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Sivabharathi RC, Rajagopalan VR, Suresh R, Sudha M, Karthikeyan G, Jayakanthan M, Raveendran M. Haplotype-based breeding: A new insight in crop improvement. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2024; 346:112129. [PMID: 38763472 DOI: 10.1016/j.plantsci.2024.112129] [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: 03/15/2024] [Revised: 05/09/2024] [Accepted: 05/15/2024] [Indexed: 05/21/2024]
Abstract
Haplotype-based breeding (HBB) is one of the cutting-edge technologies in the realm of crop improvement due to the increasing availability of Single Nucleotide Polymorphisms identified by Next Generation Sequencing technologies. The complexity of the data can be decreased with fewer statistical tests and a lower probability of spurious associations by combining thousands of SNPs into a few hundred haplotype blocks. The presence of strong genomic regions in breeding lines of most crop species facilitates the use of haplotypes to improve the efficiency of genomic and marker-assisted selection. Haplotype-based breeding as a Genomic Assisted Breeding (GAB) approach harnesses the genome sequence data to pinpoint the allelic variation used to hasten the breeding cycle and circumvent the challenges associated with linkage drag. This review article demonstrates ways to identify candidate genes, superior haplotype identification, haplo-pheno analysis, and haplotype-based marker-assisted selection. The crop improvement strategies that utilize superior haplotypes will hasten the breeding progress to safeguard global food security.
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Affiliation(s)
- R C Sivabharathi
- Department of Genetics and Plant breeding, CPBG, Tamil Nadu Agricultural University, Coimbatore 641003, India
| | - Veera Ranjani Rajagopalan
- Department of Plant Biotechnology, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, 641003, India
| | - R Suresh
- Department of Rice, CPBG, Tamil Nadu Agricultural University, Coimbatore 641003, India
| | - M Sudha
- Department of Plant Biotechnology, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, 641003, India.
| | - G Karthikeyan
- Department of Plant Pathology, CPPS, Tamil Nadu Agricultural University, Coimbatore 641003, India
| | - M Jayakanthan
- Department of Plant Molecular Biology and Bioinformatics, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore 641003, India
| | - M Raveendran
- Directorate of research, Tamil Nadu Agricultural University, Coimbatore 641003, India.
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2
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Adak A, DeSalvio AJ, Arik MA, Murray SC. Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize. G3 (BETHESDA, MD.) 2024; 14:jkae092. [PMID: 38776257 PMCID: PMC11228873 DOI: 10.1093/g3journal/jkae092] [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: 03/03/2024] [Accepted: 04/24/2024] [Indexed: 05/24/2024]
Abstract
Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.
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Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Aaron J DeSalvio
- Interdisciplinary Graduate Program in Genetics and Genomics (Department of Biochemistry and Biophysics), Texas A&M University, College Station, TX 77843-2128, USA
| | - Mustafa A Arik
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
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3
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Nagle MF, Yuan J, Kaur D, Ma C, Peremyslova E, Jiang Y, Niño de Rivera A, Jawdy S, Chen JG, Feng K, Yates TB, Tuskan GA, Muchero W, Fuxin L, Strauss SH. GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa. G3 (BETHESDA, MD.) 2024; 14:jkae026. [PMID: 38325329 PMCID: PMC10989874 DOI: 10.1093/g3journal/jkae026] [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: 11/14/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 02/09/2024]
Abstract
Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.
