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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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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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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: 18] [Impact Index Per Article: 4.5] [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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Li Q, Pan Z, Gao Y, Li T, Liang J, Zhang Z, Zhang H, Deng G, Long H, Yu M. Quantitative Trait Locus (QTLs) Mapping for Quality Traits of Wheat Based on High Density Genetic Map Combined With Bulked Segregant Analysis RNA-seq (BSR-Seq) Indicates That the Basic 7S Globulin Gene Is Related to Falling Number. FRONTIERS IN PLANT SCIENCE 2020; 11:600788. [PMID: 33424899 PMCID: PMC7793810 DOI: 10.3389/fpls.2020.600788] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 11/11/2020] [Indexed: 05/14/2023]
Abstract
Numerous quantitative trait loci (QTLs) have been identified for wheat quality; however, most are confined to low-density genetic maps. In this study, based on specific-locus amplified fragment sequencing (SLAF-seq), a high-density genetic map was constructed with 193 recombinant inbred lines derived from Chuanmai 42 and Chuanmai 39. In total, 30 QTLs with phenotypic variance explained (PVE) up to 47.99% were identified for falling number (FN), grain protein content (GPC), grain hardness (GH), and starch pasting properties across three environments. Five NAM genes closely adjacent to QGPC.cib-4A probably have effects on GPC. QGH.cib-5D was the only one detected for GH with high PVE of 33.31-47.99% across the three environments and was assumed to be related to the nearest pina-D1 and pinb-D1genes. Three QTLs were identified for FN in at least two environments, of which QFN.cib-3D had relatively higher PVE of 16.58-25.74%. The positive effect of QFN.cib-3D for high FN was verified in a double-haploid population derived from Chuanmai 42 × Kechengmai 4. The combination of these QTLs has a considerable effect on increasing FN. The transcript levels of Basic 7S globulin and Basic 7S globulin 2 in QFN.cib-3D were significantly different between low FN and high FN bulks, as observed through bulk segregant RNA-seq (BSR). These QTLs and candidate genes based on the high-density genetic map would be beneficial for further understanding of the genetic mechanism of quality traits and molecular breeding of wheat.
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Affiliation(s)
- Qiao Li
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Zhifen Pan
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- *Correspondence: Zhifen Pan, ; orcid.org/0000-0002-1692-5425
| | - Yuan Gao
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Tao Li
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junjun Liang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Zijin Zhang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Haili Zhang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Guangbing Deng
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Hai Long
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Maoqun Yu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
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