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Agnew E, Ziegler G, Lee S, Lizárraga C, Fahlgren N, Baxter I, Mockler TC, Shakoor N. Longitudinal genome-wide association study reveals early QTL that predict biomass accumulation under cold stress in sorghum. FRONTIERS IN PLANT SCIENCE 2024; 15:1278802. [PMID: 38807776 PMCID: PMC11130433 DOI: 10.3389/fpls.2024.1278802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/24/2024] [Indexed: 05/30/2024]
Abstract
Introduction Sorghum bicolor is a promising cellulosic feedstock crop for bioenergy due to its high biomass yields. However, early growth phases of sorghum are sensitive to cold stress, limiting its planting in temperate environments. Cold adaptability is crucial for cultivating bioenergy and grain sorghum at higher latitudes and elevations, or for extending the growing season. Identifying genes and alleles that enhance biomass accumulation under early cold stress can lead to improved sorghum varieties through breeding or genetic engineering. Methods We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP includes diverse accessions with dense genotyping and varied racial, geographical, and phenotypic backgrounds. Daily, non-destructive imaging allowed temporal analysis of growth-related traits and water use efficiency (WUE). A genome-wide association study (GWAS) was performed to identify genomic intervals and genes associated with cold stress response. Results The GWAS identified transient quantitative trait loci (QTL) strongly associated with growth-related traits, enabling an exploration of the genetic basis of cold stress response at different developmental stages. This analysis of daily growth traits, rather than endpoint traits, revealed early transient QTL predictive of final phenotypes. The study identified both known and novel candidate genes associated with growth-related traits and temporal responses to cold stress. Discussion The identified QTL and candidate genes contribute to understanding the genetic mechanisms underlying sorghum's response to cold stress. These findings can inform breeding and genetic engineering strategies to develop sorghum varieties with improved biomass yields and resilience to cold, facilitating earlier planting, extended growing seasons, and cultivation at higher latitudes and elevations.
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Affiliation(s)
| | | | | | | | | | | | | | - Nadia Shakoor
- Donald Danforth Plant Science Center, Saint Louis, MO, United States
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Meyer RC, Weigelt-Fischer K, Tschiersch H, Topali G, Altschmied L, Heuermann MC, Knoch D, Kuhlmann M, Zhao Y, Altmann T. Dynamic growth QTL action in diverse light environments: characterization of light regime-specific and stable QTL in Arabidopsis. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5341-5362. [PMID: 37306093 DOI: 10.1093/jxb/erad222] [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: 10/17/2022] [Accepted: 06/10/2023] [Indexed: 06/13/2023]
Abstract
Plant growth is a complex process affected by a multitude of genetic and environmental factors and their interactions. To identify genetic factors influencing plant performance under different environmental conditions, vegetative growth was assessed in Arabidopsis thaliana cultivated under constant or fluctuating light intensities, using high-throughput phenotyping and genome-wide association studies. Daily automated non-invasive phenotyping of a collection of 382 Arabidopsis accessions provided growth data during developmental progression under different light regimes at high temporal resolution. Quantitative trait loci (QTL) for projected leaf area, relative growth rate, and PSII operating efficiency detected under the two light regimes were predominantly condition-specific and displayed distinct temporal activity patterns, with active phases ranging from 2 d to 9 d. Eighteen protein-coding genes and one miRNA gene were identified as potential candidate genes at 10 QTL regions consistently found under both light regimes. Expression patterns of three candidate genes affecting projected leaf area were analysed in time-series experiments in accessions with contrasting vegetative leaf growth. These observations highlight the importance of considering both environmental and temporal patterns of QTL/allele actions and emphasize the need for detailed time-resolved analyses under diverse well-defined environmental conditions to effectively unravel the complex and stage-specific contributions of genes affecting plant growth processes.
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Affiliation(s)
- Rhonda C Meyer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Kathleen Weigelt-Fischer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Henning Tschiersch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Georgia Topali
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Lothar Altschmied
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Marc C Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Dominic Knoch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Markus Kuhlmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Breeding Research, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Thomas Altmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
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Adak A, Kang M, Anderson SL, Murray SC, Jarquin D, Wong RKW, Katzfuß M. Phenomic data-driven biological prediction of maize through field-based high-throughput phenotyping integration with genomic data. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5307-5326. [PMID: 37279568 DOI: 10.1093/jxb/erad216] [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: 04/14/2022] [Accepted: 06/02/2023] [Indexed: 06/08/2023]
Abstract
High-throughput phenotyping (HTP) has expanded the dimensionality of data in plant research; however, HTP has resulted in few novel biological discoveries to date. Field-based HTP (FHTP), using small unoccupied aerial vehicles (UAVs) equipped with imaging sensors, can be deployed routinely to monitor segregating plant population interactions with the environment under biologically meaningful conditions. Here, flowering dates and plant height, important phenological fitness traits, were collected on 520 segregating maize recombinant inbred lines (RILs) in both irrigated and drought stress trials in 2018. Using UAV phenomic, single nucleotide polymorphism (SNP) genomic, as well as combined data, flowering times were predicted using several scenarios. Untested genotypes were predicted with 0.58, 0.59, and 0.41 prediction ability for anthesis, silking, and terminal plant height, respectively, using genomic data, but prediction ability increased to 0.77, 0.76, and 0.58 when phenomic and genomic data were used together. Using the phenomic data in a genome-wide association study, a heat-related candidate gene (GRMZM2G083810; hsp18f) was discovered using temporal reflectance phenotypes belonging to flowering times (both irrigated and drought) trials where heat stress also peaked. Thus, a relationship between plants and abiotic stresses belonging to a specific time of growth was revealed only through use of temporal phenomic data. Overall, this study showed that (i) it is possible to predict complex traits using high dimensional phenomic data between different environments, and (ii) temporal phenomic data can reveal a time-dependent association between genotypes and abiotic stresses, which can help understand mechanisms to develop resilient plants.
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Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Myeongjong Kang
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | | | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL 32611, USA
| | - Raymond K W Wong
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - Matthias Katzfuß
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
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4
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Adak A, Murray SC, Anderson SL. Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions. G3 (BETHESDA, MD.) 2022; 13:6851143. [PMID: 36445027 PMCID: PMC9836347 DOI: 10.1093/g3journal/jkac294] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 10/21/2022] [Indexed: 11/30/2022]
Abstract
A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade, genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over 15 growth time points and multispectral (RGB, red-edge and near infrared) bands over 12 time points were compared across 280 unique maize hybrids. Through cross-validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP), outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in 3 other cross-validation scenarios. Genome-wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5% of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51% of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, TPP appeared to work successfully on unrelated individuals unlike GP.
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Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Seth C Murray
- Corresponding author: Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA.