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Affiliation(s)
- Michael F Nagle
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Jialin Yuan
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Damanpreet Kaur
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Cathleen Ma
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Ekaterina Peremyslova
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Yuan Jiang
- Statistics Department, Oregon State University, 239 Weniger Hall, Corvallis, OR 97331, USA
| | - Alexa Niño de Rivera
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Sara Jawdy
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Jin-Gui Chen
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Kai Feng
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Timothy B Yates
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Gerald A Tuskan
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Wellington Muchero
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Li Fuxin
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Steven H Strauss
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
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Ntakirutimana F, Tranchant-Dubreuil C, Cubry P, Chougule K, Zhang J, Wing RA, Adam H, Lorieux M, Jouannic S. Genome-wide association analysis identifies natural allelic variants associated with panicle architecture variation in African rice, Oryza glaberrima Steud. G3 (BETHESDA, MD.) 2023; 13:jkad174. [PMID: 37535690 PMCID: PMC10542218 DOI: 10.1093/g3journal/jkad174] [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: 06/12/2023] [Revised: 06/12/2023] [Accepted: 07/18/2023] [Indexed: 08/05/2023]
Abstract
African rice (Oryza glaberrima Steud), a short-day cereal crop closely related to Asian rice (Oryza sativa L.), has been cultivated in Sub-Saharan Africa for ∼ 3,000 years. Although less cultivated globally, it is a valuable genetic resource in creating high-yielding cultivars that are better adapted to diverse biotic and abiotic stresses. While inflorescence architecture, a key trait for rice grain yield improvement, has been extensively studied in Asian rice, the morphological and genetic determinants of this complex trait are less understood in African rice. In this study, using a previously developed association panel of 162 O. glaberrima accessions and new SNP variants characterized through mapping to a new version of the O. glaberrima reference genome, we conducted a genome-wide association study of four major morphological panicle traits. We have found a total of 41 stable genomic regions that are significantly associated with these traits, of which 13 co-localized with previously identified QTLs in O. sativa populations and 28 were unique for this association panel. Additionally, we found a genomic region of interest on chromosome 3 that was associated with the number of spikelets and primary and secondary branches. Within this region was localized the O. sativa ortholog of the PHYTOCHROME B gene (Oglab_006903/OgPHYB). Haplotype analysis revealed the occurrence of natural sequence variants at the OgPHYB locus associated with panicle architecture variation through modulation of the flowering time phenotype, whereas no equivalent alleles were found in O. sativa. The identification in this study of genomic regions specific to O. glaberrima indicates panicle-related intra-specific genetic variation in this species, increasing our understanding of the underlying molecular processes governing panicle architecture. Identified candidate genes and major haplotypes may facilitate the breeding of new African rice cultivars with preferred panicle traits.
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Affiliation(s)
| | | | - Philippe Cubry
- DIADE, University of Montpellier, IRD, CIRAD, 34394 Montpellier, France
| | - Kapeel Chougule
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Jianwei Zhang
- Arizona Genomics Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Rod A Wing
- Arizona Genomics Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA
- Center for Desert Agriculture, Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Hélène Adam
- DIADE, University of Montpellier, IRD, CIRAD, 34394 Montpellier, France
| | - Mathias Lorieux
- DIADE, University of Montpellier, IRD, CIRAD, 34394 Montpellier, France
| | - Stefan Jouannic
- DIADE, University of Montpellier, IRD, CIRAD, 34394 Montpellier, France
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5
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Ye Y, Wang P, Zhang M, Abbas M, Zhang J, Liang C, Wang Y, Wei Y, Meng Z, Zhang R. UAV-based time-series phenotyping reveals the genetic basis of plant height in upland cotton. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 115:937-951. [PMID: 37154288 DOI: 10.1111/tpj.16272] [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: 01/18/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/10/2023]
Abstract
Plant height (PH) is an important agronomic trait affecting crop architecture, biomass, resistance to lodging and mechanical harvesting. Elucidating the genetic governance of plant height is crucial because of the global demand for high crop yields. However, during the rapid growth period of plants the PH changes a lot on a daily basis, which makes it difficult to accurately phenotype the trait by hand on a large scale. In this study, an unmanned aerial vehicle (UAV)-based remote-sensing phenotyping platform was applied to obtain time-series PHs of 320 upland cotton accessions in three different field trials. The results showed that the PHs obtained from UAV images were significantly correlated with ground-based manual measurements, for three trials (R2 = 0.96, 0.95 and 0.96). Two genetic loci on chromosomes A01 and A11 associated with PH were identified by genome-wide association studies (GWAS). GhUBP15 and GhCUL1 were identified to influence PH in further analysis. We obtained a time series of PH values for three field conditions based on remote sensing with UAV. The key genes identified in this study are of great value for the breeding of ideal plant architecture in cotton.
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Affiliation(s)
- Yulu Ye
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Peilin Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Man Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Mubashir Abbas
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jiaxin Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Chengzhen Liang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yuan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yunxiao Wei
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhigang Meng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Rui Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
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6
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Wada KC, Hayashi A, Lee U, Tanabata T, Isobe S, Itoh H, Maeda H, Fujisako S, Kochi N. A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling-A Case Study in Leaf Lettuce. SENSORS (BASEL, SWITZERLAND) 2023; 23:6825. [PMID: 37571608 PMCID: PMC10422436 DOI: 10.3390/s23156825] [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: 06/11/2023] [Revised: 07/24/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023]
Abstract
Three-dimensional measurement is a high-throughput method that can record a large amount of information. Three-dimensional modelling of plants has the possibility to not only automate dimensional measurement, but to also enable visual assessment to be quantified, eliminating ambiguity in human judgment. In this study, we have developed new methods that could be used for the morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The latter is particularly novel in this paper. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The coefficient of determination between manual measurements and the scale-related methods were all above 0.9. Furthermore, the differences in LC calculated from the normal vector data allowed us to visualise and quantify the concavity and convexity of leaves. This technique revealed that there were differences in the time point at which leaf blistering began to develop among the varieties. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment. This research paper was able to demonstrate the following possibilities as outcomes: (1) the automation of conventional manual measurements, and (2) the elimination of variability caused by human subjectivity, thereby rendering evaluations by skilled experts unnecessary.
