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Govta N, Fatiukha A, Govta L, Pozniak C, Distelfeld A, Fahima T, Beckles DM, Krugman T. Nitrogen deficiency tolerance conferred by introgression of a QTL derived from wild emmer into bread wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:187. [PMID: 39020219 PMCID: PMC11255033 DOI: 10.1007/s00122-024-04692-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 07/04/2024] [Indexed: 07/19/2024]
Abstract
KEY MESSAGE Genetic dissection of a QTL from wild emmer wheat, QGpc.huj.uh-5B.2, introgressed into bread wheat, identified candidate genes associated with tolerance to nitrogen deficiency, and potentially useful for improving nitrogen-use efficiency. Nitrogen (N) is an important macronutrient critical to wheat growth and development; its deficiency is one of the main factors causing reductions in grain yield and quality. N availability is significantly affected by drought or flooding, that are dependent on additional factors including soil type or duration and severity of stress. In a previous study, we identified a high grain protein content QTL (QGpc.huj.uh-5B.2) derived from the 5B chromosome of wild emmer wheat, that showed a higher proportion of explained variation under water-stress conditions. We hypothesized that this QTL is associated with tolerance to N deficiency as a possible mechanism underlying the higher effect under stress. To validate this hypothesis, we introgressed the QTL into the elite bread wheat var. Ruta, and showed that under N-deficient field conditions the introgression IL99 had a 33% increase in GPC (p < 0.05) compared to the recipient parent. Furthermore, evaluation of IL99 response to severe N deficiency (10% N) for 14 days, applied using a semi-hydroponic system under controlled conditions, confirmed its tolerance to N deficiency. Fine-mapping of the QTL resulted in 26 homozygous near-isogenic lines (BC4F5) segregating to N-deficiency tolerance. The QTL was delimited from - 28.28 to - 1.29 Mb and included 13 candidate genes, most associated with N-stress response, N transport, and abiotic stress responses. These genes may improve N-use efficiency under severely N-deficient environments. Our study demonstrates the importance of WEW as a source of novel candidate genes for sustainable improvement in tolerance to N deficiency in wheat.
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Affiliation(s)
- Nikolai Govta
- Wild Cereal Gene Bank, Institute of Evolution, University of Haifa, Abba Khoushy Ave 199, 3498838, Haifa, Israel
- Department of Evolutionary and Environmental Biology, University of Haifa, Abba Khoushy Ave 199, 3498838, Haifa, Israel
| | - Andrii Fatiukha
- Department of Evolutionary and Environmental Biology, University of Haifa, Abba Khoushy Ave 199, 3498838, Haifa, Israel
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, Saskatoon, Canada
| | - Liubov Govta
- Department of Evolutionary and Environmental Biology, University of Haifa, Abba Khoushy Ave 199, 3498838, Haifa, Israel
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, Saskatoon, Canada
| | - Assaf Distelfeld
- Department of Evolutionary and Environmental Biology, University of Haifa, Abba Khoushy Ave 199, 3498838, Haifa, Israel
| | - Tzion Fahima
- Department of Evolutionary and Environmental Biology, University of Haifa, Abba Khoushy Ave 199, 3498838, Haifa, Israel
| | - Diane M Beckles
- Department of Plant Sciences, University of California, Davis, CA, 95616, USA
| | - Tamar Krugman
- Wild Cereal Gene Bank, Institute of Evolution, University of Haifa, Abba Khoushy Ave 199, 3498838, Haifa, Israel.
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Dwivedi SL, Heslop-Harrison P, Amas J, Ortiz R, Edwards D. Epistasis and pleiotropy-induced variation for plant breeding. PLANT BIOTECHNOLOGY JOURNAL 2024. [PMID: 38875130 DOI: 10.1111/pbi.14405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 05/07/2024] [Accepted: 05/24/2024] [Indexed: 06/16/2024]
Abstract
Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.
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Affiliation(s)
| | - Pat Heslop-Harrison
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- Department of Genetics and Genome Biology, Institute for Environmental Futures, University of Leicester, Leicester, UK
| | - Junrey Amas
- Centre for Applied Bioinformatics, School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - David Edwards
- Centre for Applied Bioinformatics, School of Biological Sciences, University of Western Australia, Perth, WA, Australia
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Liao S, Xu Z, Fan X, Zhou Q, Liu X, Jiang C, Ma F, Wang Y, Wang T, Feng B. Identification and validation of two major QTL for grain number per spike on chromosomes 2B and 2D in bread wheat (Triticum aestivum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:147. [PMID: 38834870 DOI: 10.1007/s00122-024-04652-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 05/16/2024] [Indexed: 06/06/2024]
Abstract
KEY MESSAGE Major QTL for grain number per spike were identified on chromosomes 2B and 2D. Haplotypes and candidate genes of QGns.cib-2B.1 were analyzed. Grain number per spike (GNS) is one of the main components of wheat yield. Genetic dissection of their regulatory factors is essential to improve the yield potential. In present study, a recombinant inbred line population comprising 180 lines developed from the cross between a high GNS line W7268 and a cultivar Chuanyu12 was employed to identify quantitative trait loci (QTL) associated with GNS across six environments. Two major QTL, QGns.cib-2B.1 and QGns.cib-2D.1, were detected in at least four environments with the phenotypic variations of 12.99-27.07% and 8.50-13.79%, respectively. And significant interactions were observed between the two major QTL. In addition, QGns.cib-2B.1 is a QTL cluster for GNS, grain number per spikelet and fertile tiller number, and they were validated in different genetic backgrounds using Kompetitive Allele Specific PCR (KASP) markers. QGns.cib-2B.1 showed pleotropic effects on other yield-related traits including plant height, spike length, and spikelet number per spike, but did not significantly affect thousand grain weight which suggested that it might be potentially applicable in breeding program. Comparison analysis suggested that QGns.cib-2B.1 might be a novel QTL. Furthermore, haplotype analysis of QGns.cib-2B.1 indicated that it is a hot spot of artificial selection during wheat improvement. Based on the expression patterns, gene annotation, orthologs analysis and sequence variations, the candidate genes of QGns.cib-2B.1 were predicted. Collectively, the major QTL and KASP markers reported here provided a wealth of information for the genetic basis of GNS and grain yield improvement.
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Affiliation(s)
- Simin Liao
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhibin Xu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Xiaoli Fan
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Qiang Zhou
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Xiaofeng Liu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Cheng Jiang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Fang Ma
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yanlin Wang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tao Wang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
- The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Bo Feng
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China.
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Li Y, Hu J, Qu Y, Qiu D, Lin H, Du J, Hou L, Ma L, Wu Q, Zhou Y, Zhang H, Yang L, Liu H, Liu Z, Zhou Y, Li H. Alleles on locus chromosome 4B from different parents confer tiller number and the yield-associated traits in wheat. BMC PLANT BIOLOGY 2024; 24:454. [PMID: 38789943 PMCID: PMC11127307 DOI: 10.1186/s12870-024-05079-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 04/28/2024] [Indexed: 05/26/2024]
Abstract
Pleiotropy is frequently detected in agronomic traits of wheat (Triticum aestivum). A locus on chromosome 4B, QTn/Ptn/Sl/Sns/Al/Tgw/Gl/Gw.caas-4B, proved to show pleiotropic effects on tiller, spike, and grain traits using a recombinant inbred line (RIL) population of Qingxinmai × 041133. The allele from Qingxinmai increased tiller numbers, and the allele from line 041133 produced better performances of spike traits and grain traits. Another 52 QTL for the eight traits investigated were detected on 18 chromosomes, except for chromosomes 5D, 6D, and 7B. Several genes in the genomic interval of the locus on chromosome 4B were differentially expressed in crown and inflorescence samples between Qingxinmai and line 041133. The development of the KASP marker specific for the locus on chromosome 4B is useful for molecular marker-assisted selection in wheat breeding.
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Affiliation(s)
- Yahui Li
- College of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China
- The National Engineering Laboratory of Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jinghuang Hu
- The National Engineering Laboratory of Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yunfeng Qu
- State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, 475001, China
| | - Dan Qiu
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Huailong Lin
- Jiushenghe Seed Industry Co. Ltd, Changji, 831100, China
| | - Jiuyuan Du
- Wheat Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, 730070, China
| | - Lu Hou
- Key Laboratory of Agricultural Integrated Pest Management, Qinghai Academy of Agricultural and Forestry Sciences, Qinghai University, Xining, 810016, China
| | - Lin Ma
- Datong Hui and Tu Autonomous County Agricultural Technology Extension Center, Xining, 810100, China
| | - Qiuhong Wu
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yang Zhou
- The National Engineering Laboratory of Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Hongjun Zhang
- The National Engineering Laboratory of Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Li Yang
- The National Engineering Laboratory of Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Hongwei Liu
- The National Engineering Laboratory of Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhiyong Liu
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yijun Zhou
- College of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China.
| | - Hongjie Li
- The National Engineering Laboratory of Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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Ding H, Wang C, Cai Y, Yu K, Zhao H, Wang F, Shi X, Cheng J, Sun H, Wu Y, Qin R, Liu C, Zhao C, Sun X, Cui F. Characterization of a wheat stable QTL for spike length and its genetic effects on yield-related traits. BMC PLANT BIOLOGY 2024; 24:292. [PMID: 38632554 PMCID: PMC11022484 DOI: 10.1186/s12870-024-04963-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 03/29/2024] [Indexed: 04/19/2024]
Abstract
Spike length (SL) is one of the most important agronomic traits affecting yield potential and stability in wheat. In this study, a major stable quantitative trait locus (QTL) for SL, i.e., qSl-2B, was detected in multiple environments in a recombinant inbred line (RIL) mapping population, KJ-RILs, derived from a cross between Kenong 9204 (KN9204) and Jing 411 (J411). The qSl-2B QTL was mapped to the 60.06-73.06 Mb region on chromosome 2B and could be identified in multiple mapping populations. An InDel molecular marker in the target region was developed based on a sequence analysis of the two parents. To further clarify the breeding use potential of qSl-2B, we analyzed its genetic effects and breeding selection effect using both the KJ-RIL population and a natural mapping population, which consisted of 316 breeding varieties/advanced lines. The results showed that the qSl-2B alleles from KN9204 showed inconsistent genetic effects on SL in the two mapping populations. Moreover, in the KJ-RILs population, the additive effects analysis of qSl-2B showed that additive effect was higher when both qSl-2D and qSl-5A harbor negative alleles under LN and HN. In China, a moderate selection utilization rate for qSl-2B was found in the Huanghuai winter wheat area and the selective utilization rate for qSl-2B continues to increase. The above findings provided a foundation for the genetic improvement of wheat SL in the future via molecular breeding strategies.
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Affiliation(s)
- Hongke Ding
- 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
| | - 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
| | - Kai Yu
- Yantai Agricultural Technology Extension Center, Yantai, 264001, China
| | - Haibo Zhao
- Yantai Agricultural Technology Extension Center, Yantai, 264001, China
| | - Faxiang 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - Cheng Liu
- Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, 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.
| | - Xiaohui Sun
- Yantai Academy of Agricultural Sciences, Yantai, Shandong, 265500, 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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Qin R, Cao M, Dong J, Chen L, Guo H, Guo Q, Cai Y, Han L, Huang Z, Xu N, Yang A, Xu H, Wu Y, Sun H, Liu X, Ling H, Zhao C, Li J, Cui F. Fine mapping of a major QTL, qKl-1BL controlling kernel length in common wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:67. [PMID: 38441674 DOI: 10.1007/s00122-024-04574-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 02/03/2024] [Indexed: 03/07/2024]
Abstract
KEY MESSAGE A major stable QTL, qKl-1BL, for kernel length of wheat was narrowed down to a 2.04-Mb interval on chromosome 1BL; the candidate genes were predicated and the genetic effects on yield-related traits were characterized. As a key factor influencing kernel weight, wheat kernel shape is closely related to yield formation, and in turn affects both wheat processing quality and market value. Fine mapping of the major quantitative trait loci (QTL) for kernel shape could provide genetic resources and a theoretical basis for the genetic improvement of wheat yield-related traits. In this study, a major QTL for kernel length (KL) on 1BL, named qKl-1BL, was identified from the recombinant inbred lines (RIL) in multiple environments based on the genetic map and physical map, with 4.76-21.15% of the phenotypic variation explained. To fine map qKl-1BL, the map-based cloning strategy was used. By using developed InDel markers, the near-isogenic line (NIL) pairs and eight key recombinants were identified from a segregating population containing 3621 individuals derived from residual heterozygous lines (RHLs) self-crossing. In combination with phenotype identification, qKl-1BL was finely positioned into a 2.04-Mb interval, KN1B:698.15-700.19 Mb, with eight differentially expressed genes enriched at the key period of kernel elongation. Based on transcriptome analysis and functional annotation information, two candidate genes for qKl-1BL controlling kernel elongation were identified. Additionally, genetic effect analysis showed that the superior allele of qKl-1BL from Jing411 could increase KL, thousand kernel weight (TKW), and yield per plant (YPP) significantly, as well as kernel bulk density and stability time. Taken together, this study identified a QTL interval for controlling kernel length with two possible candidate genes, which provides an important basis for qKl-1BL cloning, functional analysis, and application in molecular breeding programs.
