Unoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci

Abstract

Unoccupied aerial systems (UAS) were used to phenotype growth trajectories of inbred maize populations under field conditions. Three recombinant inbred line populations were surveyed on a weekly basis collecting RGB images across two irrigation regimens (irrigated and non-irrigated/rain fed). Plant height, estimated by the 95th percentile (P95) height from UAS generated 3D point clouds, exceeded 70% correlation (r) to manual ground truth measurements and 51% of experimental variance was explained by genetics. The Weibull sigmoidal function accurately modeled plant growth (R 2 : >99%; RMSE: <4 cm) from P95 genetic means. The mean asymptote was strongly correlated (r 2 = 0.66-0.77) with terminal plant height. Maximum absolute growth rates (mm/day) were weakly correlated with height and flowering time. The average inflection point ranged from 57 to 60 days after sowing (DAS) and was correlated with flowering time (r 2 = 0.45-0.68). Functional growth parameters (asymptote, inflection point, growth rate) alone identified 34 genetic loci, each explaining 3-15% of total genetic variation. Plant height was estimated at one-day intervals to 85 DAS, identifying 58 unique temporal quantitative trait loci (QTL) locations. Genomic hotspots on chromosomes 1 and 3 indicated chromosomal regions associated with functional growth trajectories influencing flowering time, growth rate, and terminal growth. Temporal QTL demonstrated unique dynamic expression patterns

Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize

“…It is likely that differences in repeatability for each flight were related to the image and stitching quality of each flight (Anderson et al., 2019; Malambo et al., 2018) as well as weed pressures especially in earliest flights (e.g., 35 DAS in G2FD). It is also likely that plant height at any point in time was impacted by the activity of many genes and interplays between genes in response to changing environmental conditions during various growth periods (Veldboom & Lee, 1996; Messmer et al., 2009; Sibov et al., 2003; Anderson et al., 2020; Dijak et al., 1999; Han et al., 2018). Since UAS allowed temporal variation of plant height to be estimated here, this variation can be used in dissecting underlying genetic mechanisms such as discovering time‐specific and colocalized genes in association mapping.…”

“…Quantitative variation of complex traits in maize ( Zea mays L.) have been challenging to dissect since they show strong environmental interactions and are generally inconsistent between populations and different screening environments (Beavis et al., 1991; Koester et al., 1993; Peiffer et al., 2014; Sari‐Gorla et al., 1999; Veldboom & Lee, 1996; Wang et al., 2006). Plant height, traditionally measured terminally at the end of the growing season with a ruler, is a prime example of a quantitative, complex trait; it is relatively easy to measure across many plots and it has high repeatability and heritability (Anderson et al., 2018,2019, 2020; Mahan et al., 2018; Peiffer et al., 2014; Rood & Major, 1981; Veldboom & Lee, 1996). Genetic mapping and theory suggest an omnigenic model supported by the genetically polygenic inheritances observed and the variable contributions of pedigree as a source of variation, consistent with a large number of loci with minor effects governing these traits (Boyle et al., 2017; Mackay, 2001; Peiffer et al., 2014; Wallace et al., 2016; Wang et al., 2006).…”

“…Genetic mapping and theory suggest an omnigenic model supported by the genetically polygenic inheritances observed and the variable contributions of pedigree as a source of variation, consistent with a large number of loci with minor effects governing these traits (Boyle et al., 2017; Mackay, 2001; Peiffer et al., 2014; Wallace et al., 2016; Wang et al., 2006). Recently, emerging high‐throughput phenotyping (HTP) tools have demonstrated that complicated phenotypically dynamic growth patterns are occurring (Anderson et al., 2020; Miao et al., 2020; Pauli et al., 2016; Wang et al., 2019; Wu & Lin, 2006) and that the importance of various loci change temporally throughout plant growth and development (Furbank & Tester, 2011).…”

Validation of Functional Polymorphisms Affecting Maize Plant Height by Unoccupied Aerial Systems (UAS) allows Novel Temporal Phenotypes

“…Recent studies showed the temporal plant height-SNPs associations in sorghum (Miao et al 2020) and maize (Anderson et al 2020), however there were no precise validation of gene effects in either early or late growth stages. In conclusion, in this study, highthroughput phenotyping technology firstly enabled the monitoring temporal shift of gene effects with high resolution during growth periods, which cannot be captured to such an extent by traditional terminal measurement methods.…”

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

“…One multi-rotor UAP with an RGB camera, and another with a multispectral camera, were combined to monitor tomato crops, so as to formulate management measures and determine the best management scheme for specific fields (Marconi et al, 2019 ). Likewise, Anderson et al ( 2020 ) employed a rotary-wing UAP and a fixed-wing UAP to monitor a track of plant height growth of an recombinant inbred line (RIL) maize population; hence, they could, for the first time, elucidate dynamic characteristics of quantitative trait loci (QTL) in real time under the field conditions. In addition, Deery et al ( 2019 ) designed a MAP with airborne thermal IR cameras and a pole-based platform (Arducrop wireless IR thermometers) and used it to continuously measure crop CT (canopy temperature).…”