Genomic Science Program
U.S. Department of Energy | Office of Science | Biological and Environmental Research Program

2024 Abstracts

Integrating Molecular Genetics and Precision Phenotyping to Elucidate the Genetic Basis for Drought Resilience in Sorghum


Andrea L. Eveland1* ([email protected]), Yuguo Xiao1, Maxwell Braud1, Indrajit Kumar1, Philip Ozersky1, Emmanuel Miguel Gonzalez2, Rajdeep S. Khangura3, Zhonghui Wang1, Todd C. Mockler1, Brian Dilkes3, Duke Pauli2


1Donald Danforth Plant Science Center; 2University of Arizona; 3Purdue University


  • Overall project objective: To define and functionally characterize genes and pathways related to drought stress tolerance in sorghum and the molecular mechanisms by which these factors drive phenotypic diversity.
  • Establish a foundation for deep explorations of gene regulatory networks in sorghum through integrative genomics analyses.
  • Enhance understanding of how genotype drives phenotype and environmental adaptation using high-resolution, field-based phenotyping of sorghum mutant collections and a novel diversity panel.
  • Map and characterize genes contributing to drought responsive phenotypes in
  • Experimentally validate predictions of gene function using molecular and genetic assays and targeted gene editing.


Development of the next generation of bioenergy feedstocks will require strategies that utilize resource-limited agricultural lands. This project investigates the innate drought resilience of sorghum (Sorghum bicolor), a bioenergy feedstock and cereal crop. Drought is a complex trait and identifying the genes underlying sorghum’s innate drought tolerance and how they are regulated in the broader context of the whole plant and its environment requires advanced approaches in genetics, genomics, and phenotyping.

This project leverages a field-based phenotyping infrastructure at Maricopa, AZ, which provides an exceptional capability for managed stress trials in a hot, arid environment through controlled irrigation. An automated field scanner system collects high-resolution phenotyping data using a variety of sensors throughout the growing season, from seedling establishment to harvest. A chemically mutagenized sorghum population was phenotyped under the field scanner to compare drought-stressed and well-watered plants. Each mutant’s genome has been sequenced so that sequence variants can be linked with phenotypes. Being able to assess the genotype-to-phenotype link in response to drought over the life cycle of the plant will facilitate discovery of genes and their functions. A custom diversity panel of sorghum lines was built that maximizes variation in water use efficiencies as well as genetics and geographic origin. Each of these lines have been sequenced through various efforts. This panel was phenotyped both under the field scanner and in controlled environment drought response experiments, and samples were collected for population level expression analyses. State-of-the-art phenotyping data analytics pipelines have been developed as part of this project and DOE-funded initiatives (see poster by Gonzalez et al.) and are being extended to define stress-related phenotypes at multiple scales. To accelerate mapping of causal loci that underlie mutants of interest, researchers use bulked segregant analysis-seq. So far, researchers have identified candidate genes underlying defects in leaf senescence, shoot and root architecture, and fertility. Regulatory maps generated from diverse sorghum lines in response to stress are being used to nominate gene candidates and place them in the larger context of a drought response network. Finally, an in-house leaf-based transformation and gene editing pipeline is being used to generate mutant alleles in sorghum for characterizing gene function.

This work will identify control points for enhancing the productivity of bioenergy crops in marginal environments through precision breeding or engineering, and thus accelerate the development of improved varieties that are high-yielding with limited water resources.

Funding Information

This work is funded by DOE BER awards #DE-SC0023305 and #DE-SC0020401.