scores ranged from 2.50 to 6.58. A comparison of physical intervals indicated several new QTL for root traits were identified. In addition, significant correlations between root and agronomic traits were detected and discussed. The results presented in this study, along with those of previous reports, suggest that chromosomes 2 and 7 likely play important roles in the growth and development of wheat roots.
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Affiliation(s)
- Ting Li
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Jian Ma
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Yaya Zou
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Guangdeng Chen
- College of Resources, Sichuan Agricultural University, Chengdu, Sichuan 611130, P.R. China
| | - Puyang Ding
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Han Zhang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Congcong Yang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Yang Mu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Huaping Tang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Yaxi Liu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Qiantao Jiang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Guoyue Chen
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Pengfei Qi
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Yuming Wei
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Youliang Zheng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
| | - Xiujin Lan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, P.R. China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R. China
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42
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Abstract
Improving the end-use quality traits is one of the primary objectives in wheat breeding programs. In the current study, a population of 127 recombinant inbred lines (RILs) derived from a cross between Glenn (PI-639273) and Traverse (PI-642780) was developed and used to identify quantitative trait loci (QTL) for 16 end-use quality traits in wheat. The phenotyping of these 16 traits was performed in nine environments in North Dakota, USA. The genotyping for the RIL population was conducted using the wheat Illumina iSelect 90K SNP assay. A high-density genetic linkage map consisting of 7,963 SNP markers identified a total of 76 additive QTL (A-QTL) and 73 digenic epistatic QTL (DE-QTL) associated with these traits. Overall, 12 stable major A-QTL and three stable DE-QTL were identified for these traits, suggesting that both A-QTL and DE-QTL played an important role in controlling end-use quality traits in wheat. The most significant A-QTL (AQ.MMLPT.ndsu.1B) was detected on chromosome 1B for mixograph middle line peak time. The AQ.MMLPT.ndsu.1B A-QTL was located very close to the position of the Glu-B1 gene encoding for a subunit of high molecular weight glutenin and explained up to 24.43% of phenotypic variation for mixograph MID line peak time. A total of 23 co-localized QTL loci were detected, suggesting the possibility of the simultaneous improvement of the end-use quality traits through selection procedures in wheat breeding programs. Overall, the information provided in this study could be used in marker-assisted selection to increase selection efficiency and to improve the end-use quality in wheat.
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43
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Li F, Wen W, Liu J, Zhang Y, Cao S, He Z, Rasheed A, Jin H, Zhang C, Yan J, Zhang P, Wan Y, Xia X. Genetic architecture of grain yield in bread wheat based on genome-wide association studies. BMC PLANT BIOLOGY 2019; 19:168. [PMID: 31035920 PMCID: PMC6489268 DOI: 10.1186/s12870-019-1781-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Accepted: 04/16/2019] [Indexed: 05/18/2023]
Abstract
BACKGROUND Identification of loci for grain yield (GY) and related traits, and dissection of the genetic architecture are important for yield improvement through marker-assisted selection (MAS). Two genome-wide association study (GWAS) methods were used on a diverse panel of 166 elite wheat varieties from the Yellow and Huai River Valleys Wheat Zone (YHRVWD) of China to detect stable loci and analyze relationships among GY and related traits. RESULTS A total of 326,570 single nucleotide polymorphism (SNP) markers from the wheat 90 K and 660 K SNP arrays were chosen for GWAS of GY and related traits, generating a physical distance of 14,064.8 Mb. One hundred and twenty common loci were detected using SNP-GWAS and Haplotype-GWAS, among which two were potentially functional genes underpinning kernel weight and plant height (PH), eight were at similar locations to the quantitative trait loci (QTL) identified in recombinant inbred line (RIL) populations in a previous study, and 78 were potentially new. Twelve pleiotropic loci were detected on eight chromosomes; among these the interval 714.4-725.8 Mb on chromosome 3A was significantly associated with GY, kernel number per spike (KNS), kernel width (KW), spike dry weight (SDW), PH, uppermost internode length (UIL), and flag leaf length (FLL). GY shared five loci with thousand kernel weight (TKW) and PH, indicating significantly affected by two traits. Compared with the total number of loci for each trait in the diverse panel, the average number of alleles for increasing phenotypic values of GY, TKW, kernel length (KL), KW, and flag leaf width (FLW) were higher, whereas the numbers for PH, UIL and FLL were lower. There were significant additive effects for each trait when favorable alleles were combined. UIL and FLL can be directly used for selecting high-yielding varieties, whereas FLW can be used to select spike number per unit area (SN) and KNS. CONCLUSIONS The loci and significant SNP markers identified in the present study can be used for pyramiding favorable alleles in developing high-yielding varieties. Our study proved that both GWAS methods and high-density genetic markers are reliable means of identifying loci for GY and related traits, and provided new insight to the genetic architecture of GY.
