### Quick Start Example

Source: https://github.com/cloufield/gwaslab/blob/main/docs/api/index.md

A basic example demonstrating how to load summary statistics and perform a basic check using the GWASLab library.

```APIDOC
## Quick start

```python
import gwaslab as gl

mysumstats = gl.Sumstats("sumstats.txt.gz", fmt="plink2")
mysumstats.basic_check()
mysumstats.plot_mqq()
```
```

--------------------------------

### bcftools Example Output

Source: https://github.com/cloufield/gwaslab/blob/main/docs/AssignCHRPOS.md

Example output from the `bcftools --version` command, indicating the installed version and associated htslib version.

```text
bcftools 1.19
Using htslib 1.19
Copyright (C) 2023 Genome Research Ltd.
License Expat: The MIT/Expat license
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
```

--------------------------------

### Install and Serve Documentation Locally

Source: https://github.com/cloufield/gwaslab/blob/main/docs/api/index.md

Install the package with documentation dependencies and serve the documentation locally for live preview. Alternatively, build the documentation statically.

```bash
pip install -e ".[docs]"
pip install zensical
zensical serve   # live preview
# or:
zensical build --clean
```

--------------------------------

### Test Installation from TestPyPI

Source: https://github.com/cloufield/gwaslab/blob/main/test/Upload_checklist.md

Install the package from TestPyPI to verify that it was uploaded correctly. Replace X.Y.Z with the actual version number.

```bash
pip install --index-url https://test.pypi.org/simple/ gwaslab==X.Y.Z
```

--------------------------------

### Test Installation from PyPI

Source: https://github.com/cloufield/gwaslab/blob/main/test/Upload_checklist.md

Install the package from the main PyPI repository to confirm a successful public release. Replace X.Y.Z with the actual version number.

```bash
pip install gwaslab==X.Y.Z
```

--------------------------------

### Complete QC Report Example

Source: https://github.com/cloufield/gwaslab/blob/main/docs/QC_Report.md

A comprehensive example demonstrating loading sumstats and generating a PDF QC report with customized options for basic checks, lead variants, MQQ plots, and regional plots.

```python
import gwaslab as gl

# Load sumstats
mysumstats = gl.Sumstats(
    "examples/0_sample_data/t2d_bbj.txt.gz",
    snpid="SNP",
    chrom="CHR",
    pos="POS",
    ea="ALT",
    nea="REF",
    neaf="Frq",
    beta="BETA",
    se="SE",
    p="P",
    build="19",
    n="N"
)

# Generate comprehensive QC report with regional plots
report_path = mysumstats.report(
    output_path="t2d_qc_report.pdf",
    report_title="T2D GWAS QC Report",
    basic_check_kwargs={
        "remove": False,
        "remove_dup": False,
        "normalize": True,
        "verbose": False
    },
    get_lead_kwargs={
        "sig_level": 5e-8,
        "windowsizekb": 500,
        "anno": False,
        "verbose": False
    },
    mqq_plot_kwargs={
        "save": True,
        "dpi": 300,
        "verbose": False
    },
    regional_plot_kwargs={
        "vcf_path": "/path/to/reference.vcf.gz",
        "region_size": 500,
        "save": True,
        "dpi": 300,
        "verbose": False
    }
)

print(f"Report generated: {report_path}")
```

--------------------------------

### Test Install from Local Wheel

Source: https://github.com/cloufield/gwaslab/blob/main/test/Upload_checklist.md

Install the package locally from the generated wheel file to verify the build artifacts. Replace X.Y.Z with the actual version number.

```bash
pip install dist/gwaslab-X.Y.Z-py3-none-any.whl --force-reinstall
```

--------------------------------

### Complete GSF Workflow Example

Source: https://github.com/cloufield/gwaslab/blob/main/docs/GSF_Format.md

A comprehensive example showing the typical workflow: loading data, performing basic processing and QC, saving to GSF format, and later loading with filters and specific columns. This covers essential operations for managing summary statistics.

```python
import gwaslab as gl

# Load data
mysumstats = gl.Sumstats("input.txt.gz", ...)

# Process and QC
mysumstats.basic_check()
mysumstats.harmonize(...)

# Save to GSF
mysumstats.to_gsf("processed_sumstats.gsf", compression="zstd")

# Later: Load with filtering
significant_variants = gl.load_gsf(
    "processed_sumstats.gsf",
    filters="P < 5e-8 & CHR in [1, 2, 3, 7, 22]",
    columns=["CHR", "POS", "EA", "NEA", "BETA", "SE", "P"]
)

# Work with filtered data
print(f"Found {len(significant_variants.data)} significant variants")
```

