1
|
Concepcion JS, Noble AD, Thompson AM, Dong Y, Olson EL. Genomic regions influencing the hyperspectral phenome of deoxynivalenol infected wheat. Sci Rep 2024; 14:19340. [PMID: 39164367 PMCID: PMC11336138 DOI: 10.1038/s41598-024-69830-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 08/09/2024] [Indexed: 08/22/2024] Open
Abstract
The quantitative nature of fusarium head blight (FHB) resistance requires further exploration of the wheat genome to identify regions conferring resistance. In this study, we explored the application of hyperspectral imaging of Fusarium-infected wheat kernels and identified regions of the wheat genome contributing significantly to the accumulation of Deoxynivalenol (DON) mycotoxin. Strong correlations were identified between hyperspectral reflectance values for 204 wavebands in the 397-673 nm range and DON mycotoxin. Dimensionality reduction using principal components was performed for all 204 wavebands and 38 sliding windows across the range of wavebands. The first principal component (PC1) of all 204 wavebands explained 70% of the total variation in waveband reflectance values and was highly correlated with DON mycotoxin. PC1 was used as a phenotype in a genome wide association study and a large effect QTL on chromosome 2D was identified for PC1 of all wavebands as well as nearly all 38 sliding windows. The allele contributing variation in PC1 values also led to a substantial reduction in DON. The 2D polymorphism affecting DON levels localized to the exon of TraesCS2D02G524600 which is upregulated in wheat spike and rachis tissues during FHB infection. This work demonstrates the value of hyperspectral imaging as a correlated trait for investigating the genetic basis of resistance and developing wheat varieties with enhanced resistance to FHB.
Collapse
Affiliation(s)
- Jonathan S Concepcion
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA
| | - Amanda D Noble
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA
| | - Addie M Thompson
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA
| | - Yanhong Dong
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, 55108, USA
| | - Eric L Olson
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA.
| |
Collapse
|
2
|
Claussen J, Wittenberg T, Uhlmann N, Gerth S. "Chamber #8" - a holistic approach of high-throughput non-destructive assessment of plant roots. FRONTIERS IN PLANT SCIENCE 2024; 14:1269005. [PMID: 38239230 PMCID: PMC10794641 DOI: 10.3389/fpls.2023.1269005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/01/2023] [Indexed: 01/22/2024]
Abstract
Introduction In the past years, it has been observed that the breeding of plants has become more challenging, as the visible difference in phenotypic data is much smaller than decades ago. With the ongoing climate change, it is necessary to breed crops that can cope with shifting climatic conditions. To select good breeding candidates for the future, phenotypic experiments can be conducted under climate-controlled conditions. Above-ground traits can be assessed with different optical sensors, but for the root growth, access to non-destructively measured traits is much more challenging. Even though MRI or CT imaging techniques have been established in the past years, they rely on an adequate infrastructure for the automatic handling of the pots as well as the controlled climate. Methods To address both challenges simultaneously, the non-destructive imaging of plant roots combined with a highly automated and standardized mid-throughput approach, we developed a workflow and an integrated scanning facility to study root growth. Our "chamber #8" contains a climate chamber, a material flow control, an irrigation system, an X-ray system, a database for automatic data collection, and post-processing. The goals of this approach are to reduce the human interaction with the various components of the facility to a minimum on one hand, and to automate and standardize the complete process from plant care via measurements to root trait calculation on the other. The user receives standardized phenotypic traits and properties that were collected objectively. Results The proposed holistic approach allows us to study root growth of plants in a field-like substrate non-destructively over a defined period and to calculate phenotypic traits of root architecture. For different crops, genotypic differences can be observed in response to climatic conditions which have already been applied to a wide variety of root structures, such as potatoes, cassava, or corn. Discussion It enables breeders and scientists non-destructive access to root traits. Additionally, due to the non-destructive nature of X-ray computed tomography, the analysis of time series for root growing experiments is possible and enables the observation of kinetic traits. Furthermore, using this automation scheme for simultaneously controlled plant breeding and non-destructive testing reduces the involvement of human resources.
Collapse
Affiliation(s)
- Joelle Claussen
- Fraunhofer Institute for Integrated Circuits (IIS), Department Development Center X-ray Technology, Fuerth, Germany
| | - Thomas Wittenberg
- Fraunhofer Institute for Integrated Circuits (IIS), Department Development Center X-ray Technology, Fuerth, Germany
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Department for Visual Computing, Erlangen, Germany
| | - Norman Uhlmann
- Fraunhofer Institute for Integrated Circuits (IIS), Department Development Center X-ray Technology, Fuerth, Germany
| | - Stefan Gerth
- Fraunhofer Institute for Integrated Circuits (IIS), Department Development Center X-ray Technology, Fuerth, Germany
| |
Collapse
|
3
|
Luo X, Yang Y, Lin X, Xiao J. Deciphering spike architecture formation towards yield improvement in wheat. J Genet Genomics 2023; 50:835-845. [PMID: 36907353 DOI: 10.1016/j.jgg.2023.02.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/28/2023] [Accepted: 02/28/2023] [Indexed: 03/12/2023]
Abstract
Wheat is the most widely grown crop globally, providing 20% of the daily consumed calories and protein content around the world. With the growing global population and frequent occurrence of extreme weather caused by climate change, ensuring adequate wheat production is essential for food security. The architecture of the inflorescence plays a crucial role in determining the grain number and size, which is a key trait for improving yield. Recent advances in wheat genomics and gene cloning techniques have improved our understanding of wheat spike development and its applications in breeding practices. Here, we summarize the genetic regulation network governing wheat spike formation, the strategies used for identifying and studying the key factors affecting spike architecture, and the progress made in breeding applications. Additionally, we highlight future directions that will aid in the regulatory mechanistic study of wheat spike determination and targeted breeding for grain yield improvement.
Collapse
Affiliation(s)
- Xumei Luo
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiman Yang
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Xuelei Lin
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Jun Xiao
- Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
| |
Collapse
|
4
|
Xiao L, Fang Y, Zhang H, Quan M, Zhou J, Li P, Wang D, Ji L, Ingvarsson PK, Wu HX, El-Kassaby YA, Du Q, Zhang D. Natural variation in the prolyl 4-hydroxylase gene PtoP4H9 contributes to perennial stem growth in Populus. THE PLANT CELL 2023; 35:4046-4065. [PMID: 37522322 PMCID: PMC10615208 DOI: 10.1093/plcell/koad212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 08/01/2023]
Abstract
Perennial trees must maintain stem growth throughout their entire lifespan to progressively increase in size as they age. The overarching question of the molecular mechanisms that govern stem perennial growth in trees remains largely unanswered. Here we deciphered the genetic architecture that underlies perennial growth trajectories using genome-wide association studies (GWAS) for measures of growth traits across years in a natural population of Populus tomentosa. By analyzing the stem growth trajectory, we identified PtoP4H9, encoding prolyl 4-hydroxylase 9, which is responsible for the natural variation in the growth rate of diameter at breast height (DBH) across years. Quantifying the dynamic genetic contribution of PtoP4H9 loci to stem growth showed that PtoP4H9 played a pivotal role in stem growth regulation. Spatiotemporal expression analysis showed that PtoP4H9 was highly expressed in cambium tissues of poplars of various ages. Overexpression and knockdown of PtoP4H9 revealed that it altered cell expansion to regulate cell wall modification and mechanical characteristics, thereby promoting stem growth in Populus. We showed that natural variation in PtoP4H9 occurred in a BASIC PENTACYSTEINE transcription factor PtoBPC1-binding promoter element controlling PtoP4H9 expression. The geographic distribution of PtoP4H9 allelic variation was consistent with the modes of selection among populations. Altogether, our study provides important genetic insights into dynamic stem growth in Populus, and we confirmed PtoP4H9 as a potential useful marker for breeding or genetic engineering of poplars.
