Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV)

Wang, Xiaqing; Zhang, Ruyang; Liang, Huagen; Song, Wei; Sun, Xuan; Luo, Meijie; Chen, Kuan; Zhang, Yunxia; Yang, Guijun; Zhao, Yanixn; Zhao, Jiuran

doi:10.1101/369884

Cited by 14 publications

(14 citation statements)

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“…A total of 183 SNP‐trait associations with P < 1.79 × 10 −6 were identified (Table S2), and they involved 149 unique SNPs. According to the LD decay distance of this maize population, a 200‐kb region (±100 kb) around each significant SNP was defined as a QTL (Deng et al ., 2017; Wang et al ., 2019). The QTLs with overlapping intervals for the same traits were merged.…”

Section: Resultsmentioning

confidence: 99%

“…Genes were annotated according to the UniProtKB (https://www.uniprot.org/) and TAIR (https://www.arabidopsis.org/) databases. According to the LD of the association population, all genes and their annotations within 200 kb (100 kb up‐ and downstream) of significant loci were identified (Li et al ., 2013; Liu et al ., 2017; Wang et al ., 2019). For functional classifications, all genes were used as queries in searches against the KOG (https://www.ncbi.nlm.nih.gov/COG/) database.…”

Section: Methodsmentioning

confidence: 99%

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Molecular dissection of maize seedling salt tolerance using a genome‐wide association analysis method

Luo

Zhang

et al. 2021

Plant Biotechnology Journal

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Salt stress is a major devastating abiotic factor that affects the yield and quality of maize. However, knowledge of the molecular mechanisms of the responses to salt stress in maize is limited. To elucidate the genetic basis of salt tolerance traits, a genome-wide association study was performed on 348 maize inbred lines under normal and salt stress conditions using 557 894 single nucleotide polymorphisms (SNPs). The phenotypic data for 27 traits revealed coefficients of variation of >25%. In total, 149 significant SNPs explaining 6.6%-11.2% of the phenotypic variation for each SNP were identified. Of the 104 identified quantitative trait loci (QTLs), 83 were related to salt tolerance and 21 to normal traits. Additionally, 13 QTLs were associated with two to five traits. Eleven and six QTLs controlling salt tolerance traits and normal root growth, respectively, co-localized with QTL intervals reported previously. Based on functional annotations, 13 candidate genes were predicted. Expression levels analysis of 12 candidate genes revealed that they were all responsive to salt stress. The CRISPR/Cas9 technology targeting three sites was applied in maize, and its editing efficiency reached 70%. By comparing the biomass of three CRISPR/Cas9 mutants of ZmCLCg and one zmpmp3 EMS mutant with their wild-type plants under salt stress, the salt tolerance function of candidate genes ZmCLCg and ZmPMP3 were confirmed. Chloride content analysis revealed that ZmCLCg regulated chloride transport under sodium chloride stress. These results help to explain genetic variations in salt tolerance and provide novel loci for generating salt-tolerant maize lines.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Molecular dissection of maize seedling salt tolerance using a genome‐wide association analysis method

Luo

Zhang

et al. 2021

Plant Biotechnology Journal

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“…In recent years, with the rapid development of high-density single nucleotide polymorphism (SNP) genotyping and the nextgeneration sequencing (NGS) technologies, genome-wide association study (GWAS) has become a powerful tool to dissect the genetic basis for the quantitative variation of complex traits in crops (Chen et al, 2019;Xiao et al, 2017). For maize, since the release of the B73 reference genome (Schnable et al, 2009), many agronomic important traits, such as plant height (Dell'Acqua et al, 2015;Farfan et al, 2015;Li et al, 2016a,b;Peiffer et al, 2014;Riedelsheimer et al, 2012;Wang et al, 2019;Weng et al, 2011;Yang et al, 2014a,b), flowering time (Buckler et al, 2009;Farfan et al, 2015;Hung et al, 2012;Li et al, 2016a,b;Van Inghelandt et al, 2012;Yang et al, 2013Yang et al, , 2014a, ear height (Dell'Acqua et al, 2015;Farfan et al, 2015;Li et al, 2016a,b;Peiffer et al, 2014;Yang et al, 2014a,b) and grain size (Dell'Acqua et al, Yang et al, 2014a,b), have been dissected through GWASs. GWAS application in agricultural traits of maize provides useful reference for revealing the phenotypic traits diversity and genetic architecture of vascular bundles in maize stem.…”