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Affiliation(s)
- Kaede C Wada
- Breeding Big Data Management and Utilization Group, Division of Smart Breeding Research, Institute of Crop Science, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0856, Japan
| | - Atsushi Hayashi
- Research Center for Agricultural Robotics, Core Technology Research Headquarters, NARO, Tsukuba 305-0856, Japan
| | - Unseok Lee
- Research Center for Agricultural Robotics, Core Technology Research Headquarters, NARO, Tsukuba 305-0856, Japan
| | - Takanari Tanabata
- Department of Frontier Research Plant Genomics and Genetics, Kazusa DNA Research Institute, Kisarazu 292-0818, Japan
| | - Sachiko Isobe
- Department of Frontier Research Plant Genomics and Genetics, Kazusa DNA Research Institute, Kisarazu 292-0818, Japan
| | - Hironori Itoh
- Breeding Big Data Management and Utilization Group, Division of Smart Breeding Research, Institute of Crop Science, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0856, Japan
| | - Hideki Maeda
- Center for Seeds and Seedlings, Nishinihon Station (NARO), Kasaoka 714-0054, Japan
| | - Satoshi Fujisako
- Center for Seeds and Seedlings, Nishinihon Station (NARO), Kasaoka 714-0054, Japan
| | - Nobuo Kochi
- Research Center for Agricultural Robotics, Core Technology Research Headquarters, NARO, Tsukuba 305-0856, Japan
- Department of Frontier Research Plant Genomics and Genetics, Kazusa DNA Research Institute, Kisarazu 292-0818, Japan
- R&D Initiative, Chuo University, Kasuga, Tokyo 112-8551, Japan
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7
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Taniguchi S, Sakamoto T, Imase R, Nonoue Y, Tsunematsu H, Goto A, Matsushita K, Ohmori S, Maeda H, Takeuchi Y, Ishii T, Yonemaru JI, Ogawa D. Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice. FRONTIERS IN PLANT SCIENCE 2022; 13:998803. [PMID: 36582650 PMCID: PMC9792801 DOI: 10.3389/fpls.2022.998803] [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: 07/20/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.
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Affiliation(s)
- Shoji Taniguchi
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Toshihiro Sakamoto
- Institute for Agro-Environmental Sciences, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Ryoji Imase
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Yasunori Nonoue
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Hiroshi Tsunematsu
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Akitoshi Goto
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Kei Matsushita
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Sinnosuke Ohmori
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Hideo Maeda
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Yoshinobu Takeuchi
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Takuro Ishii
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Jun-ichi Yonemaru
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Daisuke Ogawa
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
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Sun G, Lu H, Zhao Y, Zhou J, Jackson R, Wang Y, Xu L, Wang A, Colmer J, Ober E, Zhao Q, Han B, Zhou J. AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice. THE NEW PHYTOLOGIST 2022; 236:1584-1604. [PMID: 35901246 PMCID: PMC9796158 DOI: 10.1111/nph.18314] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/31/2022] [Indexed: 05/11/2023]
Abstract
Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.
