A Manhattan plot visualizes genome-wide association study (GWAS) results by displaying -log10 transformed p-values across chromosomal positions. Points are arranged by genomic position along the x-axis with alternating colors for each chromosome, making it easy to identify significant associations. A horizontal threshold line indicates genome-wide significance (typically p < 5×10⁻⁸). This plot is essential for identifying genetic variants associated with traits or diseases.
# anyplot.ai
# manhattan-gwas: Manhattan Plot for GWAS
# Library: makie 0.21.9 | Julia 1.11.9
# Quality: 88/100 | Created: 2026-09-05
using CairoMakie
using Colors
using Random
Random.seed!(42)
# Theme tokens (see prompts/default-style-guide.md "Theme-adaptive Chrome")
THEME = get(ENV, "ANYPLOT_THEME", "light")
PAGE_BG = THEME == "light" ? colorant"#FAF8F1" : colorant"#1A1A17"
ELEVATED_BG = THEME == "light" ? colorant"#FFFDF6" : colorant"#242420"
INK = THEME == "light" ? colorant"#1A1A17" : colorant"#F0EFE8"
INK_SOFT = THEME == "light" ? colorant"#4A4A44" : colorant"#B8B7B0"
INK_MUTED = THEME == "light" ? colorant"#6B6A63" : colorant"#A8A79F"
const IMPRINT_PALETTE = [
colorant"#009E73", colorant"#C475FD", colorant"#4467A3", colorant"#BD8233",
colorant"#AE3030", colorant"#2ABCCD", colorant"#954477", colorant"#99B314",
]
CHR_COLOR_ODD = IMPRINT_PALETTE[1] # brand green
CHR_COLOR_EVEN = IMPRINT_PALETTE[3] # blue
AMBER = colorant"#DDCC77" # significant-hit highlight
# Data: simulated GWAS summary statistics across the 22 autosomes + chromosome X
chrom_names = vcat(string.(1:22), "X")
chrom_lengths_mb = Float64[
249, 243, 198, 191, 180, 171, 159, 145, 138, 134, 135, 133,
114, 107, 102, 90, 83, 80, 59, 64, 47, 51, 156,
]
snps_per_mb = 36.0
peak_chroms = ("2", "6", "9", "17", "X")
chrom_ids = String[]
positions = Float64[]
neglog10p = Float64[]
chrom_centers = Float64[]
top_hit_pos = Float64[]
top_hit_p = Float64[]
top_hit_labels = String[]
cumulative_offset = 0.0
for (name, length_mb) in zip(chrom_names, chrom_lengths_mb)
n_snps = round(Int, length_mb * snps_per_mb)
local_pos = sort(rand(n_snps) .* length_mb)
baseline_p = -log10.(rand(n_snps)) # null distribution — mostly non-significant
if name in peak_chroms
peak_center = length_mb * rand()
peak_width = length_mb * 0.015
peak_signal = 12.0 .* exp.(-((local_pos .- peak_center) .^ 2) ./ (2 * peak_width^2))
baseline_p = baseline_p .+ peak_signal .* (0.5 .+ 0.5 .* rand(n_snps))
top_idx = argmax(baseline_p)
push!(top_hit_pos, local_pos[top_idx] + cumulative_offset)
push!(top_hit_p, baseline_p[top_idx])
push!(top_hit_labels, "rs" * string(rand(1_000_000:99_999_999)))
end
append!(chrom_ids, fill(name, n_snps))
append!(positions, local_pos .+ cumulative_offset)
append!(neglog10p, baseline_p)
push!(chrom_centers, cumulative_offset + length_mb / 2)
global cumulative_offset += length_mb
end
point_colors = [isodd(parse_index) ? CHR_COLOR_ODD : CHR_COLOR_EVEN
for parse_index in indexin(chrom_ids, chrom_names)]
genome_wide_threshold = -log10(5e-8) # ≈ 7.30 — genome-wide significance
suggestive_threshold = -log10(1e-5) # 5.0 — suggestive association
significant = neglog10p .> genome_wide_threshold
# Plot — see default-style-guide.md "Visual Sizing Defaults" for the canvas + sizing values
title_text = "manhattan-gwas · julia · makie · anyplot.ai"
fig = Figure(
resolution = (1600, 900),
fontsize = 14,
backgroundcolor = PAGE_BG,
)
ax = Axis(
fig[1, 1];
title = title_text,
titlesize = 20,
titlecolor = INK,
xlabel = "Chromosome",
ylabel = "-log10(p-value)",
xlabelsize = 14,
ylabelsize = 14,
xlabelcolor = INK,
ylabelcolor = INK,
xticklabelsize = 10,
yticklabelsize = 12,
xticklabelcolor = INK_SOFT,
yticklabelcolor = INK_SOFT,
xtickcolor = INK_SOFT,
ytickcolor = INK_SOFT,
backgroundcolor = PAGE_BG,
topspinevisible = false,
rightspinevisible = false,
leftspinecolor = INK_SOFT,
bottomspinecolor = INK_SOFT,
xgridvisible = false,
ygridcolor = RGBAf(INK.r, INK.g, INK.b, 0.12),
yminorgridvisible = false,
)
ax.xticks = (chrom_centers, chrom_names)
scatter!(
ax, positions, neglog10p;
color = point_colors, markersize = 3, alpha = 0.6, strokewidth = 0,
)
scatter!(
ax, positions[significant], neglog10p[significant];
color = AMBER, markersize = 8, strokewidth = 0.6, strokecolor = INK,
label = "Genome-wide significant SNP",
)
text!(
ax, top_hit_pos, top_hit_p .+ 0.6;
text = top_hit_labels, color = INK_SOFT, fontsize = 11,
align = (:center, :bottom),
)
# Extra headroom above the tallest peak/label keeps the top-left legend box
# (pixel-anchored) clear of the chromosome-2 peak, which otherwise sits
# closest to the axis top among the labeled hits.
ylims!(ax, -0.3, maximum(neglog10p) + 2.5)
hlines!(
ax, [suggestive_threshold];
color = INK_MUTED, linestyle = :dot, linewidth = 2,
label = "Suggestive (p < 1×10⁻⁵)",
)
hlines!(
ax, [genome_wide_threshold];
color = AMBER, linestyle = :dash, linewidth = 2.5,
label = "Genome-wide significance (p < 5×10⁻⁸)",
)
axislegend(
ax; position = :lt, backgroundcolor = ELEVATED_BG, framevisible = false,
labelcolor = INK, labelsize = 11, patchsize = (18, 4),
)
# Save
save("plot-$(THEME).png", fig; px_per_unit = 2)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/manhattan-gwas/makie/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "manhattan-gwas",
"language": "julia",
"library": "makie",
"page": "https://anyplot.ai/manhattan-gwas/julia/makie",
"hub": "https://anyplot.ai/manhattan-gwas",
"code_json": "https://api.anyplot.ai/specs/manhattan-gwas/makie/code",
"spec_json": "https://api.anyplot.ai/specs/manhattan-gwas",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/manhattan-gwas/julia/makie/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/manhattan-gwas/julia/makie/plot-dark.png",
"quality_score": 88.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Manhattan Plot for GWAS on anyplot.ai.