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Mugabe D, Yoosefzadeh-Najafabadi M, Rajcan I. Genetic diversity and genome-wide association study of partial resistance to Sclerotinia stem rot in a Canadian soybean germplasm panel. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:201. [PMID: 39127987 DOI: 10.1007/s00122-024-04708-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 07/31/2024] [Indexed: 08/12/2024]
Abstract
KEY MESSAGE Developing genetically resistant soybean cultivars is key in controlling the destructive Sclerotinia Stem Rot (SSR) disease. Here, a GWAS study in Canadian soybeans identified potential marker-trait associations and candidate genes, paving the way for more efficient breeding methods for SSR. Sclerotinia stem rot (SSR), caused by the fungal pathogen Sclerotinia sclerotiorum, is one of the most important diseases leading to significant soybean yield losses in Canada and worldwide. Developing soybean cultivars that are genetically resistant to the disease is the most inexpensive and reliable method to control the disease. However, breeding for resistance is hampered by the highly complex nature of genetic resistance to SSR in soybean. This study sought to understand the genetic basis underlying SSR resistance particularly in soybean grown in Canada. Consequently, a panel of 193 genotypes was assembled based on maturity group and genetic diversity as representative of Canadian soybean cultivars. Plants were inoculated and screened for SSR resistance in controlled environments, where variation for SSR phenotypic response was observed. The panel was also genotyped via genotyping-by-sequencing and the resulting genotypic data were imputed using BEAGLE v5 leading to a catalogue of 417 K SNPs. Through genome-wide association analyses (GWAS) using FarmCPU method with threshold of FDR-adjusted p-values < 0.1, we identified significant SNPs on chromosomes 2 and 9 with allele effects of 16.1 and 14.3, respectively. Further analysis identified three potential candidate genes linked to SSR disease resistance within a 100 Kb window surrounding each of the peak SNPs. Our results will be important in developing molecular markers that can speed up the breeding for SSR resistance in Canadian grown soybean.
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Affiliation(s)
- Deus Mugabe
- Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | | | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada.
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Potapova NA, Zlobin AS, Leonova IN, Salina EA, Tsepilov YA. The BLUP method in evaluation of breeding values of Russian spring wheat lines using micro- and macroelements in seeds. Vavilovskii Zhurnal Genet Selektsii 2024; 28:456-462. [PMID: 39027122 PMCID: PMC11253017 DOI: 10.18699/vjgb-24-51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/06/2024] [Accepted: 03/12/2023] [Indexed: 07/20/2024] Open
Abstract
Genomic selection is a technology that allows for the determination of the genetic value of varieties of agricultural plants and animal breeds, based on information about genotypes and phenotypes. The measured breeding value (BV) for varieties and breeds in relation to the target trait allows breeding stages to be thoroughly planned and the parent forms suitable for crossing to be chosen. In this work, the BLUP method was used to assess the breeding value of 149 Russian varieties and introgression lines (4 measurements for each variety or line, 596 phenotypic points) of spring wheat according to the content of seven chemical elements in the grain - K, Ca, Mg, Mn, Fe, Zn, Cu. The quality of the evaluation of breeding values was assessed using cross-validation, when the sample was randomly divided into five parts, one of which was chosen as a test population. The following average values of the Pearson correlation were obtained for predicting the concentration of trace elements: K - 0.67, Ca - 0.61, Mg - 0.4, Mn - 0.5, Fe - 0.38, Zn - 0.46, Cu - 0.48. Out of the 35 models studied, the p-value was below the nominal significant threshold (p-value < 0.05) for 28 models. For 11 models, the p-value was significant after correction for multiple testing (p-value < 0.001). For Ca and K, four out of five models and for Mn two out of five models had a p-value below the threshold adjusted for multiple testing. For 30 varieties that showed the best varietal values for Ca, K and Mn, the average breeding value was 296.43, 785.11 and 4.87 mg/kg higher, respectively, than the average breeding value of the population. The results obtained show the relevance of the application of genomic selection models even in such limited-size samples. The models for K, Ca and Mn are suitable for assessing the breeding value of Russian wheat varieties based on these characteristics.
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Affiliation(s)
- N A Potapova
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Moscow, Russia Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical-Biological Agency, Moscow, Russia
| | - A S Zlobin
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia
| | - I N Leonova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - E A Salina
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia
| | - Y A Tsepilov
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia
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Zhang D, Zhao R, Xian G, Kou Y, Ma W. A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1361716. [PMID: 38571713 PMCID: PMC10987776 DOI: 10.3389/fpls.2024.1361716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/04/2024] [Indexed: 04/05/2024]
Abstract
Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing novel high-quality crop varieties. Predicting the associations between genes and traits requires the organization and analysis of multi-dimensional scientific data. The existing methods for establishing the relationships between genomic data and phenotypic data can only elucidate the associations between genes and individual traits. However, there are relatively few methods for detecting elite polyphenotype genes. In this study, a knowledge graph for traits regulating-genes was constructed by collecting data from the PubMed database and eight other databases related to the staple food crops rice, maize, and wheat as well as the model plant Arabidopsis thaliana. On the basis of the knowledge graph, a model for predicting traits regulating-genes was constructed by combining the data attributes of the gene nodes and the topological relationship attributes of the gene nodes. Additionally, a scoring method for predicting the genes regulating specific traits was developed to screen for elite polyphenotype genes. A total of 125,591 nodes and 547,224 semantic relationships were included in the knowledge graph. The accuracy of the knowledge graph-based model for predicting traits regulating-genes was 0.89, the precision rate was 0.91, the recall rate was 0.96, and the F1 value was 0.94. Moreover, 4,447 polyphenotype genes for 31 trait combinations were identified, among which the rice polyphenotype gene IPA1 and the A. thaliana polyphenotype gene CUC2 were verified via a literature search. Furthermore, the wheat gene TraesCS5A02G275900 was revealed as a potential polyphenotype gene that will need to be further characterized. Meanwhile, the result of venn diagram analysis between the polyphenotype gene datasets (consists of genes that are predicted by our model) and the transcriptome gene datasets (consists of genes that were differential expression in response to disease, drought or salt) showed approximately 70% and 54% polyphenotype genes were identified in the transcriptome datasets of Arabidopsis and rice, respectively. The application of the model driven by knowledge graph for predicting traits regulating-genes represents a novel method for detecting elite polyphenotype genes.
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Affiliation(s)
- Dandan Zhang
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ruixue Zhao
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Integration Publishing Knowledge Mining and Knowledge Service, National Press and Publication Administration, Beijing, China
| | - Guojian Xian
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yuantao Kou
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Integration Publishing Knowledge Mining and Knowledge Service, National Press and Publication Administration, Beijing, China
| | - Weilu Ma
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
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M'hamdi O, Takács S, Palotás G, Ilahy R, Helyes L, Pék Z. A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data. PLANTS (BASEL, SWITZERLAND) 2024; 13:746. [PMID: 38475592 DOI: 10.3390/plants13050746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024]
Abstract
The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato quality, utilizing data from 48 cultivars and 28 locations in Hungary over 5 seasons. It focused on °Brix, lycopene content, and colour (a/b ratio) using extreme gradient boosting (XGBoost) and artificial neural network (ANN) models. The results revealed that XGBoost consistently outperformed ANN, achieving high accuracy in predicting °Brix (R² = 0.98, RMSE = 0.07) and lycopene content (R² = 0.87, RMSE = 0.61), and excelling in colour prediction (a/b ratio) with a R² of 0.93 and RMSE of 0.03. ANN lagged behind particularly in colour prediction, showing a negative R² value of -0.35. Shapley additive explanation's (SHAP) summary plot analysis indicated that both models are effective in predicting °Brix and lycopene content in tomatoes, highlighting different aspects of the data. SHAP analysis highlighted the models' efficiency (especially in °Brix and lycopene predictions) and underscored the significant influence of cultivar choice and environmental factors like climate and soil. These findings emphasize the importance of selecting and fine-tuning the appropriate ML model for enhancing precision agriculture, underlining XGBoost's superiority in handling complex agronomic data for quality assessment.
