DOI: 10.5061/dryad.44j0zpcf0
title: Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping

description: Plant phenotyping under field conditions plays an important role in
 agricultural research. Efficient and accurate high-throughput phenotyping
 strategies enable a better connection between genotype and phenotype.
 Unmanned aerial vehicle-based high-throughput phenotyping platforms
 (UAV-HTPPs) provide novel opportunities for large-scale proximal
 measurement of plant traits with high efficiency, high resolution, and low
 cost. The objective of this study was to use time series normalized
 difference vegetation index (NDVI) extracted from UAV-based multispectral
 imagery to characterize its pattern across development and conduct genetic
 dissection of NDVI in a large maize population. The time series NDVI data
 from the multispectral sensor were obtained at 5 time points across the
 growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster
 analysis of the acquired measurements classified 1,752 maize accessions
 into 2 groups with distinct NDVI developmental trends. To capture the
 dynamics underlying these static observations, penalized-splines
 (P-splines) model was used to obtain genotype-specific curve parameters.
 Genome-wide association study (GWAS) using static NDVI values and curve
 parameters as phenotypic traits detected signals significantly associated
 with the traits. Additionally, GWAS using the projected NDVI values from
 the P-splines models revealed the dynamic change of genetic effects,
 indicating the role of gene-environment interplay in controlling NDVI
 across the growing season. Our results demonstrated the utility of
 ultra-high spatial resolution multispectral imagery, as that acquired
 using a UAV-based remote sensing, for genetic dissection of NDVI.
description: The UAV system contains a DJI S900 UAV and an NIR converted
 multispectral Canon Rebel SL1 DSLR camera with an intervalometer and
 GPS. We conducted 5 UAV overflights across the growing season in 2017.
 Overflights were scheduled around 5 growth stages
 (V<sub>4</sub>, V<sub>8</sub>,
 V<sub>12</sub>,<sub>
 </sub>V<sub>T</sub>, and R<sub>5</sub>). The
 following image processing steps were applied to obtain high quality data
 from the raw UAV images: image pre-processing, orthomosaic generation, VIs
 calculation, and plot-level data extraction. The plot-level NDVI mean was
 calculated from the reflectance measurements in the red and NIR portion of
 the spectrum from the transect area of each plot. NDVI values generated on
 the -1 to 1 scale were rescaled by adding 1 and then multiplying by 128 to
 convert them into the [0 – 255] range. Time series NDVI values were
 obtained from five overflights for 1,752 diverse maize accessions. GWAS
 was conducted for the static NDVI values and the curve parameters derived
 across stages.
description: ## Dataset repository for the "Genetic Dissection of
 Seasonal Vegetation Index Dynamics in Maize through Aerial Based
 High-throughput Phenotyping" Project ### Outline
 of the repository<br> In order to better guide the visitors about
 the repository, here we briefly introduce the outline of the
 repository ### NDVI Distribution Dataset<br> ####
 1. Dataset for NDVI distribution analysis<br> ##### A. Name of the
 data file: AmesDP_NDVI_hand_measured_trait_with_group_infor.txt<br>
 ##### B. File Overview -Number of variables/columns:
 12 -Number of rows: 1752 -Variable
 List:     Group: Population information for each
 accession<br>     New_Group: Population information for each
 accession, different from 'Group' column is that the
 sweet/popcorn using the kernel-based criteria (doesn’t matter what the
 genetic background is, only whether the kernel type is sweet or
 pop)<br>     Order: Manually defined accession order
 number<br>     Taxa: Accession name<br>     NDVI_37DAP_2017 -
 NDVI_115DAP_2017 (5 variables in total): NDVI measurements at 37, 44, 60,
 73, and 115 DAP. DAP stands for Days after planting<br>     FT_2017:
 flowering time measurement (DAP)<br>     PH_2017: plant height
 measurement (cm)<br>     EH_2017: ear height measurement
 (cm)<br>     ### NDVI Clustering And Population
 Structure Dataset<br> #### 1. Dataset for NDVI distribution
 analysis<br> ##### A. Name of the data file:
 AmesDP_NDVI_hand_measured_trait_with_group_infor.txt<br> ##### B.