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Affiliation(s)
- Faji Li
- College of Agronomy, Xinjiang Agricultural University, Urumqi, 830052 Xinjiang China
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081 China
| | - Weie Wen
- College of Agronomy, Xinjiang Agricultural University, Urumqi, 830052 Xinjiang China
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081 China
| | - Jindong Liu
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081 China
| | - Yong Zhang
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081 China
| | - Shuanghe Cao
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081 China
| | - Zhonghu He
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081 China
- International Maize and Wheat Improvement Center (CIMMYT) China Office, c/o CAAS, 12 Zhongguancun South Street, Beijing, 100081 China
| | - Awais Rasheed
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081 China
- International Maize and Wheat Improvement Center (CIMMYT) China Office, c/o CAAS, 12 Zhongguancun South Street, Beijing, 100081 China
| | - Hui Jin
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081 China
- Sino-Russia Agricultural Scientific and Technological Cooperation Center, Heilongjiang Academy of Agricultural Sciences, 368 Xuefu Street, Harbin, 150086 Heilongjiang China
| | - Chi Zhang
- School of Chemical Science and Engineering, Royal Institute of Technology, Teknikringen 42, SE-100 44 Stockholm, Sweden
| | - Jun Yan
- Institute of Cotton Research, Chinese Academy of Agricultural Sciences (CAAS), 38 Huanghe Street, Anyang, 455000 Henan China
| | - Pingzhi Zhang
- Crop Research Institute, Anhui Academy of Agricultural Sciences, 40 Nongke South Street, Hefei, 230001 Anhui China
| | - Yingxiu Wan
- Crop Research Institute, Anhui Academy of Agricultural Sciences, 40 Nongke South Street, Hefei, 230001 Anhui China
| | - Xianchun Xia
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081 China
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Xu J, Dai X, Ramasamy RK, Wang L, Zhu T, McGuire PE, Jorgensen CM, Dehghani H, Gulick PJ, Luo MC, Müller HG, Dvorak J. Aegilops tauschii Genome Sequence: A Framework for Meta-analysis of Wheat QTLs. G3 (BETHESDA, MD.) 2019; 9:841-853. [PMID: 30670607 PMCID: PMC6404623 DOI: 10.1534/g3.118.200921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 01/12/2019] [Indexed: 12/22/2022]
Abstract
Numerous quantitative trait loci (QTL) have been mapped in tetraploid and hexaploid wheat and wheat relatives, mostly with simple sequence repeat (SSR) or single nucleotide polymorphism (SNP) markers. To conduct meta-analysis of QTL requires projecting them onto a common genomic framework, either a consensus genetic map or genomic sequence. The latter strategy is pursued here. Of 774 QTL mapped in wheat and wheat relatives found in the literature, 585 (75.6%) were successfully projected onto the Aegilops tauschii pseudomolecules. QTL mapped with SNP markers were more successfully projected (92.2%) than those mapped with SSR markers (66.2%). The QTL were not distributed homogeneously along chromosome arms. Their frequencies increased in the proximal-to-distal direction but declined in the most distal regions and were weakly correlated with recombination rates along the chromosome arms. Databases for projected SSR markers and QTL were constructed and incorporated into the Ae. tauschii JBrowse. To facilitate meta-QTL analysis, eight clusters of QTL were used to estimate standard deviations ([Formula: see text]) of independently mapped QTL projected onto the Ae. tauschii genome sequence. The standard deviations [Formula: see text] were modeled as an exponential decay function of recombination rates along the Ae. tauschii chromosomes. We implemented four hypothesis tests for determining the membership of query QTL. The hypothesis tests and estimation procedure for [Formula: see text] were implemented in a web portal for meta-analysis of projected QTL. Twenty-one QTL for Fusarium head blight resistance mapped on wheat chromosomes 3A, 3B, and 3D were analyzed to illustrate the use of the portal for meta-QTL analyses.
