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Vourlaki IT, Ramos-Onsins SE, Pérez-Enciso M, Castanera R. Evaluation of deep learning for predicting rice traits using structural and single-nucleotide genomic variants. PLANT METHODS 2024; 20:121. [PMID: 39127715 DOI: 10.1186/s13007-024-01250-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 07/28/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND Structural genomic variants (SVs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider only Single Nucleotide Polymorphisms (SNPs). Deep Learning (DL) is a promising strategy for genomic prediction, but its performance using SVs and SNPs as genetic markers remains unknown. RESULTS We used rice to investigate whether combining SVs and SNPs can result in better trait prediction over SNPs alone and examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models. Specifically, the performances of BayesC (considering additive effects) and a Bayesian Reproducible Kernel Hilbert space (RKHS) regression (considering both additive and non-additive effects) were compared to those of two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network, to explore their prediction ability by using various marker input strategies. We found that exploiting structural and nucleotide variation slightly improved prediction ability on complex traits in 87% of the cases. DL models outperformed Bayesian models in 75% of the studied cases, considering the four traits and the two validation strategies used. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models. CONCLUSIONS Our study reveals that the use of structural genomic variants can improve trait prediction in rice, independently of the methodology used. Also, our results suggest that Deep Learning (DL) networks can perform better than Bayesian models in the prediction of binary traits, and in quantitative traits when the training and target sets are not closely related. This highlights the potential of DL to enhance crop improvement in specific scenarios and the importance to consider SVs in addition to SNPs in genomic selection.
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Affiliation(s)
- Ioanna-Theoni Vourlaki
- Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, Campus UAB, Edifici CRAG, Bellaterra, 08193, Barcelona, Spain.
- IRTA (Institut de Recerca i Tecnologia Agroalimentàries), Caldes de Montbui, 08140, Barcelona, Spain.
| | - Sebastián E Ramos-Onsins
- Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, Campus UAB, Edifici CRAG, Bellaterra, 08193, Barcelona, Spain
| | - Miguel Pérez-Enciso
- Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, Campus UAB, Edifici CRAG, Bellaterra, 08193, Barcelona, Spain
- Catalan Institute for Research and Advanced Studies (ICREA), Barcelona, Spain
- Universitat Autónoma de Barcelona, 08193, Barcelona, Spain
| | - Raúl Castanera
- Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, Campus UAB, Edifici CRAG, Bellaterra, 08193, Barcelona, Spain.
- IRTA (Institut de Recerca i Tecnologia Agroalimentàries), Caldes de Montbui, 08140, Barcelona, Spain.
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Li J, Zhang D, Yang F, Zhang Q, Pan S, Zhao X, Zhang Q, Han Y, Yang J, Wang K, Zhao C. TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield. PLANT COMMUNICATIONS 2024; 5:100975. [PMID: 38751121 PMCID: PMC11287160 DOI: 10.1016/j.xplc.2024.100975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/14/2024] [Accepted: 05/11/2024] [Indexed: 06/24/2024]
Abstract
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.
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Affiliation(s)
- Jinlong Li
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Dongfeng Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Feng Yang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Qiusi Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Shouhui Pan
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Xiangyu Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Qi Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Yanyun Han
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Kaiyi Wang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
| | - Chunjiang Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
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Lee SY, Lee G, Han J, Ha SK, Lee CM, Kang K, Jin M, Suh JP, Jeung JU, Mo Y, Lee HS. GWAS analysis reveals the genetic basis of blast resistance associated with heading date in rice. FRONTIERS IN PLANT SCIENCE 2024; 15:1412614. [PMID: 38835858 PMCID: PMC11148375 DOI: 10.3389/fpls.2024.1412614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 05/06/2024] [Indexed: 06/06/2024]
Abstract
Rice blast is a destructive fungal disease affecting rice plants at various growth stages, significantly threatening global yield stability. Development of resistant rice cultivars stands as a practical means of disease control. Generally, association mapping with a diversity panel powerfully identifies new alleles controlling trait of interest. On the other hand, utilization of a breeding panel has its advantage that can be directly applied in a breeding program. In this study, we conducted a genome-wide association study (GWAS) for blast resistance using 296 commercial rice cultivars with low population structure but large phenotypic diversity. We attempt to answer the genetic basis behind rice blast resistance among early maturing cultivars by subdividing the population based on its Heading date 1 (Hd1) functionality. Subpopulation-specific GWAS using the mixed linear model (MLM) based on blast nursery screening conducted in three years revealed a total of 26 significant signals, including three nucleotide-binding site leucine-rich repeat (NBS-LRR) genes (Os06g0286500, Os06g0286700, and Os06g0287500) located at Piz locus on chromosome 6, and one at the Pi-ta locus (Os12g0281300) on chromosome 12. Haplotype analysis revealed blast resistance associated with Piz locus was exclusively specific to Type 14 hd1 among japonica rice. Our findings provide valuable insights for breeding blast resistant rice and highlight the applicability of our elite cultivar panel to detect superior alleles associated with important agronomic traits.
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Affiliation(s)
- Seung Young Lee
- Crop Breeding Division, National Institute of Crop Science, Rural Development Administration, Wanju, Republic of Korea
- Department of Crop Science and Biotechnology, Jeonbuk National University, Jeonju, Republic of Korea
| | - Gileung Lee
- Crop Breeding Division, National Institute of Crop Science, Rural Development Administration, Wanju, Republic of Korea
| | - Jiheon Han
- Department of Crop Science and Biotechnology, Jeonbuk National University, Jeonju, Republic of Korea
| | - Su-Kyung Ha
- Crop Breeding Division, National Institute of Crop Science, Rural Development Administration, Wanju, Republic of Korea
| | - Chang-Min Lee
- Crop Breeding Division, National Institute of Crop Science, Rural Development Administration, Wanju, Republic of Korea
| | - Kyeongmin Kang
- Crop Breeding Division, National Institute of Crop Science, Rural Development Administration, Wanju, Republic of Korea
| | - Mina Jin
- Crop Breeding Division, National Institute of Crop Science, Rural Development Administration, Wanju, Republic of Korea
| | - Jung-Pil Suh
- Crop Breeding Division, National Institute of Crop Science, Rural Development Administration, Wanju, Republic of Korea
| | - Ji-Ung Jeung
- Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration, Miryang, Republic of Korea
| | - Youngjun Mo
- Department of Crop Science and Biotechnology, Jeonbuk National University, Jeonju, Republic of Korea
- Institute of Agricultural Science and Technology, Jeonbuk National University, Jeonju, Republic of Korea
| | - Hyun-Sook Lee
- Crop Breeding Division, National Institute of Crop Science, Rural Development Administration, Wanju, Republic of Korea
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Sachdeva S, Singh R, Maurya A, Singh VK, Singh UM, Kumar A, Singh GP. New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study. BMC PLANT BIOLOGY 2024; 24:124. [PMID: 38373874 PMCID: PMC10877931 DOI: 10.1186/s12870-024-04810-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/08/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND Rice (Oryza sativa L.) is one of the globally important staple food crops, and yield-related traits are prerequisites for improved breeding efficiency in rice. Here, we used six different genome-wide association study (GWAS) models for 198 accessions, with 553,229 single nucleotide markers (SNPs) to identify the quantitative trait nucleotides (QTNs) and candidate genes (CGs) governing rice yield. RESULTS Amongst the 73 different QTNs in total, 24 were co-localized with already reported QTLs or loci in previous mapping studies. We obtained fifteen significant QTNs, pathway analysis revealed 10 potential candidates within 100kb of these QTNs that are predicted to govern plant height, days to flowering, and plot yield in rice. Based on their superior allelic information in 20 elite and 6 inferior genotypes, we found a higher percentage of superior alleles in the elite genotypes in comparison to inferior genotypes. Further, we implemented expression analysis and enrichment analysis enabling the identification of 73 candidate genes and 25 homologues of Arabidopsis, 19 of which might regulate rice yield traits. Of these candidate genes, 40 CGs were found to be enriched in 60 GO terms of the studied traits for instance, positive regulator metabolic process (GO:0010929), intracellular part (GO:0031090), and nucleic acid binding (GO:0090079). Haplotype and phenotypic variation analysis confirmed that LOC_OS09G15770, LOC_OS02G36710 and LOC_OS02G17520 are key candidates associated with rice yield. CONCLUSIONS Overall, we foresee that the QTNs, putative candidates elucidated in the study could summarize the polygenic regulatory networks controlling rice yield and be useful for breeding high-yielding varieties.
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Grants
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
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Affiliation(s)
- Supriya Sachdeva
- Division of Genomic Resources, ICAR-NBPGR, Pusa, New Delhi, India
| | - Rakesh Singh
- Division of Genomic Resources, ICAR-NBPGR, Pusa, New Delhi, India.
| | - Avantika Maurya
- Division of Genomic Resources, ICAR-NBPGR, Pusa, New Delhi, India
| | - Vikas K Singh
- International Rice Research Institute (IRRI), South Asia Hub, ICRISAT, Hyderabad, India
| | - Uma Maheshwar Singh
- International Rice Research Institute (IRRI), South Asia Regional Centre (ISARC), Varanasi, India
| | - Arvind Kumar
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Telangana, India
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Sachdeva S, Singh R, Maurya A, Singh VK, Singh UM, Kumar A, Singh GP. Multi-model genome-wide association studies for appearance quality in rice. FRONTIERS IN PLANT SCIENCE 2024; 14:1304388. [PMID: 38273959 PMCID: PMC10808671 DOI: 10.3389/fpls.2023.1304388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/22/2023] [Indexed: 01/27/2024]
Abstract
Improving the quality of the appearance of rice is critical to meet market acceptance. Mining putative quality-related genes has been geared towards the development of effective breeding approaches for rice. In the present study, two SL-GWAS (CMLM and MLM) and three ML-GWAS (FASTmrEMMA, mrMLM, and FASTmrMLM) genome-wide association studies were conducted in a subset of 3K-RGP consisting of 198 rice accessions with 553,831 SNP markers. A total of 594 SNP markers were identified using the mixed linear model method for grain quality traits. Additionally, 70 quantitative trait nucleotides (QTNs) detected by the ML-GWAS models were strongly associated with grain aroma (AR), head rice recovery (HRR, %), and percentage of grains with chalkiness (PGC, %). Finally, 39 QTNs were identified using single- and multi-locus GWAS methods. Among the 39 reliable QTNs, 20 novel QTNs were identified for the above-mentioned three quality-related traits. Based on annotation and previous studies, four functional candidate genes (LOC_Os01g66110, LOC_Os01g66140, LOC_Os07g44910, and LOC_Os02g14120) were found to influence AR, HRR (%), and PGC (%), which could be utilized in rice breeding to improve grain quality traits.
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Affiliation(s)
- Supriya Sachdeva
- Division of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India
| | - Rakesh Singh
- Division of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India
| | - Avantika Maurya
- Division of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India
| | - Vikas Kumar Singh
- International Rice Research Institute, South Asia Hub, International Crop Reseach Institute for Semi Arid Tropics (ICRISAT), Hyderabad, India
| | - Uma Maheshwar Singh
- International Rice Research Institute, South Asia Regional Centre (ISARC), Varanasi, India
| | - Arvind Kumar
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Telangana, India
| | - Gyanendra Pratap Singh
- Indian Council of Agricultural Research (ICAR)-National Bureau of Plant Genetic Resources, New Delhi, India
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Manjunatha PB, Aski MS, Mishra GP, Gupta S, Devate NB, Singh A, Bansal R, Kumar S, Nair RM, Dikshit HK. Genome-wide association studies for phenological and agronomic traits in mungbean ( Vigna radiata L. Wilczek). FRONTIERS IN PLANT SCIENCE 2023; 14:1209288. [PMID: 37810385 PMCID: PMC10558178 DOI: 10.3389/fpls.2023.1209288] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/25/2023] [Indexed: 10/10/2023]
Abstract
Mungbean (Vigna radiata L. Wilczek) is one of the important warm-season food legumes, contributing substantially to nutritional security and environmental sustainability. The genetic complexity of yield-associated agronomic traits in mungbean is not well understood. To dissect the genetic basis of phenological and agronomic traits, we evaluated 153 diverse mungbean genotypes for two phenological (days to heading and days to maturity) and eight agronomic traits (leaf nitrogen status using SPAD, plant height, number of primary branches, pod length, number of pods per plant, seeds per pod, 100-seed weight, and yield per plant) under two environmental conditions. A wide array of phenotypic variability was apparent among the studied genotypes for all the studied traits. The broad sense of heritability of traits ranged from 0.31 to 0.95 and 0.21 to 0.94 at the Delhi and Ludhiana locations, respectively. A total of 55,634 genome-wide single nucleotide polymorphisms (SNPs) were obtained by the genotyping-by-sequencing method, of which 15,926 SNPs were retained for genome-wide association studies (GWAS). GWAS with Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK) model identified 50 SNPs significantly associated with phenological and agronomic traits. In total, 12 SNPs were found to be significantly associated with phenological traits across environments, explaining 7%-18.5% of phenotypic variability, and 38 SNPs were significantly associated with agronomic traits, explaining 4.7%-27.6% of the phenotypic variability. The maximum number of SNPs (15) were located on chromosome 1, followed by seven SNPs each on chromosomes 2 and 8. The BLAST search identified 19 putative candidate genes that were involved in light signaling, nitrogen responses, phosphorus (P) transport and remobilization, photosynthesis, respiration, metabolic pathways, and regulating growth and development. Digital expression analysis of 19 genes revealed significantly higher expression of 12 genes, viz. VRADI01G08170, VRADI11G09170, VRADI02G00450, VRADI01G00700, VRADI07G14240, VRADI03G06030, VRADI02G14230, VRADI08G01540, VRADI09G02590, VRADI08G00110, VRADI02G14240, and VRADI02G00430 in the roots, cotyledons, seeds, leaves, shoot apical meristems, and flowers. The identified SNPs and putative candidate genes provide valuable genetic information for fostering genomic studies and marker-assisted breeding programs that improve yield and agronomic traits in mungbean.