 File Overview -Basically the same dataset as above, so
 skip the file overview part  #### 2. Dataset for
 K-means clustering result<br> ##### A. Name of the data file:
 AmesPanel_NDVI_data_with_clustering_infor_2cluster<br> ##### B. File
 Overview<br> -Number of variables/columns: 6
 -Number of rows: 1752 -Variable
 List:     Taxa: Accession name<br>    
 NDVI_37DAP_2017 - NDVI_115DAP_2017 (5 variables in total): NDVI
 measurements at 37, 44, 60, 73, and 115 DAP. DAP stands for Days after
 planting<br>     cluster: cluster category information for each
 accession, obtained from K-means clustering analysis
 #### 3. Dataset for tSNE analysis<br> ##### A. Name of the
 data file: AmesDP_genome_hmp_m1m20_s10<br> ##### B. File
 Overview<br> -Number of variables/columns: 1753
 -Number of rows: 31674 -Variable
 List:     rs#: SNP ID<br>     the rest columns:
 accession name/taxa for each accession -Dataset for SNP
 set used for tSNE analysis #### 4. Dataset for ploting
 growth curve, clustering result and tSNE result<br> ##### A. Name of
 the data file: AmesDP_2017_NDVI_cluster_tSNE_PCA_synthetic<br> #####
 B. File Overview<br> -Number of variables/columns: 17
 -Number of rows: 1752 -Variable
 List:     Group: Population information for each
 accession<br>     New_Group: Population information for each
 accession, different from 'Group' column is that the
 sweet/popcorn using the kernel-based criteria (doesn’t matter what the
 genetic background is, only whether the kernel type is sweet or
 pop)<br>     Order: Manually defined accession order
 number<br>     Taxa: Accession name<br>     NDVI_37DAP_2017 -
 NDVI_115DAP_2017 (5 variables in total): NDVI measurements at 37, 44, 60,
 73, and 115 DAP. DAP stands for Days after planting<br>     cluster:
 cluster category information for each accession, obtained from K-means
 clustering analysis<br>     pheno_tsne_Y1: value on first dimension
 of tSNE result with phenotype data<br>     pheno_tsne_Y2: value on
 second dimension of tSNE result with phenotype data<br>    
 geno_tsne_Y1: value on first dimension of tSNE result with genotype
 data<br>     geno_tsne_Y2: value on second dimension of tSNE result
 with genotype data<br>     PC1 - PC3 (3 variables): first, second,
 third dimention data from PCA analysis ### P-Spline
 Modeling NDVI growth Dataset<br> #### 1. Dataset for P-spline
 modeling<br> ##### A. Name of the data file:
 AmesDP_NDVI_hand_measured_trait<br> ##### B. File Overview
 -Number of variables/columns: 9 -Number of
 rows: 1752 -Variable List:     
 Taxa, NDVI_37DAP_2017 - NDVI_115DAP_2017, FT_2017, PH_2017, EH_2017 are
 the same as above dataset named
 AmesDP_NDVI_hand_measured_trait_with_group_infor.txt
 #### 2. Dataset for P-spline modeling<br> ##### A. Name of
 the data file: AmesDP2017_NDVI_long<br> -Basically an intermediate
 file, which is reframed from the file named
 'AmesDP_NDVI_hand_measured_trait' (in wide format) to long
 format<br> ##### B. File Overview<br> -Number of
 variables/columns: 4 -Number of rows: 1752
 -Variable List:      Gen: Accession
 name<br>     Genor: Manually assigned accession order<br>    
 Date: Days After Planting<br>     NDVI: NDVI measurement
 #### 3. Dataset for Psplines modeling parameter<br> #####
 A. Name of the data file:
 AmesDP_NDVI_Psplines_modeling_parameter.csv<br> ##### B. File
 Overview<br> -Number of variables/columns: 4
 -Number of rows: 1752 -Variable
 List:      Geno: Accession name<br>    
 asymptote: model estimated maximum NDVI value<br>     max_rate:
 model estimated maximum growth rate of NDVI<br>    
 inflection_point: point in time with maximum growth rate of NDVI
 #### 4. Dataset for Psplines predicted NDVI value<br> #####
 A. Name of the data file:
 AmesDP_NDVI_Pspline_NDVI_prediction_by_1day.csv<br> ##### B. File
 Overview -Number of variables/columns: 3
 -Number of rows: 1752 -Variable
 List:      geno: Accession name<br>    
 biomassspline: predicted biomass from 37-115 DAP with 1 day interval, the
 predicted biomass value is connected with '_'<br>    
 growthratespline: predicted growth rate from 37-115 DAP with 1 day
 interval, the predicted growth rate value is connected with
 '_' #### 5. Dataset for Psplines predicted
 NDVI value<br> ##### A. Name of the data file:
