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: plotly 6.7.0 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-15
"""
import os
import sys
import numpy as np
import pandas as pd
# Remove current directory from path to avoid shadowing plotly package
current_dir = os.path.dirname(os.path.abspath(__file__))
if current_dir in sys.path:
sys.path.remove(current_dir)
import plotly.graph_objects as go
# 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"
GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
# Okabe-Ito palette
IMPRINT = [
"#009E73", # bluish green (brand)
"#C475FD", # vermillion
"#4467A3", # blue
"#BD8233", # reddish purple
"#AE3030", # orange
"#2ABCCD", # sky blue
"#954477", # yellow
]
# Threshold line colors (theme-adaptive)
THRESHOLD_COLOR = INK_MUTED
HIGHLIGHT_COLOR = IMPRINT[1] # vermillion for significant SNPs
# Data - Simulated GWAS results
np.random.seed(42)
# Chromosome lengths (simplified, in Mb)
chr_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,
}
# Generate SNPs for each chromosome
data = []
cumulative_pos = 0
chr_centers = {}
for chrom, length in chr_lengths.items():
# Number of SNPs proportional to chromosome length
n_snps = int(length * 40)
positions = np.sort(np.random.uniform(0, length * 1e6, n_snps))
# Generate p-values (mostly non-significant, with some peaks)
pvalues = np.random.uniform(0, 1, n_snps)
# Add significant peaks on chromosomes 2, 8, and 15
if chrom == "2":
peak_idx = np.abs(positions - 100e6).argmin()
pvalues[peak_idx - 5 : peak_idx + 5] = 10 ** (-np.random.uniform(8, 12, 10))
elif chrom == "8":
peak_idx = np.abs(positions - 70e6).argmin()
pvalues[peak_idx - 3 : peak_idx + 3] = 10 ** (-np.random.uniform(7.5, 10, 6))
elif chrom == "15":
peak_idx = np.abs(positions - 50e6).argmin()
pvalues[peak_idx - 4 : peak_idx + 4] = 10 ** (-np.random.uniform(9, 14, 8))
# Calculate cumulative position
cumulative_positions = positions + cumulative_pos
chr_centers[chrom] = cumulative_pos + (length * 1e6) / 2
for i in range(n_snps):
data.append(
{
"chromosome": chrom,
"position": positions[i],
"cumulative_pos": cumulative_positions[i],
"p_value": pvalues[i],
"neg_log_p": -np.log10(pvalues[i]),
}
)
cumulative_pos += length * 1e6
df = pd.DataFrame(data)
# Alternating chromosome colors (Okabe-Ito positions 1 and 2)
def get_chr_color(chrom_num):
return IMPRINT[0] if int(chrom_num) % 2 == 1 else IMPRINT[1]
# Create figure
fig = go.Figure()
# Add scatter traces for each chromosome
for chrom in chr_lengths.keys():
chr_data = df[df["chromosome"] == chrom]
color = get_chr_color(chrom)
fig.add_trace(
go.Scatter(
x=chr_data["cumulative_pos"],
y=chr_data["neg_log_p"],
mode="markers",
marker={"size": 5, "color": color, "opacity": 0.7},
name=f"Chr {chrom}",
showlegend=False,
hovertemplate=(
f"Chr {chrom}<br>Position: %{{customdata[0]:,.0f}} bp<br>-log₁₀(p): %{{y:.2f}}<extra></extra>"
),
customdata=chr_data[["position"]].values,
)
)
# Genome-wide significance threshold (-log10(5e-8) ≈ 7.3)
significance_threshold = -np.log10(5e-8)
fig.add_shape(
type="line",
x0=0,
x1=1,
xref="paper",
y0=significance_threshold,
y1=significance_threshold,
line={"color": THRESHOLD_COLOR, "width": 2, "dash": "dash"},
)
fig.add_annotation(
text="Genome-wide significance (p = 5×10⁻⁸)",
font={"size": 16, "color": THRESHOLD_COLOR},
xref="paper",
x=0.99,
xanchor="right",
yref="y",
y=significance_threshold,
showarrow=False,
yshift=15,
)
# Suggestive threshold (-log10(1e-5) = 5)
suggestive_threshold = 5
fig.add_shape(
type="line",
x0=0,
x1=1,
xref="paper",
y0=suggestive_threshold,
y1=suggestive_threshold,
line={"color": INK_MUTED, "width": 2, "dash": "dot"},
)
fig.add_annotation(
text="Suggestive threshold (p = 10⁻⁵)",
font={"size": 16, "color": INK_MUTED},
xref="paper",
x=0.99,
xanchor="right",
yref="y",
y=suggestive_threshold,
showarrow=False,
yshift=15,
)
# Highlight significant SNPs
significant_snps = df[df["neg_log_p"] > significance_threshold]
if len(significant_snps) > 0:
fig.add_trace(
go.Scatter(
x=significant_snps["cumulative_pos"],
y=significant_snps["neg_log_p"],
mode="markers",
marker={"size": 10, "color": HIGHLIGHT_COLOR, "symbol": "diamond", "line": {"color": "white", "width": 1}},
name="Significant SNPs",
showlegend=True,
hovertemplate=(
"Significant SNP<br>"
"Chr %{customdata[0]}<br>"
"Position: %{customdata[1]:,.0f} bp<br>"
"-log₁₀(p): %{y:.2f}<extra></extra>"
),
customdata=significant_snps[["chromosome", "position"]].values,
)
)
# Chromosome tick positions and labels
chr_positions = [chr_centers[chrom] for chrom in chr_lengths.keys()]
chr_labels = list(chr_lengths.keys())
# Layout
fig.update_layout(
title={"text": "manhattan-gwas · plotly · pyplots.ai", "font": {"size": 28, "color": INK}, "x": 0.5, "xanchor": "center"},
xaxis={
"title": {"text": "Chromosome", "font": {"size": 22, "color": INK}},
"tickfont": {"size": 18, "color": INK_SOFT},
"tickmode": "array",
"tickvals": chr_positions,
"ticktext": chr_labels,
"showgrid": False,
"zeroline": False,
"linecolor": INK_SOFT,
},
yaxis={
"title": {"text": "-log₁₀(p-value)", "font": {"size": 22, "color": INK}},
"tickfont": {"size": 18, "color": INK_SOFT},
"gridcolor": GRID,
"gridwidth": 1,
"zeroline": False,
"linecolor": INK_SOFT,
},
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
legend={
"yanchor": "top",
"y": 0.99,
"xanchor": "left",
"x": 0.01,
"font": {"size": 16, "color": INK_SOFT},
"bgcolor": ELEVATED_BG,
"bordercolor": INK_SOFT,
"borderwidth": 1,
},
margin={"l": 80, "r": 50, "t": 80, "b": 80},
hovermode="closest",
)
# Save outputs with theme suffix
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Part of Manhattan Plot for GWAS on anyplot.ai.