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Si Y, Tian S, Niu J, Yu Z, Ma S, Lu Q, Wu H, Ling HQ, Zheng S. Dissection and validation of a promising QTL controlling spikelet number on 5B in bread wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:240. [PMID: 37930446 DOI: 10.1007/s00122-023-04488-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/16/2023] [Indexed: 11/07/2023]
Abstract
KEY MESSAGE Five environmentally stable QTLs for spikelet number per spike and days to heading were identified using a high-genetic map containing 95,444 SNPs, among which QSns.ucas-5B was validated using residual heterozygous line at multiple environments. Spikelet number per spike (SNS) and days to heading (DTH) play pivotal roles in the improvement of wheat yield. In this study, a high-density genetic map for a recombinant inbred lines (RILs) population derived from Zhengnong 17 (ZN17) and Yangbaimai (YBM) was constructed using 95,444 single-nucleotide polymorphism (SNP) markers from the Wheat660K SNP array. Our study identified a total of five environmentally stable QTLs for SNS and DTH, one of which was named QSns.ucas-5B, with a physical interval of approximately 545.4-552.1 Mb on the 5BL chromosome arm. Importantly, the elite haplotype within QSns.ucas-5B showed a consistent and positive effect on SNS, grain number and weight per spike, without extending the days to heading. These findings provide a foundation for future efforts to map and clone the gene(s) responsible for QSns.ucas-5B and further indicate the potential application of the developed and validated InDel marker of QSns.ucas-5B for molecular breeding purposes, aimed at improving wheat grain yield.
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Affiliation(s)
- Yaoqi Si
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shuiquan Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
- College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jianqing Niu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Zhongqing Yu
- National Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian, China
| | - Shengwei Ma
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qiao Lu
- College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Huilan Wu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
- College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hong-Qing Ling
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.
- College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Shusong Zheng
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.
- College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
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2
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Li Z, Hu Y, Ma X, Da L, She J, Liu Y, Yi X, Cao Y, Xu W, Jiao Y, Su Z. WheatCENet: A Database for Comparative Co-expression Networks Analysis of Allohexaploid Wheat and Its Progenitors. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:324-336. [PMID: 35660007 PMCID: PMC10626052 DOI: 10.1016/j.gpb.2022.04.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 03/16/2022] [Accepted: 05/08/2022] [Indexed: 06/15/2023]
Abstract
Genetic and epigenetic changes after polyploidization events could result in variable gene expression and modified regulatory networks. Here, using large-scale transcriptome data, we constructed co-expression networks for diploid, tetraploid, and hexaploid wheat species, and built a platform for comparing co-expression networks of allohexaploid wheat and its progenitors, named WheatCENet. WheatCENet is a platform for searching and comparing specific functional co-expression networks, as well as identifying the related functions of the genes clustered therein. Functional annotations like pathways, gene families, protein-protein interactions, microRNAs (miRNAs), and several lines of epigenome data are integrated into this platform, and Gene Ontology (GO) annotation, gene set enrichment analysis (GSEA), motif identification, and other useful tools are also included. Using WheatCENet, we found that the network of WHEAT ABERRANT PANICLE ORGANIZATION 1 (WAPO1) has more co-expressed genes related to spike development in hexaploid wheat than its progenitors. We also found a novel motif of CCWWWWWWGG (CArG) specifically in the promoter region of WAPO-A1, suggesting that neofunctionalization of the WAPO-A1 gene affects spikelet development in hexaploid wheat. WheatCENet is useful for investigating co-expression networks and conducting other analyses, and thus facilitates comparative and functional genomic studies in wheat. WheatCENet is freely available at http://bioinformatics.cpolar.cn/WheatCENet and http://bioinformatics.cau.edu.cn/WheatCENet.
