GWAS Circos Plot

Author

Modified

2026-01-17

The visualization of Genome-Wide Association Study (GWAS) results mainly includes SNP circular plots displayed by chromosome positions, SNP density plots, Manhattan plots for significance screening, QQ plots comparing the distribution of observed p-values with expected p-values, etc., which are used to screen candidate variant genes at the genome-wide level.

Example

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming language: R
Dependent packages: MetaNet; pcutils; igraph; dplyr

# Install packages
if (!requireNamespace("CMplot", quietly = TRUE)) {
  install.packages("CMplot")
}

# Library packages
library(CMplot)

sessioninfo::session_info("attached")

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.2 (2025-10-31)
 os       Ubuntu 24.04.3 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-01-17
 pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.8.27 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package * version date (UTC) lib source
 CMplot  * 4.5.1   2024-01-19 [1] RSPM

 [1] /home/runner/work/_temp/Library
 [2] /opt/R/4.5.2/lib/R/site-library
 [3] /opt/R/4.5.2/lib/R/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

Data Preparation

The sample data includes 60K liquid chip raw variation information data of Pig.

# Example data
data(pig60K)
data <- pig60K

# Data preview
head(data, 5)

          SNP Chromosome Position    trait1    trait2    trait3
1 ALGA0000009          1    52297 0.7738187 0.5119432 0.5119432
2 ALGA0000014          1    79763 0.7738187 0.5119432 0.5119432
3 ALGA0000021          1   209568 0.7583016 0.9840529 0.9840529
4 ALGA0000022          1   292758 0.7200305 0.4888714 0.4888714
5 ALGA0000046          1   747831 0.9736840 0.2209684 0.2209684

Visualization

1. SNP screening genome circular map

Genome circular maps can display more chromosomes and traits under the same proportion, and have a great advantage when comparing multiple traits.

# SNP screening genome circular map
p <- CMplot(
  data,
  type = "p",
  plot.type = "c",
  chr.labels = paste("Chr", c(1:18, "X", "Y"), sep = ""),
  r = 8,
  cir.axis = TRUE,
  outward = TRUE,
  cir.axis.col = "black",
  cir.chr.h = 2,
  chr.den.col = "black",
  file.output = FALSE,
  verbose = FALSE,
  mar = c(0,0,0,0)
)

p

NULL

Figure 1: SNP screening genome circular map

2. SNP Chromosome Density Map

The SNP chromosome density map shows the distribution of SNP density on each chromosome.

# SNP Chromosome Density Map
p <- capture.output(
  CMplot(
    data,
    plot.type = "d",
    bin.size = 1e6,
    chr.den.col = c("darkgreen", "yellow", "red"),
    main = "Density",
    file.output = FALSE,
    verbose = FALSE
  )
)

print(p[1])

[1] NA

3. Manhattan plot for SNP screening

The Manhattan plot for SNP screening is convenient for displaying the significance of SNPs at 5% or 1%, and it also helps identify the concentration effect of key chromosomes.

# Manhattan plot for SNP screening
p <- CMplot(
  data,
  type = "p",
  plot.type = "m",
  LOG10 = TRUE,
  threshold = NULL,
  file.output = FALSE,
  verbose = FALSE,
  chr.labels.angle = 45
)

p

NULL

Figure 3: Manhattan plot for SNP screening

Figure 4: Manhattan plot for SNP screening

Figure 5: Manhattan plot for SNP screening

4. QQ plot based on SNP

The core function of the QQ plot based on SNPs is to test whether the P-value distribution of SNPs in GWAS analysis conforms to the “expected distribution under the no-association hypothesis”, so as to judge the reliability of the analysis results.

# QQ plot based on SNP
p <- CMplot(
  data,
  plot.type = "q",
  box = FALSE,
  conf.int = TRUE,
  conf.int.col = NULL,
  threshold.col = "red",
  threshold.lty = 2,
  file.output = FALSE,
  verbose = FALSE
)

p

NULL