Collapse
Affiliation(s)
- Liang Xiao
- School of Landscape Architecture, Beijing University of Agriculture, Beijing 102206,China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
| | - Yuanyuan Fang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
| | - He Zhang
- State Key Laboratory of Protein and Plant Gene Research, School of Advanced Agricultural Sciences, Peking University, Beijing 100871,China
| | - Mingyang Quan
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
| | - Jiaxuan Zhou
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
| | - Peng Li
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
| | - Dan Wang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
| | - Li Ji
- MOE Engineering Research Center of Forestry Biomass Materials and Bioenergy, Beijing Forestry University, Beijing 100083,China
| | - Pär K Ingvarsson
- Linnean Center for Plant Biology, Department of Plant Biology, Swedish University of Agricultural Sciences, Box 7080, SE-750 07 Uppsala,Sweden
| | - Harry X Wu
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Science, 90183 Umeå,Sweden
| | - Yousry A El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, Vancouver, British Columbia V6T 1Z4,Canada
| | - Qingzhang Du
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083,China
| | - Deqiang Zhang
- School of Landscape Architecture, Beijing University of Agriculture, Beijing 102206,China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083,China
| |
Collapse
|
5
|
Guo J, Liu S, Jing D, He K, Zhang Y, Li M, Qi J, Wang Z. Genotypic variation in field-grown maize eliminates trade-offs between resistance, tolerance and growth in response to high pressure from the Asian corn borer. PLANT, CELL & ENVIRONMENT 2023; 46:3072-3089. [PMID: 36207806 DOI: 10.1111/pce.14458] [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: 07/26/2022] [Revised: 09/28/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
Insect herbivory challenges plant survival, and coordination of the interactions between growth, herbivore resistance/tolerance is a key problem faced by plants. Based on field experiments into resistance to the Asian corn borer (ACB, Ostrinia furnacalis), we selected 10 inbred maize lines, of which five were resistant and five were susceptible to ACB. We conducted ACB larval bioassays, analysed defensive chemicals, phytohormones, and relative gene expression using RNA-seq and qPCR as well as agronomic traits, and found resistant lines had weaker inducibility, but were more resistant after ACB attack than susceptible lines. Resistance was related to high levels of major benzoxazinoids, but was not related to induced levels of JA or JA-Ile. Following combination analyses of transcriptome, metabolome and larval performance data, we discovered three benzoxazinoids biosynthesis-related transcription factors, NAC60, WRKY1 and WRKY46. Protoplast transformation analysis suggested that these may regulate maize defence-growth trade-offs by increasing levels of benzoxazinoids, JA and SA but decreasing IAA. Moreover, the resistance/tolerance-growth trade-offs were not observed in the 10 lines, and genotype-specific metabolic and genetic features probably eliminated the trade-offs. This study highlights the possibility of breeding maize varieties simultaneously with improved defences and higher yield under complex field conditions.
Collapse
Affiliation(s)
- Jingfei Guo
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, MOA-CABI Joint Laboratory for Bio-safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shen Liu
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, MOA-CABI Joint Laboratory for Bio-safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Dapeng Jing
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, MOA-CABI Joint Laboratory for Bio-safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Kanglai He
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, MOA-CABI Joint Laboratory for Bio-safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yongjun Zhang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, MOA-CABI Joint Laboratory for Bio-safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mingshun Li
- Department of Economic Plants and Biotechnology, Yunnan Key Laboratory for Wild Plant Resources, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China
| | - Jinfeng Qi
- Department of Economic Plants and Biotechnology, Yunnan Key Laboratory for Wild Plant Resources, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China
| | - Zhenying Wang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, MOA-CABI Joint Laboratory for Bio-safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| |
Collapse
|
6
|
Gao J, Hu X, Gao C, Chen G, Feng H, Jia Z, Zhao P, Yu H, Li H, Geng Z, Fu J, Zhang J, Cheng Y, Yang B, Pang Z, Xiang D, Jia J, Su H, Mao H, Lan C, Chen W, Yan W, Gao L, Yang W, Li Q. Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat. PLANT BIOTECHNOLOGY JOURNAL 2023; 21:1966-1977. [PMID: 37392004 PMCID: PMC10502759 DOI: 10.1111/pbi.14104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/11/2023] [Accepted: 06/07/2023] [Indexed: 07/02/2023]
Abstract
Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.
Collapse
Affiliation(s)
- Jie Gao
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Xin Hu
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Chunyan Gao
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Guang Chen
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Hui Feng
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Zhen Jia
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Peimin Zhao
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Haiyang Yu
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Huaiwen Li
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Zedong Geng
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Jingbo Fu
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Jun Zhang
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Yikeng Cheng
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Bo Yang
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Zhanghan Pang
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Daoquan Xiang
- Aquatic and Crop Resource DevelopmentNational Research Council CanadaSaskatoonSaskatchewanCanada
| | - Jizeng Jia
- Institute of Crop SciencesChinese Academy of Crop Sciences (CAAS)BeijingChina
| | - Handong Su
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Hailiang Mao
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Caixia Lan
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Wei Chen
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Wenhao Yan
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Lifeng Gao
- Institute of Crop SciencesChinese Academy of Crop Sciences (CAAS)BeijingChina
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Qiang Li
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
- The Center of Crop NanobiotechnologyHuazhong Agricultural UniversityWuhanChina
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Shi R, Seiler C, Knoch D, Junker A, Altmann T. Integrated phenotyping of root and shoot growth dynamics in maize reveals specific interaction patterns in inbreds and hybrids and in response to drought. FRONTIERS IN PLANT SCIENCE 2023; 14:1233553. [PMID: 37719228 PMCID: PMC10502302 DOI: 10.3389/fpls.2023.1233553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/07/2023] [Indexed: 09/19/2023]
Abstract
In recent years, various automated methods for plant phenotyping addressing roots or shoots have been developed and corresponding platforms have been established to meet the diverse requirements of plant research and breeding. However, most platforms are only either able to phenotype shoots or roots of plants but not both simultaneously. This substantially limits the opportunities offered by a joint assessment of the growth and development dynamics of both organ systems, which are highly interdependent. In order to overcome these limitations, a root phenotyping installation was integrated into an existing automated non-invasive high-throughput shoot phenotyping platform. Thus, the amended platform is now capable of conducting high-throughput phenotyping at the whole-plant level, and it was used to assess the vegetative root and shoot growth dynamics of five maize inbred lines and four hybrids thereof, as well as the responses of five inbred lines to progressive drought stress. The results showed that hybrid vigour (heterosis) occurred simultaneously in roots and shoots and was detectable as early as 4 days after transplanting (4 DAT; i.e., 8 days after seed imbibition) for estimated plant height (EPH), total root length (TRL), and total root volume (TRV). On the other hand, growth dynamics responses to progressive drought were different in roots and shoots. While TRV was significantly reduced 10 days after the onset of the water deficit treatment, the estimated shoot biovolume was significantly reduced about 6 days later, and EPH showed a significant decrease even 2 days later (8 days later than TRV) compared with the control treatment. In contrast to TRV, TRL initially increased in the water deficit period and decreased much later (not earlier than 16 days after the start of the water deficit treatment) compared with the well-watered plants. This may indicate an initial response of the plants to water deficit by forming longer but thinner roots before growth was inhibited by the overall water deficit. The magnitude and the dynamics of the responses were genotype-dependent, as well as under the influence of the water consumption, which was related to plant size.
Collapse
Affiliation(s)
- Rongli Shi
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | - Christiane Seiler
- Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute (JKI), Quedlinburg, Germany
| | - Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | - Astrid Junker
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| |
Collapse
|
10
|
Susmitha P, Kumar P, Yadav P, Sahoo S, Kaur G, Pandey MK, Singh V, Tseng TM, Gangurde SS. Genome-wide association study as a powerful tool for dissecting competitive traits in legumes. FRONTIERS IN PLANT SCIENCE 2023; 14:1123631. [PMID: 37645459 PMCID: PMC10461012 DOI: 10.3389/fpls.2023.1123631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 08/31/2023]
Abstract
Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.
Collapse
Affiliation(s)
- Pusarla Susmitha
- Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Andhra Pradesh, India
| | - Pawan Kumar
- Department of Genetics and Plant Breeding, College of Agriculture, Chaudhary Charan Singh (CCS) Haryana Agricultural University, Hisar, India
| | - Pankaj Yadav
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Rajasthan, India
| | - Smrutishree Sahoo
- Department of Genetics and Plant Breeding, School of Agriculture, Gandhi Institute of Engineering and Technology (GIET) University, Odisha, India
| | - Gurleen Kaur
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Manish K. Pandey
- Department of Genomics, Prebreeding and Bioinformatics, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Varsha Singh
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Te Ming Tseng
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Sunil S. Gangurde
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
| |
Collapse
|
11
|
Virus-Induced Gene Silencing (VIGS): A Powerful Tool for Crop Improvement and Its Advancement towards Epigenetics. Int J Mol Sci 2023; 24:ijms24065608. [PMID: 36982682 PMCID: PMC10057534 DOI: 10.3390/ijms24065608] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 01/26/2023] [Accepted: 02/02/2023] [Indexed: 03/17/2023] Open
Abstract
Virus-induced gene silencing (VIGS) is an RNA-mediated reverse genetics technology that has evolved into an indispensable approach for analyzing the function of genes. It downregulates endogenous genes by utilizing the posttranscriptional gene silencing (PTGS) machinery of plants to prevent systemic viral infections. Based on recent advances, VIGS can now be used as a high-throughput tool that induces heritable epigenetic modifications in plants through the viral genome by transiently knocking down targeted gene expression. As a result of the progression of DNA methylation induced by VIGS, new stable genotypes with desired traits are being developed in plants. In plants, RNA-directed DNA methylation (RdDM) is a mechanism where epigenetic modifiers are guided to target loci by small RNAs, which play a major role in the silencing of the target gene. In this review, we described the molecular mechanisms of DNA and RNA-based viral vectors and the knowledge obtained through altering the genes in the studied plants that are not usually accessible to transgenic techniques. We showed how VIGS-induced gene silencing can be used to characterize transgenerational gene function(s) and altered epigenetic marks, which can improve future plant breeding programs.