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Affiliation(s)
- Zhongqiu Li
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yiheng Hu
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xuelian Ma
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Lingling Da
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Jiajie She
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yue Liu
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Xin Yi
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Yaxin Cao
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Wenying Xu
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yuannian Jiao
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhen Su
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
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3
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Zanella CM, Rotondo M, McCormick‐Barnes C, Mellers G, Corsi B, Berry S, Ciccone G, Day R, Faralli M, Galle A, Gardner KA, Jacobs J, Ober ES, Sánchez del Rio A, Van Rie J, Lawson T, Cockram J. Longer epidermal cells underlie a quantitative source of variation in wheat flag leaf size. THE NEW PHYTOLOGIST 2023; 237:1558-1573. [PMID: 36519272 PMCID: PMC10107444 DOI: 10.1111/nph.18676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 11/17/2022] [Indexed: 06/17/2023]
Abstract
The wheat flag leaf is the main contributor of photosynthetic assimilates to developing grains. Understanding how canopy architecture strategies affect source strength and yield will aid improved crop design. We used an eight-founder population to investigate the genetic architecture of flag leaf area, length, width and angle in European wheat. For the strongest genetic locus identified, we subsequently created a near-isogenic line (NIL) pair for more detailed investigation across seven test environments. Genetic control of traits investigated was highly polygenic, with colocalisation of replicated quantitative trait loci (QTL) for one or more traits identifying 24 loci. For QTL QFll.niab-5A.1 (FLL5A), development of a NIL pair found the FLL5A+ allele commonly conferred a c. 7% increase in flag and second leaf length and a more erect leaf angle, resulting in higher flag and/or second leaf area. Increased FLL5A-mediated flag leaf length was associated with: (1) longer pavement cells and (2) larger stomata at lower density, with a trend for decreased maximum stomatal conductance (Gsmax ) per unit leaf area. For FLL5A, cell size rather than number predominantly determined leaf length. The observed trade-offs between leaf size and stomatal morphology highlight the need for future studies to consider these traits at the whole-leaf level.
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Affiliation(s)
| | - Marilena Rotondo
- NIAB93 Lawrence Weaver RoadCambridgeCB3 0LEUK
- University of MessinaMessina98122Italy
| | | | | | | | | | - Giulia Ciccone
- NIAB93 Lawrence Weaver RoadCambridgeCB3 0LEUK
- University of MessinaMessina98122Italy
| | - Rob Day
- NIAB93 Lawrence Weaver RoadCambridgeCB3 0LEUK
| | - Michele Faralli
- School of Biological SciencesUniversity of EssexColchesterCO4 3SQUK
| | - Alexander Galle
- BASF Belgium Coordination Center (BBCC) – Innovation Center GhentTechnologiepark‐Zwijnaarde 1019052GhentBelgium
| | | | - John Jacobs
- BASF Belgium Coordination Center (BBCC) – Innovation Center GhentTechnologiepark‐Zwijnaarde 1019052GhentBelgium
| | | | | | - Jeroen Van Rie
- BASF Belgium Coordination Center (BBCC) – Innovation Center GhentTechnologiepark‐Zwijnaarde 1019052GhentBelgium
| | - Tracy Lawson
- School of Biological SciencesUniversity of EssexColchesterCO4 3SQUK
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4
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Wang Z, Dhakal S, Cerit M, Wang S, Rauf Y, Yu S, Maulana F, Huang W, Anderson JD, Ma XF, Rudd JC, Ibrahim AMH, Xue Q, Hays DB, Bernardo A, St. Amand P, Bai G, Baker J, Baker S, Liu S. QTL mapping of yield components and kernel traits in wheat cultivars TAM 112 and Duster. FRONTIERS IN PLANT SCIENCE 2022; 13:1057701. [PMID: 36570880 PMCID: PMC9768232 DOI: 10.3389/fpls.2022.1057701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/04/2022] [Indexed: 06/17/2023]
Abstract
In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL map-based cloning.