--------------------------------

### Example Workflow: Harmonize, Infer EAF, and Check AF

Source: https://github.com/cloufield/gwaslab/blob/main/docs/InferAF.md

This example demonstrates a typical workflow: harmonizing sumstats, inferring EAF using the faster `infer_af2` method, and then checking the inferred AF values.

```python
import gwaslab as gl

mysumstats = gl.Sumstats("sumstats.txt.gz")
mysumstats.harmonize(basic_check=True)
mysumstats.infer_af2(vcf_path="reference.vcf.gz", ref_alt_freq="AF")
mysumstats.check_af2(vcf_path="reference.vcf.gz", ref_alt_freq="AF")
```

--------------------------------

### Example: Save and Load Sumstats using dump_pickle()

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Pickle.md

This example shows how to process sumstats, save the object using the `gl.dump_pickle()` function, and then load it back using `gl.load_pickle()`.

```python
import gwaslab as gl

# Process sumstats
mysumstats = gl.Sumstats("sumstats.txt.gz", fmt="plink2")
mysumstats.basic_check()

# Save using function
gl.dump_pickle(mysumstats, "./first.pickle", overwrite=True)

# Load it back
mysumstats2 = gl.load_pickle("./first.pickle")
```

--------------------------------

### Example: Save and Load Sumstats using to_pickle()

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Pickle.md

This example demonstrates loading sumstats, performing basic checks and fixes, saving the processed object using `to_pickle()` with overwrite enabled, and then loading it back.

```python
import gwaslab as gl

# Load and process sumstats
mysumstats = gl.Sumstats("sumstats.txt.gz", fmt="plink2")
mysumstats.basic_check()
mysumstats.fix_chr()
mysumstats.fix_pos()

# Save the processed object
mysumstats.to_pickle("./processed_sumstats.pickle", overwrite=True)

# Later, load it back
mysumstats2 = gl.load_pickle("./processed_sumstats.pickle")
```

--------------------------------

### Verify bcftools Installation

Source: https://github.com/cloufield/gwaslab/blob/main/docs/AssignCHRPOS.md

Verify that bcftools is installed and accessible in your PATH. This is a prerequisite for processing VCF files.

```bash
bcftools --version
```

--------------------------------

### Install Weasyprint for PDF Generation

Source: https://github.com/cloufield/gwaslab/blob/main/docs/QC_Report.md

Install the 'weasyprint' library using pip for PDF report generation. System dependencies may also be required on some platforms.

```bash
pip install weasyprint
```

```bash
# Ubuntu/Debian:
sudo apt-get install python3-weasyprint
```

--------------------------------

### Install GWASLab via uv

Source: https://github.com/cloufield/gwaslab/blob/main/docs/index.md

Installs the latest version of GWASLab using the uv package installer. uv is known for its speed.

```bash
uv pip install -U gwaslab
```

--------------------------------

### Log Operation Start

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Logging.md

Logs the beginning of an operation, including its name and an optional version.

```python
log_operation_start(operation_name, version=None, indent=0)
```

--------------------------------

### Install GWASLab in Conda Environment

Source: https://github.com/cloufield/gwaslab/blob/main/README.md

Create and activate a Conda environment with a compatible Python version, then install GWASLab. This ensures dependency management.

```bash
conda env create -n gwaslab -c conda-forge python=3.12

conda activate gwaslab

pip install gwaslab
```

```bash
conda env create -n gwaslab -f environment.yml
```

--------------------------------

### Build Wheel Package

Source: https://github.com/cloufield/gwaslab/blob/main/test/Upload_checklist.md

Create a wheel (binary distribution) of the Python package. This is a pre-compiled format that speeds up installation.

```bash
python -m build --wheel
```

--------------------------------

### GWASLab CLI - Show Version

Source: https://github.com/cloufield/gwaslab/blob/main/docs/index.md

Display the installed GWASLab version using the CLI.

```bash
gwaslab version
```

--------------------------------

### Import GWASLab and Check Version

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Extract_LD_proxy.md

Import the GWASLab library and display its version information. This is the initial setup step for using the library.

```python
import gwaslab as gl
gl.show_version()
```