Collapse
|
12
|
Wang W, Guo W, Le L, Yu J, Wu Y, Li D, Wang Y, Wang H, Lu X, Qiao H, Gu X, Tian J, Zhang C, Pu L. Integration of high-throughput phenotyping, GWAS, and predictive models reveals the genetic architecture of plant height in maize. MOLECULAR PLANT 2023; 16:354-373. [PMID: 36447436 DOI: 10.1016/j.molp.2022.11.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/05/2022] [Accepted: 11/27/2022] [Indexed: 06/16/2023]
Abstract
Plant height (PH) is an essential trait in maize (Zea mays) that is tightly associated with planting density, biomass, lodging resistance, and grain yield in the field. Dissecting the dynamics of maize plant architecture will be beneficial for ideotype-based maize breeding and prediction, as the genetic basis controlling PH in maize remains largely unknown. In this study, we developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages. Time-resolved i-traits with novel digital phenotypes and complex correlations with agronomic traits were characterized to reveal the dynamics of maize growth. An i-trait-based genome-wide association study identified 4945 trait-associated SNPs, 2603 genetic loci, and 1974 corresponding candidate genes. We found that rapid growth of maize plants occurs mainly at two developmental stages, stage 2 (S2) to S3 and S5 to S6, accounting for the final PH indicators. By integrating the PH-association network with the transcriptome profiles of specific internodes, we revealed 13 hub genes that may play vital roles during rapid growth. The candidate genes and novel i-traits identified at multiple growth stages may be used as potential indicators for final PH in maize. One candidate gene, ZmVATE, was functionally validated and shown to regulate PH-related traits in maize using genetic mutation. Furthermore, machine learning was used to build predictive models for final PH based on i-traits, and their performance was assessed across developmental stages. Moderate, strong, and very strong correlations between predictions and experimental datasets were achieved from the early S4 (tenth-leaf) stage. Colletively, our study provides a valuable tool for dissecting the spatiotemporal formation of specific internodes and the genetic architecture of PH, as well as resources and predictive models that are useful for molecular design breeding and predicting maize varieties with ideal plant architectures.
Collapse
Affiliation(s)
- Weixuan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China
| | - Weijun Guo
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Liang Le
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jia Yu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yue Wu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Dongwei Li
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yifan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Huan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xiaoduo Lu
- Institute of Molecular Breeding for Maize, Qilu Normal University, Jinan 250200, China
| | - Hong Qiao
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA; Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Xiaofeng Gu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jian Tian
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Chunyi Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Sanya Institute, Hainan Academy of Agricultural Sciences, Sanya 572000, China.
| | - Li Pu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China.
| |
Collapse
|
13
|
Zandberg JD, Fernandez CT, Danilevicz MF, Thomas WJW, Edwards D, Batley J. The Global Assessment of Oilseed Brassica Crop Species Yield, Yield Stability and the Underlying Genetics. PLANTS (BASEL, SWITZERLAND) 2022; 11:2740. [PMID: 36297764 PMCID: PMC9610009 DOI: 10.3390/plants11202740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/08/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
The global demand for oilseeds is increasing along with the human population. The family of Brassicaceae crops are no exception, typically harvested as a valuable source of oil, rich in beneficial molecules important for human health. The global capacity for improving Brassica yield has steadily risen over the last 50 years, with the major crop Brassica napus (rapeseed, canola) production increasing to ~72 Gt in 2020. In contrast, the production of Brassica mustard crops has fluctuated, rarely improving in farming efficiency. The drastic increase in global yield of B. napus is largely due to the demand for a stable source of cooking oil. Furthermore, with the adoption of highly efficient farming techniques, yield enhancement programs, breeding programs, the integration of high-throughput phenotyping technology and establishing the underlying genetics, B. napus yields have increased by >450 fold since 1978. Yield stability has been improved with new management strategies targeting diseases and pests, as well as by understanding the complex interaction of environment, phenotype and genotype. This review assesses the global yield and yield stability of agriculturally important oilseed Brassica species and discusses how contemporary farming and genetic techniques have driven improvements.
Collapse
Affiliation(s)
- Jaco D. Zandberg
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | | | - Monica F. Danilevicz
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | - William J. W. Thomas
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | - David Edwards
- Center for Applied Bioinformatics, School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | - Jacqueline Batley
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| |
Collapse
|
14
|
Rössner C, Lotz D, Becker A. VIGS Goes Viral: How VIGS Transforms Our Understanding of Plant Science. ANNUAL REVIEW OF PLANT BIOLOGY 2022; 73:703-728. [PMID: 35138878 DOI: 10.1146/annurev-arplant-102820-020542] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Virus-induced gene silencing (VIGS) has developed into an indispensable approach to gene function analysis in a wide array of species, many of which are not amenable to stable genetic transformation. VIGS utilizes the posttranscriptional gene silencing (PTGS) machinery of plants to restrain viral infections systemically and is used to downregulate the plant's endogenous genes. Here, we review the molecular mechanisms of DNA- and RNA-virus-based VIGS, its inherent connection to PTGS, and what is known about the systemic spread of silencing. Recently, VIGS-based technologies have been expanded to enable not only gene silencing but also overexpression [virus-induced overexpression (VOX)], genome editing [virus-induced genome editing (VIGE)], and host-induced gene silencing (HIGS). These techniques expand the genetic toolbox for nonmodel organisms even more. Further, we illustrate the versatility of VIGS and the methods derived from it in elucidating molecular mechanisms, using tomato fruit ripening and programmed cell death as examples. Finally, we discuss challenges of and future perspectives on the use of VIGS to advance gene function analysis in nonmodel plants in the postgenomic era.
Collapse
Affiliation(s)
- Clemens Rössner
- Institute of Botany, Justus-Liebig University Gießen, Gießen, Germany;
| | - Dominik Lotz
- Institute of Botany, Justus-Liebig University Gießen, Gießen, Germany;
| | - Annette Becker
- Institute of Botany, Justus-Liebig University Gießen, Gießen, Germany;
| |
Collapse
|
15
|
The Use of DArTseq Technology to Identify New SNP and SilicoDArT Markers Related to the Yield-Related Traits Components in Maize. Genes (Basel) 2022; 13:genes13050848. [PMID: 35627233 PMCID: PMC9142088 DOI: 10.3390/genes13050848] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 02/04/2023] Open
Abstract
In the last decade, many scientists have used molecular biology methods in their research to locate the grain-yield-determining loci and yield structure characteristics in maize. Large-scale molecular analyses in maize do not only focus on the identification of new markers and quantitative trait locus (QTL) regions. DNA analysis in the selection of parental components for heterotic crosses is a very important tool for breeders. The aim of this research was to identify and select new markers for maize (SNP and SilicoDArT) linked to genes influencing the size of the yield components in maize. The plant material used for the research was 186 inbred maize lines. The field experiment was established in twolocations. The yield and six yield components were analyzed. For identification of SNP and SilicoDArT markers related to the yield and yield components, next-generation sequencing was used. As a result of the biometric measurements analysis, differentiation in the average elevation of the analyzed traits for the lines in both locations was found. The above-mentioned results indicate the existence of genotype–environment interactions. The analysis of variance for the observed quality between genotypes indicated a statistically significant differentiation between genotypes and a statistically significant differentiation for all the observed properties betweenlocations. A canonical variable analysis was applied to present a multi-trait assessment of the similarity of the tested maize genotypes in a lower number of dimensions with the lowest possible loss of information. No grouping of lines due to the analyzed was observed. As a result of next-generation sequencing, the molecular markers SilicoDArT (53,031) and SNP (28,571) were obtained. The genetic distance between the analyzed lines was estimated on the basis of these markers. Out of 81,602 identified SilicoDArT and SNP markers, 15,409 (1559 SilicoDArT and 13,850 SNPs) significantly related to the analyzed yield components were selected as a result of association mapping. The greatest numbers of molecular markers were associated with cob length (1203), cob diameter (1759), core length (1201) and core diameter (2326). From 15,409 markers significantly related to the analyzed traits of the yield components, 18 DArT markers were selected, which were significant for the same four traits (cob length, cob diameter, core length, core diameter) in both Kobierzyce and Smolice. These markers were used for physical mapping. As a result of the analyses, it was found that 6 out of 18 (1818; 14,506; 2317; 3233; 11,657; 12,812) identified markers are located inside genes. These markers are located on chromosomes 8, 9, 7, 3, 5, and 1, respectively.
Collapse
|
16
|
Xiao Q, Bai X, Zhang C, He Y. Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review. J Adv Res 2022; 35:215-230. [PMID: 35003802 PMCID: PMC8721248 DOI: 10.1016/j.jare.2021.05.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 05/05/2021] [Accepted: 05/09/2021] [Indexed: 01/22/2023] Open
Abstract
Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In recent years, genome-wide association studies (GWASs) have been applied extensively to interpret relationships between genes and traits. Successful GWAS application requires comprehensive genomic and phenotypic data from large populations. Although multiple high-throughput DNA sequencing approaches are available for the generation of genomics data, the capacity to generate high-quality phenotypic data is lagging far behind. Traditional methods for plant phenotyping mostly rely on manual measurements, which are laborious, inaccurate, and time-consuming, greatly impairing the acquisition of phenotypic data from large populations. In contrast, high-throughput phenotyping has unique advantages, facilitating rapid, non-destructive, and high-throughput detection, and, in turn, addressing the shortcomings of traditional methods. Aim of Review: This review summarizes the current status with regard to the integration of high-throughput phenotyping and GWAS in plants, in addition to discussing the inherent challenges and future prospects. Key Scientific Concepts of Review: High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance the unraveling of genetic structures of complex plant traits. In conclusion, high-throughput phenotyping integration with GWAS could facilitate the revealing of coding information in plant genomes.
