1
|
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.
Collapse
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
| |
Collapse
|
2
|
Adak A, Murray SC, Washburn JD. Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations. THE NEW PHYTOLOGIST 2024; 242:121-136. [PMID: 38348523 DOI: 10.1111/nph.19575] [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: 10/05/2023] [Accepted: 01/11/2024] [Indexed: 03/08/2024]
Abstract
Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques. In this study, we analyzed the growth patterns of two different maize (Zea mays L.) populations using high-throughput phenotyping with a maize population consisting of 515 recombinant inbred lines (RILs) grown in Texas and a hybrid population containing 1090 hybrids grown in Missouri. Two models, Gaussian peak and functional principal component analysis (FPCA), were employed to study the Normalized Green-Red Difference Index (NGRDI) scores. The Gaussian peak model showed strong correlations (c. 0.94 for RILs and c. 0.97 for hybrids) between modeled and non-modeled temporal trajectories. Functional principal component analysis differentiated NGRDI trajectories in RILs under different conditions, capturing substantial variability (75%, 20%, and 5% for RILs; 88% and 12% for hybrids). By comparing these models with conventional BLUP values, common quantitative trait loci (QTLs) were identified, containing candidate genes of brd1, pin11, zcn8 and rap2. The harmony between these loci's additive effects and growing degree days, as well as the differentiation of RIL haplotypes across growth stages, underscores the significant interplay of these loci in driving plant development. These findings contribute to advancing understanding of plant-environment interactions and have implications for crop improvement strategies.
Collapse
Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, 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, USA
| | | |
Collapse
|
3
|
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
|
4
|
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
|
5
|
Li D, Bai D, Tian Y, Li YH, Zhao C, Wang Q, Guo S, Gu Y, Luan X, Wang R, Yang J, Hawkesford MJ, Schnable JC, Jin X, Qiu LJ. Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2023; 65:117-132. [PMID: 36218273 DOI: 10.1111/jipb.13380] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data. Here, we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time-series data, as it may represent an important trait for weed control. Genome-wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single-time-point studies. Moreover, this time-series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time-series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.
Collapse
Affiliation(s)
- Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Dong Bai
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yu Tian
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Ying-Hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Chaosen Zhao
- Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang, 330200, China
| | - Qi Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Shiyu Guo
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, China
| | - Yongzhe Gu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiaoyan Luan
- Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, 150086, China
| | - Ruizhen Wang
- Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang, 330200, China
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, 68583, USA
| | - Malcolm J Hawkesford
- Plant Sciences Department, Rothamsted Research, West Common, Harpenden, Hertfordshire, AL5 2JQ, UK
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, 68583, USA
| | - Xiuliang Jin
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Li-Juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| |
Collapse
|
6
|
Adak A, Murray SC, Anderson SL. Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions. G3 (BETHESDA, MD.) 2022; 13:6851143. [PMID: 36445027 PMCID: PMC9836347 DOI: 10.1093/g3journal/jkac294] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 10/21/2022] [Indexed: 11/30/2022]
Abstract
A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade, genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over 15 growth time points and multispectral (RGB, red-edge and near infrared) bands over 12 time points were compared across 280 unique maize hybrids. Through cross-validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP), outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in 3 other cross-validation scenarios. Genome-wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5% of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51% of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, TPP appeared to work successfully on unrelated individuals unlike GP.
Collapse
Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Seth C Murray
- Corresponding author: Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA.
