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Du B, Wu J, Wang Q, Sun C, Sun G, Zhou J, Zhang L, Xiong Q, Ren X, Lu B. Genome-wide screening of meta-QTL and candidate genes controlling yield and yield-related traits in barley (Hordeum vulgare L.). PLoS One 2024; 19:e0303751. [PMID: 38768114 PMCID: PMC11104655 DOI: 10.1371/journal.pone.0303751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 04/30/2024] [Indexed: 05/22/2024] Open
Abstract
Increasing yield is an important goal of barley breeding. In this study, 54 papers published from 2001-2022 on QTL mapping for yield and yield-related traits in barley were collected, which contained 1080 QTLs mapped to the barley high-density consensus map for QTL meta-analysis. These initial QTLs were integrated into 85 meta-QTLs (MQTL) with a mean confidence interval (CI) of 2.76 cM, which was 7.86-fold narrower than the CI of the initial QTL. Among these 85 MQTLs, 68 MQTLs were validated in GWAS studies, and 25 breeder's MQTLs were screened from them. Seventeen barley orthologs of yield-related genes in rice and maize were identified within the hcMQTL region based on comparative genomics strategy and were presumed to be reliable candidates for controlling yield-related traits. The results of this study provide useful information for molecular marker-assisted breeding and candidate gene mining of yield-related traits in barley.
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Affiliation(s)
- Binbin Du
- College of Biotechnology and Pharmaceutical Engineering, West Anhui University, Lu’an, China
| | - Jia Wu
- College of Biotechnology and Pharmaceutical Engineering, West Anhui University, Lu’an, China
| | | | - Chaoyue Sun
- College of Biotechnology and Pharmaceutical Engineering, West Anhui University, Lu’an, China
| | - Genlou Sun
- Biology Department, Saint Mary’s University, Halifax, Canada
| | - Jie Zhou
- Lu’an Academy of Agricultural Science, Lu’an, China
| | - Lei Zhang
- Lu’an Academy of Agricultural Science, Lu’an, China
| | | | - Xifeng Ren
- Hubei Hongshan Laboratory, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Baowei Lu
- College of Biotechnology and Pharmaceutical Engineering, West Anhui University, Lu’an, China
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Kopeć P. Climate Change-The Rise of Climate-Resilient Crops. PLANTS (BASEL, SWITZERLAND) 2024; 13:490. [PMID: 38498432 PMCID: PMC10891513 DOI: 10.3390/plants13040490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/31/2024] [Accepted: 02/06/2024] [Indexed: 03/20/2024]
Abstract
Climate change disrupts food production in many regions of the world. The accompanying extreme weather events, such as droughts, floods, heat waves, and cold snaps, pose threats to crops. The concentration of carbon dioxide also increases in the atmosphere. The United Nations is implementing the climate-smart agriculture initiative to ensure food security. An element of this project involves the breeding of climate-resilient crops or plant cultivars with enhanced resistance to unfavorable environmental conditions. Modern agriculture, which is currently homogeneous, needs to diversify the species and cultivars of cultivated plants. Plant breeding programs should extensively incorporate new molecular technologies, supported by the development of field phenotyping techniques. Breeders should closely cooperate with scientists from various fields of science.
