diff --git a/plots/manhattan-gwas/implementations/matplotlib.py b/plots/manhattan-gwas/implementations/matplotlib.py
new file mode 100644
index 0000000000..a33f3220ce
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+++ b/plots/manhattan-gwas/implementations/matplotlib.py
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+""" pyplots.ai
+manhattan-gwas: Manhattan Plot for GWAS
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+# Data - Simulate GWAS results for 22 chromosomes
+np.random.seed(42)
+
+# Define chromosome sizes (approximate in Mb, scaled down for simulation)
+chrom_sizes = {
+    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: 103,
+    16: 90,
+    17: 81,
+    18: 78,
+    19: 59,
+    20: 63,
+    21: 48,
+    22: 51,
+}
+
+# Generate SNPs for each chromosome
+chromosomes = []
+positions = []
+p_values = []
+
+for chrom, size in chrom_sizes.items():
+    n_snps = int(size * 40)  # ~40 SNPs per Mb
+    chrom_positions = np.sort(np.random.randint(1, size * 1_000_000, n_snps))
+
+    # Generate p-values (mostly non-significant, with some significant peaks)
+    chrom_pvals = np.random.uniform(0, 1, n_snps)
+
+    # Add some significant SNPs in certain chromosomes (simulating real signals)
+    if chrom in [2, 6, 11, 16]:
+        peak_idx = np.random.choice(n_snps, size=np.random.randint(3, 8), replace=False)
+        chrom_pvals[peak_idx] = 10 ** (-np.random.uniform(8, 15, len(peak_idx)))
+
+    # Add suggestive hits in more chromosomes
+    if chrom in [1, 3, 8, 12, 19]:
+        suggestive_idx = np.random.choice(n_snps, size=np.random.randint(2, 5), replace=False)
+        chrom_pvals[suggestive_idx] = 10 ** (-np.random.uniform(5, 7.5, len(suggestive_idx)))
+
+    chromosomes.extend([chrom] * n_snps)
+    positions.extend(chrom_positions)
+    p_values.extend(chrom_pvals)
+
+# Create DataFrame
+df = pd.DataFrame({"chromosome": chromosomes, "position": positions, "p_value": p_values})
+
+# Calculate -log10(p-value)
+df["-log10p"] = -np.log10(df["p_value"])
+
+# Calculate cumulative position for x-axis
+df["chrom_num"] = df["chromosome"]
+df = df.sort_values(["chrom_num", "position"])
+
+# Add cumulative position offset
+cumulative_offset = 0
+chrom_centers = {}
+for chrom in sorted(df["chrom_num"].unique()):
+    chrom_mask = df["chrom_num"] == chrom
+    df.loc[chrom_mask, "cumulative_pos"] = df.loc[chrom_mask, "position"] + cumulative_offset
+    chrom_centers[chrom] = cumulative_offset + df.loc[chrom_mask, "position"].median()
+    cumulative_offset += df.loc[chrom_mask, "position"].max() + 10_000_000  # Gap between chromosomes
+
+# Define thresholds
+genome_wide_threshold = -np.log10(5e-8)  # ~7.3
+suggestive_threshold = -np.log10(1e-5)  # 5
+
+# Define colors
+colors = ["#306998", "#6699CC"]  # Python Blue and lighter blue for alternating
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot points by chromosome with alternating colors
+for i, chrom in enumerate(sorted(df["chrom_num"].unique())):
+    chrom_data = df[df["chrom_num"] == chrom]
+    color = colors[i % 2]
+
+    # Smaller markers for dense data, slightly larger for significant hits
+    significant_mask = chrom_data["-log10p"] >= genome_wide_threshold
+    regular_data = chrom_data[~significant_mask]
+    significant_data = chrom_data[significant_mask]
+
+    # Plot regular points
+    ax.scatter(
+        regular_data["cumulative_pos"],
+        regular_data["-log10p"],
+        c=color,
+        s=15,
+        alpha=0.6,
+        edgecolors="none",
+        rasterized=True,
+    )
+
+    # Plot significant points with emphasis
+    if len(significant_data) > 0:
+        ax.scatter(
+            significant_data["cumulative_pos"],
+            significant_data["-log10p"],
+            c="#E74C3C",  # Red for significant hits
+            s=50,
+            alpha=0.9,
+            edgecolors="white",
+            linewidths=0.5,
+            zorder=5,
+            rasterized=True,
+        )
+
+# Add threshold lines
+ax.axhline(
+    y=genome_wide_threshold,
+    color="#E74C3C",
+    linestyle="--",
+    linewidth=2,
+    label="Genome-wide significance (p < 5×10⁻⁸)",
+    alpha=0.8,
+)
+ax.axhline(
+    y=suggestive_threshold,
+    color="#FFD43B",
+    linestyle="--",
+    linewidth=2,
+    label="Suggestive threshold (p < 1×10⁻⁵)",
+    alpha=0.8,
+)
+
+# Set x-axis with chromosome labels
+ax.set_xticks([chrom_centers[c] for c in sorted(chrom_centers.keys())])
+ax.set_xticklabels([str(c) for c in sorted(chrom_centers.keys())], fontsize=14)
+ax.set_xlim(0, df["cumulative_pos"].max() * 1.01)
+
+# Set y-axis
+ax.set_ylim(0, df["-log10p"].max() * 1.1)
+
+# Labels and styling
+ax.set_xlabel("Chromosome", fontsize=20)
+ax.set_ylabel("-log₁₀(p-value)", fontsize=20)
+ax.set_title("manhattan-gwas · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="y", labelsize=16)
+ax.tick_params(axis="x", labelsize=14)
+
+# Legend
+ax.legend(fontsize=14, loc="upper right", framealpha=0.9)
+
+# Remove top and right spines for cleaner look
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")
diff --git a/plots/manhattan-gwas/metadata/matplotlib.yaml b/plots/manhattan-gwas/metadata/matplotlib.yaml
new file mode 100644
index 0000000000..6f865b880c
--- /dev/null
+++ b/plots/manhattan-gwas/metadata/matplotlib.yaml
@@ -0,0 +1,27 @@
+library: matplotlib
+specification_id: manhattan-gwas
+created: '2025-12-31T05:31:23Z'
+updated: '2025-12-31T05:37:51Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612787947
+issue: 2925
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent chromosome visualization with alternating blue colors providing clear
+    visual distinction between chromosomes
+  - Proper highlighting of significant SNPs with larger red markers and white edges
+    that stand out clearly
+  - Realistic GWAS data simulation with appropriate chromosome sizes, SNP densities,
+    and significant peaks on specific chromosomes
+  - Clean code structure with good use of rasterized=True for performance optimization
+    with 110k+ data points
+  - Both genome-wide and suggestive threshold lines included with clear legend
+  weaknesses:
+  - No grid lines present (minor - acceptable for Manhattan plots to reduce clutter)
+  - Y-axis label could include clearer notation