Collapse
Affiliation(s)
- Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| |
Collapse
|
17
|
Langstroff A, Heuermann MC, Stahl A, Junker A. Opportunities and limits of controlled-environment plant phenotyping for climate response traits. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1-16. [PMID: 34302493 PMCID: PMC8741719 DOI: 10.1007/s00122-021-03892-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 06/17/2021] [Indexed: 05/19/2023]
Abstract
Rising temperatures and changing precipitation patterns will affect agricultural production substantially, exposing crops to extended and more intense periods of stress. Therefore, breeding of varieties adapted to the constantly changing conditions is pivotal to enable a quantitatively and qualitatively adequate crop production despite the negative effects of climate change. As it is not yet possible to select for adaptation to future climate scenarios in the field, simulations of future conditions in controlled-environment (CE) phenotyping facilities contribute to the understanding of the plant response to special stress conditions and help breeders to select ideal genotypes which cope with future conditions. CE phenotyping facilities enable the collection of traits that are not easy to measure under field conditions and the assessment of a plant's phenotype under repeatable, clearly defined environmental conditions using automated, non-invasive, high-throughput methods. However, extrapolation and translation of results obtained under controlled environments to field environments is ambiguous. This review outlines the opportunities and challenges of phenotyping approaches under controlled environments complementary to conventional field trials. It gives an overview on general principles and introduces existing phenotyping facilities that take up the challenge of obtaining reliable and robust phenotypic data on climate response traits to support breeding of climate-adapted crops.
Collapse
Affiliation(s)
- Anna Langstroff
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich Buff-Ring 26, 35392, Giessen, Germany
| | - Marc C Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, OT Gatersleben, 06466, Seeland, Germany
| | - Andreas Stahl
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich Buff-Ring 26, 35392, Giessen, Germany
- Institute for Resistance Research and Stress Tolerance, Federal Research Centre for Cultivated Plants, Julius Kühn-Institut (JKI), Erwin-Baur-Strasse 27, 06484, Quedlinburg, Germany
| | - Astrid Junker
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, OT Gatersleben, 06466, Seeland, Germany.
| |
Collapse
|
18
|
Li K, Wang J, Kuang L, Tian Z, Wang X, Dun X, Tu J, Wang H. Genome-wide association study and transcriptome analysis reveal key genes affecting root growth dynamics in rapeseed. BIOTECHNOLOGY FOR BIOFUELS 2021; 14:178. [PMID: 34507599 PMCID: PMC8431925 DOI: 10.1186/s13068-021-02032-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/30/2021] [Indexed: 05/02/2023]
Abstract
BACKGROUND In terms of global demand, rapeseed is the third-largest oilseed crop after soybeans and palm, which produces vegetable oil for human consumption and biofuel for industrial production. Roots are vital organs for plant to absorb water and attain mineral nutrients, thus they are of great importance to plant productivity. However, the genetic mechanisms regulating root development in rapeseed remain unclear. In the present study, seven root-related traits and shoot biomass traits in 280 Brassica napus accessions at five continuous vegetative stages were measured to establish the genetic basis of root growth in rapeseed. RESULTS The persistent and stage-specific genetic mechanisms were revealed by root dynamic analysis. Sixteen persistent and 32 stage-specific quantitative trait loci (QTL) clusters were identified through genome-wide association study (GWAS). Root samples with contrasting (slow and fast) growth rates throughout the investigated stages and those with obvious stage-specific changes in growth rates were subjected to transcriptome analysis. A total of 367 differentially expressed genes (DEGs) with persistent differential expressions throughout root development were identified, and these DEGs were significantly enriched in GO terms, such as energy metabolism and response to biotic or abiotic stress. Totally, 485 stage-specific DEGs with different expressions at specific stage were identified, and these DEGs were enriched in GO terms, such as nitrogen metabolism. Four candidate genes were identified as key persistent genetic factors and eight as stage-specific ones by integrating GWAS, weighted gene co-expression network analysis (WGCNA), and differential expression analysis. These candidate genes were speculated to regulate root system development, and they were less than 100 kb away from peak SNPs of QTL clusters. The homologs of three genes (BnaA03g52990D, BnaA06g37280D, and BnaA09g07580D) out of 12 candidate genes have been reported to regulate root development in previous studies. CONCLUSIONS Sixteen QTL clusters and four candidate genes controlling persistently root development, and 32 QTL clusters and eight candidate genes stage-specifically regulating root growth in rapeseed were detected in this study. Our results provide new insights into the temporal genetic mechanisms of root growth by identifying key candidate QTL/genes in rapeseed.
Collapse
Affiliation(s)
- Keqi Li
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences/Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, 430062 China
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430062 China
| | - Jie Wang
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences/Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, 430062 China
| | - Lieqiong Kuang
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences/Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, 430062 China
| | - Ze Tian
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences/Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, 430062 China
| | - Xinfa Wang
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences/Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, 430062 China
| | - Xiaoling Dun
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences/Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, 430062 China
| | - Jinxing Tu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430062 China
| | - Hanzhong Wang
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences/Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, 430062 China
| |
Collapse
|
19
|
Freitas Moreira F, Rojas de Oliveira H, Lopez MA, Abughali BJ, Gomes G, Cherkauer KA, Brito LF, Rainey KM. High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production. FRONTIERS IN PLANT SCIENCE 2021; 12:715983. [PMID: 34539708 PMCID: PMC8446606 DOI: 10.3389/fpls.2021.715983] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R 2 = 0.92-0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.
Collapse
Affiliation(s)
| | | | - Miguel Angel Lopez
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Bilal Jamal Abughali
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States
| | - Guilherme Gomes
- Department of Statistics, Purdue University, West Lafayette, IN, United States
| | - Keith Aric Cherkauer
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States
| | - Luiz Fernando Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Katy Martin Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| |
Collapse
|
20
|
Miculan M, Nelissen H, Ben Hassen M, Marroni F, Inzé D, Pè ME, Dell’Acqua M. A forward genetics approach integrating genome-wide association study and expression quantitative trait locus mapping to dissect leaf development in maize (Zea mays). THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 107:1056-1071. [PMID: 34087008 PMCID: PMC8519057 DOI: 10.1111/tpj.15364] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 05/31/2021] [Indexed: 05/13/2023]
Abstract
The characterization of the genetic basis of maize (Zea mays) leaf development may support breeding efforts to obtain plants with higher vigor and productivity. In this study, a mapping panel of 197 biparental and multiparental maize recombinant inbred lines (RILs) was analyzed for multiple leaf traits at the seedling stage. RNA sequencing was used to estimate the transcription levels of 29 573 gene models in RILs and to derive 373 769 single nucleotide polymorphisms (SNPs), and a forward genetics approach combining these data was used to pinpoint candidate genes involved in leaf development. First, leaf traits were correlated with gene expression levels to identify transcript-trait correlations. Then, leaf traits were associated with SNPs in a genome-wide association (GWA) study. An expression quantitative trait locus mapping approach was followed to associate SNPs with gene expression levels, prioritizing candidate genes identified based on transcript-trait correlations and GWAs. Finally, a network analysis was conducted to cluster all transcripts in 38 co-expression modules. By integrating forward genetics approaches, we identified 25 candidate genes highly enriched for specific functional categories, providing evidence supporting the role of vacuolar proton pumps, cell wall effectors, and vesicular traffic controllers in leaf growth. These results tackle the complexity of leaf trait determination and may support precision breeding in maize.
Collapse
Affiliation(s)
- Mara Miculan
- Institute of Life SciencesScuola Superiore Sant’AnnaPisa56127Italy
| | - Hilde Nelissen
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- Center for Plant Systems Biology, VIBGhent9052Belgium
| | - Manel Ben Hassen
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- Center for Plant Systems Biology, VIBGhent9052Belgium
| | - Fabio Marroni
- IGA Technology ServicesUdine33100Italy
- Department of Agricultural, FoodAT, Environmental and Animal Sciences (DI4A)University of UdineUdine33100Italy
| | - Dirk Inzé
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- Center for Plant Systems Biology, VIBGhent9052Belgium
| | - Mario Enrico Pè
- Institute of Life SciencesScuola Superiore Sant’AnnaPisa56127Italy
| | | |
Collapse
|
21
|
Razzaq A, Kaur P, Akhter N, Wani SH, Saleem F. Next-Generation Breeding Strategies for Climate-Ready Crops. FRONTIERS IN PLANT SCIENCE 2021; 12:620420. [PMID: 34367194 PMCID: PMC8336580 DOI: 10.3389/fpls.2021.620420] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 06/14/2021] [Indexed: 05/17/2023]
Abstract
Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.
