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Kaur N, Lozada DN, Bhatta M, Barchenger DW, Khokhar ES, Nourbakhsh SS, Sanogo S. Insights into the genetic architecture of Phytophthora capsici root rot resistance in chile pepper (Capsicum spp.) from multi-locus genome-wide association study. BMC PLANT BIOLOGY 2024; 24:416. [PMID: 38760676 PMCID: PMC11100198 DOI: 10.1186/s12870-024-05097-2] [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: 06/21/2023] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Phytophthora root rot, a major constraint in chile pepper production worldwide, is caused by the soil-borne oomycete, Phytophthora capsici. This study aimed to detect significant regions in the Capsicum genome linked to Phytophthora root rot resistance using a panel consisting of 157 Capsicum spp. genotypes. Multi-locus genome wide association study (GWAS) was conducted using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing (GBS). Individual plants were separately inoculated with P. capsici isolates, 'PWB-185', 'PWB-186', and '6347', at the 4-8 leaf stage and were scored for disease symptoms up to 14-days post-inoculation. Disease scores were used to calculate disease parameters including disease severity index percentage, percent of resistant plants, area under disease progress curve, and estimated marginal means for each genotype. RESULTS Most of the genotypes displayed root rot symptoms, whereas five accessions were completely resistant to all the isolates and displayed no symptoms of infection. A total of 55,117 SNP markers derived from GBS were used to perform multi-locus GWAS which identified 330 significant SNP markers associated with disease resistance. Of these, 56 SNP markers distributed across all the 12 chromosomes were common across the isolates, indicating association with more durable resistance. Candidate genes including nucleotide-binding site leucine-rich repeat (NBS-LRR), systemic acquired resistance (SAR8.2), and receptor-like kinase (RLKs), were identified within 0.5 Mb of the associated markers. CONCLUSIONS Results will be used to improve resistance to Phytophthora root rot in chile pepper by the development of Kompetitive allele-specific markers (KASP®) for marker validation, genomewide selection, and marker-assisted breeding.
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Affiliation(s)
- Navdeep Kaur
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
- Current address: Department of Horticultural Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Dennis N Lozada
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA.
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, 88003, USA.
| | | | | | - Ehtisham S Khokhar
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
| | - Seyed Shahabeddin Nourbakhsh
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
- Department of Extension Plant Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
| | - Soum Sanogo
- Department of Entomology, Plant Pathology and Weed Science, New Mexico State University, Las Cruces, NM, 88003, USA
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Wondimu Z, Dong H, Paterson AH, Worku W, Bantte K. Genome-wide association study reveals genomic loci influencing agronomic traits in Ethiopian sorghum ( Sorghum bicolor (L.) Moench) landraces. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:32. [PMID: 37312746 PMCID: PMC10248676 DOI: 10.1007/s11032-023-01381-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 04/11/2023] [Indexed: 06/15/2023]
Abstract
Uncovering the genetic basis of agronomic traits in sorghum landraces that have adapted to various agro-climatic conditions would contribute to sorghum improvement efforts around the world. To identify quantitative trait nucleotides (QTNs) associated with nine agronomic traits in a panel of 304 sorghum accessions collected from diverse environments across Ethiopia (considered to be the center of origin and diversity), multi-locus genome-wide association studies (ML-GWAS) were performed using 79,754 high quality single nucleotide polymorphism (SNP) markers. Association analyses using six ML-GWAS models identified a set of 338 significantly (LOD ≥ 3)-associated QTNs for nine agronomic traits of sorghum accessions evaluated in two environments (E1 and E2) and their combined dataset (Em). Of these, 121 reliable QTNs, including 13 for flowering time (DF), 13 for plant height (PH), 9 for tiller number (TN), 15 for panicle weight (PWT), 30 for grain yield per panicle (GYP), 12 for structural panicle mass (SPM), 13 for hundred seed weight (HSW), 6 for grain number per panicle (GNP), and 10 for panicle exertion (PE) were consistently detected by at least three ML-GWAS methods and/or in two different environments. Notably, Ethylene responsive transcription factor gene AP2/ERF, known for regulation of plant growth, and the sorghum Terminal flower1/TF1 gene, which functions in the control of floral architecture, were identified as strong candidate genes associated with PH and HSW, respectively. This study provides an entry point for further validation studies to elucidate complex mechanisms controlling important agronomic traits in sorghum. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01381-5.
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Affiliation(s)
- Zeleke Wondimu
- College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Hongxu Dong
- Plant Genome Mapping Laboratory, University of Georgia, Athens, GA 30602 USA
| | - Andrew H. Paterson
- Plant Genome Mapping Laboratory, University of Georgia, Athens, GA 30602 USA
| | - Walelign Worku
- College of Agriculture, Hawassa University, P.O. Box 05, Hawassa, Ethiopia
| | - Kassahun Bantte
- College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
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Zhang A, Zhao T, Hu X, Zhou Y, An Y, Pei H, Sun D, Sun G, Li C, Ren X. Identification of QTL underlying the main stem related traits in a doubled haploid barley population. FRONTIERS IN PLANT SCIENCE 2022; 13:1063988. [PMID: 36531346 PMCID: PMC9751491 DOI: 10.3389/fpls.2022.1063988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/14/2022] [Indexed: 06/17/2023]
Abstract
Lodging reduces grain yield in cereal crops. The height, diameter and strength of stem are crucial for lodging resistance, grain yield, and photosynthate transport in barley. Understanding the genetic basis of stem benefits barley breeding. Here, we evaluated 13 stem related traits after 28 days of heading in a barley DH population in two consecutive years. Significant phenotypic correlations between lodging index (LI) and other stem traits were observed. Three mapping methods using the experimental data and the BLUP data, detected 27 stable and major QTLs, and 22 QTL clustered regions. Many QTLs were consistent with previously reported traits for grain filling rate, internodes, panicle and lodging resistance. Further, candidate genes were predicted for stable and major QTLs and were associated with plant development and adverse stress in the transition from vegetative stage to reproductive stage. This study provided potential genetic basis and new information for exploring barley stem morphology, and laid a foundation for map-based cloning and further fine mapping of these QTLs.