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Affiliation(s)
- Zhen Wang
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Smit Dhakal
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Mustafa Cerit
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Shichen Wang
- Genomics and Bioinformatics Service Center, Texas A&M AgriLife Research, College Station, TX, United States
| | - Yahya Rauf
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Shuhao Yu
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Frank Maulana
- Noble Research Institute, Ardmore, OK, United States
| | - Wangqi Huang
- Noble Research Institute, Ardmore, OK, United States
| | | | - Xue-Feng Ma
- Noble Research Institute, Ardmore, OK, United States
| | - Jackie C. Rudd
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Amir M. H. Ibrahim
- Department of Soil and Crop Science, Texas A&M University, College Station, TX, United States
| | - Qingwu Xue
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Dirk B. Hays
- Department of Soil and Crop Science, Texas A&M University, College Station, TX, United States
| | - Amy Bernardo
- Central Small Grain Genotyping Lab and Hard Winter Wheat Genetics Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Manhattan, KS, United States
| | - Paul St. Amand
- Central Small Grain Genotyping Lab and Hard Winter Wheat Genetics Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Manhattan, KS, United States
| | - Guihua Bai
- Central Small Grain Genotyping Lab and Hard Winter Wheat Genetics Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Manhattan, KS, United States
| | - Jason Baker
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Shannon Baker
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Shuyu Liu
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
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5
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Peters Haugrud AR, Zhang Q, Green AJ, Xu SS, Faris JD. Identification of stable QTL controlling multiple yield components in a durum × cultivated emmer wheat population under field and greenhouse conditions. G3 (BETHESDA, MD.) 2022; 13:6762085. [PMID: 36250796 PMCID: PMC9911061 DOI: 10.1093/g3journal/jkac281] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 09/27/2022] [Indexed: 11/09/2022]
Abstract
Crop yield gains are needed to keep pace with a growing global population and decreasing resources to produce food. Cultivated emmer wheat is a progenitor of durum wheat and a useful source of genetic variation for trait improvement in durum. Here, we evaluated a recombinant inbred line population derived from a cross between the North Dakota durum wheat variety Divide and the cultivated emmer wheat accession PI 272527 consisting of 219 lines. The population was evaluated in 3 field environments and 2 greenhouse experiments to identify quantitative trait locus associated with 11 yield-related traits that were expressed in a consistent manner over multiple environments. We identified 27 quantitative trait locus expressed in at least 2 field environments, 17 of which were also expressed under greenhouse conditions. Seven quantitative trait locus regions on chromosomes 1B, 2A, 2B, 3A, 3B, 6A, and 7B had pleiotropic effects on multiple yield-related traits. The previously cloned genes Q and FT-B1, which are known to be associated with development and morphology, were found to consistently be associated with multiple traits across environments. PI 272527 contributed beneficial alleles for quantitative trait locus associated with multiple traits, especially for seed morphology quantitative trait locus on chromosomes 1B, 2B, and 6A. Three recombinant inbred lines with increased grain size and weight compared to Divide were identified and demonstrated the potential for improvement of durum wheat through deployment of beneficial alleles from the cultivated emmer parent. The findings from this study provide knowledge regarding stable and robust quantitative trait locus that breeders can use for improving yield in durum wheat.
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Affiliation(s)
| | - Qijun Zhang
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Andrew J Green
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Steven S Xu
- USDA-ARS Western Regional Research Center, Albany, CA 94710, USA
| | - Justin D Faris
- Corresponding author: Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, Fargo, ND 58102, USA.
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6
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Wittern LM, Barrero JM, Bovill WD, Verbyla KL, Hughes T, Swain SM, Steed G, Webb AAR, Gardner K, Greenland A, Jacobs J, Frohberg C, Schmidt RC, Cavanagh C, Rohde A, Davey MW, Hannah MA. Overexpression of the WAPO-A1 gene increases the number of spikelets per spike in bread wheat. Sci Rep 2022; 12:14229. [PMID: 35987959 PMCID: PMC9392761 DOI: 10.1038/s41598-022-18614-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 08/16/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractTwo homoeologous QTLs for number of spikelets per spike (SPS) were mapped on chromosomes 7AL and 7BL using two wheat MAGIC populations. Sets of lines contrasting for the QTL on 7AL were developed which allowed for the validation and fine mapping of the 7AL QTL and for the identification of a previously described candidate gene, WHEAT ORTHOLOG OF APO1 (WAPO1). Using transgenic overexpression in both a low and a high SPS line, we provide a functional validation for the role of this gene in determining SPS also in hexaploid wheat. We show that the expression levels of this gene positively correlate with SPS in multiple MAGIC founder lines under field conditions as well as in transgenic lines grown in the greenhouse. This work highlights the potential use of WAPO1 in hexaploid wheat for further yield increases. The impact of WAPO1 and SPS on yield depends on other genetic and environmental factors, hence, will require a finely balanced expression level to avoid the development of detrimental pleiotropic phenotypes.