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Affiliation(s)
- Jiale Xu
- Department of Plant Sciences, University of California, Davis, California
| | - Xiongtao Dai
- Department of Statistics, Iowa State University, Iowa
| | - Ramesh K Ramasamy
- Department of Plant Sciences, University of California, Davis, California
| | - Le Wang
- Department of Plant Sciences, University of California, Davis, California
| | - Tingting Zhu
- Department of Plant Sciences, University of California, Davis, California
| | - Patrick E McGuire
- Department of Plant Sciences, University of California, Davis, California
| | - Chad M Jorgensen
- Department of Plant Sciences, University of California, Davis, California
| | - Hamid Dehghani
- Department of Plant Sciences, University of California, Davis, California
- Department of Plant Breeding, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran, and
| | - Patrick J Gulick
- Department of Biology, Concordia University, Montreal, Quebec, Canada
| | - Ming-Cheng Luo
- Department of Plant Sciences, University of California, Davis, California
| | - Hans-Georg Müller
- Department of Statistics, University of California, Davis, California
| | - Jan Dvorak
- Department of Plant Sciences, University of California, Davis, California,
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45
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Goel S, Singh K, Singh B, Grewal S, Dwivedi N, Alqarawi AA, Abd Allah EF, Ahmad P, Singh NK. Analysis of genetic control and QTL mapping of essential wheat grain quality traits in a recombinant inbred population. PLoS One 2019; 14:e0200669. [PMID: 30840619 PMCID: PMC6402682 DOI: 10.1371/journal.pone.0200669] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 02/04/2019] [Indexed: 11/17/2022] Open
Abstract
Wheat cultivars are genetically crossed to improve end-use quality for traits as per demands of baking industry and broad consumer preferences. The processing and baking qualities of bread wheat are influenced by a variety of genetic make-ups, environmental factors and their interactions. Two wheat cultivars, WL711 and C306, derived recombinant inbred lines (RILs) with a population of 206, were used for phenotyping of quality-related traits. The genetic analysis of quality traits showed considerable variation for measurable quality traits, with normal distribution and transgressive segregation across the years. From the 206 RILs, few RILs were found to be superior to those of the parental cultivars for key quality traits, indicating their potential use for the improvement of end-use quality and suggesting the probability of finding new alleles and allelic combinations from the RIL population. Mapping analysis identified 38 putative QTLs for 13 quality-related traits, with QTLs explaining 7.9-16.8% phenotypic variation spanning over 14 chromosomes, i.e., 1A, 1B, 1D, 2A, 2D, 3B, 3D, 4A, 4B, 4D, 5D, 6A, 7A and 7B. In-silico analysis based on homology to the annotated wheat genes present in database, identified six putative candidate genes within QTL for total grain protein content, qGPC.1B.1 region. Major QTL regions for other quality traits such as TKW have been identified on 1B, 2A, and 7A chromosomes in the studied RIL population. This study revealed the importance of the combination of stable QTLs with region-specific QTLs for better phenotyping, and the QTLs presented in our study will be useful for the improvement of wheat grain and bread-making quality.
