feat(letsplot): implement manhattan-gwas (#2958)

## Implementation: `manhattan-gwas` - letsplot

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

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

**Parent Issue:** #2925

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612795129)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/manhattan-gwas/implementations/letsplot.py

@@ -0,0 +1,112 @@
+""" pyplots.ai
+manhattan-gwas: Manhattan Plot for GWAS
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+
+
+LetsPlot.setup_html()
+
+# Set seed for reproducibility
+np.random.seed(42)
+
+# Generate simulated GWAS data
+n_snps_per_chrom = 2000
+chromosomes = [str(i) for i in range(1, 23)]
+
+data = []
+cumulative_pos = 0
+chrom_centers = {}
+
+for i, chrom in enumerate(chromosomes):
+    # Random positions within chromosome (scaled by chromosome "size")
+    chrom_size = 250_000_000 - i * 5_000_000  # Varying chromosome sizes
+    positions = np.sort(np.random.randint(1, chrom_size, n_snps_per_chrom))
+
+    # Generate p-values - mostly non-significant with some peaks
+    p_values = np.random.uniform(0, 1, n_snps_per_chrom)
+
+    # Add some significant peaks (simulate real GWAS signals)
+    if chrom in ["2", "6", "11", "17"]:
+        peak_idx = np.random.choice(n_snps_per_chrom, size=15, replace=False)
+        p_values[peak_idx] = 10 ** (-np.random.uniform(6, 12, 15))
+
+    # Add suggestive signals to more chromosomes
+    if chrom in ["1", "5", "8", "14", "19"]:
+        suggestive_idx = np.random.choice(n_snps_per_chrom, size=10, replace=False)
+        p_values[suggestive_idx] = 10 ** (-np.random.uniform(4.5, 7, 10))
+
+    # Calculate cumulative position for x-axis
+    cumulative_positions = positions + cumulative_pos
+
+    # Store chromosome center for labeling
+    chrom_centers[chrom] = cumulative_pos + chrom_size / 2
+
+    for pos, cum_pos, pval in zip(positions, cumulative_positions, p_values):
+        data.append(
+            {
+                "chromosome": chrom,
+                "position": pos,
+                "cumulative_pos": cum_pos,
+                "p_value": pval,
+                "neg_log10_p": -np.log10(pval),
+            }
+        )
+
+    cumulative_pos += chrom_size
+
+df = pd.DataFrame(data)
+
+# Create chromosome index for coloring (alternating pattern)
+df["chrom_idx"] = df["chromosome"].astype(int) % 2
+
+# Assign colors based on alternating pattern
+df["color_group"] = df["chrom_idx"].map({0: "even", 1: "odd"})
+
+# Significance thresholds
+genome_wide_threshold = -np.log10(5e-8)  # ~7.3
+suggestive_threshold = -np.log10(1e-5)  # 5
+
+# Mark significant SNPs
+df["significant"] = df["neg_log10_p"] >= genome_wide_threshold
+
+# Create x-axis tick positions and labels
+tick_positions = [chrom_centers[c] for c in chromosomes]
+
+# Create the Manhattan plot
+plot = (
+    ggplot(df, aes(x="cumulative_pos", y="neg_log10_p", color="color_group"))
+    + geom_point(size=1.5, alpha=0.7)
+    + scale_color_manual(values=["#306998", "#7A9BBD"], guide="none")
+    # Highlight significant points
+    + geom_point(
+        data=df[df["significant"]], mapping=aes(x="cumulative_pos", y="neg_log10_p"), color="#DC2626", size=3, alpha=0.9
+    )
+    # Genome-wide significance threshold line
+    + geom_hline(yintercept=genome_wide_threshold, linetype="dashed", color="#DC2626", size=0.8)
+    # Suggestive threshold line
+    + geom_hline(yintercept=suggestive_threshold, linetype="dotted", color="#666666", size=0.6)
+    + labs(title="manhattan-gwas · letsplot · pyplots.ai", x="Chromosome", y="-log₁₀(p-value)")
+    + scale_x_continuous(breaks=tick_positions, labels=chromosomes)
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=28, face="bold"),
+        axis_title_x=element_text(size=22),
+        axis_title_y=element_text(size=22),
+        axis_text_x=element_text(size=14),
+        axis_text_y=element_text(size=16),
+        panel_grid_major_x=element_blank(),
+        panel_grid_minor=element_blank(),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save as PNG (scale 3x for 4800x2700)
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save interactive HTML version
+ggsave(plot, "plot.html", path=".")

plots/manhattan-gwas/metadata/letsplot.yaml

@@ -0,0 +1,25 @@
+library: letsplot
+specification_id: manhattan-gwas
+created: '2025-12-31T05:34:36Z'
+updated: '2025-12-31T05:46:54Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612795129
+issue: 2925
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/letsplot/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent Manhattan plot structure with proper cumulative genomic positions and
+    chromosome labeling
+  - Effective use of alternating blue/gray colors for chromosome distinction
+  - Significant SNPs highlighted in red above genome-wide threshold line
+  - Both genome-wide (dashed red) and suggestive (dotted gray) threshold lines included
+  - Good data generation with realistic GWAS patterns including peaks on specific
+    chromosomes
+  weaknesses:
+  - Chromosome labels on x-axis could be slightly larger for better readability at
+    full resolution