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Adak A, Murray SC, Anderson SL, Popescu SC, Malambo L, Romay MC, de Leon N. Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize. THE PLANT GENOME 2021; 14:e20102. [PMID: 34009740 DOI: 10.1002/tpg2.20102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
Traditional phenotyping methods, coupled with genetic mapping in segregating populations, have identified loci governing complex traits in many crops. Unoccupied aerial systems (UAS)-based phenotyping has helped to reveal a more novel and dynamic relationship between time-specific associated loci with complex traits previously unable to be evaluated. Over 1,500 maize (Zea mays L.) hybrid row plots containing 280 different replicated maize hybrids from the Genomes to Fields (G2F) project were evaluated agronomically and using UAS in 2017. Weekly UAS flights captured variation in plant heights during the growing season under three different management conditions each year: optimal planting with irrigation (G2FI), optimal dryland planting without irrigation (G2FD), and a stressed late planting (G2LA). Plant height of different flights were ranked based on importance for yield using a random forest (RF) algorithm. Plant heights captured by early flights in G2FI trials had higher importance (based on Gini scores) for predicting maize grain yield (GY) but also higher accuracies in genomic predictions which fluctuated for G2FD (-0.06∼0.73), G2FI (0.33∼0.76), and G2LA (0.26∼0.78) trials. A genome-wide association analysis discovered 52 significant single nucleotide polymorphisms (SNPs), seven were found consistently in more than one flights or trial; 45 were flight or trial specific. Total cumulative marker effects for each chromosome's contributions to plant height also changed depending on flight. Using UAS phenotyping, this study showed that many candidate genes putatively play a role in the regulation of plant architecture even in relatively early stages of maize growth and development.
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Affiliation(s)
- Alper Adak
- Dept. of Soil and Crop Sciences, Texas A&M Univ., College Station, TX, 77843-2474, USA
| | - Seth C Murray
- Dept. of Soil and Crop Sciences, Texas A&M Univ., College Station, TX, 77843-2474, USA
| | - Steven L Anderson
- Dept. of Environmental Hort., Institute of Food and Agricultural Sciences, Mid-Florida Research and Education Center, University of Florida, Apopka, FL, USA
| | - Sorin C Popescu
- Dept. of Ecosystem Science and Management, Texas A&M Univ., College Station, TX, 77843-2120, USA
| | - Lonesome Malambo
- Dept. of Ecosystem Science and Management, Texas A&M Univ., College Station, TX, 77843-2120, USA
| | - M Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI, 53706, USA
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6
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Prakash PT, Banan D, Paul RE, Feldman MJ, Xie D, Freyfogle L, Baxter I, Leakey ADB. Correlation and co-localization of QTL for stomatal density, canopy temperature, and productivity with and without drought stress in Setaria. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5024-5037. [PMID: 33893796 PMCID: PMC8219040 DOI: 10.1093/jxb/erab166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 04/23/2021] [Indexed: 05/04/2023]
Abstract
Mechanistic modeling indicates that stomatal conductance could be reduced to improve water use efficiency (WUE) in C4 crops. Genetic variation in stomatal density and canopy temperature was evaluated in the model C4 genus, Setaria. Recombinant inbred lines (RILs) derived from a Setaria italica×Setaria viridis cross were grown with ample or limiting water supply under field conditions in Illinois. An optical profilometer was used to rapidly assess stomatal patterning, and canopy temperature was measured using infrared imaging. Stomatal density and canopy temperature were positively correlated but both were negatively correlated with total above-ground biomass. These trait relationships suggest a likely interaction between stomatal density and the other drivers of water use such as stomatal size and aperture. Multiple quantitative trait loci (QTL) were identified for stomatal density and canopy temperature, including co-located QTL on chromosomes 5 and 9. The direction of the additive effect of these QTL on chromosome 5 and 9 was in accordance with the positive phenotypic relationship between these two traits. This, along with prior experiments, suggests a common genetic architecture between stomatal patterning and WUE in controlled environments with canopy transpiration and productivity in the field, while highlighting the potential of Setaria as a model to understand the physiology and genetics of WUE in C4 species.
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Affiliation(s)
- Parthiban Thathapalli Prakash
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- International Rice Research Institute, Los Baños, Philippines
| | - Darshi Banan
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Rachel E Paul
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Dan Xie
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, USA
| | - Luke Freyfogle
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St Louis, MO, USA
| | - Andrew D B Leakey
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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7
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Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041403] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.
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8
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Meyer RC, Weigelt-Fischer K, Knoch D, Heuermann M, Zhao Y, Altmann T. Temporal dynamics of QTL effects on vegetative growth in Arabidopsis thaliana. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:476-490. [PMID: 33080013 DOI: 10.1093/jxb/eraa490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/19/2020] [Indexed: 06/11/2023]
Abstract
We assessed early vegetative growth in a population of 382 accessions of Arabidopsis thaliana using automated non-invasive high-throughput phenotyping. All accessions were imaged daily from 7 d to 18 d after sowing in three independent experiments and genotyped using the Affymetrix 250k SNP array. Projected leaf area (PLA) was derived from image analysis and used to calculate relative growth rates (RGRs). In addition, initial seed size was determined. The generated datasets were used jointly for a genome-wide association study that identified 238 marker-trait associations (MTAs) individually explaining up to 8% of the total phenotypic variation. Co-localization of MTAs occurred at 33 genomic positions. At 21 of these positions, sequential co-localization of MTAs for 2-9 consecutive days was observed. The detected MTAs for PLA and RGR could be grouped according to their temporal expression patterns, emphasizing that temporal variation of MTA action can be observed even during the vegetative growth phase, a period of continuous formation and enlargement of seemingly similar rosette leaves. This indicates that causal genes may be differentially expressed in successive periods. Analyses of the temporal dynamics of biological processes are needed to gain important insight into the molecular mechanisms of growth-controlling processes in plants.
