Performing Department
(N/A)
Non Technical Summary
Excessively high temperatures during flowering and boll development are important drivers of yield loss in the southern United States. Many cotton production regions routinely exceed the optimum temperature range for multiple physiological processes, and the frequency, duration, and intensity of extreme heat wave events is only expected to increase in the decades to come. Additionally, low genetic diversity among modern cotton cultivars has resulted in a shallow gene pool from which to select for thermotolerance, and targeted selection for heat tolerance has been non-existent. The overrching goal of the proposed research is to accelerate the development of targeted heat tolerance selection in upland cotton. Objectives aimed at meeting this goal include 1) characterizing reproductive tolerance to a simulated heat wave event for a diversity panel of upland cotton that accounts for 97% of all allelic diversity within the species, 2) identifying functional source leaf traits that are indicative of genetic variation in reproductive thermotolerance, and 3) mapping functional traits conferring heat tolerance to specific regions of the cotton genome to develop molecular markers for heat tolerance selection. To date, no such effort has been successfully conducted for upland cotton, and achieving these three goals are the deliverables of our proposed research.
Animal Health Component
0%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
Goal: Accelerate the development of targeted heat tolerance selection in upland cotton by 1) deepening our understanding of genotypic variation in heat tolerance within upland cotton, 2) identifying functional traits that are predictive of genetic differences in heat tolerance, and 3) mapping functional traits conferring heat tolerance to specific regions of the cotton genome.Objective 1: characterize reproductive tolerance to a simulated heat wave event for a diverse collection of upland cotton. Objective 2: Identify functional source leaf traits that are indicative of genetic variation in heat tolerance for upland cotton.Objective 3: Map functional traits conferring heat tolerance to specific regions of the cotton genome and develop molecular markers for heat tolerance selection.
Project Methods
For objective 1 a controlled-environment experiment will be conducted at the University of Georgia Envirotron Facility. A diversity panel of approximately 325 upland cotton genotypes will be evaluated under an optimal day/night temperature regime (30/20 °C) or a simulated heat wave temperature regime (40/30 °C day/night temperature) during flowering. For yield component assessments, all bolls will be hand harvested from each plant at maturity. Samples will be ginned to obtain lint and seed weight and the mass of 100 seed will also be determined (seed index). From these data, a number of yield component parameters will be estimated: lint and seedcotton yield per plant, bolls plant-1, seedcotton weight boll-1 (g), lint percent, seed number boll-1, seed surface area (SSA). From the yield component data obtained above and high volume instrumentation (HVI) fiber quality properties (length, strength, micronaire, and fiber uniformity), the estimated average weight of an individual cotton fiber, fibers seed-1 and fiber density will be determined. A mixed-effects analysis of variance will determine the effect of genotype (G), temperature (T) and their interactive effects (G × T) on yield and yield components. Replicates (experimental run) and whole plots will be considered random effects and G, T, and interaction effects [G × T] will be treated as fixed effects (P < 0.05 will indicate significant main effects or interactions). To characterize the tolerance of any measured trait to high temperature, actual values will be converted into relative values for each genotype by dividing the observed value at high temperature by the mean value under optimal temperature. A significant genotype effect for relative values indicates significant genotypic differences in heat tolerance for yield or a specific yield component within the diversity panel.For objective 2, at the end of the heat wave event noted above, targeted physiological and biochemical assessments will be conducted on subtending leaves. These measurements will include dark respiration, rapid chlorophyll fluorescence induction measurements, light-saturated net carbon assimilation rates, stomatal conductance, light-saturated photosynthetic electron transport rates, leaf temperature, and intercellular CO2 concentration inside the leaf. Calculated variables will include partitioning of electron flux to assimilation and photorespiration, CO2 concentration in the chloroplast, and mesophyll conductance. The response of net photosynthetic rate to intercellular CO2 concentration will be used to determine the maximum rate of Rubisco carboxylation and RuBP regeneration. Samples will be collected from the subtending leaf for lipid extraction and an automated electrospray ionization-tandem mass spectrometry (ESI-MS/MS) approach will be followed for lipid profiling. The extracted leaf tissue will be dried at 105ºC for 24 h and weighed to express lipid content on a dry weight basis. Statistical analysis will include a mixed-effects analysis of variance as described above. The next phase of our analysis will involve correlation and regression analysis to identify the functional leaf traits that are most strongly associated with heat tolerance. All values will be averaged across all three replicates prior to analysis, and multi-parametric correlation analysis will be deployed to determine what associations may exist between genetic variation in yield-specific heat tolerance and biochemical or physiological traits of the subtending leaf. Regression analysis (the most appropriate functions to be determined) will be utilized to develop functional relationships between subtending leaf traits and yield-specific heat tolerance.For objective 3, we will use the diversity panel of 325 upland cotton accessions to identify marker-trait associations for functional source leaf traits and heat tolerance using GWAS. We will then use select biparental RIL population(s) of the elite NAM populations to validate the marker-trait associations for functional traits and thermotolerance. For GWAS, we will use 325 accessions with phenotypic data on subtending leaf physiological and biochemical traits associated with thermotolerance. The combined SNP data set on the previously mentioned diversity panel will be used to perform basic genetic diversity and population structure analysis to aid GWAS. To validate the GWAS results, we will test for marker-trait associations by linkage analysis in biparental RIL populations. Based on the phenotypic data collected from years 1 and 2, and the results from GWAS on the contrasting QTL effects, we will select up to two best RIL populations from the elite upland cotton NAM population for the marker-trait validations. We will phenotype the selected RIL populations for heat tolerance associated traits in years 3 and 4.SNP markers (less than 12) that show significant associations with heat tolerance associated traits in GWAS and RIL based mapping will be selected for KASP marker development. The probe sequences of these markers will be used for designing two forward allele-specific primers (ASP1 and ASP2) and a common reverse primer (CRP). The allele-specific primers will be tagged with HEX (GAAGGTCGGAGTCAACGGATT) and FAM (GAAGGTGACCAAGTTCATGCT) fluorophore tail sequences from the PACE assay designing service and primers will be synthesized from Integrated DNA Technologies (Coralville, Iowa). KASP/PACE assays of the targeted SNP markers on the RIL population will be performed.Phenotypic data on the RILs and parents will be analyzed using the linear mixed models and the variance components for each trait will be estimated by ANOVA using SAS. Broad sense heritability will be calculated using SAS. Independent T-test and correlation coefficients between traits will be calculated using SPSS version 25.0 (IBM Corp. Armonk, NY). Segregation of the polymorphic SNP markers in the RIL population will be tested using the chi-square test (P < 0.05) and a segregation distortion region will be identified when at least three adjacent markers show significant (P < 0.05) segregation distortion using JoinMap 4.1. QTL of the phenotypic traits will be detected using the composite interval mapping (CIM) method using WinQTLCart2.5. Based on the results of a 1000-time permutation procedure, LOD ≥ 3 with at least one years' PVE ≥ 5.0 will be used as the threshold for a QTL identified. The resulting linkage map with identified QTLs will be drawn using MapChart version 2.32. The marker-defined QTL regions with DNA sequence information will be BLAST searched on Cotton Functional Genomics Database (https://cottonfgd.org/) for identifying the possible candidate genes for QTLs controlling functional source leaf traits contributing to thermotolerance.