Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

ABSTRACT: Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows along the proximodistal axis of the adult leaf blade. Bulliform cell patterning affects leaf angle and is presumed to function during leaf rolling, thereby reducing water loss during temperature extremes and drought. In this study, epidermal leaf impressions were collected from a genetically and anatomically diverse population of maize inbred lines. Subsequently, convolutional neural networks were employed to measure microscopic, bulliform cell-patterning phenotypes in high-throughput. A genome-wide association study, combined with RNAseq analyses of the bulliform cell ontogenic zone, identified candidate regulatory genes affecting bulliform cel

SUBMITTER: Qiao P 

PROVIDER: S-EPMC6893188 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature