pylocuszoom 0.3.0

Regional association plots for GWAS results with LD coloring, gene tracks, and recombination rate overlays

pyLocusZoom

Regional association plots for GWAS results with LD coloring, gene tracks, and recombination rate overlays.

Inspired by LocusZoom and locuszoomr.

Features

• LD coloring: SNPs colored by linkage disequilibrium (R²) with lead variant
• Gene track: Annotated gene/exon positions below the association plot
• Recombination rate: Overlay showing recombination rate across region (Canis lupus familiaris only)
• SNP labels: Automatic labeling of top SNPs with RS ID or nearest gene
• Species support: Built-in Canis lupus familiaris (CanFam3.1/CanFam4), Felis catus (FelCat9), or custom species
• CanFam4 support: Automatic coordinate liftover for recombination maps
• Multiple backends: matplotlib (static), plotly (interactive), bokeh (dashboards)
• Stacked plots: Compare multiple GWAS/phenotypes vertically
• eQTL overlay: Expression QTL data as separate panel
• PySpark support: Handles large-scale genomics DataFrames

Installation

pip install pylocuszoom

Or with uv:

uv add pylocuszoom

Or with conda (Bioconda):

conda install -c bioconda pylocuszoom

Quick Start

from pylocuszoom import LocusZoomPlotter

# Initialize plotter (loads reference data for canine)
plotter = LocusZoomPlotter(species="canine")

# Create regional plot
fig = plotter.plot(
    gwas_df,                    # DataFrame with ps, p_wald, rs columns
    chrom=1,
    start=1000000,
    end=2000000,
    lead_pos=1500000,           # Highlight lead SNP
)

fig.savefig("regional_plot.png", dpi=150)

Full Example

from pylocuszoom import LocusZoomPlotter

plotter = LocusZoomPlotter(
    species="canine",                   # or "feline", or None for custom
    plink_path="/path/to/plink",        # Optional, auto-detects if on PATH
)

fig = plotter.plot(
    gwas_df,
    chrom=1,
    start=1000000,
    end=2000000,
    lead_pos=1500000,
    ld_reference_file="genotypes.bed",  # For LD calculation
    genes_df=genes_df,                  # Gene annotations
    exons_df=exons_df,                  # Exon annotations
    show_recombination=True,            # Overlay recombination rate
    snp_labels=True,                    # Label top SNPs
    label_top_n=5,                      # How many to label
    pos_col="ps",                       # Column name for position
    p_col="p_wald",                     # Column name for p-value
    rs_col="rs",                        # Column name for SNP ID
    figsize=(12, 8),
)

Genome Builds

The default genome build for canine is CanFam3.1. For CanFam4 data:

plotter = LocusZoomPlotter(species="canine", genome_build="canfam4")

Recombination maps are automatically lifted over from CanFam3.1 to CanFam4 coordinates using the UCSC liftOver chain file.

Using with Other Species

# Feline (LD and gene tracks, user provides recombination data)
plotter = LocusZoomPlotter(species="feline")

# Custom species (provide all reference data)
plotter = LocusZoomPlotter(
    species=None,
    recomb_data_dir="/path/to/recomb_maps/",
)

# Or provide data per-plot
fig = plotter.plot(
    gwas_df,
    chrom=1, start=1000000, end=2000000,
    recomb_df=my_recomb_dataframe,
    genes_df=my_genes_df,
)

Backends

pyLocusZoom supports multiple rendering backends:

# Static publication-quality plot (default)
fig = plotter.plot(gwas_df, chrom=1, start=1000000, end=2000000, backend="matplotlib")
fig.savefig("plot.png", dpi=150)

# Interactive Plotly (hover tooltips, pan/zoom)
fig = plotter.plot(gwas_df, chrom=1, start=1000000, end=2000000, backend="plotly")
fig.write_html("plot.html")

# Interactive Bokeh (dashboard-ready)
fig = plotter.plot(gwas_df, chrom=1, start=1000000, end=2000000, backend="bokeh")

Backend	Output	Best For
matplotlib	Static PNG/PDF/SVG	Publications, presentations
plotly	Interactive HTML	Web reports, data exploration
bokeh	Interactive HTML	Dashboards, web apps

Note: All backends support gene track, recombination overlay, and LD legend. SNP labels (auto-positioned with adjustText) are matplotlib-only.