Collapse
Affiliation(s)
- Ali Razzaq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
| | - Parwinder Kaur
- UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia
| | - Naheed Akhter
- College of Allied Health Professional, Faculty of Medical Sciences, Government College University Faisalabad, Faisalabad, Pakistan
| | - Shabir Hussain Wani
- Mountain Research Center for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India
| | - Fozia Saleem
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
| |
Collapse
|
22
|
Grzybowski M, Wijewardane NK, Atefi A, Ge Y, Schnable JC. Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges. PLANT COMMUNICATIONS 2021; 2:100209. [PMID: 34327323 PMCID: PMC8299078 DOI: 10.1016/j.xplc.2021.100209] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/23/2021] [Accepted: 05/24/2021] [Indexed: 05/05/2023]
Abstract
Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.
Collapse
Affiliation(s)
- Marcin Grzybowski
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Department of Plant Molecular Ecophysiology, Institute of Plant Experimental Biology and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Nuwan K. Wijewardane
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
- Department of Agricultural Biological Engineering, Mississippi State University, Starkville, MS, USA
| | - Abbas Atefi
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - James C. Schnable
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Corresponding author
| |
Collapse
|
23
|
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.
Collapse
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
| |
Collapse
|
24
|
Massel K, Lam Y, Wong ACS, Hickey LT, Borrell AK, Godwin ID. Hotter, drier, CRISPR: the latest edit on climate change. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1691-1709. [PMID: 33420514 DOI: 10.1007/s00122-020-03764-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/30/2020] [Indexed: 05/23/2023]
Abstract
Integrating CRISPR/Cas9 genome editing into modern breeding programs for crop improvement in cereals. Global climate trends in many agricultural regions have been rapidly changing over the past decades, and major advances in global food systems are required to ensure food security in the face of these emerging challenges. With increasing climate instability due to warmer temperatures and rising CO2 levels, the productivity of global agriculture will continue to be negatively impacted. To combat these growing concerns, creative approaches will be required, utilising all the tools available to produce more robust and tolerant crops with increased quality and yields under more extreme conditions. The integration of genome editing and transgenics into current breeding strategies is one promising solution to accelerate genetic gains through targeted genetic modifications, producing crops that can overcome the shifting climate realities. This review focuses on how revolutionary genome editing tools can be directly implemented into breeding programs for cereal crop improvement to rapidly counteract many of the issues affecting agriculture production in the years to come.
Collapse
Affiliation(s)
- Karen Massel
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Yasmine Lam
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Albert C S Wong
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Lee T Hickey
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Andrew K Borrell
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ian D Godwin
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| |
Collapse
|
25
|
Dodig D, Božinović S, Nikolić A, Zorić M, Vančetović J, Ignjatović-Micić D, Delić N, Weigelt-Fischer K, Altmann T, Junker A. Dynamics of Maize Vegetative Growth and Drought Adaptability Using Image-Based Phenotyping Under Controlled Conditions. FRONTIERS IN PLANT SCIENCE 2021; 12:652116. [PMID: 34046050 PMCID: PMC8146906 DOI: 10.3389/fpls.2021.652116] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/15/2021] [Indexed: 06/12/2023]
Abstract
Changes in climate are likely to have a negative impact on water availability and soil fertility in many maize-growing agricultural areas. The development of high-throughput phenotyping platforms provides a new prospect for dissecting the dynamic complex plant traits such as abiotic stress tolerance into simple components. The growth phenotypes of 20 maize (Zea mays L.) inbred lines were monitored in a non-invasive way under control, nitrogen, and water limitation as well as under combined nitrogen and water stress using an automated phenotyping system in greenhouse conditions. Thirteen biomass-related and morphophysiological traits were extracted from RGB images acquired at 33 time points covering developmental stages from leaf count 5 at the first imaging date to leaf count 10-13 at the final harvest. For these traits, genetic differences were identified and dynamic developmental trends during different maize growth stages were analyzed. The difference between control and water stress was detectable 3-10 days after the beginning of stress depending on the genotype, while the effect of limited nitrogen supply only induced subtle phenotypic effects. Phenotypic traits showed different response dynamics as well as multiple and changing interaction patterns with stress progression. The estimated biovolume, leaf area index, and color ratios were found to be stress-responsive at different stages of drought stress progression and thereby represent valuable reference indicators in the selection of drought-adaptive genotypes. Furthermore, genotypes could be grouped according to two typical growth dynamic patterns in water stress treatments by c-means clustering analysis. Inbred lines with high drought adaptability across time and development were identified and could serve as a basis for designing novel genotypes with desired, stage-specific growth phenotypes under water stress through pyramiding. Drought recovery potential may play an equal role as drought tolerance in plant drought adaptation.
Collapse
Affiliation(s)
- Dejan Dodig
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade-Zemun, Serbia
| | - Sofija Božinović
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade-Zemun, Serbia
| | - Ana Nikolić
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade-Zemun, Serbia
| | - Miroslav Zorić
- Department for Maize, Institute for Field and Vegetable Crops, Novi Sad, Serbia
| | - Jelena Vančetović
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade-Zemun, Serbia
| | - Dragana Ignjatović-Micić
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade-Zemun, Serbia
| | - Nenad Delić
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade-Zemun, Serbia
| | - Kathleen Weigelt-Fischer
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany
| | - Astrid Junker
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany
| |
Collapse
|
26
|
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.
Collapse
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
| |
Collapse
|
27
|
Zhang Y, Wang J, Du J, Zhao Y, Lu X, Wen W, Gu S, Fan J, Wang C, Wu S, Wang Y, Liao S, Zhao C, Guo X. Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high-throughput phenotypic analysis. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:35-50. [PMID: 32569428 PMCID: PMC7769239 DOI: 10.1111/pbi.13437] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 06/03/2020] [Accepted: 06/15/2020] [Indexed: 05/27/2023]
Abstract
High-throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual phenotyping of vascular bundles is tedious and time-consuming, which lags behind the rapid development of functional genomics in maize. More robust and automated techniques of phenotyping vascular bundles traits at high-throughput are urgently needed for large crop populations. In this study, we developed a standard process for stem micro-CT data acquisition and an automatic CT image process pipeline to obtain vascular bundle traits of stems including geometry-related, morphology-related and distribution-related traits. Next, we analysed the phenotypic variation of stem vascular bundles between natural population subgroup (480 inbred lines) based on 48 comprehensively phenotypic information. Also, the first database for stem micro-phenotypes, MaizeSPD, was established, storing 554 pieces of basic information of maize inbred lines, 523 pieces of experimental information, 1008 pieces of CT scanning images and processed images, and 24 192 pieces of phenotypic data. Combined with genome-wide association studies (GWASs), a total of 1562 significant single nucleotide polymorphism (SNPs) were identified for 30 stem micro-phenotypic traits, and 84 unique genes of 20 traits such as VBNum, VBAvArea and PZVBDensity were detected. Candidate genes identified by GWAS mainly encode enzymes involved in cell wall metabolism, transcription factors, protein kinase and protein related to plant signal transduction and stress response. The results presented here will advance our knowledge about phenotypic trait components of stem vascular bundles and provide useful information for understanding the genetic controls of vascular bundle formation and development.
Collapse
Affiliation(s)
- Ying Zhang
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Jinglu Wang
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Jianjun Du
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Yanxin Zhao
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular BreedingMaize Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Xianju Lu
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Weiliang Wen
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Shenghao Gu
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Jiangchuan Fan
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Chuanyu Wang
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Sheng Wu
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Yongjian Wang
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Shengjin Liao
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Chunjiang Zhao
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Xinyu Guo
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| |
Collapse
|
28
|
Li H, Feng H, Guo C, Yang S, Huang W, Xiong X, Liu J, Chen G, Liu Q, Xiong L, Liu K, Yang W. High-throughput phenotyping accelerates the dissection of the dynamic genetic architecture of plant growth and yield improvement in rapeseed. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:2345-2353. [PMID: 32367649 PMCID: PMC7589443 DOI: 10.1111/pbi.13396] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 04/25/2020] [Accepted: 04/28/2020] [Indexed: 05/21/2023]
Abstract
Rapeseed is the second most important oil crop species and is widely cultivated worldwide. However, overcoming the 'phenotyping bottleneck' has remained a significant challenge. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In addition, it is important to explore the dynamic genetic architecture underlying rapeseed plant growth and its contribution to final yield. In this work, a high-throughput phenotyping facility was used to dynamically screen a rapeseed intervarietal substitution line population during two growing seasons. We developed an automatic image analysis pipeline to quantify 43 dynamic traits across multiple developmental stages, with 12 time points. The time-resolved i-traits could be extracted to reflect shoot growth and predict the final yield of rapeseed. Broad phenotypic variation and high heritability were observed for these i-traits across all developmental stages. A total of 337 and 599 QTLs were identified, with 33.5% and 36.1% consistent QTLs for each trait across all 12 time points in the two growing seasons, respectively. Moreover, the QTLs responsible for yield indicators colocalized with those of final yield, potentially providing a new mechanism of yield regulation. Our results indicate that high-throughput phenotyping can provide novel insights into the dynamic genetic architecture of rapeseed growth and final yield, which would be useful for future genetic improvements in rapeseed.
