feat(plotly): implement manhattan-gwas (#2945)

## Implementation: `manhattan-gwas` - plotly

Implements the **plotly** version of `manhattan-gwas`.

**File:** `plots/manhattan-gwas/implementations/plotly.py`

**Parent Issue:** #2925

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612787650)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/manhattan-gwas/implementations/plotly.py

@@ -0,0 +1,210 @@
+""" pyplots.ai
+manhattan-gwas: Manhattan Plot for GWAS
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+
+
+# 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 colors for chromosomes (Python Blue and a gray variant)
+colors = {"odd": "#306998", "even": "#7A9FBF"}
+
+# Create figure
+fig = go.Figure()
+
+# Add scatter traces for each chromosome
+for i, chrom in enumerate(chr_lengths.keys()):
+    chr_data = df[df["chromosome"] == chrom]
+    color = colors["odd"] if int(chrom) % 2 == 1 else colors["even"]
+
+    fig.add_trace(
+        go.Scatter(
+            x=chr_data["cumulative_pos"],
+            y=chr_data["neg_log_p"],
+            mode="markers",
+            marker=dict(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=dict(color="#E53935", width=2, dash="dash"),
+)
+fig.add_annotation(
+    text="Genome-wide significance (p = 5×10⁻⁸)",
+    font=dict(size=16, color="#E53935"),
+    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=dict(color="#FFD43B", width=2, dash="dot"),
+)
+fig.add_annotation(
+    text="Suggestive threshold (p = 10⁻⁵)",
+    font=dict(size=16, color="#B8860B"),
+    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=dict(size=10, color="#E53935", symbol="diamond", line=dict(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=dict(text="manhattan-gwas · plotly · pyplots.ai", font=dict(size=32), x=0.5, xanchor="center"),
+    xaxis=dict(
+        title=dict(text="Chromosome", font=dict(size=24)),
+        tickfont=dict(size=16),
+        tickmode="array",
+        tickvals=chr_positions,
+        ticktext=chr_labels,
+        showgrid=False,
+        zeroline=False,
+    ),
+    yaxis=dict(
+        title=dict(text="-log₁₀(p-value)", font=dict(size=24)),
+        tickfont=dict(size=18),
+        gridcolor="rgba(0,0,0,0.1)",
+        gridwidth=1,
+        zeroline=False,
+    ),
+    template="plotly_white",
+    legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01, font=dict(size=16), bgcolor="rgba(255,255,255,0.8)"),
+    margin=dict(l=80, r=50, t=80, b=80),
+    plot_bgcolor="white",
+)
+
+# Save as PNG (4800 x 2700 px)
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+
+# Save as interactive HTML
+fig.write_html("plot.html", include_plotlyjs="cdn")

plots/manhattan-gwas/metadata/plotly.yaml

@@ -0,0 +1,27 @@
+library: plotly
+specification_id: manhattan-gwas
+created: '2025-12-31T05:32:43Z'
+updated: '2025-12-31T05:42:28Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612787650
+issue: 2925
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/plotly/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent implementation of the Manhattan plot with proper GWAS conventions (alternating
+    chromosome colors, cumulative positions, threshold lines)
+  - Clear visual hierarchy with significant SNPs highlighted as distinct red diamonds
+    above the main point cloud
+  - Comprehensive hover information showing chromosome, position, and -log10(p) value
+    with proper formatting
+  - Both PNG and HTML outputs generated for static and interactive viewing
+  - Proper threshold line annotations positioned to avoid data overlap
+  weaknesses:
+  - Could leverage more Plotly-specific interactive features like chromosome filtering
+    dropdown or range slider for genomic position
+  - Grid lines could be slightly more visible (currently very faint at alpha 0.1)