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Geyer M, Mohler V, Hartl L. Genetics of the Inverse Relationship between Grain Yield and Grain Protein Content in Common Wheat. PLANTS 2022; 11:plants11162146. [PMID: 36015449 PMCID: PMC9413592 DOI: 10.3390/plants11162146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/05/2022] [Accepted: 08/16/2022] [Indexed: 12/01/2022]
Abstract
Grain protein content (GPC) is one of the most important criteria to determine the quality of common wheat (Triticum aestivum). One of the major obstacles for bread wheat production is the negative correlation between GPC and grain yield (GY). Previous studies demonstrated that the deviation from this inverse relationship is highly heritable. However, little is known about the genetics controlling these deviations in common wheat. To fill this gap, we performed quantitative trait locus (QTL) analysis for GY, GPC, and four derived GY-GPC indices using an eight-way multiparent advanced generation intercross population comprising 394 lines. Interval mapping was conducted using phenotypic data from up to nine environments and genotypic data from a 20k single-nucleotide polymorphism array. The four indices were highly heritable (0.76–0.88) and showed distinct correlations to GY and GPC. Interval mapping revealed that GY, GPC, and GY-GPC indices were controlled by 6, 12, and 12 unique QTL, of which each explained only a small amount of phenotypic variance (R2 ≤ 10%). Ten of the 12 index QTL were independent of loci affecting GY and GPC. QTL regions harboured several candidate genes, including Rht-1, WAPO-A1, TaTEF-7A, and NRT2.6-7A. The study confirmed the usefulness of indices to mitigate the inverse GY-GPC relationship in breeding, though the selection method should reflect their polygenic inheritance.
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Hussain B, Akpınar BA, Alaux M, Algharib AM, Sehgal D, Ali Z, Aradottir GI, Batley J, Bellec A, Bentley AR, Cagirici HB, Cattivelli L, Choulet F, Cockram J, Desiderio F, Devaux P, Dogramaci M, Dorado G, Dreisigacker S, Edwards D, El-Hassouni K, Eversole K, Fahima T, Figueroa M, Gálvez S, Gill KS, Govta L, Gul A, Hensel G, Hernandez P, Crespo-Herrera LA, Ibrahim A, Kilian B, Korzun V, Krugman T, Li Y, Liu S, Mahmoud AF, Morgounov A, Muslu T, Naseer F, Ordon F, Paux E, Perovic D, Reddy GVP, Reif JC, Reynolds M, Roychowdhury R, Rudd J, Sen TZ, Sukumaran S, Ozdemir BS, Tiwari VK, Ullah N, Unver T, Yazar S, Appels R, Budak H. Capturing Wheat Phenotypes at the Genome Level. FRONTIERS IN PLANT SCIENCE 2022; 13:851079. [PMID: 35860541 PMCID: PMC9289626 DOI: 10.3389/fpls.2022.851079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.
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Affiliation(s)
- Babar Hussain
- Department of Biological Sciences, Middle East Technical University, Ankara, Turkey
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore, Pakistan
| | | | - Michael Alaux
- Université Paris-Saclay, INRAE, URGI, Versailles, France
| | - Ahmed M. Algharib
- Department of Environment and Bio-Agriculture, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt
| | - Deepmala Sehgal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Zulfiqar Ali
- Institute of Plant Breeding and Biotechnology, MNS University of Agriculture, Multan, Pakistan
| | - Gudbjorg I. Aradottir
- Department of Pathology, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Arnaud Bellec
- French Plant Genomic Resource Center, INRAE-CNRGV, Castanet Tolosan, France
| | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Halise B. Cagirici
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Fred Choulet
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - James Cockram
- The John Bingham Laboratory, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Francesca Desiderio
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Pierre Devaux
- Research & Innovation, Florimond Desprez Group, Cappelle-en-Pévèle, France
| | - Munevver Dogramaci
- USDA, Agricultural Research Service, Edward T. Schafer Agricultural Research Center, Fargo, ND, United States
| | - Gabriel Dorado
- Department of Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Córdoba, Spain
| | | | - David Edwards
- University of Western Australia, Perth, WA, Australia
| | - Khaoula El-Hassouni
- State Plant Breeding Institute, The University of Hohenheim, Stuttgart, Germany
| | - Kellye Eversole
- International Wheat Genome Sequencing Consortium (IWGSC), Bethesda, MD, United States
| | - Tzion Fahima
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Melania Figueroa
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Canberra, ACT, Australia
| | - Sergio Gálvez
- Department of Languages and Computer Science, ETSI Informática, Campus de Teatinos, Universidad de Málaga, Andalucía Tech, Málaga, Spain
| | - Kulvinder S. Gill
- Department of Crop Science, Washington State University, Pullman, WA, United States
| | - Liubov Govta
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Alvina Gul
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Goetz Hensel
- Center of Plant Genome Engineering, Heinrich-Heine-Universität, Düsseldorf, Germany
- Division of Molecular Biology, Centre of Region Haná for Biotechnological and Agriculture Research, Czech Advanced Technology and Research Institute, Palacký University, Olomouc, Czechia