Collapse
Affiliation(s)
- Haitao Li
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanChina
- State Key Laboratory of Biocatalysis and Enzyme Engineering, and Hubei Collaborative Innovation Center for Green Transformation of Bio‐resourcesSchool of Life SciencesHubei UniversityWuhanChina
| | - Hui Feng
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanChina
| | - Chaocheng Guo
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanChina
| | - Shanjing Yang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanChina
| | - Wan Huang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanChina
| | - Xiong Xiong
- Britton Chance Center for Biomedical PhotonicsWuhan National Laboratory for Optoelectronics, and Key Laboratory of Ministry of Education for Biomedical PhotonicsDepartment of Biomedical EngineeringHuazhong University of Science and TechnologyWuhanChina
| | - Jianxiao Liu
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanChina
| | - Guoxing Chen
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanChina
| | - Qian Liu
- Britton Chance Center for Biomedical PhotonicsWuhan National Laboratory for Optoelectronics, and Key Laboratory of Ministry of Education for Biomedical PhotonicsDepartment of Biomedical EngineeringHuazhong University of Science and TechnologyWuhanChina
| | - Lizhong Xiong
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanChina
| | - Kede Liu
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanChina
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanChina
| |
Collapse
|
29
|
Mikołajczak K, Ogrodowicz P, Ćwiek-Kupczyńska H, Weigelt-Fischer K, Mothukuri SR, Junker A, Altmann T, Krystkowiak K, Adamski T, Surma M, Kuczyńska A, Krajewski P. Image Phenotyping of Spring Barley ( Hordeum vulgare L.) RIL Population Under Drought: Selection of Traits and Biological Interpretation. FRONTIERS IN PLANT SCIENCE 2020; 11:743. [PMID: 32582262 PMCID: PMC7296146 DOI: 10.3389/fpls.2020.00743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 05/08/2020] [Indexed: 05/03/2023]
Abstract
Image-based phenotyping is a non-invasive method that permits the dynamic evaluation of plant features during growth, which is especially important for understanding plant adaptation and temporal dynamics of responses to environmental cues such as water deficit or drought. The aim of the present study was to use high-throughput imaging in order to assess the variation and dynamics of growth and development during drought in a spring barley population and to investigate associations between traits measured in time and yield-related traits measured after harvesting. Plant material covered recombinant inbred line population derived from a cross between European and Syrian cultivars. After placing the plants on the platform (28th day after sowing), drought stress was applied for 2 weeks. Top and side cameras were used to capture images daily that covered the visible range of the light spectrum, fluorescence signals, and the near infrared spectrum. The image processing provided 376 traits that were subjected to analysis. After 32 days of image phenotyping, the plants were cultivated in the greenhouse under optimal watering conditions until ripening, when several architecture and yield-related traits were measured. The applied data analysis approach, based on the clustering of image-derived traits into groups according to time profiles of statistical and genetic parameters, permitted to select traits representative for inference from the experiment. In particular, drought effects for 27 traits related to convex hull geometry, texture, proportion of brown pixels and chlorophyll intensity were found to be highly correlated with drought effects for spike traits and thousand grain weight.
Collapse
Affiliation(s)
| | - Piotr Ogrodowicz
- Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland
| | | | | | | | - Astrid Junker
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Thomas Altmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | | | - Tadeusz Adamski
- Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland
| | - Maria Surma
- Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland
| | - Anetta Kuczyńska
- Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland
| | - Paweł Krajewski
- Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland
| |
Collapse
|
30
|
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: 20] [Impact Index Per Article: 5.0] [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.
Collapse
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
| |
Collapse
|
31
|
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.
Collapse
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
| |
Collapse
|
32
|
Watt M, Fiorani F, Usadel B, Rascher U, Muller O, Schurr U. Phenotyping: New Windows into the Plant for Breeders. ANNUAL REVIEW OF PLANT BIOLOGY 2020; 71:689-712. [PMID: 32097567 DOI: 10.1146/annurev-arplant-042916-041124] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.
Collapse
Affiliation(s)
- Michelle Watt
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Fabio Fiorani
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Björn Usadel
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
- Institute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany
| | - Uwe Rascher
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Onno Muller
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Ulrich Schurr
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| |
Collapse
|
33
|
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.
Collapse
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
| |
Collapse
|
34
|
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.
Collapse
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
| |
Collapse
|
35
|
Schmidt J, Claussen J, Wörlein N, Eggert A, Fleury D, Garnett T, Gerth S. Drought and heat stress tolerance screening in wheat using computed tomography. PLANT METHODS 2020; 16:15. [PMID: 32082405 PMCID: PMC7017466 DOI: 10.1186/s13007-020-00565-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 02/06/2020] [Indexed: 05/19/2023]
Abstract
BACKGROUND Improving abiotic stress tolerance in wheat requires large scale screening of yield components such as seed weight, seed number and single seed weight, all of which is very laborious, and a detailed analysis of seed morphology is time-consuming and visually often impossible. Computed tomography offers the opportunity for much faster and more accurate assessment of yield components. RESULTS An X-ray computed tomographic analysis was carried out on 203 very diverse wheat accessions which have been exposed to either drought or combined drought and heat stress. Results demonstrated that our computed tomography pipeline was capable of evaluating grain set with an accuracy of 95-99%. Most accessions exposed to combined drought and heat stress developed smaller, shrivelled seeds with an increased seed surface. As expected, seed weight and seed number per ear as well as single seed size were significantly reduced under combined drought and heat compared to drought alone. Seed weight along the ear was significantly reduced at the top and bottom of the wheat spike. CONCLUSIONS We were able to establish a pipeline with a higher throughput with scanning times of 7 min per ear and accuracy than previous pipelines predicting a set of agronomical important seed traits and to visualize even more complex traits such as seed deformations. The pipeline presented here could be scaled up to use for high throughput, high resolution phenotyping of tens of thousands of heads, greatly accelerating breeding efforts to improve abiotic stress tolerance.
Collapse
Affiliation(s)
- Jessica Schmidt
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA Australia
| | - Joelle Claussen
- Fraunhofer Development Center X-Ray Technology, Fürth, Germany
| | - Norbert Wörlein
- Fraunhofer Development Center X-Ray Technology, Fürth, Germany
| | - Anja Eggert
- Fraunhofer Development Center X-Ray Technology, Fürth, Germany
| | - Delphine Fleury
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA Australia
- Innolea, 6 chemin de Panedautes, 31700 Mondonville, France
| | - Trevor Garnett
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA Australia
| | - Stefan Gerth
- Fraunhofer Development Center X-Ray Technology, Fürth, Germany
| |
Collapse
|
36
|
Dos Santos JPR, Fernandes SB, McCoy S, Lozano R, Brown PJ, Leakey ADB, Buckler ES, Garcia AAF, Gore MA. Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum. G3 (BETHESDA, MD.) 2020; 10:769-781. [PMID: 31852730 PMCID: PMC7003104 DOI: 10.1534/g3.119.400759] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 12/15/2019] [Indexed: 11/23/2022]
Abstract
The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.
Collapse
Affiliation(s)
- Jhonathan P R Dos Santos
- Plant Breeding and Genetics Section, School of Integrative Plant Science
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil
| | | | | | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science
| | - Patrick J Brown
- Section of Agricultural Plant Biology, Department of Plant Sciences, University of California Davis, 95616, and
| | - Andrew D B Leakey
- Department of Crop Science
- Institute for Genomic Biology
- Department of Plant Biology, University of Illinois at Urbana Champaign, 61801
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science
- United States Department of Agriculture, Agricultural Research Service, R. W. Holley Center, Ithaca, New York 14853
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
| | - Antonio A F Garcia
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil,
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
| |
Collapse
|
37
|
Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. MOLECULAR PLANT 2020; 13:187-214. [PMID: 31981735 DOI: 10.1016/j.molp.2020.01.008] [Citation(s) in RCA: 239] [Impact Index Per Article: 59.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/06/2020] [Accepted: 01/10/2020] [Indexed: 05/18/2023]
Abstract
Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
Collapse
Affiliation(s)
- Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crops Science/College of Agronomy, Henan Agricultural University, Zhengzhou 450002, P.R. China
| | - Jian Zhang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - John H Doonan
- The National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | | | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| |
Collapse
|
38
|
Knoch D, Abbadi A, Grandke F, Meyer RC, Samans B, Werner CR, Snowdon RJ, Altmann T. Strong temporal dynamics of QTL action on plant growth progression revealed through high-throughput phenotyping in canola. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:68-82. [PMID: 31125482 PMCID: PMC6920335 DOI: 10.1111/pbi.13171] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 05/13/2019] [Accepted: 05/15/2019] [Indexed: 05/08/2023]
Abstract
A major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high-throughput phenotyping in conjunction with genome-wide association studies to elucidate genotype-phenotype relationships at high temporal resolution. A diverse Brassica napus population from a commercial breeding programme was analysed by automated non-invasive phenotyping. Time-resolved data for early growth-related traits, including estimated biovolume, projected leaf area, early plant height and colour uniformity, were established and complemented by fresh and dry weight biomass. Genome-wide SNP array data provided the framework for genome-wide association analyses. Using time point data and relative growth rates, multiple robust main effect marker-trait associations for biomass and related traits were detected. Candidate genes involved in meristem development, cell wall modification and transcriptional regulation were detected. Our results demonstrate that early plant growth is a highly complex trait governed by several medium and many small effect loci, most of which act only during short phases. These observations highlight the importance of taking the temporal patterns of QTL/allele actions into account and emphasize the need for detailed time-resolved analyses to effectively unravel the complex and stage-specific contributions of genes affecting growth processes that operate at different developmental phases.
