Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data
Data files
Nov 14, 2023 version files 61.83 GB
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2018_Multi_flights.zip
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2018_RGB_flights.zip
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2018_SHP.zip
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2018_SHP2.zip
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erad216_suppl_supplementary_table_s1.pdf
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Phenomic_data.zip
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README.md
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.
README
Datasets included:
- 2018_RGB_Flights.zip This file contains 19 orthomosaics captured by the DJI Phantom 3 Professional, equipped with an RGB sensor (12-megapixel DJI FC300X camera), and flown at an altitude of 25 meters. The flight dates are designated as yyyy/mm/dd. The first three orthomosaics (20180314, 20180330, and 20180404) were not utilized and were included to display the bare ground without vegetation. The days after planting corresponding to the RGB flight times can be seen in Figure 1.
- 2018_Multi_flights.zip This file contains 8 orthomosaics captured by a fixed-wing Tuffwing UAV mapper equipped with a multispectral camera, the MicaSense RedEdge-MX, flown at an altitude of 120 meters. The flight date is designated as mm/dd/yy. The days after planting corresponding to the multispectral flight times can be seen in Figure 1.
- 2018_SHP.zip This file contains a plot-based shape files for drought and irrigated trials in ESRI shapefile format and designed for RGB orthomosaics. The compiled geospatial dataset comprises four components: .shp, .shx, .prj, and .dbf extensions. The drought trials are denoted as 'YYCD,' while the irrigated trials are denoted as 'YYCI.' For the drought trial, the files 2018_YYCD.dbf, 2018_YYCD.prj, 2018_YYCD.shp, and 2018_YYCD.shx are utilized, and for the irrigated trial, the corresponding files are 2018_YYCI.dbf, 2018_YYCI.prj, 2018_YYCI.shp, and 2018_YYCI.shx. These shape files should be viewed alongside the RGB orthomosaics to observe the plots in both drought and irrigated trials. Notably, there are no space between the shape files 2018_YYCD.shp and 2018_YYCI.shp. However, spaces are introduced between shape files across rows and ranges in experimental design, by additional shape files with "_buff" extensions. Specifically, for the drought trial, the files 2018_YYCD_buff.dbf, 2018_YYCD_buff.prj, 2018_YYCD_buff.shp, and 2018_YYCD_buff.shx are generated, and for the irrigated trial, the corresponding files are 2018_YYCI_buff.dbf, 2018_YYCI_buff.prj, 2018_YYCI_buff.shp, and 2018_YYCI_buff.shx. These shape files, with spaces between them, should be viewed alongside the RGB orthomosaics to analyze the plots in the respective trials. In this study, the 2018_YYCD_buff.shp and 2018_YYCI_buff.shp files were utilized.
- 2018_SHP2.zip This file contains a plot-based shape files for drought and irrigated trials in ESRI shapefile format and designed for Multispectral orthomosaics. The compiled geospatial dataset comprises four components: .shp, .shx, .prj, and .dbf extensions. The drought trials are denoted as 'YYCD,' while the irrigated trials are denoted as 'YYCI.' For the drought trial, the files 2018_YYCD.dbf, 2018_YYCD.prj, 2018_YYCD.shp, and 2018_YYCD.shx are utilized, and for the irrigated trial, the corresponding files are 2018_YYCI.dbf, 2018_YYCI.prj, 2018_YYCI.shp, and 2018_YYCI.shx. These shape files should be viewed alongside the Multispectral orthomosaics to observe the plots in both drought and irrigated trials. Notably, there are no space between the shape files 2018_YYCD.shp and 2018_YYCI.shp. However, spaces are introduced between shape files across rows and ranges in experimental design, by additional shape files with "_buff" extensions. Specifically, for the drought trial, the files 2018_YYCD_buff.dbf, 2018_YYCD_buff.prj, 2018_YYCD_buff.shp, and 2018_YYCD_buff.shx are generated, and for the irrigated trial, the corresponding files are 2018_YYCI_buff.dbf, 2018_YYCI_buff.prj, 2018_YYCI_buff.shp, and 2018_YYCI_buff.shx. These shape files, with spaces between them, should be viewed alongside the RGB orthomosaics to analyze the plots in the respective trials. In this study, the 2018_YYCD_buff.shp and 2018_YYCI_buff.shp files were utilized.
- Phenomic_data.zip This file contains the four phenomic data. 'YYCD18_MULTI.csv' contains 83 VIs plus red, green, blue, red-edge and NIR bands belonging to each plot in drought trial along with the planting information. 'YYCI18_MULTI.csv' contains 83 VIs plus red, green, blue, red-edge and NIR bands belonging to each plot in irrigated trial along with the planting information. 'YYCD18_RGB.csv' contains 32 VIs plus red, green and blue bands belonging to each plot in drought trial along with the planting information. 'YYCI18_RGB.csv' contains 32 VIs plus red, green and blue bands belonging to each plot in irrigated trial along with the planting information. Please see the supplementary table 1 for vegetation indices (VIs), it is attached here as well. Equation 1 in the article was used to analyze each phenomic data stored here.
- To view the orthomosaics and shape files, it is recommended to use QGIS (https://www.qgis.org/en/site/#), which is a free and open source geographic information system software.
Methods
Two different UAV platforms were used in the study to observe the RIL population in both drought and irrigated trials. One was a rotary wing UAV, the DJI Phantom 3 Professional, equipped with an RGB sensor (12-megapixel DJI FC300X camera) and flown at 25 meters, providing approximately 1 cm per pixel resolution. The other was a fixed-wing Tuffwing UAV mapper equipped with a multispectral camera, the MicaSense RedEdge-MX, flown at 120 meters, yielding roughly 7.5 cm per pixel resolution.To create orthomosaics for each flight, we processed the raw images from each flight using Pix4Dmapper for RGB data and Agisoft PhotoScan for multispectral data. Subsequently, we conducted plot-based data extraction on each orthomosaic using FIELDimageR packge in R.