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Affiliation(s)
- Rhonda C Meyer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Kathleen Weigelt-Fischer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Dominic Knoch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Marc Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Breeding Research, Research Group Quantitative Genetics, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Thomas Altmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
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He Q, Zhi H, Tang S, Xing L, Wang S, Wang H, Zhang A, Li Y, Gao M, Zhang H, Chen G, Dai S, Li J, Yang J, Liu H, Zhang W, Jia Y, Li S, Liu J, Qiao Z, Guo E, Jia G, Liu J, Diao X. QTL mapping for foxtail millet plant height in multi-environment using an ultra-high density bin map. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:557-572. [PMID: 33128073 DOI: 10.1007/s00122-020-03714-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 10/23/2020] [Indexed: 05/20/2023]
Abstract
Using a fixed RIL population derived from a widely used foxtail millet backbone breeding line and an elite cultivar, we constructed a high-density bin map and identified six novel multi-environment effect QTLs and seven candidate genes for dwarf phenotype. Plant height is an important trait that determines tradeoffs between competition and resource allocation, which is crucial for yield potential. To improve the C4 model plant foxtail millet (Setaria italica) productivity, it is necessary to isolate plant height-related genes that contribute to ideal plant architecture in breeding. In the present study, we generated a foxtail millet population of 333 recombinant inbred lines (RILs) derived from a cross between a backbone line Ai 88 and an elite cultivar Liaogu 1. We evaluated plant height in 13 environmental conditions across 4 years, the mean plant height of the RIL population ranged from 89.5 to 149.9 cm. Using deep re-sequencing data, we constructed a high-density bin map with 3744 marker bins. Quantitative trait locus (QTL) mapping identified 26 QTLs significantly associated with plant height. Of these, 13 QTLs were repeatedly detected under multiple environments, including six novel QTLs that have not been reported before. Seita.1G242300, a gene encodes gibberellin 2-oxidase-8, which was detected in nine environments in a 1.54-Mb interval of qPH1.3, was considered as an important candidate gene. Moreover, other six genes involved in GA biosynthesis or signaling pathways, and fifteen genes encode F-box domain proteins which might function as E3 ligases, were also considered as candidate genes in different QTLs. These QTLs and candidate genes identified in this study will help to elucidate the genetic basis of foxtail millet plant height, and the linked markers will be useful for marker-assistant selection of varieties with ideal plant architecture and high yield potential.
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Affiliation(s)
- Qiang He
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, Haidian, 100081, China
| | - Hui Zhi
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, Haidian, 100081, China
| | - Sha Tang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, Haidian, 100081, China
| | - Lu Xing
- Anyang Academy of Agricultural Sciences, Anyang, 455000, Henan, China
| | - Suying Wang
- Anyang Academy of Agricultural Sciences, Anyang, 455000, Henan, China
| | - Haigang Wang
- Institute of Crop Germplasm, Shanxi Academy of Agricultural Sciences, Taiyuan, 030000, China
| | - Aiying Zhang
- Institute of Millet Crops, Shanxi Academy of Agricultural Sciences, Changzhi, 046000, Shanxi, China
| | - Yuhui Li
- Institute of Millet Crops, Shanxi Academy of Agricultural Sciences, Changzhi, 046000, Shanxi, China
| | - Ming Gao
- Institute of Crop Sciences, Jilin Academy of Agricultural Sciences, Gongzhuling, Jilin, 136100, China
| | - Haijin Zhang
- Institute of Dry-Land Agriculture and Forestry, Liaoning Academy of Agricultural Sciences, Chaoyang, 122000, Liaoning, China
| | - Guoqiu Chen
- Institute of Dry-Land Agriculture and Forestry, Liaoning Academy of Agricultural Sciences, Chaoyang, 122000, Liaoning, China
| | - Shutao Dai
- Institute of Crop Sciences, Henan Academy of Agricultural Sciences, Zhengzhou, 450000, Henan, China
| | - Junxia Li
- Institute of Crop Sciences, Henan Academy of Agricultural Sciences, Zhengzhou, 450000, Henan, China
| | - Junjun Yang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, Haidian, 100081, China
| | - Huifang Liu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, Haidian, 100081, China
| | - Wei Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, Haidian, 100081, China
| | - Yanchao Jia
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, Haidian, 100081, China
| | - Shujie Li
- Institute of Crop Sciences, Jilin Academy of Agricultural Sciences, Gongzhuling, Jilin, 136100, China
| | - Jinrong Liu
- Anyang Academy of Agricultural Sciences, Anyang, 455000, Henan, China
| | - Zhijun Qiao
- Institute of Crop Germplasm, Shanxi Academy of Agricultural Sciences, Taiyuan, 030000, China
| | - Erhu Guo
- Institute of Millet Crops, Shanxi Academy of Agricultural Sciences, Changzhi, 046000, Shanxi, China
| | - Guanqing Jia
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, Haidian, 100081, China
| | - Jun Liu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, Haidian, 100081, China
| | - Xianmin Diao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, Haidian, 100081, China.
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10
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Shao MR, Jiang N, Li M, Howard A, Lehner K, Mullen JL, Gunn SL, McKay JK, Topp CN. Complementary Phenotyping of Maize Root System Architecture by Root Pulling Force and X-Ray Imaging. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9859254. [PMID: 34870229 PMCID: PMC8603028 DOI: 10.34133/2021/9859254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 10/05/2021] [Indexed: 05/11/2023]
Abstract
The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.
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Affiliation(s)
- M. R. Shao
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - N. Jiang
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - M. Li
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - A. Howard
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - K. Lehner
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - J. L. Mullen
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - S. L. Gunn
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - J. K. McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - C. N. Topp
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
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Miao C, Xu Y, Liu S, Schnable PS, Schnable JC. Increased Power and Accuracy of Causal Locus Identification in Time Series Genome-wide Association in Sorghum. PLANT PHYSIOLOGY 2020; 183:1898-1909. [PMID: 32461303 PMCID: PMC7401099 DOI: 10.1104/pp.20.00277] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 05/20/2020] [Indexed: 05/18/2023]
Abstract
The phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track these phenotypic changes. Identifying the genetic loci regulating differences in the pattern of phenotypic change remains challenging. This study used functional principal component analysis (FPCA) to achieve this aim. Time series phenotype data were collected from a sorghum (Sorghum bicolor) diversity panel using a number of technologies including conventional color photography and hyperspectral imaging. This imaging lasted for 37 d and centered on reproductive transition. A new higher density marker set was generated for the same population. Several genes known to control trait variation in sorghum have been previously cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotypes. Partitioning was consistent with the known molecular function of the individual cloned genes. These data demonstrate that FPCA-based genome-wide association studies can enable robust time series mapping analyses in a wide range of contexts. Moreover, time series analysis can increase the accuracy and power of quantitative genetic analyses.