Collapse
Affiliation(s)
- Dominic Knoch
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| | - Amine Abbadi
- Norddeutsche Pflanzenzucht Innovation GmbH (NPZi)HoltseeGermany
| | - Fabian Grandke
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
| | - Rhonda C. Meyer
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| | - Birgit Samans
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
- Present address:
Technische Hochschule Mittelhessen (THM), University of Applied SciencesFachbereich Gesundheit35390GiessenGermany
| | - Christian R. Werner
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
- Present address:
The Roslin InstituteUniversity of EdinburghEaster Bush CampusMidlothianEH25 9RGUK
| | - Rod J. Snowdon
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
| | - Thomas Altmann
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| |
Collapse
|
39
|
Harfouche AL, Jacobson DA, Kainer D, Romero JC, Harfouche AH, Scarascia Mugnozza G, Moshelion M, Tuskan GA, Keurentjes JJ, Altman A. Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence. Trends Biotechnol 2019; 37:1217-1235. [DOI: 10.1016/j.tibtech.2019.05.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 05/18/2019] [Accepted: 05/23/2019] [Indexed: 12/20/2022]
|
40
|
Jiang N, Floro E, Bray AL, Laws B, Duncan KE, Topp CN. Three-Dimensional Time-Lapse Analysis Reveals Multiscale Relationships in Maize Root Systems with Contrasting Architectures. THE PLANT CELL 2019; 31:1708-1722. [PMID: 31123089 DOI: 10.1101/381046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 05/08/2019] [Accepted: 07/01/2019] [Indexed: 05/28/2023]
Abstract
Understanding how an organism's phenotypic traits are conditioned by genetic and environmental variation is a central goal of biology. Root systems are one of the most important but poorly understood aspects of plants, largely due to the three-dimensional (3D), dynamic, and multiscale phenotyping challenge they pose. A critical gap in our knowledge is how root systems build in complexity from a single primary root to a network of thousands of roots that collectively compete for ephemeral, heterogeneous soil resources. We used time-lapse 3D imaging and mathematical modeling to assess root system architectures (RSAs) of two maize (Zea mays) inbred genotypes and their hybrid as they grew in complexity from a few to many roots. Genetically driven differences in root branching zone size and lateral branching densities along a single root, combined with differences in peak growth rate and the relative allocation of carbon resources to new versus existing roots, manifest as sharply distinct global RSAs over time. The 3D imaging of mature field-grown root crowns showed that several genetic differences in seedling architectures could persist throughout development and across environments. This approach connects individual and system-wide scales of root growth dynamics, which could eventually be used to predict genetic variation for complex RSAs and their functions.
Collapse
Affiliation(s)
- Ni Jiang
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Eric Floro
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Adam L Bray
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
- Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211
| | - Benjamin Laws
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Keith E Duncan
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | | |
Collapse
|
41
|
Dodig D, Božinović S, Nikolić A, Zorić M, Vančetović J, Ignjatović-Micić D, Delić N, Weigelt-Fischer K, Junker A, Altmann T. Image-Derived Traits Related to Mid-Season Growth Performance of Maize Under Nitrogen and Water Stress. FRONTIERS IN PLANT SCIENCE 2019; 10:814. [PMID: 31297124 PMCID: PMC6607059 DOI: 10.3389/fpls.2019.00814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 06/06/2019] [Indexed: 06/09/2023]
Abstract
Phenotypic measurements under controlled cultivation conditions are essential to gain a mechanistic understanding of plant responses to environmental impacts and thus for knowledge-based improvement of their performance under natural field conditions. Twenty maize inbred lines (ILs) were phenotyped in response to two levels of water and nitrogen supply (control and stress) and combined nitrogen and water deficit. Over a course of 5 weeks (from about 4-leaf stage to the beginning of the reproductive stage), maize phenology and growth were monitored by using a high-throughput phenotyping platform for daily acquisition of images in different spectral ranges. The focus of the present study is on the measurements taken at the time of maximum water stress (for traits that reflect plant physiological properties) and at the end of the experiment (for traits that reflect plant architectural and biomass-related traits). Twenty-five phenotypic traits extracted from the digital image data that support biological interpretation of plant growth were selected for their predictive value for mid-season shoot biomass accumulation. Measured fresh and dry weights after harvest were used to calculate various indices (water-use efficiency, physiological nitrogen-use efficiency, specific plant weight) and to establish correlations with image-derived phenotypic features. Also, score indices based on dry weight were used to identify contrasting ILs in terms of productivity and tolerance to stress, and their means for image-derived and manually measured traits were compared. Color-related traits appear to be indicative of plant performance and photosystem II operating efficiency might be an importance physiological parameter of biomass accumulation, particularly under severe stress conditions. Also, genotypes showing greater leaf area may be better adapted to abiotic stress conditions.
Collapse
Affiliation(s)
- Dejan Dodig
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade, Serbia
| | - Sofija Božinović
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade, Serbia
| | - Ana Nikolić
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade, Serbia
| | - Miroslav Zorić
- Department for Maize, Institute of Field and Vegetable Crops, Novi Sad, Serbia
| | - Jelena Vančetović
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade, Serbia
| | | | - Nenad Delić
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade, Serbia
| | - Kathleen Weigelt-Fischer
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Astrid Junker
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| |
Collapse
|
42
|
Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J. Crop Phenomics: Current Status and Perspectives. FRONTIERS IN PLANT SCIENCE 2019; 10:714. [PMID: 31214228 PMCID: PMC6557228 DOI: 10.3389/fpls.2019.00714] [Citation(s) in RCA: 136] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 05/14/2019] [Indexed: 05/19/2023]
Abstract
Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called 'phenomics.' The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.
Collapse
|
43
|
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.
Collapse
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
| |
Collapse
|
44
|
Mir RR, Reynolds M, Pinto F, Khan MA, Bhat MA. High-throughput phenotyping for crop improvement in the genomics era. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:60-72. [PMID: 31003612 DOI: 10.1016/j.plantsci.2019.01.007] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 12/10/2018] [Accepted: 01/09/2019] [Indexed: 05/24/2023]
Abstract
Tremendous progress has been made with continually expanding genomics technologies to unravel and understand crop genomes. However, the impact of genomics data on crop improvement is still far from satisfactory, in large part due to a lack of effective phenotypic data; our capacity to collect useful high quality phenotypic data lags behind the current capacity to generate high-throughput genomics data. Thus, the research bottleneck in plant sciences is shifting from genotyping to phenotyping. This article review the current status of efforts made in the last decade to systematically collect phenotypic data to alleviate this 'phenomics bottlenecks' by recording trait data through sophisticated non-invasive imaging, spectroscopy, image analysis, robotics, high-performance computing facilities and phenomics databases. These modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution. It is believed that this revolution will provide plant scientists with the knowledge and tools necessary for unlocking information coded in plant genomes. Efforts have been also made to present the advances made in the last 10 years in phenomics platforms and their use in generating phenotypic data on different traits in several major crops including rice, wheat, barley, and maize. The article also highlights the need for phenomics databases and phenotypic data sharing for crop improvement. The phenomics data generated has been used to identify genes/QTL through QTL mapping, association mapping and genome-wide association studies (GWAS) for genomics-assisted breeding (GAB) for crop improvement.
Collapse
Affiliation(s)
- Reyazul Rouf Mir
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India.
| | - Mathew Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Mohd Anwar Khan
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India
| | - Mohd Ashraf Bhat
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India
| |
Collapse
|
45
|
Marchadier E, Hanemian M, Tisné S, Bach L, Bazakos C, Gilbault E, Haddadi P, Virlouvet L, Loudet O. The complex genetic architecture of shoot growth natural variation in Arabidopsis thaliana. PLoS Genet 2019; 15:e1007954. [PMID: 31009456 PMCID: PMC6476473 DOI: 10.1371/journal.pgen.1007954] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Accepted: 01/11/2019] [Indexed: 12/16/2022] Open
Abstract
One of the main outcomes of quantitative genetics approaches to natural variation is to reveal the genetic architecture underlying the phenotypic space. Complex genetic architectures are described as including numerous loci (or alleles) with small-effect and/or low-frequency in the populations, interactions with the genetic background, environment or age. Linkage or association mapping strategies will be more or less sensitive to this complexity, so that we still have an unclear picture of its extent. By combining high-throughput phenotyping under two environmental conditions with classical QTL mapping approaches in multiple Arabidopsis thaliana segregating populations as well as advanced near isogenic lines construction and survey, we have attempted to improve our understanding of quantitative phenotypic variation. Integrative traits such as those related to vegetative growth used in this work (highlighting either cumulative growth, growth rate or morphology) all showed complex and dynamic genetic architecture with respect to the segregating population and condition. The more resolutive our mapping approach, the more complexity we uncover, with several instances of QTLs visible in near isogenic lines but not detected with the initial QTL mapping, indicating that our phenotyping accuracy was less limiting than the mapping resolution with respect to the underlying genetic architecture. In an ultimate approach to resolve this complexity, we intensified our phenotyping effort to target specifically a 3Mb-region known to segregate for a major quantitative trait gene, using a series of selected lines recombined every 100kb. We discovered that at least 3 other independent QTLs had remained hidden in this region, some with trait- or condition-specific effects, or opposite allelic effects. If we were to extrapolate the figures obtained on this specific region in this particular cross to the genome- and species-scale, we would predict hundreds of causative loci of detectable phenotypic effect controlling these growth-related phenotypes.
