3-Day Beginner Training Course: R for Rice Genome-Wide Association Mapping

📚 Overview

This repository contains materials for a 3-day beginner-level training course on performing Genome-Wide Association Studies (GWAS) in rice using R. The course covers essential data preprocessing, visualization, and analysis workflows, utilizing publicly available datasets.

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📅 Course Outline

Day 1: Introduction to R and GWAS Datasets

• Objective: Familiarize with R basics, loading datasets, and understanding GWAS datasets.
  
• Topics Covered:
  
  • R basics and syntax
    
  • Loading and exploring phenotype and genotype datasets
    
• Files Used:
  
  • phenotype_data.csv
    
  • genotype_data.csv
    
  • day1_intro.R
    

Script Highlights:
- Load and explore phenotype data.
- Load and explore genotype data.
- Understand data structure and contents.

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Day 2: Data Analysis and Visualization

• Objective: Learn data visualization techniques with R and create a Manhattan Plot.
  
• Topics Covered:
  
  • Data exploration and preprocessing
    
  • Visualization using ggplot2
    
• Files Used:
  
  • manhattan_data.csv
    
  • day2_visualization.R
    

Script Highlights:
- Create a Manhattan Plot to visualize SNP associations.
- Interpret GWAS visualization outputs.

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Day 3: Performing GWAS with GAPIT

• Objective: Conduct GWAS using the GAPIT (Genome Association and Prediction Integrated Tool) package.
  
• Topics Covered:
  
  • Introduction to GAPIT
    
  • Running GWAS analysis
    
  • Interpreting GWAS outputs
    
• Files Used:
  
  • phenotype_data.csv
    
  • genotype_data.csv
    
  • day3_gwas_pipeline.R
    

Script Highlights:
- Run a GWAS pipeline using GAPIT.
- Generate and interpret GWAS summary statistics.

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🛠️ Installation and Setup

Prerequisites

Ensure you have the following installed: - R (≥4.0) - RStudio (Recommended) - Required R Packages:
r install.packages(c("ggplot2", "GAPIT"))

Automated Sample Scripts and Datasets for Rice GWAS Training

This repository contains automated sample scripts and dataset recommendations for a 3-day training program on rice Genome-Wide Association Studies (GWAS) using R.

📅 Day 1: Introduction to R and GWAS Concepts

Session 1: Getting Started with R

Script 1: R Basics & Data Import

```r

Basic R Operations

x <- 5 y <- 10 sum <- x + y print(sum)

Install necessary packages

install.packages(c("readr", "dplyr", "ggplot2"))

Load dataset (example dataset from Rice SNP-Seek Database)

library(readr) phenotypedata <- readcsv("phenotypedata.csv") head(phenotypedata) ```

Dataset: Example phenotype data (e.g., plant height, grain yield) in CSV format.

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Session 2: Introduction to GWAS

Script 2: Understanding Phenotypic and Genotypic Data

```r

Explore phenotype data

summary(phenotypedata) str(phenotypedata)

Sample Genotypic Data (Example SNP data)

genotypedata <- readcsv("genotypedata.csv") head(genotypedata) ```

Datasets: - phenotype_data.csv: Sample phenotype data (e.g., Plant Height, Yield). - genotype_data.csv: Example SNP dataset (e.g., marker names, genotypes).

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Session 3: Exploring Rice GWAS Data

Script 3: Data Cleaning and Preprocessing

```r

Clean Phenotype Data

library(dplyr) phenotypedata <- phenotypedata %>% filter(!is.na(Trait1)) %>% mutate(Group = as.factor(Group))

Clean Genotype Data

genotypedata <- genotypedata %>% filter(!is.na(SNP1)) ```

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📅 Day 2: Data Analysis and Visualization in R

Session 1: Data Manipulation in R

Script 4: Data Wrangling

```r

Summarize phenotype data

library(dplyr) summarystats <- phenotypedata %>% groupby(Group) %>% summarise(meanheight = mean(Height, na.rm = TRUE))

print(summary_stats) ```

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Session 2: Phenotypic Data Analysis

Script 5: Descriptive Statistics and Correlation

```r

Basic Statistics

summary(phenotype_data$Height)

Correlation

cormatrix <- cor(phenotypedata[, c("Height", "Yield")], use = "complete.obs") print(cor_matrix) ```

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Session 3: Data Visualization

Script 6: Manhattan and QQ Plots

```r library(ggplot2)

Sample Manhattan Plot Data

manhattandata <- data.frame( SNP = paste0("SNP", 1:1000), Chromosome = sample(1:12, 1000, replace = TRUE), Position = runif(1000, 1, 1e6), Pvalue = runif(1000, 0, 0.05) )

Manhattan Plot

ggplot(manhattandata, aes(x = Position, y = -log10(Pvalue), color = as.factor(Chromosome))) + geompoint() + thememinimal() + labs(title = "Manhattan Plot", x = "Genomic Position", y = "-log10(P-value)") ```

Dataset: manhattan_data.csv (or generated dynamically in the script).

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📅 Day 3: Performing GWAS in R

Session 1: GWAS Analysis Using R Packages

Script 7: GWAS with GAPIT

```r

Install GAPIT

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("GAPIT")

library(GAPIT)

Example GWAS Run

gwasresult <- GAPIT( Y = phenotypedata, # Phenotype data G = genotype_data, # Genotype data PCA.total = 3 # Number of principal components ) ```

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Session 2: Interpreting GWAS Results

Script 8: SNP Annotation and Result Interpretation

```r

Filter significant SNPs

significantsnps <- gwasresult$GWAS %>% filter(P.value < 0.05)

Display top SNPs

head(significant_snps) ```

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Session 3: Practical GWAS Workflow

Script 9: Full GWAS Pipeline Automation

```r

Load necessary libraries

library(GAPIT) library(dplyr)

Run GWAS

gwasresult <- GAPIT( Y = phenotypedata, G = genotype_data, PCA.total = 3 )

Visualize results

library(qqman) manhattan(gwasresult$GWAS, col = c("blue4", "orange3")) qq(gwasresult$GWAS$P.value) ```

Datasets: - phenotype_data.csv - genotype_data.csv

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📂 Dataset Files

• phenotype_data.csv: Example phenotype data.
• genotype_data.csv: Example genotype SNP data.
• manhattan_data.csv: Example GWAS plot data.

🛠️ Dependencies

• R version >= 4.0
• R Packages: readr, dplyr, ggplot2, GAPIT, qqman

📖 References

• GAPIT documentation: https://zzlab.net/GAPIT/
• SNP-Seek Database: https://snp-seek.irri.org/

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🤝 Contributing

Contributions are welcome! Please open an issue or pull request.

📜 License

This project is licensed under the MIT License.

Happy analyzing! 🌾📊✨