| | | |
Collapse
|
7
|
Zhang M, Liu YH, Wang Y, Sze SH, Scheuring CF, Qi X, Ekinci O, Pekar J, Murray SC, Zhang HB. Genome-wide identification of genes enabling accurate prediction of hybrid performance from parents across environments and populations for gene-based breeding in maize. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 324:111424. [PMID: 35995113 DOI: 10.1016/j.plantsci.2022.111424] [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: 06/14/2022] [Revised: 08/07/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Accurate prediction of hybrid offspring complex trait phenotype from parents is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report genome-wide identification of genes enabling accurate prediction of hybrid offspring complex traits from parents using maize grain yield as the target trait. We identified 181 ZmF1GY genes enabling prediction of maize (Zea mays L.) F1 hybrid grain yield from parents and tested their utility and efficiency for predicting F1 hybrid grain yields from parents using their expressions, genic SNPs, and number of favorable alleles (NFAs), respectively. The ZmF1GY genes predicted hybrid grain yields from parents at an accuracy of 0.86, presented by correlation coefficient between predicted and observed phenotypes, within an environment, 0.74 across environments, and 0.64 across populations, outperforming genomic prediction by 27-406%, 23%, and 40%, respectively. Furthermore, we identified nine of the ZmF1GY genes containing SNPs or InDels in parents that increased or decreased hybrid grain yields by 14-46%. When the NFAs of these nine ZmF1GY genes were used for hybrid grain yield prediction from parents, they predicted hybrid grain yields at an accuracy of 0.79, outperforming genomic prediction by 21% that was based on up to tens of thousands of genome-wide SNPs. These results demonstrate the feasibility of developing a gene toolkit for a species enabling gene-based breeding across environments and populations that is much more powerful and efficient than current breeding, thereby helping secure the world's food production. The methodology is applicable to all crops, livestock, and humans.
Collapse
Affiliation(s)
- Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Yun-Hua Liu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Yinglei Wang
- Department of Computer Science, Cornell University, Ithaca, NY 14853, USA.
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA.
| | - Chantel F Scheuring
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Xiaoli Qi
- College of Life Science, Jiamusi University, Jiamusi, Heilongjiang 154007, China.
| | - Ozge Ekinci
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Jacob Pekar
- Department of Soil and Crop Sciences, 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, USA.
| | - Hong-Bin Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| |
Collapse
|
8
|
Thomson MJ, Biswas S, Tsakirpaloglou N, Septiningsih EM. Functional Allele Validation by Gene Editing to Leverage the Wealth of Genetic Resources for Crop Improvement. Int J Mol Sci 2022; 23:ijms23126565. [PMID: 35743007 PMCID: PMC9223900 DOI: 10.3390/ijms23126565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 02/05/2023] Open
Abstract
Advances in molecular technologies over the past few decades, such as high-throughput DNA marker genotyping, have provided more powerful plant breeding approaches, including marker-assisted selection and genomic selection. At the same time, massive investments in plant genetics and genomics, led by whole genome sequencing, have led to greater knowledge of genes and genetic pathways across plant genomes. However, there remains a gap between approaches focused on forward genetics, which start with a phenotype to map a mutant locus or QTL with the goal of cloning the causal gene, and approaches using reverse genetics, which start with large-scale sequence data and work back to the gene function. The recent establishment of efficient CRISPR-Cas-based gene editing promises to bridge this gap and provide a rapid method to functionally validate genes and alleles identified through studies of natural variation. CRISPR-Cas techniques can be used to knock out single or multiple genes, precisely modify genes through base and prime editing, and replace alleles. Moreover, technologies such as protoplast isolation, in planta transformation, and the use of developmental regulatory genes promise to enable high-throughput gene editing to accelerate crop improvement.
Collapse
|
9
|
DeSalvio AJ, Adak A, Murray SC, Wilde SC, Isakeit T. Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms. Sci Rep 2022; 12:7571. [PMID: 35534655 PMCID: PMC9085875 DOI: 10.1038/s41598-022-11591-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 04/19/2022] [Indexed: 11/09/2022] Open
Abstract
Current methods in measuring maize (Zea mays L.) southern rust (Puccinia polyspora Underw.) and subsequent crop senescence require expert observation and are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. In total, 36 vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated hybrid performance as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92-98%) and lower root mean squared error (RMSE) for rust and senescence scores (linear model RMSE ranged from 65.8 to 2396.5 across all traits, machine learning regressions RMSE ranged from 0.3 to 17.0). UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.
Collapse
Affiliation(s)
- Aaron J DeSalvio
- Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX, 77843-2128, USA
| | - Alper Adak
- 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.
| | - Scott C Wilde
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843-2474, USA
| | - Thomas Isakeit
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, 77843-2474, USA
| |
Collapse
|
10
|
Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection. REMOTE SENSING 2021. [DOI: 10.3390/rs13193976] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial.
Collapse
|
11
|
Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression. REMOTE SENSING 2021. [DOI: 10.3390/rs13112141] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.
Collapse
|