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

This study is the first to combine machine learning approaches, transcriptomics, and genomics to study bulliform cell patterning, and the first to utilize natural variation to investigate the genetic architecture of this microscopic trait. In addition, this study provides insight toward the improvement of macroscopic traits such as drought resistance and plant architecture in an agronomically important crop plant.

Source: G3: Genes Genomes Genetics - December 2, 2019 Category: Genetics & Stem Cells Authors: Qiao, P., Lin, M., Vasquez, M., Matschi, S., Chamness, J., Baseggio, M., Smith, L. G., Sabuncu, M. R., Gore, M. A., Scanlon, M. J. Tags: Investigations Source Type: research