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: matplotlib 3.10.9 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-15
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Theme tokens
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"
BRAND = "#009E73"
# Okabe-Ito palette for chromosome alternation
CHROM_COLORS = ["#009E73", "#C475FD"] # Brand green and vermillion, alternating
# Data - Simulate GWAS results for 22 chromosomes
np.random.seed(42)
# Define chromosome sizes (approximate in Mb, scaled down for simulation)
chrom_sizes = {
1: 249,
2: 243,
3: 198,
4: 191,
5: 182,
6: 171,
7: 159,
8: 146,
9: 141,
10: 136,
11: 135,
12: 134,
13: 115,
14: 107,
15: 103,
16: 90,
17: 81,
18: 78,
19: 59,
20: 63,
21: 48,
22: 51,
}
# Generate SNPs for each chromosome
chromosomes = []
positions = []
p_values = []
for chrom, size in chrom_sizes.items():
n_snps = int(size * 40)
chrom_positions = np.sort(np.random.randint(1, size * 1_000_000, n_snps))
chrom_pvals = np.random.uniform(0, 1, n_snps)
# Add some significant SNPs in certain chromosomes
if chrom in [2, 6, 11, 16]:
peak_idx = np.random.choice(n_snps, size=np.random.randint(3, 8), replace=False)
chrom_pvals[peak_idx] = 10 ** (-np.random.uniform(8, 15, len(peak_idx)))
# Add suggestive hits in more chromosomes
if chrom in [1, 3, 8, 12, 19]:
suggestive_idx = np.random.choice(n_snps, size=np.random.randint(2, 5), replace=False)
chrom_pvals[suggestive_idx] = 10 ** (-np.random.uniform(5, 7.5, len(suggestive_idx)))
chromosomes.extend([chrom] * n_snps)
positions.extend(chrom_positions)
p_values.extend(chrom_pvals)
# Create DataFrame
df = pd.DataFrame({"chromosome": chromosomes, "position": positions, "p_value": p_values})
# Calculate -log10(p-value)
df["-log10p"] = -np.log10(df["p_value"])
# Calculate cumulative position for x-axis
df["chrom_num"] = df["chromosome"]
df = df.sort_values(["chrom_num", "position"]).reset_index(drop=True)
# Add cumulative position offset
chrom_centers = {}
cumulative_offset = 0
for chrom in sorted(df["chrom_num"].unique()):
chrom_mask = df["chrom_num"] == chrom
chrom_data = df.loc[chrom_mask].copy()
chrom_positions = chrom_data["position"].values + cumulative_offset
df.loc[chrom_mask, "cumulative_pos"] = chrom_positions
chrom_centers[chrom] = cumulative_offset + chrom_data["position"].median()
cumulative_offset += chrom_data["position"].max() + 10_000_000
# Define thresholds
genome_wide_threshold = -np.log10(5e-8)
suggestive_threshold = -np.log10(1e-5)
# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Plot points by chromosome with alternating Okabe-Ito colors
for i, chrom in enumerate(sorted(df["chrom_num"].unique())):
chrom_data = df[df["chrom_num"] == chrom]
color = CHROM_COLORS[i % 2]
# Smaller markers for dense data
significant_mask = chrom_data["-log10p"] >= genome_wide_threshold
regular_data = chrom_data[~significant_mask]
significant_data = chrom_data[significant_mask]
# Plot regular points
ax.scatter(
regular_data["cumulative_pos"],
regular_data["-log10p"],
c=color,
s=15,
alpha=0.6,
edgecolors="none",
rasterized=True,
)
# Plot significant points with emphasis (using brand color)
if len(significant_data) > 0:
ax.scatter(
significant_data["cumulative_pos"],
significant_data["-log10p"],
c=BRAND,
s=50,
alpha=0.9,
edgecolors=INK_SOFT,
linewidths=0.5,
zorder=5,
rasterized=True,
)
# Add threshold lines
ax.axhline(
y=genome_wide_threshold,
color=INK_SOFT,
linestyle="--",
linewidth=2,
label="Genome-wide significance (p < 5×10⁻⁸)",
alpha=0.6,
)
ax.axhline(
y=suggestive_threshold,
color=INK_SOFT,
linestyle=":",
linewidth=2,
label="Suggestive threshold (p < 1×10⁻⁵)",
alpha=0.4,
)
# Set x-axis with chromosome labels
ax.set_xticks([chrom_centers[c] for c in sorted(chrom_centers.keys())])
ax.set_xticklabels([str(c) for c in sorted(chrom_centers.keys())], fontsize=16)
ax.set_xlim(0, df["cumulative_pos"].max() * 1.01)
# Set y-axis
ax.set_ylim(0, df["-log10p"].max() * 1.1)
# Style
ax.set_xlabel("Chromosome", fontsize=20, color=INK)
ax.set_ylabel("-log₁₀(p-value)", fontsize=20, color=INK)
ax.set_title("manhattan-gwas · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="y", labelsize=16, colors=INK_SOFT, labelcolor=INK_SOFT)
ax.tick_params(axis="x", labelsize=16, colors=INK_SOFT, labelcolor=INK_SOFT)
# Spine styling
for spine in ("left", "bottom"):
ax.spines[spine].set_color(INK_SOFT)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
# Legend
leg = ax.legend(fontsize=16, loc="upper right", frameon=True)
if leg:
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
for text in leg.get_texts():
text.set_color(INK_SOFT)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Manhattan Plot for GWAS on anyplot.ai.