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Affiliation(s)
- Anyong Zhang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Ting Zhao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xue Hu
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yu Zhou
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yue An
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Haiyi Pei
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Dongfa Sun
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Genlou Sun
- Department of Biology, Saint Mary’s University, Halifax, NS, Canada
| | - Chengdao Li
- College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Xifeng Ren
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
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Gahlaut V, Jaiswal V, Balyan HS, Joshi AK, Gupta PK. Multi-Locus GWAS for Grain Weight-Related Traits Under Rain-Fed Conditions in Common Wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2021; 12:758631. [PMID: 34745191 PMCID: PMC8568012 DOI: 10.3389/fpls.2021.758631] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 09/20/2021] [Indexed: 05/04/2023]
Abstract
In wheat, a multi-locus genome-wide association study (ML-GWAS) was conducted for the four grain weight-related traits (days to anthesis, grain filling duration, grain number per ear, and grain weight per ear) using data recorded under irrigated (IR) and rain-fed (RF) conditions. Seven stress-related indices were estimated for these four traits: (i) drought resistance index (DI), (ii) geometric mean productivity (GMP), (iii) mean productivity index (MPI), (iv) relative drought index (RDI), (v) stress tolerance index (STI), (vi) yield index, and (vii) yield stability index (YSI). The association panel consisted of a core collection of 320 spring wheat accessions representing 28 countries. The panel was genotyped using 9,627 single nucleotide polymorphisms (SNPs). The genome-wide association (GWA) analysis provided 30 significant marker-trait associations (MTAs), distributed as follows: (i) IR (15 MTAs), (ii) RF (14 MTAs), and (iii) IR+RF (1 MTA). In addition, 153 MTAs were available for the seven stress-related indices. Five MTAs co-localized with previously reported QTLs/MTAs. Candidate genes (CGs) associated with different MTAs were also worked out. Gene ontology (GO) analysis and expression analysis together allowed the selection of the two CGs, which may be involved in response to drought stress. These two CGs included: TraesCS1A02G331000 encoding RNA helicase and TraesCS4B02G051200 encoding microtubule-associated protein 65. The results supplemented the current knowledge on genetics for drought tolerance in wheat. The results may also be used for future wheat breeding programs to develop drought-tolerant wheat cultivars.
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Affiliation(s)
- Vijay Gahlaut
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
- Council of Scientific & Industrial Research-Institute of Himalayan Bioresource Technology, Palampur, India
| | - Vandana Jaiswal
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
- Council of Scientific & Industrial Research-Institute of Himalayan Bioresource Technology, Palampur, India
| | - Harindra S. Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| | - Arun Kumar Joshi
- International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India
- Borlaug Institute for South Asia (BISA), New Delhi, India
| | - Pushpendra K. Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
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Khan SU, Saeed S, Khan MHU, Fan C, Ahmar S, Arriagada O, Shahzad R, Branca F, Mora-Poblete F. Advances and Challenges for QTL Analysis and GWAS in the Plant-Breeding of High-Yielding: A Focus on Rapeseed. Biomolecules 2021; 11:1516. [PMID: 34680149 PMCID: PMC8533950 DOI: 10.3390/biom11101516] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/07/2021] [Accepted: 10/11/2021] [Indexed: 12/15/2022] Open
Abstract
Yield is one of the most important agronomic traits for the breeding of rapeseed (Brassica napus L), but its genetic dissection for the formation of high yield remains enigmatic, given the rapid population growth. In the present review, we review the discovery of major loci underlying important agronomic traits and the recent advancement in the selection of complex traits. Further, we discuss the benchmark summary of high-throughput techniques for the high-resolution genetic breeding of rapeseed. Biparental linkage analysis and association mapping have become powerful strategies to comprehend the genetic architecture of complex agronomic traits in crops. The generation of improved crop varieties, especially rapeseed, is greatly urged to enhance yield productivity. In this sense, the whole-genome sequencing of rapeseed has become achievable to clone and identify quantitative trait loci (QTLs). Moreover, the generation of high-throughput sequencing and genotyping techniques has significantly enhanced the precision of QTL mapping and genome-wide association study (GWAS) methodologies. Furthermore, this study demonstrates the first attempt to identify novel QTLs of yield-related traits, specifically focusing on ovule number per pod (ON). We also highlight the recent breakthrough concerning single-locus-GWAS (SL-GWAS) and multi-locus GWAS (ML-GWAS), which aim to enhance the potential and robust control of GWAS for improved complex traits.