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Affiliation(s)
- P. B. Manjunatha
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Muraleedhar S. Aski
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Gyan Prakash Mishra
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Soma Gupta
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Narayana Bhat Devate
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Akanksha Singh
- Amity Institute of Organic Agriculture, Amity University, Noida, India
| | - Ruchi Bansal
- Division of Plant Physiology, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Shiv Kumar
- International Centre for Agricultural Research in the Dry Areas (ICARDA), New Delhi, India
| | | | - Harsh Kumar Dikshit
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
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Sepehrdoust H, Tartar M, Zamani Shabkhaneh S, Heydari Parvin S. Environmental Sustainability in Selected OPEC Countries: Do the Influence of FDI and ICT Matter? ENVIRONMENTAL HEALTH INSIGHTS 2023; 17:11786302231188244. [PMID: 37577381 PMCID: PMC10422914 DOI: 10.1177/11786302231188244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 06/23/2023] [Indexed: 08/15/2023]
Abstract
Considering the undeniable role and importance of the environment in people's lives, the present study is designed to investigate the combined effect of information and communication technology (ICT) and foreign direct investment (FDI) on achieving environmental sustainability. Since the increasing emission of carbon in society and its destructive environmental effects on social economic aspects and even political tensions have become a challenge, the main question of the research is what strategies have governments, especially oil exporting countries, used in the past to reduce the level they have discovered pollution and what policies do they want to follow in the future? Among the policies undertaken by the OPEC oil exporting countries, has the action for foreign direct investment (FDI) and the development of information and communication technology (ICT) been effective in preventing harmful environmental effects? For this purpose, data on renewable energy consumption, the intensity of use of information and communication technology, foreign direct investment (FDI), and urbanization have been used as explanatory variables, and carbon dioxide (CO2) emission as a dependent variable. The target countries selected are oil exporting countries (OPEC) for the period 2000 to 2020, and the analysis method used is panel VAR. The results showed that creating a shock in FDI, labor force, urban population, and renewable energy consumption decreases CO2 while creating a shock in Gross capital formation increases CO2. The impact of shock of ICT on CO2 is also insignificant and can be ignored. The results of variance analysis also showed that urban population, labor force, and FDI variables have the largest contribution in explaining the behavior of CO2; therefore, it is necessary to pay attention to FDI and try to increase the attraction of foreign direct investment to reduce CO2 in OPEC countries. JEL: C23, F43, F64.
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Affiliation(s)
- Hamid Sepehrdoust
- Faculty of Economics & Social Science, Department of Economics, Bu-Ali-Sina University, Hamedan, Islamic Republic of Iran
| | - Mohsen Tartar
- Faculty of Economics & Social Science, Department of Economics, Bu-Ali-Sina University, Hamedan, Islamic Republic of Iran
| | - Saber Zamani Shabkhaneh
- Faculty of Economics & Social Science, Department of Economics, Bu-Ali-Sina University, Hamedan, Islamic Republic of Iran
| | - Shaghayegh Heydari Parvin
- Faculty of Economics & Social Science, Department of Economics, Bu-Ali-Sina University, Hamedan, Islamic Republic of Iran
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Ashfaq M, Rasheed A, Zhu R, Ali M, Javed MA, Anwar A, Tabassum J, Shaheen S, Wu X. Genome-Wide Association Mapping for Yield and Yield-Related Traits in Rice ( Oryza Sativa L.) Using SNPs Markers. Genes (Basel) 2023; 14:genes14051089. [PMID: 37239449 DOI: 10.3390/genes14051089] [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: 03/20/2023] [Revised: 05/10/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Rice (Oryza sativa L.) is a staple food for more than 50% of the world's population. Rice cultivar improvement is critical in order to feed the world's growing population. Improving yield is one of the main aims of rice breeders. However, yield is a complex quantitative trait controlled by many genes. The presence of genetic diversity is the key factor to improve the yield hence, the presence of diversity in any germplasm is important for yield improvement. In the current study, the rice germplasm was collected from Pakistan and the United States of America and a panel of 100 diverse genotypes was utilized to identify important yield and yield-related traits. For this, a genome-wide association study (GWAS) was performed to identify the genetic loci related to yield. The GWAS on the diverse germplasm will lead to the identification of new genes which can be utilized in the breeding program for improvement of yield. For this reason, firstly, the germplasm was phenotypically evaluated in two growing seasons for yield and yield-related traits. The analysis of variance results showed significant differences among traits which showed the presence of diversity in the current germplasm. Secondly, the germplasm was also genotypically evaluated using 10K SNP. Genetic structure analysis showed the presence of four groups which showed that enough genetic diversity was present in the rice germplasm to be used for association mapping analysis. The results of GWAS identified 201 significant marker trait associations (MTAs. 16 MTAs were identified for plant height, 49 for days to flowering, three for days to maturity, four for tillers per plant, four for panicle length, eight for grains per panicle, 20 unfilled grains per panicle, 81 for seed setting %, four for thousand-grain weight, five for yield per plot and seven for yield per hectare. Apart from this, some pleiotropic loci were also identified. The results showed that panicle length (PL) and thousand-grain weight (TGW) were controlled by a pleiotropic locus OsGRb23906 on chromosome 1 at 10,116,371 cM. The loci OsGRb25803 and OsGRb15974 on chromosomes 4 and 8 at the position of 14,321,111 cM and 6,205,816 cM respectively, showed pleiotropic effects for seed setting % (SS) and unfilled grain per panicle (UG/P). A locus OsGRb09180 on chromosome 4 at 19,850,601 cM was significantly linked with SS and yield/ha. Furthermore, gene annotation was performed, and results indicated that the 190 candidate genes or QTLs that closely linked with studied traits. These candidate genes and novel significant markers could be useful in marker-assisted gene selection and QTL pyramiding to improve rice yield and the selection of potential parents, recombinants and MTAs which could be used in rice breeding programs to develop high-yielding rice varieties for sustainable food security.
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Affiliation(s)
- Muhammad Ashfaq
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore 54590, Pakistan
| | - Abdul Rasheed
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore 54590, Pakistan
| | - Renshan Zhu
- Department of Genetics, College of Life Sciences, Wuhan University, Wuhan 430072, China
| | - Muhammad Ali
- Department of Entomology, Faculty of Agricultural Sciences, University of the Punjab, Lahore 54590, Pakistan
| | - Muhammad Arshad Javed
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore 54590, Pakistan
| | - Alia Anwar
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore 54590, Pakistan
| | - Javaria Tabassum
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore 54590, Pakistan
| | - Shabnum Shaheen
- Department of Botany, Lahore College for Women University, Lahore 54590, Pakistan
| | - Xianting Wu
- Department of Genetics, College of Life Sciences, Wuhan University, Wuhan 430072, China
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Jabran M, Ali MA, Zahoor A, Muhae-Ud-Din G, Liu T, Chen W, Gao L. Intelligent reprogramming of wheat for enhancement of fungal and nematode disease resistance using advanced molecular techniques. FRONTIERS IN PLANT SCIENCE 2023; 14:1132699. [PMID: 37235011 PMCID: PMC10206142 DOI: 10.3389/fpls.2023.1132699] [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: 12/27/2022] [Accepted: 04/19/2023] [Indexed: 05/28/2023]
Abstract
Wheat (Triticum aestivum L.) diseases are major factors responsible for substantial yield losses worldwide, which affect global food security. For a long time, plant breeders have been struggling to improve wheat resistance against major diseases by selection and conventional breeding techniques. Therefore, this review was conducted to shed light on various gaps in the available literature and to reveal the most promising criteria for disease resistance in wheat. However, novel techniques for molecular breeding in the past few decades have been very fruitful for developing broad-spectrum disease resistance and other important traits in wheat. Many types of molecular markers such as SCAR, RAPD, SSR, SSLP, RFLP, SNP, and DArT, etc., have been reported for resistance against wheat pathogens. This article summarizes various insightful molecular markers involved in wheat improvement for resistance to major diseases through diverse breeding programs. Moreover, this review highlights the applications of marker assisted selection (MAS), quantitative trait loci (QTL), genome wide association studies (GWAS) and the CRISPR/Cas-9 system for developing disease resistance against most important wheat diseases. We also reviewed all reported mapped QTLs for bunts, rusts, smuts, and nematode diseases of wheat. Furthermore, we have also proposed how the CRISPR/Cas-9 system and GWAS can assist breeders in the future for the genetic improvement of wheat. If these molecular approaches are used successfully in the future, they can be a significant step toward expanding food production in wheat crops.
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Affiliation(s)
- Muhammad Jabran
- State Key Laboratory for Biology of Plant Diseases, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Amjad Ali
- Department of Plant Pathology, University of Agriculture, Faisalabad, Pakistan
| | - Adil Zahoor
- Department of Biotechnology, Chonnam National University, Yeosu, Republic of Korea
| | - Ghulam Muhae-Ud-Din
- State Key Laboratory for Biology of Plant Diseases, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Taiguo Liu
- State Key Laboratory for Biology of Plant Diseases, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wanquan Chen
- State Key Laboratory for Biology of Plant Diseases, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Li Gao
- State Key Laboratory for Biology of Plant Diseases, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
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10
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Jin SK, Xu LN, Yang QQ, Zhang MQ, Wang SL, Wang RA, Tao T, Hong LM, Guo QQ, Jia SW, Song T, Leng YJ, Cai XL, Gao JP. High-resolution quantitative trait locus mapping for rice grain quality traits using genotyping by sequencing. FRONTIERS IN PLANT SCIENCE 2023; 13:1050882. [PMID: 36714703 PMCID: PMC9878556 DOI: 10.3389/fpls.2022.1050882] [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: 09/22/2022] [Accepted: 12/23/2022] [Indexed: 06/18/2023]
Abstract
Rice is a major food crop that sustains approximately half of the world population. Recent worldwide improvements in the standard of living have increased the demand for high-quality rice. Accurate identification of quantitative trait loci (QTLs) for rice grain quality traits will facilitate rice quality breeding and improvement. In the present study, we performed high-resolution QTL mapping for rice grain quality traits using a genotyping-by-sequencing approach. An F2 population derived from a cross between an elite japonica variety, Koshihikari, and an indica variety, Nona Bokra, was used to construct a high-density genetic map. A total of 3,830 single nucleotide polymorphism markers were mapped to 12 linkage groups spanning a total length of 2,456.4 cM, with an average genetic distance of 0.82 cM. Seven grain quality traits-the percentage of whole grain, percentage of head rice, percentage of area of head rice, transparency, percentage of chalky rice, percentage of chalkiness area, and degree of chalkiness-of the F2 population were investigated. In total, 15 QTLs with logarithm of the odds (LOD) scores >4 were identified, which mapped to chromosomes 6, 7, and 9. These loci include four QTLs for transparency, four for percentage of chalky rice, four for percentage of chalkiness area, and three for degree of chalkiness, accounting for 0.01%-61.64% of the total phenotypic variation. Of these QTLs, only one overlapped with previously reported QTLs, and the others were novel. By comparing the major QTL regions in the rice genome, several key candidate genes reported to play crucial roles in grain quality traits were identified. These findings will expedite the fine mapping of these QTLs and QTL pyramiding, which will facilitate the genetic improvement of rice grain quality.
