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Zhou J, Liu Q, Tian R, Chen H, Wang J, Yang Y, Zhao C, Liu Y, Tang H, Deng M, Xu Q, Jiang Q, Chen G, Qi P, Jiang Y, Chen G, Tang L, Ren Y, Zheng Z, Liu C, Zheng Y, He Y, Wei Y, Ma J. A co-located QTL for seven spike architecture-related traits shows promising breeding use potential in common wheat (Triticum aestivum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:31. [PMID: 38267732 DOI: 10.1007/s00122-023-04536-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 12/27/2023] [Indexed: 01/26/2024]
Abstract
KEY MESSAGE A co-located novel QTL for TFS, FPs, FMs, FFS, FFPs, KWS, and KWPs with potential of improving wheat yield was identified and validated. Spike-related traits, including fertile florets per spike (FFS), kernel weight per spike (KWS), total florets per spike (TFS), florets per spikelet (FPs), florets in the middle spikelet (FMs), fertile florets per spikelet (FFPs), and kernel weight per spikelet (KWPs), are key traits in improving wheat yield. In the present study, quantitative trait loci (QTL) for these traits evaluated under various environments were detected in a recombinant inbred line population (msf/Chuannong 16) mainly genotyped using the 16 K SNP array. Ultimately, we identified 60 QTL, but only QFFS.sau-MC-1A for FFS was a major and stably expressed QTL. It was located on chromosome arm 1AS, where loci for TFS, FPs, FMs, FFS, FFPs, KWS, and KWPs were also simultaneously co-mapped. The effect of QFFS.sau-MC-1A was further validated in three independent segregating populations using a Kompetitive Allele-Specific PCR marker. For the co-located QTL, QFFS.sau-MC-1A, the presence of a positive allele from msf was associate with increases for all traits: + 12.29% TFS, + 10.15% FPs, + 13.97% FMs, + 17.12% FFS, + 14.75% FFPs, + 22.17% KWS, and + 19.42% KWPs. Furthermore, pleiotropy analysis showed that the positive allele at QFFS.sau-MC-1A simultaneously increased the spike length, spikelet number per spike, and thousand-kernel weight. QFFS.sau-MC-1A represents a novel QTL for marker-assisted selection with the potential for improving wheat yield. Four genes, TraesCS1A03G0012700, TraesCS1A03G0015700, TraesCS1A03G0016000, and TraesCS1A03G0016300, which may affect spike development, were predicted in the physical interval harboring QFFS.sau-MC-1A. Our results will help in further fine mapping QFFS.sau-MC-1A and be useful for improving wheat yield.
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Affiliation(s)
- Jieguang Zhou
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Qian Liu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Rong Tian
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Huangxin Chen
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Jian Wang
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yaoyao Yang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Conghao Zhao
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yanlin Liu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Huaping Tang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Mei Deng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Qiang Xu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Qiantao Jiang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guoyue Chen
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Pengfei Qi
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yunfeng Jiang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guangdeng Chen
- College of Resources, Sichuan Agricultural University, Chengdu, China
| | - Liwei Tang
- Panzhihua Academy of Agricultural and Forestry Sciences, Panzhihua, China
| | - Yong Ren
- Mianyang Academy of Agricultural Science/Crop Characteristic Resources Creation and Utilization Key Laboratory of Sichuan Providence, Mianyang, China
| | - Zhi Zheng
- CSIRO Agriculture and Food, 306 Carmody Road, St Lucia, QLD, 4067, Australia
| | - Chunji Liu
- CSIRO Agriculture and Food, 306 Carmody Road, St Lucia, QLD, 4067, Australia
| | - Youliang Zheng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yuanjiang He
- Mianyang Academy of Agricultural Science/Crop Characteristic Resources Creation and Utilization Key Laboratory of Sichuan Providence, Mianyang, China.
| | - Yuming Wei
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China.
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China.
| | - Jian Ma
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China.
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China.
