Location: Plant Physiology and Genetics Research
Project Number: 2020-21410-007-000-D
Project Type: In-House Appropriated
Start Date: Jun 16, 2018
End Date: Jun 15, 2023
The objectives of the plan concentrate on utilizing advanced phenomic and genomic approaches to genetically improve cotton, oilseed crops, bioenergy and industrial crops and expand their use for food, feed, fuel, and fiber production for United States agricultural sectors and global use. To reach that goal our specific objectives are: Objective 1: Use existing and newly developed field-based phenotyping methods to evaluate cotton, oilseeds, and other industrial and biofuel crops, and utilize the results to enable effective use of high-throughput phenotyping (HTP) methodology for crop genetic improvement and management. Sub-objective 1A: Field-based evaluation of cotton using high-throughput phenotyping and conventional methods for germplasm improvement and crop management. Sub-objective 1B: Field-based phenotypic evaluations for biofuel crop camelina using high-throughput and traditional phenotyping technologies for traits related to drought stress. Sub-objective 1C: Use high-throughput and traditional phenotyping strategies to identify soybean germplasm with abiotic stress tolerance traits. Sub-objective 1D: Phenotypic characterization of USDA guayule collection under abiotic stress conditions and Arizona growing conditions using traditional and high-throughput phenotyping technologies. Objective 2: Utilize various new and conventional genetic approaches to identify genes and associated molecular markers conditioning abiotic stress tolerance in arid environments, and determine relationships with important agronomic traits. Sub-objective 2A: Identify molecular markers associated with genes involved in temporal patterns with abiotic stress tolerance and agronomic traits in cotton using high-throughput phenotyping. Sub-objective 2B: Identifying alleles/genes and associated molecular markers conditioning yield and abiotic stress tolerance and related traits in bioenergy crop, camelina. Sub-objective 2C: Identify genes/alleles and associated molecular markers conditioning yield and abiotic stress tolerance in soybean.
The objectives of the plan will be carried out using various high through-put phenotyping (HTP) approaches used to identify and improve cotton, camelina, soybean and guayule crops with increased tolerance to abiotic stress and stable productivity. For each crop, a genetic population/diversity panel will be planted under well-watered (WW) and water-limited treatments, based on agronomic recommendations of each crop, in replicated design over several years. The HTP data will be collected on a weekly basis throughout the growing season using HTP platforms that use electronic sensors to measure crop height, canopy multi-spectral reflectances and canopy temperature. In addition to HTP measurements, morphological, physiological and agronomic traits including plant height, lodging score, and flowering date will be collected during the growing season. At physiological maturities, plots will be harvested and seed/lint yield will be determined. Oil and leaf wax contents and compositions will be quantified using standard gas chromatography analysis. For guayule, rubber and resin will be determined using an Ion chromatography system. Traits will be analyzed using MIXED model in statistical analysis software (SAS) software, where water treatments, different environments and accessions will be considered as fixed effects and replicates will be the random effect. Differences among lines within each water treatment will be determined with a Bonferroni adjustment for multiplicity test. G×E interaction analysis will be conducted for recorded traits where water treatments, replicates, environments, and accessions will be considered as random effects. Quantitative trait loci (QTL)/alleles/genes associated with complex traits like heat and drought stress tolerances will also be identified. Cotton recombinant inbred line (RIL) population and camelina and soybean diversity panels will be genotyped using Genotyping-by-Sequencing technology. Genome-Wide Association Studies (GWAS) and QTL analyses will be used to identify molecular markers that are associated with and controlling the dynamic changes in plant growth under stress conditions, crop productivity traits and stability and oil and wax content and quality (Objective 1). Best linear unbiased predictors (BLUPs) of each phenotypic trait will be determined using mixed model of SAS software. GWAS analyses will be conducted using the trait analysis by association, evolution and linkage (TASSEL) package. To find the best model that is able to detect the associations between phenotypic traits and single nucleotide polymorphism (SNP) markers, and reduce the number of false-positive associations, the Mixed Linear Models (MLM) approach of TASSEL will be used. Candidate genes from multiple GWAS analyses will be identified from genomic intervals in the reference genome assemblies. In cotton, QTL analyses will be conducted using the inclusive composite interval mapping (ICIM) program.