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Affiliation(s)
- Shahid Ullah Khan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Sumbul Saeed
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Muhammad Hafeez Ullah Khan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Chuchuan Fan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Sunny Ahmar
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3465548, Chile;
| | - Osvin Arriagada
- Departamento de Ciencias Vegetales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile;
| | - Raheel Shahzad
- Department of Biotechnology, Faculty of Science & Technology, Universitas Muhammadiyah Bandung, Bandung 40614, Indonesia;
| | - Ferdinando Branca
- Department of Agriculture, Food and Environment (Di3A), University of Catania, 95123 Catania, Italy;
| | - Freddy Mora-Poblete
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3465548, Chile;
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6
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Zhong H, Liu S, Sun T, Kong W, Deng X, Peng Z, Li Y. Multi-locus genome-wide association studies for five yield-related traits in rice. BMC PLANT BIOLOGY 2021; 21:364. [PMID: 34376143 PMCID: PMC8353822 DOI: 10.1186/s12870-021-03146-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 07/27/2021] [Indexed: 05/14/2023]
Abstract
BACKGROUND Improving the overall production of rice with high quality is a major target of breeders. Mining potential yield-related loci have been geared towards developing efficient rice breeding strategies. In this study, one single-locus genome-wide association studies (SL-GWAS) method (MLM) in conjunction with five multi-locus genome-wide association studies (ML-GWAS) approaches (mrMLM, FASTmrMLM, pLARmEB, pKWmEB, and ISIS EM-BLASSO) were conducted in a panel consisting of 529 rice core varieties with 607,201 SNPs. RESULTS A total of 152, 106, 12, 111, and 64 SNPs were detected by the MLM model associated with the five yield-related traits, namely grain length (GL), grain width (GW), grain thickness (GT), thousand-grain weight (TGW), and yield per plant (YPP), respectively. Furthermore, 74 significant quantitative trait nucleotides (QTNs) were presented across at least two ML-GWAS methods to be associated with the above five traits successively. Finally, 20 common QTNs were simultaneously discovered by both SL-GWAS and ML-GWAS methods. Based on genome annotation, gene expression analysis, and previous studies, two candidate key genes (LOC_Os09g02830 and LOC_Os07g31450) were characterized to affect GW and TGW, separately. CONCLUSIONS These outcomes will provide an indication for breeding high-yielding rice varieties in the immediate future.
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Affiliation(s)
- Hua Zhong
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, People's Republic of China, 430072
| | - Shuai Liu
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Starkville, MS, 39762, USA
| | - Tong Sun
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, People's Republic of China, 430072
| | - Weilong Kong
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, People's Republic of China, 430072
| | - Xiaoxiao Deng
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, People's Republic of China, 430072
| | - Zhaohua Peng
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Starkville, MS, 39762, USA
| | - Yangsheng Li
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, People's Republic of China, 430072.
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7
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Rahman H, Kebede B. Mapping of seed quality traits in the C genome of Brassica napus by using a population carrying genome content of B. oleracea and their effect on other traits. THE PLANT GENOME 2021; 14:e20078. [PMID: 33818008 DOI: 10.1002/tpg2.20078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 11/21/2020] [Indexed: 06/12/2023]
Abstract
Increasing seed oil and protein contents and reducing the content of seed glucosinolates (GSLs) in Brassica oilseed crops are important objectives in breeding. By using an oilseed rape (B. napus L.) doubled-haploid (DH) population carrying genome content introgressed from Chinese kale (B. oleracea L.), we mapped quantitative trait loci (QTL) for these seed quality traits and investigated their effect on other traits including seed yield. A stable QTL for seed oil content was identified on chromosome C5 at 40-42 Mb position and a QTL for seed GSL content was identified on C9 at 7-8 Mb position. The C5 and C9 QTL alleles for high oil and GSL contents were derived from Chinese kale, demonstrating that high-oil QTL allele can be found in the parental species of oilseed rape. The low-GSL QTL allele of C9 exerted a significant positive effect on seed protein content, demonstrating that selection for this QTL allele contributed to higher protein content in canola seed. These two QTL were not affected by field environmental conditions and did not exert a significant effect on days to flowering and seed yield. Thus, the genomic regions and the molecular markers identified in this study should be useful in molecular breeding of the seed quality traits in oilseed rape.
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Affiliation(s)
- Habibur Rahman
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Berisso Kebede
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada
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Li S, Zhang C, Yang D, Lu M, Qian Y, Jin F, Liu X, Wang Y, Liu W, Li X. Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS. Sci Rep 2021; 11:1764. [PMID: 33469070 PMCID: PMC7815807 DOI: 10.1038/s41598-020-80391-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 12/21/2020] [Indexed: 11/10/2022] Open
Abstract
Maize is China’s largest grain crop. Mechanical grain harvesting is the key technology in maize production, and the kernel moisture concentration (KMC) is the main controlling factor in mechanical maize harvesting in China. The kernel dehydration rate (KDR) is closely related to the KMC. Thus, it is important to conduct genome-wide association studies (GWAS) of the KMC and KDR in maize, detect relevant quantitative trait nucleotides (QTNs), and mine relevant candidate genes. Here, 132 maize inbred lines were used to measure the KMC every 5 days from 10 to 40 days after pollination (DAP) in order to calculate the KDR. These lines were genotyped using a maize 55K single-nucleotide polymorphism array. QTNs for the KMC and KDR were detected based on five methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, and ISIS EM-BLASSO) in the package mrMLM. A total of 334 significant QTNs were found for both the KMC and KDR, including 175 QTNs unique to the KMC and 178 QTNs unique to the KDR; 116 and 58 QTNs were detected among the 334 QTNs by two and more than two methods, respectively; and 9 and 5 QTNs among 58 QTNs were detected in 2 and 3 years, respectively. A significant enrichment in cellular component was revealed by Gene Ontology enrichment analysis of candidate genes in the intervals adjacent to the 14 QTNs and this category contained five genes. The information provided in this study may be useful for further mining of genes associated with the KMC and KDR in maize.