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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
| | - Mingsu Cao
- 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
| | - Linqu Chen
- 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
| | - Haoru Guo
- 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
| | - Qingjie Guo
- 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
| | - Lei Han
- 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
| | - Zhenjie Huang
- 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
| | - Ninghao 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
| | - Aoyu 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
| | - 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
| | - 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
| | - Xigang Liu
- Ministry of Education Key Laboratory of Molecular and Cellular Biology, Hebei Collaboration Innovation Center for Cell Signaling and Environmental Adaptation, Hebei Key Laboratory of Molecular and Cellular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, 050000, China
| | - Hongqing Ling
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, 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.
| | - Junming Li
- Ministry of Education Key Laboratory of Molecular and Cellular Biology, Hebei Collaboration Innovation Center for Cell Signaling and Environmental Adaptation, Hebei Key Laboratory of Molecular and Cellular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, 050000, 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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Zhao D, Hu W, Fang Z, Cheng X, Liao S, Fu L. Two QTL regions for spike length showing pleiotropic effects on Fusarium head blight resistance and thousand-grain weight in bread wheat ( Triticum aestivum L.). MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:82. [PMID: 37974900 PMCID: PMC10645863 DOI: 10.1007/s11032-023-01427-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023]
Abstract
Spike length (SL) plays an important role in the yield improvement of wheat and is significantly associated with other traits. Here, we used a recombinant inbred line (RIL) population derived from a cross between Yangmai 12 (YM12) and Yanzhan 1 (YZ1) to construct a genetic linkage map and identify quantitative trait loci (QTL) for SL. A total of 5 QTL were identified for SL, among which QSl.yaas-3A and QSl.yaas-5B are two novel QTL for SL. The YZ1 alleles at QSl.yaas-2D and QSl.yaas-5A, and the YM12 alleles at QSl.yaas-2A, QSl.yaas-3A, and QSl.yaas-5B conferred increasing SL effects. Two major QTL QSl.yaas-5A and QSl.yaas-5B explained 9.11-15.85% and 9.01-12.85% of the phenotypic variations, respectively. Moreover, the positive alleles of QSl.yaas-5A and QSl.yaas-5B could significantly increase Fusarium head blight (FHB) resistance (soil surface inoculation and spray inoculation were used) and thousand-grain weight (TGW) in the RIL population. Kompetitive allele-specific PCR (KASP) markers for QSl.yaas-5A and QSl.yaas-5B were developed and validated in an additional panel of 180 wheat cultivars/lines. The cultivars/lines harboring both the positive alleles of QSl.yaas-5A and QSl.yaas-5B accounted for only 28.33% of the validation populations and had the longest SL, best FHB resistance (using spray inoculation), and highest TGW. A total of 358 and 200 high-confidence annotated genes in QSl.yaas-5A and QSl.yaas-5B were identified, respectively. Some of the genes in these two regions were involved in cell development, disease resistance, and so on. The results of this study will provide a basis for directional breeding of longer SL, higher TGW, and better FHB resistance varieties and a solid foundation for fine-mapping QSl.yaas-5A and QSl.yaas-5B in future. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01427-8.
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Affiliation(s)
- Die Zhao
- College of Agriculture, Yangtze University, Jingzhou, 434025 China
| | - Wenjing Hu
- Key Laboratory of Wheat Biology and Genetic Improvement for Low Middle Yangtze Valley, Ministry of Agriculture and Rural Affairs, Lixiahe Institute of Agricultural Sciences, Yangzhou, 225007 China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops / Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding / Jiangsu Key Laboratory of Crop Genetics and Physiology, Agricultural College, Yangzhou University, Yangzhou, 225009 Jiangsu China
| | - Zhengwu Fang
- College of Agriculture, Yangtze University, Jingzhou, 434025 China
| | - Xiaoming Cheng
- Key Laboratory of Wheat Biology and Genetic Improvement for Low Middle Yangtze Valley, Ministry of Agriculture and Rural Affairs, Lixiahe Institute of Agricultural Sciences, Yangzhou, 225007 China
| | - Sen Liao
- Key Laboratory of Wheat Biology and Genetic Improvement for Low Middle Yangtze Valley, Ministry of Agriculture and Rural Affairs, Lixiahe Institute of Agricultural Sciences, Yangzhou, 225007 China
| | - Luping Fu
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops / Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding / Jiangsu Key Laboratory of Crop Genetics and Physiology, Agricultural College, Yangzhou University, Yangzhou, 225009 Jiangsu China
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8
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Gao J, Hu X, Gao C, Chen G, Feng H, Jia Z, Zhao P, Yu H, Li H, Geng Z, Fu J, Zhang J, Cheng Y, Yang B, Pang Z, Xiang D, Jia J, Su H, Mao H, Lan C, Chen W, Yan W, Gao L, Yang W, Li Q. Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat. PLANT BIOTECHNOLOGY JOURNAL 2023; 21:1966-1977. [PMID: 37392004 PMCID: PMC10502759 DOI: 10.1111/pbi.14104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/11/2023] [Accepted: 06/07/2023] [Indexed: 07/02/2023]
Abstract
Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.
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Affiliation(s)
- Jie Gao
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Xin Hu
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Chunyan Gao
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Guang Chen
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Hui Feng
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Zhen Jia
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Peimin Zhao
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Haiyang Yu
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Huaiwen Li
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Zedong Geng
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Jingbo Fu
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Jun Zhang
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Yikeng Cheng
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Bo Yang
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Zhanghan Pang
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Daoquan Xiang
- Aquatic and Crop Resource DevelopmentNational Research Council CanadaSaskatoonSaskatchewanCanada
| | - Jizeng Jia
- Institute of Crop SciencesChinese Academy of Crop Sciences (CAAS)BeijingChina
| | - Handong Su
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Hailiang Mao
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Caixia Lan
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Wei Chen
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Wenhao Yan
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Lifeng Gao
- Institute of Crop SciencesChinese Academy of Crop Sciences (CAAS)BeijingChina
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Qiang Li
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- The Center of Crop NanobiotechnologyHuazhong Agricultural UniversityWuhanChina
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9
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Zhou C, Xiong H, Fu M, Guo H, Zhao L, Xie Y, Gu J, Zhao S, Ding Y, Li Y, Li X, Liu L. Genetic mapping and identification of Rht8-B1 that regulates plant height in wheat. BMC PLANT BIOLOGY 2023; 23:333. [PMID: 37349717 DOI: 10.1186/s12870-023-04343-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 06/11/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND Plant height (PH) and spike compactness (SC) are important agronomic traits that affect yield improvement in wheat crops. The identification of the loci or genes responsible for these traits is thus of great importance for marker-assisted selection in wheat breeding. RESULTS In this study, we used a recombinant inbred line (RIL) population with 139 lines derived from a cross between the mutant Rht8-2 and the local wheat variety NongDa5181 (ND5181) to construct a high-density genetic linkage map by applying the Wheat 40 K Panel. We identified seven stable QTLs for PH (three) and SC (four) in two environments using the RIL population, and found that Rht8-B1 is the causal gene of qPH2B.1 by further genetic mapping, gene cloning and gene editing analyses. Our results also showed that two natural variants from GC to TT in the coding region of Rht8-B1 resulted in an amino acid change from G (ND5181) to V (Rht8-2) at the 175th position, reducing PH by 3.6%~6.2% in the RIL population. Moreover, gene editing analysis suggested that the height of T2 generation in Rht8-B1 edited plants was reduced by 5.6%, and that the impact of Rht8-B1 on PH was significantly lower than Rht8-D1. Additionally, analysis of the distribution of Rht8-B1 in various wheat resources suggested that the Rht8-B1b allele has not been widely utilized in modern wheat breeding. CONCLUSIONS The combination of Rht8-B1b with other favorable Rht genes might be an alternative approach for developing lodging-resistant crops. Our study provides important information for marker-assisted selection in wheat breeding.
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Affiliation(s)
- Chunyun Zhou
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China
| | - Hongchun Xiong
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China
| | - Meiyu Fu
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China
| | - Huijun Guo
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China
| | - Linshu Zhao
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China
| | - Yongdun Xie
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China
| | - Jiayu Gu
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China
| | - Shirong Zhao
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China
| | - Yuping Ding
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China
| | - Yuting Li
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China
| | - Xuejun Li
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China.
| | - Luxiang Liu
- Institute of Crop Sciences, National Engineering Laboratory for Crop Molecular Breeding, Chinese Academy of Agricultural Sciences, National Center of Space Mutagenesis for Crop Improvement, Beijing, China.
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10
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Abdi H, Alipour H, Bernousi I, Jafarzadeh J, Rodrigues PC. Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS. Sci Rep 2023; 13:9927. [PMID: 37336905 DOI: 10.1038/s41598-023-36134-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/30/2023] [Indexed: 06/21/2023] Open
Abstract
Principal component analysis (PCA) is widely used in various genetics studies. In this study, the role of classical PCA (cPCA) and robust PCA (rPCA) was evaluated explicitly in genome-wide association studies (GWAS). We evaluated 294 wheat genotypes under well-watered and rain-fed, focusing on spike traits. First, we showed that some phenotypic and genotypic observations could be outliers based on cPCA and different rPCA algorithms (Proj, Grid, Hubert, and Locantore). Hubert's method provided a better approach to identifying outliers, which helped to understand the nature of these samples. These outliers led to the deviation of the heritability of traits from the actual value. Then, we performed GWAS with 36,000 single nucleotide polymorphisms (SNPs) based on the traditional approach and two robust strategies. In the conventional approach and using the first three components of cPCA as population structure, 184 and 139 marker-trait associations (MTAs) were identified for five traits in well-watered and rain-fed environments, respectively. In the first robust strategy and when rPCA was used as population structure in GWAS, we observed that the Hubert and Grid methods identified new MTAs, especially for yield and spike weight on chromosomes 7A and 6B. In the second strategy, we followed the classical and robust principal component-based GWAS, where the first two PCs obtained from phenotypic variables were used instead of traits. In the recent strategy, despite the similarity between the methods, some new MTAs were identified that can be considered pleiotropic. Hubert's method provided a better linear combination of traits because it had the most MTAs in common with the traditional approach. Newly identified SNPs, including rs19833 (5B) and rs48316 (2B), were annotated with important genes with vital biological processes and molecular functions. The approaches presented in this study can reduce the misleading GWAS results caused by the adverse effect of outlier observations.