| | - Pilar Hernandez
- Institute for Sustainable Agriculture (IAS-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
| | | | - Amir Ibrahim
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | | | | | - Tamar Krugman
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Yinghui Li
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Shuyu Liu
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Amer F. Mahmoud
- Department of Plant Pathology, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Alexey Morgounov
- Food and Agriculture Organization of the United Nations, Riyadh, Saudi Arabia
| | - Tugdem Muslu
- Molecular Biology, Genetics and Bioengineering, Sabanci University, Istanbul, Turkey
| | - Faiza Naseer
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Frank Ordon
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Etienne Paux
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - Dragan Perovic
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Gadi V. P. Reddy
- USDA-Agricultural Research Service, Southern Insect Management Research Unit, Stoneville, MS, United States
| | - Jochen Christoph Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Rajib Roychowdhury
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Jackie Rudd
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Taner Z. Sen
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | | | | | | | - Naimat Ullah
- Institute of Biological Sciences (IBS), Gomal University, D. I. Khan, Pakistan
| | - Turgay Unver
- Ficus Biotechnology, Ostim Teknopark, Ankara, Turkey
| | - Selami Yazar
- General Directorate of Research, Ministry of Agriculture, Ankara, Turkey
| | | | - Hikmet Budak
- Montana BioAgriculture, Inc., Missoula, MT, United States
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9
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Yang F, Zhang J, Zhao Y, Liu Q, Islam S, Yang W, Ma W. Wheat glutamine synthetase TaGSr-4B is a candidate gene for a QTL of thousand grain weight on chromosome 4B. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2369-2384. [PMID: 35588016 PMCID: PMC9271121 DOI: 10.1007/s00122-022-04118-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/25/2022] [Indexed: 06/14/2023]
Abstract
Glutamine synthetase TaGSr-4B is a candidate gene for a QTL of thousand grain weight on 4B, and the gene marker is ready for wheat breeding. A QTL for thousand grain weight (TGW) in wheat was previously mapped on chromosome 4B in a DH population of Westonia × Kauz. For identifying the candidate genes of the QTL, wheat 90 K SNP array was used to saturate the existing linkage map, and four field trials plus one glasshouse experiment over five locations were conducted to refine the QTL. Three nitrogen levels were applied to two of those field trials, resulting in a TGW phenotype data set from nine environments. A robust TGW QTL cluster including 773 genes was detected in six environments with the highest LOD value of 13.4. Based on differentiate gene expression within the QTL cluster in an RNAseq data of Westonia and Kauz during grain filling, a glutamine synthesis gene (GS: TaGSr-4B) was selected as a potential candidate gene for the QTL. A SNP on the promoter region between Westonia and Kauz was used to develop a cleaved amplified polymorphic marker for TaGSr-4B gene mapping and QTL reanalysing. As results, TGW QTL appeared in seven environments, and in four out of seven environments, the TGW QTL were localized on the TaGSr-4B locus and showed significant contributions to the phenotype. Based on the marker, two allele groups of Westonia and Kauz formed showed significant differences on TGW in eight environments. In agreement with the roles of GS genes on nitrogen and carbon remobilizations, TaGSr-4B is likely the candidate gene of the TGW QTL on 4B and the TaGSr-4B gene marker is ready for wheat breeding.
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Affiliation(s)
- Fan Yang
- Australian-China Joint Centre for Wheat Improvement, Food Futures Institute, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
- Crop Research Institute, Sichuan Academy of Agricultural Sciences, 4 Shizishan Road, Chengdu, 610066, China
| | - Jingjuan Zhang
- Australian-China Joint Centre for Wheat Improvement, Food Futures Institute, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia.
| | - Yun Zhao
- Australian-China Joint Centre for Wheat Improvement, Food Futures Institute, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
- College of Agronomy, Qingdao Agriculture University, Qingdao, 266109, China
| | - Qier Liu
- Australian-China Joint Centre for Wheat Improvement, Food Futures Institute, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
| | - Shahidul Islam
- Australian-China Joint Centre for Wheat Improvement, Food Futures Institute, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
| | - Wuyun Yang
- Crop Research Institute, Sichuan Academy of Agricultural Sciences, 4 Shizishan Road, Chengdu, 610066, China
| | - Wujun Ma
- Australian-China Joint Centre for Wheat Improvement, Food Futures Institute, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia.
- College of Agronomy, Qingdao Agriculture University, Qingdao, 266109, China.