 AmesDP2017_observed_Psplines_model_fitted_value<br> ##### B. File
 Overview<br> -Number of variables/columns: 21
 -Number of rows: 1752 -Variable
 List:      geno: Accession name<br>    
 NDVI_37DAP_2017 - NDVI_115DAP_2017 (5 variables in total): NDVI
 measurements at 37, 44, 60, 73, and 115 DAP.  DAP stands for Days after
 planting<br>     Pspline_37DAP - Pspline_115DAP (15 variables in
 total): predicted NDVI value at 37, 44, 44, 51, 58, 65, 72, 79, 86, 93,
 100, 107, 114, 60, 73, 115 DAP #### 6. Dataset for
 plotting growth curve, and correlation between observed and predicted NDVI
 value<br> ##### A. Name of the data file:
 AmesPanel2017_observed_Psplines_model_fitted_with_pop_structure<br>
 ##### B. File Overview<br> -Number of variables/columns:
 14 -Number of rows: 1752 -Variable
 List:      Group: Population information for each
 accession<br>     New_Group: Population information for each
 accession, different from 'Group' column is that the
 sweet/popcorn using the kernel-based criteria (doesn’t matter what the
 genetic background is, only whether the kernel type is sweet or
 pop)<br>     Order: Manually defined accession order
 number<br>     Taxa: Accession name<br>     NDVI_37DAP_2017 -
 NDVI_115DAP_2017 (5 variables in total): NDVI measurements at 37, 44, 60,
 73, and 115 DAP. DAP stands for Days after planting<br>    
 Pspline_37DAP - Pspline_115DAP (5 variables in total): predicted NDVI
 value at 37, 44, 60, 73, and 115 DAP.  ### GWAS of NDVI
 and Pspline Curve Parameters Dataset #### 1. Dataset
 for plotting Manhattan plot for NDVI and Pspline Curve
 Parameters<br> ##### A. Name of the data file:
 GWAS-21M_SNPs.txt<br> ##### B. File Overview 
 -Number of variables/columns: 2 -Number of
 rows: 21129389 -Variable List:     
 CHROM: Chromosome number<br>     POS: Position on correspongind
 chromosome #### 2. Dataset for plotting Manhattan plot
 for NDVI and Pspline Curve Parameters<br> ##### A. Name of the data
 file: NDVI_candidate_gene_list_FDR<br> ##### B. File
 Overview  -Number of variables/columns: 13
 -Number of rows: 93 -Variable List: 
     Trait: trait name<br>     Gene_name: Gene
 name<br>     Gene_ID_v3: Gene ID from maize B73 genome Version
 3<br>     Gene_Chr_V3: chromosome number basedon B73 genome Version
 3<br>     Gene_S: start position on chromosome<br>     Gene_E:
 end position on chromosome<br>     Tag_SNP: tagged SNP position on
 chromosome<br>     Distance: the distance from tagged SNP to
 gene<br>     Abt_distance: the absoute distance from tagged SNP to
 gene<br>     Alias: gene alias name<br>     Significant:
 whether the tagged SNP is significantly associated with the
 trait<br>     AtID: gene ID in arabidopsis<br>     OsID: gene
 ID in rice #### 3. Dataset for plotting Manhattan plot
 for NDVI and Pspline Curve Parameters<br> ##### A. Name of the data
 file: NDVI_GWAS_FDR_threshold<br> ##### B. File Overview
 -Number of variables/columns: 5 -Number of
 rows: 25 -Variable List:      Trait:
 Trait name<br>     p_value: p-value obtained from GWAS<br>    
 Threshold: calculted FDR threshold #### 4. Dataset for
 plotting Manhattan plot for NDVI and Pspline Curve Parameters<br>
 ##### A. Name of the data file: ##### A. Name of the data file:
 AmesDP_NDVI_73DAP_2017_genome_wide_gwas_results_sorted_log2_wh,
 mesDP_NDVI_115DAP_2017_genome_wide_gwas_results_sorted_log2_wh,
 AmesDP_max_rate_genome_wide_gwas_results_sorted_log2_wh,
 AmesDP_asymptote_genome_wide_gwas_results_sorted_log2_wh<br> #####
 B. File Overview<br> - GWAS output from GAPIT for 4 different trait,
 NDVI_73DAP, NDVI_115DAP, asymptote, max rate. normal GAPIT output file.
 Skip the description for file overview ### Dynamic
 Changes of Allelic Effect Dataset<br> ##### A. Name of the data
 file: Psplines_SNP_effect<br> ##### B. File Overview
 Number of variables/columns:9 -Number of rows:
 45 -Variable List:      SNP: SNP
 ID<br>     Position: SNP position<br>     P.value:
 P-value<br>     maf: minor allele frequency<br>    
 Rsquare.of.Model.without.SNP: Rsquare of model when not including the
 SNP<br>     Rsquare.of.Model.with.SNP: Rsquare of model when
 including the SNP<br>     FDR_Adjusted_P.values: FDR adjusted
 p_values <br>     effect: SNP effect<br>     Chro: Chromosome
 number