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Affiliation(s)
- Gang Sun
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Hengyun Lu
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Yan Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Jie Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Robert Jackson
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
| | - Yongchun Wang
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Ling‐xiang Xu
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Ahong Wang
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Joshua Colmer
- Earlham InstituteNorwich Research ParkNorwichNR4 7UHUK
| | - Eric Ober
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
| | - Qiang Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Bin Han
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Ji Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
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9
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Shu M, Fei S, Zhang B, Yang X, Guo Y, Li B, Ma Y. Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:9802585. [PMID: 36158531 PMCID: PMC9489231 DOI: 10.34133/2022/9802585] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 08/07/2022] [Indexed: 12/03/2022]
Abstract
High-throughput estimation of phenotypic traits from UAV (unmanned aerial vehicle) images is helpful to improve the screening efficiency of breeding maize. Accurately estimating phenotyping traits of breeding maize at plot scale helps to promote gene mining for specific traits and provides a guarantee for accelerating the breeding of superior varieties. Constructing an efficient and accurate estimation model is the key to the application of UAV-based multiple sensors data. This study aims to apply the ensemble learning model to improve the feasibility and accuracy of estimating maize phenotypic traits using UAV-based red-green-blue (RGB) and multispectral sensors. The UAV images of four growth stages were obtained, respectively. The reflectance of visible light bands, canopy coverage, plant height (PH), and texture information were extracted from RGB images, and the vegetation indices were calculated from multispectral images. We compared and analyzed the estimation accuracy of single-type feature and multiple features for LAI (leaf area index), fresh weight (FW), and dry weight (DW) of maize. The basic models included ridge regression (RR), support vector machine (SVM), random forest (RF), Gaussian process (GP), and K-neighbor network (K-NN). The ensemble learning models included stacking and Bayesian model averaging (BMA). The results showed that the ensemble learning model improved the accuracy and stability of maize phenotypic traits estimation. Among the features extracted from UAV RGB images, the highest accuracy was obtained by the combination of spectrum, structure, and texture features. The model had the best accuracy constructed using all features of two sensors. The estimation accuracies of ensemble learning models, including stacking and BMA, were higher than those of the basic models. The coefficient of determination (R 2) of the optimal validation results were 0.852, 0.888, and 0.929 for LAI, FW, and DW, respectively. Therefore, the combination of UAV-based multisource data and ensemble learning model could accurately estimate phenotyping traits of breeding maize at plot scale.
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Affiliation(s)
- Meiyan Shu
- College of Land Science and Technology, China Agricultural University, Beijing 100091, China
| | - Shuaipeng Fei
- College of Land Science and Technology, China Agricultural University, Beijing 100091, China
| | - Bingyu Zhang
- College of Land Science and Technology, China Agricultural University, Beijing 100091, China
| | - Xiaohong Yang
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center of China, China Agricultural University, Beijing 100091, China
| | - Yan Guo
- College of Land Science and Technology, China Agricultural University, Beijing 100091, China
| | - Baoguo Li
- College of Land Science and Technology, China Agricultural University, Beijing 100091, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100091, China
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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.
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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
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Bhat JA, Yu D, Bohra A, Ganie SA, Varshney RK. Features and applications of haplotypes in crop breeding. Commun Biol 2021; 4:1266. [PMID: 34737387 PMCID: PMC8568931 DOI: 10.1038/s42003-021-02782-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 10/09/2021] [Indexed: 12/17/2022] Open
Abstract
Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.
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Affiliation(s)
- Javaid Akhter Bhat
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Deyue Yu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Abhishek Bohra
- Crop Improvement Division, ICAR- Indian Institute of Pulses Research (ICAR- IIPR), Kanpur, India
| | - Showkat Ahmad Ganie
- Department of Biotechnology, Visva-Bharati, Santiniketan, 731235, WB, India.
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India.
- State Agricultural Biotechnology Centre, Centre for Crop & Food Research Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, Australia.
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Yang CJ, Edmondson RN, Piepho HP, Powell W, Mackay I. Crafting for a better MAGIC: systematic design and test for Multiparental Advanced Generation Inter-Cross population. G3 GENES|GENOMES|GENETICS 2021; 11:6354367. [PMID: 34849794 PMCID: PMC8527519 DOI: 10.1093/g3journal/jkab295] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/15/2021] [Indexed: 12/04/2022]
Abstract
Multiparental Advanced Generation Inter-Cross (MAGIC) populations are valuable crop resources with a wide array of research uses including genetic mapping of complex traits, management of genetic resources and breeding of new varieties. Multiple founders are crossed to create a rich mosaic of highly recombined founder genomes in the MAGIC recombinant inbred lines (RILs). Many variations of MAGIC population designs exist; however, a large proportion of the currently available populations have been created empirically and based on similar designs. In our evaluations of five MAGIC populations, we found that the choice of designs has a large impact on the recombination landscape in the RILs. The most popular design used in many MAGIC populations has been shown to have a bias in recombinant haplotypes and low level of unique recombinant haplotypes, and therefore is not recommended. To address this problem and provide a remedy for the future, we have developed the “magicdesign” R package for creating and testing any MAGIC population design via simulation. A Shiny app version of the package is available as well. Our “magicdesign” package provides a unifying tool and a framework for creativity and innovation in MAGIC population designs. For example, using this package, we demonstrate that MAGIC population designs can be found which are very effective in creating haplotype diversity without the requirement for very large crossing programs. Furthermore, we show that interspersing cycles of crossing with cycles of selfing is effective in increasing haplotype diversity. These approaches are applicable in species that are hard to cross or in which resources are limited.