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Affiliation(s)
- Chenyong Miao
- Quantitative Life Science Initiative, Center for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
| | - Yuhang Xu
- Department of Applied Statistics and Operations Research, Bowling Green State University, Bowling Green, Ohio 43403
| | - Sanzhen Liu
- Department of Plant Pathology, Kansas State University, Manhattan, Kansas 66506
| | | | - James C Schnable
- Quantitative Life Science Initiative, Center for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
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12
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Saade S, Brien C, Pailles Y, Berger B, Shahid M, Russell J, Waugh R, Negrão S, Tester M. Dissecting new genetic components of salinity tolerance in two-row spring barley at the vegetative and reproductive stages. PLoS One 2020; 15:e0236037. [PMID: 32701981 PMCID: PMC7377408 DOI: 10.1371/journal.pone.0236037] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 06/27/2020] [Indexed: 11/18/2022] Open
Abstract
Soil salinity imposes an agricultural and economic burden that may be alleviated by identifying the components of salinity tolerance in barley, a major crop and the most salt tolerant cereal. To improve our understanding of these components, we evaluated a diversity panel of 377 two-row spring barley cultivars during both the vegetative, in a controlled environment, and the reproductive stages, in the field. In the controlled environment, a high-throughput phenotyping platform was used to assess the growth-related traits under both control and saline conditions. In the field, the agronomic traits were measured from plots irrigated with either fresh or saline water. Association mapping for the different components of salinity tolerance enabled us to detect previously known associations, such as HvHKT1;5. Using an "interaction model", which took into account the interaction between treatment (control and salt) and genetic markers, we identified several loci associated with yield components related to salinity tolerance. We also observed that the two developmental stages did not share genetic regions associated with the components of salinity tolerance, suggesting that different mechanisms play distinct roles throughout the barley life cycle. Our association analysis revealed that genetically defined regions containing known flowering genes (Vrn-H3, Vrn-H1, and HvNAM-1) were responsive to salt stress. We identified a salt-responsive locus (7H, 128.35 cM) that was associated with grain number per ear, and suggest a gene encoding a vacuolar H+-translocating pyrophosphatase, HVP1, as a candidate. We also found a new QTL on chromosome 3H (139.22 cM), which was significant for ear number per plant, and a locus on chromosome 2H (141.87 cM), previously identified using a nested association mapping population, which associated with a yield component and interacted with salinity stress. Our study is the first to evaluate a barley diversity panel for salinity stress under both controlled and field conditions, allowing us to identify contributions from new components of salinity tolerance which could be used for marker-assisted selection when breeding for marginal and saline regions.
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Affiliation(s)
- Stephanie Saade
- Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Chris Brien
- School of Agriculture, Food and Wine, Waite Research Precinct, University of Adelaide, Urrbrae, South Australia, Australia
- School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia
- The Plant Accelerator, Australian Plant Phenomics Facility, Waite Research Precinct, University of Adelaide, Urrbrae, South Australia, Australia
| | - Yveline Pailles
- Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Bettina Berger
- School of Agriculture, Food and Wine, Waite Research Precinct, University of Adelaide, Urrbrae, South Australia, Australia
- The Plant Accelerator, Australian Plant Phenomics Facility, Waite Research Precinct, University of Adelaide, Urrbrae, South Australia, Australia
| | - Mohammad Shahid
- International Center for Biosaline Agriculture (ICBA), Dubai, United Arab Emirates
| | - Joanne Russell
- Cell and Molecular Sciences, The James Hutton Institute, Invergowrie, Dundee, Scotland
| | - Robbie Waugh
- School of Agriculture, Food and Wine, Waite Research Precinct, University of Adelaide, Urrbrae, South Australia, Australia
- Cell and Molecular Sciences, The James Hutton Institute, Invergowrie, Dundee, Scotland
- Division of Plant Sciences, School of Life Sciences, University of Dundee at The James Hutton Institute, Invergowrie, Dundee, Scotland
| | - Sónia Negrão
- School of Biology and Environmental Sciences, University College Dublin, Belfield, Dublin, Ireland
| | - Mark Tester
- Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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13
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QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants. G3-GENES GENOMES GENETICS 2020; 10:2411-2421. [PMID: 32430305 PMCID: PMC7341141 DOI: 10.1534/g3.120.401122] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as the training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.
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14
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Ellsworth PZ, Feldman MJ, Baxter I, Cousins AB. A genetic link between leaf carbon isotope composition and whole-plant water use efficiency in the C 4 grass Setaria. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 102:1234-1248. [PMID: 31968138 DOI: 10.1111/tpj.14696] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 12/18/2019] [Accepted: 01/02/2020] [Indexed: 05/13/2023]
Abstract
Genetic selection for whole-plant water use efficiency (yield per transpiration; WUEplant ) in any crop-breeding programme requires high-throughput phenotyping of component traits of WUEplant such as intrinsic water use efficiency (WUEi ; CO2 assimilation rate per stomatal conductance). Measuring WUEi by gas exchange measurements is laborious and time consuming and may not reflect an integrated WUEi over the life of the leaf. Alternatively, leaf carbon stable isotope composition (δ13 Cleaf ) has been suggested as a potential time-integrated proxy for WUEi that may provide a tool to screen for WUEplant . However, a genetic link between δ13 Cleaf and WUEplant in a C4 species has not been well established. Therefore, to determine if there is a genetic relationship in a C4 plant between δ13 Cleaf and WUEplant under well watered and water-limited growth conditions, a high-throughput phenotyping facility was used to measure WUEplant in a recombinant inbred line (RIL) population created between the C4 grasses Setaria viridis and S. italica. Three quantitative trait loci (QTL) for δ13 Cleaf were found and co-localized with transpiration, biomass accumulation, and WUEplant . Additionally, WUEplant for each of the δ13 Cleaf QTL allele classes was negatively correlated with δ13 Cleaf , as would be predicted when WUEi influences WUEplant . These results demonstrate that δ13 Cleaf is genetically linked to WUEplant , likely to be through their relationship with WUEi , and can be used as a high-throughput proxy to screen for WUEplant in these C4 species.
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Affiliation(s)
- Patrick Z Ellsworth
- School of Biological Sciences, Washington State University, Pullman, WA, USA
| | - Max J Feldman
- Donald Danforth Plant Sciences Center, St. Louis, MO, USA
| | - Ivan Baxter
- Donald Danforth Plant Sciences Center, St. Louis, MO, USA
| | - Asaph B Cousins
- School of Biological Sciences, Washington State University, Pullman, WA, USA
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15
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Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. FRONTIERS IN PLANT SCIENCE 2020; 11:681. [PMID: 32528513 PMCID: PMC7264266 DOI: 10.3389/fpls.2020.00681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/30/2020] [Indexed: 05/28/2023]
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
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Affiliation(s)
- Fabiana F. Moreira
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey J. Volenec
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katy M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
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16
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Anderson SL, Murray SC, Chen Y, Malambo L, Chang A, Popescu S, Cope D, Jung J. Unoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci. PLANT DIRECT 2020; 4:e00223. [PMID: 32399510 PMCID: PMC7212003 DOI: 10.1002/pld3.223] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 03/25/2020] [Accepted: 04/15/2020] [Indexed: 05/06/2023]
Abstract
Unoccupied aerial systems (UAS) were used to phenotype growth trajectories of inbred maize populations under field conditions. Three recombinant inbred line populations were surveyed on a weekly basis collecting RGB images across two irrigation regimens (irrigated and non-irrigated/rain fed). Plant height, estimated by the 95th percentile (P95) height from UAS generated 3D point clouds, exceeded 70% correlation (r) to manual ground truth measurements and 51% of experimental variance was explained by genetics. The Weibull sigmoidal function accurately modeled plant growth (R 2: >99%; RMSE: <4 cm) from P95 genetic means. The mean asymptote was strongly correlated (r 2 = 0.66-0.77) with terminal plant height. Maximum absolute growth rates (mm/day) were weakly correlated with height and flowering time. The average inflection point ranged from 57 to 60 days after sowing (DAS) and was correlated with flowering time (r 2 = 0.45-0.68). Functional growth parameters (asymptote, inflection point, growth rate) alone identified 34 genetic loci, each explaining 3-15% of total genetic variation. Plant height was estimated at one-day intervals to 85 DAS, identifying 58 unique temporal quantitative trait loci (QTL) locations. Genomic hotspots on chromosomes 1 and 3 indicated chromosomal regions associated with functional growth trajectories influencing flowering time, growth rate, and terminal growth. Temporal QTL demonstrated unique dynamic expression patterns not previously observable, and no QTL were significantly expressed throughout the entire growing season. UAS technologies improved phenotypic selection accuracy and permitted monitoring traits on a temporal scale previously infeasible using manual measurements, furthering understanding of crop development and biological trajectories.
