通过高光谱成像技术和全基因组关联研究对水稻谷物蛋白质含量进行表型。

IF 7.6 1区 农林科学 Q1 AGRONOMY
Plant Phenomics Pub Date : 2024-07-08 eCollection Date: 2024-01-01 DOI:10.34133/plantphenomics.0200
Hengbiao Zheng, Weijie Tang, Tao Yang, Meng Zhou, Caili Guo, Tao Cheng, Weixing Cao, Yan Zhu, Yunhui Zhang, Xia Yao
{"title":"通过高光谱成像技术和全基因组关联研究对水稻谷物蛋白质含量进行表型。","authors":"Hengbiao Zheng, Weijie Tang, Tao Yang, Meng Zhou, Caili Guo, Tao Cheng, Weixing Cao, Yan Zhu, Yunhui Zhang, Xia Yao","doi":"10.34133/plantphenomics.0200","DOIUrl":null,"url":null,"abstract":"<p><p>Efficient and accurate acquisition of the rice grain protein content (GPC) is important for selecting high-quality rice varieties, and remote sensing technology is an attractive potential method for this task. However, the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands. Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics. However, the small size of typical datasets is a constraint for model construction for estimating GPC, limiting their accuracy and reducing their ability to generalize to a wide range of varieties. In this study, we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks (DCGANs) to generate simulated data to improve the model accuracy. Features sensitive to GPC were extracted after applying a continuous wavelet transform (CWT), and the estimated GPC model was constructed by partial least squares regression (PLSR). Finally, a genome-wide association study (GWAS) was applied to the measured and generated datasets to detect GPC loci. The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values. The wavelet feature (WF<sub>1743, 2</sub>), obtained from the data with the addition of 200 simulated samples, exhibited the highest GPC estimation accuracy (<i>R</i> <sup>2</sup> = 0.58 and RRMSE = 6.70%). The GWAS analysis showed that the estimated values based on the simulated data detected the same loci as the measured values, including the <i>OsmtSSB1L</i> gene related to grain storage protein. This study provides a new technique for the efficient genetic study of phenotypic traits in rice based on hyperspectral technology.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11227985/pdf/","citationCount":"0","resultStr":"{\"title\":\"Grain Protein Content Phenotyping in Rice via Hyperspectral Imaging Technology and a Genome-Wide Association Study.\",\"authors\":\"Hengbiao Zheng, Weijie Tang, Tao Yang, Meng Zhou, Caili Guo, Tao Cheng, Weixing Cao, Yan Zhu, Yunhui Zhang, Xia Yao\",\"doi\":\"10.34133/plantphenomics.0200\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Efficient and accurate acquisition of the rice grain protein content (GPC) is important for selecting high-quality rice varieties, and remote sensing technology is an attractive potential method for this task. However, the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands. Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics. However, the small size of typical datasets is a constraint for model construction for estimating GPC, limiting their accuracy and reducing their ability to generalize to a wide range of varieties. In this study, we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks (DCGANs) to generate simulated data to improve the model accuracy. Features sensitive to GPC were extracted after applying a continuous wavelet transform (CWT), and the estimated GPC model was constructed by partial least squares regression (PLSR). Finally, a genome-wide association study (GWAS) was applied to the measured and generated datasets to detect GPC loci. The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values. The wavelet feature (WF<sub>1743, 2</sub>), obtained from the data with the addition of 200 simulated samples, exhibited the highest GPC estimation accuracy (<i>R</i> <sup>2</sup> = 0.58 and RRMSE = 6.70%). The GWAS analysis showed that the estimated values based on the simulated data detected the same loci as the measured values, including the <i>OsmtSSB1L</i> gene related to grain storage protein. This study provides a new technique for the efficient genetic study of phenotypic traits in rice based on hyperspectral technology.</p>\",\"PeriodicalId\":20318,\"journal\":{\"name\":\"Plant Phenomics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11227985/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Plant Phenomics\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.34133/plantphenomics.0200\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plant Phenomics","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.34133/plantphenomics.0200","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0

摘要

高效、准确地获取稻米籽粒蛋白质含量(GPC)对于选择优质稻米品种非常重要,而遥感技术是完成这项任务的一种极具吸引力的潜在方法。然而,由于光谱波段较宽,大多数多光谱传感器对 GPC 的预测能力较差。高光谱技术为缩小表型组学和基因组学之间的差距提供了一种新的分析技术。然而,典型数据集的规模较小,制约了用于估算 GPC 的模型的构建,限制了其准确性,并降低了其推广到广泛品种的能力。在本研究中,我们使用了来自 515 个粳稻品种的稻谷高光谱数据和深度卷积生成对抗网络(DCGAN)生成模拟数据,以提高模型的准确性。在应用连续小波变换(CWT)后提取了对 GPC 敏感的特征,并通过偏最小二乘回归(PLSR)构建了估计的 GPC 模型。最后,对测量和生成的数据集进行了全基因组关联研究(GWAS),以检测 GPC 基因位点。结果表明,8000 个历时后生成的模拟 GPC 值与测量值最为接近。在数据中加入 200 个模拟样本后得到的小波特征(WF1743, 2)显示出最高的 GPC 估计精度(R 2 = 0.58 和 RRMSE = 6.70%)。GWAS 分析表明,基于模拟数据的估计值检测到了与测量值相同的基因位点,包括与谷物储藏蛋白相关的 OsmtSSB1L 基因。该研究为基于高光谱技术的水稻表型性状的高效遗传研究提供了一种新技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Grain Protein Content Phenotyping in Rice via Hyperspectral Imaging Technology and a Genome-Wide Association Study.

Efficient and accurate acquisition of the rice grain protein content (GPC) is important for selecting high-quality rice varieties, and remote sensing technology is an attractive potential method for this task. However, the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands. Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics. However, the small size of typical datasets is a constraint for model construction for estimating GPC, limiting their accuracy and reducing their ability to generalize to a wide range of varieties. In this study, we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks (DCGANs) to generate simulated data to improve the model accuracy. Features sensitive to GPC were extracted after applying a continuous wavelet transform (CWT), and the estimated GPC model was constructed by partial least squares regression (PLSR). Finally, a genome-wide association study (GWAS) was applied to the measured and generated datasets to detect GPC loci. The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values. The wavelet feature (WF1743, 2), obtained from the data with the addition of 200 simulated samples, exhibited the highest GPC estimation accuracy (R 2 = 0.58 and RRMSE = 6.70%). The GWAS analysis showed that the estimated values based on the simulated data detected the same loci as the measured values, including the OsmtSSB1L gene related to grain storage protein. This study provides a new technique for the efficient genetic study of phenotypic traits in rice based on hyperspectral technology.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信