Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data.
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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 high-throughput phenotyping (FHTP), using small unoccupied aerial vehicles (UAV) 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 recombinant maize inbred lines (RILs) in both irrigated and drought stress trials in 2018. Using UAV phenomic, 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 using genomic data; but prediction ability increased 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 relation 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 time-dependent association between genotypes and abiotic stresses, which can help understand mechanisms to develop resilient plants.