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Affiliation(s)
- Hossein Abdi
- Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
| | - Hadi Alipour
- Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
| | - Iraj Bernousi
- Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran.
| | - Jafar Jafarzadeh
- Dryland Agricultural Research Institute (DARI), Agriculture Research, Education and Extension Organization (AREEO), Maragheh, Iran
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11
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Han X, Zhang M, Gao M, Yuan Y, Yuan Y, Zhang G, An Y, Guo Y, Kong F, Li S. QTL Mapping and Candidate Gene Identifying for N, P, and K Use Efficiency at the Maturity Stages in Wheat. Genes (Basel) 2023; 14:1168. [PMID: 37372348 DOI: 10.3390/genes14061168] [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: 05/05/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023] Open
Abstract
Nitrogen (N), phosphorus (P), and potassium (K) are the three most important mineral nutrients for crop growth and development. We previously constructed a genetic map of unigenes (UG-Map) based on their physical positions using a RIL population derived from the cross of "TN18 × LM6" (TL-RILs). In this study, a total of 18 traits related to mineral use efficiency (MUE) of N/P/K were investigated under three growing seasons using TL-RILs. A total of 54 stable QTLs were detected, distributed across 19 chromosomes except for 3A and 5B. There were 50 QTLs associated with only one trait, and the other four QTLs were associated with two traits. A total of 73 candidate genes for stable QTLs were identified. Of these, 50 candidate genes were annotated in Chinese Spring (CS) RefSeq v1.1. The average number of candidate genes per QTL was 1.35, with 45 QTLs containing only one candidate gene and nine QTLs containing two or more candidate genes. The candidate gene TraesCS6D02G132100 (TaPTR gene) for QGnc-6D-3306 belongs to the NPF (NRT1/PTR) gene family. We speculate that the TaPTR gene should regulate the GNC trait.
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Affiliation(s)
- Xu Han
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an 271018, China
| | - Mingxia Zhang
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an 271018, China
| | - Minggang Gao
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an 271018, China
- Key Laboratory of Biochemistry and Molecular Biology, College of Biological and Agricultural Engineering, Weifang University, Weifang 261061, China
| | - Yuanyuan Yuan
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an 271018, China
- Jinan Academy of Agricultural Science, Jinan 250316, China
| | - Yapei Yuan
- National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China
| | - Guizhi Zhang
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an 271018, China
- Institute of Industrial Crops, Shandong Academy of Agricultural Sciences, Jinan 250108, China
| | - Yanrong An
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an 271018, China
| | - Ying Guo
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an 271018, China
| | - Fanmei Kong
- National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China
| | - Sishen Li
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an 271018, China
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12
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Wang D, Li Y, Wang H, Xu Y, Yang Y, Zhou Y, Chen Z, Zhou Y, Gui L, Guo Y, Zhou C, Tang W, Zheng S, Wang L, Guo X, Zhang Y, Cui F, Lin X, Jiao Y, He Y, Li J, He F, Liu X, Xiao J. Boosting wheat functional genomics via an indexed EMS mutant library of KN9204. PLANT COMMUNICATIONS 2023:100593. [PMID: 36945776 PMCID: PMC10363553 DOI: 10.1016/j.xplc.2023.100593] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/02/2023] [Accepted: 03/17/2023] [Indexed: 06/18/2023]
Abstract
A better understanding of wheat functional genomics can improve targeted breeding for better agronomic traits and environmental adaptation. However, the lack of gene-indexed mutants and the low transformation efficiency of wheat limit in-depth gene functional studies and genetic manipulation for breeding. In this study, we created a library for KN9204, a popular wheat variety in northern China, with a reference genome, transcriptome, and epigenome of different tissues, using ethyl methyl sulfonate (EMS) mutagenesis. This library contains a vast developmental diversity of critical tissues and transition stages. Exome capture sequencing of 2090 mutant lines using KN9204 genome-designed probes revealed that 98.79% of coding genes had mutations, and each line had an average of 1383 EMS-type SNPs. We identified new allelic variations for crucial agronomic trait-related genes such as Rht-D1, Q, TaTB1, and WFZP. We tested 100 lines with severe mutations in 80 NAC transcription factors (TFs) under drought and salinity stress and identified 13 lines with altered sensitivity. Further analysis of three lines using transcriptome and chromatin accessibility data revealed hundreds of direct NAC targets with altered transcription patterns under salt or drought stress, including SNAC1, DREB2B, CML16, and ZFP182, factors known to respond to abiotic stress. Thus, we have generated and indexed a KN9204 EMS mutant library that can facilitate functional genomics research and offer resources for genetic manipulation of wheat.
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Affiliation(s)
- Dongzhi Wang
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yongpeng 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; Center for Agricultural Resources Research, Institute of Genetics and Development Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China
| | - Haojie Wang
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongxin Xu
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiman Yang
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yuxin Zhou
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhongxu Chen
- Department of Life Science, Tcuni, Inc, Chengdu 610000, China
| | - Yuqing Zhou
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lixuan Gui
- Department of Life Science, Tcuni, Inc, Chengdu 610000, China
| | - Yi Guo
- 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
| | - Chunjiang Zhou
- 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
| | - Wenqiang Tang
- 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
| | - Shuzhi Zheng
- 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
| | - Lei Wang
- Center for Agricultural Resources Research, Institute of Genetics and Development Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China
| | - Xiulin Guo
- Plant Genetic Engineering Center of Hebei Province, Institute of Biotechnology and Food Science, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, 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 050035, 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
| | - Xuelei Lin
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yuling Jiao
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, School of Life Sciences, Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Yuehui He
- State Key Laboratory of Protein and Plant Gene Research, School of Advanced Agricultural Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, 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; Center for Agricultural Resources Research, Institute of Genetics and Development Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China.
| | - Fei He
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Centre of Excellence for Plant and Microbial Science (CEPAMS), JIC-CAS, Beijing 100101, China.
| | - Xigang Liu
- 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.
| | - Jun Xiao
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Centre of Excellence for Plant and Microbial Science (CEPAMS), JIC-CAS, Beijing 100101, China.
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13
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Khaipho-Burch M, Ferebee T, Giri A, Ramstein G, Monier B, Yi E, Romay MC, Buckler ES. Elucidating the patterns of pleiotropy and its biological relevance in maize. PLoS Genet 2023; 19:e1010664. [PMID: 36943844 PMCID: PMC10030035 DOI: 10.1371/journal.pgen.1010664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/09/2023] [Indexed: 03/23/2023] Open
Abstract
Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.
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Affiliation(s)
| | - Taylor Ferebee
- Department of Computational Biology, Cornell University, Ithaca, New York
| | - Anju Giri
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Guillaume Ramstein
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Emily Yi
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - M Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Edward S Buckler
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
- USDA-ARS, Ithaca, New York, United States of America
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14
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Halder J, Gill HS, Zhang J, Altameemi R, Olson E, Turnipseed B, Sehgal SK. Genome-wide association analysis of spike and kernel traits in the U.S. hard winter wheat. THE PLANT GENOME 2023; 16:e20300. [PMID: 36636831 DOI: 10.1002/tpg2.20300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/20/2022] [Indexed: 05/10/2023]
Abstract
A better understanding of the genetic control of spike and kernel traits that have higher heritability can help in the development of high-yielding wheat varieties. Here, we identified the marker-trait associations (MTAs) for various spike- and kernel-related traits in winter wheat (Triticum aestivum L.) through genome-wide association studies (GWAS). An association mapping panel comprising 297 hard winter wheat accessions from the U.S. Great Plains was evaluated for eight spike- and kernel-related traits in three different environments. A GWAS using 15,590 single-nucleotide polymorphisms (SNPs) identified a total of 53 MTAs for seven spike- and kernel-related traits, where the highest number of MTAs were identified for spike length (16) followed by the number of spikelets per spike (15) and spikelet density (11). Out of 53 MTAs, 14 were considered to represent stable quantitative trait loci (QTL) as they were identified in multiple environments. Five multi-trait MTAs were identified for various traits including the number of spikelets per spike (NSPS), spikelet density (SD), kernel width (KW), and kernel area (KA) that could facilitate the pyramiding of yield-contributing traits. Further, a significant additive effect of accumulated favorable alleles on the phenotype of four spike-related traits suggested that breeding lines and cultivars with a higher number of favorable alleles could be a valuable resource for breeders to improve yield-related traits. This study improves the understanding of the genetic basis of yield-related traits in hard winter wheat and provides reliable molecular markers that will facilitate marker-assisted selection (MAS) in wheat breeding programs.
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Affiliation(s)
- Jyotirmoy Halder
- Dep. of Agronomy, Horticulture & Plant Science, South Dakota State Univ., Brookings, SD, 57007, USA
| | - Harsimardeep S Gill
- Dep. of Agronomy, Horticulture & Plant Science, South Dakota State Univ., Brookings, SD, 57007, USA
| | - Jinfeng Zhang
- Dep. of Agronomy, Horticulture & Plant Science, South Dakota State Univ., Brookings, SD, 57007, USA
| | - Rami Altameemi
- Dep. of Agronomy, Horticulture & Plant Science, South Dakota State Univ., Brookings, SD, 57007, USA
| | - Eric Olson
- Dep. of Plant, Soil and Microbial Sciences, Michigan State Univ., East Lansing, MI, 48824, USA
| | - Brent Turnipseed
- Dep. of Agronomy, Horticulture & Plant Science, South Dakota State Univ., Brookings, SD, 57007, USA
| | - Sunish K Sehgal
- Dep. of Agronomy, Horticulture & Plant Science, South Dakota State Univ., Brookings, SD, 57007, USA
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15
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Wei W, Li S, Li P, Yu K, Fan G, Wang Y, Zhao F, Zhang X, Feng X, Shi G, Zhang W, Song G, Dan W, Wang F, Zhang Y, Li X, Wang D, Zhang W, Pei J, Wang X, Zhao Z. QTL analysis of important agronomic traits and metabolites in foxtail millet ( Setaria italica) by RIL population and widely targeted metabolome. FRONTIERS IN PLANT SCIENCE 2023; 13:1035906. [PMID: 36704173 PMCID: PMC9872001 DOI: 10.3389/fpls.2022.1035906] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 12/19/2022] [Indexed: 06/18/2023]
Abstract
As a bridge between genome and phenotype, metabolome is closely related to plant growth and development. However, the research on the combination of genome, metabolome and multiple agronomic traits in foxtail millet (Setaria italica) is insufficient. Here, based on the linkage analysis of 3,452 metabolites via with high-quality genetic linkage maps, we detected a total of 1,049 metabolic quantitative trait loci (mQTLs) distributed in 11 hotspots, and 28 metabolite-related candidate genes were mined from 14 mQTLs. In addition, 136 single-environment phenotypic QTL (pQTLs) related to 63 phenotypes were identified by linkage analysis, and there were 12 hotspots on these pQTLs. We futher dissected 39 candidate genes related to agronomic traits through metabolite-phenotype correlation and gene function analysis, including Sd1 semidwarf gene, which can affect plant height by regulating GA synthesis. Combined correlation network and QTL analysis, we found that flavonoid-lignin pathway maybe closely related to plant architecture and yield in foxtail millet. For example, the correlation coefficient between apigenin 7-rutinoside and stem diameter reached 0.98, and they were co-located at 41.33-44.15 Mb of chromosome 5, further gene function analysis revealed that 5 flavonoid pathway genes, as well as Sd1, were located in this interval . Therefore, the correlation and co-localization between flavonoid-lignins and plant architecture may be due to the close linkage of their regulatory genes in millet. Besides, we also found that a combination of genomic and metabolomic for BLUP analysis can better predict plant agronomic traits than genomic or metabolomic data, independently. In conclusion, the combined analysis of mQTL and pQTL in millet have linked genetic, metabolic and agronomic traits, and is of great significance for metabolite-related molecular assisted breeding.