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Affiliation(s)
- Przemysław Kopeć
- The Franciszek Górski Institute of Plant Physiology, Polish Academy of Sciences, Niezapominajek 21, 30-239 Kraków, Poland
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Liu S, Jiang Y, Wang Y, Huo H, Cilkiz M, Chen P, Han Y, Li L, Wang K, Zhao M, Zhu L, Lei J, Wang Y, Zhang M. Genetic and molecular dissection of ginseng ( Panax ginseng Mey.) germplasm using high-density genic SNP markers, secondary metabolites, and gene expressions. FRONTIERS IN PLANT SCIENCE 2023; 14:1165349. [PMID: 37575919 PMCID: PMC10416250 DOI: 10.3389/fpls.2023.1165349] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 06/27/2023] [Indexed: 08/15/2023]
Abstract
Genetic and molecular knowledge of a species is crucial to its gene discovery and enhanced breeding. Here, we report the genetic and molecular dissection of ginseng, an important herb for healthy food and medicine. A mini-core collection consisting of 344 cultivars and landraces was developed for ginseng that represents the genetic variation of ginseng existing in its origin and diversity center. We sequenced the transcriptomes of all 344 cultivars and landraces; identified over 1.5 million genic SNPs, thereby revealing the genic diversity of ginseng; and analyzed them with 26,600 high-quality genic SNPs or a selection of them. Ginseng had a wide molecular diversity and was clustered into three subpopulations. Analysis of 16 ginsenosides, the major bioactive components for healthy food and medicine, showed that ginseng had a wide variation in the contents of all 16 ginsenosides and an extensive correlation of their contents, suggesting that they are synthesized through a single or multiple correlated pathways. Furthermore, we pair-wisely examined the relationships between the cultivars and landraces, revealing their relationships in gene expression, gene variation, and ginsenoside biosynthesis. These results provide new knowledge and new genetic and genic resources for advanced research and breeding of ginseng and related species.
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Affiliation(s)
- Sizhang Liu
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
| | - Yue Jiang
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
| | - Yanfang Wang
- College of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, Jilin, China
| | - Huimin Huo
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
| | - Mustafa Cilkiz
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Ping Chen
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
- Research Center for Ginseng Genetic Resources Development and Utilization, Jilin Province, Jilin Agricultural University, Changchun, Jilin, China
| | - Yilai Han
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
| | - Li Li
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
| | - Kangyu Wang
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
- Research Center for Ginseng Genetic Resources Development and Utilization, Jilin Province, Jilin Agricultural University, Changchun, Jilin, China
| | - Mingzhu Zhao
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
- Research Center for Ginseng Genetic Resources Development and Utilization, Jilin Province, Jilin Agricultural University, Changchun, Jilin, China
| | - Lei Zhu
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
| | - Jun Lei
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
- Research Center for Ginseng Genetic Resources Development and Utilization, Jilin Province, Jilin Agricultural University, Changchun, Jilin, China
| | - Yi Wang
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
- Research Center for Ginseng Genetic Resources Development and Utilization, Jilin Province, Jilin Agricultural University, Changchun, Jilin, China
| | - Meiping Zhang
- College of Life Science, Jilin Agricultural University, Changchun, Jilin, China
- Research Center for Ginseng Genetic Resources Development and Utilization, Jilin Province, Jilin Agricultural University, Changchun, Jilin, China
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4
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Ma J, Cao Y, Wang Y, Ding Y. Development of the maize 5.5K loci panel for genomic prediction through genotyping by target sequencing. FRONTIERS IN PLANT SCIENCE 2022; 13:972791. [PMID: 36438102 PMCID: PMC9691890 DOI: 10.3389/fpls.2022.972791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Genotyping platforms are important for genetic research and molecular breeding. In this study, a low-density genotyping platform containing 5.5K SNP markers was successfully developed in maize using genotyping by target sequencing (GBTS) technology with capture-in-solution. Two maize populations (Pop1 and Pop2) were used to validate the GBTS panel for genetic and molecular breeding studies. Pop1 comprised 942 hybrids derived from 250 inbred lines and four testers, and Pop2 contained 540 hybrids which were generated from 123 new-developed inbred lines and eight testers. The genetic analyses showed that the average polymorphic information content and genetic diversity values ranged from 0.27 to 0.38 in both populations using all filtered genotyping data. The mean missing rate was 1.23% across populations. The Structure and UPGMA tree analyses revealed similar genetic divergences (76-89%) in both populations. Genomic prediction analyses showed that the prediction accuracy of reproducing kernel Hilbert space (RKHS) was slightly lower than that of genomic best linear unbiased prediction (GBLUP) and three Bayesian methods for general combining ability of grain yield per plant and three yield-related traits in both populations, whereas RKHS with additive effects showed superior advantages over the other four methods in Pop1. In Pop1, the GBLUP and three Bayesian methods with additive-dominance model improved the prediction accuracies by 4.89-134.52% for the four traits in comparison to the additive model. In Pop2, the inclusion of dominance did not improve the accuracy in most cases. In general, low accuracies (0.33-0.43) were achieved for general combing ability of the four traits in Pop1, whereas moderate-to-high accuracies (0.52-0.65) were observed in Pop2. For hybrid performance prediction, the accuracies were moderate to high (0.51-0.75) for the four traits in both populations using the additive-dominance model. This study suggests a reliable genotyping platform that can be implemented in genomic selection-assisted breeding to accelerate maize new cultivar development and improvement.