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Affiliation(s)
- Oussama M'hamdi
- Institute of Horticultural Sciences, Hungarian University of Agriculture and Life Sciences, Páter K. Str. 1, 2100 Gödöllö, Hungary
- Doctoral School of Plant Science, Hungarian University of Agriculture and Life Sciences, Páter K. Str. 1, 2100 Gödöllö, Hungary
| | - Sándor Takács
- Institute of Horticultural Sciences, Hungarian University of Agriculture and Life Sciences, Páter K. Str. 1, 2100 Gödöllö, Hungary
| | - Gábor Palotás
- Univer Product Zrt, Szolnoki út 35, 6000 Kecskemét, Hungary
| | - Riadh Ilahy
- Laboratory of Horticulture, National Agricultural Research Institute of Tunisia (INRAT), University of Carthage, Ariana 1004, Tunisia
| | - Lajos Helyes
- Institute of Horticultural Sciences, Hungarian University of Agriculture and Life Sciences, Páter K. Str. 1, 2100 Gödöllö, Hungary
| | - Zoltán Pék
- Institute of Horticultural Sciences, Hungarian University of Agriculture and Life Sciences, Páter K. Str. 1, 2100 Gödöllö, Hungary
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Fagerstedt KV. Use of GWAS analysis in deciphering the inability of barley seeds to germinate after hypoxia. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:3883-3886. [PMID: 37536060 PMCID: PMC10400110 DOI: 10.1093/jxb/erad198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
This article comments on:
Gómez-Álvarez EM, Tondelli A, Nghi KN, Voloboeva V, Giordano G, Valè G, Perata P, Pucciariello C. 2023. The inability of barley to germinate after submergence depends on hypoxia-induced secondary dormancy. Journal of Experimental Botany 74, 4277–4289
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Affiliation(s)
- Kurt V Fagerstedt
- University of Helsinki, Faculty of Biological and Environmental Sciences, Organismal and Evolutionary Biology Research Programme, FI-00014 University of Helsinki, Finland
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6
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Wang X, Liu D, Luo J, Kong D, Zhang Y. Exploring the Role of Enhancer-Mediated Transcriptional Regulation in Precision Biology. Int J Mol Sci 2023; 24:10843. [PMID: 37446021 PMCID: PMC10342031 DOI: 10.3390/ijms241310843] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/18/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023] Open
Abstract
The emergence of precision biology has been driven by the development of advanced technologies and techniques in high-resolution biological research systems. Enhancer-mediated transcriptional regulation, a complex network of gene expression and regulation in eukaryotes, has attracted significant attention as a promising avenue for investigating the underlying mechanisms of biological processes and diseases. To address biological problems with precision, large amounts of data, functional information, and research on the mechanisms of action of biological molecules is required to address biological problems with precision. Enhancers, including typical enhancers and super enhancers, play a crucial role in gene expression and regulation within this network. The identification and targeting of disease-associated enhancers hold the potential to advance precision medicine. In this review, we present the concepts, progress, importance, and challenges in precision biology, transcription regulation, and enhancers. Furthermore, we propose a model of transcriptional regulation for multi-enhancers and provide examples of their mechanisms in mammalian cells, thereby enhancing our understanding of how enhancers achieve precise regulation of gene expression in life processes. Precision biology holds promise in providing new tools and platforms for discovering insights into gene expression and disease occurrence, ultimately benefiting individuals and society as a whole.
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Affiliation(s)
- Xueyan Wang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Danli Liu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Jing Luo
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Dashuai Kong
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Yubo Zhang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
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7
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Alemu A, Batista L, Singh PK, Ceplitis A, Chawade A. Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:92. [PMID: 37009920 PMCID: PMC10068637 DOI: 10.1007/s00122-023-04352-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
Linkage disequilibrium (LD)-based haplotyping with subsequent SNP tagging improved the genomic prediction accuracy up to 0.07 and 0.092 for Fusarium head blight resistance and spike width, respectively, across six different models. Genomic prediction is a powerful tool to enhance genetic gain in plant breeding. However, the method is accompanied by various complications leading to low prediction accuracy. One of the major challenges arises from the complex dimensionality of marker data. To overcome this issue, we applied two pre-selection methods for SNP markers viz. LD-based haplotype-tagging and GWAS-based trait-linked marker identification. Six different models were tested with preselected SNPs to predict the genomic estimated breeding values (GEBVs) of four traits measured in 419 winter wheat genotypes. Ten different sets of haplotype-tagged SNPs were selected by adjusting the level of LD thresholds. In addition, various sets of trait-linked SNPs were identified with different scenarios from the training-test combined and only from the training populations. The BRR and RR-BLUP models developed from haplotype-tagged SNPs had a higher prediction accuracy for FHB and SPW by 0.07 and 0.092, respectively, compared to the corresponding models developed without marker pre-selection. The highest prediction accuracy for SPW and FHB was achieved with tagged SNPs pruned at weak LD thresholds (r2 < 0.5), while stringent LD was required for spike length (SPL) and flag leaf area (FLA). Trait-linked SNPs identified only from training populations failed to improve the prediction accuracy of the four studied traits. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs. Furthermore, the method could pave the way for developing low-cost genotyping methods through customized genotyping platforms targeting key SNP markers tagged to essential haplotype blocks.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - Pawan K Singh
- International Maize and Wheat Improvement Center, Texcoco, Mexico
| | | | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
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Taylor J, Jorgensen D, Moffat CS, Chalmers KJ, Fox R, Hollaway GJ, Cook MJ, Neate SM, See PT, Shankar M. An international wheat diversity panel reveals novel sources of genetic resistance to tan spot in Australia. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:61. [PMID: 36912976 PMCID: PMC10011302 DOI: 10.1007/s00122-023-04332-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
KEY MESSAGE Novel sources of genetic resistance to tan spot in Australia have been discovered using one-step GWAS and genomic prediction models that accounts for additive and non-additive genetic variation. Tan spot is a foliar disease in wheat caused by the fungal pathogen Pyrenophora tritici-repentis (Ptr) and has been reported to generate up to 50% yield losses under favourable disease conditions. Although farming management practices are available to reduce disease, the most economically sustainable approach is establishing genetic resistance through plant breeding. To further understand the genetic basis for disease resistance, we conducted a phenotypic and genetic analysis study using an international diversity panel of 192 wheat lines from the Maize and Wheat Improvement Centre (CIMMYT), the International Centre for Agriculture in the Dry Areas (ICARDA) and Australian (AUS) wheat research programmes. The panel was evaluated using Australian Ptr isolates in 12 experiments conducted in three Australian locations over two years, with assessment for tan spot symptoms at various plant development stages. Phenotypic modelling indicated high heritability for nearly all tan spot traits with ICARDA lines displaying the greatest average resistance. We then conducted a one-step whole-genome analysis of each trait using a high-density SNP array, revealing a large number of highly significant QTL exhibiting a distinct lack of repeatability across the traits. To better summarise the genetic resistance of the lines, a one-step genomic prediction of each tan spot trait was conducted by combining the additive and non-additive predicted genetic effects of the lines. This revealed multiple CIMMYT lines with broad genetic resistance across the developmental stages of the plant which can be utilised in Australian wheat breeding programmes to improve tan spot disease resistance.