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Affiliation(s)
- Su-Kui Jin
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Li-Na Xu
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Qing-Qing Yang
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Ming-Qiu Zhang
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Shui-Lian Wang
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Ruo-An Wang
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Tao Tao
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Lian-Min Hong
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Qian-Qian Guo
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Shu-Wen Jia
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Tao Song
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yu-Jia Leng
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Xiu-Ling Cai
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Ji-Ping Gao
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
- Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, China
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11
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Abhijith KP, Gopala Krishnan S, Ravikiran KT, Dhawan G, Kumar P, Vinod KK, Bhowmick PK, Nagarajan M, Seth R, Sharma R, Badhran SK, Bollinedi H, Ellur RK, Singh AK. Genome-wide association study reveals novel genomic regions governing agronomic and grain quality traits and superior allelic combinations for Basmati rice improvement. FRONTIERS IN PLANT SCIENCE 2022; 13:994447. [PMID: 36544876 PMCID: PMC9760805 DOI: 10.3389/fpls.2022.994447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/09/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Basmati is a speciality segment in the rice genepool characterised by explicit grain quality. For the want of suitable populations, genome-wide association study (GWAS) in Basmati rice has not been attempted. MATERIALS To address this gap, we have performed a GWAS on a panel of 172 elite Basmati multiparent population comprising of potential restorers and maintainers. Phenotypic data was generated for various agronomic and grain quality traits across seven different environments during two consecutive crop seasons. Based on the observed phenotypic variation, three agronomic traits namely, days to fifty per cent flowering, plant height and panicle length, and three grain quality traits namely, kernel length before cooking, length breadth ratio and kernel length after cooking were subjected to GWAS. Genotyped with 80K SNP array, the population was subjected to principal component analysis to stratify the underlying substructure and subjected to the association analysis using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model. RESULTS We identified 32 unique MTAs including 11 robust MTAs for the agronomic traits and 25 unique MTAs including two robust MTAs for the grain quality traits. Six out of 13 robust MTAs were novel. By genome annotation, six candidate genes associated with the robust MTAs were identified. Further analysis of the allelic combinations of the robust MTAs enabled the identification of superior allelic combinations in the population. This information was utilized in selecting 77 elite Basmati rice genotypes from the panel. CONCLUSION This is the first ever GWAS study in Basmati rice which could generate valuable information usable for further breeding through marker assisted selection, including enhancing of heterosis.
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Affiliation(s)
- Krishnan P. Abhijith
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - S. Gopala Krishnan
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | | | - Gaurav Dhawan
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Pankaj Kumar
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | | | | | - Mariappan Nagarajan
- Rice Breeding and Genetics Research Centre, ICAR-Indian Agricultural Research Institute, Aduthurai, Tamil Nadu, India
| | - Rakesh Seth
- Regional Station, ICAR-Indian Agricultural Research Institute, Karnal, Haryana, India
| | - Ritesh Sharma
- Basmati Export Development Foundation (BEDF), Meerut, Uttar Pradesh, India
| | | | - Haritha Bollinedi
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Ranjith Kumar Ellur
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Ashok Kumar Singh
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
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12
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Zia MAB, Yousaf MF, Asim A, Naeem M. An overview of genome-wide association mapping studies in Poaceae species (model crops: wheat and rice). Mol Biol Rep 2022; 49:12077-12090. [DOI: 10.1007/s11033-022-08036-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
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13
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Duan G, Wu G, Chen X, Tian D, Li Z, Sun Y, Du Z, Hao L, Song S, Gao Y, Xiao J, Zhang Z, Bao Y, Tang B, Zhao W. HGD: an integrated homologous gene database across multiple species. Nucleic Acids Res 2022; 51:D994-D1002. [PMID: 36318261 PMCID: PMC9825607 DOI: 10.1093/nar/gkac970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/28/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022] Open
Abstract
Homology is fundamental to infer genes' evolutionary processes and relationships with shared ancestry. Existing homolog gene resources vary in terms of inferring methods, homologous relationship and identifiers, posing inevitable difficulties for choosing and mapping homology results from one to another. Here, we present HGD (Homologous Gene Database, https://ngdc.cncb.ac.cn/hgd), a comprehensive homologs resource integrating multi-species, multi-resources and multi-omics, as a complement to existing resources providing public and one-stop data service. Currently, HGD houses a total of 112 383 644 homologous pairs for 37 species, including 19 animals, 16 plants and 2 microorganisms. Meanwhile, HGD integrates various annotations from public resources, including 16 909 homologs with traits, 276 670 homologs with variants, 398 573 homologs with expression and 536 852 homologs with gene ontology (GO) annotations. HGD provides a wide range of omics gene function annotations to help users gain a deeper understanding of gene function.
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Affiliation(s)
| | | | - Xiaoning Chen
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongmei Tian
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zhaohua Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanling Sun
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zhenglin Du
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Lili Hao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Shuhui Song
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuan Gao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhang Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiming Bao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bixia Tang
- Correspondence may also be addressed to Bixia Tang.
| | - Wenming Zhao
- To whom correspondence should be addressed. Tel: +86 1084097636; Fax: +86 1084097720;
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14
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Vourlaki IT, Castanera R, Ramos-Onsins SE, Casacuberta JM, Pérez-Enciso M. Transposable element polymorphisms improve prediction of complex agronomic traits in rice. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3211-3222. [PMID: 35931838 PMCID: PMC9482605 DOI: 10.1007/s00122-022-04180-2] [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: 01/20/2022] [Accepted: 07/12/2022] [Indexed: 06/15/2023]
Abstract
Transposon insertion polymorphisms can improve prediction of complex agronomic traits in rice compared to using SNPs only, especially when accessions to be predicted are less related to the training set. Transposon insertion polymorphisms (TIPs) are significant sources of genetic variation. Previous work has shown that TIPs can improve detection of causative loci on agronomic traits in rice. Here, we quantify the fraction of variance explained by single nucleotide polymorphisms (SNPs) compared to TIPs, and we explore whether TIPs can improve prediction of traits when compared to using only SNPs. We used eleven traits of agronomic relevance from by five different rice population groups (Aus, Indica, Aromatic, Japonica, and Admixed), 738 accessions in total. We assess prediction by applying data split validation in two scenarios. In the within-population scenario, we predicted performance of improved Indica varieties using the rest of Indica accessions. In the across population scenario, we predicted all Aromatic and Admixed accessions using the rest of populations. In each scenario, Bayes C and a Bayesian reproducible kernel Hilbert space regression were compared. We find that TIPs can explain an important fraction of total genetic variance and that they also improve genomic prediction. In the across population prediction scenario, TIPs outperformed SNPs in nine out of the eleven traits analyzed. In some traits like leaf senescence or grain width, using TIPs increased predictive correlation by 30-50%. Our results evidence, for the first time, that TIPs genotyping can improve prediction on complex agronomic traits in rice, especially when accessions to be predicted are less related to training accessions.
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Affiliation(s)
- Ioanna-Theoni Vourlaki
- Universitat Autònoma de Barcelona, Department of Animal Production, 08193, Bellaterra, Barcelona, Spain.
- Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, 08193, Bellaterra, Barcelona, Spain.
| | - Raúl Castanera
- Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, 08193, Bellaterra, Barcelona, Spain
| | - Sebastián E Ramos-Onsins
- Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, 08193, Bellaterra, Barcelona, Spain
| | - Josep M Casacuberta
- Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, 08193, Bellaterra, Barcelona, Spain
| | - Miguel Pérez-Enciso
- Universitat Autònoma de Barcelona, Department of Animal Production, 08193, Bellaterra, Barcelona, Spain.
- Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, 08193, Bellaterra, Barcelona, Spain.
- Catalan Institute for Research and Advanced studies, ICREA, 08010, Barcelona, Spain.
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15
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Gill HS, Halder J, Zhang J, Rana A, Kleinjan J, Amand PS, Bernardo A, Bai G, Sehgal SK. Whole-genome analysis of hard winter wheat germplasm identifies genomic regions associated with spike and kernel traits. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2953-2967. [PMID: 35939073 DOI: 10.1007/s00122-022-04160-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Genetic dissection of yield component traits including spike and kernel characteristics is essential for the continuous improvement in wheat yield. Genome-wide association studies (GWAS) have been frequently used to identify genetic determinants for spike and kernel-related traits in wheat, though none have been employed in hard winter wheat (HWW) which represents a major class in US wheat acreage. Further, most of these studies relied on assembled diversity panels instead of adapted breeding lines, limiting the transferability of results to practical wheat breeding. Here we assembled a population of advanced/elite breeding lines and well-adapted cultivars and evaluated over four environments for phenotypic analysis of spike and kernel traits. GWAS identified 17 significant multi-environment marker-trait associations (MTAs) for various traits, representing 12 putative quantitative trait loci (QTLs), with five QTLs affecting multiple traits. Four of these QTLs mapped on three chromosomes 1A, 5B, and 7A for spike length, number of spikelets per spike (NSPS), and kernel length are likely novel. Further, a highly significant QTL was detected on chromosome 7AS that has not been previously associated with NSPS and putative candidate genes were identified in this region. The allelic frequencies of important quantitative trait nucleotides (QTNs) were deduced in a larger set of 1,124 accessions which revealed the importance of identified MTAs in the US HWW breeding programs. The results from this study could be directly used by the breeders to select the lines with favorable alleles for making crosses, and reported markers will facilitate marker-assisted selection of stable QTLs for yield components in wheat breeding.
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Affiliation(s)
- Harsimardeep S Gill
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Jyotirmoy Halder
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Jinfeng Zhang
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Anshul Rana
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Jonathan Kleinjan
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Paul St Amand
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66506, USA
| | - Amy Bernardo
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66506, USA
| | - Guihua Bai
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66506, USA
| | - Sunish K Sehgal
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA.
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16
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de la Fuente Cantó C, Vigouroux Y. Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches. BMC Genomics 2022; 23:490. [PMID: 35794552 PMCID: PMC9258084 DOI: 10.1186/s12864-022-08718-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 06/20/2022] [Indexed: 11/22/2022] Open
Abstract
Background Bulk segregant analysis (BSA) combined with next generation sequencing is a powerful tool to identify quantitative trait loci (QTL). The impact of the size of the study population and the percentage of extreme genotypes analysed have already been assessed. But a good comparison of statistical approaches designed to identify QTL regions using next generation sequencing (NGS) technologies for BSA is still lacking. Results We developed an R code to simulate QTLs in bulks of F2 contrasted lines. We simulated a range of recombination rates based on estimations using different crop species. The simulations were used to benchmark the ability of statistical methods identify the exact location of true QTLs. A single QTL led to a shift in allele frequency across a large fraction of the chromosome for plant species with low recombination rate. The smoothed version of all statistics performed best notably the smoothed Euclidean distance-based statistics was always found to be more accurate in identifying the location of QTLs. We propose a simulation approach to build confidence interval statistics for the detection of QTLs. Conclusion We highlight the statistical methods best suited for BSA studies using NGS technologies in crops even when recombination rate is low. We also provide simulation codes to build confidence intervals and to assess the impact of recombination for application to other studies. This computational study will help select NGS-based BSA statistics that are useful to the broad scientific community. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08718-y.
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17
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Thomson MJ, Biswas S, Tsakirpaloglou N, Septiningsih EM. Functional Allele Validation by Gene Editing to Leverage the Wealth of Genetic Resources for Crop Improvement. Int J Mol Sci 2022; 23:ijms23126565. [PMID: 35743007 PMCID: PMC9223900 DOI: 10.3390/ijms23126565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 02/05/2023] Open
Abstract
Advances in molecular technologies over the past few decades, such as high-throughput DNA marker genotyping, have provided more powerful plant breeding approaches, including marker-assisted selection and genomic selection. At the same time, massive investments in plant genetics and genomics, led by whole genome sequencing, have led to greater knowledge of genes and genetic pathways across plant genomes. However, there remains a gap between approaches focused on forward genetics, which start with a phenotype to map a mutant locus or QTL with the goal of cloning the causal gene, and approaches using reverse genetics, which start with large-scale sequence data and work back to the gene function. The recent establishment of efficient CRISPR-Cas-based gene editing promises to bridge this gap and provide a rapid method to functionally validate genes and alleles identified through studies of natural variation. CRISPR-Cas techniques can be used to knock out single or multiple genes, precisely modify genes through base and prime editing, and replace alleles. Moreover, technologies such as protoplast isolation, in planta transformation, and the use of developmental regulatory genes promise to enable high-throughput gene editing to accelerate crop improvement.
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Vinarao R, Proud C, Snell P, Fukai S, Mitchell J. Genomic Regions and Floral Traits Contributing to Low Temperature Tolerance at Young Microspore Stage in a Rice ( Oryza sativa L.) Recombinant Inbred Line Population of Sherpa/IRAT109. FRONTIERS IN PLANT SCIENCE 2022; 13:873677. [PMID: 35574104 PMCID: PMC9100824 DOI: 10.3389/fpls.2022.873677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 03/31/2022] [Indexed: 06/15/2023]
Abstract
Aerobic rice production (AP) consumes less water compared to flooded systems. Developing genotypes and identifying genomic regions associated with low temperature (LT) tolerance at the young microspore stage (YMS) is imperative for AP, particularly for temperate regions. Using a recombinant inbred line population derived from the Australian LT tolerant variety Sherpa, experiments were conducted to map and dissect quantitative trait loci (QTL) associated with spikelet sterility (SS) after exposure to LT and to investigate floral traits contributing to the development of lower SS. Significant genotypic variation for SS was observed in the population after exposure to LT at YMS. Three genomic regions associated with SS, qYMCT3, qYMCT4, and qYMCT8.1 were identified in chromosomes 3, 4, and 8 respectively, using multiple QTL models explaining 22.4% of the genotypic variation. Introgression of the favorable allele from qYMCT3 was estimated to reduce SS by up to 15.4%. A co-locating genomic region with qYMCT3, qDTHW3.1 was identified as the major QTL affecting days to heading and explained as much as 44.7% of the genotypic variation. Whole-genome sequence and bioinformatic analyses demonstrated OsMADS50 as the candidate gene for qYMCT3/qDTHW3.1 and to our knowledge, this was the first attempt in connecting the role of OsMADS50 in both LT and flowering in rice. Differential sets selected for extreme SS showed LT tolerant genotype group produced higher total pollen per spikelet resulting in a higher number of dehisced anthers and pollen on stigma and eventually, lower SS than THE sensitive group. The relationship between these key floral traits with SS was induced only after exposure to LT and was not observed in warm ideal temperature conditions. Identification of elite germplasm with favorable QTL allele and combinations, gene cloning, and pyramiding with additional high-value QTL for key traits should empower breeders to develop AP adapted genotypes for temperate growing regions, and ultimately produce climate-resilient rice.
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Affiliation(s)
- Ricky Vinarao
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Christopher Proud
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Peter Snell
- Department of Primary Industries, Yanco Agricultural Institute, Yanco, NSW, Australia
| | - Shu Fukai
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Jaquie Mitchell
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD, Australia
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19
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Utilization Strategies of Two Environment Phenotypes in Genomic Prediction. Genes (Basel) 2022; 13:genes13050722. [PMID: 35627107 PMCID: PMC9141986 DOI: 10.3390/genes13050722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 02/01/2023] Open
Abstract
Multiple environment phenotypes may be utilized to implement genomic prediction in plant breeding, while it is unclear about optimal utilization strategies according to its different availability. It is necessary to assess the utilization strategies of genomic prediction models based on different availability of multiple environment phenotypes. Here, we compared the prediction accuracy of three genomic prediction models (genomic prediction model (genomic best linear unbiased prediction (GBLUP), genomic best linear unbiased prediction (GFBLUP), and multi-trait genomic best linear unbiased prediction (mtGBLUP)) which leveraged diverse information from multiple environment phenotypes using a rice dataset containing 19 agronomic traits in two disparate seasons. We found that the prediction accuracy of genomic prediction models considering multiple environment phenotypes (GFBLUP and mtGBLUP) was better than the classical genomic prediction model (GBLUP model). The deviation of prediction accuracy of between GBLUP and mtGBLUP or GFBLUP was associated with the phenotypic correlation. In summary, the genomic prediction models considering multiple environment phenotypes (GFBLUP and mtGBLUP) demonstrated better prediction accuracy. In addition, we could utilize different genomic prediction strategies according to different availability of multiple environment phenotypes.
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20
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DNA-Based Tools to Certify Authenticity of Rice Varieties—An Overview. Foods 2022; 11:foods11030258. [PMID: 35159410 PMCID: PMC8834242 DOI: 10.3390/foods11030258] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/04/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023] Open
Abstract
Rice (Oryza sativa L.) is one of the most cultivated and consumed crops worldwide. It is mainly produced in Asia but, due to its large genetic pool, it has expanded to several ecosystems, latitudes and climatic conditions. Europe is a rice producing region, especially in the Mediterranean countries, that grow mostly typical japonica varieties. The European consumer interest in rice has increased over the last decades towards more exotic types, often more expensive (e.g., aromatic rice) and Europe is a net importer of this commodity. This has increased food fraud opportunities in the rice supply chain, which may deliver mixtures with lower quality rice, a problem that is now global. The development of tools to clearly identify undesirable mixtures thus became urgent. Among the various tools available, DNA-based markers are considered particularly reliable and stable for discrimination of rice varieties. This review covers aspects ranging from rice diversity and fraud issues to the DNA-based methods used to distinguish varieties and detect unwanted mixtures. Although not exhaustive, the review covers the diversity of strategies and ongoing improvements already tested, highlighting important advantages and disadvantages in terms of costs, reliability, labor-effort and potential scalability for routine fraud detection.
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21
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Peringottillam M, Kunhiraman Vasumathy S, Selvakumar HKK, Alagu M. Genetic diversity and population structure of rice (Oryza sativa L.) landraces from Kerala, India analyzed through genotyping-by-sequencing. Mol Genet Genomics 2022; 297:169-182. [PMID: 35039933 DOI: 10.1007/s00438-021-01844-4] [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: 05/11/2021] [Accepted: 11/28/2021] [Indexed: 11/24/2022]
Abstract
Researchers stand at the vanguard of advancement and application of next-generation sequencing technology for developing dominant strategies for the sustainable management of genetically diverse crops. We attempt to fill the existing research lacuna in the molecular characterization of potent rice landraces in Kerala. Genotyping-by-sequencing (GBS) was performed on 96 Kerala rice accessions to identify single-nucleotide polymorphisms (SNPs), to examine the genetic diversity, population structure, and to delineate linkage disequilibrium (LD) pattern. GBS identified 5856 high-quality SNPs. The structure analysis indicated three subpopulations with the highest probability for population clustering with significant genetic differentiation, confirmed by principal component analysis. The genome-wide LD decay distance was 772 kb, at which the r2 dropped to half its maximum value. The analysis of genetic properties of the identified SNP panel with an average polymorphism information content (PIC) value of 0.22 and a minor allele frequency (MAF) > 0.1 unveiled their efficacy in genome-wide association studies (GWAS). High FST (0.266) and low Nm (0.692) portray a strong genetic differentiation among the rice landraces, complementing the genetic structuring observed in the studied population. Slow LD decay in the rice landraces reflects their self-pollinating behavior and the indirect selection of desired traits by domestication. Moreover, the high LD entails only a minimum number of SNP markers for detecting marker-trait association. The diverse germplasm utilized in this study can be further utilized to disclose genetic variants associated with phenotypic traits and define signatures of selection via GWAS and selective sweep, respectively.
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Affiliation(s)
- Maya Peringottillam
- Department of Genomic Science, Central University of Kerala, Tejaswini Hills, Periye (PO), Kasaragod, Kerala, 671316, India
| | - Smitha Kunhiraman Vasumathy
- Department of Genomic Science, Central University of Kerala, Tejaswini Hills, Periye (PO), Kasaragod, Kerala, 671316, India
| | - Hari Krishna Kumar Selvakumar
- Department of Genomic Science, Central University of Kerala, Tejaswini Hills, Periye (PO), Kasaragod, Kerala, 671316, India
| | - Manickavelu Alagu
- Department of Genomic Science, Central University of Kerala, Tejaswini Hills, Periye (PO), Kasaragod, Kerala, 671316, India.
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22
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Phumichai C, Aiemnaka P, Nathaisong P, Hunsawattanakul S, Fungfoo P, Rojanaridpiched C, Vichukit V, Kongsil P, Kittipadakul P, Wannarat W, Chunwongse J, Tongyoo P, Kijkhunasatian C, Chotineeranat S, Piyachomkwan K, Wolfe MD, Jannink JL, Sorrells ME. Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:145-171. [PMID: 34661695 DOI: 10.1007/s00122-021-03956-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 09/25/2021] [Indexed: 06/13/2023]
Abstract
GWAS identified eight yield-related, peak starch type of waxy and wild-type starch and 21 starch pasting property-related traits (QTLs). Prediction ability of eight GS models resulted in low to high predictability, depending on trait, heritability, and genetic architecture. Cassava is both a food and an industrial crop in Africa, South America, and Asia, but knowledge of the genes that control yield and starch pasting properties remains limited. We carried out a genome-wide association study to clarify the molecular mechanisms underlying these traits and to explore marker-based breeding approaches. We estimated the predictive ability of genomic selection (GS) using parametric, semi-parametric, and nonparametric GS models with a panel of 276 cassava genotypes from Thai Tapioca Development Institute, International Center for Tropical Agriculture, International Institute of Tropical Agriculture, and other breeding programs. The cassava panel was genotyped via genotyping-by-sequencing, and 89,934 single-nucleotide polymorphism (SNP) markers were identified. A total of 31 SNPs associated with yield, starch type, and starch properties traits were detected by the fixed and random model circulating probability unification (FarmCPU), Bayesian-information and linkage-disequilibrium iteratively nested keyway and compressed mixed linear model, respectively. GS models were developed, and forward predictabilities using all the prediction methods resulted in values of - 0.001-0.71 for the four yield-related traits and 0.33-0.82 for the seven starch pasting property traits. This study provides additional insight into the genetic architecture of these important traits for the development of markers that could be used in cassava breeding programs.
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Affiliation(s)
- Chalermpol Phumichai
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand.
- Center for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand.
- Center of Excellence On Agricultural Biotechnology: (AG-BIO/MHESI), Bangkok, 10900, Thailand.
| | - Pornsak Aiemnaka
- Thai Tapioca Development Institute, Lumpini Tower, 1168/26 Rama IV Road, Bangkok, 10120, Thailand
| | - Piyaporn Nathaisong
- Thai Tapioca Development Institute, Lumpini Tower, 1168/26 Rama IV Road, Bangkok, 10120, Thailand
| | - Sirikan Hunsawattanakul
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
- Center for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand
- Center of Excellence On Agricultural Biotechnology: (AG-BIO/MHESI), Bangkok, 10900, Thailand
| | - Phasakorn Fungfoo
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | | | - Vichan Vichukit
- Thai Tapioca Development Institute, Lumpini Tower, 1168/26 Rama IV Road, Bangkok, 10120, Thailand
| | - Pasajee Kongsil
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | - Piya Kittipadakul
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | - Wannasiri Wannarat
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | - Julapark Chunwongse
- Department of Horticulture, Faculty of Agriculture Kamphaeng Saen, Kasetsart University, Nakhon Pathom, 73140, Thailand
| | - Pumipat Tongyoo
- Center for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand
| | - Chookiat Kijkhunasatian
- Cassava and Starch Technology Research Team, National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand
| | - Sunee Chotineeranat
- Cassava and Starch Technology Research Team, National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand
| | - Kuakoon Piyachomkwan
- Cassava and Starch Technology Research Team, National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand
| | - Marnin D Wolfe
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, 14850, USA
| | - Jean-Luc Jannink
- United States Department of Agriculture - Agriculture Research Service, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, 14850, USA
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23
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Cruz M, Arbelaez JD, Loaiza K, Cuasquer J, Rosas J, Graterol E. Genetic and phenotypic characterization of rice grain quality traits to define research strategies for improving rice milling, appearance, and cooking qualities in Latin America and the Caribbean. THE PLANT GENOME 2021; 14:e20134. [PMID: 34510797 DOI: 10.1002/tpg2.20134] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/23/2021] [Indexed: 06/13/2023]
Abstract
Rice (Oryza sativa L.)grain quality is a set of complex interrelated traits that include grain milling, appearance, cooking, and edible properties. As consumer preferences in Latin America and the Caribbean evolve, determining what traits best capture regional grain quality preferences is fundamental for breeding and cultivar release. In this study, a genome-wide association study (GWAS), marker-assisted selection (MAS), and genomic selection (GS) were evaluated to help guide the development of new breeding strategies for rice grain quality improvement. For this purpose, 284 rice lines representing over 20 yr of breeding in Latin America and the Caribbean were genotyped and phenotyped for 10 different traits including grain milling, appearance, cooking, and edible quality traits. Genetic correlations among the 10 traits ranged from -0.83 to 0.85. A GWAS identified 19 significant marker/trait combinations associated with eight grain quality traits. Four functional markers, three located in the Waxy and one in the starch synthase IIa genes, were significantly associated with six grain-quality traits. These markers individually explained 51-75% of the phenotypic variance depending on the trait, clearly indicating their potential utility for MAS. Cross-validation studies to evaluate predictive abilities of four different GS models for each of the 10 quality traits were conducted and predictive abilities ranged from 0.3 to 0.72. Overall, the machine learning model random forest had the highest predictive abilities and was especially effective for traits where large effect quantitative trait loci were identified. This study provides the foundation for deploying effective molecular breeding strategies for grain quality in Latin American rice breeding programs.
