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: bokeh 3.9.0 | Python 3.13.13
Quality: 85/100 | Updated: 2026-05-15
"""
import os
import time
from pathlib import Path
import numpy as np
import pandas as pd
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, HoverTool, Label, Span
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# 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"
# Okabe-Ito palette for data colors
CHROM_COLOR_1 = "#009E73" # Okabe-Ito position 1 (first series)
CHROM_COLOR_2 = "#C475FD" # Okabe-Ito position 2 (alternating)
SIG_COLOR = "#954477" # Okabe-Ito position 7 (significant SNPs highlight)
# Data - Simulated GWAS data with significant peaks
np.random.seed(42)
chrom_sizes = {
"1": 248,
"2": 242,
"3": 198,
"4": 190,
"5": 181,
"6": 170,
"7": 159,
"8": 145,
"9": 138,
"10": 133,
"11": 135,
"12": 133,
"13": 114,
"14": 107,
"15": 101,
"16": 90,
"17": 83,
"18": 80,
"19": 58,
"20": 64,
"21": 46,
"22": 50,
}
# Generate SNP data
n_snps_per_chrom = 2000
data = []
cumulative_pos = 0
chrom_centers = {}
for chrom, size in chrom_sizes.items():
positions = np.sort(np.random.randint(1, size * 1_000_000, n_snps_per_chrom))
p_values = np.random.uniform(0.001, 1.0, n_snps_per_chrom)
if chrom in ["2", "6", "11", "17"]:
n_significant = np.random.randint(5, 15)
peak_indices = np.random.choice(n_snps_per_chrom, n_significant, replace=False)
p_values[peak_indices] = 10 ** np.random.uniform(-12, -8, n_significant)
cumulative_positions = positions + cumulative_pos
chrom_centers[chrom] = cumulative_pos + (size * 1_000_000) / 2
for i in range(n_snps_per_chrom):
data.append(
{
"chromosome": chrom,
"position": positions[i],
"cumulative_pos": cumulative_positions[i],
"p_value": p_values[i],
"neg_log_p": -np.log10(p_values[i]),
}
)
cumulative_pos += size * 1_000_000
df = pd.DataFrame(data)
# Assign alternating colors based on chromosome parity
chrom_int = df["chromosome"].astype(int)
df["color"] = df["chromosome"].apply(lambda x: CHROM_COLOR_1 if int(x) % 2 == 1 else CHROM_COLOR_2)
df["color_label"] = df["chromosome"].apply(lambda x: "Odd chromosome" if int(x) % 2 == 1 else "Even chromosome")
# Highlight significant SNPs
significance_threshold = -np.log10(5e-8)
significant_mask = df["neg_log_p"] >= significance_threshold
df.loc[significant_mask, "color"] = SIG_COLOR
df.loc[significant_mask, "color_label"] = "Significant SNP (p < 5×10⁻⁸)"
# Adjust point sizes
df["size"] = 6
df.loc[significant_mask, "size"] = 12
# Plot
source = ColumnDataSource(df)
p = figure(
width=4800,
height=2700,
title="manhattan-gwas · bokeh · anyplot.ai",
x_axis_label="Genomic Position",
y_axis_label="-log₁₀(p-value)",
tools="pan,wheel_zoom,box_zoom,reset,save",
)
# Scatter plot with legend
p.scatter(
x="cumulative_pos",
y="neg_log_p",
source=source,
size="size",
color="color",
alpha=0.7,
line_color=None,
legend_field="color_label",
)
# Significance threshold line
significance_line = Span(
location=significance_threshold, dimension="width", line_color=INK_SOFT, line_dash="dashed", line_width=3
)
p.add_layout(significance_line)
# Suggestive threshold line
suggestive_threshold = -np.log10(1e-5)
suggestive_line = Span(
location=suggestive_threshold, dimension="width", line_color=INK_SOFT, line_dash="dotted", line_width=2
)
p.add_layout(suggestive_line)
# Threshold labels
sig_label = Label(
x=cumulative_pos * 0.98,
y=significance_threshold + 0.3,
text="p = 5×10⁻⁸",
text_font_size="18pt",
text_color=INK_SOFT,
)
p.add_layout(sig_label)
sug_label = Label(
x=cumulative_pos * 0.98, y=suggestive_threshold + 0.3, text="p = 1×10⁻⁵", text_font_size="18pt", text_color=INK_SOFT
)
p.add_layout(sug_label)
# Add chromosome labels
for chrom, center in chrom_centers.items():
chrom_label = Label(x=center, y=-0.8, text=chrom, text_font_size="16pt", text_align="center", text_color=INK_SOFT)
p.add_layout(chrom_label)
# Add HoverTool for interactivity
hover = HoverTool(
tooltips=[
("Chromosome", "@chromosome"),
("Position", "@{position:0,0}"),
("-log₁₀(p)", "@{neg_log_p:.2f}"),
("Type", "@color_label"),
]
)
p.add_tools(hover)
# Style the plot
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "22pt"
p.yaxis.axis_label_text_font_size = "22pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "0pt"
p.yaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_color = INK_SOFT
# Hide x-axis ticks (using chromosome labels instead)
p.xaxis.major_tick_line_color = None
p.xaxis.minor_tick_line_color = None
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
# Grid styling
p.xgrid.grid_line_alpha = 0.10
p.ygrid.grid_line_alpha = 0.10
p.xgrid.grid_line_color = INK
p.ygrid.grid_line_color = INK
# Background and borders
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
# Legend styling
if p.legend:
p.legend.background_fill_color = ELEVATED_BG
p.legend.border_line_color = INK_SOFT
p.legend.label_text_color = INK_SOFT
p.legend.label_text_font_size = "16pt"
p.legend.location = "top_right"
# Set y-axis range to accommodate chromosome labels
p.y_range.start = -1.5
p.y_range.end = df["neg_log_p"].max() + 1
# Save HTML
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Chrome via Selenium
W, H = 4800, 2700
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Manhattan Plot for GWAS on anyplot.ai.