Section: Introductionmentioning

confidence: 99%

Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high‐throughput phenotypic analysis

Zhang

Wang

et al. 2020

Plant Biotechnology Journal

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High-throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual phenotyping of vascular bundles is tedious and time-consuming, which lags behind the rapid development of functional genomics in maize. More robust and automated techniques of phenotyping vascular bundles traits at highthroughput are urgently needed for large crop populations. In this study, we developed a standard process for stem micro-CT data acquisition and an automatic CT image process pipeline to obtain vascular bundle traits of stems including geometry-related, morphology-related and distribution-related traits. Next, we analysed the phenotypic variation of stem vascular bundles between natural population subgroup (480 inbred lines) based on 48 comprehensively phenotypic information. Also, the first database for stem micro-phenotypes, MaizeSPD, was established, storing 554 pieces of basic information of maize inbred lines, 523 pieces of experimental information, 1008 pieces of CT scanning images and processed images, and 24 192 pieces of phenotypic data. Combined with genome-wide association studies (GWASs), a total of 1562 significant single nucleotide polymorphism (SNPs) were identified for 30 stem micro-phenotypic traits, and 84 unique genes of 20 traits such as VBNum, VBAvArea and PZVBDensity were detected. Candidate genes identified by GWAS mainly encode enzymes involved in cell wall metabolism, transcription factors, protein kinase and protein related to plant signal transduction and stress response. The results presented here will advance our knowledge about phenotypic trait components of stem vascular bundles and provide useful information for understanding the genetic controls of vascular bundle formation and development.

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“…Imaged-based phenotyping has been used to detect QTL associated with plant root 32 and shoot [33][34][35] architecture, plant height 36 , salt stress 37 , and yield 38,39 among other traits. Although imagebased phenotyping has become increasingly common to quantify plant disease symptoms [4][5][6][7][11][12][13][14][15][16] , to our knowledge, it has yet to be used in the identification of new genetic loci for disease resistance.…”

Section: Benefits Of Image-based Phenotypingmentioning

confidence: 99%

Image-based assessment of plant disease progression identifies new genetic loci for resistance

Meline.

Caldwell

et al. 2021

Preprint

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A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best means of disease control is plant resistance, but the identification of genes that promote resistance has been limited by the subjective quantification of disease, which is typically scored by the human eye. We hypothesized that image-based quantification of disease phenotypes would enable the identification of new disease resistance loci. We tested this using the interaction between tomato and Ralstonia solanacearum , a soilborne pathogen that causes bacterial wilt disease. We acquired over 40,000 time-series images of disease progression in a tomato recombinant inbred line population, and developed an image analysis pipeline providing a suite of ten traits to quantify wilt disease based on plant shape and size. Quantitative trait loci (QTL) analyses using image-based phenotyping identified QTL that were both unique and shared compared with those identified by human assessment of wilting. When shared loci were identified, image-based phenotyping could detect some QTL several days earlier than human assessment. Thus, expanding the phenotypic space of disease with image-based phenotyping allowed both earlier detection and identified new genetic components of resistance.

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Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV)

Cited by 14 publications

References 50 publications

Molecular dissection of maize seedling salt tolerance using a genome‐wide association analysis method

Molecular dissection of maize seedling salt tolerance using a genome‐wide association analysis method

Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high‐throughput phenotypic analysis

Image-based assessment of plant disease progression identifies new genetic loci for resistance

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