Genetic Architecture of Sorghum Biomass Yield Component Traits Identified Using High-Throughput, Field-Based Phenotyping Technologies

2012 Awardee

Investigators: Schnable, P. S.; Salas-Fernandez, M.; Tang, L.

Institution: Iowa State University

Non-Technical Summary: Sorghum is one of the most promising and productive plant species for biomass production in the United States. It will be possible to quickly exploit the understanding gained from this study of the genetic architecture of biochemical and physical traits in applied sorghum biomass breeding programs. Although essential for plant growth, excess light can damage plants. Plants have developed “photo-protection” mechanisms to protect themselves from the consequences of excess light. The hypothesis that variation in growth rate can be explained by variation in photosynthetic rates and/or amounts of photo-protection will be tested. One of the key breeding objectives for biomass crops is increased yield, which is affected by growth rate. The genetic control of sorghum growth rates will be determined. To do so, biomass volumes of a large genetically diverse collection of sorghum lines will be assayed at multiple time points during the growing season. Identifying the genetic control of biomass growth rates will allow breeders to genetically “stack” genes that control maximal growth rates, thereby paving a path to producing higher yielding hybrids. To identify the genetic control of biomass growth rates, it will be necessary to collect trait data at multiple times during multiple growing seasons. It would be extremely challenging to do so using conventional approaches. Instead, these data will be collected using a sophisticated, high-throughput, field-based, plant phenotyping system that will be developed during the project. Over the last several years, substantial progress has been achieved in the development of automated phenotyping systems. But to date most such automated phenotyping systems have been laboratory- or greenhouse-based. These systems suffer from the limitation that plant performance in laboratories or greenhouses is often only poorly correlated with field performance. Hence, the field-based phenotyping system that will be developed during the project has the potential to revolutionize the collection of phenotypic data from field-based biomass yield trials. As such, this robotic system is expected contribute widely to the genetic improvement of biomass crops of importance to the U.S. economy.

Objectives: A systems approach (Genome-wide Association Studies; GWAS) will be used to identify the genetic control of rates of photosynthesis, photo-protection, and biomass growth, as well as a series of biomass yield-related plant architecture traits (e.g., plant height, stalk diameter, leaf number, leaf width, leaf length, leaf angle, leaf area index) in the C4 grass sorghum [Sorghum bicolor (L.) Moench], a promising and productive biomass crop. These experiments will identify SNP markers within or closely linked to the genes that control these traits. Using these identified SNPs it will be possible to predict the phenotypes of sorghum lines based on their underlying genotypes and conduct genomic selection experiments designed to improve biomass yields of sorghum hybrids. Higher photosynthetic rates contribute to increase biomass yields. Although essential for photosynthesis, excess light can lead to the generation of harmful reactive oxygen species (ROS). Plants have developed several mechanisms to protect themselves from the consequences of excess light, which are collectively termed photo-protection. The genetic regulation of photosynthetic rates and photo-protection will be identified. In addition, the hypotheses that significant amounts of variation in growth rate can be explained by variation in photosynthetic rates and/or the amount of photo-protection will be tested. Although total biomass yield is a function of growth rate and growth duration, growth rates are typically not constant throughout the growing season. Hence, the potential exists to identify distinct genetic loci that control growth rates and plant architecture at different times in the growing season. Once favorable alleles of loci that control rates of photosynthesis, photo-protection and biomass growth, as well as plant architecture traits have been identified, breeders can use genomic selection methods to produce sorghum hybrids having higher biomass yields. To identify the genetic control of dynamic changes in biomass growth rates and plant architecture, it will be necessary to collect trait data at multiple times during two growing seasons. It would be extremely challenging to do so using conventional approaches. Instead, these data will be collected using a sophisticated, high-throughput, field-based, plant phenotyping system that will be developed during the project. Over the last several years, substantial progress has been achieved in the development of automated phenotyping systems. But to date most automated phenotyping systems have been laboratory- or greenhouse-based. These systems suffer from the limitation that plant performance in laboratories or greenhouses is often only poorly correlated with field performance. Hence, the field-based phenotyping system that will be developed during the project has the potential to revolutionize the collection of phenotypic data from field-based biomass yield trials. As such, this robotic system is expected contribute widely to the genetic improvement of biomass crops.

Approach: Objective 1: Identify the genetic control of growth rates, photosynthetic rates and amounts of photo-protection, and dynamic changes in plant architecture GWAS will be performed on two sets of sorghum lines: the “Diversity Panel” and the “Yu Panel.” The “diversity panel” consists of 387 photoperiod insensitive lines and the “Yu Panel” consists of 300 photoperiod sensitive lines. Using the robotic system developed as part of this project, 3D images will be collected from both panels. These 3D images will be used to determine a variety of plant architecture traits such as plant height, leaf number, leaf width, leaf length and leaf angle. These measurements will be used to determine total leaf area and plant volume, which is well correlated with total dry matter. Because growth rate is not constant throughout the growing season, plant architecture traits will be measured in each biological replication of each entry every second week during each growing season. To test the hypothesis that variation in growth rates can be explained at least in part by variation in photosynthesis and photo-protection, carbon assimilation and fluorescence parameters (utilized to determine photo-protection levels and other photosynthetic parameters) will be measured in the Diversity Panel. GWAS will be conducted separately on the two panels for all the collected traits using existing genotyping data. Objective 2: Develop a high-throughput phenotyping robot to measure growth rates. Because growth rate is not constant throughout the growing season it will be necessary to measure biomass volumes of each biological replication of entry at multiple dates during each growing season. It would be extremely challenging to collect this much phenotypic data using conventional approaches. Biomass volumes will instead be measured using a high-throughput, field-based, plant phenotyping system. Specifically, automated robotic technologies will be used for the high-throughput collection of biomass volumes from field plantings. The system will be developed and optimized based on an existing in-field plant sensing system designed to measure plant population and interplant spacing. This existing sensing system, which is based on state-of-the-art 3D Time-of-Flight of light sensing technology, has great potential for measuring plant morphological features in a high-throughput fashion. In addition, with the availability of 3D spatial data, it is anticipated that it will be possible to delineate 3D morphological features of sorghum plants such as height, volume, and leaf number, leaf angle, leaf width, and leaf length. Reliable methods exist by which the resulting 3D images can be accurately converted to total leaf area and plant volume, which is well correlated with total dry matter. To validate these automatically collected and calculated data, we will also measure “by hand” all of the phenotypes from a limited number of entries in our field trials. In addition, to test the accuracy of this automated approach, total dry matter will be measured directly from a subset of the lines (~10%) and compared to estimated total dry matter yields as calculated from the 3D measurements.

Project Contact
Name: Pat Schnable
Phone: 515-294-0975
Email: [email protected]