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10
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White J, Sharma R, Balding D, Cockram J, Mackay IJ. Genome-wide association mapping of Hagberg falling number, protein content, test weight, and grain yield in U.K. wheat. CROP SCIENCE 2022; 62:965-981. [PMID: 35915786 PMCID: PMC9314726 DOI: 10.1002/csc2.20692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 12/14/2021] [Indexed: 05/12/2023]
Abstract
Association mapping using crop cultivars allows identification of genetic loci of direct relevance to breeding. Here, 150 U.K. wheat (Triticum aestivum L.) cultivars genotyped with 23,288 single nucleotide polymorphisms (SNPs) were used for genome-wide association studies (GWAS) using historical phenotypic data for grain protein content, Hagberg falling number (HFN), test weight, and grain yield. Power calculations indicated experimental design would enable detection of quantitative trait loci (QTL) explaining ≥20% of the variation (PVE) at a relatively high power of >80%, falling to 40% for detection of a SNP with an R2 ≥ .5 with the same QTL. Genome-wide association studies identified marker-trait associations for all four traits. For HFN (h 2 = .89), six QTL were identified, including a major locus on chromosome 7B explaining 49% PVE and reducing HFN by 44 s. For protein content (h 2 = 0.86), 10 QTL were found on chromosomes 1A, 2A, 2B, 3A, 3B, and 6B, together explaining 48.9% PVE. For test weight, five QTL were identified (one on 1B and four on 3B; 26.3% PVE). Finally, 14 loci were identified for grain yield (h 2 = 0.95) on eight chromosomes (1A, 2A, 2B, 2D, 3A, 5B, 6A, 6B; 68.1% PVE), of which five were located within 16 Mbp of genetic regions previously identified as under breeder selection in European wheat. Our study demonstrates the utility of exploiting historical crop datasets, identifying genomic targets for independent validation, and ultimately for wheat genetic improvement.
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Affiliation(s)
- Jon White
- Genetics and Breeding Dep.NIAB93 Lawrence Weaver RoadCambridge, CB3 0LEUK
- Institute of GeneticsUniv. College LondonLondon, WC1E 6BTUK
| | - Rajiv Sharma
- Scotland's Rural College (SRUC)Kings Buildings, West Mains RoadEdinburgh, EH9 3JGUK
| | - David Balding
- Institute of GeneticsUniv. College LondonLondon, WC1E 6BTUK
- Current address: Melbourne Integrative GenomicsUniv. of MelbourneMelbourneAustralia
| | - James Cockram
- Genetics and Breeding Dep.NIAB93 Lawrence Weaver RoadCambridge, CB3 0LEUK
| | - Ian J. Mackay
- Scotland's Rural College (SRUC)Kings Buildings, West Mains RoadEdinburgh, EH9 3JGUK
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11
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Yield-Related QTL Clusters and the Potential Candidate Genes in Two Wheat DH Populations. Int J Mol Sci 2021; 22:ijms222111934. [PMID: 34769361 PMCID: PMC8585063 DOI: 10.3390/ijms222111934] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/21/2021] [Accepted: 10/28/2021] [Indexed: 11/17/2022] Open
Abstract
In the present study, four large-scale field trials using two doubled haploid wheat populations were conducted in different environments for two years. Grain protein content (GPC) and 21 other yield-related traits were investigated. A total of 227 QTL were mapped on 18 chromosomes, which formed 35 QTL clusters. The potential candidate genes underlying the QTL clusters were suggested. Furthermore, adding to the significant correlations between yield and its related traits, correlation variations were clearly shown within the QTL clusters. The QTL clusters with consistently positive correlations were suggested to be directly utilized in wheat breeding, including 1B.2, 2A.2, 2B (4.9–16.5 Mb), 2B.3, 3B (68.9–214.5 Mb), 4A.2, 4B.2, 4D, 5A.1, 5A.2, 5B.1, and 5D. The QTL clusters with negative alignments between traits may also have potential value for yield or GPC improvement in specific environments, including 1A.1, 2B.1, 1B.3, 5A.3, 5B.2 (612.1–613.6 Mb), 7A.1, 7A.2, 7B.1, and 7B.2. One GPC QTL (5B.2: 671.3–672.9 Mb) contributed by cultivar Spitfire was positively associated with nitrogen use efficiency or grain protein yield and is highly recommended for breeding use. Another GPC QTL without negatively pleiotropic effects on 2A (50.0–56.3 Mb), 2D, 4D, and 6B is suggested for quality wheat breeding.
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