Genetic Correlation Analysis in R

Overview

This R script performs genetic correlation analysis using GBS-derived SNP data (HapMap format) and phenotypic trait data. It is designed for plant breeders and geneticists who want to quantify the relationships between traits based on additive genetic variance estimated from the genomic relationship matrix (GRM).

The script allows users to:

• Import and process GBS SNP data in HapMap format.
• Convert genotype calls (AA, AG, GG) into numeric values (0, 1, 2).
• Construct the genomic relationship matrix (GRM) using the rrBLUP package.
• Fit multivariate mixed models using sommer.
• Estimate variance components and genetic correlations between traits.
• Generate a genetic correlation heatmap for visualization.
• Export results as .csv files for reporting and downstream use.

This workflow enables breeders to understand trait-trait relationships at the genetic level, helping in selection strategies and multi-trait improvement.

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Features

• Supports multiple traits in one dataset.
• Fits multivariate models with genomic relationship matrix (GRM).
• Estimates variance-covariance components.
• Calculates genetic correlations between all trait pairs.
• Saves results as:
  • Pairwise correlations (genetic_correlations.csv)
  • Correlation matrix (genetic_correlation_matrix.csv)
  • Correlation matrix with significance (genetic_correlations_with_significance.csv)
• Generates a heatmap plot of genetic correlations.

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Input Requirements

1. Genotypic Data (HapMap format)

   • SNP file in HapMap format (.hmp.txt)
   • The script skips the first 10 header lines automatically.
   • Genotype calls: AA, AG/GA, GG.

2. Phenotypic Data (CSV file)

   • Must include:
     • geno – genotype names (matching HapMap file)
     • Trait columns (numeric values for measured traits)

⚠️ Important: Genotype names in the phenotypic file must match the HapMap file to align correctly with the GRM.

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Output

The script generates:

• Genetic correlation values for all trait pairs (genetic_correlations.csv).
• Genetic correlation matrix across all traits (genetic_correlation_matrix.csv).
• Heatmap plot of genetic correlations (ggplot2 visualization).
• Optional significance testing with p-values and annotated correlation matrix.

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Citation

If you use this script, please cite the underlying R packages:

• Endelman, J.B. (2011). Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP. Plant Genome 4(3):250-255.
  https://doi.org/10.3835/plantgenome2011.08.0024

• Covarrubias-Pazaran, G. (2016). Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer. PLoS ONE 11(6): e0156744.
  https://doi.org/10.1371/journal.pone.0156744

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Author

Ehtisham Khokhar
New Mexico State University
Email: ehtishamshakeel@gmail.com