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Affiliation(s)
- Shufang Li
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Chunxiao Zhang
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Deguang Yang
- College of Agronomy, Northeast Agricultural University, Harbin, 150030, China
| | - Ming Lu
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, 136100, China
| | - Yiliang Qian
- Maize Research Center, Anhui Academy of Agricultural Science, Hefei, 230001, China
| | - Fengxue Jin
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Xueyan Liu
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Yu Wang
- Gongzhuling Meteorological Bureau, Gongzhuling, 136100, China
| | - Wenguo Liu
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, 136100, China.
| | - Xiaohui Li
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China.
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Lin Y, Zhou K, Hu H, Jiang X, Yu S, Wang Q, Li C, Ma J, Chen G, Yang Z, Liu Y. Multi-Locus Genome-Wide Association Study of Four Yield-Related Traits in Chinese Wheat Landraces. FRONTIERS IN PLANT SCIENCE 2021; 12:665122. [PMID: 34484253 PMCID: PMC8415402 DOI: 10.3389/fpls.2021.665122] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 07/20/2021] [Indexed: 05/13/2023]
Abstract
Wheat (Triticum aestivum L.) is one of the most important crops in the world. Here, four yield-related traits, namely, spike length, spikelets number, tillers number, and thousand-kernel weight, were evaluated in 272 Chinese wheat landraces in multiple environments. Five multi-locus genome-wide association studies (FASTmrEMMA, ISIS EN-BLASSO, mrMLM, pKWmEB, and pLARmEB) were performed using 172,711 single-nucleotide polymorphisms (SNPs) to identify yield-related quantitative trait loci (QTL). A total of 27 robust QTL were identified by more than three models. Nine of these QTL were consistent with those in previous studies. The remaining 18 QTL may be novel. We identified a major QTL, QTkw.sicau-4B, with up to 18.78% of phenotypic variation explained. The developed kompetitive allele-specific polymerase chain reaction marker for QTkw.sicau-4B was validated in two recombinant inbred line populations with an average phenotypic difference of 16.07%. After combined homologous function annotation and expression analysis, TraesCS4B01G272300 was the most likely candidate gene for QTkw.sicau-4B. Our findings provide new insights into the genetic basis of yield-related traits and offer valuable QTL to breed wheat cultivars via marker-assisted selection.
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Affiliation(s)
- Yu Lin
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Kunyu Zhou
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Haiyan Hu
- School of Life Science and Technology, Henan Institute of Science and Technology, Xinxiang, China
| | - Xiaojun Jiang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Shifan Yu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Qing Wang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Caixia Li
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Jian Ma
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guangdeng Chen
- College of Resources, Sichuan Agricultural University, Chengdu, China
| | - Zisong Yang
- College of Resources and Environment, Aba Teachers University, Wenchuan, China
| | - Yaxi Liu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
- *Correspondence: Yaxi Liu, , orcid.org/0000-0001-6814-7218
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10
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Yang Y, Chai Y, Zhang X, Lu S, Zhao Z, Wei D, Chen L, Hu YG. Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network. FRONTIERS IN PLANT SCIENCE 2020; 11:1091. [PMID: 32849679 PMCID: PMC7411135 DOI: 10.3389/fpls.2020.01091] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/02/2020] [Indexed: 05/20/2023]
Abstract
In wheat breeding, improved quality traits, including grain quality and dough rheological properties, have long been a critical goal. To understand the genetic basis of key quality traits of wheat, two single-locus and five multi-locus GWAS models were performed for six grain quality traits and three dough rheological properties based on 19, 254 SNPs in 267 bread wheat accessions. As a result, 299 quantitative trait nucleotides (QTNs) within 105 regions were identified to be associated with these quality traits in four environments. Of which, 40 core QTN regions were stably detected in at least three environments, 19 of which were novel. Compared with the previous studies, these novel QTN regions explained smaller phenotypic variation, which verified the advantages of the multi-locus GWAS models in detecting important small effect QTNs associated with complex traits. After characterization of the function and expression in-depth, 67 core candidate genes involved in protein/sugar synthesis, histone modification and the regulation of transcription factor were observed to be associated with the formation of grain quality, which showed that multi-level regulations influenced wheat grain quality. Finally, a preliminary network of gene regulation that may affect wheat quality formation was inferred. This study verified the power and reliability of multi-locus GWAS methods in wheat quality trait research, and increased the understanding of wheat quality formation mechanisms. The detected QTN regions and candidate genes in this study could be further used for gene cloning and marker-assisted selection in high-quality breeding of bread wheat.