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Affiliation(s)
- Julian Taylor
- School of Agriculture, Food and Wine, Waite Research Institute, University of Adelaide, Glen Osmond, SA, 5064, Australia.
| | - Dorthe Jorgensen
- Department of Primary Industries and Regional Development, Agriculture and Food, 3 Baron Hay Ct, South Perth, WA, 6151, Australia
| | - Caroline S Moffat
- Centre for Crop Disease and Management, School of Molecular and Life Sciences, Curtin University, Kent St, Bentley, WA, 6102, Australia
| | - Ken J Chalmers
- School of Agriculture, Food and Wine, Waite Research Institute, University of Adelaide, Glen Osmond, SA, 5064, Australia
| | - Rebecca Fox
- School of Agriculture, Food and Wine, Waite Research Institute, University of Adelaide, Glen Osmond, SA, 5064, Australia
| | - Grant J Hollaway
- Agriculture Victoria, Private Bag 260, Horsham, VIC, 3401, Australia
| | - Melissa J Cook
- Agriculture Victoria, Private Bag 260, Horsham, VIC, 3401, Australia
| | - Stephen M Neate
- Centre for Crop Health, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Pao Theen See
- Centre for Crop Disease and Management, School of Molecular and Life Sciences, Curtin University, Kent St, Bentley, WA, 6102, Australia
| | - Manisha Shankar
- Department of Primary Industries and Regional Development, Agriculture and Food, 3 Baron Hay Ct, South Perth, WA, 6151, Australia.
- School of Agriculture and Environment, University of Western Australia, 35 Stirling Hwy, Crawley, WA, 6009, Australia.
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Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T. Microorganisms 2023; 11:microorganisms11020331. [PMID: 36838296 PMCID: PMC9961961 DOI: 10.3390/microorganisms11020331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 02/03/2023] Open
Abstract
Plant growth-promoting bacteria (PGPB) can enhance plant health by facilitating nutrient uptake, nitrogen fixation, protection from pathogens, stress tolerance and/or boosting plant productivity. The genetic determinants that drive the plant-bacteria association remain understudied. To identify genetic loci highly correlated with traits responsive to PGPB, we performed a genome-wide association study (GWAS) using an Arabidopsis thaliana population treated with Azoarcus olearius DQS-4T. Phenotypically, the 305 Arabidopsis accessions tested responded differently to bacterial treatment by improving, inhibiting, or not affecting root system or shoot traits. GWA mapping analysis identified several predicted loci associated with primary root length or root fresh weight. Two statistical analyses were performed to narrow down potential gene candidates followed by haplotype block analysis, resulting in the identification of 11 loci associated with the responsiveness of Arabidopsis root fresh weight to bacterial inoculation. Our results showed considerable variation in the ability of plants to respond to inoculation by A. olearius DQS-4T while revealing considerable complexity regarding statistically associated loci with the growth traits measured. This investigation is a promising starting point for sustainable breeding strategies for future cropping practices that may employ beneficial microbes and/or modifications of the root microbiome.
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Ogrodowicz P, Mikołajczak K, Kempa M, Mokrzycka M, Krajewski P, Kuczyńska A. Genome-wide association study of agronomical and root-related traits in spring barley collection grown under field conditions. FRONTIERS IN PLANT SCIENCE 2023; 14:1077631. [PMID: 36760640 PMCID: PMC9902773 DOI: 10.3389/fpls.2023.1077631] [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: 10/24/2022] [Accepted: 01/06/2023] [Indexed: 06/18/2023]
Abstract
The root system is a key component for plant survival and productivity. In particular, under stress conditions, developing plants with a better root architecture can ensure productivity. The objectives of this study were to investigate the phenotypic variation of selected root- and yield-related traits in a diverse panel of spring barley genotypes. By performing a genome-wide association study (GWAS), we identified several associations underlying the variations occurring in root- and yield-related traits in response to natural variations in soil moisture. Here, we report the results of the GWAS based on both individual single-nucleotide polymorphism markers and linkage disequilibrium (LD) blocks of markers for 11 phenotypic traits related to plant morphology, grain quality, and root system in a group of spring barley accessions grown under field conditions. We also evaluated the root structure of these accessions by using a nondestructive method based on electrical capacitance. The results showed the importance of two LD-based blocks on chromosomes 2H and 7H in the expression of root architecture and yield-related traits. Our results revealed the importance of the region on the short arm of chromosome 2H in the expression of root- and yield-related traits. This study emphasized the pleiotropic effect of this region with respect to heading time and other important agronomic traits, including root architecture. Furthermore, this investigation provides new insights into the roles played by root traits in the yield performance of barley plants grown under natural conditions with daily variations in soil moisture content.
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11
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Song S, Wang S, Li N, Chang S, Dai S, Guo Y, Wu X, Cheng Y, Zeng S. Genome-wide association study to identify SNPs and candidate genes associated with body size traits in donkeys. Front Genet 2023; 14:1112377. [PMID: 36926587 PMCID: PMC10011486 DOI: 10.3389/fgene.2023.1112377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/14/2023] [Indexed: 03/08/2023] Open
Abstract
The Yangyuan donkey is a domestic animal breed mainly distributed in the northwest region of Hebei Province. Donkey body shape is the most direct production index, can fully reflect the donkey's growth status, and is closely related to important economic traits. As one of the main breeding selection criteria, body size traits have been widely used to monitor animal growth and evaluate the selection response. Molecular markers genetically linked to body size traits have the potential to accelerate the breeding process of animals via marker-assisted selection. However, the molecular markers of body size in Yangyuan donkeys have yet to be explored. In this study, we performed a genome-wide association study to identify the genomic variations associated with body size traits in a population of 120 Yangyuan donkeys. We screened 16 single nucleotide polymorphisms that were significantly associated with body size traits. Some genes distributed around these significant SNPs were considered candidates for body size traits, including SMPD4, RPS6KA6, LPAR4, GLP2R, BRWD3, MAGT1, ZDHHC15, and CYSLTR1. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated that these genes were mainly involved in the P13K-Akt signaling pathway, Rap1 signaling pathway, regulation of actin cytoskeleton, calcium signaling pathway, phospholipase D signaling pathway, and neuroactive ligand-receptor interactions. Collectively, our study reported on a list of novel markers and candidate genes associated with body size traits in donkeys, providing useful information for functional gene studies and offering great potential for accelerating Yangyuan donkey breeding.