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Affiliation(s)
- Sonia Goel
- ICAR-National Research centre on Plant Biotechnology, New Delhi, India
| | - Kalpana Singh
- Water Technology Centre, Indian Agriculture Research Institute, New Delhi, India
| | - Balwant Singh
- ICAR-National Research centre on Plant Biotechnology, New Delhi, India
| | - Sapna Grewal
- ICAR-National Research centre on Plant Biotechnology, New Delhi, India
| | - Neeta Dwivedi
- Water Technology Centre, Indian Agriculture Research Institute, New Delhi, India
| | - Abdulaziz A Alqarawi
- Plant Production Department, College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Elsayed Fathi Abd Allah
- Plant Production Department, College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Parvaiz Ahmad
- Botany and Microbiology Department, College of Science, King Saud University, Riyadh, Saudi Arabia.,Department of Botany, S.P. College, Srinagar, Jammu and Kashmir, India
| | - N K Singh
- ICAR-National Research centre on Plant Biotechnology, New Delhi, India
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46
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Mérida-García R, Liu G, He S, Gonzalez-Dugo V, Dorado G, Gálvez S, Solís I, Zarco-Tejada PJ, Reif JC, Hernandez P. Genetic dissection of agronomic and quality traits based on association mapping and genomic selection approaches in durum wheat grown in Southern Spain. PLoS One 2019; 14:e0211718. [PMID: 30811415 PMCID: PMC6392243 DOI: 10.1371/journal.pone.0211718] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 01/19/2019] [Indexed: 01/12/2023] Open
Abstract
Climatic conditions affect the growth, development and final crop production. As wheat is of paramount importance as a staple crop in the human diet, there is a growing need to study its abiotic stress adaptation through the performance of key breeding traits. New and complementary approaches, such as genome-wide association studies (GWAS) and genomic selection (GS), are used for the dissection of different agronomic traits. The present study focused on the dissection of agronomic and quality traits of interest (initial agronomic score, yield, gluten index, sedimentation index, specific weight, whole grain protein and yellow colour) assessed in a panel of 179 durum wheat lines (Triticum durum Desf.), grown under rainfed conditions in different Mediterranean environments in Southern Spain (Andalusia). The findings show a total of 37 marker-trait associations (MTAs) which affect phenotype expression for three quality traits (specific weight, gluten and sedimentation indexes). MTAs could be mapped on the A and B durum wheat subgenomes (on chromosomes 1A, 1B, 2A, 2B and 3A) through the recently available bread wheat reference assembly (IWGSC RefSeqv1). Two of the MTAs found for quality traits (gluten index and SDS) corresponded to the known Glu-B1 and Glu-A1 loci, for which candidate genes corresponding to high molecular weight glutenin subunits could be located. The GS prediction ability values obtained from the breeding materials analyzed showed promising results for traits as grain protein content, sedimentation and gluten indexes, which can be used in plant breeding programs.
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Affiliation(s)
- Rosa Mérida-García
- Instituto de Agricultura Sostenible (IAS) Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo s/n, Córdoba, Spain
| | - Guozheng Liu
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Stadt Seeland, Germany
| | - Sang He
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Stadt Seeland, Germany
| | - Victoria Gonzalez-Dugo
- Instituto de Agricultura Sostenible (IAS) Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo s/n, Córdoba, Spain
| | - Gabriel Dorado
- Departamento de Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Córdoba, Spain
| | - Sergio Gálvez
- Universidad de Málaga, Andalucía Tech, ETSI Informática, Campus de Teatinos s/n, Málaga, Spain
| | - Ignacio Solís
- ETSIA (University of Seville), Ctra de Utrera km1, Seville, Spain
| | - Pablo J. Zarco-Tejada
- Instituto de Agricultura Sostenible (IAS) Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo s/n, Córdoba, Spain
| | - Jochen C. Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Stadt Seeland, Germany
| | - Pilar Hernandez
- Instituto de Agricultura Sostenible (IAS) Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo s/n, Córdoba, Spain
- * E-mail:
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Yoshioka M, Takenaka S, Nitta M, Li J, Mizuno N, Nasuda S. Genetic dissection of grain morphology in hexaploid wheat by analysis of the NBRP-Wheat core collection. Genes Genet Syst 2019; 94:35-49. [DOI: 10.1266/ggs.18-00045] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Affiliation(s)
- Motohiro Yoshioka
- Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto University
| | - Shotaro Takenaka
- Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto University
- Department of Plant Life Science, Faculty of Agriculture, Ryukoku University
| | - Miyuki Nitta
- Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto University
| | - Jianjian Li
- Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto University
| | - Nobuyuki Mizuno
- Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto University
| | - Shuhei Nasuda
- Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto University
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48
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Kumari S, Jaiswal V, Mishra VK, Paliwal R, Balyan HS, Gupta PK. QTL mapping for some grain traits in bread wheat ( Triticum aestivum L.). PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2018; 24:909-920. [PMID: 30150865 PMCID: PMC6103944 DOI: 10.1007/s12298-018-0552-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 04/07/2018] [Accepted: 05/08/2018] [Indexed: 05/19/2023]
Abstract
Grain traits are important agronomic attributes with the market value as well as milling yield of bread wheat. In the present study, quantitative trait loci (QTL) regulating grain traits in wheat were identified. Data for grain area size (GAS), grain width (GWid), factor form density (FFD), grain length-width ratio (GLWR), thousand grain weight (TGW), grain perimeter length (GPL) and grain length (GL) were recorded on a recombinant inbred line derived from the cross of NW1014 × HUW468 at Meerut and Varanasi locations. A linkage map of 55 simple sequence repeat markers for 8 wheat chromosomes was used for QTL analysis by Composite interval mapping. Eighteen QTLs distributed on 8 chromosomes were identified for seven grain traits. Of these, five QTLs for GLWR were found on chromosomes 1A, 6A, 2B, and 7B, three QTLs for GPL were located on chromosomes 4A, 5A and 7B and three QTLs for GAS were mapped on 5D and 7D. Two QTLs were identified on chromosomes 4A and 5A for GL and two QTLs for GWid were identified on chromosomes 7D and 6A. Similarly, two QTLs for FFD were found on chromosomes 1A and 5D. A solitary QTL for TGW was identified on chromosome 2B. For several traits, QTLs were also co-localized on chromosomes 2B, 4A, 5A, 6A, 5D, 7B and 7D. The QTLs detected in the present study may be validated for specific crosses and then used for marker-assisted selection to improve grain quality in bread wheat.
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Affiliation(s)
- Supriya Kumari
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P. India
| | - Vandana Jaiswal
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P. India
- School of Life Science, Jawaharlal Nehru University, New Delhi, India
| | - Vinod Kumar Mishra
- Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, U.P. India
| | - Rajneesh Paliwal
- International Institute of Tropical Agriculture (IITA), Ibadan, PMB 5320 Nigeria
| | - Harindra Singh Balyan
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P. India
| | - Pushpendra Kumar Gupta
- Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P. India
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49
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Optimising Genomic Selection in Wheat: Effect of Marker Density, Population Size and Population Structure on Prediction Accuracy. G3-GENES GENOMES GENETICS 2018; 8:2889-2899. [PMID: 29970398 PMCID: PMC6118301 DOI: 10.1534/g3.118.200311] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genomic selection applied to plant breeding enables earlier estimates of a line’s performance and significant reductions in generation interval. Several factors affecting prediction accuracy should be well understood if breeders are to harness genomic selection to its full potential. We used a panel of 10,375 bread wheat (Triticum aestivum) lines genotyped with 18,101 SNP markers to investigate the effect and interaction of training set size, population structure and marker density on genomic prediction accuracy. Through assessing the effect of training set size we showed the rate at which prediction accuracy increases is slower beyond approximately 2,000 lines. The structure of the panel was assessed via principal component analysis and K-means clustering, and its effect on prediction accuracy was examined through a novel cross-validation analysis according to the K-means clusters and breeding cohorts. Here we showed that accuracy can be improved by increasing the diversity within the training set, particularly when relatedness between training and validation sets is low. The breeding cohort analysis revealed that traits with higher selection pressure (lower allelic diversity) can be more accurately predicted by including several previous cohorts in the training set. The effect of marker density and its interaction with population structure was assessed for marker subsets containing between 100 and 17,181 markers. This analysis showed that response to increased marker density is largest when using a diverse training set to predict between poorly related material. These findings represent a significant resource for plant breeders and contribute to the collective knowledge on the optimal structure of calibration panels for genomic prediction.
