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: pygal 3.1.0 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-15
"""
import os
import numpy as np
import pygal
from pygal.style import Style
np.random.seed(42)
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477")
# Chromosome lengths (simplified, in Mb)
chrom_lengths = {
"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": 102,
"16": 90,
"17": 83,
"18": 80,
"19": 59,
"20": 64,
"21": 47,
"22": 51,
}
chromosomes = list(chrom_lengths.keys())
n_snps_per_chrom = 500
# Storage for data
all_chroms = []
all_cumulative_pos = []
all_pvalues = []
# Track chromosome boundaries for x-axis labels
chrom_midpoints = []
cumulative_offset = 0
for chrom in chromosomes:
length = chrom_lengths[chrom]
positions = np.sort(np.random.uniform(0, length, n_snps_per_chrom))
# Store chromosome midpoint for x-axis label
chrom_midpoints.append(cumulative_offset + length / 2)
# Generate p-values: mostly uniform with some significant hits
pvalues = np.random.uniform(0, 1, n_snps_per_chrom)
# Add significant peaks on selected chromosomes
if chrom in ["6", "11", "16"]:
n_sig = np.random.randint(3, 8)
sig_indices = np.random.choice(n_snps_per_chrom, n_sig, replace=False)
pvalues[sig_indices] = 10 ** (-np.random.uniform(8, 15, n_sig))
# Add suggestive signals
n_sugg = np.random.randint(5, 15)
sugg_indices = np.random.choice(n_snps_per_chrom, n_sugg, replace=False)
pvalues[sugg_indices] = 10 ** (-np.random.uniform(5, 8, n_sugg))
cumulative_positions = positions + cumulative_offset
all_chroms.extend([chrom] * n_snps_per_chrom)
all_cumulative_pos.extend(cumulative_positions)
all_pvalues.extend(pvalues)
cumulative_offset += length
# Convert to -log10 p-values
neg_log_pvalues = -np.log10(np.array(all_pvalues))
# Thresholds
genome_wide_sig = -np.log10(5e-8) # ~7.3
suggestive_sig = 5.0 # -log10(1e-5)
# Custom style with theme-adaptive colors
custom_style = Style(
background=PAGE_BG,
plot_background=PAGE_BG,
foreground=INK,
foreground_strong=INK,
foreground_subtle=INK_MUTED,
colors=IMPRINT,
title_font_size=28,
label_font_size=22,
major_label_font_size=18,
legend_font_size=16,
value_font_size=14,
)
# Create XY scatter chart
chart = pygal.XY(
width=4800,
height=2700,
style=custom_style,
title="manhattan-gwas · pygal · anyplot.ai",
x_title="Chromosome",
y_title="-log₁₀(p-value)",
show_legend=True,
legend_at_bottom=True,
legend_at_bottom_columns=5,
legend_box_size=16,
stroke=False,
dots_size=4,
show_x_guides=False,
show_y_guides=True,
truncate_label=-1,
print_values=False,
x_label_rotation=0,
range=(0, 16),
xrange=(0, cumulative_offset),
tooltip_border_radius=5,
explicit_size=True,
spacing=20,
margin=50,
margin_bottom=150,
margin_top=80,
)
# Set x-axis labels at chromosome midpoints
chart.x_labels = chrom_midpoints
def format_x_label(x_val):
"""Find closest chromosome midpoint and return chromosome number."""
if not chrom_midpoints:
return ""
min_dist = float("inf")
closest_idx = 0
for i, pos in enumerate(chrom_midpoints):
dist = abs(x_val - pos)
if dist < min_dist:
min_dist = dist
closest_idx = i
# Only return label if close to midpoint
if min_dist < 50:
return str(closest_idx + 1)
return ""
chart.x_value_formatter = lambda x: format_x_label(x)
# Prepare data by chromosome with alternating colors
odd_chrom_points = []
even_chrom_points = []
significant_points = []
for idx, chrom in enumerate(chromosomes):
chrom_mask = [c == chrom for c in all_chroms]
chrom_x = [all_cumulative_pos[j] for j in range(len(all_chroms)) if chrom_mask[j]]
chrom_y = [neg_log_pvalues[j] for j in range(len(all_chroms)) if chrom_mask[j]]
for x, y in zip(chrom_x, chrom_y, strict=True):
point = {"value": (x, y), "label": f"Chr {chrom}: {x:.1f} Mb, -log₁₀(p)={y:.2f}"}
if y >= genome_wide_sig:
significant_points.append(point)
elif idx % 2 == 0:
odd_chrom_points.append(point)
else:
even_chrom_points.append(point)
# Add data series
chart.add("Odd chromosomes", odd_chrom_points, stroke=False, show_dots=True)
chart.add("Even chromosomes", even_chrom_points, stroke=False, show_dots=True)
chart.add("Significant (p<5×10⁻⁸)", significant_points, stroke=False, show_dots=True)
# Add threshold lines
n_line_points = 200
threshold_x = np.linspace(10, cumulative_offset - 10, n_line_points)
gw_line_points = [
{"value": (x, genome_wide_sig), "label": f"Genome-wide threshold: -log₁₀(5×10⁻⁸) = {genome_wide_sig:.1f}"}
for x in threshold_x
]
chart.add("p = 5×10⁻⁸ threshold", gw_line_points, stroke=True, show_dots=True, dots_size=2)
sugg_line_points = [
{"value": (x, suggestive_sig), "label": f"Suggestive threshold: -log₁₀(1×10⁻⁵) = {suggestive_sig:.1f}"}
for x in threshold_x
]
chart.add("p = 1×10⁻⁵ threshold", sugg_line_points, stroke=True, show_dots=True, dots_size=2)
# Save outputs
chart.render_to_file(f"plot-{THEME}.html")
chart.render_to_png(f"plot-{THEME}.png")
Part of Manhattan Plot for GWAS on anyplot.ai.