SunflowerAssociationMappingFieldSaltStress

Overview The study investigates the genetic tolerance of sunflower (Helianthus annuus) to high salinity soils. It involves genome-wide association studies using the Sunflower Association Mapping panel grown in naturally occurring saline soils. The analysis covers six phenotypes: days to bloom, height, leaf area, leaf mass, oil percentage, and yield.

Contents R Scripts:

1. BLUP_ANOVA_example_OilPercentage.R: Performs BLUP (Best Linear Unbiased Prediction) analysis for oil percentage.
2. Kriging.R: Implements kriging to estimate soil salinity for individual plots.
3. outlier_removal.R: Removes outliers from the phenotypic data.

Input CSV Files:

1. Phenotypes_with_Salinity.csv: Contains phenotypic data with salinity measurements.
2. oil_noOutlier.csv: Oil percentage data with outliers removed.
3. krig_train_data.csv: Training data for kriging analysis.
4. krig_test_data.csv: Test data for kriging analysis.

Usage

1. Ensure you have R and the required packages installed (dplyr, ggplot2, scales, magrittr, gstat, sp, asreml, ASRgenomics, ASRgwas).
2. Clone this repository: clone https://github.com/your-username/sunflower-salinity-tolerance.git
3. Set your working directory to the cloned repository.
4. Run the R scripts in the following order:
   • First, run outlier_removal.R to clean the data.
   • Then, run Kriging.R to estimate soil salinity for individual plots.
   • Finally, run BLUP_ANOVA_example_OilPercentage.R for the BLUP analysis of oil percentage.

Key Analyses

1. Outlier Removal: The outlier_removal.R script removes outliers from various phenotypic traits using influence plots and studentized residuals.
2. Kriging: The Kriging.R script performs kriging to estimate soil salinity for individual plots, using data from two years (2016 and 2017) and two soil depths (0-6 cm and 6-24 cm).
3. BLUP Analysis: The BLUP_ANOVA_example_OilPercentage.R script demonstrates the BLUP analysis for oil percentage, including model fitting and heritability calculation.

Results The scripts generate various outputs including:

• Cleaned phenotypic data
• Estimated soil salinity for individual plots
• BLUP values for oil percentage
• Variance components and heritability estimates These results were used in the genome-wide association study to identify loci associated with salinity tolerance in sunflower.

Citation If you use this code or data in your research, please cite: McNellie, J.P., May, W.E., Rieseberg, L.H. & Hulke, B.S. (2024). Association studies of salinity tolerance in sunflower provide robust breeding and selection strategies under climate change. Theoretical and Applied Genetics. https://doi.org/10.1007/s00122-024-04672-3

License This work is licensed under the Creative Commons Zero v1.0 Universal (CC0 1.0) Public Domain Dedication. This means that the creators of this work have waived all copyright and related rights to the fullest extent allowed by law. You are free to:

• Copy, modify, distribute, and perform the work
• Even for commercial purposes
• All without asking permission Under the following terms:
• No warranties are given. The license may not give you all of the permissions necessary for your intended use. For more information, see https://creativecommons.org/publicdomain/zero/1.0/

Contact For questions regarding this code or data, please contact:

Dr. Brent S. Hulke
Research Geneticist
Sunflower and Plant Biology Research Unit
USDA-ARS Edward T Schafer Agricultural Research Center
1616 Albrecht Blvd. N.
Fargo, ND 58102
USA

Email: brent.hulke@usda.gov

ORCID: Brent S. Hulke's ORCID