Collapse
Affiliation(s)
- Elodie Marchadier
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Mathieu Hanemian
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Sébastien Tisné
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Liên Bach
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Christos Bazakos
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Elodie Gilbault
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Parham Haddadi
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Laetitia Virlouvet
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Olivier Loudet
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
- * E-mail:
| |
Collapse
|
46
|
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.
Collapse
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
| |
Collapse
|
47
|
Jiang L, Sun L, Ye M, Wang J, Wang Y, Bogard M, Lacaze X, Fournier A, Beauchêne K, Gouache D, Wu R. Functional mapping of N deficiency‐induced response in wheat yield‐component traits by implementing high‐throughput phenotyping. THE PLANT JOURNAL 2019; 97:1105-1119. [PMID: 30536457 DOI: 10.1111/tpj.14186] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 11/09/2018] [Accepted: 11/23/2018] [Indexed: 05/25/2023]
Affiliation(s)
- Libo Jiang
- Center for Computational Biology College of Biological Sciences and Technology Beijing Forestry University Beijing 100083 China
| | - Lidan Sun
- Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding National Engineering Research Center for Floriculture College of Landscape Architecture Beijing Forestry University Beijing 100083 China
| | - Meixia Ye
- Center for Computational Biology College of Biological Sciences and Technology Beijing Forestry University Beijing 100083 China
| | - Jing Wang
- Center for Computational Biology College of Biological Sciences and Technology Beijing Forestry University Beijing 100083 China
| | - Yaqun Wang
- Department of Biostatistics Rutgers University New Brunswick NJ 08901 USA
| | - Matthieu Bogard
- Arvalis Institut du Végétal 3‐5 Rue Joseph et Marie Hackin 75116 Paris France
| | - Xavier Lacaze
- Arvalis Institut du Végétal 3‐5 Rue Joseph et Marie Hackin 75116 Paris France
| | - Antoine Fournier
- Arvalis Institut du Végétal 3‐5 Rue Joseph et Marie Hackin 75116 Paris France
| | - Katia Beauchêne
- Arvalis Institut du Végétal 3‐5 Rue Joseph et Marie Hackin 75116 Paris France
| | - David Gouache
- Arvalis Institut du Végétal 3‐5 Rue Joseph et Marie Hackin 75116 Paris France
| | - Rongling Wu
- Center for Computational Biology College of Biological Sciences and Technology Beijing Forestry University Beijing 100083 China
- Center for Statistical Genetics Departments of Public Health Sciences and Statistics Pennsylvania State University Hershey PA 17033 USA
| |
Collapse
|
48
|
Dommes AB, Gross T, Herbert DB, Kivivirta KI, Becker A. Virus-induced gene silencing: empowering genetics in non-model organisms. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:757-770. [PMID: 30452695 DOI: 10.1093/jxb/ery411] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 11/08/2018] [Indexed: 05/19/2023]
Abstract
Virus-induced gene silencing (VIGS) is an RNA interference-based technology used to transiently knock down target gene expression by utilizing modified plant viral genomes. VIGS can be adapted to many angiosperm species that cover large phylogenetic distances, allowing the analysis of gene functions in species that are not amenable to stable genetic transformation. With a vast amount of sequence information already available and even more likely to become available in the future, VIGS provides a means to analyze the functions of candidate genes identified in large genomic or transcriptomic screens. Here, we provide a comprehensive overview of target species and VIGS vector systems, assess recent key publications in the field, and explain how plant viruses are modified to serve as VIGS vectors. As many reports on the VIGS technique are being published, we also propose minimal reporting guidelines for carrying out these experiments, with the aim of increasing comparability between experiments. Finally, we propose methods for the statistical evaluation of phenotypic results obtained with VIGS-treated plants, as analysis is challenging due to the predominantly transient nature of the silencing effect.
Collapse
Affiliation(s)
- Anna B Dommes
- Institute of Botany, Justus-Liebig-University, Heinrich-Buff-Ring, Gießen, Germany
| | - Thomas Gross
- Institute of Botany, Justus-Liebig-University, Heinrich-Buff-Ring, Gießen, Germany
| | - Denise B Herbert
- Institute of Botany, Justus-Liebig-University, Heinrich-Buff-Ring, Gießen, Germany
| | - Kimmo I Kivivirta
- Institute of Botany, Justus-Liebig-University, Heinrich-Buff-Ring, Gießen, Germany
| | - Annette Becker
- Institute of Botany, Justus-Liebig-University, Heinrich-Buff-Ring, Gießen, Germany
| |
Collapse
|
49
|
Sun L, Sang M, Zheng C, Wang D, Shi H, Liu K, Guo Y, Cheng T, Zhang Q, Wu R. The genetic architecture of heterochrony as a quantitative trait: lessons from a computational model. Brief Bioinform 2018; 19:1430-1439. [PMID: 28575183 DOI: 10.1093/bib/bbx056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2017] [Indexed: 11/14/2022] Open
Abstract
Heterochrony is known as a developmental change in the timing or rate of ontogenetic events across phylogenetic lineages. It is a key concept synthesizing development into ecology and evolution to explore the mechanisms of how developmental processes impact on phenotypic novelties. A number of molecular experiments using contrasting organisms in developmental timing have identified specific genes involved in heterochronic variation. Beyond these classic approaches that can only identify single genes or pathways, quantitative models derived from current next-generation sequencing data serve as a more powerful tool to precisely capture heterochronic variation and systematically map a complete set of genes that contribute to heterochronic processes. In this opinion note, we discuss a computational framework of genetic mapping that can characterize heterochronic quantitative trait loci that determine the pattern and process of development. We propose a unifying model that charts the genetic architecture of heterochrony that perceives and responds to environmental perturbations and evolves over geologic time. The new model may potentially enhance our understanding of the adaptive value of heterochrony and its evolutionary origins, providing a useful context for designing new organisms that can best use future resources.
Collapse
Affiliation(s)
- Lidan Sun
- Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture at Beijing Forestry University
| | - Mengmeng Sang
- Computational Genetics in the Center for Computational Biology at Beijing Forestry University
| | - Chenfei Zheng
- Computational Genetics in the Center for Computational Biology at Beijing Forestry University
| | - Dongyang Wang
- Computational Biology Center for Computational Biology at Beijing Forestry University
| | - Hexin Shi
- Computational Biology Center for Computational Biology at Beijing Forestry University
| | - Kaiyue Liu
- Computational Biology Center for Computational Biology at Beijing Forestry University
| | - Yanfang Guo
- Computational Biology Center for Computational Biology at Beijing Forestry University
| | - Tangren Cheng
- National Engineering Research Center for Floriculture at Beijing Forestry University
| | - Qixiang Zhang
- National Engineering Research Center for Floriculture at Beijing Forestry University
| | - Rongling Wu
- Center for Computational Biology at Beijing Forestry University
| |
Collapse
|
50
|
Scheben A, Edwards D. Towards a more predictable plant breeding pipeline with CRISPR/Cas-induced allelic series to optimize quantitative and qualitative traits. CURRENT OPINION IN PLANT BIOLOGY 2018; 45:218-225. [PMID: 29752075 DOI: 10.1016/j.pbi.2018.04.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 04/22/2018] [Accepted: 04/23/2018] [Indexed: 05/20/2023]
Abstract
The rate of crop improvement must increase to meet rising global demand for food. Crop breeding pipelines can be hampered by the imprecision and multi-generational timeframe of methods such as intercrossing and stochastic mutagenesis used to generate variation. Genome editing can generate targeted allelic series of trait-related genes and regulatory elements, creating a series of variable phenotypes for breeding within a single generation. Disrupting genic and regulatory regions is particularly effective for engineering quantitative traits. Although qualitative traits can be more difficult to engineer using disruption, precise base editing may allow an efficient path to rationally improve qualitative traits if protein function can be accurately modelled. As functional understanding of genes and regulatory elements increases, genome editing can enhance the predictability of plant breeding outcomes and will ensure food security.
Collapse
Affiliation(s)
- Armin Scheben
- School of Plant Biology and Institute of Agriculture, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
| | - David Edwards
- School of Plant Biology and Institute of Agriculture, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia.
| |
Collapse
|