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Affiliation(s)
- Steven L. Anderson
- Department of Soil and Crop SciencesTexas A&M UniversityCollege StationTXUSA
- Present address:
Department of Environmental HorticultureInstitute of Food and Agricultural SciencesMid‐Florida Research and Education CenterUniversity of FloridaApopkaFLUSA
| | - Seth C. Murray
- Department of Soil and Crop SciencesTexas A&M UniversityCollege StationTXUSA
| | - Yuanyuan Chen
- Department of Soil and Crop SciencesTexas A&M UniversityCollege StationTXUSA
- Present address:
National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Lonesome Malambo
- Department of Ecosystem Science and ManagementTexas A&M UniversityCollege StationTXUSA
| | - Anjin Chang
- School of Engineering and Computer SciencesTexas A&M University – Corpus ChristiCorpus ChristiTXUSA
| | - Sorin Popescu
- Department of Ecosystem Science and ManagementTexas A&M UniversityCollege StationTXUSA
| | - Dale Cope
- Department of Mechanical EngineeringTexas A&M UniversityCollege StationTXUSA
| | - Jinha Jung
- School of Engineering and Computer SciencesTexas A&M University – Corpus ChristiCorpus ChristiTXUSA
- Present address:
Department of Civil EngineeringPurdue UniversityWest LafayetteINUSA
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17
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:1885-1898. [PMID: 32097472 PMCID: PMC7094083 DOI: 10.1093/jxb/erz545] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 02/19/2020] [Indexed: 05/08/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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18
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020. [PMID: 32097472 DOI: 10.17632/pkxpkw6j43.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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19
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Brien C, Jewell N, Watts-Williams SJ, Garnett T, Berger B. Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data. PLANT METHODS 2020; 16:36. [PMID: 32180825 PMCID: PMC7065360 DOI: 10.1186/s13007-020-00577-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 02/27/2020] [Indexed: 05/14/2023]
Abstract
BACKGROUND Non-destructive high-throughput plant phenotyping is becoming increasingly used and various methods for growth analysis have been proposed. Traditional longitudinal or repeated measures analyses that model growth using statistical models are common. However, often the variation in the data is inappropriately modelled, in part because the required models are complicated and difficult to fit. We provide a novel, computationally efficient technique that is based on smoothing and extraction of traits (SET), which we compare with the alternative traditional longitudinal analysis methods. RESULTS The SET-based and longitudinal analyses were applied to a tomato experiment to investigate the effects on plant growth of zinc (Zn) addition and growing plants in soil inoculated with arbuscular mycorrhizal fungi (AMF). Conclusions from the SET-based and longitudinal analyses are similar, although the former analysis results in more significant differences. They showed that added Zn had little effect on plants grown in inoculated soils, but that growth depended on the amount of added Zn for plants grown in uninoculated soils. The longitudinal analysis of the unsmoothed data fitted a mixed model that involved both fixed and random regression modelling with splines, as well as allowing for unequal variances and autocorrelation between time points. CONCLUSIONS A SET-based analysis can be used in any situation in which a traditional longitudinal analysis might be applied, especially when there are many observed time points. Two reasons for deploying the SET-based method are (i) biologically relevant growth parameters are required that parsimoniously describe growth, usually focussing on a small number of intervals, and/or (ii) a computationally efficient method is required for which a valid analysis is easier to achieve, while still capturing the essential features of the exhibited growth dynamics. Also discussed are the statistical models that need to be considered for traditional longitudinal analyses and it is demonstrated that the oft-omitted unequal variances and autocorrelation may be required for a valid longitudinal analysis. With respect to the separate issue of the subjective choice of mathematical growth functions or splines to characterize growth, it is recommended that, for both SET-based and longitudinal analyses, an evidence-based procedure is adopted.
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Affiliation(s)
- Chris Brien
- The Plant Accelerator, Australian Plant Phenomics Facility, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
- School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
- School of Information Technology and Information Sciences, University of South Australia, GPO Box 2471, Adelaide, SA 5001 Australia
| | - Nathaniel Jewell
- The Plant Accelerator, Australian Plant Phenomics Facility, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
- School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
| | | | - Trevor Garnett
- The Plant Accelerator, Australian Plant Phenomics Facility, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
- School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
| | - Bettina Berger
- The Plant Accelerator, Australian Plant Phenomics Facility, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
- School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
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20
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Ubbens J, Cieslak M, Prusinkiewicz P, Parkin I, Ebersbach J, Stavness I. Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:5801869. [PMID: 33313558 PMCID: PMC7706325 DOI: 10.34133/2020/5801869] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 12/15/2019] [Indexed: 05/05/2023]
Abstract
Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.
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Affiliation(s)
- Jordan Ubbens
- Department of Computer Science, University of Saskatchewan, Canada
| | - Mikolaj Cieslak
- Department of Computer Science, University of Calgary, Canada
| | | | - Isobel Parkin
- Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
| | | | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Canada
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21
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Ward B, Brien C, Oakey H, Pearson A, Negrão S, Schilling RK, Taylor J, Jarvis D, Timmins A, Roy SJ, Tester M, Berger B, van den Hengel A. High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare). THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 98:555-570. [PMID: 30604470 PMCID: PMC6850118 DOI: 10.1111/tpj.14225] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Revised: 12/17/2018] [Accepted: 12/19/2018] [Indexed: 05/11/2023]
Abstract
To optimize shoot growth and structure of cereals, we need to understand the genetic components controlling initiation and elongation. While measuring total shoot growth at high throughput using 2D imaging has progressed, recovering the 3D shoot structure of small grain cereals at a large scale is still challenging. Here, we present a method for measuring defined individual leaves of cereals, such as wheat and barley, using few images. Plant shoot modelling over time was used to measure the initiation and elongation of leaves in a bi-parental barley mapping population under low and high soil salinity. We detected quantitative trait loci (QTL) related to shoot growth per se, using both simple 2D total shoot measurements and our approach of measuring individual leaves. In addition, we detected QTL specific to leaf elongation and not to total shoot size. Of particular importance was the detection of a QTL on chromosome 3H specific to the early responses of leaf elongation to salt stress, a locus that could not be detected without the computer vision tools developed in this study.
