• Genome Wide Studies
  • Welcome to CDI – GWAS for Population Health
    • 🌐 The CDI Learning Path
  • I DATA EXPLORATION
  • 1 How do you create a GWAS project directory ready for analysis?
    • 1.1 Explanation
    • 1.2 Bash (Terminal)
    • 1.3 Python Code
    • 1.4 R Code
    • 1.5 Import libraries
  • 2 How do you prepare a public GWAS dataset for R-based analysis?
    • 2.1 Explanation
    • 2.2 Bash Script
    • 2.3 File Structure
  • 3 How do you efficiently load and store GWAS data files in R?
    • 3.1 Explanation
    • 3.2 R Code
  • 4 How do you inspect the structure and contents of GWAS input files in R?
    • 4.1 Explanation
    • 4.2 R Code
  • 5 How do you tidy the genotype matrix from a .ped file in R?
    • 5.1 Explanation
    • 5.2 R Code
  • 6 How do you recode allele strings into numeric count format for GWAS?
    • 6.1 Explanation
    • 6.2 R Code
  • 7 How do you filter SNPs and samples based on missing data and minor allele frequency?
    • 7.1 Explanation
    • 7.2 R Code
  • 8 How do you impute missing genotype values before GWAS analysis?
    • 8.1 Explanation
    • 8.2 R Code
  • 9 How do you perform PCA on genotype data to assess population structure?
    • 9.1 Explanation
    • 9.2 R Code
  • 10 How do you include PCA covariates in a GWAS model?
    • 10.1 Explanation
    • 10.2 R Code
  • 11 How do you interpret GWAS model results with PCA covariates?
    • 11.1 Explanation
    • 11.2 R Model Output Summary
  • II GWAS ANALYSIS & VIZ
  • 12 How do you perform a genome-wide SNP scan to generate GWAS results?
    • 12.1 Explanation
    • 12.2 R Code
  • 13 How do you create a Manhattan plot from GWAS results using the ggplot2 package?
    • 13.1 Explanation
    • 13.2 R Code
  • 14 How do you create a Manhattan plot from GWAS results using the qqman package?
    • 14.1 Explanation
    • 14.2 R Code
  • 15 How do you create a QQ plot from GWAS results using qqman and ggplot2?
    • 15.1 Explanation
    • 15.2 A. Using the qqman package
    • 15.3 B. Using ggplot2 for more control
  • 16 How do you apply multiple testing correction to GWAS results?
    • 16.1 Explanation
    • 16.2 R Code
    • 16.3 Interpretation
      • 16.3.1 Summary Table
  • 17 How do you create a volcano plot from GWAS results using ggplot2?
    • 17.1 Explanation
    • 17.2 R Code
  • 18 How do you identify genome-wide significant SNP hits and save them for downstream analysis?
    • 18.1 Explanation
    • 18.2 R Code
  • 19 How do you visualize significant SNPs from a Bonferroni-corrected GWAS results file?
    • 19.1 Explanation
    • 19.2 Python Code
    • 19.3 R Code
  • Explore More Guides

Learning GWAS One Step at a Time with R

Learning GWAS One Step at a Time with R


Last updated: July 31, 2025