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Unoccupied aerial system enabled functional modeling of maize (Zea mays L.) height reveals dynamic expression of loci associated to temporal growth

Citation

Anderson, Steven et al. (2019), Unoccupied aerial system enabled functional modeling of maize (Zea mays L.) height reveals dynamic expression of loci associated to temporal growth, Dryad, Dataset, https://doi.org/10.5061/dryad.q573n5tf2

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 to manual ground truth measurements and 51% of experimental variance was explained by genetics. The Weibull sigmoidal function accurately modeled plant growth (R2: >99%; RMSE: < 4 cm) from P95 genetic means. The mean asymptote was strongly correlated (r2=0.66-0.77) with terminal plant height. Maximum absolute growth rates (mm d-1) were weakly correlated to height and flowering time. The average inflection point ranged from 57 to 60 days after sowing (DAS) and was correlated with flowering time (r2=0.45-0.68). Functional growth parameters (asymptote, inflection point, growth rate) alone identified 34 genetic loci, each explaining 3 to 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 chromosome 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 observable previously, 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.

Funding

USDA-NIFA-AFRI, Award: 017-67013-26185

USDA-NIFA Hatch funds

Texas A&M AgriLife Research

Texas Corn Producers Board

Iowa Corn Promotion Board

Eugene Butler Endowed Chair in Biotechnology

Texas A&M College of Agriculture and Life Sciences, Award: Tom Slick Senior Graduate Fellowship