Stacked Plots

Compare multiple GWAS results vertically with shared x-axis:

fig = plotter.plot_stacked(
    [gwas_height, gwas_bmi, gwas_whr],
    chrom=1,
    start=1000000,
    end=2000000,
    panel_labels=["Height", "BMI", "WHR"],
    genes_df=genes_df,
)

eQTL Overlay

Add expression QTL data as a separate panel:

eqtl_df = pd.DataFrame({
    "pos": [1000500, 1001200, 1002000],
    "p_value": [1e-6, 1e-4, 0.01],
    "gene": ["BRCA1", "BRCA1", "BRCA1"],
})

fig = plotter.plot_stacked(
    [gwas_df],
    chrom=1, start=1000000, end=2000000,
    eqtl_df=eqtl_df,
    eqtl_gene="BRCA1",
    genes_df=genes_df,
)

PySpark Support

For large-scale genomics data, pass PySpark DataFrames directly:

from pylocuszoom import LocusZoomPlotter, to_pandas

# PySpark DataFrame (automatically converted)
fig = plotter.plot(spark_gwas_df, chrom=1, start=1000000, end=2000000)

# Or convert manually with sampling for very large data
pandas_df = to_pandas(spark_gwas_df, sample_size=100000)

Install PySpark support: uv add pylocuszoom[spark]

Data Formats

GWAS Results DataFrame

Required columns (names configurable via pos_col, p_col, rs_col):

Column	Type	Required	Description
ps	int	Yes	Genomic position in base pairs (1-based). Must match coordinate system of genes/recombination data.
p_wald	float	Yes	Association p-value (0 < p ≤ 1). Values are -log10 transformed for plotting.
rs	str	No	SNP identifier (e.g., "rs12345" or "chr1:12345"). Used for labeling top SNPs if snp_labels=True.

Example:

gwas_df = pd.DataFrame({
    "ps": [1000000, 1000500, 1001000],
    "p_wald": [1e-8, 1e-6, 0.05],
    "rs": ["rs123", "rs456", "rs789"],
})

Genes DataFrame

Column	Type	Required	Description
chr	str or int	Yes	Chromosome identifier. Accepts "1", "chr1", or 1. The "chr" prefix is stripped for matching.
start	int	Yes	Gene start position (bp, 1-based). Transcript start for strand-aware genes.
end	int	Yes	Gene end position (bp, 1-based). Must be ≥ start.
gene_name	str	Yes	Gene symbol displayed in track (e.g., "BRCA1", "TP53"). Keep short for readability.

Example:

genes_df = pd.DataFrame({
    "chr": ["1", "1", "1"],
    "start": [1000000, 1050000, 1100000],
    "end": [1020000, 1080000, 1150000],
    "gene_name": ["GENE1", "GENE2", "GENE3"],
})

Exons DataFrame (optional)

Provides exon/intron structure. If omitted, genes are drawn as simple rectangles.

Column	Type	Required	Description
chr	str or int	Yes	Chromosome identifier.
start	int	Yes	Exon start position (bp).
end	int	Yes	Exon end position (bp).
gene_name	str	Yes	Parent gene symbol. Must match gene_name in genes DataFrame.

Recombination DataFrame

Column	Type	Required	Description
pos	int	Yes	Genomic position (bp). Should span the plotted region with reasonable density (every ~10kb).
rate	float	Yes	Recombination rate in centiMorgans per megabase (cM/Mb). Typical range: 0-50 cM/Mb.

Example:

recomb_df = pd.DataFrame({
    "pos": [1000000, 1010000, 1020000],
    "rate": [0.5, 2.3, 1.1],
})

Recombination Map Files

When using recomb_data_dir, files must be named chr{N}_recomb.tsv (e.g., chr1_recomb.tsv, chrX_recomb.tsv).

Format: Tab-separated with header row:

Column	Description
chr	Chromosome number (without "chr" prefix)
pos	Position in base pairs
rate	Recombination rate (cM/Mb)
cM	Cumulative genetic distance (optional, not used for plotting)

chr	pos	rate	cM
1	10000	0.5	0.005
1	20000	1.2	0.017
1	30000	0.8	0.025

Reference Data

Canine recombination maps are downloaded from Campbell et al. 2016 on first use.

To manually download:

from pylocuszoom import download_canine_recombination_maps

download_canine_recombination_maps()

Logging

Logging uses loguru and is configured via the log_level parameter (default: "INFO"):

# Suppress logging
plotter = LocusZoomPlotter(log_level=None)

# Enable DEBUG level for troubleshooting
plotter = LocusZoomPlotter(log_level="DEBUG")

Requirements

• Python >= 3.10
• matplotlib >= 3.5.0
• pandas >= 1.4.0
• numpy >= 1.21.0
• loguru >= 0.7.0
• plotly >= 5.0.0
• bokeh >= 3.8.2
• kaleido >= 0.2.0 (for plotly static export)
• pyliftover >= 0.4 (for CanFam4 coordinate liftover)
• PLINK 1.9 (for LD calculations) - must be on PATH or specify plink_path

Optional:

• pyspark >= 3.0.0 (for PySpark DataFrame support) - uv add pylocuszoom[spark]

License

GPL-3.0-or-later