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Affiliation(s)
- Maribel Cruz
- FLAR (Fondo Latinoamericano para Arroz de Riego), CIAT (International Center for Tropical Agriculture), Kilómetro 17 c, CP, Cali, Valle del Cauca, 763537, Colombia
| | - Juan David Arbelaez
- Dep. of Crop Sciences, Univ. of Illinois, Urbana-Champaign, Turner Hall N-211|1102 S. Goodwin Ave. | 046, Urbana, IL, 61801, USA
| | - Katherine Loaiza
- FLAR (Fondo Latinoamericano para Arroz de Riego), CIAT (International Center for Tropical Agriculture), Kilómetro 17 c, CP, Cali, Valle del Cauca, 763537, Colombia
| | - Juan Cuasquer
- CIAT (International Center for Tropical Agriculture), Kilómetro 17 Recta Cali, Palmira, CP, Cali, Valle del Cauca, 763537, Colombia
| | - Juan Rosas
- INIA (Instituto Nacional de Investigación Agropecuaria), Ruta 8 Km. 281/33000, Treinta y Tres, Uruguay
| | - Eduardo Graterol
- FLAR (Fondo Latinoamericano para Arroz de Riego), CIAT (International Center for Tropical Agriculture), Kilómetro 17 c, CP, Cali, Valle del Cauca, 763537, Colombia
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24
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Khan SU, Saeed S, Khan MHU, Fan C, Ahmar S, Arriagada O, Shahzad R, Branca F, Mora-Poblete F. Advances and Challenges for QTL Analysis and GWAS in the Plant-Breeding of High-Yielding: A Focus on Rapeseed. Biomolecules 2021; 11:1516. [PMID: 34680149 PMCID: PMC8533950 DOI: 10.3390/biom11101516] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/07/2021] [Accepted: 10/11/2021] [Indexed: 12/15/2022] Open
Abstract
Yield is one of the most important agronomic traits for the breeding of rapeseed (Brassica napus L), but its genetic dissection for the formation of high yield remains enigmatic, given the rapid population growth. In the present review, we review the discovery of major loci underlying important agronomic traits and the recent advancement in the selection of complex traits. Further, we discuss the benchmark summary of high-throughput techniques for the high-resolution genetic breeding of rapeseed. Biparental linkage analysis and association mapping have become powerful strategies to comprehend the genetic architecture of complex agronomic traits in crops. The generation of improved crop varieties, especially rapeseed, is greatly urged to enhance yield productivity. In this sense, the whole-genome sequencing of rapeseed has become achievable to clone and identify quantitative trait loci (QTLs). Moreover, the generation of high-throughput sequencing and genotyping techniques has significantly enhanced the precision of QTL mapping and genome-wide association study (GWAS) methodologies. Furthermore, this study demonstrates the first attempt to identify novel QTLs of yield-related traits, specifically focusing on ovule number per pod (ON). We also highlight the recent breakthrough concerning single-locus-GWAS (SL-GWAS) and multi-locus GWAS (ML-GWAS), which aim to enhance the potential and robust control of GWAS for improved complex traits.
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Affiliation(s)
- Shahid Ullah Khan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Sumbul Saeed
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Muhammad Hafeez Ullah Khan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Chuchuan Fan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Sunny Ahmar
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3465548, Chile;
| | - Osvin Arriagada
- Departamento de Ciencias Vegetales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile;
| | - Raheel Shahzad
- Department of Biotechnology, Faculty of Science & Technology, Universitas Muhammadiyah Bandung, Bandung 40614, Indonesia;
| | - Ferdinando Branca
- Department of Agriculture, Food and Environment (Di3A), University of Catania, 95123 Catania, Italy;
| | - Freddy Mora-Poblete
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3465548, Chile;
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25
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A high-resolution genome-wide association study of the grain ionome and agronomic traits in rice Oryza sativa subsp. indica. Sci Rep 2021; 11:19230. [PMID: 34584121 PMCID: PMC8478900 DOI: 10.1038/s41598-021-98573-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/09/2021] [Indexed: 02/08/2023] Open
Abstract
This study presents a comprehensive study of the genetic bases controlling variation in the rice ionome employing genome-wide association studies (GWAS) with a diverse panel of indica accessions, each genotyped with 5.2 million markers. GWAS was performed for twelve elements including B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, P, and Zn and four agronomic traits including days to 50% flowering, grain yield, plant height and thousand grain weight. GWAS identified 128 loci associated with the grain elements and 57 associated with the agronomic traits. There were sixteen co-localization regions containing QTL for two or more traits. Fourteen grain element quantitative trait loci were stable across growing environments, which can be strong candidates to be used in marker-assisted selection to improve the concentrations of nutritive elements in rice grain. Potential candidate genes were revealed including OsNAS3 linked to the locus that controls the variation of Zn and Co concentrations. The effects of starch synthesis and grain filling on multiple grain elements were elucidated through the likely involvement of OsSUS1 and OsGSSB1 genes. Overall, our study provides crucial insights into the genetic basis of ionomic variations in rice and will facilitate improvement in breeding for trace mineral content.
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26
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Panahabadi R, Ahmadikhah A, McKee LS, Ingvarsson PK, Farrokhi N. Genome-Wide Association Mapping of Mixed Linkage (1,3;1,4)-β-Glucan and Starch Contents in Rice Whole Grain. FRONTIERS IN PLANT SCIENCE 2021; 12:665745. [PMID: 34512678 PMCID: PMC8424012 DOI: 10.3389/fpls.2021.665745] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 07/28/2021] [Indexed: 05/27/2023]
Abstract
The glucan content of rice is a key factor defining its nutritional and economic value. Starch and its derivatives have many industrial applications such as in fuel and material production. Non-starch glucans such as (1,3;1,4)-β-D-glucan (mixed-linkage β-glucan, MLG) have many benefits in human health, including lowering cholesterol, boosting the immune system, and modulating the gut microbiome. In this study, the genetic variability of MLG and starch contents were analyzed in rice (Oryza sativa L.) whole grain, by performing a new quantitative analysis of the polysaccharide content of rice grains. The 197 rice accessions investigated had an average MLG content of 252 μg/mg, which was negatively correlated with the grain starch content. A new genome-wide association study revealed seven significant quantitative trait loci (QTLs) associated with the MLG content and two QTLs associated with the starch content in rice whole grain. Novel genes associated with the MLG content were a hexose transporter and anthocyanidin 5,3-O-glucosyltransferase. Also, the novel gene associated with the starch content was a nodulin-like domain. The data pave the way for a better understanding of the genes involved in determining both MLG and starch contents in rice grains and should facilitate future plant breeding programs.
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Affiliation(s)
- Rahele Panahabadi
- Department of Plant Science and Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
- Division of Glycoscience, Department of Chemistry, KTH Royal Institute of Technology, AlbaNova University Centre, Stockholm, Sweden
| | - Asadollah Ahmadikhah
- Department of Plant Science and Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Lauren S. McKee
- Division of Glycoscience, Department of Chemistry, KTH Royal Institute of Technology, AlbaNova University Centre, Stockholm, Sweden
- Wallenberg Wood Science Centre, Stockholm, Sweden
| | - Pär K. Ingvarsson
- Linnean Centre for Plant Biology, Department of Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Naser Farrokhi
- Department of Plant Science and Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
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27
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Marker-Assisted Introgression and Stacking of Major QTLs Controlling Grain Number ( Gn1a) and Number of Primary Branching ( WFP) to NERICA Cultivars. PLANTS 2021; 10:plants10050844. [PMID: 33922112 PMCID: PMC8143528 DOI: 10.3390/plants10050844] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/19/2021] [Accepted: 04/19/2021] [Indexed: 11/16/2022]
Abstract
The era of the green revolution has significantly improved rice yield productivity. However, with the growing population and decreasing arable land, rice scientists must find new ways to improve rice productivity. Although hundreds of rice yield-related QTLs were already mapped and some of them were cloned, only a few were utilized for actual systematic introgression breeding programs. In this study, the major yield QTLs Grain Number 1a (Gn1a) and Wealthy Farmer’s Panicle (WFP) were introgressed and stacked in selected NERICA cultivars by marker-assisted backcross breeding (MABB). The DNA markers RM3360, RM3452, and RM5493 were used for foreground selection. At BC3F4 and BC3F5 generation, a combination of marker-assisted selection and phenotypic evaluation were carried out to select lines with target alleles and traits. Further, genotyping-by-sequencing (GBS) was conducted to validate the introgression and determine the recurrent parent genome recovery (RPGR) of the selected lines. The Gn1a and/or WFP introgression lines showed significantly higher numbers of spikelets per panicle and primary branching compared to the recurrent parents. In addition, lines with Gn1a and/or WFP alleles were comparatively similar to the recurrent parents (RP) in most yield-related traits. This study demonstrates the success of utilizing yield QTLs and marker-assisted selection to develop and improve rice cultivars.
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28
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Rabier CE, Delmas C. The SgenoLasso and its cousins for selective genotyping and extreme sampling: application to association studies and genomic selection. STATISTICS-ABINGDON 2021. [DOI: 10.1080/02331888.2021.1881785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Charles-Elie Rabier
- ISEM, CNRS, EPHE, IRD, Université de Montpellier, Montpellier, France
- IMAG, CNRS, Université de Montpellier, Montpellier, France
- LIRMM, CNRS, Université de Montpellier, Montpellier, France
| | - Céline Delmas
- INRAE, UR MIAT, Université de Toulouse, Castanet-Tolosan, France
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29
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Al-Tamimi N, Oakey H, Tester M, Negrão S. Assessing Rice Salinity Tolerance: From Phenomics to Association Mapping. Methods Mol Biol 2021; 2238:339-375. [PMID: 33471343 DOI: 10.1007/978-1-0716-1068-8_23] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Rice is the most salt-sensitive cereal, suffering yield losses above 50% with soil salinity of 6 dS/m. Thus, understanding the mechanisms of rice salinity tolerance is key to address food security. In this chapter, we provide guidelines to assess rice salinity tolerance using a high-throughput phenotyping platform (HTP) with digital imaging at seedling/early tillering stage and suggest improved analysis methods using stress indices. The protocols described here also include computer scripts for users to improve their experimental design, run genome-wide association studies (GWAS), perform multi-testing corrections, and obtain the Manhattan plots, enabling the identification of loci associated with salinity tolerance. Notably, the computer scripts provided here can be used for any stress or GWAS experiment and independently of HTP.
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Affiliation(s)
- Nadia Al-Tamimi
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Helena Oakey
- School of Agriculture Food and Wine, University of Adelaide, Urrbrae, SA, Australia
| | - Mark Tester
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Sónia Negrão
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland.
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30
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Hou L, Chen W, Zhang Z, Pang X, Li Y. Genome-wide association studies of fruit quality traits in jujube germplasm collections using genotyping-by-sequencing. THE PLANT GENOME 2020; 13:e20036. [PMID: 33217218 DOI: 10.1002/tpg2.20036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 05/06/2020] [Accepted: 05/24/2020] [Indexed: 06/11/2023]
Abstract
Chinese jujube (Ziziphus jujuba Mill.) is an important fruit crop and harbors many highly diverse traits of potential economic importance. Fruit size, stone size, and fruit cracking have an important influence on the commercial value of jujube. This study is the first to conduct a genome-wide association study (GWAS) on 180 accessions of jujube and focuses on locating single-nucleotide polymorphisms (SNPs) associated with nine important fruit quality traits. Genotyping was performed using genotyping-by-sequencing and 4651 high-quality SNPs were identified. A genetic diversity analysis revealed the presence of three distinct groups, and rapid linkage disequilibrium decay was observed in this jujube population. Using a mixed linear model, a total of 45 significant SNP-trait associations were detected, among which 33 SNPs had associations with fruit size-related traits, nine were associated with stone size-related traits, and three with fruit cracking-related traits. In total, 21 candidate genes involved in cell expansion, abiotic stress responses, hormone signaling, and growth development were identified from the genome sequences of jujube. These results are useful as basic data for GWAS of other jujube traits, and these significant SNP loci and candidate genes should aid marker-assisted breeding and genomic selection of improved jujube cultivars.
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Affiliation(s)
- Lu Hou
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Wu Chen
- The Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, 650201, China
| | - Zhiyong Zhang
- Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, Beijing Laboratory of Urban and Rural Ecological Environment, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, School of Landscape Architecture, Beijing Forestry University, Beijing, 100083, China
| | - Xiaoming Pang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Yingyue Li
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
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Weckwerth W, Ghatak A, Bellaire A, Chaturvedi P, Varshney RK. PANOMICS meets germplasm. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:1507-1525. [PMID: 32163658 PMCID: PMC7292548 DOI: 10.1111/pbi.13372] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 02/17/2020] [Accepted: 02/26/2020] [Indexed: 05/14/2023]
Abstract
Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.