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Dai K, Wang X, Liu H, Qiao P, Wang J, Shi W, Guo J, Diao X. Efficient identification of QTL for agronomic traits in foxtail millet (Setaria italica) using RTM- and MLM-GWAS. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:18. [PMID: 38206376 DOI: 10.1007/s00122-023-04522-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/07/2023] [Indexed: 01/12/2024]
Abstract
KEY MESSAGE Eleven QTLs for agronomic traits were identified by RTM- and MLM-GWAS, putative candidate genes were predicted and two markers for grain weight were developed and validated. Foxtail millet (Setaria italica), the second most cultivated millet crop after pearl millet, is an important grain crop in arid regions. Seven agronomic traits of 408 diverse foxtail millet accessions from 15 provinces in China were evaluated in three environments. They were clustered into two divergent groups based on genotypic data using ADMIXTURE, which was highly consistent with their geographical distribution. Two models for genome-wide association studies (GWAS), namely restricted two-stage multi-locus multi-allele (RTM)-GWAS and mixed linear model (MLM)-GWAS, were used to dissect the genetic architecture of the agronomic traits based on 13,723 SNPs. Eleven quantitative trait loci (QTLs) for seven traits were identified using two models (RTM- and MLM-GWAS). Among them, five were considered stable QTLs that were identified in at least two environments using MLM-GWAS. One putative candidate gene (SETIT_006045mg, Chr4: 744,701-746,852) that can enhance grain weight per panicle was identified based on homologous gene comparison and gene expression analysis and was validated by haplotype analysis of 330 accessions with high-depth (10×) resequencing data (unpublished). In addition, homologous gene comparison and haplotype analysis identified one putative foxtail millet ortholog (SETIT_032906mg, Chr2: 5,020,600-5,029,771) with rice affecting the target traits. Two markers (cGWP6045 and kTGW2906) were developed and validated and can be used for marker-assisted selection of foxtail millet with high grain weight. The results provide a fundamental resource for foxtail millet genetic research and breeding and demonstrate the power of integrating RTM- and MLM-GWAS approaches as a complementary strategy for investigating complex traits in foxtail millet.
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Affiliation(s)
- Keli Dai
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Xin Wang
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Hanxiao Liu
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Pengfei Qiao
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Jiaxue Wang
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Weiping Shi
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China.
| | - Jie Guo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China.
| | - Xianmin Diao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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Al-Ashkar I, Sallam M, Ibrahim A, Ghazy A, Al-Suhaibani N, Ben Romdhane W, Al-Doss A. Identification of Wheat Ideotype under Multiple Abiotic Stresses and Complex Environmental Interplays by Multivariate Analysis Techniques. PLANTS (BASEL, SWITZERLAND) 2023; 12:3540. [PMID: 37896004 PMCID: PMC10610392 DOI: 10.3390/plants12203540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/04/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023]
Abstract
Multiple abiotic stresses negatively impact wheat production all over the world. We need to increase productivity by 60% to provide food security to the world population of 9.6 billion by 2050; it is surely time to develop stress-tolerant genotypes with a thorough comprehension of the genetic basis and the plant's capacity to tolerate these stresses and complex environmental reactions. To approach these goals, we used multivariate analysis techniques, the additive main effects and multiplicative interaction (AMMI) model for prediction, linear discriminant analysis (LDA) to enhance the reliability of the classification, multi-trait genotype-ideotype distance index (MGIDI) to detect the ideotype, and the weighted average of absolute scores (WAASB) index to recognize genotypes with stability that are highly productive. Six tolerance multi-indices were used to test twenty wheat genotypes grown under multiple abiotic stresses. The AMMI model showed varying differences with performance indices, which disagreed with the trait and genotype differences used. The G01, G12, G16, and G02 were selected as the appropriate and stable genotypes using the MGIDI with the six tolerance multi-indices. The biplot features the genotypes (G01, G03, G11, G16, G17, G18, and G20) that were most stable and had high tolerance across the environments. The pooled analyses (LDA, MGIDI, and WAASB) showed genotype G01 as the most stable candidate. The genotype (G01) is considered a novel genetic resource for improving productivity and stabilizing wheat programs under multiple abiotic stresses. Hence, these techniques, if used in an integrated manner, strongly support the plant breeders in multi-environment trials.
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Affiliation(s)
- Ibrahim Al-Ashkar
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (M.S.); (A.I.); (A.G.); (N.A.-S.); (W.B.R.); (A.A.-D.)
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