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Zhang M, Liu YH, Wang Y, Sze SH, Scheuring CF, Qi X, Ekinci O, Pekar J, Murray SC, Zhang HB. Genome-wide identification of genes enabling accurate prediction of hybrid performance from parents across environments and populations for gene-based breeding in maize. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 324:111424. [PMID: 35995113 DOI: 10.1016/j.plantsci.2022.111424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/07/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Accurate prediction of hybrid offspring complex trait phenotype from parents is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report genome-wide identification of genes enabling accurate prediction of hybrid offspring complex traits from parents using maize grain yield as the target trait. We identified 181 ZmF1GY genes enabling prediction of maize (Zea mays L.) F1 hybrid grain yield from parents and tested their utility and efficiency for predicting F1 hybrid grain yields from parents using their expressions, genic SNPs, and number of favorable alleles (NFAs), respectively. The ZmF1GY genes predicted hybrid grain yields from parents at an accuracy of 0.86, presented by correlation coefficient between predicted and observed phenotypes, within an environment, 0.74 across environments, and 0.64 across populations, outperforming genomic prediction by 27-406%, 23%, and 40%, respectively. Furthermore, we identified nine of the ZmF1GY genes containing SNPs or InDels in parents that increased or decreased hybrid grain yields by 14-46%. When the NFAs of these nine ZmF1GY genes were used for hybrid grain yield prediction from parents, they predicted hybrid grain yields at an accuracy of 0.79, outperforming genomic prediction by 21% that was based on up to tens of thousands of genome-wide SNPs. These results demonstrate the feasibility of developing a gene toolkit for a species enabling gene-based breeding across environments and populations that is much more powerful and efficient than current breeding, thereby helping secure the world's food production. The methodology is applicable to all crops, livestock, and humans.
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Affiliation(s)
- Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Yun-Hua Liu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Yinglei Wang
- Department of Computer Science, Cornell University, Ithaca, NY 14853, USA.
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA.
| | - Chantel F Scheuring
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Xiaoli Qi
- College of Life Science, Jiamusi University, Jiamusi, Heilongjiang 154007, China.
| | - Ozge Ekinci
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Jacob Pekar
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Hong-Bin Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
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Liu YH, Zhang M, Sze SH, Smith CW, Zhang HB. Analysis of the genes controlling cotton fiber length reveals the molecular basis of plant breeding and the genetic potential of current cultivars for continued improvement. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 321:111318. [PMID: 35696918 DOI: 10.1016/j.plantsci.2022.111318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 05/02/2022] [Accepted: 05/08/2022] [Indexed: 06/15/2023]
Abstract
Stagnated crop improvement has raised questions of whether and how current crop cultivars can be further improved. Genes are the core determinants of performance of all cultivars. Here, we report the molecular basis of plant breeding and address these questions by analyzing 226 GFL genes controlling and accurately predicting fiber length, an important breeding objective trait, in cotton (Gossypium sp.). We first identified the favorable allele and the number of favorable alleles (NFAs) of each GFL gene, calculated the total NFAs of the 226 GFL genes accumulated in 198 advanced breeding lines, and analyzed them against fiber lengths. Fiber lengths of the breeding lines were strongly correlated with the total NFAs of the GFL genes (r = 0.85, P < 0.0001), suggesting that accumulation of the favorable alleles of the genes controlling objective traits is the molecular basis of cotton breeding. Surprisingly, a breeding line with a fiber length of present cultivars having the longest fibers contained only about 51% of the total NFAs of the 226 GFL genes. The genetic potentials of current cultivars were then predicted using linear and non-linear models, respectively, revealing that a breeding line or cultivar with a fiber length of 33.8 mm could be further improved in fiber length by up to 118%. Finally, we showed that the genetic potential of such a breeding line can be realized through gene-based breeding. Therefore, these findings shed light on continued crop improvement in general and provide 740 genic biomarkers desirable for enhanced cotton fiber breeding.