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Affiliation(s)
- Wolfram Weckwerth
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
- Vienna Metabolomics Center (VIME)University of ViennaViennaAustria
| | - Arindam Ghatak
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Anke Bellaire
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Palak Chaturvedi
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadTelanganaIndia
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Weckwerth W, Ghatak A, Bellaire A, Chaturvedi P, Varshney RK. PANOMICS meets germplasm. PLANT BIOTECHNOLOGY JOURNAL 2020; 18. [PMID: 32163658 PMCID: PMC7292548 DOI: 10.1111/pbi.13372,10.13140/rg.2.1.1233.5760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.
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Affiliation(s)
- Wolfram Weckwerth
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
- Vienna Metabolomics Center (VIME)University of ViennaViennaAustria
| | - Arindam Ghatak
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Anke Bellaire
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Palak Chaturvedi
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadTelanganaIndia
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Manimekalai R, Suresh G, Govinda Kurup H, Athiappan S, Kandalam M. Role of NGS and SNP genotyping methods in sugarcane improvement programs. Crit Rev Biotechnol 2020; 40:865-880. [PMID: 32508157 DOI: 10.1080/07388551.2020.1765730] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Sugarcane (Saccharum spp.) is one of the most economically significant crops because of its high sucrose content and it is a promising biomass feedstock for biofuel production. Sugarcane genome sequencing and analysis is a difficult task due to its heterozygosity and polyploidy. Long sequence read technologies, PacBio Single-Molecule Real-Time (SMRT) sequencing, the Illumina TruSeq, and the Oxford Nanopore sequencing could solve the problem of genome assembly. On the applications side, next generation sequencing (NGS) technologies played a major role in the discovery of single nucleotide polymorphism (SNP) and the development of low to high throughput genotyping platforms. The two mainstream high throughput genotyping platforms are the SNP microarray and genotyping by sequencing (GBS). This paper reviews the NGS in sugarcane genomics, genotyping methodologies, and the choice of these methods. Array-based SNP genotyping is robust, provides consistent SNPs, and relatively easier downstream data analysis. The GBS method identifies large scale SNPs across the germplasm. A combination of targeted GBS and array-based genotyping methods should be used to increase the accuracy of genomic selection and marker-assisted breeding.
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Affiliation(s)
- Ramaswamy Manimekalai
- Crop Improvement Division, ICAR - Sugarcane Breeding Institute, Indian Council of Agricultural Research (ICAR), Coimbatore, Tamil Nadu, India
| | - Gayathri Suresh
- Crop Improvement Division, ICAR - Sugarcane Breeding Institute, Indian Council of Agricultural Research (ICAR), Coimbatore, Tamil Nadu, India
| | - Hemaprabha Govinda Kurup
- Crop Improvement Division, ICAR - Sugarcane Breeding Institute, Indian Council of Agricultural Research (ICAR), Coimbatore, Tamil Nadu, India
| | - Selvi Athiappan
- Crop Improvement Division, ICAR - Sugarcane Breeding Institute, Indian Council of Agricultural Research (ICAR), Coimbatore, Tamil Nadu, India
| | - Mallikarjuna Kandalam
- Business Development, Asia Pacific Japan region, Thermo Fisher Scientific, Waltham, MA, USA
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Pavan S, Delvento C, Ricciardi L, Lotti C, Ciani E, D'Agostino N. Recommendations for Choosing the Genotyping Method and Best Practices for Quality Control in Crop Genome-Wide Association Studies. Front Genet 2020; 11:447. [PMID: 32587600 PMCID: PMC7299185 DOI: 10.3389/fgene.2020.00447] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 04/14/2020] [Indexed: 12/19/2022] Open
Abstract
High-throughput genotyping boosts genome-wide association studies (GWAS) in crop species, leading to the identification of single-nucleotide polymorphisms (SNPs) associated with economically important traits. Choosing a cost-effective genotyping method for crop GWAS requires careful examination of several aspects, namely, the purpose and the scale of the study, crop-specific genomic features, and technical and economic matters associated with each genotyping option. Once genotypic data have been obtained, quality control (QC) procedures must be applied to avoid bias and false signals in genotype–phenotype association tests. QC for human GWAS has been extensively reviewed; however, QC for crop GWAS may require different actions, depending on the GWAS population type. Here, we review most popular genotyping methods based on next-generation sequencing (NGS) and array hybridization, and report observations that should guide the investigator in the choice of the genotyping method for crop GWAS. We provide recommendations to perform QC in crop species, and deliver an overview of bioinformatics tools that can be used to accomplish all needed tasks. Overall, this work aims to provide guidelines to harmonize those procedures leading to SNP datasets ready for crop GWAS.
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Affiliation(s)
- Stefano Pavan
- Department of Soil, Plant and Food Science, Section of Genetics and Plant Breeding, University of Bari Aldo Moro, Bari, Italy.,Institute of Biomedical Technologies, National Research Council (CNR), Bari, Italy
| | - Chiara Delvento
- Department of Soil, Plant and Food Science, Section of Genetics and Plant Breeding, University of Bari Aldo Moro, Bari, Italy
| | - Luigi Ricciardi
- Department of Soil, Plant and Food Science, Section of Genetics and Plant Breeding, University of Bari Aldo Moro, Bari, Italy
| | - Concetta Lotti
- Department of Agricultural, Food and Environmental Sciences, University of Foggia, Foggia, Italy
| | - Elena Ciani
- Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari Aldo Moro, Bari, Italy
| | - Nunzio D'Agostino
- Department of Agricultural Sciences, University of Naples Federico II, Naples, Italy
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Hamazaki K, Kajiya-Kanegae H, Yamasaki M, Ebana K, Yabe S, Nakagawa H, Iwata H. Choosing the optimal population for a genome-wide association study: A simulation of whole-genome sequences from rice. THE PLANT GENOME 2020; 13:e20005. [PMID: 33016626 DOI: 10.1002/tpg2.20005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/15/2019] [Indexed: 06/11/2023]
Abstract
A genome-wide association study (GWAS) needs to have a suitable population. The factors that affect a GWAS (e.g. population structure, sample size, and sequence analysis and field testing costs) need to be considered. Mixed populations containing subpopulations of different genetic backgrounds may be suitable populations. We conducted simulation experiments to see if a population with high genetic diversity, such as a diversity panel, should be added to a target population, especially when the target population harbors small genetic diversity. The target population was 112 accessions of Oryza sativa L. subsp. japonica, mainly developed in Japan. We combined the target population with three populations that had higher genetic diversity. These were 100 indica accessions, 100 japonica accessions, and 100 accessions with various genetic backgrounds. The results showed that the GWAS's power with a mixed population was generally higher than with a separate population. Also, the optimal GWAS populations varied depending on the fixation index (FST ) of the quantitative trait nucleotides (QTNs) and the polymorphism of QTNs in each population. When a QTN was polymorphic in a target population, a target population combined with a higher diversity population improved the QTN's detection power. By investigating FST and the expected heterozygosity (He ) as factors influencing the detection power, we showed that single nucleotide polymorphisms with high FST or low He are less likely to be detected by GWAS with mixed populations. Sequenced or genotyped germplasm collections can improve the GWAS's detection power by using a subset of the collections with a target population.
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Affiliation(s)
- Kosuke Hamazaki
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
| | - Hiromi Kajiya-Kanegae
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
- Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, 3-1-1 Kannondai, Tsukuba, Ibaraki, 305-8517, Japan
| | - Masanori Yamasaki
- Food Resources Education and Research Center, Graduate School of Agricultural Science, Kobe Univ., 1348 Uzurano, Kasai, Hyogo, 675-2103, Japan
| | - Kaworu Ebana
- Genetic Resources Center, National Agriculture and Food Research Organization, 2-1-2 Kannondai, Tsukuba, Ibaraki, 305-8602, Japan
| | - Shiori Yabe
- Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2 Kannondai, Tsukuba, Ibaraki, 305-8518, Japan
| | - Hiroshi Nakagawa
- Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki, 305-8604, Japan
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
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36
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Oreiro EG, Grimares EK, Atienza-Grande G, Quibod IL, Roman-Reyna V, Oliva R. Genome-Wide Associations and Transcriptional Profiling Reveal ROS Regulation as One Underlying Mechanism of Sheath Blight Resistance in Rice. MOLECULAR PLANT-MICROBE INTERACTIONS : MPMI 2020; 33:212-222. [PMID: 31634039 DOI: 10.1094/mpmi-05-19-0141-r] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Rice sheath blight, caused by the necrotrophic fungus Rhizoctonia solani Kühn, continues to be an important and challenging rice disease worldwide. Here, we used genome-wide association studies over a high-density rice array to facilitate the identification of potential novel genes and quantitative trait loci related to sheath blight resistance. We identified multiple regions that significantly associated with independent disease components in chromosomes 1, 4, and 11 under controlled condition. In particular, we investigated qLN1128, a quantitative trait locus enriched with defense-related genes that reduce disease lesions in a near-isogenic line. RNA profiling of the line carrying qLN1128 showed a number of differentially expressed genes related to the reactive oxygen species (ROS)-redox pathway. Histochemical staining revealed less ROS accumulation on the resistant line, suggesting efficient ROS deregulation that delays pathogen colonization. The detection of genomic regions controlling multiple mechanisms of resistance to sheath blight will provide tools to design effective breeding interventions in rice.
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Affiliation(s)
- Eula Gems Oreiro
- Rice Breeding Platform, International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Earlyn Kate Grimares
- Rice Breeding Platform, International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
- Institute of Weed Science, Entomology and Plant Pathology, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Genelou Atienza-Grande
- Rice Breeding Platform, International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
- Institute of Weed Science, Entomology and Plant Pathology, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Ian Lorenzo Quibod
- Rice Breeding Platform, International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Veronica Roman-Reyna
- Rice Breeding Platform, International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Ricardo Oliva
- Rice Breeding Platform, International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
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Kim KH, Kim JY, Lim WJ, Jeong S, Lee HY, Cho Y, Moon JK, Kim N. Genome-wide association and epistatic interactions of flowering time in soybean cultivar. PLoS One 2020; 15:e0228114. [PMID: 31968016 PMCID: PMC6975553 DOI: 10.1371/journal.pone.0228114] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 01/07/2020] [Indexed: 12/02/2022] Open
Abstract
Genome-wide association studies (GWAS) have enabled the discovery of candidate markers that play significant roles in various complex traits in plants. Recently, with increased interest in the search for candidate markers, studies on epistatic interactions between single nucleotide polymorphism (SNP) markers have also increased, thus enabling the identification of more candidate markers along with GWAS on single-variant-additive-effect. Here, we focused on the identification of candidate markers associated with flowering time in soybean (Glycine max). A large population of 2,662 cultivated soybean accessions was genotyped using the 180k Axiom® SoyaSNP array, and the genomic architecture of these accessions was investigated to confirm the population structure. Then, GWAS was conducted to evaluate the association between SNP markers and flowering time. A total of 93 significant SNP markers were detected within 59 significant genes, including E1 and E3, which are the main determinants of flowering time. Based on the GWAS results, multilocus epistatic interactions were examined between the significant and non-significant SNP markers. Two significant and 16 non-significant SNP markers were discovered as candidate markers affecting flowering time via interactions with each other. These 18 candidate SNP markers mapped to 18 candidate genes including E1 and E3, and the 18 candidate genes were involved in six major flowering pathways. Although further biological validation is needed, our results provide additional information on the existing flowering time markers and present another option to marker-assisted breeding programs for regulating flowering time of soybean.
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Affiliation(s)
- Kyoung Hyoun Kim
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
- Department of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology (UST), Daejeon, Republic of Korea
| | - Jae-Yoon Kim
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
- Department of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology (UST), Daejeon, Republic of Korea
| | - Won-Jun Lim
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
- Department of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology (UST), Daejeon, Republic of Korea
| | - Seongmun Jeong
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
| | - Ho-Yeon Lee
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
- Department of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology (UST), Daejeon, Republic of Korea
| | - Youngbum Cho
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
- Department of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology (UST), Daejeon, Republic of Korea
| | - Jung-Kyung Moon
- National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
| | - Namshin Kim
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
- Department of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology (UST), Daejeon, Republic of Korea
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Genome wide association study (GWAS) and identification of candidate genes for yield and oil yield related traits in oil palm (Eleaeis guineensis) using SNPs by genotyping-based sequencing. Genomics 2020; 112:1011-1020. [DOI: 10.1016/j.ygeno.2019.06.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 06/03/2019] [Accepted: 06/17/2019] [Indexed: 12/13/2022]
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Melandri G, Sikirou M, Arbelaez JD, Shittu A, Semwal VK, Konaté KA, Maji AT, Ngaujah SA, Akintayo I, Govindaraj V, Shi Y, Agosto-Peréz FJ, Greenberg AJ, Atlin G, Ramaiah V, McCouch SR. Multiple Small-Effect Alleles of Indica Origin Enhance High Iron-Associated Stress Tolerance in Rice Under Field Conditions in West Africa. FRONTIERS IN PLANT SCIENCE 2020; 11:604938. [PMID: 33584748 PMCID: PMC7874229 DOI: 10.3389/fpls.2020.604938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/15/2020] [Indexed: 05/03/2023]
Abstract
Understanding the genetics of field-based tolerance to high iron-associated (HIA) stress in rice can accelerate the development of new varieties with enhanced yield performance in West African lowland ecosystems. To date, few field-based studies have been undertaken to rigorously evaluate rice yield performance under HIA stress conditions. In this study, two NERICA × O. sativa bi-parental rice populations and one O.sativa diversity panel consisting of 296 rice accessions were evaluated for grain yield and leaf bronzing symptoms over multiple years in four West African HIA stress and control sites. Mapping of these traits identified a large number of QTLs and single nucleotide polymorphisms (SNPs) associated with stress tolerance in the field. Favorable alleles associated with tolerance to high levels of iron in anaerobic rice soils were rare and almost exclusively derived from the indica subpopulation, including the most favorable alleles identified in NERICA varieties. These findings highlight the complex genetic architecture underlying rice response to HIA stress and suggest that a recurrent selection program focusing on an expanded indica genepool could be productively used in combination with genomic selection to increase the efficiency of selection in breeding programs designed to enhance tolerance to this prevalent abiotic stress in West Africa.
