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 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.


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.

Description

Keywords

uas, uav, dynamic qtl, functional modeling, maize, temporal growth, uas, uav, dynamic qtl, functional modeling, maize, temporal growth

Sponsorship

This project was made possible by financial support from USDA-NIFA-AFRI Award No. 2017-67013-26185, USDA-NIFA Hatch funds, Texas A&M AgriLife Research, the Texas Corn Producers Board, the Iowa Corn Promotion Board, and the Eugene Butler Endowed Chair in Biotechnology. Steven Anderson was funded for one year by the Texas A&M College of Agriculture and Life Sciences Tom Slick Senior Graduate Fellowship. Special thank you to AgReliant Genetics, LLC. and Dr. Ivan D. Barrero Farfan for conducting the genotyping for this research in kind.
This project was made possible by financial support from USDA-NIFA-AFRI Award No. 2017-67013-26185, USDA-NIFA Hatch funds, Texas A&M AgriLife Research, the Texas Corn Producers Board, the Iowa Corn Promotion Board, and the Eugene Butler Endowed Chair in Biotechnology. Steven Anderson was funded for one year by the Texas A&M College of Agriculture and Life Sciences Tom Slick Senior Graduate Fellowship. Special thank you to AgReliant Genetics, LLC. and Dr. Ivan D. Barrero Farfan for conducting the genotyping for this research in kind.

Rights:

Attribution 4.0 International, Attribution 4.0 International

Citation

Anderson, S.L., Murray, S.C., Chen, Y., Malambo, L., Chang, A., Popescu, S., Cope, D. and Jung, J., 2020. Unoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci. Plant Direct, 4(5), p.e00223.
Anderson, S.L., Murray, S.C., Chen, Y., Malambo, L., Chang, A., Popescu, S., Cope, D. and Jung, J., 2020. Unoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci. Plant Direct, 4(5), p.e00223.