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Affiliation(s)
- Yun-Hua Liu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering, and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA.
| | - C Wayne Smith
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Hong-Bin Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
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7
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Conaty WC, Broughton KJ, Egan LM, Li X, Li Z, Liu S, Llewellyn DJ, MacMillan CP, Moncuquet P, Rolland V, Ross B, Sargent D, Zhu QH, Pettolino FA, Stiller WN. Cotton Breeding in Australia: Meeting the Challenges of the 21st Century. FRONTIERS IN PLANT SCIENCE 2022; 13:904131. [PMID: 35646011 PMCID: PMC9136452 DOI: 10.3389/fpls.2022.904131] [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: 03/25/2022] [Accepted: 04/08/2022] [Indexed: 06/15/2023]
Abstract
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program's commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.
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Affiliation(s)
| | | | - Lucy M. Egan
- CSIRO Agriculture and Food, Narrabri, NSW, Australia
| | - Xiaoqing Li
- CSIRO Agriculture and Food, Canberra, ACT, Australia
| | - Zitong Li
- CSIRO Agriculture and Food, Canberra, ACT, Australia
| | - Shiming Liu
- CSIRO Agriculture and Food, Narrabri, NSW, Australia
| | | | | | | | | | - Brett Ross
- Cotton Seed Distributors Ltd., Wee Waa, NSW, Australia
| | - Demi Sargent
- CSIRO Agriculture and Food, Narrabri, NSW, Australia
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
| | - Qian-Hao Zhu
- CSIRO Agriculture and Food, Canberra, ACT, Australia
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8
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Liu YH, Zhang M, Scheuring CF, Cilkiz M, Sze SH, Smith CW, Murray SC, Xu W, Zhang HB. Accurate prediction of complex traits for individuals and offspring from parents using a simple, rapid, and efficient method for gene-based breeding in cotton and maize. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 316:111153. [PMID: 35151437 DOI: 10.1016/j.plantsci.2021.111153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/11/2021] [Indexed: 06/14/2023]
Abstract
Accurate, simple, rapid, and inexpensive prediction of complex traits controlled by numerous genes is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report a novel method that enables accurate, simple, and rapid prediction of complex traits of individuals or offspring from parents based on the number of favorable alleles (NFAs) of the genes controlling the objective traits. The NFAs of 226 cotton fiber length (GFL) genes and nine maize hybrid grain yield related (ZmF1GY) genes were directly used to predict cotton fiber lengths of individual plants and maize grain yields of F1 hybrids from parents, respectively, using prediction model-based methods as controls. The NFAs of the 226 GFL genes predicted cotton fiber lengths at an accuracy of 0.85, as the model methods and outperforming genomic prediction by 82 % - 170 %. The NFAs of the nine ZmF1GY genes predicted grain yields of maize hybrids from parents at an accuracy of 0.80, outperforming genomic prediction by 67 %. Moreover, the prediction accuracies of these traits were consistent across years, environments, and eco-agricultural systems. Importantly, the accurate prediction of these traits directly using the NFAs of the genes allows breeding to be performed in greenhouse, phytotron, or off-season, without the need of the model training and validation steps essential and costly for model-based genomic or genic prediction. Therefore, this new method dramatically outperforms the current model-based genomic methods used for phenotype prediction and streamlines the process of breeding, thus promising to substantially enhance current plant and animal breeding.