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Affiliation(s)
- Shuang Song
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shiwei Wang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Nan Li
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Siyu Chang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shizhen Dai
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yajun Guo
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xuan Wu
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yuanweilu Cheng
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shenming Zeng
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
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Gangurde SS, Xavier A, Naik YD, Jha UC, Rangari SK, Kumar R, Reddy MSS, Channale S, Elango D, Mir RR, Zwart R, Laxuman C, Sudini HK, Pandey MK, Punnuri S, Mendu V, Reddy UK, Guo B, Gangarao NVPR, Sharma VK, Wang X, Zhao C, Thudi M. Two decades of association mapping: Insights on disease resistance in major crops. FRONTIERS IN PLANT SCIENCE 2022; 13:1064059. [PMID: 37082513 PMCID: PMC10112529 DOI: 10.3389/fpls.2022.1064059] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/10/2022] [Indexed: 05/03/2023]
Abstract
Climate change across the globe has an impact on the occurrence, prevalence, and severity of plant diseases. About 30% of yield losses in major crops are due to plant diseases; emerging diseases are likely to worsen the sustainable production in the coming years. Plant diseases have led to increased hunger and mass migration of human populations in the past, thus a serious threat to global food security. Equipping the modern varieties/hybrids with enhanced genetic resistance is the most economic, sustainable and environmentally friendly solution. Plant geneticists have done tremendous work in identifying stable resistance in primary genepools and many times other than primary genepools to breed resistant varieties in different major crops. Over the last two decades, the availability of crop and pathogen genomes due to advances in next generation sequencing technologies improved our understanding of trait genetics using different approaches. Genome-wide association studies have been effectively used to identify candidate genes and map loci associated with different diseases in crop plants. In this review, we highlight successful examples for the discovery of resistance genes to many important diseases. In addition, major developments in association studies, statistical models and bioinformatic tools that improve the power, resolution and the efficiency of identifying marker-trait associations. Overall this review provides comprehensive insights into the two decades of advances in GWAS studies and discusses the challenges and opportunities this research area provides for breeding resistant varieties.
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Affiliation(s)
- Sunil S. Gangurde
- Crop Genetics and Breeding Research, United States Department of Agriculture (USDA) - Agriculture Research Service (ARS), Tifton, GA, United States
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
| | - Alencar Xavier
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | | | - Uday Chand Jha
- Indian Council of Agricultural Research (ICAR), Indian Institute of Pulses Research (IIPR), Kanpur, Uttar Pradesh, India
| | | | - Raj Kumar
- Dr. Rajendra Prasad Central Agricultural University (RPCAU), Bihar, India
| | - M. S. Sai Reddy
- Dr. Rajendra Prasad Central Agricultural University (RPCAU), Bihar, India
| | - Sonal Channale
- Crop Health Center, University of Southern Queensland (USQ), Toowoomba, QLD, Australia
| | - Dinakaran Elango
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Reyazul Rouf Mir
- Faculty of Agriculture, Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST), Sopore, India
| | - Rebecca Zwart
- Crop Health Center, University of Southern Queensland (USQ), Toowoomba, QLD, Australia
| | - C. Laxuman
- Zonal Agricultural Research Station (ZARS), Kalaburagi, University of Agricultural Sciences, Raichur, Karnataka, India
| | - Hari Kishan Sudini
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, India
| | - Manish K. Pandey
- Crop Health Center, University of Southern Queensland (USQ), Toowoomba, QLD, Australia
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, India
| | - Somashekhar Punnuri
- College of Agriculture, Family Sciences and Technology, Dr. Fort Valley State University, Fort Valley, GA, United States
| | - Venugopal Mendu
- Department of Plant Science and Plant Pathology, Montana State University, Bozeman, MT, United States
| | - Umesh K. Reddy
- Department of Biology, West Virginia State University, West Virginia, WV, United States
| | - Baozhu Guo
- Crop Genetics and Breeding Research, United States Department of Agriculture (USDA) - Agriculture Research Service (ARS), Tifton, GA, United States
| | | | - Vinay K. Sharma
- Dr. Rajendra Prasad Central Agricultural University (RPCAU), Bihar, India
| | - Xingjun Wang
- Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences (SAAS), Jinan, China
| | - Chuanzhi Zhao
- Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences (SAAS), Jinan, China
| | - Mahendar Thudi
- Dr. Rajendra Prasad Central Agricultural University (RPCAU), Bihar, India
- Crop Health Center, University of Southern Queensland (USQ), Toowoomba, QLD, Australia
- Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences (SAAS), Jinan, China
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Genome-Wide Association Analysis for Hybrid Breeding in Wheat. Int J Mol Sci 2022; 23:ijms232315321. [PMID: 36499647 PMCID: PMC9740285 DOI: 10.3390/ijms232315321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/24/2022] [Accepted: 11/27/2022] [Indexed: 12/12/2022] Open
Abstract
Disclosure of markers that are significantly associated with plant traits can help develop new varieties with desirable properties. This study determined the genome-wide associations based on DArTseq markers for six agronomic traits assessed in eight environments for wheat. Moreover, the association study for heterosis and analysis of the effects of markers grouped by linkage disequilibrium were performed based on mean values over all experiments. All results were validated using data from post-registration trials. GWAS revealed 1273 single nucleotide polymorphisms with biologically significant effects. Most polymorphisms were predicted to be modifiers of protein translation, with only two having a more pronounced effect. Markers significantly associated with the considered set of features were clustered within chromosomes based on linkage disequilibrium in 327 LD blocks. A GWAS for heterosis revealed 1261 markers with significant effects.
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Song J, Pang Y, Wang C, Zhang X, Zeng Z, Zhao D, Zhang L, Zhang Y. QTL mapping and genomic prediction of resistance to wheat head blight caused by Fusarium verticillioides. Front Genet 2022; 13:1039841. [PMID: 36353117 PMCID: PMC9638129 DOI: 10.3389/fgene.2022.1039841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/12/2022] [Indexed: 08/04/2023] Open
Abstract
Fusarium head blight (FHB), is one of the destructive fugue diseases of wheat worldwide caused by the Fusarium verticillioides (F.v). In this study, a population consisting of 262 recombinant inbred lines (RILs) derived from Zhongmai 578 and Jimai 22 was used to map Quantitative Trait Locus (QTL) for FHB resistance, with the genotype data using the wheat 50 K single nucleotide polymorphism (SNP) array. The percentage of symptomatic spikelet (PSS) and the weighted average of PSS (PSSW) were collected for each RIL to represent their resistance to wheat head blight caused by F.v. In total, 22 QTL associated with FHB resistance were identified on chromosomes 1D, 2B, 3B, 4A, 5D, 7A, 7B, and 7D, respectively, from which 10 and 12 QTL were detected from PSS and PSSW respectively, explaining 3.82%-10.57% of the phenotypic variances using the inclusive composite interval mapping method. One novel QTL, Qfhb. haust-4A.1, was identified, explaining 10.56% of the phenotypic variation. One stable QTL, Qfhb. haust-1D.1 was detected on chromosome 1D across multiple environments explaining 4.39%-5.70% of the phenotypic variation. Forty-seven candidate genes related to disease resistance were found in the interval of Qfhb. haust-1D.1 and Qfhb. haust-4A.1. Genomic prediction accuracies were estimated from the five-fold cross-validation scheme ranging from 0.34 to 0.40 for PSS, and from 0.34 to 0.39 for PSSW in in-vivo inoculation treatment. This study provided new insight into the genetic analysis of resistance to wheat head blight caused by F.v, and genomic selection (GS) as a potential approach for improving the resistance of wheat head blight.