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Affiliation(s)
- Ben Ward
- Australian Center for Visual TechnologiesUniversity of AdelaideAdelaideSA5005Australia
| | - Chris Brien
- Australian Plant Phenomics FacilityThe Plant AcceleratorSchool of Agriculture Food & WineUniversity of AdelaideUrrbraeSA5064Australia
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Phenomics and Bioinformatics Research CentreSchool of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaide5001Australia
| | - Helena Oakey
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
| | - Allison Pearson
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- ARC Centre of Excellence in Plant Energy BiologyThe University of AdelaidePMB 1, Glen OsmondAdelaideSouth Australia5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Sónia Negrão
- Division of Biological and Environmental Sciences and Engineering (BESE)King Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Saudi Arabia
| | - Rhiannon K. Schilling
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Julian Taylor
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
| | - David Jarvis
- Division of Biological and Environmental Sciences and Engineering (BESE)King Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Saudi Arabia
| | - Andy Timmins
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Stuart J. Roy
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Mark Tester
- Division of Biological and Environmental Sciences and Engineering (BESE)King Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Saudi Arabia
| | - Bettina Berger
- Australian Plant Phenomics FacilityThe Plant AcceleratorSchool of Agriculture Food & WineUniversity of AdelaideUrrbraeSA5064Australia
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
| | - Anton van den Hengel
- Australian Center for Visual TechnologiesUniversity of AdelaideAdelaideSA5005Australia
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22
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Pham AT, Maurer A, Pillen K, Brien C, Dowling K, Berger B, Eglinton JK, March TJ. Genome-wide association of barley plant growth under drought stress using a nested association mapping population. BMC PLANT BIOLOGY 2019; 19:134. [PMID: 30971212 PMCID: PMC6458831 DOI: 10.1186/s12870-019-1723-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 03/17/2019] [Indexed: 05/22/2023]
Abstract
BACKGROUND Barley (Hordeum vulgare L.) is the fourth most important cereal crop worldwide. Barley production is compromised by many abiotic stresses including drought. Wild barley is a valuable source of alleles that can improve adaptation of cultivated barley to drought stress. RESULTS In the present study, a nested association mapping population named HEB-25, consisting of 1420 BC1S3 lines that were developed by crossing 25 different wild barley accessions to the elite barley cultivar 'Barke', was evaluated under both control and drought-stressed conditions in the Australian Plant Phenomics Facility, University of Adelaide. Overall, 14 traits reflecting the performance of individual plants in each treatment were calculated from non-destructive imaging over time and destructive end-of-experiment measurements. For each trait, best linear unbiased estimators (BLUEs) were calculated and used for genome-wide association study (GWAS) analysis. Among the quantitative trait loci (QTL) identified for the 14 traits, many co-localise with known inflorescence and developmental genes. We identified a QTL on chromosome 4H where, under drought and control conditions, wild barley alleles increased biomass by 10 and 17% respectively compared to the Barke allele. CONCLUSIONS Across all traits, QTL which increased phenotypic values were identified, providing a wider range of genetic diversity for the improvement of drought tolerance in barley.
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Affiliation(s)
- Anh-Tung Pham
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
| | - Andreas Maurer
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Klaus Pillen
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Chris Brien
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
- Phenomics and Bioinformatics Research Centre, University of South Australia, North Terrace, Adelaide, SA 5000 Australia
- Australian Plant Phenomics Facility, The Plant Accelerator, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
| | - Kate Dowling
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
- Phenomics and Bioinformatics Research Centre, University of South Australia, North Terrace, Adelaide, SA 5000 Australia
- Australian Plant Phenomics Facility, The Plant Accelerator, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
| | - Bettina Berger
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
- Australian Plant Phenomics Facility, The Plant Accelerator, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
| | - Jason K. Eglinton
- Sugar Research Australia, 71378 Bruce Highway, Gordonvale, QLD 4865 Australia
| | - Timothy J. March
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
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23
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Sun J, Luu NS, Chen Z, Chen B, Cui X, Wu J, Zhang Z, Lu T. Generation and Characterization of a Foxtail Millet ( Setaria italica) Mutant Library. FRONTIERS IN PLANT SCIENCE 2019; 10:369. [PMID: 31001298 PMCID: PMC6455083 DOI: 10.3389/fpls.2019.00369] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 03/11/2019] [Indexed: 05/20/2023]
Abstract
Foxtail millet (Setaria italica) is attractive to plant scientists as a model plant because of several distinct characteristics, such as its short stature, rapid life cycle, sufficient seed production per plant, self-compatibility, true diploid nature, high photosynthetic efficiency, small genome size, and tolerance to abiotic and biotic stress. However, the study on the genetic resources of foxtail millet largely lag behind those of the other model plants such as Arabidopsis, rice and maize. Mutagenized populations cannot only create new germplasm resources, but also provide materials for gene function research. In this manuscript, an ethyl methanesulfonate (EMS)-induced foxtail millet population comprising ∼15,000 individual M1 lines was established. Total 1353 independent lines with diverse abnormal phenotypes of leaf color, plant morphologies and panicle shapes were identified in M2. Resequencing of sixteen randomly selected M2 plants showed an average estimated mutation density of 1 loci/213 kb. Moreover, we provided an example for rapid cloning of the WP1 gene by a map-based cloning method. A white panicle mutant, named as wp1.a, exhibited significantly reduced chlorophyll (Chl) and carotenoid contents in leaf and panicle. Map-based cloning results showed an eight-base pair deletion located at the sixth exon of wp1.a in LOC101786849, which caused the premature termination. WP1 encoded phytoene synthase. Moreover, the sequencing analysis and cross test verified that a white panicle mutant wp1.b was an allelic mutant of wp1.a. The filed phenotypic observation and gene cloning example showed that our foxtail millet EMS-induced mutant population would be used as an important resource for functional genomics studies of foxtail millet.
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Affiliation(s)
| | | | | | | | | | | | | | - Tiegang Lu
- *Correspondence: Zhiguo Zhang, Tiegang Lu,
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24
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Berry JC, Fahlgren N, Pokorny AA, Bart RS, Veley KM. An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping. PeerJ 2018; 6:e5727. [PMID: 30310752 PMCID: PMC6174877 DOI: 10.7717/peerj.5727] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 09/10/2018] [Indexed: 12/11/2022] Open
Abstract
High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.