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Affiliation(s)
- Giovanni Melandri
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | - Mouritala Sikirou
- Africa Rice Center, Ibadan, Nigeria
- School of Horticulture and Green Landscaping, Kétou, Bénin
| | - Juan D. Arbelaez
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | | | | | | | | | | | - Inoussa Akintayo
- Central Agricultural Research Institute, Suakoko, Liberia
- Africa Rice Center, Suakoko, Liberia
| | - Vishnu Govindaraj
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | - Yuxin Shi
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | | | | | - Gary Atlin
- Bill & Melinda Gates Foundation, Seattle, WA, United States
| | | | - Susan R. McCouch
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
- Venuprasad Ramaiah,
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40
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Exploring genetic architecture of grain yield and quality traits in a 16-way indica by japonica rice MAGIC global population. Sci Rep 2019; 9:19605. [PMID: 31862941 PMCID: PMC6925145 DOI: 10.1038/s41598-019-55357-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 11/18/2019] [Indexed: 12/27/2022] Open
Abstract
Identification of Quantitative Trait Loci (QTL) has been a challenge for complex traits due to the use of populations with narrow genetic base. Most of QTL mapping studies were carried out from crosses made within the subspecies, either indica × indica or japonica × japonica. In this study we report advantages of using Multi-parent Advanced Generation Inter-Crosses global population, derived from a combination of eight indica and eight japonica elite parents, in QTL discovery for yield and grain quality traits. Genome-wide association study and interval mapping identified 38 and 34 QTLs whereas Bayesian networking detected 60 QTLs with 22 marker-marker associations, 32 trait-trait associations and 65 marker-trait associations. Notably, nine known QTLs/genes qPH1/OsGA20ox2, qDF3/OsMADS50, PL, QDg1, qGW-5b, grb7-2, qGL3/GS3, Amy6/Wx gene and OsNAS3 were consistently identified by all approaches for nine traits whereas qDF3/OsMADS50 was co-located for both yield and days-to-flowering traits on chromosome 3. Moreover, we identified a number of candidate QTLs in either one or two analyses but further validations will be needed. The results indicate that this new population has enabled identifications of significant QTLs and interactions for 16 traits through multiple approaches. Pyramided recombinant inbred lines provide a valuable source for integration into future breeding programs.
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Nyine M, Uwimana B, Akech V, Brown A, Ortiz R, Doležel J, Lorenzen J, Swennen R. Association genetics of bunch weight and its component traits in East African highland banana (Musa spp. AAA group). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:3295-3308. [PMID: 31529270 PMCID: PMC6820618 DOI: 10.1007/s00122-019-03425-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 09/06/2019] [Indexed: 05/06/2023]
Abstract
The major quantitative trait loci associated with bunch weight and its component traits in the East African highland banana-breeding population are located on chromosome 3. Bunch weight increase is one of the major objectives of banana improvement programs, but little is known about the loci controlling bunch weight and its component traits. Here we report for the first time some genomic loci associated with bunch weight and its component traits in banana as revealed through a genome-wide association study. A banana-breeding population of 307 genotypes varying in ploidy was phenotyped in three locations under different environmental conditions, and data were collected on bunch weight, number of hands and fruits; fruit length and circumference; and diameter of both fruit and pulp for three crop cycles. The population was genotyped with genotyping by sequencing and 27,178 single nucleotide polymorphisms (SNPs) were generated. The association between SNPs and the best linear unbiased predictors of traits was performed with TASSEL v5 using a mixed linear model accounting for population structure and kinship. Using Bonferroni correction, false discovery rate, and long-range linkage disequilibrium (LD), 25 genomic loci were identified with significant SNPs and most were localized on chromosome 3. Most SNPs were located in genes encoding uncharacterized and hypothetical proteins, but some mapped to transcription factors and genes involved in cell cycle regulation. Inter-chromosomal LD of SNPs was present in the population, but none of the SNPs were significantly associated with the traits. The clustering of significant SNPs on chromosome 3 supported our hypothesis that fruit filling in this population was under control of a few quantitative trait loci with major effects.
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Affiliation(s)
- Moses Nyine
- International Institute of Tropical Agriculture, P.O. Box 7878, Kampala, Uganda
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA
| | - Brigitte Uwimana
- International Institute of Tropical Agriculture, P.O. Box 7878, Kampala, Uganda
| | - Violet Akech
- International Institute of Tropical Agriculture, P.O. Box 7878, Kampala, Uganda
| | - Allan Brown
- International Institute of Tropical Agriculture c/o Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 101, 23053, Alnarp, Sweden
| | - Jaroslav Doležel
- Institute of Experimental Botany, Centre of the Region Haná for Biotechnological and Agricultural Research, 78371, Olomouc, Czech Republic
| | - Jim Lorenzen
- International Institute of Tropical Agriculture, P.O. Box 7878, Kampala, Uganda
- Bill and Melinda Gates Foundation, Seattle, 23350, USA
| | - Rony Swennen
- International Institute of Tropical Agriculture c/o Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania.
- Laboratory of Tropical Crop Improvement, Division of Crop Biotechnics, Katholieke Universiteit, 3001, Leuven, Belgium.
- Bioversity International, 3001, Leuven, Belgium.
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Ladejobi O, Mackay IJ, Poland J, Praud S, Hibberd JM, Bentley AR. Reference Genome Anchoring of High-Density Markers for Association Mapping and Genomic Prediction in European Winter Wheat. FRONTIERS IN PLANT SCIENCE 2019; 10:1278. [PMID: 31781130 PMCID: PMC6857554 DOI: 10.3389/fpls.2019.01278] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 09/12/2019] [Indexed: 05/28/2023]
Abstract
In this study, we anchored genotyping-by-sequencing data to the International Wheat Genome Sequencing Consortium Reference Sequence v1.0 assembly to generate over 40,000 high quality single nucleotide polymorphism markers on a panel of 376 elite European winter wheat varieties released between 1946 and 2007. We compared association mapping and genomic prediction accuracy for a range of productivity traits with previous results based on lower density dominant DArT markers. The results demonstrate that the availability of RefSeq v1.0 supports higher precision trait mapping and provides the density of markers required to obtain accurate predictions of traits controlled by multiple small effect loci, including grain yield.
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Affiliation(s)
- Olufunmilayo Ladejobi
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
- Department of Plant Sciences, The University of Cambridge, Cambridge, United Kingdom
| | - Ian J. Mackay
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
- IMplant Consultancy Ltd., Chelmsford, United Kingdom
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | | | - Julian M. Hibberd
- Department of Plant Sciences, The University of Cambridge, Cambridge, United Kingdom
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Genome-wide association study for leaf area, rachis length and total dry weight in oil palm (Eleaeisguineensis) using genotyping by sequencing. PLoS One 2019; 14:e0220626. [PMID: 31390382 PMCID: PMC6685610 DOI: 10.1371/journal.pone.0220626] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 07/20/2019] [Indexed: 11/24/2022] Open
Abstract
The marker-trait association for complex traits using genotyping by sequencing (GBS) method is being widely spread in plants. The study aimed to identify significant single nucleotide polymorphism (SNP) associations for rachis length (RL), leaf area (LA) and total dry weight (TrDW) in oil palm among diverse African germplasm. The Illumina NextSeq platform has been used for SNP genotyping and retained 4031 fully informative SNPs after applying the filter criterion. These 4031 SNPs were used for genome wide association study for the above three traits. The LD decay rates of the African germplasm using GBS data of SNP is observed to be 25 Kb at 0.45 of average pair wise correlation coefficient (r2). Association mapping led to the identification of seven significant associations for three traits using MLM approach at a P value of ≤ 0.001. Three associations were identified for total dry weight, two each for leaf area index and rachis length. The qtlLA1 was found to be highly significant at a P value of 7.39E-05 (18.4% phenotypic variance) which is located on chromosome 4. Two QTLs (qtlLA2 and qtlRL1) were located on chromosome 1, which explained 11.9% and 12.4% of phenotypic variance respectively. Three QTLs for total dry weight were located on chromosome 2, 14 and 16, all-together explained 40% phenotypic variance. The results showed that the SNP-trait associations identified in the present study could be used in selection of elite oil palm germplasm for higher yields.
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Arbelaez JD, Dwiyanti MS, Tandayu E, Llantada K, Jarana A, Ignacio JC, Platten JD, Cobb J, Rutkoski JE, Thomson MJ, Kretzschmar T. 1k-RiCA (1K-Rice Custom Amplicon) a novel genotyping amplicon-based SNP assay for genetics and breeding applications in rice. RICE (NEW YORK, N.Y.) 2019; 12:55. [PMID: 31350673 PMCID: PMC6660535 DOI: 10.1186/s12284-019-0311-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 07/02/2019] [Indexed: 05/04/2023]
Abstract
BACKGROUND While a multitude of genotyping platforms have been developed for rice, the majority of them have not been optimized for breeding where cost, turnaround time, throughput and ease of use, relative to density and informativeness are critical parameters of their utility. With that in mind we report the development of the 1K-Rice Custom Amplicon, or 1k-RiCA, a robust custom sequencing-based amplicon panel of ~ 1000-SNPs that are uniformly distributed across the rice genome, designed to be highly informative within indica rice breeding pools, and tailored for genomic prediction in elite indica rice breeding programs. RESULTS Empirical validation tests performed on the 1k-RiCA showed average marker call rates of 95% with marker repeatability and concordance rates of 99%. These technical properties were not affected when two common DNA extraction protocols were used. The average distance between SNPs in the 1k-RiCA was 1.5 cM, similar to the theoretical distance which would be expected between 1,000 uniformly distributed markers across the rice genome. The average minor allele frequencies on a panel of indica lines was 0.36 and polymorphic SNPs estimated on pairwise comparisons between indica by indica accessions and indica by japonica accessions were on average 430 and 450 respectively. The specific design parameters of the 1k-RiCA allow for a detailed view of genetic relationships and unambiguous molecular IDs within indica accessions and good cost vs. marker-density balance for genomic prediction applications in elite indica germplasm. Predictive abilities of Genomic Selection models for flowering time, grain yield, and plant height were on average 0.71, 0.36, and 0.65 respectively based on cross-validation analysis. Furthermore the inclusion of important trait markers associated with 11 different genes and QTL adds value to parental selection in crossing schemes and marker-assisted selection in forward breeding applications. CONCLUSIONS This study validated the marker quality and robustness of the 1k-RiCA genotypic platform for genotyping populations derived from indica rice subpopulation for genetic and breeding purposes including MAS and genomic selection. The 1k-RiCA has proven to be an alternative cost-effective genotyping system for breeding applications.