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Affiliation(s)
- Junqiao Song
- College of Agronomy, Henan University of Science and Technology, Luoyang, China
- The Shennong Laboratory, Zhengzhou, Henan, China
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Anyang Academy of Agricultural Sciences, Anyang, China
| | - Yuhui Pang
- College of Agronomy, Henan University of Science and Technology, Luoyang, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Chunping Wang
- College of Agronomy, Henan University of Science and Technology, Luoyang, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Zhankui Zeng
- College of Agronomy, Henan University of Science and Technology, Luoyang, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Dehui Zhao
- College of Agronomy, Henan University of Science and Technology, Luoyang, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Leiyi Zhang
- College of Agronomy, Henan University of Science and Technology, Luoyang, China
- The Shennong Laboratory, Zhengzhou, Henan, China
| | - Yong Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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Ballén-Taborda C, Lyerly J, Smith J, Howell K, Brown-Guedira G, Babar MA, Harrison SA, Mason RE, Mergoum M, Murphy JP, Sutton R, Griffey CA, Boyles RE. Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat. Front Genet 2022; 13:964684. [PMID: 36276956 PMCID: PMC9585219 DOI: 10.3389/fgene.2022.964684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled1 breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the ‘lme4’ R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the ‘STPGA’ R package. Third, for each TP, phenotypic values and SNP data were incorporated into the ‘rrBLUP’ mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.
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Affiliation(s)
- Carolina Ballén-Taborda
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
| | - Jeanette Lyerly
- Crop and Soil Sciences Department, North Carolina State University, Raleigh, NC, United States
| | - Jared Smith
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Raleigh, NC, United States
| | - Kimberly Howell
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Raleigh, NC, United States
| | - Gina Brown-Guedira
- Crop and Soil Sciences Department, North Carolina State University, Raleigh, NC, United States
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Raleigh, NC, United States
| | - Md. Ali Babar
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Stephen A. Harrison
- School of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Richard E. Mason
- College of Agricultural Sciences, Colorado State University, Fort Collins, CO, United States
| | - Mohamed Mergoum
- Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
| | - J. Paul Murphy
- Crop and Soil Sciences Department, North Carolina State University, Raleigh, NC, United States
| | - Russell Sutton
- Department of Soil and Crop Sciences, Texas A&M University, Commerce, TX, United States
| | - Carl A. Griffey
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Richard E. Boyles
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
- *Correspondence: Richard E. Boyles,
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Rabieyan E, Bihamta MR, Moghaddam ME, Mohammadi V, Alipour H. Genome-wide association mapping and genomic prediction for pre‑harvest sprouting resistance, low α-amylase and seed color in Iranian bread wheat. BMC PLANT BIOLOGY 2022; 22:300. [PMID: 35715737 PMCID: PMC9204952 DOI: 10.1186/s12870-022-03628-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 05/05/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Pre-harvest sprouting (PHS) refers to a phenomenon, in which the physiologically mature seeds are germinated on the spike before or during the harvesting practice owing to high humidity or prolonged period of rainfall. Pre-harvest sprouting (PHS) remarkably decreases seed quality and yield in wheat; hence it is imperative to uncover genomic regions responsible for PHS tolerance to be used in wheat breeding. A genome-wide association study (GWAS) was carried out using 298 bread wheat landraces and varieties from Iran to dissect the genomic regions of PHS tolerance in a well-irrigated environment. Three different approaches (RRBLUP, GBLUP and BRR) were followed to estimate prediction accuracies in wheat genomic selection. RESULTS Genomes B, A, and D harbored the largest number of significant marker pairs (MPs) in both landraces (427,017, 328,006, 92,702 MPs) and varieties (370,359, 266,708, 63,924 MPs), respectively. However, the LD levels were found the opposite, i.e., genomes D, A, and B have the highest LD, respectively. Association mapping by using GLM and MLM models resulted in 572 and 598 marker-trait associations (MTAs) for imputed SNPs (- log10 P > 3), respectively. Gene ontology exhibited that the pleitropic MPs located on 1A control seed color, α-Amy activity, and PHS. RRBLUP model indicated genetic effects better than GBLUP and BRR, offering a favorable tool for wheat genomic selection. CONCLUSIONS Gene ontology exhibited that the pleitropic MPs located on 1A can control seed color, α-Amy activity, and PHS. The verified markers in the current work can provide an opportunity to clone the underlying QTLs/genes, fine mapping, and genome-assisted selection.Our observations uncovered key MTAs related to seed color, α-Amy activity, and PHS that can be exploited in the genome-mediated development of novel varieties in wheat.
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Affiliation(s)
- Ehsan Rabieyan
- Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
| | - Mohammad Reza Bihamta
- Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
| | | | - Valiollah Mohammadi
- Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
| | - Hadi Alipour
- Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
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Li Y, Shi F, Lin Z, Robinson H, Moody D, Rattey A, Godoy J, Mullan D, Keeble-Gagnere G, Hayden MJ, Tibbits JFG, Daetwyler HD. Benefit of Introgression Depends on Level of Genetic Trait Variation in Cereal Breeding Programmes. FRONTIERS IN PLANT SCIENCE 2022; 13:786452. [PMID: 35783964 PMCID: PMC9240786 DOI: 10.3389/fpls.2022.786452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
We investigated the benefit from introgression of external lines into a cereal breeding programme and strategies that accelerated introgression of the favourable alleles while minimising linkage drag using stochastic computer simulation. We simulated genomic selection for disease resistance and grain yield in two environments with a high level of genotype-by-environment interaction (G × E) for the latter trait, using genomic data of a historical barley breeding programme as the base generation. Two populations (existing and external) were created from this base population with different allele frequencies for few (N = 10) major and many (N ~ 990) minor simulated disease quantitative trait loci (QTL). The major disease QTL only existed in the external population and lines from the external population were introgressed into the existing population which had minor disease QTL with low, medium and high allele frequencies. The study revealed that the benefit of introgression depended on the level of genetic variation for the target trait in the existing cereal breeding programme. Introgression of external resources into the existing population was beneficial only when the existing population lacked variation in disease resistance or when minor disease QTL were already at medium or high frequency. When minor disease QTL were at low frequencies, no extra genetic gain was achieved from introgression. More benefit in the disease trait was obtained from the introgression if the major disease QTL had larger effect sizes, more selection emphasis was applied on disease resistance, or more external lines were introgressed. While our strategies to increase introgression of major disease QTL were generally successful, most were not able to completely avoid negative impacts on selection for grain yield with the only exception being when major introgression QTL effects were very large. Breeding programmes are advised to carefully consider the level of genetic variation in a trait available in their breeding programme before deciding to introgress germplasms.
