Open AccessUnoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci
Author(s) -
Anderson Steven L.,
Murray Seth C.,
Chen Yuanyuan,
Malambo Lonesome,
Chang Anjin,
Popescu Sorin,
Cope Dale,
Jung Jinha
Publication year - 2020
Publication title -
plant direct
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.211
H-Index - 11
ISSN - 2475-4455
DOI - 10.1002/pld3.223
Subject(s) - quantitative trait locus , biology , inflection point , quantitative genetics , heritability , genetic variation , inbred strain , agronomy , genetics , mathematics , gene , geometry
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 not previously observable, and no QTL were significantly expressed throughout the entire growing season. UAS technologies improved phenotypic selection accuracy and permitted monitoring traits on a temporal scale previously infeasible using manual measurements, furthering understanding of crop development and biological trajectories.