Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data

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.