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Affiliation(s)
- Yongjun Li
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Fan Shi
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Zibei Lin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | | | | | | | | | | | | | - Matthew J. Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | | | - Hans D. Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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18
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Bapela T, Shimelis H, Tsilo TJ, Mathew I. Genetic Improvement of Wheat for Drought Tolerance: Progress, Challenges and Opportunities. PLANTS (BASEL, SWITZERLAND) 2022; 11:1331. [PMID: 35631756 PMCID: PMC9144332 DOI: 10.3390/plants11101331] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/27/2022] [Accepted: 05/04/2022] [Indexed: 06/01/2023]
Abstract
Wheat production and productivity are challenged by recurrent droughts associated with climate change globally. Drought and heat stress resilient cultivars can alleviate yield loss in marginal production agro-ecologies. The ability of some crop genotypes to thrive and yield in drought conditions is attributable to the inherent genetic variation and environmental adaptation, presenting opportunities to develop drought-tolerant varieties. Understanding the underlying genetic, physiological, biochemical, and environmental mechanisms and their interactions is key critical opportunity for drought tolerance improvement. Therefore, the objective of this review is to document the progress, challenges, and opportunities in breeding for drought tolerance in wheat. The paper outlines the following key aspects: (1) challenges associated with breeding for adaptation to drought-prone environments, (2) opportunities such as genetic variation in wheat for drought tolerance, selection methods, the interplay between above-ground phenotypic traits and root attributes in drought adaptation and drought-responsive attributes and (3) approaches, technologies and innovations in drought tolerance breeding. In the end, the paper summarises genetic gains and perspectives in drought tolerance breeding in wheat. The review will serve as baseline information for wheat breeders and agronomists to guide the development and deployment of drought-adapted and high-performing new-generation wheat varieties.
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Affiliation(s)
- Theresa Bapela
- African Centre for Crop Improvement, University of Kwa-Zulu Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa; (H.S.); (I.M.)
- Agricultural Research Council—Small Grain, Bethlehem 9700, South Africa;
| | - Hussein Shimelis
- African Centre for Crop Improvement, University of Kwa-Zulu Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa; (H.S.); (I.M.)
| | - Toi John Tsilo
- Agricultural Research Council—Small Grain, Bethlehem 9700, South Africa;
| | - Isack Mathew
- African Centre for Crop Improvement, University of Kwa-Zulu Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa; (H.S.); (I.M.)
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Puglisi D, Visioni A, Ozkan H, Kara İ, Lo Piero AR, Rachdad FE, Tondelli A, Valè G, Cattivelli L, Fricano A. High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings. G3 GENES|GENOMES|GENETICS 2022; 12:6517783. [PMID: 35099521 PMCID: PMC8895982 DOI: 10.1093/g3journal/jkac022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 01/21/2022] [Indexed: 11/24/2022]
Abstract
In plants, the study of belowground traits is gaining momentum due to their importance on yield formation and the uptake of water and nutrients. In several cereal crops, seminal root number and seminal root angle are proxy traits of the root system architecture at the mature stages, which in turn contributes to modulating the uptake of water and nutrients. Along with seminal root number and seminal root angle, experimental evidence indicates that the transpiration rate response to evaporative demand or vapor pressure deficit is a key physiological trait that might be targeted to cope with drought tolerance as the reduction of the water flux to leaves for limiting transpiration rate at high levels of vapor pressure deficit allows to better manage soil moisture. In the present study, we examined the phenotypic diversity of seminal root number, seminal root angle, and transpiration rate at the seedling stage in a panel of 8-way Multiparent Advanced Generation Inter-Crosses lines of winter barley and correlated these traits with grain yield measured in different site-by-season combinations. Second, phenotypic and genotypic data of the Multiparent Advanced Generation Inter-Crosses population were combined to fit and cross-validate different genomic prediction models for these belowground and physiological traits. Genomic prediction models for seminal root number were fitted using threshold and log-normal models, considering these data as ordinal discrete variable and as count data, respectively, while for seminal root angle and transpiration rate, genomic prediction was implemented using models based on extended genomic best linear unbiased predictors. The results presented in this study show that genome-enabled prediction models of seminal root number, seminal root angle, and transpiration rate data have high predictive ability and that the best models investigated in the present study include first-order additive × additive epistatic interaction effects. Our analyses indicate that beyond grain yield, genomic prediction models might be used to predict belowground and physiological traits and pave the way to practical applications for barley improvement.
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Affiliation(s)
- Damiano Puglisi
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania , 95123 Catania, Italy
| | - Andrea Visioni
- Biodiversity and Crop Improvement Program, International Center for Agricultural Research in the Dry Areas , 6299 Rabat, Morocco
| | - Hakan Ozkan
- Faculty of Agriculture, Department of Field Crops, University of Cukurova , 01330 Adana, Turkey
| | - İbrahim Kara
- Bahri Dagdas International Agricultural Research Institute , Km Karatay/Konya 42020, Turkey
| | - Angela Roberta Lo Piero
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania , 95123 Catania, Italy
| | - Fatima Ezzahra Rachdad
- Biodiversity and Crop Improvement Program, International Center for Agricultural Research in the Dry Areas , 6299 Rabat, Morocco
- Faculty of Sciences Ben M’sik, Department of Biology, Environment and Ecology Laboratory, Hassan II University of Casablanca , 7955 Casablanca, Morocco
| | - Alessandro Tondelli
- Council for Agricultural Research and Economics—Research Centre for Genomics and Bioinformatics , 29017 Fiorenzuola d’Arda (PC), Italy
| | - Giampiero Valè
- DiSIT, Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale , 13100 Vercelli, Italy
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics—Research Centre for Genomics and Bioinformatics , 29017 Fiorenzuola d’Arda (PC), Italy
| | - Agostino Fricano
- Council for Agricultural Research and Economics—Research Centre for Genomics and Bioinformatics , 29017 Fiorenzuola d’Arda (PC), Italy
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20
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Brainard SH, Ellison SL, Simon PW, Dawson JC, Goldman IL. Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:605-622. [PMID: 34782932 PMCID: PMC8866378 DOI: 10.1007/s00122-021-03988-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 10/27/2021] [Indexed: 06/13/2023]
Abstract
The principal phenotypic determinants of market class in carrot-the size and shape of the root-are under primarily additive, but also highly polygenic, genetic control. The size and shape of carrot roots are the primary determinants not only of yield, but also market class. These quantitative phenotypes have historically been challenging to objectively evaluate, and thus subjective visual assessment of market class remains the primary method by which selection for these traits is performed. However, advancements in digital image analysis have recently made possible the high-throughput quantification of size and shape attributes. It is therefore now feasible to utilize modern methods of genetic analysis to investigate the genetic control of root morphology. To this end, this study utilized both genome wide association analysis (GWAS) and genomic-estimated breeding values (GEBVs) and demonstrated that the components of market class are highly polygenic traits, likely under the influence of many small effect QTL. Relatively large proportions of additive genetic variance for many of the component phenotypes support high predictive ability of GEBVs; average prediction ability across underlying market class traits was 0.67. GWAS identified multiple QTL for four of the phenotypes which compose market class: length, aspect ratio, maximum width, and root fill, a previously uncharacterized trait which represents the size-independent portion of carrot root shape. By combining digital image analysis with GWAS and GEBVs, this study represents a novel advance in our understanding of the genetic control of market class in carrot. The immediate practical utility and viability of genomic selection for carrot market class is also described, and concrete guidelines for the design of training populations are provided.
