Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping

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.