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Affiliation(s)
- Wei Wei
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Shuangdong Li
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Peiyu Li
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, China
| | - Kuohai Yu
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, China
| | - Guangyu Fan
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Yixiang Wang
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, China
| | - Fang Zhao
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Xiaolei Zhang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Xiaolei Feng
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Gaolei Shi
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Weiqin Zhang
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, China
| | - Guoliang Song
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Wenhan Dan
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, China
| | - Feng Wang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Yali Zhang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Xinru Li
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Dequan Wang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Wenying Zhang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Jingjing Pei
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Xiaoming Wang
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
| | - Zhihai Zhao
- Institute of Millet, Zhangjiakou Academy of Agricultural Science, Zhangjiakou, China
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Wang Y, Qiao L, Yang C, Li X, Zhao J, Wu B, Zheng X, Li P, Zheng J. Identification of genetic loci for flag-leaf-related traits in wheat ( Triticum aestivum L.) and their effects on grain yield. FRONTIERS IN PLANT SCIENCE 2022; 13:990287. [PMID: 36160981 PMCID: PMC9493265 DOI: 10.3389/fpls.2022.990287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 08/08/2022] [Indexed: 06/16/2023]
Abstract
Flag-leaf-related traits including length (FLL), width (FLW), area (FLA), thickness (FLT), and volume (FLV) of flag leaves are the most important determinants of plant architecture and yield in wheat. Understanding the genetic basis of these traits could accelerate the breeding of high yield wheat varieties. In this study, we constructed a doubled haploid (DH) population and analyzed flag-leaf-related traits in five experimental locations/years using the wheat 90K single-nucleotide polymorphism array. It's worth noting that a novel method was used to measure FLT and FLV easily. Leaf thickness at two-thirds of the leaf length from tip to collar represented the average leaf thickness as measured with freehand sections and was used to calculate the leaf volume. In addition, flag-leaf-related traits showed positive correlations with yield related traits under two different water regimes. A total of 79 quantitative trait loci (QTL) controlling the five traits were detected among all chromosomes except 4D and 5A, explaining 3.09-14.52% of the phenotypic variation. Among them, 15 stable QTL were identified in more than three environments, including two major QTL for FLT, six for FLW, three for FLA, two for FLT and two for FLV. DH lines with positive alleles at both QTL regions had an average FLL (9.90%), FLW (32.87%), FLT (6.62%), FLA (18.47%), and FLV (20.87%) greater than lines with contrasting alleles. QFLT-2B, QFLV-2A, and QFLV-7D were co-located with yield-related traits. The 15 QTL were validated by tightly linked kompetitive allele specific PCR (KASP) markers in a recombinant inbred line (RIL) population derived from a different cross. QFLL-4A, QFLW-4B, QFLA-5D.1, QFLA-7A, QFLA-7D.1, QFLT-2B, QFLT-6A, QFLV-2A, and QFLV-7D are likely novel loci. These results provide a better understanding of the genetic basis underlying flag-leaf-related traits. Also, target regions for fine mapping and marker-assisted selection were identified and these will be valuable for breeding high yielding bread wheat.
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Affiliation(s)
- Ying Wang
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
- School of Life Sciences, Shanxi University, Taiyuan, China
| | - Ling Qiao
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Chenkang Yang
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
- School of Life Sciences, Shanxi University, Taiyuan, China
| | - Xiaohua Li
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Jiajia Zhao
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Bangbang Wu
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Xingwei Zheng
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Pengbo Li
- Institute of Cotton Research, Shanxi Agricultural University, Yuncheng, China
| | - Jun Zheng
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
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17
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Liu X, Xu Z, Feng B, Zhou Q, Ji G, Guo S, Liao S, Lin D, Fan X, Wang T. Quantitative trait loci identification and breeding value estimation of grain weight-related traits based on a new wheat 50K single nucleotide polymorphism array-derived genetic map. FRONTIERS IN PLANT SCIENCE 2022; 13:967432. [PMID: 36110352 PMCID: PMC9468616 DOI: 10.3389/fpls.2022.967432] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/04/2022] [Indexed: 06/01/2023]
Abstract
Mining novel and less utilized thousand grain weight (TGW) related genes are useful for improving wheat yield. In this study, a recombinant inbred line population from a cross between Zhongkemai 138 (ZKM138, high TGW) and Chuanmai 44 (CM44, low TGW) was used to construct a new Wheat 50K SNP array-derived genetic map that spanned 1,936.59 cM and contained 4, 139 markers. Based on this map, ninety-one quantitative trait loci (QTL) were detected for eight grain-related traits in six environments. Among 58 QTLs, whose superior alleles were contributed by ZKM138, QTgw.cib-6A was a noticeable major stable QTL and was also highlighted by bulked segregant analysis with RNA sequencing (BSR-Seq). It had a pyramiding effect on TGW enhancement but no significant trade-off effect on grain number per spike or tiller number, with two other QTLs (QTgw.cib-2A.2 and QTgw.cib-6D), possibly explaining the excellent grain performance of ZKM138. After comparison with known loci, QTgw.cib-6A was deduced to be a novel locus that differed from nearby TaGW2 and TaBT1. Seven simple sequence repeat (SSR) and thirty-nine kompetitive allele-specific PCR (KASP) markers were finally developed to narrow the candidate interval of QTgw.cib-6A to 4.1 Mb. Only six genes in this interval were regarded as the most likely candidate genes. QTgw.cib-6A was further validated in different genetic backgrounds and presented 88.6% transmissibility of the ZKM138-genotype and a 16.4% increase of TGW in ZKM138 derivatives. And the geographic pattern of this locus revealed that its superior allele is present in only 6.47% of 433 Chinese modern wheat varieties, indicating its potential contribution to further high-yield breeding.
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Affiliation(s)
- Xiaofeng Liu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhibin Xu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Bo Feng
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Qiang Zhou
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Guangsi Ji
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Shaodan Guo
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Simin Liao
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Dian Lin
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoli Fan
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Tao Wang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China
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18
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Chen H, Wei J, Tian R, Zeng Z, Tang H, Liu Y, Xu Q, Deng M, Jiang Q, Chen G, Liu Y, Li W, Qi P, Jiang Y, Jiang Y, Tang L, Wei Y, Zheng Y, Lan X, Ma J. A major quantitative trait locus for wheat total root length associated with precipitation distribution. FRONTIERS IN PLANT SCIENCE 2022; 13:995183. [PMID: 36092437 PMCID: PMC9451531 DOI: 10.3389/fpls.2022.995183] [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: 07/18/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Optimizing root system architecture (RSA) allows crops to better capture water and nutrients and adapt to harsh environment. Parental reproductive environment (PRE) has been reported to significantly affect growth and development throughout the life cycle of the next generation. In this study, 10 RSA-related traits were evaluated in seedling stage from five independent hydroponic tests using seeds harvested from five different PREs. Based on the Wheat55K SNP array-based genetic map, quantitative trait loci (QTL) for these traits were detected in a recombinant inbred line population. Twenty-eight putative QTL for RSA-related traits were detected, covering thirteen chromosomal regions. A major QTL, QTrl.sicau-2SY-4D for total root length (TRL), which was likely independent of PREs, explained 15.81-38.48% of phenotypic variations and was located at 14.96-19.59 Mb on chromosome arm 4DS. Interestingly, it showed pleiotropic effects on TRL, root area, root volume, root forks, root dry weight, and shoot dry weight. The functional marker KASP-Rht-D1 for Rht-D1 was used to genotype 2SY population and remapping QTL for TRL showed that QTrl.sicau-2SY-4D was not linked to Rht-D1. The kompetitive allele-specific PCR (KASP) marker, KASP-AX-110527441 linked to this major QTL, was developed and used to successfully validate its effect in three different genetic populations. Further analysis suggested that the positive allele at QTrl.sicau-2SY-4D was mainly utilized in wheat breeding of northwest China where precipitation was significantly lower, indicating that wheat requires longer TRL to capture water and nutrients in arid or semi-arid regions due to deficient precipitation. Additionally, four genes (TraesCS4D03G0059800, TraesCS4D03G0057800, TraesCS4D03G0064000, and TraesCS4D03G0064400) possibly related to root development were predicted in physical interval of QTrl.sicau-2SY-4D. Taken together, these results enrich our understanding on the genetic basis of RSA and provide a potentially valuable TRL QTL for wheat breeding.
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Affiliation(s)
- Huangxin 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
| | - Jiatai Wei
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Rong Tian
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Zhaoyong Zeng
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Huaping Tang
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yanlin Liu
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Qiang Xu
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Mei Deng
- 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
| | - Yaxi 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
| | - Wei Li
- College of Agronomy, 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
| | - Yun Jiang
- Institute of Biotechnology and Nuclear Technology Research, Sichuan Academy of Agricultural Sciences, Chengdu, China
| | - Liwei Tang
- Panzhihua Academy of Agricultural and Forestry Sciences, Panzhihua, 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
| | - Youliang Zheng
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Xiujin Lan
- 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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19
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Yu Q, Feng B, Xu Z, Fan X, Zhou Q, Ji G, Liao S, Gao P, Wang T. Genetic Dissection of Three Major Quantitative Trait Loci for Spike Compactness and Length in Bread Wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2022; 13:882655. [PMID: 35677243 PMCID: PMC9168683 DOI: 10.3389/fpls.2022.882655] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 03/24/2022] [Indexed: 06/15/2023]
Abstract
Spike compactness (SC) and length (SL) are the components of spike morphology and are strongly related to grain yield in wheat (Triticum aestivum L.). To investigate quantitative trait loci (QTL) associated with SC and SL, a recombinant inbred lines (RIL) population derived from the cross of Bailangmai (BLM, a Tibet landrace) and Chuanyu 20 (CY20, an improved variety) was employed in six environments. Three genomic regions responsible for SC and SL traits were identified on chromosomes 2A and 2D using bulked segregant exome sequencing (BSE-Seq). By constructing genetic maps, six major QTL were repeatedly detected in more than four environments and the best linear unbiased estimation (BLUE) datasets, explaining 7.00-28.56% of the phenotypic variation and the logarithm of the odd (LOD) score varying from 2.50 to 13.22. They were co-located on three loci, designed as QSc/Sl.cib-2AS, QSc/Sl.cib-2AL, and QSc/Sl.cib-2D, respectively. Based on the flanking markers, their interactions and effects on the corresponding trait and other agronomic traits were also analyzed. Comparison analysis showed that QSc/Sl.cib-2AS and QSc/Sl.cib-2AL were possibly two novel loci for SC and SL. QSc/Sl.cib-2AS and QSc/Sl.cib-2D showed pleiotropic effects on plant height and grain morphology, while QSc/Sl.cib-2AL showed effects on spikelet number per spike (SNS) and grain width (GW). Based on the gene annotation, orthologous search, and spatiotemporal expression patterns of genes, TraesCS2A03G0410600 and TraesCS2A03G0422300 for QSc/Sl.cib-2AS, and TraesCS2D03G1129300 and TraesCS2D03G1131500 for QSc/Sl.cib-2D were considered as potential candidate genes, respectively. These results will be useful for fine mapping and developing new varieties with high yield in the future.
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Affiliation(s)
- Qin Yu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- College of Life Sciences, Sichuan University, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Bo Feng
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Zhibin Xu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Xiaoli Fan
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Qiang Zhou
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Guangsi Ji
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Simin Liao
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ping Gao
- College of Life Sciences, Sichuan University, Chengdu, China
| | - Tao Wang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
- Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China
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20
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Li T, Li Q, Wang J, Yang Z, Tang Y, Su Y, Zhang J, Qiu X, Pu X, Pan Z, Zhang H, Liang J, Liu Z, Li J, Yan W, Yu M, Long H, Wei Y, Deng G. High-resolution detection of quantitative trait loci for seven important yield-related traits in wheat (Triticum aestivum L.) using a high-density SLAF-seq genetic map. BMC Genom Data 2022; 23:37. [PMID: 35562674 PMCID: PMC9107147 DOI: 10.1186/s12863-022-01050-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 04/06/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Yield-related traits including thousand grain weight (TGW), grain number per spike (GNS), grain width (GW), grain length (GL), plant height (PH), spike length (SL), and spikelet number per spike (SNS) are greatly associated with grain yield of wheat (Triticum aestivum L.). To detect quantitative trait loci (QTL) associated with them, 193 recombinant inbred lines derived from two elite winter wheat varieties Chuanmai42 and Chuanmai39 were employed to perform QTL mapping in six/eight environments. RESULTS A total of 30 QTLs on chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 4A, 5A, 5B, 6A, 6D, 7A, 7B and 7D were identified. Among them, six major QTLs QTgw.cib-6A.1, QTgw.cib-6A.2, QGw.cib-6A, QGl.cib-3A, QGl.cib-6A, and QSl.cib-2D explaining 5.96-23.75% of the phenotypic variance were detected in multi-environments and showed strong and stable effects on corresponding traits. Three QTL clusters on chromosomes 2D and 6A containing 10 QTLs were also detected, which showed significant pleiotropic effects on multiple traits. Additionally, three Kompetitive Allele Specific PCR (KASP) markers linked with five of these major QTLs were developed. Candidate genes of QTgw.cib-6A.1/QGl.cib-6A and QGl.cib-3A were analyzed based on the spatiotemporal expression patterns, gene annotation, and orthologous search. CONCLUSIONS Six major QTLs for TGW, GL, GW and SL were detected. Three KASP markers linked with five of these major QTLs were developed. These QTLs and KASP markers will be useful for elucidating the genetic architecture of grain yield and developing new wheat varieties with high and stable yield in wheat.