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Affiliation(s)
- Yun-Hua Liu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Chantel F Scheuring
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Mustafa Cilkiz
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA
| | - C Wayne Smith
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Wenwei Xu
- Texas A&M AgriLife Research, Lubbock, TX 79403, USA
| | - Hong-Bin Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
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9
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Shariatipour N, Heidari B, Tahmasebi A, Richards C. Comparative Genomic Analysis of Quantitative Trait Loci Associated With Micronutrient Contents, Grain Quality, and Agronomic Traits in Wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2021; 12:709817. [PMID: 34712248 PMCID: PMC8546302 DOI: 10.3389/fpls.2021.709817] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/06/2021] [Indexed: 05/02/2023]
Abstract
Comparative genomics and meta-quantitative trait loci (MQTLs) analysis are important tools for the identification of reliable and stable QTLs and functional genes controlling quantitative traits. We conducted a meta-analysis to identify the most stable QTLs for grain yield (GY), grain quality traits, and micronutrient contents in wheat. A total of 735 QTLs retrieved from 27 independent mapping populations reported in the last 13 years were used for the meta-analysis. The results showed that 449 QTLs were successfully projected onto the genetic consensus map which condensed to 100 MQTLs distributed on wheat chromosomes. This consolidation of MQTLs resulted in a three-fold reduction in the confidence interval (CI) compared with the CI for the initial QTLs. Projection of QTLs revealed that the majority of QTLs and MQTLs were in the non-telomeric regions of chromosomes. The majority of micronutrient MQTLs were located on the A and D genomes. The QTLs of thousand kernel weight (TKW) were frequently associated with QTLs for GY and grain protein content (GPC) with co-localization occurring at 55 and 63%, respectively. The co- localization of QTLs for GY and grain Fe was found to be 52% and for QTLs of grain Fe and Zn, it was found to be 66%. The genomic collinearity within Poaceae allowed us to identify 16 orthologous MQTLs (OrMQTLs) in wheat, rice, and maize. Annotation of promising candidate genes (CGs) located in the genomic intervals of the stable MQTLs indicated that several CGs (e.g., TraesCS2A02G141400, TraesCS3B02G040900, TraesCS4D02G323700, TraesCS3B02G077100, and TraesCS4D02G290900) had effects on micronutrients contents, yield, and yield-related traits. The mapping refinements leading to the identification of these CGs provide an opportunity to understand the genetic mechanisms driving quantitative variation for these traits and apply this information for crop improvement programs.