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Affiliation(s)
| | | | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart 70593, Germany
| | - Wayne Powell
- Scotland’s Rural College (SRUC), Edinburgh EH9 3JG, UK
| | - Ian Mackay
- Scotland’s Rural College (SRUC), Edinburgh EH9 3JG, UK
- IMplant Consultancy Ltd., Chelmsford, UK
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Effect of the Solar Zenith Angles at Different Latitudes on Estimated Crop Vegetation Indices. DRONES 2021. [DOI: 10.3390/drones5030080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Normalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications to ensure reliability, consistency, and interpretability of time-series multispectral data acquired using an unmanned aerial vehicle (UAV). Different models have been developed to characterize the bidirectional reflectance distribution function. However, the substantial variation in crop breeding trials, in terms of vegetation structure configuration, creates challenges to such modeling approaches. This study evaluated the variation in standard vegetation indices and its relationship with ground-reference data (measured crop traits such as seed/grain yield) in multiple crop breeding trials as a function of solar zenith angles (SZA). UAV-based multispectral images were acquired and utilized to extract vegetation indices at SZA across two different latitudes. The pea and chickpea breeding materials were evaluated in a high latitude (46°36′39.92″ N) zone, whereas the rice lines were assessed in a low latitude (3°29′42.43″ N) zone. In general, several of the vegetation index data were affected by SZA (e.g., normalized difference vegetation index, green normalized difference vegetation index, normalized difference red-edge index, etc.) in both latitudes. Nevertheless, the simple ratio index (SR) showed less variability across SZA in both latitude zones amongst these indices. In addition, it was interesting to note that the correlation between vegetation indices and ground-reference data remained stable across SZA in both latitude zones. In summary, SR was found to have a minimum anisotropic reflectance effect in both zones, and the other vegetation indices can be utilized to evaluate relative differences in crop performances, although the absolute data would be affected by SZA.
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Ogawa D, Sakamoto T, Tsunematsu H, Kanno N, Nonoue Y, Yonemaru JI. Remote-Sensing-Combined Haplotype Analysis Using Multi-Parental Advanced Generation Inter-Cross Lines Reveals Phenology QTLs for Canopy Height in Rice. FRONTIERS IN PLANT SCIENCE 2021; 12:715184. [PMID: 34721450 PMCID: PMC8553969 DOI: 10.3389/fpls.2021.715184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/13/2021] [Indexed: 05/13/2023]
Abstract
High-throughput phenotyping systems with unmanned aerial vehicles (UAVs) enable observation of crop lines in the field. In this study, we show the ability of time-course monitoring of canopy height (CH) to identify quantitative trait loci (QTLs) and to characterise their pleiotropic effect on various traits. We generated a digital surface model from low-altitude UAV-captured colour digital images and investigated CH data of rice multi-parental advanced generation inter-cross (MAGIC) lines from tillering and heading to maturation. Genome-wide association studies (GWASs) using the CH data and haplotype information of the MAGIC lines revealed 11 QTLs for CH. Each QTL showed haplotype effects on different features of CH such as stage-specificity and constancy. Haplotype analysis revealed relationships at the QTL level between CH and, vegetation fraction and leaf colour [derived from UAV red-green-blue (RGB) data], and CH and yield-related traits. Noticeably, haplotypes with canopy lowering effects at qCH1-4, qCH2, and qCH10-2 increased the ratio of panicle weight to leaf and stem weight, suggesting biomass allocation to grain yield or others through growth regulation of CH. Allele mining using gene information with eight founders of the MAGIC lines revealed the possibility that qCH1-4 contains multiple alleles of semi-dwarf 1 (sd1), the IR-8 allele of which significantly contributed to the "green revolution" in rice. This use of remote-sensing-derived phenotyping data into genetics using the MAGIC lines gives insight into how rice plants grow, develop, and produce grains in phenology and provides information on effective haplotypes for breeding with ideal plant architecture and grain yield.
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Affiliation(s)
- Daisuke Ogawa
- Institute of Crop Science, National Agricultural and Food Research Organization, Tsukuba, Japan
- *Correspondence: Daisuke Ogawa
| | - Toshihiro Sakamoto
- Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Hiroshi Tsunematsu
- Institute of Crop Science, National Agricultural and Food Research Organization, Tsukuba, Japan
| | - Noriko Kanno
- Institute of Crop Science, National Agricultural and Food Research Organization, Tsukuba, Japan
| | - Yasunori Nonoue
- Institute of Crop Science, National Agricultural and Food Research Organization, Tsukuba, Japan
| | - Jun-ichi Yonemaru
- Institute of Crop Science, National Agricultural and Food Research Organization, Tsukuba, Japan
- Jun-ichi Yonemaru
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