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Affiliation(s)
- Jeffrey C. Berry
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
| | | | - Rebecca S. Bart
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
| | - Kira M. Veley
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
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25
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Feldman MJ, Ellsworth PZ, Fahlgren N, Gehan MA, Cousins AB, Baxter I. Components of Water Use Efficiency Have Unique Genetic Signatures in the Model C 4 Grass Setaria. PLANT PHYSIOLOGY 2018; 178:699-715. [PMID: 30093527 PMCID: PMC6181048 DOI: 10.1104/pp.18.00146] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 07/02/2018] [Indexed: 05/04/2023]
Abstract
Plant growth and water use are interrelated processes influenced by genetically controlled morphological and biochemical characteristics. Improving plant water use efficiency (WUE) to sustain growth in different environments is an important breeding objective that can improve crop yields and enhance agricultural sustainability. However, genetic improvement of WUE using traditional methods has proven difficult due to the low throughput and environmental heterogeneity of field settings. To overcome these limitations, this study utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific Setaria italica × Setaria viridis recombinant inbred line population at daily intervals in both well-watered and water-limited conditions. Our findings indicate that measurements of plant size and water use are correlated strongly in this system; therefore, a linear modeling approach was used to partition this relationship into predicted values of plant size given water use and deviations from this relationship at the genotype level. The resulting traits describing plant size, water use, and WUE all were heritable and responsive to soil water availability, allowing for a genetic dissection of the components of plant WUE under different watering treatments. Linkage mapping identified major loci underlying two different pleiotropic components of WUE. This study indicates that alleles controlling WUE derived from both wild and domesticated accessions can be utilized to predictably modulate trait values given a specified precipitation regime in the model C4 genus Setaria.
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Affiliation(s)
- Max J Feldman
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Patrick Z Ellsworth
- School of Biological Sciences, Washington State University, Pullman, Washington 99164
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Malia A Gehan
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Asaph B Cousins
- School of Biological Sciences, Washington State University, Pullman, Washington 99164
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
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26
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Ørsted M, Hoffmann AA, Rohde PD, Sørensen P, Kristensen TN. Strong impact of thermal environment on the quantitative genetic basis of a key stress tolerance trait. Heredity (Edinb) 2018; 122:315-325. [PMID: 30050062 DOI: 10.1038/s41437-018-0117-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 06/20/2018] [Accepted: 06/21/2018] [Indexed: 12/16/2022] Open
Abstract
Most organisms experience variable and sometimes suboptimal environments in their lifetime. While stressful environmental conditions are normally viewed as a strong selective force, they can also impact directly on the genetic basis of traits such as through environment-dependent gene action. Here, we used the Drosophila melanogaster Genetic Reference Panel to investigate the impact of developmental temperature on variance components and evolutionary potential of cold tolerance. We reared 166 lines at five temperatures and assessed cold tolerance of adult male flies from each line and environment. We show (1) that the expression of genetic variation for cold tolerance is highly dependent on developmental temperature, (2) that the genetic correlation of cold tolerance between environments decreases as developmental temperatures become more distinct, (3) that the correlation between cold tolerance at individual developmental temperatures and plasticity for cold tolerance differs across developmental temperatures, and even switches sign across the thermal developmental gradient, and (4) that evolvability decrease with increasing developmental temperatures. Our results show that the quantitative genetic basis of low temperature tolerance is environment specific. This conclusion is important for the understanding of evolution in variable thermal environments and for designing experiments aimed at pinpointing candidate genes and performing functional analyses of thermal resistance.
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Affiliation(s)
- Michael Ørsted
- Department of Chemistry and Bioscience, Section of Biology and Environmental Science, Aalborg University, Aalborg E, 9220, Denmark. .,Department of Bioscience, Section of Genetics, Ecology and Evolution, Aarhus University, Aarhus C, 8000, Denmark.
| | - Ary Anthony Hoffmann
- Department of Chemistry and Bioscience, Section of Biology and Environmental Science, Aalborg University, Aalborg E, 9220, Denmark.,School of Biosciences, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Palle Duun Rohde
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, 8830, Denmark
| | - Peter Sørensen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, 8830, Denmark
| | - Torsten Nygaard Kristensen
- Department of Chemistry and Bioscience, Section of Biology and Environmental Science, Aalborg University, Aalborg E, 9220, Denmark.,Department of Bioscience, Section of Genetics, Ecology and Evolution, Aarhus University, Aarhus C, 8000, Denmark
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27
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Hu H, Mauro-Herrera M, Doust AN. Domestication and Improvement in the Model C4 Grass, Setaria. FRONTIERS IN PLANT SCIENCE 2018; 9:719. [PMID: 29896214 PMCID: PMC5986938 DOI: 10.3389/fpls.2018.00719] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Accepted: 05/14/2018] [Indexed: 05/17/2023]
Abstract
Setaria viridis (green foxtail) and its domesticated relative S. italica (foxtail millet) are diploid C4 panicoid grasses that are being developed as model systems for studying grass genomics, genetics, development, and evolution. According to archeological evidence, foxtail millet was domesticated from green foxtail approximately 9,000 to 6,000 YBP in China. Under long-term human selection, domesticated foxtail millet developed many traits adapted to human cultivation and agricultural production. In comparison with its wild ancestor, foxtail millet has fewer vegetative branches, reduced grain shattering, delayed flowering time and less photoperiod sensitivity. Foxtail millet is the only present-day crop in the genus Setaria, although archeological records suggest that other species were domesticated and later abandoned in the last 10,000 years. We present an overview of domestication in foxtail millet, by reviewing recent studies on the genetic regulation of several domesticated traits in foxtail millet and discuss how the foxtail millet and green foxtail system could be further developed to both better understand its domestication history, and to provide more tools for future breeding efforts.
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Affiliation(s)
| | | | - Andrew N. Doust
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States
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28
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Tovar JC, Hoyer JS, Lin A, Tielking A, Callen ST, Elizabeth Castillo S, Miller M, Tessman M, Fahlgren N, Carrington JC, Nusinow DA, Gehan MA. Raspberry Pi-powered imaging for plant phenotyping. APPLICATIONS IN PLANT SCIENCES 2018; 6:e1031. [PMID: 29732261 PMCID: PMC5895192 DOI: 10.1002/aps3.1031] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 10/24/2017] [Indexed: 05/22/2023]
Abstract
PREMISE OF THE STUDY Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data. METHODS AND RESULTS We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV. CONCLUSIONS This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.