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Affiliation(s)
- Nikwan Shariatipour
- Department of Plant Production and Genetics, School of Agriculture, Shiraz University, Shiraz, Iran
| | - Bahram Heidari
- Department of Plant Production and Genetics, School of Agriculture, Shiraz University, Shiraz, Iran
| | - Ahmad Tahmasebi
- Department of Plant Production and Genetics, School of Agriculture, Shiraz University, Shiraz, Iran
| | - Christopher Richards
- USDA ARS National Laboratory for Genetic Resources Preservation, Fort Collins, CO, United States
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10
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Lyra DH, Griffiths CA, Watson A, Joynson R, Molero G, Igna AA, Hassani-Pak K, Reynolds MP, Hall A, Paul MJ. Gene-based mapping of trehalose biosynthetic pathway genes reveals association with source- and sink-related yield traits in a spring wheat panel. Food Energy Secur 2021; 10:e292. [PMID: 34594548 PMCID: PMC8459250 DOI: 10.1002/fes3.292] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/12/2021] [Accepted: 04/12/2021] [Indexed: 12/11/2022] Open
Abstract
Trehalose 6‐phosphate (T6P) signalling regulates carbon use and allocation and is a target to improve crop yields. However, the specific contributions of trehalose phosphate synthase (TPS) and trehalose phosphate phosphatase (TPP) genes to source‐ and sink‐related traits remain largely unknown. We used enrichment capture sequencing on TPS and TPP genes to estimate and partition the genetic variation of yield‐related traits in a spring wheat (Triticum aestivum) breeding panel specifically built to capture the diversity across the 75,000 CIMMYT wheat cultivar collection. Twelve phenotypes were correlated to variation in TPS and TPP genes including plant height and biomass (source), spikelets per spike, spike growth and grain filling traits (sink) which showed indications of both positive and negative gene selection. Individual genes explained proportions of heritability for biomass and grain‐related traits. Three TPS1 homologues were particularly significant for trait variation. Epistatic interactions were found within and between the TPS and TPP gene families for both plant height and grain‐related traits. Gene‐based prediction improved predictive ability for grain weight when gene effects were combined with the whole‐genome markers. Our study has generated a wealth of information on natural variation of TPS and TPP genes related to yield potential which confirms the role for T6P in resource allocation and in affecting traits such as grain number and size confirming other studies which now opens up the possibility of harnessing natural genetic variation more widely to better understand the contribution of native genes to yield traits for incorporation into breeding programmes.
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Affiliation(s)
- Danilo H Lyra
- Computational & Analytical Sciences Rothamsted Research Harpenden UK
| | | | - Amy Watson
- Plant Sciences Rothamsted Research Harpenden UK
| | | | - Gemma Molero
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT) Texcoco Mexico
| | | | | | - Matthew P Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT) Texcoco Mexico
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11
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Song JM, Arif M, Zi Y, Sze SH, Zhang M, Zhang HB. Molecular and genetic dissection of the USDA rice mini-core collection using high-density SNP markers. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2021; 308:110910. [PMID: 34034867 DOI: 10.1016/j.plantsci.2021.110910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 04/05/2021] [Accepted: 04/10/2021] [Indexed: 06/12/2023]
Abstract
Molecular tools and knowledge of crop germplasm are vital for their effective utilization. In this study, we developed 40,866 high-quality and well distributed SNPs for a rice mini-core collection (RMC) developed by the United States Department of Agriculture (USDA). The high-quality SNPs clustered the USDA-RMC into five subpopulations (Ind, indica; Aus, aus; Afr, African rice; TeJ, temperate japonica; TrJ, tropical japonica) and one admixture (Adm). This classification was further confirmed by phylogenetic and principal component analyses. The rice ARO (aromatic) subpopulation of previous studies was re-assigned with Adm and the WD (wild-type) subpopulation was re-defined to the Afr subpopulation because most of its accessions are African cultivated rice. The Aus and Ind subpopulations had a substantially wider genetic variation than the TrJ and TeJ subpopulations. The genetic diversities were much larger between the Ind or Aus subpopulation and the TrJ or TeJ subpopulation than between the Afr subpopulation and the Ind, Aus, TrJ or TeJ subpopulation. Comparative agronomic trait analysis between the subpopulations also supported the genetic structure and variation of the RMC, and suggested the existence of extensive variation in the genes controlling agronomic traits among them. Furthermore, analysis of ancestral membership of the RMC accessions revealed that reproductive barrier or wide incompatibility existed between the Indica and Japonica groups, while gene flow occurred between them. These results provide high-quality SNPs and knowledge of genetic structure and diversity of the USDA-RMC necessary for enhanced rice research and breeding.
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Affiliation(s)
- Jian-Min Song
- Crop Research Institute/National Engineering Laboratory for Wheat and Maize, Shandong Academy of Agricultural Sciences (SAAS), Jinan, 250100, PR China; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843-2474, USA.
| | - Muhammad Arif
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843-2474, USA; Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering, Faisalabad, Pakistan.
| | - Yan Zi
- Crop Research Institute/National Engineering Laboratory for Wheat and Maize, Shandong Academy of Agricultural Sciences (SAAS), Jinan, 250100, PR China
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA.
| | - Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843-2474, USA.
| | - Hong-Bin Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843-2474, USA.