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Affiliation(s)
- Scott H Brainard
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, 53706, USA.
| | - Shelby L Ellison
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Philipp W Simon
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Vegetable Crops Research Unit, US Department of Agriculture-Agricultural Research Service, Madison, WI, 53706, USA
| | - Julie C Dawson
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Irwin L Goldman
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, 53706, USA
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Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:1. [PMID: 37309486 PMCID: PMC10248672 DOI: 10.1007/s11032-021-01272-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.
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Affiliation(s)
- Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Jagmohan Singh
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| | - Anand Kumar
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, 202002 India
| | - Sumandeep Bazzer
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211 USA
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
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22
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Liu H, Mullan D, Zhao S, Zhang Y, Ye J, Wang Y, Zhang A, Zhao X, Liu G, Zhang C, Chan K, Lu Z, Yan G. Genomic regions controlling yield-related traits in spring wheat: A mini review and a case study for rainfed environments in Australia and China. Genomics 2022; 114:110268. [PMID: 35065191 DOI: 10.1016/j.ygeno.2022.110268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 01/11/2022] [Accepted: 01/15/2022] [Indexed: 01/17/2023]
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Dalla Lana F, Madden LV, Paul PA. Logistic Models Derived via LASSO Methods for Quantifying the Risk of Natural Contamination of Maize Grain with Deoxynivalenol. PHYTOPATHOLOGY 2021; 111:2250-2267. [PMID: 34009008 DOI: 10.1094/phyto-03-21-0104-r] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Models were developed to quantify the risk of deoxynivalenol (DON) contamination of maize grain based on weather, cultural practices, hybrid resistance, and Gibberella ear rot (GER) intensity. Data on natural DON contamination of 15 to 16 hybrids and weather were collected from 10 Ohio locations over 4 years. Logistic regression with 10-fold cross-validation was used to develop models to predict the risk of DON ≥1 ppm. The presence and severity of GER predicted DON risk with an accuracy of 0.81 and 0.87, respectively. Temperature, relative humidity, surface wetness, and rainfall were used to generate 37 weather-based predictor variables summarized over each of six 15-day windows relative to maize silking (R1). With these variables, least absolute shrinkage and selection operator (LASSO) followed by all-subsets variable selection and logistic regression with 10-fold cross-validation were used to build single-window weather-based models, from which 11 with one or two predictors were selected based on performance metrics and simplicity. LASSO logistic regression was also used to build more complex multiwindow models with up to 22 predictors. The performance of the best single-window models was comparable to that of the best multiwindow models, with accuracy ranging from 0.81 to 0.83 for the former and 0.83 to 0.87 for the latter group of models. These results indicated that the risk of DON ≥1 ppm can be accurately predicted with simple models built using temperature- and moisture-based predictors from a single window. These models will be the foundation for developing tools to predict the risk of DON contamination of maize grain.
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Affiliation(s)
- Felipe Dalla Lana
- Department of Plant Pathology, The Ohio State University, Ohio Agricultural Research, and Development Center, Wooster, OH 44691
| | - Laurence V Madden
- Department of Plant Pathology, The Ohio State University, Ohio Agricultural Research, and Development Center, Wooster, OH 44691
| | - Pierce A Paul
- Department of Plant Pathology, The Ohio State University, Ohio Agricultural Research, and Development Center, Wooster, OH 44691
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Yoosefzadeh-Najafabadi M, Torabi S, Tulpan D, Rajcan I, Eskandari M. Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods. FRONTIERS IN PLANT SCIENCE 2021; 12:777028. [PMID: 34880894 PMCID: PMC8647880 DOI: 10.3389/fpls.2021.777028] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 10/18/2021] [Indexed: 05/12/2023]
Abstract
In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.
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Affiliation(s)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
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25
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McGaugh SE, Lorenz AJ, Flagel LE. The utility of genomic prediction models in evolutionary genetics. Proc Biol Sci 2021; 288:20210693. [PMID: 34344180 PMCID: PMC8334854 DOI: 10.1098/rspb.2021.0693] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 07/15/2021] [Indexed: 12/25/2022] Open
Abstract
Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.
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Affiliation(s)
- Suzanne E. McGaugh
- Ecology, Evolution, and Behavior, University of Minnesota, 140 Gortner Lab, 1479 Gortner Avenue, Saint Paul, MN 55108, USA
| | - Aaron J. Lorenz
- Agronomy and Plant Genetics, University of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Circle, Saint Paul, MN 55108, USA
| | - Lex E. Flagel
- Plant and Microbial Biology, University of Minnesota, 140 Gortner Lab, 1479 Gortner Avenue, Saint Paul, MN 55108, USA
- Bayer Crop Science, 700 W Chesterfield Parkway, Chesterfield, MO 63017, USA
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26
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Amalova A, Abugalieva S, Babkenov A, Babkenova S, Turuspekov Y. Genome-wide association study of yield components in spring wheat collection harvested under two water regimes in Northern Kazakhstan. PeerJ 2021; 9:e11857. [PMID: 34395089 PMCID: PMC8323601 DOI: 10.7717/peerj.11857] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 07/05/2021] [Indexed: 12/13/2022] Open
Abstract
Background Bread wheat is the most important cereal in Kazakhstan, where it is grown on over 12 million hectares. One of the major constraints affecting wheat grain yield is drought due to the limited water supply. Hence, the development of drought-resistant cultivars is critical for ensuring food security in this country. Therefore, identifying quantitative trait loci (QTLs) associated with drought tolerance as an essential step in modern breeding activities, which rely on a marker-assisted selection approach. Methods A collection of 179 spring wheat accessions was tested under irrigated and rainfed conditions in Northern Kazakhstan over three years (2018, 2019, and 2020), during which data was collected on nine traits: heading date (HD), seed maturity date (SMD), plant height (PH), peduncle length (PL), number of productive spikes (NPS), spike length (SL), number of kernels per spike (NKS), thousand kernel weight (TKW), and kernels yield per m2 (YM2). The collection was genotyped using a 20,000 (20K) Illumina iSelect SNP array, and 8,662 polymorphic SNP markers were selected for a genome-wide association study (GWAS) to identify QTLs for targeted agronomic traits. Results Out of the total of 237 discovered QTLs, 50 were identified as being stable QTLs for irrigated and rainfed conditions in the Akmola region, Northern Kazakhstan; the identified QTLs were associated with all the studied traits except PH. The results indicate that nine QTLs for HD and 11 QTLs for SMD are presumably novel genetic factors identified in the irrigated and rainfed conditions of Northern Kazakhstan. The identified SNP markers of the QTLs for targeted traits in rainfed conditions can be applied to develop new competitive spring wheat cultivars in arid zones using a marker-assisted selection approach.