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Affiliation(s)
- Tao Li
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China.,Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.,State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, 611130, China
| | - Qiao Li
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Jinhui Wang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Zhao Yang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Yanyan Tang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Yan Su
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Juanyu Zhang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Xvebing Qiu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Xi Pu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Zhifen Pan
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Haili Zhang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Junjun Liang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Zehou Liu
- Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, Sichuan, China
| | - Jun Li
- Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, Sichuan, China
| | - Wuyun Yan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, 611130, China
| | - Maoqun Yu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Hai Long
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Yuming Wei
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.,State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, 611130, China
| | - Guangbing Deng
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China.
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21
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Zhang X, Qiao L, Li X, Yang Z, Liu C, Guo H, Zheng J, Zhang S, Chang L, Chen F, Jia J, Yan L, Chang Z. Genetic Incorporation of the Favorable Alleles for Three Genes Associated With Spikelet Development in Wheat. FRONTIERS IN PLANT SCIENCE 2022; 13:892642. [PMID: 35592560 PMCID: PMC9111956 DOI: 10.3389/fpls.2022.892642] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/14/2022] [Indexed: 06/15/2023]
Abstract
The number of spikelets per spike is an important trait that directly affects grain yield in wheat. Three quantitative trait loci (QTLs) associated with spikelet nodes per spike (SNS) were mapped in a population of recombinant inbred lines generated from a cross between two advanced breeding lines of winter wheat based on the phenotypic variation evaluated over six locations/years. Two of the three QTLs are QSns.sxau-2A at the WHEATFRIZZY PANICLE (WFZP) loci and QSns.sxau-7A at the WHEAT ORTHOLOG OF APO1 (WAPO1) loci. The WFZP-A1b allele with a 14-bp deletion at QSns.sxau-2A was associated with increased spikelets per spike. WAPO-A1e, as a novel allele at WAPO1, were regulated at the transcript level that was associated with the SNS trait. The third SNS QTL, QSns.sxau-7D on chromosome 7D, was not associated with homoeologous WAPO-D1 or any other genes known to regulate SNS. The favorable alleles for each of WZFP-A1, WAPO-A1, and QSns.sxau-7D are identified and incorporated to increase up to 3.4 spikelets per spike in the RIL lines. Molecular markers for the alleles were developed. This study has advanced our understanding of the genetic basis of natural variation in spikelet development in wheat.
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Affiliation(s)
- Xiaojun Zhang
- State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), College of Agronomy, Shanxi Agricultural University, Taiyuan, China
| | - Linyi Qiao
- State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), College of Agronomy, Shanxi Agricultural University, Taiyuan, China
- Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, United States
| | - Xin Li
- State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), College of Agronomy, Shanxi Agricultural University, Taiyuan, China
| | - Zujun Yang
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Liu
- Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Huijuan Guo
- State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), College of Agronomy, Shanxi Agricultural University, Taiyuan, China
| | - Jun Zheng
- Institute of Wheat Research, Shanxi Agricultural University, Linfen, China
| | - Shuwei Zhang
- State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), College of Agronomy, Shanxi Agricultural University, Taiyuan, China
| | - Lifang Chang
- State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), College of Agronomy, Shanxi Agricultural University, Taiyuan, China
| | - Fang Chen
- State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), College of Agronomy, Shanxi Agricultural University, Taiyuan, China
| | - Juqing Jia
- State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), College of Agronomy, Shanxi Agricultural University, Taiyuan, China
| | - Liuling Yan
- Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, United States
| | - Zhijian Chang
- State Key Laboratory of Sustainable Dryland Agriculture (in Preparation), College of Agronomy, Shanxi Agricultural University, Taiyuan, China
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Javed T, I I, Singhal RK, Shabbir R, Shah AN, Kumar P, Jinger D, Dharmappa PM, Shad MA, Saha D, Anuragi H, Adamski R, Siuta D. Recent Advances in Agronomic and Physio-Molecular Approaches for Improving Nitrogen Use Efficiency in Crop Plants. FRONTIERS IN PLANT SCIENCE 2022; 13:877544. [PMID: 35574130 PMCID: PMC9106419 DOI: 10.3389/fpls.2022.877544] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/11/2022] [Indexed: 05/05/2023]
Abstract
The efficiency with which plants use nutrients to create biomass and/or grain is determined by the interaction of environmental and plant intrinsic factors. The major macronutrients, especially nitrogen (N), limit plant growth and development (1.5-2% of dry biomass) and have a direct impact on global food supply, fertilizer demand, and concern with environmental health. In the present time, the global consumption of N fertilizer is nearly 120 MT (million tons), and the N efficiency ranges from 25 to 50% of applied N. The dynamic range of ideal internal N concentrations is extremely large, necessitating stringent management to ensure that its requirements are met across various categories of developmental and environmental situations. Furthermore, approximately 60 percent of arable land is mineral deficient and/or mineral toxic around the world. The use of chemical fertilizers adds to the cost of production for the farmers and also increases environmental pollution. Therefore, the present study focused on the advancement in fertilizer approaches, comprising the use of biochar, zeolite, and customized nano and bio-fertilizers which had shown to be effective in improving nitrogen use efficiency (NUE) with lower soil degradation. Consequently, adopting precision farming, crop modeling, and the use of remote sensing technologies such as chlorophyll meters, leaf color charts, etc. assist in reducing the application of N fertilizer. This study also discussed the role of crucial plant attributes such as root structure architecture in improving the uptake and transport of N efficiency. The crosstalk of N with other soil nutrients plays a crucial role in nutrient homeostasis, which is also discussed thoroughly in this analysis. At the end, this review highlights the more efficient and accurate molecular strategies and techniques such as N transporters, transgenes, and omics, which are opening up intriguing possibilities for the detailed investigation of the molecular components that contribute to nitrogen utilization efficiency, thus expanding our knowledge of plant nutrition for future global food security.
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Affiliation(s)
- Talha Javed
- College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Department of Agronomy, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Indu I
- Indian Council of Agricultural Research (ICAR)-Indian Grassland and Fodder Research Institute, Jhansi, India
| | - Rajesh Kumar Singhal
- Indian Council of Agricultural Research (ICAR)-Indian Grassland and Fodder Research Institute, Jhansi, India
| | - Rubab Shabbir
- College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Department of Plant Breeding and Genetics, Seed Science and Technology, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Adnan Noor Shah
- Department of Agricultural Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Pawan Kumar
- Indian Council of Agricultural Research (ICAR)-Central Institute for Arid Horticulture, Bikaner, India
| | - Dinesh Jinger
- Research Centre, Indian Council of Agricultural Research (ICAR)-Indian Institute of Soil and Water Conservation, Anand, India
| | - Prathibha M. Dharmappa
- Indian Council of Agricultural Research (ICAR)-Indian Institute of Horticultural Research, Bengaluru, India
| | - Munsif Ali Shad
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene, Hubei Hongshan Laboratory, Wuhan, China
| | - Debanjana Saha
- Centurion University of Technology and Management, Jatni, India
| | - Hirdayesh Anuragi
- Indian Council of Agricultural Research (ICAR)- Central Agroforestry Research Institute, Jhansi, India
| | - Robert Adamski
- Faculty of Process and Environmental Engineering, Łódź University of Technology, Łódź, Poland
| | - Dorota Siuta
- Faculty of Process and Environmental Engineering, Łódź University of Technology, Łódź, Poland
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Xu X, Li X, Zhang D, Zhao J, Jiang X, Sun H, Ru Z. Identification and validation of QTLs for kernel number per spike and spike length in two founder genotypes of wheat. BMC PLANT BIOLOGY 2022; 22:146. [PMID: 35346053 PMCID: PMC8962171 DOI: 10.1186/s12870-022-03544-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 03/16/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Kernel number per spike (KNS) and spike length (SL) are important spike-related traits in wheat variety improvement. Discovering genetic loci controlling these traits is necessary to elucidate the genetic basis of wheat yield traits and is very important for marker-assisted selection breeding. RESULTS In the present study, we used a recombinant inbred line population with 248 lines derived from the two founder genotypes of wheat, Bima4 and BainongAK58, to construct a high-density genetic map using wheat 55 K genotyping assay. The final genetic linkage map consists of 2356 bin markers (14,812 SNPs) representing all 21 wheat chromosomes, and the entire map spanned 4141.24 cM. A total of 7 and 18 QTLs were identified for KNS and SL, respectively, and they were distributed on 11 chromosomes. The allele effects of the flanking markers for 12 stable QTLs, including four QTLs for KNS and eight QTLs for SL, were estimated based on phenotyping data collected from 15 environments in a diverse wheat panel including 384 elite cultivars and breeding lines. The positive alleles at seven loci, namely, QKns.his-7D2-1, QKns.his-7D2-2, QSl.his-4A-1, QSl.his-5D1, QSl.his-4D2-2, QSl.his-5B and QSl.his-5A-2, significantly increased KNS or SL in the diverse panel, suggesting they are more universal in their effects and are valuable for gene pyramiding in breeding programs. The transmission of Bima4 allele indicated that the favorite alleles at five loci (QKns.his-7D2-1, QSl.his-5A-2, QSl.his-2D1-1, QSl.his-3A-2 and QSl.his-3B) showed a relatively high frequency or an upward trend following the continuity of generations, suggesting that they underwent rigorous selection during breeding. At two loci (QKns.his-7D2-1 and QSl.his-5A-2) that the positive effects of the Bima4 alleles have been validated in the diverse panel, two and one kompetitive allele-specific PCR (KASP) markers were further developed, respectively, and they are valuable for marker-assisted selection breeding. CONCLUSION Important chromosome regions controlling KNS and SL were identified in the founder parents. Our results are useful for knowing the molecular mechanisms of founder parents and future molecular breeding in wheat.
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Affiliation(s)
- Xin Xu
- School of Life Sciences and Basic Medicine, Xinxiang University, Xinxiang, 453003, China
| | - Xiaojun Li
- School of Life Science and Technology, Collaborative Innovation Center of Modern Biological Breeding, Henan Province, Henan Institute of Science and Technology, Xinxiang, 453003, China.
| | - Dehua Zhang
- School of Life Science and Technology, Collaborative Innovation Center of Modern Biological Breeding, Henan Province, Henan Institute of Science and Technology, Xinxiang, 453003, China
| | - Jishun Zhao
- School of Life Science and Technology, Collaborative Innovation Center of Modern Biological Breeding, Henan Province, Henan Institute of Science and Technology, Xinxiang, 453003, China
| | - Xiaoling Jiang
- School of Life Science and Technology, Collaborative Innovation Center of Modern Biological Breeding, Henan Province, Henan Institute of Science and Technology, Xinxiang, 453003, China
| | - Haili Sun
- School of Life Science and Technology, Collaborative Innovation Center of Modern Biological Breeding, Henan Province, Henan Institute of Science and Technology, Xinxiang, 453003, China
| | - Zhengang Ru
- School of Life Science and Technology, Collaborative Innovation Center of Modern Biological Breeding, Henan Province, Henan Institute of Science and Technology, Xinxiang, 453003, China.
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Xu H, Zhang R, Wang M, Li L, Yan L, Wang Z, Zhu J, Chen X, Zhao A, Su Z, Xing J, Sun Q, Ni Z. Identification and characterization of QTL for spike morphological traits, plant height and heading date derived from the D genome of natural and resynthetic allohexaploid wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:389-403. [PMID: 34674009 DOI: 10.1007/s00122-021-03971-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
QHd.cau-7D.1 for heading date was delimited into the physical interval of approximately 17.38 Mb harboring three CONSTANS-like zinc finger genes. Spike morphological traits, plant height and heading date play important roles in yield improvement of wheat. To reveal the genetic factors that controlling spike morphological traits, plant height and heading date on the D genome, we conducted analysis of quantitative traits locus (QTL) using 198 F7:8 recombinant inbred lines (RILs) derived from a cross between the common wheat TAA10 and resynthesized allohexaploid wheat XX329 with similar AABB genomes. A total of 23 environmentally stable QTL on the D sub-genome for spike length (SL), fertile spikelet number per spike (FSN), sterile spikelet number per spike (SSN), total spikelet number per spike (TSN), spike compactness (SC), plant height (PHT) and heading date (HD) were detected, among which eight appeared to be novel QTL. Furthermore, QHd.cau-7D.1 and QPht.cau-7D.2 shared identical confidence interval and were delimited into the physical interval of approximately 17.38 Mb with 145 annotated genes, including three CONSTANS-like zinc finger genes (TraesCS7D02G209000, TraesCS7D02G213000 and TraesCS7D02G220300). This study will help elucidate the molecular mechanism of the seven traits (SL, FSN, SSN, TSN, SC, PHT and HD) and provide a potentially valuable resource for genetic improvement.