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Analysis of the genes controlling three quantitative traits in three diverse plant species reveals the molecular basis of quantitative traits. Sci Rep 2020; 10:10074. [PMID: 32572040 PMCID: PMC7308372 DOI: 10.1038/s41598-020-66271-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 04/28/2020] [Indexed: 02/08/2023] Open
Abstract
Most traits of agricultural importance are quantitative traits controlled by numerous genes. However, it remains unclear about the molecular mechanisms underpinning quantitative traits. Here, we report the molecular characteristics of the genes controlling three quantitative traits randomly selected from three diverse plant species, including ginsenoside biosynthesis in ginseng (Panax ginseng C.A. Meyer), fiber length in cotton (Gossypium hirsutum L. and G. barbadense L.) and grain yield in maize (Zea mays L.). We found that a vast majority of the genes controlling a quantitative trait were significantly more likely spliced into multiple transcripts while they expressed. Nevertheless, only one to four, but not all, of the transcripts spliced from each of the genes were significantly correlated with the phenotype of the trait. The genes controlling a quantitative trait were multiple times more likely to form a co-expression network than other genes expressed in an organ. The network varied substantially among genotypes of a species and was associated with their phenotypes. These findings indicate that the genes controlling a quantitative trait are more likely pleiotropic and functionally correlated, thus providing new insights into the molecular basis underpinning quantitative traits and knowledge necessary to develop technologies for efficient manipulation of quantitative traits.
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Liu YH, Xu Y, Zhang M, Cui Y, Sze SH, Smith CW, Xu S, Zhang HB. Accurate Prediction of a Quantitative Trait Using the Genes Controlling the Trait for Gene-Based Breeding in Cotton. FRONTIERS IN PLANT SCIENCE 2020; 11:583277. [PMID: 33281846 PMCID: PMC7690289 DOI: 10.3389/fpls.2020.583277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 10/15/2020] [Indexed: 05/03/2023]
Abstract
Accurate phenotype prediction of quantitative traits is paramount to enhanced plant research and breeding. Here, we report the accurate prediction of cotton fiber length, a typical quantitative trait, using 474 cotton (Gossypium ssp.) fiber length (GFL) genes and nine prediction models. When the SNPs/InDels contained in 226 of the GFL genes or the expressions of all 474 GFL genes was used for fiber length prediction, a prediction accuracy of r = 0.83 was obtained, approaching the maximally possible prediction accuracy of a quantitative trait. This has improved by 116%, the prediction accuracies of the fiber length thus far achieved for genomic selection using genome-wide random DNA markers. Moreover, analysis of the GFL genes identified 125 of the GFL genes that are key to accurate prediction of fiber length, with which a prediction accuracy similar to that of all 474 GFL genes was obtained. The fiber lengths of the plants predicted with expressions of the 125 key GFL genes were significantly correlated with those predicted with the SNPs/InDels of the above 226 SNP/InDel-containing GFL genes (r = 0.892, P = 0.000). The prediction accuracies of fiber length using both genic datasets were highly consistent across environments or generations. Finally, we found that a training population consisting of 100-120 plants was sufficient to train a model for accurate prediction of a quantitative trait using the genes controlling the trait. Therefore, the genes controlling a quantitative trait are capable of accurately predicting its phenotype, thereby dramatically improving the ability, accuracy, and efficiency of phenotype prediction and promoting gene-based breeding in cotton and other species.
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Affiliation(s)
- Yun-Hua Liu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Yang Xu
- Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Yanru Cui
- Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - C. Wayne Smith
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Shizhong Xu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
- *Correspondence: Shizhong Xu,
| | - Hong-Bin Zhang
- Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
- Hong-Bin Zhang,
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