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Affiliation(s)
- Yang Yang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Yongmao Chai
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Xuan Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Shan Lu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Zhangchen Zhao
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Di Wei
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Liang Chen
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Yin-Gang Hu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
- Institute of Water Saving Agriculture in Arid Regions of China, Northwest A&F University, Yangling, China
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11
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Li Z, Lhundrup N, Guo G, Dol K, Chen P, Gao L, Chemi W, Zhang J, Wang J, Nyema T, Dawa D, Li H. Characterization of Genetic Diversity and Genome-Wide Association Mapping of Three Agronomic Traits in Qingke Barley ( Hordeum Vulgare L.) in the Qinghai-Tibet Plateau. Front Genet 2020; 11:638. [PMID: 32719715 PMCID: PMC7351530 DOI: 10.3389/fgene.2020.00638] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 05/26/2020] [Indexed: 12/18/2022] Open
Abstract
Barley (Hordeum vulgare L.) is one of the most important cereal crops worldwide. In the Qinghai-Tibet Plateau, six-rowed hulless (or naked) barley, called “qingke” in Chinese or “nas” in Tibetan, is produced mainly in Tibet. The complexity of the environment in the Qinghai-Tibet Plateau has provided unique opportunities for research on the breeding and adaptability of qingke barley. However, the genetic architecture of many important agronomic traits for qingke barley remains elusive. Heading date (HD), plant height (PH), and spike length (SL) are three prominent agronomic traits in barley. Here, we used genome-wide association (GWAS) mapping and GWAS with eigenvector decomposition (EigenGWAS) to detect quantitative trait loci (QTL) and selective signatures for HD, PH, and SL in a collection of 308 qingke barley accessions. The accessions were genotyped using a newly-developed, proprietary genotyping-by-sequencing (tGBS) technology, that yielded 14,970 high quality single nucleotide polymorphisms (SNPs). We found that the number of SNPs was higher in the varieties than in the landraces, which suggested that Tibetan varieties and varieties in the Tibetan area may have originated from different landraces in different areas. We have identified 62 QTLs associated with three important traits, and the observed phenotypic variation is well-explained by the identified QTLs. We mapped 114 known genes that include, but are not limited to, vernalization, and photoperiod genes. We found that 83.87% of the identified QTLs are located in the non-coding regulatory regions of annotated barley genes. Forty-eight of the QTLs are first reported here, 28 QTLs have pleotropic effects, and three QTL are located in the regions of the well-characterized genes HvVRN1, HvVRN3, and PpD-H2. EigenGWAS analysis revealed that multiple heading-date-related loci bear signatures of selection. Our results confirm that the barley panel used in this study is highly diverse, and showed a great promise for identifying the genetic basis of adaptive traits. This study should increase our understanding of complex traits in qingke barley, and should facilitate genome-assisted breeding for qingke barley improvement.
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Affiliation(s)
- Zhiyong Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Namgyal Lhundrup
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, Tibet Academy of Agriculture and Animal Sciences, Lhasa, China
| | - Ganggang Guo
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Kar Dol
- Tibet Agricultural and Animal Husbandry College, Nyingchi, China
| | - Panpan Chen
- Tibet Agricultural and Animal Husbandry College, Nyingchi, China
| | - Liyun Gao
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, Tibet Academy of Agriculture and Animal Sciences, Lhasa, China
| | - Wangmo Chemi
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, Tibet Academy of Agriculture and Animal Sciences, Lhasa, China
| | - Jing Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jiankang Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Tashi Nyema
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, Tibet Academy of Agriculture and Animal Sciences, Lhasa, China
| | - Dondrup Dawa
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, Tibet Academy of Agriculture and Animal Sciences, Lhasa, China
| | - Huihui Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.,International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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12
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Weckwerth W, Ghatak A, Bellaire A, Chaturvedi P, Varshney RK. PANOMICS meets germplasm. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:1507-1525. [PMID: 32163658 PMCID: PMC7292548 DOI: 10.1111/pbi.13372] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 02/17/2020] [Accepted: 02/26/2020] [Indexed: 05/14/2023]
Abstract
Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.
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Affiliation(s)
- Wolfram Weckwerth
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
- Vienna Metabolomics Center (VIME)University of ViennaViennaAustria
| | - Arindam Ghatak
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Anke Bellaire
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Palak Chaturvedi
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadTelanganaIndia
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13
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Weckwerth W, Ghatak A, Bellaire A, Chaturvedi P, Varshney RK. PANOMICS meets germplasm. PLANT BIOTECHNOLOGY JOURNAL 2020; 18. [PMID: 32163658 PMCID: PMC7292548 DOI: 10.1111/pbi.13372,10.13140/rg.2.1.1233.5760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.
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Affiliation(s)
- Wolfram Weckwerth
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
- Vienna Metabolomics Center (VIME)University of ViennaViennaAustria
| | - Arindam Ghatak
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Anke Bellaire
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Palak Chaturvedi
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadTelanganaIndia
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14
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Peng H, Wang K, Chen Z, Cao Y, Gao Q, Li Y, Li X, Lu H, Du H, Lu M, Yang X, Liang C. MBKbase for rice: an integrated omics knowledgebase for molecular breeding in rice. Nucleic Acids Res 2020; 48:D1085-D1092. [PMID: 31624841 PMCID: PMC7145604 DOI: 10.1093/nar/gkz921] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 10/04/2019] [Accepted: 10/08/2019] [Indexed: 11/25/2022] Open
Abstract
To date, large amounts of genomic and phenotypic data have been accumulated in the fields of crop genetics and genomic research, and the data are increasing very quickly. However, the bottleneck to using big data in breeding is integrating the data and developing tools for revealing the relationship between genotypes and phenotypes. Here, we report a rice sub-database of an integrated omics knowledgebase (MBKbase-rice, www.mbkbase.org/rice), which integrates rice germplasm information, multiple reference genomes with a united set of gene loci, population sequencing data, phenotypic data, known alleles and gene expression data. In addition to basic data search functions, MBKbase provides advanced web tools for genotype searches at the population level and for visually displaying the relationship between genotypes and phenotypes. Furthermore, the database also provides online tools for comparing two samples by their genotypes and finding target germplasms by genotype or phenotype information, as well as for analyzing the user submitted SNP or sequence data to find important alleles in the germplasm. A soybean sub-database is planned for release in 3 months and wheat and maize will be added in 1–2 years. The data and tools integrated in MBKbase will facilitate research in crop functional genomics and molecular breeding.