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Affiliation(s)
- Huanwen Xu
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Runqi Zhang
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Mingming Wang
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Linghong Li
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Lei Yan
- Institute of Crop Sciences (ICS), Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
| | - Zhen Wang
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Jun Zhu
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Xiyong Chen
- Hebei Crop Genetic Breeding Laboratory, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, 050035, China
| | - Aiju Zhao
- Hebei Crop Genetic Breeding Laboratory, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, 050035, China
| | - Zhenqi Su
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Jiewen Xing
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Qixin Sun
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- National Plant Gene Research Centre, Beijing, 100193, China
| | - Zhongfu Ni
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
- National Plant Gene Research Centre, Beijing, 100193, China.
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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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You J, Liu H, Wang S, Luo W, Gou L, Tang H, Mu Y, Deng M, Jiang Q, Chen G, Qi P, Peng Y, Tang L, Habib A, Wei Y, Zheng Y, Lan X, Ma J. Spike Density Quantitative Trait Loci Detection and Analysis in Tetraploid and Hexaploid Wheat Recombinant Inbred Line Populations. FRONTIERS IN PLANT SCIENCE 2021; 12:796397. [PMID: 34975986 PMCID: PMC8716915 DOI: 10.3389/fpls.2021.796397] [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: 10/16/2021] [Accepted: 11/26/2021] [Indexed: 05/15/2023]
Abstract
Spike density (SD) is an agronomically important character in wheat. In addition, an optimized spike structure is a key basis for high yields. Identification of quantitative trait loci (QTL) for SD has provided a genetic basis for constructing ideal spike morphologies in wheat. In this study, two recombinant inbred line (RIL) populations (tetraploid RIL AM and hexaploid RIL 20828/SY95-71 (2SY)) previously genotyped using the wheat55K SNP array were used to identify SD QTL. A total of 18 QTL were detected, and three were major and one was stably expressed (QSd.sau-2SY-7A.2, QSd.sau-AM-5A.2, QSd.sau-AM-7B, and QSd.sau-2SY-2D). They can explain up to 23.14, 19.97, 12.00, and 9.44% of phenotypic variation, respectively. QTL × environment and epistatic interactions for SD were further analyzed. In addition, pyramiding analysis further revealed that there were additive effects between QSd.sau-2SY-2D and QSd.sau-2SY-7A.2 in 2SY, and QSd.sau-AM-5A.2 and QSd.sau-AM-7B in AM. Pearson's correlation between SD and other agronomic traits, and effects of major or stable QTL on yield related traits indicated SD significantly impacted spike length (SL), spikelet number per spike (SNS) and kernel length (KL). Several genes related to spike development within the physical intervals of major or stable QTL were predicted and discussed. Collectively, our research identified QTL with potential applications for modern wheat breeding and broadening the genetic basis of SD.
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Affiliation(s)
- Jianing You
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Hang Liu
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Surong Wang
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Wei Luo
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Lulu Gou
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Huaping Tang
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yang Mu
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Mei Deng
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Qiantao Jiang
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guoyue Chen
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Pengfei Qi
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yuanying Peng
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Liwei Tang
- Panzhihua Academy of Agricultural and Forestry Sciences, Panzhihua, China
| | - Ahsan Habib
- Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna, Bangladesh
| | - Yuming Wei
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Youliang Zheng
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Xiujin Lan
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Jian Ma
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
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Satrio RD, Fendiyanto MH, Supena EDJ, Suharsono S, Miftahudin M. Genome-wide SNP discovery, linkage mapping, and analysis of QTL for morpho-physiological traits in rice during vegetative stage under drought stress. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2021; 27:2635-2650. [PMID: 34924715 PMCID: PMC8639969 DOI: 10.1007/s12298-021-01095-y] [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: 06/16/2021] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 06/14/2023]
Abstract
Drought tolerance in rice is controlled by several genes and is inherited quantitatively. Low genetic map density and the use of phenotypic traits that do not reflect the corresponding tolerance level have been obstacles in genetic analyses performed to identify genes that control drought-tolerant traits in rice. The current study aimed to construct a genetic map from high-density single-nucleotide polymorphism (SNP) markers generated from genome sequences of recombinant inbred lines (RILs), derived from IR64 × Hawara Bunar. Moreover, it sought to analyze the quantitative trait loci (QTL) and identify the drought tolerance candidate genes. A linkage map along 1980 cM on the 12 rice chromosomes was constructed employing 55,205 SNP markers resulting from the RIL genome sequences. A total of 175 morpho-physiological traits pertaining to drought stress were determined. A total of 41 QTLs were detected in 13 regions on rice chromosomes 1, 3, 6, 8, 9, and 12. Moreover, three hotspot QTL regions were found on chromosomes 6 and 8, along with two major QTL on chromosome 9. Differential gene expression for the loci within the QTL physical map intervals revealed many potential candidate genes. The markers tightly linked to the QTL and their candidate genes can potentially be used for pyramiding in marker-assisted breeding in order to achieve genetic improvement concerning the tolerance of rice to drought stress. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12298-021-01095-y.
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Affiliation(s)
- Rizky Dwi Satrio
- Plant Biology Graduate Program, Department of Biology, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (IPB University), Kampus IPB Dramaga, Bogor, 16680 Indonesia
- Department of Biology, Faculty of Military Mathematics and Natural Sciences, The Republic of Indonesia Defense University (Unhan RI), Komplek Indonesia Peace and Security Center (IPSC) Sentul, Bogor, 16810 Indonesia
| | - Miftahul Huda Fendiyanto
- Plant Biology Graduate Program, Department of Biology, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (IPB University), Kampus IPB Dramaga, Bogor, 16680 Indonesia
- Department of Biology, Faculty of Military Mathematics and Natural Sciences, The Republic of Indonesia Defense University (Unhan RI), Komplek Indonesia Peace and Security Center (IPSC) Sentul, Bogor, 16810 Indonesia
| | - Ence Darmo Jaya Supena
- Department of Biology, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (IPB University), Kampus IPB Dramaga, Bogor, 16680 Indonesia
- Faculty of Military Mathematics and Natural Sciences, The Republic of Indonesia Defense University (Unhan RI), Komplek Indonesia Peace and Security Center (IPSC) Sentul, Bogor, 16810 Indonesia
| | - Sony Suharsono
- Department of Biology, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (IPB University), Kampus IPB Dramaga, Bogor, 16680 Indonesia
| | - Miftahudin Miftahudin
- Department of Biology, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (IPB University), Kampus IPB Dramaga, Bogor, 16680 Indonesia
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Li T, Deng G, Su Y, Yang Z, Tang Y, Wang J, Qiu X, Pu X, Li J, Liu Z, Zhang H, Liang J, Yang W, Yu M, Wei Y, Long H. Identification and validation of two major QTLs for spike compactness and length in bread wheat (Triticum aestivum L.) showing pleiotropic effects on yield-related traits. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3625-3641. [PMID: 34309684 DOI: 10.1007/s00122-021-03918-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/15/2021] [Indexed: 05/27/2023]
Abstract
Two major and stable QTLs for spike compactness and length were detected and validated in multiple genetic backgrounds and environments, and their pleiotropic effects on yield-related traits were analyzed. Spike compactness (SC) and length (SL) are greatly associated with wheat (Triticum aestivum L.) grain yield. To detect quantitative trait loci (QTL) associated with SC and SL, two biparental populations derived from crosses of Chuanmai42/Kechengmai1 and Chuanmai42/Chuannong16 were employed to perform QTL mapping in five environments. A total of 34 QTLs were identified, in which six major QTLs were repeatedly detected in more than four environments and the best linear unbiased prediction datasets, explaining 7.13-33.6% of phenotypic variation. These major QTLs were co-located in two genomic regions on chromosome 5A and 6A, namely QSc/Sl.cib-5A and QSc/Sl.cib-6A, respectively. By developing kompetitive allele-specific PCR (KASP) markers that linked to them, the two loci were validated in different genetic backgrounds, and their interactions were also analyzed. Comparison analysis showed that QSc/Sl.cib-5A was not Vrn-A1 and Q, and QSc/Sl.cib-6A was likely a new locus for SC and SL. Both QSc/Sl.cib-5A and QSc/Sl.cib-6A had pleiotropic effects on other yield-related traits including plant height, thousand grain weight and grain length. Therefore, the two loci combined with the developed KASP markers might be potentially applicable in wheat breeding. Furthermore, based on the spatiotemporal expression patterns, gene annotation, orthologous search and sequence differences, TraesCS5A01G301400 and TraesCS6A01G090300 were considered as potential candidates for QSc/Sl.cib-5A and QSc/Sl.cib-6A, respectively. These results provided valuable information for fine mapping and cloning of the two loci in the future.
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Affiliation(s)
- Tao Li
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Guangbing Deng
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Yan Su
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Zhao Yang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Yanyan Tang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Jinhui Wang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Xvebing Qiu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Xi Pu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Jun Li
- Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, Sichuan, China
| | - Zehou Liu
- Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, Sichuan, China
| | - Haili Zhang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Junjun Liang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Wuyun Yang
- Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, Sichuan, China
| | - Maoqun Yu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Yuming Wei
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Hai Long
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China.
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Saini DK, Chopra Y, Pal N, Chahal A, Srivastava P, Gupta PK. Meta-QTLs, ortho-MQTLs and candidate genes for nitrogen use efficiency and root system architecture in bread wheat ( Triticum aestivum L.). PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2021; 27:2245-2267. [PMID: 34744364 PMCID: PMC8526679 DOI: 10.1007/s12298-021-01085-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/26/2021] [Accepted: 09/27/2021] [Indexed: 05/04/2023]
Abstract
In wheat, meta-QTLs (MQTLs), ortho-MQTLs, and candidate genes (CGs) were identified for nitrogen use efficiency and root system architecture. For this purpose, 1788 QTLs were available from 24 studies published during 2006-2020. Of these, 1098 QTLs were projected onto the consensus map resulting in 118 MQTLs. The average confidence interval (CI) of MQTLs was reduced up to 8.56 folds in comparison to the average CI of QTLs. Of the 118 MQTLs, 112 were anchored to the physical map of the wheat reference genome. The physical interval of MQTLs ranged from 0.02 to 666.18 Mb with a mean of 94.36 Mb. Eighty-eight of these 112 MQTLs were verified by marker-trait associations (MTAs) identified in published genome-wide association studies (GWAS); the MQTLs that were verified using GWAS also included 9 most robust MQTLs, which are particularly useful for breeders; we call them 'Breeder's QTLs'. Some selected wheat MQTLs were further utilized for the identification of ortho-MQTLs for wheat and maize; 9 such ortho-MQTLs were available. As many as 1991 candidate genes (CGs) were also detected, which included 930 CGs with an expression level of > 2 transcripts per million in relevant organs/tissues. Among the CGs, 97 CGs with functions previously reported as important for the traits under study were selected. Based on homology analysis and expression patterns, 49 orthologues of 35 rice genes were also identified in MQTL regions. The results of the present study may prove useful for the improvement of selection strategy for yield potential, stability, and performance under N-limiting conditions. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12298-021-01085-0.