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Affiliation(s)
- Juan David Arbelaez
- International Rice Research Institute, DAPO Box 7777, 1301 Los Baños, Metro Manila Philippines
| | | | - Erwin Tandayu
- International Rice Research Institute, DAPO Box 7777, 1301 Los Baños, Metro Manila Philippines
| | - Krizzel Llantada
- International Rice Research Institute, DAPO Box 7777, 1301 Los Baños, Metro Manila Philippines
| | - Annalhea Jarana
- International Rice Research Institute, DAPO Box 7777, 1301 Los Baños, Metro Manila Philippines
| | - John Carlos Ignacio
- International Rice Research Institute, DAPO Box 7777, 1301 Los Baños, Metro Manila Philippines
| | - John Damien Platten
- International Rice Research Institute, DAPO Box 7777, 1301 Los Baños, Metro Manila Philippines
| | - Joshua Cobb
- International Rice Research Institute, DAPO Box 7777, 1301 Los Baños, Metro Manila Philippines
| | - Jessica Elaine Rutkoski
- International Rice Research Institute, DAPO Box 7777, 1301 Los Baños, Metro Manila Philippines
| | - Michael J. Thomson
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Houston, TX 77843 USA
| | - Tobias Kretzschmar
- Southern Cross Plant Sciences, Southern Cross University, PO Box 157, Lismore, NSW 2480 Australia
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Ramakrishnan SM, Sidhu JS, Ali S, Kaur N, Wu J, Sehgal SK. Molecular characterization of bacterial leaf streak resistance in hard winter wheat. PeerJ 2019; 7:e7276. [PMID: 31341737 PMCID: PMC6637926 DOI: 10.7717/peerj.7276] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 06/07/2019] [Indexed: 11/20/2022] Open
Abstract
Bacterial leaf streak (BLS) caused by Xanthomonas campestris pv. translucens is one of the major bacterial diseases threatening wheat production in the United States Northern Great Plains (NGP) region. It is a sporadic but widespread wheat disease that can cause significant loss in grain yield and quality. Identification and characterization of genomic regions in wheat that confer resistance to BLS will help track resistance genes/QTLs in future wheat breeding. In this study, we evaluated a hard winter wheat association mapping panel (HWWAMP) containing 299 hard winter wheat lines from the US hard winter wheat growing region for their reactions to BLS. We observed a range of BLS responses among the lines, importantly, we identified ten genotypes that showed a resistant reaction both in greenhouse and field evaluation. -Genome-wide association analysis with 15,990 SNPs was conducted using an exponentially compressed mixed linear model. Five genomic regions (p < 0.001) that regulate the resistance to BLS were identified on chromosomes 1AL, 1BS, 3AL, 4AL, and 7AS. The QTLs Q.bls.sdsu-1AL, Q.bls.sdsu-1BS, Q.bls.sdsu-3AL, Q.bls.sdsu-4AL, and Q.bls.sdsu-7AS explain a total of 42% of the variation. In silico analysis of sequences in the candidate regions on chromosomes 1AL, 1BS, 3AL, 4AL, and 7AS identified 10, 25, 22, eight, and nine genes, respectively with known plant defense-related functions. Comparative analysis with rice showed two syntenic regions in rice that harbor genes for bacterial leaf streak resistance. The ten BLS resistant genotypes and SNP markers linked to the QTLs identified in our study could facilitate breeding for BLS resistance in winter wheat.
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Affiliation(s)
- Sai Mukund Ramakrishnan
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
| | - Jagdeep Singh Sidhu
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
| | - Shaukat Ali
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
| | - Navjot Kaur
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
| | - Jixiang Wu
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
| | - Sunish K. Sehgal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
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Arifuzzaman M, Oladzadabbasabadi A, McClean P, Rahman M. Shovelomics for phenotyping root architectural traits of rapeseed/canola (Brassica napus L.) and genome-wide association mapping. Mol Genet Genomics 2019; 294:985-1000. [PMID: 30968249 DOI: 10.1007/s00438-019-01563-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 04/03/2019] [Indexed: 01/22/2023]
Abstract
Root system in plants plays an important role in mining moisture and nutrients from the soil and is positively correlated to yield in many crops including rapeseed/canola (Brassica napus L.). Substantial phenotypic diversity in root architectural traits among the B. napus growth types leads to a scope of root system improvement in breeding populations. In this study, 216 diverse genotypes were phenotyped for five different root architectural traits following shovelomics approach in the field condition during 2015 and 2016. A single nucleotide polymorphism (SNP) marker panel consisting of 30,262 SNPs was used to conduct genome-wide association study to detect marker/trait association. A total of 31 significant marker loci were identified at 0.01 percentile tail P value cutoff for different root traits. Six marker loci for soil-level taproot diameter (R1Dia), six loci for belowground taproot diameter (R2Dia), seven loci for number of primary root branches (PRB), eight loci for root angle, and eight loci for root score (RS) were detected in this study. Several markers associated with root diameters R1Dia and R2Dia were also associated with PRB and RS. Significant phenotypic correlation between these traits was observed in both environments. Therefore, taproot diameter appears to be a major determinant of the canola root system architecture and can be used as proxy for other root traits. Fifteen candidate genes related to root traits and root development were detected within 100 kbp upstream and downstream of different significant markers. The identified markers associated with different root architectural traits can be considered for marker-assisted selection for root traits in canola in future.
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Affiliation(s)
| | | | - Phillip McClean
- Departemnt of Plant Sciences, North Dakota State University, Fargo, ND, USA
| | - Mukhlesur Rahman
- Departemnt of Plant Sciences, North Dakota State University, Fargo, ND, USA.
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Kausch AP, Nelson-Vasilchik K, Hague J, Mookkan M, Quemada H, Dellaporta S, Fragoso C, Zhang ZJ. Edit at will: Genotype independent plant transformation in the era of advanced genomics and genome editing. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 281:186-205. [PMID: 30824051 DOI: 10.1016/j.plantsci.2019.01.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 12/07/2018] [Accepted: 01/10/2019] [Indexed: 05/21/2023]
Abstract
The combination of advanced genomics, genome editing and plant transformation biology presents a powerful platform for basic plant research and crop improvement. Together these advances provide the tools to identify genes as targets for direct editing as single base pair changes, deletions, insertions and site specific homologous recombination. Recent breakthrough technologies using morphogenic regulators in plant transformation creates the ability to introduce reagents specific toward their identified targets and recover stably transformed and/or edited plants which are genotype independent. These technologies enable the possibility to alter a trait in any variety, without genetic disruption which would require subsequent extensive breeding, but rather to deliver the same variety with one trait changed. Regulatory issues regarding this technology will predicate how broadly these technologies will be implemented. In addition, education will play a crucial role for positive public acceptance. Taken together these technologies comprise a platform for advanced breeding which is an imperative for future world food security.
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Affiliation(s)
- Albert P Kausch
- Department of Cell and Molecular Biology, University of Rhode Island, RI 02892, USA.
| | | | - Joel Hague
- Department of Cell and Molecular Biology, University of Rhode Island, RI 02892, USA
| | - Muruganantham Mookkan
- Plant Transformation Core Facility, Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA
| | | | - Stephen Dellaporta
- Yale University, New Haven, CT 06520, USA; Verinomics Inc., New Haven, CT 06520, USA
| | | | - Zhanyuan J Zhang
- Plant Transformation Core Facility, Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA
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Uthup TK, Rajamani A, Ravindran M, Saha T. Distinguishing CPT gene family members and vetting the sequence structure of a putative rubber synthesizing variant in Hevea brasiliensis. Gene 2019; 689:183-193. [PMID: 30528269 DOI: 10.1016/j.gene.2018.12.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 11/21/2018] [Accepted: 12/01/2018] [Indexed: 11/19/2022]
Abstract
cis-Prenyltransferases (cis-PTs) constitute a large family of enzymes conserved during evolution and present in all domains of life. cis-PTs catalyze the cis-1,4-polymerization of isoprene units to generate isoprenoids with carbon skeletons varying from C10 (neryl pyrophosphate) to C > 10,000 (natural rubber). Though the previously reported CPTs in Hevea are designated based on sequence variations, their classification was done mostly by phylogenetic analysis using a mixture of partial as well as full length sequences often excluding the UTRs. In this context an attempt was made to reclassify the CPTs strictly based on their sequence similarity and distinguish the members putatively associated with rubber biosynthesis from the others. Extensive computational analysis was carried out on CPT sequences obtained from public resources and whole genome assemblies of Hevea. Based on the results from BLAST analysis, multiple sequence alignments of protein, nucleotide and untranslated regions, open reading frame analysis, gene prediction analysis and sequence length variations, we conclude that there exists mainly three CPTs namely RubCPT1, RubCPT2 and RubCPT3 putatively associated with rubber biosynthesis in Hevea brasiliensis. The rest were categorised as variants of dehydrodolichyl diphosphate synthase (DHDDS) involved in the synthesis of dolichols having short chain isoprenoids. Analysis of the sequence structure of the most highly expressed RubCPT1 in latex revealed the allele richness and diversity of this important variant prevailing in the popular rubber clones. Haplotypes consisting of SNPs with high degree of heterozygosity were also identified. Segregation and linkage disequilibrium analysis confirmed that recombination is the major contributor towards the generation of allelic diversity rather than point mutations. Alternatively, gene expression analysis indicated the possibility of association between specific haplotypes and RubCPT1 expression in Hevea clones which may have downstream impact up to the level of rubber production. The conclusions from this study may pave way for the identification and better understanding of CPTs directly involved with natural rubber biosynthesis in Hevea and the SNP data generated may aid in the development of molecular markers putatively associated with yield in rubber.
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Affiliation(s)
- Thomas Kadampanattu Uthup
- Genome Analysis Laboratory, Rubber Research Institute of India, Rubber Board P O, Kottayam, Kerala PIN-686009, India.
| | - Anantharamanan Rajamani
- Genome Analysis Laboratory, Rubber Research Institute of India, Rubber Board P O, Kottayam, Kerala PIN-686009, India
| | - Minimol Ravindran
- Genome Analysis Laboratory, Rubber Research Institute of India, Rubber Board P O, Kottayam, Kerala PIN-686009, India
| | - Thakurdas Saha
- Genome Analysis Laboratory, Rubber Research Institute of India, Rubber Board P O, Kottayam, Kerala PIN-686009, India
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Eizenga GC, Jia MH, Jackson AK, Boykin DL, Ali ML, Shakiba E, Tran NT, McCouch SR, Edwards JD. Validation of Yield Component Traits Identified by Genome-Wide Association Mapping in a tropical japonica × tropical japonica Rice Biparental Mapping Population. THE PLANT GENOME 2019; 12:180021. [PMID: 30951093 DOI: 10.3835/plantgenome2018.04.0021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The Rice Diversity Panel 1 (RDP1) was developed for genome-wide association (GWA) studies to explore five rice ( L.) subpopulations (, , , , and ). The RDP1 was evaluated for over 30 traits, including agronomic, panicle architecture, seed, and disease traits and genotyped with 700,000 single nucleotide polymorphisms (SNPs). Most rice grown in the southern United States is and thus the diversity in this subpopulation is interesting to U.S. breeders. Among the RDP1 accessions, 'Estrela' and 'NSFTV199' are both phenotypically and genotypically diverse, thus making them excellent parents for a biparental mapping population. The objectives were to (i) ascertain the GWA QTLs from the RDP1 GWA studies that overlapped with the QTLs uncovered in an Estrela × NSFTV199 recombinant inbred line (RIL) population evaluated for 15 yield traits, and (ii) identify known or novel genes potentially controlling specific yield component traits. The 256 RILs were genotyped with 132 simple sequence repeat markers and 70 QTLs were found. Perl scripts were developed for automatic identification of the underlying candidate genes in the GWA QTL regions. Approximately 100 GWA QTLs overlapped with 41 Estrela × NSFTV199 QTL (RIL QTL) regions and 47 known genes were identified. Two seed trait RIL QTLs with overlapping GWA QTLs were not associated with a known gene. Segregating SNPs in the overlapping GWA QTLs for RIL QTLs with high values will be evaluated as potential DNA markers useful to breeding programs for the associated yield trait.
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Platten JD, Cobb JN, Zantua RE. Criteria for evaluating molecular markers: Comprehensive quality metrics to improve marker-assisted selection. PLoS One 2019; 14:e0210529. [PMID: 30645632 PMCID: PMC6333336 DOI: 10.1371/journal.pone.0210529] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Accepted: 12/26/2018] [Indexed: 11/18/2022] Open
Abstract
Despite strong interest over many years, the usage of quantitative trait loci in plant breeding has often failed to live up to expectations. A key weak point in the utilisation of QTLs is the “quality” of markers used during marker-assisted selection (MAS): unreliable markers result in variable outcomes, leading to a perception that MAS products fail to achieve reliable improvement. Most reports of markers used for MAS focus on markers derived from the mapping population. There are very few studies that examine the reliability of these markers in other genetic backgrounds, and critically, no metrics exist to describe and quantify this reliability. To improve the MAS process, this work proposes five core metrics that fully describe the reliability of a marker. These metrics give a comprehensive and quantitative measure of the ability of a marker to correctly classify germplasm as QTL[+]/[–], particularly against a background of high allelic diversity. Markers that score well on these metrics will have far higher reliability in breeding, and deficiencies in specific metrics give information on circumstances under which a marker may not be reliable. The metrics are applicable across different marker types and platforms, allowing an objective comparison of the performance of different markers irrespective of the platform. Evaluating markers using these metrics demonstrates that trait-specific markers consistently out-perform markers designed for other purposes. These metrics also provide a superb set of criteria for designing superior marker systems for a target QTL, enabling the selection of an optimal marker set before committing to design.
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