feat(altair): implement manhattan-gwas (#2953)

## Implementation: `manhattan-gwas` - altair

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

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

**Parent Issue:** #2925

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612790460)*

---------

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

Author: github-actions[bot]

plots/manhattan-gwas/implementations/altair.py

@@ -0,0 +1,168 @@
+""" pyplots.ai
+manhattan-gwas: Manhattan Plot for GWAS
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - Simulated GWAS data with random p-values and significant peaks
+np.random.seed(42)
+
+# Chromosome lengths (approximate human chromosome sizes in Mb)
+chrom_lengths = {
+    "1": 249,
+    "2": 243,
+    "3": 198,
+    "4": 191,
+    "5": 182,
+    "6": 171,
+    "7": 159,
+    "8": 145,
+    "9": 138,
+    "10": 134,
+    "11": 135,
+    "12": 133,
+    "13": 114,
+    "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
+chrom_centers = {}
+
+for chrom, length in chrom_lengths.items():
+    # Number of SNPs proportional to chromosome length
+    n_snps = int(length * 20)  # ~20 SNPs per Mb
+    positions = np.sort(np.random.randint(1, length * 1_000_000, n_snps))
+
+    # Base p-values (mostly non-significant)
+    pvalues = np.random.uniform(0.001, 1.0, n_snps)
+
+    # Add some significant peaks on specific chromosomes
+    if chrom in ["2", "6", "8", "15"]:
+        n_sig = np.random.randint(5, 15)
+        sig_indices = np.random.choice(n_snps, n_sig, replace=False)
+        pvalues[sig_indices] = 10 ** np.random.uniform(-10, -7, n_sig)
+
+    # Add suggestive hits on other chromosomes
+    if chrom in ["3", "11", "19"]:
+        n_sug = np.random.randint(3, 8)
+        sug_indices = np.random.choice(n_snps, n_sug, replace=False)
+        pvalues[sug_indices] = 10 ** np.random.uniform(-6, -5, n_sug)
+
+    # Calculate cumulative positions
+    cum_positions = positions + cumulative_pos
+    chrom_centers[chrom] = cumulative_pos + (length * 1_000_000) / 2
+
+    for pos, cum_pos, pval in zip(positions, cum_positions, pvalues, strict=True):
+        data.append(
+            {
+                "chromosome": chrom,
+                "position": pos,
+                "cumulative_position": cum_pos,
+                "p_value": pval,
+                "neg_log_p": -np.log10(pval),
+            }
+        )
+
+    cumulative_pos += length * 1_000_000
+
+df = pd.DataFrame(data)
+
+# Threshold values
+genome_wide_threshold = -np.log10(5e-8)  # ~7.3
+suggestive_threshold = -np.log10(1e-5)  # 5.0
+
+# Create chromosome label data
+chrom_label_df = pd.DataFrame(
+    [{"chrom_label": chrom, "center": center, "y_pos": -0.5} for chrom, center in chrom_centers.items()]
+)
+
+# Define color scheme - alternating for chromosomes
+color_scale = alt.Scale(
+    domain=[str(i) for i in range(1, 23)],
+    range=["#306998", "#7F7F7F"] * 11,  # Alternating Python blue and gray
+)
+
+# Main scatter plot
+points = (
+    alt.Chart(df)
+    .mark_circle(opacity=0.7)
+    .encode(
+        x=alt.X(
+            "cumulative_position:Q",
+            axis=alt.Axis(title="Chromosome", labels=False, ticks=False, titleFontSize=22),
+            scale=alt.Scale(domain=[0, cumulative_pos]),
+        ),
+        y=alt.Y(
+            "neg_log_p:Q",
+            title="-log₁₀(p-value)",
+            axis=alt.Axis(titleFontSize=22, labelFontSize=16),
+            scale=alt.Scale(domain=[0, max(df["neg_log_p"]) + 1]),
+        ),
+        color=alt.Color("chromosome:N", scale=color_scale, legend=None),
+        size=alt.condition(
+            alt.datum.neg_log_p > genome_wide_threshold,
+            alt.value(100),  # Larger for significant hits
+            alt.value(30),  # Smaller for others
+        ),
+        tooltip=["chromosome:N", "position:Q", "p_value:Q", "neg_log_p:Q"],
+    )
+)
+
+# Genome-wide significance threshold line
+gw_line = (
+    alt.Chart(pd.DataFrame({"y": [genome_wide_threshold]}))
+    .mark_rule(strokeDash=[8, 4], color="#E74C3C", strokeWidth=2)
+    .encode(y="y:Q")
+)
+
+# Suggestive threshold line
+sug_line = (
+    alt.Chart(pd.DataFrame({"y": [suggestive_threshold]}))
+    .mark_rule(strokeDash=[4, 4], color="#F39C12", strokeWidth=2)
+    .encode(y="y:Q")
+)
+
+# Chromosome labels as text marks at bottom
+chrom_text = (
+    alt.Chart(chrom_label_df)
+    .mark_text(fontSize=14, fontWeight="bold", baseline="top", dy=5)
+    .encode(x=alt.X("center:Q", scale=alt.Scale(domain=[0, cumulative_pos])), text="chrom_label:N")
+)
+
+# Combine layers - points with threshold lines
+main_chart = alt.layer(points, gw_line, sug_line).properties(width=1500, height=750)
+
+# Chromosome labels at bottom
+labels_chart = (
+    alt.Chart(chrom_label_df)
+    .mark_text(fontSize=16, baseline="middle")
+    .encode(x=alt.X("center:Q", scale=alt.Scale(domain=[0, cumulative_pos]), axis=None), text="chrom_label:N")
+    .properties(width=1500, height=30)
+)
+
+# Vertical concat with shared x-axis
+chart = (
+    alt.vconcat(main_chart, labels_chart, spacing=0)
+    .properties(title=alt.Title("manhattan-gwas · altair · pyplots.ai", fontSize=28, anchor="middle"))
+    .configure_axis(labelFontSize=16, titleFontSize=22, grid=True, gridOpacity=0.3)
+    .configure_view(strokeWidth=0)
+)
+
+# Save as PNG and HTML
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/manhattan-gwas/metadata/altair.yaml

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+library: altair
+specification_id: manhattan-gwas
+created: '2025-12-31T05:34:14Z'
+updated: '2025-12-31T05:45:37Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612790460
+issue: 2925
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/altair/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent Manhattan plot with clear alternating chromosome colors (blue/gray)
+    for visual distinction
+  - Both genome-wide (red dashed) and suggestive (orange dashed) threshold lines correctly
+    positioned at -log10(5e-8) and -log10(1e-5)
+  - Smart use of conditional sizing - significant hits above threshold are larger
+    (100px) vs non-significant (30px)
+  - Chromosome labels clearly positioned below the plot with proper centering
+  - 'Good use of Altair features: layered charts, declarative encoding, tooltips for
+    interactivity'
+  - 'Title follows exact format specification: manhattan-gwas · altair · pyplots.ai'
+  weaknesses:
+  - Y-axis label uses subscript notation (-log₁₀) which displays well, but some tick
+    labels on the right side of smaller chromosomes (19-22) appear slightly cramped