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Affiliation(s)
- Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
- Present Address: Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583 USA
| | - Neeraj Pal
- Department of Molecular Biology and Genetic Engineering, G. B. Pant, University of Agriculture and Technology, Pantnagar, Uttarakhand 263145 India
| | - Amneek Chahal
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, 250004 India
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Zhou J, Li C, You J, Tang H, Mu Y, Jiang Q, Liu Y, Chen G, Wang J, Qi P, Ma J, Gao Y, Habib A, Wei Y, Zheng Y, Lan X, Ma J. Genetic identification and characterization of chromosomal regions for kernel length and width increase from tetraploid wheat. BMC Genomics 2021; 22:706. [PMID: 34592925 PMCID: PMC8482559 DOI: 10.1186/s12864-021-08024-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 09/13/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Improvement of wheat gercTriticum aestivum L.) yield could relieve global food shortages. Kernel size, as an important component of 1000-kernel weight (TKW), is always a significant consideration to improve yield for wheat breeders. Wheat related species possesses numerous elite genes that can be introduced into wheat breeding. It is thus vital to explore, identify, and introduce new genetic resources for kernel size from wheat wild relatives to increase wheat yield. RESULTS In the present study, quantitative trait loci (QTL) for kernel length (KL) and width (KW) were detected in a recombinant inbred line (RIL) population derived from a cross between a wild emmer accession 'LM001' and a Sichuan endemic tetraploid wheat 'Ailanmai' using the Wheat 55 K single nucleotide polymorphism (SNP) array-based constructed linkage map and phenotype from six different environments. We identified eleven QTL for KL and KW including two major ones QKL.sicau-AM-3B and QKW.sicau-AM-4B, the positive alleles of which were from LM001 and Ailanmai, respectively. They explained 17.57 to 44.28% and 13.91 to 39.01% of the phenotypic variance, respectively. For these two major QTL, Kompetitive allele-specific PCR (KASP) markers were developed and used to successfully validate their effects in three F3 populations and two natural populations containing a panel of 272 Chinese wheat landraces and that of 300 Chinese wheat cultivars, respectively. QKL.sicau-AM-3B was located at 675.6-695.4 Mb on chromosome arm 3BL. QKW.sicau-AM-4B was located at 444.2-474.0 Mb on chromosome arm 4BL. Comparison with previous studies suggested that these two major QTL were likely new loci. Further analysis indicated that the positive alleles of QKL.sicau-AM-3B and QKW.sicau-AM-4B had a great additive effect increasing TKW by 6.01%. Correlation analysis between KL and other agronomic traits showed that KL was significantly correlated to spike length, length of uppermost internode, TKW, and flag leaf length. KW was also significantly correlated with TKW. Four genes, TRIDC3BG062390, TRIDC3BG062400, TRIDC4BG037810, and TRIDC4BG037830, associated with kernel development were predicted in physical intervals harboring these two major QTL on wild emmer and Chinese Spring reference genomes. CONCLUSIONS Two stable and major QTL for KL and KW across six environments were detected and verified in three biparental populations and two natural populations. Significant relationships between kernel size and yield-related traits were identified. KASP markers tightly linked the two major QTL could contribute greatly to subsequent fine mapping. These results suggested the application potential of wheat related species in wheat genetic improvement.
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Affiliation(s)
- Jieguang Zhou
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Cong Li
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Jianing You
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Huaping Tang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yang Mu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Qiantao Jiang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yaxi Liu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Guoyue Chen
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Jirui Wang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Pengfei Qi
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Jun Ma
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China
| | - Yutian Gao
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China
| | - Ahsan Habib
- Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna, 9208, Bangladesh
| | - Yuming Wei
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Youliang Zheng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Xiujin Lan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Jian Ma
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Triticeae Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
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Ji G, Xu Z, Fan X, Zhou Q, Yu Q, Liu X, Liao S, Feng B, Wang T. Identification of a major and stable QTL on chromosome 5A confers spike length in wheat ( Triticum aestivum L.). MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:56. [PMID: 37309397 PMCID: PMC10236030 DOI: 10.1007/s11032-021-01249-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 08/29/2021] [Indexed: 06/14/2023]
Abstract
Spike length (SL) is the key determinant of plant architecture and yield potential. In this study, 193 recombinant inbred lines (RILs) derived from a cross between 13F10 and Chuanmai 42 (CM42) were evaluated for spike length in six environments. Sixty RILs consisting of 30 high and 30 low SLs were genotyped using the bulked segregant analysis exome sequencing (BSE-Seq) analysis for preliminary quantitative trait locus (QTL) mapping. A 6.69 Mb (518.43-525.12 Mb) region on chromosome 5AL was found to have a significant effect on the SL trait. Fifteen competitive allele-specific PCR (KASP) markers were successfully converted from the single nucleotide polymorphisms (SNPs) in the SL target region. Combined with four novel simple sequence repeat (SSR) markers, a genetic linkage map spanning 21.159 cM was constructed. The mapping result confirmed the identity of a major and stable QTL named QSl.cib-5A in the targeted region that explained 7.88-26.60% of the phenotypic variation in SL. QSl.cib-5A was narrowed to a region of 4.84 cM interval corresponding to a 4.67 Mb (516.60-521.27 Mb) physical region in the Chinese Spring RefSeq v2.0 containing 17 high-confidence genes with 25 transcripts. In addition, this QTL exhibited pleiotropic effects on spikelet density (SD), with the phenotypic variances proportion ranging from 11.34 to 19.92%. This study provides a foundational step for cloning the QSl.cib-5A, which is involved in the regulation of spike morphology in common wheat. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01249-6.
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Affiliation(s)
- Guangsi Ji
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zhibin Xu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041 China
| | - Xiaoli Fan
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041 China
| | - Qiang Zhou
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041 China
| | - Qin Yu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Xiaofeng Liu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Simin Liao
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Bo Feng
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041 China
| | - Tao Wang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041 China
- The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101 China
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Pretini N, Vanzetti LS, Terrile II, Donaire G, González FG. Mapping QTL for spike fertility and related traits in two doubled haploid wheat (Triticum aestivum L.) populations. BMC PLANT BIOLOGY 2021; 21:353. [PMID: 34311707 PMCID: PMC8314532 DOI: 10.1186/s12870-021-03061-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 05/23/2021] [Indexed: 06/02/2023]
Abstract
BACKGROUND In breeding programs, the selection of cultivars with the highest yield potential consisted in the selection of the yield per se, which resulted in cultivars with higher grains per spike (GN) and occasionally increased grain weight (GW) (main numerical components of the yield). In this study, quantitative trait loci (QTL) for GW, GN and spike fertility traits related to GN determination were mapped using two doubled haploid (DH) populations (Baguette Premium 11 × BioINTA 2002 and Baguette 19 × BioINTA 2002). RESULTS In total 305 QTL were identified for 14 traits, out of which 12 QTL were identified in more than three environments and explained more than 10% of the phenotypic variation in at least one environment. Eight hotspot regions were detected on chromosomes 1A, 2B, 3A, 5A, 5B, 7A and 7B in which at least two major and stable QTL sheared confidence intervals. QTL on two of these regions (R5A.1 and R5A.2) have previously been described, but the other six regions are novel. CONCLUSIONS Based on the pleiotropic analysis within a robust physiological model we conclude that two hotspot genomic regions (R5A.1 and R5A.2) together with the QGW.perg-6B are of high relevance to be used in marker assisted selection in order to improve the spike yield potential. All the QTL identified for the spike related traits are the first step to search for their candidate genes, which will allow their better manipulation in the future.
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Affiliation(s)
- Nicole Pretini
- Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA-UNSADA), Monteagudo 2772 CP 2700, Pergamino, Buenos Aires, Argentina.
| | - Leonardo S Vanzetti
- Instituto Nacional de Tecnología Agropecuaria (INTA), EEA INTA Marcos Juárez. Ruta 12 s/n CP 2850, Marcos Juárez, Córdoba, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290 CP C1425FQB, Buenos Aires, Argentina
| | - Ignacio I Terrile
- Instituto Nacional de Tecnología Agropecuaria (INTA), EEA INTA Pergamino. Ruta 32, km 4,5 CP 2700, Pergamino, Buenos Aires, Argentina
| | - Guillermo Donaire
- Instituto Nacional de Tecnología Agropecuaria (INTA), EEA INTA Marcos Juárez. Ruta 12 s/n CP 2850, Marcos Juárez, Córdoba, Argentina
| | - Fernanda G González
- Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA-UNSADA), Monteagudo 2772 CP 2700, Pergamino, Buenos Aires, Argentina
- Instituto Nacional de Tecnología Agropecuaria (INTA), EEA INTA Pergamino. Ruta 32, km 4,5 CP 2700, Pergamino, Buenos Aires, Argentina
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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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Pretini N, Alonso MP, Vanzetti LS, Pontaroli AC, González FG. The physiology and genetics behind fruiting efficiency: a promising spike trait to improve wheat yield potential. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:3987-4004. [PMID: 33681978 DOI: 10.1093/jxb/erab080] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/17/2021] [Indexed: 06/12/2023]
Abstract
Fruiting efficiency (FE, grains per g of spike dry weight at anthesis) was proposed as a promising spike trait to improve wheat yield potential, based on its functional relationship with grain number determination and the evidence of trait variability in elite germplasm. During the last few years, we have witnessed great advances in the understanding of the physiological and genetic basis of this trait. The present review summarizes the recent heritability estimations and the genetic gains obtained when fruiting efficiency was measured at maturity (FEm, grains per g of chaff) and used as selection criterion. In addition, we propose spike ideotypes for contrasting fruiting efficiencies based on the fertile floret efficiency (FFE, fertile florets per g of spike dry weight at anthesis) and grain set (grains per fertile floret), together with other spike fertility-related traits. We also review novel genes and quantitative trait loci available for using marker-assisted selection for fruiting efficiency and other spike fertility traits. The possible trade-off between FE and grain weight and the genes reported to alter this relation are also considered. Finally, we discuss the benefits and future steps towards the use of fruiting efficiency as a selection criterion in breeding programs.
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Affiliation(s)
- Nicole Pretini
- Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA-UNSADA), Monteagudo 2772 CP 2700, Pergamino, Buenos Aires, Argentina
| | - María P Alonso
- Unidad Integrada Balcarce [Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata -Instituto Nacional de Tecnología Agropecuaria (INTA), EEA INTA Balcarce], Ruta 226 km 73.5 CP 7620, Balcarce, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290 CP C1425FQB, Buenos Aires, Argentina
| | - Leonardo S Vanzetti
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290 CP C1425FQB, Buenos Aires, Argentina
- Instituto Nacional de Tecnología Agropecuaria (INTA). EEA INTA Marcos Juárez, Ruta 12 s/n CP 2850, Marcos Juárez, Córdoba, Argentina
| | - Ana C Pontaroli
- Unidad Integrada Balcarce [Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata -Instituto Nacional de Tecnología Agropecuaria (INTA), EEA INTA Balcarce], Ruta 226 km 73.5 CP 7620, Balcarce, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290 CP C1425FQB, Buenos Aires, Argentina
| | - Fernanda G González
- Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA-UNSADA), Monteagudo 2772 CP 2700, Pergamino, Buenos Aires, Argentina
- Instituto Nacional de Tecnología Agropecuaria (INTA). EEA INTA Pergamino, Ruta 32, km 4,5 CP 2700, Pergamino, Buenos Aires, Argentina
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Ren T, Fan T, Chen S, Li C, Chen Y, Ou X, Jiang Q, Ren Z, Tan F, Luo P, Chen C, Li Z. Utilization of a Wheat55K SNP array-derived high-density genetic map for high-resolution mapping of quantitative trait loci for important kernel-related traits in common wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:807-821. [PMID: 33388883 DOI: 10.1007/s00122-020-03732-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 11/18/2020] [Indexed: 05/19/2023]
Abstract
This study mapped QTLs associated with kernel-related traits by high-density genetic map. Five new major and stable QTLs for KL, KDR, SN, and KWPS were mapped in multiple environments. In the present study, a recombinant inbred line population including 371 lines derived from the cross of Chuannong18 and T1208 was genotyped using the Wheat55K single nucleotide polymorphism array. A novel high-density genetic map consisting of 11,583 markers spanning 4192.62 cM and distributed across 21 wheat chromosomes was constructed. QTLs for important kernel-related traits were mapped in multiple environments. A total of 96 and 151 QTLs were mapped by using the ICIM method and the MET method, respectively. And a total of 114 digenic epistatic QTLs were also detected across 21 chromosomes, and the epistatic effects of each trait were analyzed. BLAST analysis showed that 23 QTLs for different kernel-related traits were first time mapped and five of them were major and stable QTLs for kernel diameter ratio (121.34-126.83 cM on 4BS), spike number per square meter (71.32-73.84 cM on 2DS), kernel weight per spike (71.32-75.26 cM on 2DS), and kernel length (16.78-31.64 cM on 6A and 51.63-58.40 cM on 3D), respectively. Fifteen QTL clusters that contained 58 QTLs were also detected, and all most stable QTLs were contained in these QTL clusters. Significant correlations between different traits were detected and discussed. These results lay the foundation for fine mapping and cloning of the gene(s) underlying the stable QTLs detected in this study.
