Location: Plant Physiology and Genetics Research
2022 Annual Report
Objectives
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.
Approach
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.
Progress Report
This report documents progress for project 2020-21410-007-000D, titled “Enhancing Abiotic Stress Tolerance of Cotton, Oilseeds, and Other Industrial and Biofuel Crops Using High Throughput Phenotyping and Other Genetic Approaches” which was certified in May 2018 and continues research from project 2020-21410-006-000D titled, “Genetic Improvement and Phenotyping of Cotton, Bioenergy and Other Industrial Crops.” The following documents the research progress made in fiscal year 2022.
In support of Sub-objective 1A with USDA collaborators at Maricopa, Arizona, Florence, South Carolina, and College Station, Texas, a shuttle breeding population has been grown at all three locations since 2017. Each year, after field evaluations including conventional and high-throughput approaches, lines were selected for potential release as germplasm for the cotton breeding community. As of 2022, nine cotton germplasm lines have been released from this project. In 2021 and 2022, two germplasm lines were grown in specialized field-physiology trials to determine traits associated with adaptation to the Arizona low desert. These lines are intended for release in 2023. In support of Sub-objectives 1A and 2A, a reciprocal recombinant inbred line (RRIL) population was developed using single seed and single boll descent. The population was evaluated in the field in 2021 for leaf chlorophyll content, chlorophyll fluorescence, and leaf morphology traits using a combination of conventional and high-throughput approaches. The population is undergoing evaluation in 2022 for the same traits. The population will be sequenced this year as part of a National Institute of Food and Agricultural (NIFA) grant award. Compiled genotype and phenotype information will be used to identify genomic regions and molecular markers associated with adaptation.
In support of Sub-objective 1C, a diverse panel of 200 soybean genotypes were grown under well-irrigated and reduced-irrigated conditions at Maricopa, Arizona. The high-throughput phenotyping (HTP) data were collected on a weekly basis throughout the growing season. In addition to HTP measurements, drought tolerance-related traits such as canopy wilting, carbon isotope discrimination, and leaf wax content and compositions were collected. Data showed high phenotypic variation among soybean genotypes on studied traits. For each trait, certain genotypes showed stable changes when grown under well-irrigated and reduced-irrigated conditions. Preliminary statistical analyses over environments indicated significant effects for genotypes, environments, and Genotype x Environment (GxE) for the studied traits.
In support of Sub-objective 1D, the USDA guayule accessions and Mariola wild accessions were grown in field trials at Maricopa, Arizona, under two irrigation regimes (well-irrigation vs. reduced irrigation). Mariola is the closest relative to guayule and had been used to transfer cold tolerance traits to guayule to expand its planting regions up to Colorado. Morphological, resin, rubber, and HTP-related traits, and traits related to drought-tolerance such as carbon isotope discrimination (d13C), an indicator of water use efficiency (WUE), were collected. Results indicated significant effects for water stress treatments as well as genotypes for the studied traits, where high phenotypic variations among guayule and mirola genotypes and the variable effects of drought stress conditions on studied genotypes were observed. For each trait, some genotypes showed stable changes when grown under well-irrigated and reduced-irrigated conditions, suggesting them as parental for next cycle of genetic improvement.
To achieve Sub-objective 2B, Genome-Wide Association Studies (GWAS) analyses were performed to identify candidate genes controlling the variations in camelina flowering time, a trait related to stress tolerance as well final seed production. Flowering times data were recorded for 250 camelina accessions that were collected from different parts of the world and planted at Maricopa, Arizona, in 2-year field trials. Results indicated wide variation in flowering time in spring camelina germplasm. The current study showed significant genotypic, environmental and year to year effects on flowering time. The high heritability estimate of flowering time suggests that breeding will be effective in developing early flowering camelina varieties. A total of 20 significant trait-associated single nucleotide polymorphisms (SNPs) were identified to be colocalized within/or near a variety of transcription factors or protein families containing specific functional domains. These transcription factors interact with key regulatory genes in the four major pathways (i.e., photoperiod, autonomous, vernalization and gibberellic acid pathways) to cooperatively regulate floral transition. Whole-genome prediction was also conducted to estimate the predictive ability to develop early flowering in camelina that will speed the exploration and genetic improvements of flower development and timing in a camelina.
To develop genomic tools and information for genomic regions controlling traits related to drought stress tolerance in soybean that support Sub-objective 2C, the phenotypic data of soybean diversity panel that were planted under stress conditions was used to conduct GWAS analyses. The results reveled significant single nucleotide polymorphism (SNP) markers on different chromosomes that were associated with drought stress tolerance related traits under well-irrigated and reduced-irrigation conditions. Initial results indicated that some associated SNP that discovered within genes might be actively triggered under either of the two conditions. Data from more environments, and years will be combined for more comprehensive GWAS analyses.
Accomplishments
1. New cotton germplasm provides stable fiber quality. Cotton fiber quality characteristics are used to determine the market value of cotton for global imports and exports. Certain fiber quality scores ensure growers get the best market value for their crop. Fiber quality can be difficult ensure, as changing environmental conditions impact cotton fiber development, altering overall fiber quality. To provide growers with sustainable solutions to this problem, cotton germplasm that can adapt to different climate conditions and maintain fiber quality are needed. ARS researchers in Maricopa, Arizona, generated new cotton germplasm lines that will become a valuable source of climate resilient cotton that will improve cultivars for sustainable fiber quality.
2. Discovering candidate genes related to flowering time in camelina. Flowering time is an important factor in the adaptation of Camelina sativa to a wide range of growing zones. In collaboration with scientists at the Donald Danforth Plant Science Center in St. Louis, Missouri; the University of Nebraska, Lincoln, Nebraska; ARS researchers at Morris, Minnesota, and scientists at the University of Florida in Quincy, Florida, ARS researchers in Maricopa, Arizona, used the genome-wide association approach to identify 20 putative molecular markers that are co-localized within/near a variety of transcription factors or protein families related to floral development in Camelina sativa. In addition, the predictive ability to use the current set of molecular markers for future genomic selection for early flowering was estimated. This study lays or laid the foundation for future molecular breeding efforts to develop early flowering camelina varieties with desirable characteristics such as high seed yield, high oil production and abiotic stress tolerance suitable for sustainable agricultural systems.
Review Publications
Kim, J.Y. 2021. Software design for image mapping and analytics for high throughput phenotyping. Computers and Electronics in Agriculture. 191. Article 106550. https://doi.org/10.1016/j.compag.2021.106550.
Luo, L.L., Fahlgren, N., Kutchan, T., Schachtman, D.P., Ge, Y., Gesch, R.W., George, S., Dyer, J.M., Abdel-Haleem, H.A. 2021. Discovering candidate genes related to flowering time in the spring panel of Camelina sativa. Industrial Crops and Products. 173. Article 114104. https://doi.org/10.1016/j.indcrop.2021.114104.
Zhang, J., McDonald, S.C., Wu, C., Ingwers, M.W., Abdel-Haleem, H.A., Chen, P., Li, Z. 2022. Quantitative trait loci underlying flooding tolerance in soybean (Glycine max). Plant Breeding. 141(2):236-245. https://doi.org/10.1111/pbr.13008.
Melandri, G., Thorp, K.R., Broeckling, C., Thompson, A.L., Hinze, L.L., Pauli, D. 2021. Assessing drought and heat stress-induced changes in the cotton leaf metabolome and their relationship with hyperspectral reflectance. Frontiers in Plant Science. 12. Article 751868. https://doi.org/10.3389/fpls.2021.751868.