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Affiliation(s)
- Hua Peng
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kai Wang
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhuo Chen
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yinghao Cao
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China
| | - Qiang Gao
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China
| | - Yan Li
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiuxiu Li
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongwei Lu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huilong Du
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Min Lu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Yang
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China
| | - Chengzhi Liang
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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15
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Gahlaut V, Jaiswal V, Singh S, Balyan HS, Gupta PK. Multi-Locus Genome Wide Association Mapping for Yield and Its Contributing Traits in Hexaploid Wheat under Different Water Regimes. Sci Rep 2019; 9:19486. [PMID: 31862891 PMCID: PMC6925107 DOI: 10.1038/s41598-019-55520-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 11/29/2019] [Indexed: 11/20/2022] Open
Abstract
Multi-locus genome wide association study was undertaken using a set of 320 diverse spring wheat accessions, which were each genotyped for 9,626 SNPs. The association panel was grown in replicated trials in four environments [two each in irrigated (IR) and rainfed (RF) environments], and phenotypic data were recorded for five traits including days to heading, days to maturity, plant height, thousand grain weight and grain yield. Forty-six significant marker-trait associations (MTAs) were identified for five traits. These included 20 MTAs in IR and 19 MTAs in RF environments; seven additional MTAs were common to both the environments. Five of these MTAs were co-localized with previously known QTL/MTAs and the remaining MTAs were novel and add to the existing knowledge. Three desirable haplotypes for agronomic traits, one for improvement in RF environment and two for improvement in IR environment were identified. Eighteen (18) promising candidate genes (CGs) involved in seven different biological activities were also identified. The expression profiles of four (Trehalose-6-Phosphate, APETALA2/Ethylene-responsive factor, DNA-binding One Zinc Finger and Gibberellin-dioxygenases) of the 18 genes showed that they were induced by drought stress in the wheat seedlings. The MTAs, haplotypes and CG-based markers may be used in marker-assisted breeding for drought tolerance in wheat.
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Affiliation(s)
- Vijay Gahlaut
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, India
- Department of Plant Molecular Biology, University of Delhi, South Campus, New Delhi, India
| | - Vandana Jaiswal
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, India
- School of Life Sciences, Jawaharlal Nehru University, New Delhi, India
| | | | - H S Balyan
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, India
| | - P K Gupta
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, India.
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16
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Abed A, Belzile F. Comparing Single-SNP, Multi-SNP, and Haplotype-Based Approaches in Association Studies for Major Traits in Barley. THE PLANT GENOME 2019; 12:1-14. [PMID: 33016584 DOI: 10.3835/plantgenome2019.05.0036] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 07/15/2019] [Indexed: 05/12/2023]
Abstract
The multiple single nucleotide polymorphism (multi-SNP) and haplotype-based approaches that jointly consider multiple markers unveiled a larger number of associations, some of which were shared with the single-SNP approach. A larger overlap of quantitative trait loci (QTLs) between the single-SNP and haplotype-based approaches was obtained than with the multi-SNP approach. Despite a limited overlap between the QTLs detected by these approaches, each uncovered QTLs reported previously, suggesting that each approach is capable of uncovering a different subset of QTLs. We demonstrated the efficiency of an integrated genome-wide association study (GWAS) procedure, combining single-locus and multilocus approaches to improve the capacity and reliability of association analysis to detect key QTLs. The efficiency of barley breeding programs may be improved by the practical use of QTLs identified in this study. Genome-wide association studies (GWAS) have been widely used to identify quantitative trait loci (QTLs) underlying complex agronomic traits. The conventional GWAS model is based on a single-locus model, which may prove inaccurate if a trait is controlled by multiple loci, which is the case for most agronomic traits in barley (Hordeum vulgare L.). Additionally, an individual single nucleotide polymorphism (SNP) will prove incapable of capturing underlying allelic diversity. A multilocus model could potentially represent a better alternative for QTL identification. This study aimed to explore different GWAS approaches (single-SNP, multi-SNP, and haplotype-based) to establish SNP-trait associations and to potentially describe the complex genetic architecture of seven key traits in spring barley. The multi-SNP and haplotype-based approaches unveiled a larger number of significant associations, some of which were shared with the single-SNP approach. Globally, the multi-SNP approach explained more of the phenotypic variance (cumulative R2 ) and provided the best fit with the genetic model [Bayesian information criterion (BIC)]. Compared with the multi-SNP approach, the single-SNP and haplotype-based approaches were relatively similar in terms of cumulative R2 and BIC, with an improvement with the haplotype-based approach. Despite limited overlap between detected QTLs, each approach discovered QTLs that had been validated previously, suggesting that each approach can uncover a different subset of QTLs. An integrated GWAS procedure, considering single-locus and multilocus GWAS approaches jointly, may improve the capacity of association studies to detect key QTLs and to provide a more complete picture of the genetic architecture of complex traits in barley.