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Affiliation(s)
- Tianheng Ren
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China.
| | - Tao Fan
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Shulin Chen
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- Provincial Key Laboratory for Plant Genetics and Breeding, WenjiangChengdu, 611130, Sichuan, China
| | - Chunsheng Li
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- Provincial Key Laboratory for Plant Genetics and Breeding, WenjiangChengdu, 611130, Sichuan, China
| | - Yongyan Chen
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- Provincial Key Laboratory for Plant Genetics and Breeding, WenjiangChengdu, 611130, Sichuan, China
| | - Xia Ou
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- Provincial Key Laboratory for Plant Genetics and Breeding, WenjiangChengdu, 611130, Sichuan, China
| | - Qing Jiang
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- Provincial Key Laboratory for Plant Genetics and Breeding, WenjiangChengdu, 611130, Sichuan, China
| | - Zhenglong Ren
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- Provincial Key Laboratory for Plant Genetics and Breeding, WenjiangChengdu, 611130, Sichuan, China
| | - Feiquan Tan
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- Provincial Key Laboratory for Plant Genetics and Breeding, WenjiangChengdu, 611130, Sichuan, China
| | - Peigao Luo
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- Provincial Key Laboratory for Plant Genetics and Breeding, WenjiangChengdu, 611130, Sichuan, China
| | | | - Zhi Li
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China.
- Provincial Key Laboratory for Plant Genetics and Breeding, WenjiangChengdu, 611130, Sichuan, China.
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Mangini G, Blanco A, Nigro D, Signorile MA, Simeone R. Candidate Genes and Quantitative Trait Loci for Grain Yield and Seed Size in Durum Wheat. PLANTS 2021; 10:plants10020312. [PMID: 33562879 PMCID: PMC7916090 DOI: 10.3390/plants10020312] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 11/22/2022]
Abstract
Grain yield (YLD) is affected by thousand kernel weight (TKW) which reflects the combination of grain length (GL), grain width (GW) and grain area (AREA). Grain weight is also influenced by heading time (HT) and plant height (PH). To detect candidate genes and quantitative trait loci (QTL) of yield components, a durum wheat recombinant inbred line (RIL) population was evaluated in three field trials. The RIL was genotyped with a 90K single nucleotide polymorphism (SNP) array and a high-density genetic linkage map with 5134 markers was obtained. A total of 30 QTL were detected including 23 QTL grouped in clusters on 1B, 2A, 3A, 4B and 6B chromosomes. A QTL cluster on 2A chromosome included a major QTL for HT co-located with QTL for YLD, TKW, GL, GW and AREA, respectively. The photoperiod sensitivity (Ppd-A1) gene was found in the physical position of this cluster. Serine carboxypeptidase, Big grain 1 and β-fructofuranosidase candidate genes were mapped in clusters containing QTL for seed size. This study showed that yield components and phenological traits had higher inheritances than grain yield, allowing an accurate QTL cluster detection. This was a requisite to physically map QTL on durum genome and to identify candidate genes affecting grain yield.
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Affiliation(s)
- Giacomo Mangini
- Institute of Biosciences and Bioresources, National Research Council, Via Amendola 165/A, 70126 Bari, Italy
- Department of Soil, Plant and Food Sciences, Genetics and Plant Breeding Section, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy; (A.B.); (D.N.); (M.A.S.); (R.S.)
- Correspondence:
| | - Antonio Blanco
- Department of Soil, Plant and Food Sciences, Genetics and Plant Breeding Section, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy; (A.B.); (D.N.); (M.A.S.); (R.S.)
| | - Domenica Nigro
- Department of Soil, Plant and Food Sciences, Genetics and Plant Breeding Section, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy; (A.B.); (D.N.); (M.A.S.); (R.S.)
| | - Massimo Antonio Signorile
- Department of Soil, Plant and Food Sciences, Genetics and Plant Breeding Section, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy; (A.B.); (D.N.); (M.A.S.); (R.S.)
| | - Rosanna Simeone
- Department of Soil, Plant and Food Sciences, Genetics and Plant Breeding Section, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy; (A.B.); (D.N.); (M.A.S.); (R.S.)
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37
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Islam S, Zhang J, Zhao Y, She M, Ma W. Genetic regulation of the traits contributing to wheat nitrogen use efficiency. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2021; 303:110759. [PMID: 33487345 DOI: 10.1016/j.plantsci.2020.110759] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/14/2020] [Accepted: 11/11/2020] [Indexed: 05/25/2023]
Abstract
High nitrogen application aimed at increasing crop yield is offset by higher production costs and negative environmental consequences. For wheat, only one third of the applied nitrogen is utilized, which indicates there is scope for increasing Nitrogen Use Efficiency (NUE). However, achieving greater NUE is challenged by the complexity of the trait, which comprises processes associated with nitrogen uptake, transport, reduction, assimilation, translocation and remobilization. Thus, knowledge of the genetic regulation of these processes is critical in increasing NUE. Although primary nitrogen uptake and metabolism-related genes have been well studied, the relative influence of each towards NUE is not fully understood. Recent attention has focused on engineering transcription factors and identification of miRNAs acting on expression of specific genes related to NUE. Knowledge obtained from model species needs to be translated into wheat using recently-released whole genome sequences, and by exploring genetic variations of NUE-related traits in wild relatives and ancient germplasm. Recent findings indicate the genetic basis of NUE is complex. Pyramiding various genes will be the most effective approach to achieve a satisfactory level of NUE in the field.
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Affiliation(s)
- Shahidul Islam
- State Agricultural Biotechnology Center, Murdoch University, Perth, WA, 6150, Australia
| | - Jingjuan Zhang
- State Agricultural Biotechnology Center, Murdoch University, Perth, WA, 6150, Australia
| | - Yun Zhao
- State Agricultural Biotechnology Center, Murdoch University, Perth, WA, 6150, Australia
| | - Maoyun She
- State Agricultural Biotechnology Center, Murdoch University, Perth, WA, 6150, Australia
| | - Wujun Ma
- State Agricultural Biotechnology Center, Murdoch University, Perth, WA, 6150, Australia.
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Ruan Y, Yu B, Knox RE, Zhang W, Singh AK, Cuthbert R, Fobert P, DePauw R, Berraies S, Sharpe A, Fu BX, Sangha J. Conditional Mapping Identified Quantitative Trait Loci for Grain Protein Concentration Expressing Independently of Grain Yield in Canadian Durum Wheat. FRONTIERS IN PLANT SCIENCE 2021; 12:642955. [PMID: 33841470 PMCID: PMC8024689 DOI: 10.3389/fpls.2021.642955] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/26/2021] [Indexed: 05/22/2023]
Abstract
Grain protein concentration (GPC) is an important trait in durum cultivar development as a major determinant of the nutritional value of grain and end-use product quality. However, it is challenging to simultaneously select both GPC and grain yield (GY) due to the negative correlation between them. To characterize quantitative trait loci (QTL) for GPC and understand the genetic relationship between GPC and GY in Canadian durum wheat, we performed both traditional and conditional QTL mapping using a doubled haploid (DH) population of 162 lines derived from Pelissier × Strongfield. The population was grown in the field over 5 years and GPC was measured. QTL contributing to GPC were detected on chromosome 1B, 2B, 3A, 5B, 7A, and 7B using traditional mapping. One major QTL on 3A (QGpc.spa-3A.3) was consistently detected over 3 years accounting for 9.4-18.1% of the phenotypic variance, with the favorable allele derived from Pelissier. Another major QTL on 7A (QGpc.spa-7A) detected in 3 years explained 6.9-14.8% of the phenotypic variance, with the beneficial allele derived from Strongfield. Comparison of the QTL described here with the results previously reported led to the identification of one novel major QTL on 3A (QGpc.spa-3A.3) and five novel minor QTL on 1B, 2B and 3A. Four QTL were common between traditional and conditional mapping, with QGpc.spa-3A.3 and QGpc.spa-7A detected in multiple environments. The QTL identified by conditional mapping were independent or partially independent of GY, making them of great importance for development of high GPC and high yielding durum.
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Affiliation(s)
- Yuefeng Ruan
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, Canada
- Yuefeng Ruan
| | - Bianyun Yu
- Aquatic and Crop Resource Development, National Research Council Canada, Saskatoon, SK, Canada
- *Correspondence: Bianyun Yu
| | - Ron E. Knox
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, Canada
| | - Wentao Zhang
- Aquatic and Crop Resource Development, National Research Council Canada, Saskatoon, SK, Canada
| | - Asheesh K. Singh
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, Canada
| | - Richard Cuthbert
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, Canada
| | - Pierre Fobert
- Aquatic and Crop Resource Development, National Research Council Canada, Ottawa, ON, Canada
| | - Ron DePauw
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, Canada
| | - Samia Berraies
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, Canada
| | - Andrew Sharpe
- Aquatic and Crop Resource Development, National Research Council Canada, Saskatoon, SK, Canada
| | - Bin Xiao Fu
- Grain Research Laboratory, Canadian Grain Commission, Winnipeg, MB, Canada
| | - Jatinder Sangha
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, Canada
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Shi T, Zhu A, Jia J, Hu X, Chen J, Liu W, Ren X, Sun D, Fernie AR, Cui F, Chen W. Metabolomics analysis and metabolite-agronomic trait associations using kernels of wheat (Triticum aestivum) recombinant inbred lines. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:279-292. [PMID: 32073701 PMCID: PMC7383920 DOI: 10.1111/tpj.14727] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/17/2020] [Accepted: 02/07/2020] [Indexed: 05/21/2023]
Abstract
Plants produce numerous metabolites that are important for their development and growth. However, the genetic architecture of the wheat metabolome has not been well studied. Here, utilizing a high-density genetic map, we conducted a comprehensive metabolome study via widely targeted LC-MS/MS to analyze the wheat kernel metabolism. We further combined agronomic traits and dissected the genetic relationship between metabolites and agronomic traits. In total, 1260 metabolic features were detected. Using linkage analysis, 1005 metabolic quantitative trait loci (mQTLs) were found distributed unevenly across the genome. Twenty-four candidate genes were found to modulate the levels of different metabolites, of which two were functionally annotated by in vitro analysis to be involved in the synthesis and modification of flavonoids. Combining the correlation analysis of metabolite-agronomic traits with the co-localization of methylation quantitative trait locus (mQTL) and phenotypic QTL (pQTL), genetic relationships between the metabolites and agronomic traits were uncovered. For example, a candidate was identified using correlation and co-localization analysis that may manage auxin accumulation, thereby affecting number of grains per spike (NGPS). Furthermore, metabolomics data were used to predict the performance of wheat agronomic traits, with metabolites being found that provide strong predictive power for NGPS and plant height. This study used metabolomics and association analysis to better understand the genetic basis of the wheat metabolism which will ultimately assist in wheat breeding.
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Affiliation(s)
- Taotao Shi
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhan430070China
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan430070China
| | - Anting Zhu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhan430070China
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan430070China
| | - Jingqi Jia
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhan430070China
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan430070China
| | - Xin Hu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhan430070China
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan430070China
| | - Jie Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhan430070China
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan430070China
| | - Wei Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhan430070China
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan430070China
| | - Xifeng Ren
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan430070China
| | - Dongfa Sun
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan430070China
| | - Alisdair R. Fernie
- Max‐Planck‐Institute of Molecular Plant PhysiologyPotsdam‐Golm14476Germany
| | - Fa Cui
- Wheat Molecular Breeding Innovation Research GroupKey Laboratory of Molecular Module‐Based Breeding of High Yield and Abiotic Resistant Plants in Universities of ShandongSchool of AgricultureLudong UniversityYantaiChina
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhan430070China
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan430070China
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40
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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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