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: altair 6.1.0 | Python 3.13.13
Quality: 88/100 | Updated: 2026-05-15
"""
import os
import altair as alt
import numpy as np
import pandas as pd
# Theme configuration
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
RULE = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
# Okabe-Ito palette for alternating chromosomes
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
# Data generation
np.random.seed(42)
# Chromosome lengths (approximate human chromosome sizes in Mb)
chrom_lengths = {
"1": 249,
"2": 243,
"3": 198,
"4": 191,
"5": 182,
"6": 171,
"7": 159,
"8": 145,
"9": 138,
"10": 134,
"11": 135,
"12": 133,
"13": 114,
"14": 107,
"15": 102,
"16": 90,
"17": 83,
"18": 80,
"19": 59,
"20": 64,
"21": 47,
"22": 51,
}
# Generate SNPs for each chromosome
data = []
cumulative_pos = 0
chrom_centers = {}
for chrom, length in chrom_lengths.items():
# Number of SNPs proportional to chromosome length
n_snps = int(length * 20) # ~20 SNPs per Mb
positions = np.sort(np.random.randint(1, length * 1_000_000, n_snps))
# Base p-values (mostly non-significant)
pvalues = np.random.uniform(0.001, 1.0, n_snps)
# Add some significant peaks on specific chromosomes
if chrom in ["2", "6", "8", "15"]:
n_sig = np.random.randint(5, 15)
sig_indices = np.random.choice(n_snps, n_sig, replace=False)
pvalues[sig_indices] = 10 ** np.random.uniform(-10, -7, n_sig)
# Add suggestive hits on other chromosomes
if chrom in ["3", "11", "19"]:
n_sug = np.random.randint(3, 8)
sug_indices = np.random.choice(n_snps, n_sug, replace=False)
pvalues[sug_indices] = 10 ** np.random.uniform(-6, -5, n_sug)
# Calculate cumulative positions
cum_positions = positions + cumulative_pos
chrom_centers[chrom] = cumulative_pos + (length * 1_000_000) / 2
for pos, cum_pos, pval in zip(positions, cum_positions, pvalues, strict=True):
data.append(
{
"chromosome": chrom,
"position": pos,
"cumulative_position": cum_pos,
"p_value": pval,
"neg_log_p": -np.log10(pval),
}
)
cumulative_pos += length * 1_000_000
df = pd.DataFrame(data)
# Threshold values
genome_wide_threshold = -np.log10(5e-8) # ~7.3
suggestive_threshold = -np.log10(1e-5) # 5.0
# Create chromosome label data
chrom_label_df = pd.DataFrame([{"chrom_label": chrom, "center": center} for chrom, center in chrom_centers.items()])
# Create alternating color mapping with Okabe-Ito palette
chrom_list = list(chrom_lengths.keys())
color_mapping = {}
for i, chrom in enumerate(chrom_list):
color_mapping[chrom] = IMPRINT[i % len(IMPRINT)]
color_scale = alt.Scale(domain=chrom_list, range=[color_mapping[chrom] for chrom in chrom_list])
# Main scatter plot
points = (
alt.Chart(df)
.mark_circle(opacity=0.7, size=80)
.encode(
x=alt.X(
"cumulative_position:Q",
axis=alt.Axis(title="Genomic Position", labels=False, ticks=False, titleFontSize=22, titleColor=INK),
scale=alt.Scale(domain=[0, cumulative_pos]),
),
y=alt.Y(
"neg_log_p:Q",
title="-log₁₀(p-value)",
axis=alt.Axis(
titleFontSize=22,
labelFontSize=18,
titleColor=INK,
labelColor=INK_SOFT,
domainColor=INK_SOFT,
tickColor=INK_SOFT,
gridColor=INK,
gridOpacity=0.10,
),
scale=alt.Scale(domain=[0, max(df["neg_log_p"]) + 1]),
),
color=alt.Color("chromosome:N", scale=color_scale, legend=None),
size=alt.condition(
alt.datum.neg_log_p > genome_wide_threshold,
alt.value(100), # Larger for significant hits
alt.value(60), # Smaller for others
),
tooltip=[
"chromosome:N",
alt.Tooltip("position:Q", format=","),
alt.Tooltip("p_value:Q", format=".2e"),
"neg_log_p:Q",
],
)
)
# Genome-wide significance threshold line
gw_line = (
alt.Chart(pd.DataFrame({"y": [genome_wide_threshold]}))
.mark_rule(strokeDash=[8, 4], color=INK_MUTED, size=2, opacity=0.8)
.encode(y="y:Q")
)
# Suggestive threshold line
sug_line = (
alt.Chart(pd.DataFrame({"y": [suggestive_threshold]}))
.mark_rule(strokeDash=[4, 4], color=INK_MUTED, size=1.5, opacity=0.6)
.encode(y="y:Q")
)
# Chromosome labels at bottom
chrom_text = (
alt.Chart(chrom_label_df)
.mark_text(fontSize=16, baseline="top", dy=10, color=INK_SOFT, fontWeight="normal")
.encode(x=alt.X("center:Q", axis=None, scale=alt.Scale(domain=[0, cumulative_pos])), text="chrom_label:N")
)
# Combine layers
main_chart = alt.layer(points, gw_line, sug_line).properties(width=1600, height=850)
# Final chart with labels
chart = (
alt.vconcat(main_chart, chrom_text.properties(width=1600, height=40), spacing=5)
.properties(
title=alt.Title(
"Manhattan Plot: GWAS Results",
fontSize=28,
anchor="middle",
color=INK,
subtitle="Genome-wide association study with significance thresholds",
),
background=PAGE_BG,
)
.configure_view(fill=PAGE_BG, stroke=INK_SOFT, strokeWidth=1)
.configure_axis(labelFontSize=18, titleFontSize=22, labelColor=INK_SOFT, titleColor=INK)
.configure_title(fontSize=28, color=INK, subtitleFontSize=16, subtitleColor=INK_SOFT)
.configure_legend(fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK)
)
# Save with theme-specific filenames
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")
Part of Manhattan Plot for GWAS on anyplot.ai.