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Affiliation(s)
- Amina Abed
- Dép. de phytologie, Pavillon Charles-Eugène, Marchand 1030, Ave., de la Médecine, Quebec City, QC, G1V 0A6, Canada
| | - François Belzile
- Dép. de phytologie, Pavillon Charles-Eugène, Marchand 1030, Ave., de la Médecine, Quebec City, QC, G1V 0A6, Canada
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17
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Almerekova S, Sariev B, Abugalieva A, Chudinov V, Sereda G, Tokhetova L, Ortaev A, Tsygankov V, Blake T, Chao S, Genievskaya Y, Abugalieva S, Turuspekov Y. Association mapping for agronomic traits in six-rowed spring barley from the USA harvested in Kazakhstan. PLoS One 2019; 14:e0221064. [PMID: 31404111 PMCID: PMC6690582 DOI: 10.1371/journal.pone.0221064] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 07/29/2019] [Indexed: 11/19/2022] Open
Abstract
In barley, six-rowed barley is advantageous over two-rowed barley for feed due to the larger number of seeds per spike and the higher seed protein content. The growth of six-rowed barley is potentially important for breeding in agriculturally oriented countries, such as Kazakhstan. Nevertheless, until recently, very little attention was given to six-rowed barley in breeding projects in Kazakhstan, one of the largest countries in the world. In this study, phenotyping and single nucleotide polymorphism (SNP) genotyping data were generated from 275 accessions originating from six different breeding organizations in the USA as well as 9 accessions from Kazakhstan in field trials at six breeding institutions. The USA six-rowed barley was tested in comparison to local accessions over three years (2009–2011) based on analyses of key agronomic traits. It was determined that the average yield in the USA accessions in comparison to local lines showed heavier yield in all six tested sites. Principal Coordinate Analysis based on 1618 polymorphic SNP markers separated Kazakh lines from six USA barley origin groups based on PC1 (77.9%), and Montana lines from the remaining five USA groups based on PC2 (15.1%). A genome-wide association study based on eighteen field trials allowed the identification of 47 stable marker-trait associations (MTA) for ten agronomic traits, including key yield related characters such as yield per square meter, thousand grain weight, number of kernels per spike, and productive tillers. The comparison of chromosomal positions of identified MTA with positions of known genes and quantitative trait loci suggests that 25 out of those 47 MTAs are presumably novel. The analysis of 42 SNPs associated with 47 MTAs in the Ensemble genome annotation system (http://ensemblgenomes.org) suggested that 40 SNPs were in genic positions of the genome, as their sequences successfully aligned with corresponding Gen ID.
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Affiliation(s)
| | - Burabai Sariev
- Kazakh Research Institute of Agriculture and Plant Industry, Almalybak, Almaty region, Kazakhstan
| | - Aigul Abugalieva
- Kazakh Research Institute of Agriculture and Plant Industry, Almalybak, Almaty region, Kazakhstan
| | | | - Grigoriy Sereda
- Karaganda Breeding Station, Tsentralnoe, Karaganda region, Kazakhstan
| | | | - Anarbai Ortaev
- Krasnovodopad Breeding Station, Sarkyrama, Turkestan region, Kazakhstan
| | | | - Thomas Blake
- Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, United States of America
| | - Shiaoman Chao
- USDA-ARS Biosciences Research Lab, Fargo, ND, United States of America
| | | | - Saule Abugalieva
- Institute of Plant Biology and Biotechnology, Almaty, Kazakhstan
- Al-Farabi Kazakh National University, Department of Biodiversity and Bioresources, Almaty, Kazakhstan
| | - Yerlan Turuspekov
- Institute of Plant Biology and Biotechnology, Almaty, Kazakhstan
- Al-Farabi Kazakh National University, Department of Biodiversity and Bioresources, Almaty, Kazakhstan
- * E-mail:
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18
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Su J, Wang C, Hao F, Ma Q, Wang J, Li J, Ning X. Genetic Detection of Lint Percentage Applying Single-Locus and Multi-Locus Genome-Wide Association Studies in Chinese Early-Maturity Upland Cotton. FRONTIERS IN PLANT SCIENCE 2019; 10:964. [PMID: 31428110 PMCID: PMC6688134 DOI: 10.3389/fpls.2019.00964] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 07/10/2019] [Indexed: 05/28/2023]
Abstract
Upland cotton (Gossypium hirsutum L.) is the most important source of natural fiber in the world. Early-maturity upland cotton varieties are commonly planted in China. Nevertheless, lint yield of early-maturity upland cotton varieties is strikingly lower than that of middle- and late-maturity ones. How to effectively improve lint yield of early maturing cotton, becomes a focus of cotton research. Here, based on 72,792 high-quality single nucleotide polymorphisms of 160 early-maturing upland cotton accessions, we performed genome-wide association studies (GWASs) for lint percentage (LP), one of the most lint-yield component traits, applying one single-locus method and six multi-locus methods. A total of 4 and 45 significant quantitative trait nucleotides (QTNs) were respectively identified to be associated with LP. Interestingly, in two of four planting environments, two of these QTNs (A02_74713290 and A02_75551547) were simultaneously detected via both one single-locus and three or more multi-locus GWAS methods. Among the 42 genes within a genomic region (A02: 74.31-75.95 Mbp) containing the above two peak QTNs, Gh_A02G1269, Gh_A02G1280, and Gh_A02G1295 had the highest expression levels in ovules during seed development from 20 to 25 days post anthesis, whereas Gh_A02G1278 was preferentially expressed in the fibers rather than other organs. These results imply that the four potential candidate genes might be closely related to cotton LP by regulating the proportion of seed weight and fiber yield. The QTNs and potential candidate genes for LP, identified in this study, provide valuable resource for cultivating novel cotton varieties with earliness and high lint yield in the future.
