gwasvizr

Are you tired of ugly plots? Meet gwasvizr!

Beautiful by default, customizable when needed.

gwasvizr produces publication-ready Manhattan and Miami plots for genome-wide association studies (GWAS) with beautiful defaults and minimal code.

Required Input Format

Your data needs just 3 columns:

Column	Type	Description
chr	character/integer	Chromosome (1-22, X, Y, MT)
pos	numeric	Base pair position
p	numeric	P-value (0 to 1)

That's it. The package handles the rest.

Flexible Chromosome Handling

✅ Accepts: "1", 1, "chr1", "CHR1"
✅ Accepts: "X", "x", "chrX"
✅ Accepts: "MT", "M", "chrM", "mito"

All formats are automatically standardized internally.

Installation

# Development version
devtools::install_github("jleebio/gwasvizr")

Quick Start

Manhattan Plot Example

Using bladder calculus GWAS data with peak highlighting:

library(gwasvizr)

# Load bladder calculus GWAS data using the built-in function
bladder_calculus_data <- load_gwas_data(
    file = "bladder_calculus.h.tsv.gz",
    chrom_col = "chromosome",
    pos_col = "base_pair_location", 
    pval_col = "p_value",
    snp_col = "variant_id"
)

# Define SNPs to highlight and their corresponding gene names
highlight_snps <- c("16_20381010_G_A", "5_177371039_T_G")
label_text <- c("16_20381010_G_A" = "PDILT", "5_177371039_T_G" = "RGS14")

# Create Manhattan plot with peak highlighting and custom gene labels
manhattan_plot(
    data = bladder_calculus_data,           # Your GWAS data
    highlight_mode = "peak",                # "peak" or "snp" highlighting
    peak_window = 500000,                   # Window size around peaks (bp)
    highlight = highlight_snps,             # Specific SNPs to highlight
    highlight_color = "red3",               # Color for highlighted points
    highlight_shape = 16,                   # Shape: 16=circle, 17=triangle, etc.
    highlight_size = 2.0,                   # Size of highlighted points
    label_highlight = TRUE,                 # Show labels on highlights
    label_text = label_text,                # Custom gene labels
    label_color = "black",                  # Color for label text
    label_size = 4,                         # Size for label text
    label_fontface = "plain",               # Font style (plain, bold, italic)
    sig_threshold = 1e-20,                  # Main significance line
    suggestive_threshold = 5e-8,            # Suggestive significance line
    sig_line_color = "darkblue",            # Main significance line color
    suggestive_line_color = "orange"        # Suggestive line color
)

Miami Plot Example

# Load Heart Failure data
heart_failure <- load_gwas_data(
    file = "heart_failure.h.tsv.gz",
    chrom_col = "chromosome",
    pos_col = "base_pair_location",
    pval_col = "p_value", 
    snp_col = "variant_id"
)

# Create Miami plot comparing metabolic vs cardiovascular traits
miami_plot(
    top_data = koges_bmi,                    # Metabolic trait (top)
    bottom_data = heart_failure,             # Cardiovascular trait (bottom)
    top_label = "KOGES BMI",                 # Top panel title
    bottom_label = "Heart Failure",          # Bottom panel title
    chr_colors = c("forestgreen", "seashell2"), # Custom color scheme
    highlight_color = "deeppink4",           # Highlight color for both panels
    highlight_shape = 8,                     # Star shape for highlights
    highlight_size = 2.0,                    # Medium highlight size
    label_highlight = TRUE,                  # Show labels on significant hits
    sig_line_color = "#d32f2f",              # Red significance lines
    suggestive_line_color = "#1976d2"       # Blue suggestive lines
)

Customization Options

Common Parameters for Both Plot Types:

• chr_colors: Two-color vector for alternating chromosomes c("color1", "color2")
• highlight_color: Color for highlighted points (e.g., "red", "#FF0000")
• highlight_shape: Point shapes (16=circle, 17=triangle, 18=diamond, 19=filled circle, 8=star)
• highlight_size: Size of highlighted points (numeric, e.g., 1.5, 2.0, 3.0)
• label_highlight: Show labels on highlighted points (TRUE/FALSE)
• sig_line_color: Main significance line color
• suggestive_line_color: Suggestive significance line color

Manhattan Plot Specific:

• highlight_mode: "peak" (region highlighting) or "snp" (individual points)
• peak_window: Window size around peaks in base pairs (e.g., 500000, 1000000)
• label_text: Named vector for custom SNP labels (e.g., c("rs123" = "GENE1", "rs456" = "GENE2"))
• label_color: Color of label text (e.g., "black", "red")
• label_size: Size of label text (numeric, e.g., 3, 4, 5)
• label_fontface: Font style of labels ("plain", "bold", "italic")
• sig_threshold: Main significance threshold (e.g., 5e-8, 1e-5)
• suggestive_threshold: Suggestive significance threshold (e.g., 1e-5, 1e-3)
• chr_label_size: Size of chromosome labels (numeric)
• y_axis_tick_size: Size of Y-axis tick labels (numeric)

Miami Plot Specific:

• top_data / bottom_data: Data frames for each panel
• top_label / bottom_label: Panel titles/labels
• gap_ratio: Space between panels (negative values create overlap, e.g., -0.14)
• highlight_a / highlight_b: SNPs to highlight in each panel
• top_sig_threshold / bottom_sig_threshold: Panel-specific significance thresholds
• chr_label_size: Chromosome label size (numeric)
• chr_label_fontface: Label style ("plain", "bold", "italic")
• y_axis_tick_size: Y-axis tick label size (numeric)
• x_axis_tick_size: X-axis tick label size (numeric)

Citation

If you use gwasvizr in your research, please cite:

Lee, J. (2026). gwasvizr: R package version 1.0.0. https://github.com/jleebio/gwasvizr

BibTeX entry:

@software{lee2026gwasvizr,
  author = {Lee, Jeongah},
  title = {gwasvizr},
  year = {2026},
  version = {1.0.0},
  url = {https://github.com/jleebio/gwasvizr},
  note = {R package}
}

Your citation helps others find and use this tool, and it supports the development of open-source software. Thank you!

License

MIT License - see LICENSE file for details.