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Affiliation(s)
- Jose C. Tovar
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
| | - J. Steen Hoyer
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
- Computational and Systems Biology ProgramWashington University in St. LouisOne Brookings DriveSt. LouisMissouri63130USA
| | - Andy Lin
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
| | - Allison Tielking
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
| | - Steven T. Callen
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
| | | | - Michael Miller
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
| | - Monica Tessman
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
| | - Noah Fahlgren
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
| | - James C. Carrington
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
| | - Dmitri A. Nusinow
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
| | - Malia A. Gehan
- Donald Danforth Plant Science Center975 North Warson RoadSt. LouisMissouri63132USA
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29
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Tovar JC, Hoyer JS, Lin A, Tielking A, Callen ST, Elizabeth Castillo S, Miller M, Tessman M, Fahlgren N, Carrington JC, Nusinow DA, Gehan MA. Raspberry Pi-powered imaging for plant phenotyping. APPLICATIONS IN PLANT SCIENCES 2018. [PMID: 29732261 DOI: 10.1002/aps31031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
PREMISE OF THE STUDY Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data. METHODS AND RESULTS We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV. CONCLUSIONS This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.
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Affiliation(s)
- Jose C Tovar
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
| | - J Steen Hoyer
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
- Computational and Systems Biology Program Washington University in St. Louis One Brookings Drive St. Louis Missouri 63130 USA
| | - Andy Lin
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
| | - Allison Tielking
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
| | - Steven T Callen
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
| | - S Elizabeth Castillo
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
| | - Michael Miller
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
| | - Monica Tessman
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
| | - Noah Fahlgren
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
| | - James C Carrington
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
| | - Dmitri A Nusinow
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
| | - Malia A Gehan
- Donald Danforth Plant Science Center 975 North Warson Road St. Louis Missouri 63132 USA
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30
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Banan D, Paul RE, Feldman MJ, Holmes MW, Schlake H, Baxter I, Jiang H, Leakey AD. High-fidelity detection of crop biomass quantitative trait loci from low-cost imaging in the field. PLANT DIRECT 2018; 2:e00041. [PMID: 31245708 PMCID: PMC6508524 DOI: 10.1002/pld3.41] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 01/17/2018] [Accepted: 01/18/2018] [Indexed: 05/20/2023]
Abstract
Field-based, rapid, and nondestructive techniques for assessing plant productivity are needed to accelerate the discovery of genotype-to-phenotype relationships in next-generation biomass grass crops. The use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships in a field-grown grass crop. Plant area index (PAI) estimated from below-canopy hemispherical images, as well as a suite of thirteen traits assessed by manual destructive harvests, were measured in a Setaria recombinant inbred line mapping population segregating for aboveground productivity and architecture. A significant correlation was observed between PAI and biomass production across the population at maturity (r 2 = .60), as well as for select diverse genotypes sampled repeatedly over the growing season (r 2 = .79). Twenty-seven quantitative trait loci (QTL) were detected for manually collected traits associated with biomass production. Of these, twenty-one were found in four clusters of colocalized QTL. Analysis of image-based estimates of PAI successfully identified all four QTL hot spots for biomass production. QTL for PAI had greater overlap with those detected for traits associated with biomass production than with those for plant architecture and biomass partitioning. Hemispherical imaging is an affordable and scalable method, which demonstrates how high-throughput phenotyping can identify QTL related to biomass production of field trials in place of destructive harvests that are labor, time, and material intensive.
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Affiliation(s)
- Darshi Banan
- University of Illinois at Urbana‐ChampaignUrbanaILUSA
| | | | | | | | | | - Ivan Baxter
- USDA‐ARSDonald Danforth Plant Science CenterSt. LouisMOUSA
| | - Hui Jiang
- Donald Danforth Plant Science CenterSt. LouisMOUSA
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31
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Gehan MA, Fahlgren N, Abbasi A, Berry JC, Callen ST, Chavez L, Doust AN, Feldman MJ, Gilbert KB, Hodge JG, Hoyer JS, Lin A, Liu S, Lizárraga C, Lorence A, Miller M, Platon E, Tessman M, Sax T. PlantCV v2: Image analysis software for high-throughput plant phenotyping. PeerJ 2017; 5:e4088. [PMID: 29209576 PMCID: PMC5713628 DOI: 10.7717/peerj.4088] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 11/03/2017] [Indexed: 12/11/2022] Open
Abstract
Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.
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Affiliation(s)
- Malia A. Gehan
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Arash Abbasi
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Jeffrey C. Berry
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Steven T. Callen
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Monsanto Company, St. Louis, MO, United States of America
| | - Leonardo Chavez
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Andrew N. Doust
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - Max J. Feldman
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Kerrigan B. Gilbert
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - John G. Hodge
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - J. Steen Hoyer
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Computational and Systems Biology Program, Washington University in St. Louis, St. Louis, MO, United States of America
| | - Andy Lin
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Unidev, St. Louis, MO, United States of America
| | - Suxing Liu
- Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, United States of America
- Current affiliation: Department of Plant Biology, University of Georgia, Athens, GA, United States of America
| | - César Lizárraga
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: CiBO Technologies, Cambridge, MA, United States of America
| | - Argelia Lorence
- Arkansas Biosciences Institute, Department of Chemistry and Physics, Arkansas State University, Jonesboro, AR, United States of America
| | - Michael Miller
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Department of Agronomy and Horticulture, Center for Plant Science Innovation, Beadle Center for Biotechnology, University of Nebraska - Lincoln, Lincoln, NE, United States of America
| | | | - Monica Tessman
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - Tony Sax
- Missouri University of Science and Technology, Rolla, MO, United States of America
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32
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Acharya BR, Roy Choudhury S, Estelle AB, Vijayakumar A, Zhu C, Hovis L, Pandey S. Optimization of Phenotyping Assays for the Model Monocot Setaria viridis. FRONTIERS IN PLANT SCIENCE 2017; 8:2172. [PMID: 29312412 PMCID: PMC5743732 DOI: 10.3389/fpls.2017.02172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 12/11/2017] [Indexed: 05/02/2023]
Abstract
Setaria viridis (green foxtail) is an important model plant for the study of C4 photosynthesis in panicoid grasses, and is fast emerging as a system of choice for the study of plant development, domestication, abiotic stress responses and evolution. Basic research findings in Setaria are expected to advance research not only in this species and its close relative S. italica (foxtail millet), but also in other panicoid grasses, many of which are important food or bioenergy crops. Here we report on the standardization of multiple growth and development assays for S. viridis under controlled conditions, and in response to several phytohormones and abiotic stresses. We optimized these assays at three different stages of the plant's life: seed germination and post-germination growth using agar plate-based assays, early seedling growth and development using germination pouch-based assays, and adult plant growth and development under environmentally controlled growth chambers and greenhouses. These assays will be useful for the community to perform large scale phenotyping analyses, mutant screens, comparative physiological analysis, and functional characterization of novel genes of Setaria or other related agricultural crops. Precise description of various growth conditions, effective treatment conditions and description of the resultant phenotypes will help expand the use of S. viridis as an effective model system.
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