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Affiliation(s)
- Junji Su
- Gansu Provincial Key Laboratory of Aridland Crop Science, College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Caixiang Wang
- Gansu Provincial Key Laboratory of Aridland Crop Science, College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Fushun Hao
- State Key Laboratory of Cotton Biology, Henan Key Laboratory of Plant Stress Biology, College of Life Science, Henan University, Kaifeng, China
| | - Qi Ma
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Ji Wang
- State Key Laboratory of Cotton Biology, Henan Key Laboratory of Plant Stress Biology, College of Life Science, Henan University, Kaifeng, China
| | - Jilian Li
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Xinzhu Ning
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
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19
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Wang Q, Sun G, Ren X, Du B, Cheng Y, Wang Y, Li C, Sun D. Dissecting the Genetic Basis of Grain Size and Weight in Barley ( Hordeum vulgare L.) by QTL and Comparative Genetic Analyses. FRONTIERS IN PLANT SCIENCE 2019; 10:469. [PMID: 31105718 PMCID: PMC6491919 DOI: 10.3389/fpls.2019.00469] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 03/28/2019] [Indexed: 05/23/2023]
Abstract
Grain size and weight are crucial components of barley yield and quality and are the target characteristics of domestication and modern breeding. Despite this, little is known about the genetic and molecular mechanisms of grain size and weight in barley. Here, we evaluated nine traits determining grain size and weight, including thousand grain weight (Tgw), grain length (Gl), grain width (Gw), grain length-width ratio (Lwr), grain area (Ga), grain perimeter (Gp), grain diameter (Gd), grain roundness (Gr), and factor form density (Ffd), in a double haploid (DH) population for three consecutive years. Using five mapping methods, we successfully identified 60 reliable QTLs and 27 hotspot regions that distributed on all chromosomes except 6H which controls the nine traits of grain size and weight. Moreover, we also identified 164 barley orthologs of 112 grain size/weight genes from rice, maize, wheat and 38 barley genes that affect grain yield. A total of 45 barley genes or orthologs were identified as potential candidate genes for barley grain size and weight, including 12, 20, 9, and 4 genes or orthologs for barley, rice, maize, and wheat, respectively. Importantly, 20 of them were located in the 14 QTL hotspot regions on chromosome 1H, 2H, 3H, 5H, and 7H, which controls barley grain size and weight. These results indicated that grain size/weight genes of other cereal species might have the same or similar functions in barley. Our findings provide new insights into the understanding of the genetic basis of grain size and weight in barley, and new information to facilitate high-yield breeding in barley. The function of these potential candidate genes identified in this study are worth exploring and studying in detail.
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Affiliation(s)
- Qifei Wang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Genlou Sun
- Department of Biology, Saint Mary’s University, Halifax, NS, Canada
| | - Xifeng Ren
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Binbin Du
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yun Cheng
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yixiang Wang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Chengdao Li
- School of Veterinary and Life Sciences, Murdoch University, Murdoch, WA, Australia
| | - Dongfa Sun
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- Hubei Collaborative Innovation Centre for Grain Industry, Yangtze University, Jingzhou, China
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Zhang M, Fu MM, Qiu CW, Cao F, Chen ZH, Zhang G, Wu F. Response of Tibetan Wild Barley Genotypes to Drought Stress and Identification of Quantitative Trait Loci by Genome-Wide Association Analysis. Int J Mol Sci 2019; 20:E791. [PMID: 30759829 PMCID: PMC6387302 DOI: 10.3390/ijms20030791] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 02/06/2019] [Accepted: 02/07/2019] [Indexed: 11/23/2022] Open
Abstract
Tibetan wild barley has been identified to show large genetic variation and stress tolerance. A genome-wide association (GWA) analysis was performed to detect quantitative trait loci (QTLs) for drought tolerance using 777 Diversity Array Technology (DArT) markers and morphological and physiological traits of 166 Tibetan wild barley accessions in both hydroponic and pot experiments. Large genotypic variation for these traits was found; and population structure and kinship analysis identified three subpopulations among these barley genotypes. The average LD (linkage disequilibrium) decay distance was 5.16 cM, with the minimum on 6H (0.03 cM) and the maximum on 4H (23.48 cM). A total of 91 DArT markers were identified to be associated with drought tolerance-related traits, with 33, 26, 16, 1, 3, and 12 associations for morphological traits, H⁺K⁺-ATPase activity, antioxidant enzyme activities, malondialdehyde (MDA) content, soluble protein content, and potassium concentration, respectively. Furthermore, 7 and 24 putative candidate genes were identified based on the reference Meta-QTL map and by searching the Barleymap. The present study implicated that Tibetan annual wild barley from Qinghai⁻Tibet Plateau is rich in genetic variation for drought stress. The QTLs detected by genome-wide association analysis could be used in marker-assisting breeding for drought-tolerant barley genotypes and provide useful information for discovery and functional analysis of key genes in the future.
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Affiliation(s)
- Mian Zhang
- Department of Agronomy, College of Agriculture and Biotechnology, Zijingang Campus, Zhejiang University, Hangzhou 310058, China.
- Institute of Applied Biology, Shanxi University, Taiyuan 030006, China.
| | - Man-Man Fu
- Department of Agronomy, College of Agriculture and Biotechnology, Zijingang Campus, Zhejiang University, Hangzhou 310058, China.
| | - Cheng-Wei Qiu
- Department of Agronomy, College of Agriculture and Biotechnology, Zijingang Campus, Zhejiang University, Hangzhou 310058, China.
| | - Fangbin Cao
- Department of Agronomy, College of Agriculture and Biotechnology, Zijingang Campus, Zhejiang University, Hangzhou 310058, China.
| | - Zhong-Hua Chen
- School of Science and Health, Hawkesbury Campus, University of Western Sydney, Penrith, NSW 2751, Australia.
| | - Guoping Zhang
- Department of Agronomy, College of Agriculture and Biotechnology, Zijingang Campus, Zhejiang University, Hangzhou 310058, China.
| | - Feibo Wu
- Department of Agronomy, College of Agriculture and Biotechnology, Zijingang Campus, Zhejiang University, Hangzhou 310058, China.
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China.
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