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Affiliation(s)
- Akerke Amalova
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty, Kazakhstan.,Institute of Plant Biology and Biotechnology, Almaty, Kazakhstan
| | - Saule Abugalieva
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty, Kazakhstan.,Institute of Plant Biology and Biotechnology, Almaty, Kazakhstan
| | - Adylkhan Babkenov
- A.I. Barayev Research and Production Centre of Grain Farming, Shortandy, Akmola Region, Kazakhstan
| | - Sandukash Babkenova
- A.I. Barayev Research and Production Centre of Grain Farming, Shortandy, Akmola Region, Kazakhstan
| | - Yerlan Turuspekov
- Institute of Plant Biology and Biotechnology, Almaty, Kazakhstan.,Faculty of Agrobiology, Kazakh National Agrarian University, Almaty, Kazakhstan
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27
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An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits. PLANTS 2021; 10:plants10040745. [PMID: 33920359 PMCID: PMC8069980 DOI: 10.3390/plants10040745] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/06/2021] [Accepted: 04/08/2021] [Indexed: 11/17/2022]
Abstract
Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.
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Ma P, Wu L, Xu Y, Xu H, Zhang X, Wang W, Liu C, Wang B. Bulked Segregant RNA-Seq Provides Distinctive Expression Profile Against Powdery Mildew in the Wheat Genotype YD588. FRONTIERS IN PLANT SCIENCE 2021; 12:764978. [PMID: 34925412 PMCID: PMC8677838 DOI: 10.3389/fpls.2021.764978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 11/03/2021] [Indexed: 05/07/2023]
Abstract
Wheat powdery mildew, caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt), is a destructive disease leading to huge yield losses in production. Host resistance can greatly contribute to the control of the disease. To explore potential genes related to the powdery mildew (Pm) resistance, in this study, we used a resistant genotype YD588 to investigate the potential resistance components and profiled its expression in response to powdery mildew infection. Genetic analysis showed that a single dominant gene, tentatively designated PmYD588, conferred resistance to powdery mildew in YD588. Using bulked segregant RNA-Seq (BSR-Seq) and single nucleotide polymorphism (SNP) association analysis, two high-confidence candidate regions were detected in the chromosome arm 2B, spanning 453,752,054-506,356,791 and 584,117,809-664,221,850 bp, respectively. To confirm the candidate region, molecular markers were developed using the BSR-Seq data and mapped PmYD588 to an interval of 4.2 cM by using the markers YTU588-004 and YTU588-008. The physical position was subsequently locked into the interval of 647.1-656.0 Mb, which was different from those of Pm6, Pm33, Pm51, Pm52, Pm63, Pm64, PmQ, PmKN0816, MlZec1, and MlAB10 on the same chromosome arm in its position, suggesting that it is most likely a new Pm gene. To explore the potential regulatory genes of the R gene, 2,973 differentially expressed genes (DEGs) between the parents and bulks were analyzed using gene ontology (GO), clusters of orthologous group (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Based on the data, we selected 23 potential regulated genes in the enriched pathway of plant-pathogen interaction and detected their temporal expression patterns using an additional set of wheat samples and time-course analysis postinoculation with Bgt. As a result, six disease-related genes showed distinctive expression profiles after Bgt invasion and can serve as key candidates for the dissection of resistance mechanisms and improvement of durable resistance to wheat powdery mildew.
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Affiliation(s)
- Pengtao Ma
- School of Life Sciences, Yantai University, Yantai, China
- *Correspondence: Pengtao Ma,
| | - Liru Wu
- School of Life Sciences, Yantai University, Yantai, China
| | - Yufei Xu
- School of Life Sciences, Yantai University, Yantai, China
| | - Hongxing Xu
- School of Life Sciences, Henan University, Kaifeng, China
| | - Xu Zhang
- School of Life Sciences, Yantai University, Yantai, China
- School of Life Sciences, Henan University, Kaifeng, China
| | - Wenrui Wang
- School of Life Sciences, Yantai University, Yantai, China
| | - Cheng Liu
- Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, China
- Cheng Liu,
| | - Bo Wang
- School of Life Sciences, Yantai University, Yantai, China
- Bo Wang,
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29
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Muhammad A, Hu W, Li Z, Li J, Xie G, Wang J, Wang L. Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS. Int J Mol Sci 2020; 21:ijms21165649. [PMID: 32781752 PMCID: PMC7460857 DOI: 10.3390/ijms21165649] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/02/2020] [Accepted: 08/05/2020] [Indexed: 12/14/2022] Open
Abstract
Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB) and three single-locus models (FarmCPU, MLM, and MLMM), respectively. Among the 111 SNPs detected by the multi-locus models, 24 SNPs were simultaneously detected across multiple models, including seven for kernel length, six for kernel width, six for kernels per spike, and five for thousand kernel weight. Interestingly, the five most stable SNPs (RAC875_29540_391, Kukri_07961_503, tplb0034e07_1581, BS00074341_51, and BobWhite_049_3064) were simultaneously detected by at least three multi-locus models. Integrating these newly developed multi-locus GWAS models to unravel the genetic architecture of kernel traits, the mrMLM approach detected the maximum number of SNPs. Furthermore, a total of 41 putative candidate genes were predicted to likely be involved in the genetic architecture underlining kernel traits. These findings can facilitate a better understanding of the complex genetic mechanisms of kernel traits and may lead to the genetic improvement of grain yield in wheat.
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Affiliation(s)
- Ali Muhammad
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
| | - Weicheng Hu
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
| | - Zhaoyang Li
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
| | - Jianguo Li
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, 100 Daxue Rd., Nanning 530004, China
| | - Guosheng Xie
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
| | - Jibin Wang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, 100 Daxue Rd., Nanning 530004, China
- Correspondence: (J.W.); (L.W.)
| | - Lingqiang Wang
- College of Plant Science and Technology & Biomass and Bioenergy Research Center, Huazhong Agricultural University, Wuhan 430070, China; (A.M.); (W.H.); (Z.L.); (J.L.); (G.X.)
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, 100 Daxue Rd., Nanning 530004, China
- Correspondence: (J.W.); (L.W.)
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Jiang Y, Weise S, Graner A, Reif JC. Using Genome-Wide Predictions to Assess the Phenotypic Variation of a Barley ( Hordeum sp.) Gene Bank Collection for Important Agronomic Traits and Passport Information. FRONTIERS IN PLANT SCIENCE 2020; 11:604781. [PMID: 33505414 PMCID: PMC7829250 DOI: 10.3389/fpls.2020.604781] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/14/2020] [Indexed: 05/10/2023]
Abstract
Genome-wide predictions are a powerful tool for predicting trait performance. Against this backdrop we aimed to evaluate the potential and limitations of genome-wide predictions to inform the barley collection of the Federal ex situ Genebank for Agricultural and Horticultural Crops with phenotypic data on complex traits including flowering time, plant height, thousand grain weight, as well as on growth habit and row type. We used previously published sequence data, providing information on 306,049 high-quality SNPs for 20,454 barley accessions. The prediction abilities of the two unordered categorical traits row type and growth type as well as the quantitative traits flowering time, plant height and thousand grain weight were investigated using different cross validation scenarios. Our results demonstrate that the unordered categorical traits can be predicted with high precision. In this way genome-wide prediction can be routinely deployed to extract information pertinent to the taxonomic status of gene bank accessions. In addition, the three quantitative traits were also predicted with high precision, thereby increasing the amount of information available for genotyped but not phenotyped accessions. Deeply phenotyped core collections, such as the barley 1,000 core set of the IPK Gatersleben, are a promising training population to calibrate genome-wide prediction models. Consequently, genome-wide predictions can substantially contribute to increase the attractiveness of gene bank collections and help evolve gene banks into bio-digital resource centers.
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