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50
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Liu K, Sun X, Ning T, Duan X, Wang Q, Liu T, An Y, Guan X, Tian J, Chen J. Genetic dissection of wheat panicle traits using linkage analysis and a genome-wide association study. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2018; 131:1073-1090. [PMID: 29470622 DOI: 10.1007/s00122-018-3059-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 01/19/2018] [Indexed: 05/18/2023]
Abstract
Coincident regions on chromosome 4B for GW, on 5A for SD and TSS, and on 3A for SL and GNS were detected through an integration of a linkage analysis and a genome-wide association study (GWAS). In addition, six stable QTL clusters on chromosomes 2D, 3A, 4B, 5A and 6A were identified with high PVE% on a composite map. The panicle traits of wheat, such as grain number per spike and 1000-grain weight, are closely correlated with grain yield. Superior and effective alleles at loci related to panicles developments play a crucial role in the progress of molecular improvement in wheat yield breeding. Here, we revealed several notable allelic variations of seven panicle-related traits through an integration of genome-wide association mapping and a linkage analysis. The linkage analysis was performed using a recombinant inbred line (RIL) population (173 lines of F8:9) with a high-density genetic map constructed with 90K SNP arrays, Diversity Arrays Technology (DArT) and simple sequence repeat (SSR) markers in five environments. Thirty-five additive quantitative trait loci (QTL) were discovered, including eleven stable QTLs on chromosomes 1A, 2D, 4B, 5B, 6B, and 6D. The marker interval between EX_C101685 and RAC875_C27536 on chromosome 4B exhibited pleiotropic effects for GW, SL, GNS, FSN, SSN, and TSS, with the phenotypic variation explained (PVE) ranging from 5.40 to 37.70%. In addition, an association analysis was conducted using a diverse panel of 205 elite wheat lines with a composite map (24,355 SNPs) based on the Illumina Infinium assay in four environments. A total of 73 significant marker-trait associations (MTAs) were detected for panicle traits, which were distributed across all wheat chromosomes except for 4D, 5D, and 6D. Consensus regions between RAC875_C27536_611 and Tdurum_contig4974_355 on chromosome 4B for GW in multiple environments, between QTSS5A.7-43 and BS00021805_51 on 5A for SD and TSS, and between QSD3A.2-164 and RAC875_c17479_359 on 3A for SL and GNS in multiple environments were detected through linkage analysis and a genome-wide association study (GWAS). In addition, six stable QTL clusters on chromosomes 2D, 3A, 4B, 5A, and 6A were identified with high PVE% on a composite map. This study provides potentially valuable information on the dissection of yield-component traits and valuable genetic alleles for molecular-design breeding or functional gene exploration.
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Affiliation(s)
- Kai Liu
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, People's Republic of China
| | - Xiaoxiao Sun
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, People's Republic of China
| | - Tangyuan Ning
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, People's Republic of China
| | - Xixian Duan
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, People's Republic of China
| | - Qiaoling Wang
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, People's Republic of China
| | - Tongtong Liu
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, People's Republic of China
| | - Yuling An
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, People's Republic of China
| | - Xin Guan
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, People's Republic of China
| | - Jichun Tian
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, People's Republic of China.
| | - Jiansheng Chen
- State Key Laboratory of Crop Biology/Key Laboratory of Crop Water Physiology and Drought-Tolerance Germplasm Improvement, Ministry of Agriculture/Group of Wheat Quality Breeding, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, People's Republic of China.
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