Jian Zhang , Jun Ren , Jingjing Yang , Shenzao Fu , XiaoFei Zhang , Changxuan Xia , Hong Zhao , Kun Yang , Changlong Wen
{"title":"Evaluation of SNP fingerprinting for variety identification of tomato by DUS testing","authors":"Jian Zhang , Jun Ren , Jingjing Yang , Shenzao Fu , XiaoFei Zhang , Changxuan Xia , Hong Zhao , Kun Yang , Changlong Wen","doi":"10.1016/j.agrcom.2023.100006","DOIUrl":"https://doi.org/10.1016/j.agrcom.2023.100006","url":null,"abstract":"<div><p>Variety identification is crucial for PBR (plant breeders’ rights) protection and PVR (plant variety registration). DUS (Distinctness, Uniformity and Stability) testing, utilizing field-based morphological inspection and DNA fingerprinting with molecular markers in the laboratory are commonly employed methods for variety identification. However, the limited number of molecular markers used in DNA fingerprinting often lacks close linkage to DUS traits. In this study, 116 tomato varieties were well identified both by SNP fingerprinting and DUS testing. PCA (Principal Component Analysis) and population classification demonstrated a highly consistent outcome between SNP fingerprinting and DUS testing, resulting in the division of 116 varieties into three groups: big fruit, cherry, and processing tomatoes. Furthermore, we selected a new set of 16 core SNPs and 18 core DUS traits, which exhibited higher efficiency in variety identification due to their convenient and easy processing. Moreover, the observed variations in SNP markers among each pair of tomato varieties were linearly correlated with those comparison of all different DUS traits (R<sup>2</sup> = 0.85), and the linear correlation was also obtained based on the comparison of different core SNP fingerprints with those of the core DUS traits (R<sup>2</sup> = 0.86). In conclusion, we evaluated SNP fingerprinting for variety identification in comparison to DUS testing, and found these two methods had consistent result. This study also highlights the potential of limited core DUS traits and core SNP fingerprints for effective identification and discrimination of tomato varieties.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"1 1","pages":"Article 100006"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49720463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using Bayesian threshold model and machine learning method to improve the accuracy of genomic prediction for ordered categorical traits in fish","authors":"Hailiang Song, Tian Dong, Xiaoyu Yan, Wei Wang, Zhaohui Tian, Hongxia Hu","doi":"10.1016/j.agrcom.2023.100005","DOIUrl":"https://doi.org/10.1016/j.agrcom.2023.100005","url":null,"abstract":"<div><p>Ordered categorical traits are commonly used in fish breeding programs as they are easier to obtain than continuous observations. However, most studies treat ordered categorical traits as linear traits and analyze them using linear models, which can lead to a serious reduction in prediction accuracy by violating the basic assumptions of linear models. The aim of this study was to evaluate the advantages of Bayesian threshold model and machine learning method in genomic prediction of ordered categorical traits in fish. The study was based on the analyses of simulated data and real data of Atlantic salmon. Ordinal categorical traits were simulated with varying numbers of categories (2, 3 and 4) and levels of heritabilities (0.1, 0.3 and 0.5). Linear and threshold models with BayesA and BayesCπ methods, as well as a machine learning method, support vector regression with default (SVRdef) and tuning (SVRtuning) hyperparameters were used to investigate their prediction abilities. The results showed that Bayesian threshold models yielded 2.1%, 2.6% and 2.9% higher prediction accuracies on average for 2-, 3- and 4-category traits, respectively, than Bayesian linear models. Furthermore, SVRtuning produced higher prediction accuracy compared with SVRdef and Bayesian threshold models in all scenarios. For real data, Bayesian threshold models yielded 1.2% higher prediction accuracy than Bayesian linear models, and SVRdef and SVRtuning yielded 3.3% and 6.6% higher prediction accuracies than Bayesian methods, respectively. In conclusion, the use of Bayesian threshold model and machine learning method was beneficial for genomic prediction of ordered categorical traits in fish.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"1 1","pages":"Article 100005"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49720528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Superoxide dismutase promotes early flowering in Triticum aestivum L.","authors":"Hao-yu Guo , Yong-jie Liu , Shao-hua Yuan , Jie-ru Yue, Yan-mei Li, Xiang-zheng Liao, Sheng-kai Ying, Zi-han Liu, Jian-fang Bai, Li-ping Zhang","doi":"10.1016/j.agrcom.2023.100007","DOIUrl":"https://doi.org/10.1016/j.agrcom.2023.100007","url":null,"abstract":"<div><p>Superoxide dismutase (SOD) is a first-line-defense antioxidant enzyme that plays a crucial role in scavenging reactive oxygen species (ROS) to maintain homeostasis in plants. SOD catalyzes the conversion of superoxide (O<sub>2</sub><sup>-</sup>) into oxygen (O<sub>2</sub>) and hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), and besides its role in stress resistance, SOD also impacts plant growth and development. Here, we cloned and characterized a <em>TaCSOD</em> gene from the wheat photo-thermosensitive genic male sterile line BS366. Phylogenetic and motif analyses identified <em>TaCSOD</em> as a Cu/Zn-dependent SOD due to the presence of conserved Cu<sup>2+</sup> and Zn<sup>2+</sup> binding sites. Overexpression of <em>TaCSOD</em> enhanced drought and salt tolerance in both <em>Arabidopsis thaliana</em> and yeast. In addition, seed germination rate, primary root length, and fresh weight of the transgenic plants were higher than those of the wild-type under drought- and salt-stressed conditions. The <em>Arabidopsis TaCSOD</em> overexpression lines also exhibited an early flowering phenotype, with fewer leaves and shorter flowering period. Nitroblue tetrazolium (NBT) and 3, 3-diaminobenzidine (DAB) staining, along with transcriptome analysis, demonstrated that <em>TaCSOD</em> regulates ROS homeostasis and flowering time through carbohydrate signaling, aging, vernalization, and gibberellic acid pathways. Our study provides valuable insights into the functions of <em>SOD</em> genes in regulating flowering through the regulation of ROS homeostasis in plants.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"1 1","pages":"Article 100007"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49720464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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