Research in the Schnable Lab@UNL in an interdisciplinary business. Our projects involving collaborations with engineers, computer scientists, statisticians, and food scientists. Current work in the lab focuses on three major overlapping areas

Linking Genotype and Phenotype Across Environments and Across Grass Species

Lots of interesting (where "interesting" can mean agronomically useful, or evolutionarily interesting, or just cool) traits show much more variation between species than within populations of a single species. One of the long term goals of our lab is to develop tools to study functional genetic variation across multiple species. Several of the currently funded projects within the lab fall under this umbrella.

Identifying Mechanisms Conferring Low Temperature Tolerance in Maize, Sorghum, and Frost Tolerant Relatives

Low temperature is a key constraint on crop productivity and growing ranges and even within the native ranges of crop species, unseasonable early or late cold can both reduce yield and decrease the quality of any surviving harvest. To date, the majority of research into the mechanisms responsible for cold tolerance have been focused in the model eudictot species A. thaliana. However, more recent studies of cold tolerance in the Pooideae grass subfamily (wheat, barley, rye etc) have revealed novel cool temperature adaptive mechanisms which are not shared by closely allied subfamilies within the grasses such as the Ehrhartoideae (rice). This finding suggests different plant lineages have adapted to growth in cool season environments using distinct genetic and physiological mechanisms. This project will determine a set of genes and metabolic pathways critical for low temperature tolerance in the Panicoideae, a grass subfamily which includes the major, cold-sensitive, crops corn (Z. mays) and sorghum (S. bicolor) through the characterization of parallel adaptations to cool and cold environments present within the subfamily. The completion of this project will both increase the number of known mechanisms by which plant species can become low temperature tolerant and generate a specific set of gene and metabolite changes required for cold and freezing tolerance in panicoid species. These outcomes will generate a toolkit that will enable the development of low temperature tolerant corn and sorghum through both the mining of natural allelic variation and engineering approaches.

Supported by a grant from the USDA NIFA to the Schnable lab and our collaborators in the Roston Lab (Biochemistry): 2016-67013-24613

Automated Plant Phenotyping

The Schnable lab works with both the "Data Analysis" team within the Center for Integrative Translational Biology and the Plant Vision Initiative in developing and deploying new algorithms, tools, and datasets for high throughput plant phenotyping. Our work in this area is supported by the USDA, and internal funding from the University of Nebraska Lincoln. In 2017, we will be creating and disseminating a reference high throughput imaging dataset for the Sorghum Association Panel. Check back for more details starting in March 2017.

A High Throughput Phenotyping Reference Dataset for GWAS in Sorghum

The Consortium for Integrated Translational Biology was founded with the goal of linking variation in phenotype to variation in genotype and translate research from the greenhouse to the field. The initial validation studies of the Scanalyzer 3D were not able to include sufficiently large populations to allow the de novo mapping of genes controlling either traditional or novel phenotypic traits. This proposal aims to generate a first generation sorghum high throughput phenotype reference dataset through the phenotyping and ground truth analysis of 363 accessions from the Sorghum Association Panel (SAP). A publicly available dataset from this population will enable and encourage researchers to develop and validate new computational methods of scoring plant phenotypes using conventional or hyperspectral image data. And, when a trait is validated, it will be possible to perform GWAS to identify traits associated SNPs (TASs) associated with the genes controlling variation in those phenotypes in sorghum.

We propose developing new software tools and statistical models to measure reproductive stage phenotypes from conventional RGB image data, measure sorghum plant composition phenotypes from hyperspectral data, and identify genes or candidate intervals for genes responsible for controlling variation in plant architecture, plant composition, and derived traits (ie ratios between specific traits, and rates of change in trait measurements over time). The creation of this dataset will stimulate the development of new methods, and serve as critical preliminary data for future research proposals by both the proposing research teams and other faculty working on sorghum, maize, and related crops.

Supported by an internal award from the Agricultural Research Division to our lab and multiple collaborators at UNL.

PAPM EAGER: Transitioning to the Next Generation Plant Phenotyping Robots

This project is to develop next generation plant phenotyping robots that can collect in-vivo human-like measurement of plant physiological and chemical traits to complement image analysis. This project is to address the perceived "phenotyping bottleneck". There are three specific research aims. The first is to design and develop robotic grippers integrating specialized plant sensors. The second is to develop a novel vision system for the localization and approaching of plant leaves by the robotic arm and eye-hand coordination. The third is to test and validate the plant phenotyping robot in UNLs high throughput phenotyping greenhouse. The PIs will also work to develop several novel educational and outreach activities in this project.

Supported by a grant from the USDA NIFA to our collaborators in the Ge Lab (Biological Systems Engineering): 2017-67007-25941

High-throughput, High-Resolution Phenotyping of Nitrogen Use Efficiency Using Coupled In-Plant and In-Soil Sensors

The team will develop a novel technology toolset consisting of two types of sensors to accelerate plant breeding for nitrogen uptake and nitrogen use efficiency. The team will design and build a novel silicon microneedle in-plant nitrogen sensor and a microfluidic soil nitrogen sensor. Incorporating the new soil and in-plant sensors into real world field trials will improve and accelerate the effort to identify, select and commercialize new crops with improved nitrogen use efficiency.

Supported by an announced but in negotiation grant to our collaborators in the Liang Dong lab at Iowa State.

Engineering the Human Gut Microbiome Using Plant Metabolites

The newest research focus within the Schnable lab emphasizes combining quantitative genetic tools with high throughput screening of microbial communities to understand what components of food alter the population structure of the microbiomal community in the human gut. Working with the newly founded Food for Health Center, we will use these perturbations to understand how different micriobal taxa can influence human health in positive or negative fashions.

Miscellaneous Other Cool Science

Genomes to Fields

Working with the Rodriguez lab and the Ge Lab, we conduct field trials and high throughput phenotyping of exPVP maize hybrids ("the closest to what farmers in Nebraska grow today as we're legally allowed to work with") as part of the Genomes to Fields public-private consortium working at catalzying the translation of maize genomic information into adances beneficial to growers, consumers, and society. In 2016, members of the consortium collected yield and phenotype data from maize hybrids grown at 29 locations distributed across 17 states.

Our participation in Genomes to Fields is supported by the Nebraska Corn Board

Genomic Selection Guided Breeding of Pearl Millet Hybrids

Members of the Schnable Lab@UNL are working with ICRISAT and the Schnable Lab@ISU to increase the rate of genetic gain in the yield of pearl millet, a drought and stress tolerant crop widely grown for both forage and human consumption in India and Africa.

This work is supported by an award from ICRISAT that is in turn supported by funds from USAID