--------------------------------

### Generate PDF QC Report

Source: https://github.com/cloufield/gwaslab/blob/main/docs/QC_Report.md

Generate a PDF QC report by specifying a .pdf file extension. This requires the 'weasyprint' library to be installed.

```python
# Generate HTML report
mysumstats.report("report.html")

# Generate PDF report (requires weasyprint)
mysumstats.report("report.pdf")
```

--------------------------------

### Get Associations for a Specific Variant (get_all=True)

Source: https://github.com/cloufield/gwaslab/blob/main/docs/GWASCatalogAPI.md

Retrieve all associations for a given variant rsID using `gl.get_associations()` with `get_all=True`. This example also demonstrates printing the head of the resulting DataFrame.

```python
import gwaslab as gl

# Get all associations for rs1050316
associations = gl.get_associations(rs_id="rs1050316", get_all=True)
print(associations.head())
```

--------------------------------

### Get Reference GTF Data and Filter

Source: https://github.com/cloufield/gwaslab/blob/main/docs/GTF.md

Retrieves reference GTF data (e.g., Ensembl hg38) for a specific chromosome and build. The retrieved data can then be filtered, for example, to isolate protein-coding genes.

```python
from gwaslab.io.io_gtf import get_gtf

# Get Ensembl hg38 GTF for chromosome 1
gtf = get_gtf(chrom="1", build="38", source="ensembl")

# Filter for protein-coding genes
protein_coding = gtf[gtf["gene_biotype"] == "protein_coding"]
print(f"Protein-coding genes on chr1: {len(protein_coding)}")
```

--------------------------------

### Load and Plot GWAS Summary Statistics

Source: https://github.com/cloufield/gwaslab/blob/main/README.md

Quick start example demonstrating how to load GWAS summary statistics from a file using different format detection methods (plink2, auto, or manual column specification) and then generate Manhattan and QQ plots.

```python
import gwaslab as gl

# load plink2 output
mysumstats = gl.Sumstats("sumstats.txt.gz", fmt="plink2")

# or load sumstats with auto mode (auto-detecting commonly used headers) 
# assuming ALT/A1 is EA, and frq is EAF
mysumstats = gl.Sumstats("sumstats.txt.gz", fmt="auto")

# or you can specify the columns:
mysumstats = gl.Sumstats("sumstats.txt.gz",
             snpid="SNP",
             chrom="CHR",
             pos="POS",
             ea="ALT",
             nea="REF",
             eaf="Frq",
             beta="BETA",
             se="SE",
             p="P",
             direction="Dir",
             n="N",
             build="19")

# manhattan and qq plot
mysumstats.plot_mqq()
...
```

--------------------------------

### Rename Gene Start and End Columns

Source: https://github.com/cloufield/gwaslab/blob/main/examples/7_visualization/gwplot.ipynb

Rename the 'Gene start' and 'Gene end' columns in the Sumstats object's data to 'START' and 'END' respectively for convenience.

```python
mapping_gls.data = mapping_gls.data.rename(columns={"Gene start":"START","Gene end":"END"})
```

--------------------------------

### Partitioned GSF Workflow Example

Source: https://github.com/cloufield/gwaslab/blob/main/docs/GSF_Format.md

Demonstrates a workflow for handling partitioned GSF files, including saving data partitioned by a column and loading specific partitions (e.g., a single chromosome) for processing. This is useful for very large datasets.

```python
# Save partitioned by chromosome
mysumstats.to_gsf(
    "sumstats_partitioned",
    partition_cols=["CHR"],
    compression="zstd"
)

# Load specific chromosome
chr7_data = gl.load_gsf("sumstats_partitioned", filters="CHR == 7")

# Process chromosome by chromosome
for chr_num in range(1, 23):
    chr_data = gl.load_gsf("sumstats_partitioned", filters=f"CHR == {chr_num}")
    # Process chromosome data
    ...

```

--------------------------------

### Install GWASLab Package

Source: https://github.com/cloufield/gwaslab/blob/main/examples/1_main_tutorial/tutorial_v4.ipynb

Installs the GWASLab package from pip. This is a prerequisite for using the library.

```python
# !pip install gwaslab==4.0.0
```

--------------------------------

### Example SSF validation success output

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Format.md

This is an example of the output when an SSF file passes all validation checks.

```text
✓ SSF validation successful: File passes all validation checks
```

--------------------------------

### List Downloaded Sample Data

Source: https://github.com/cloufield/gwaslab/blob/main/docs/tutorial_v4.md

After cloning, use this command to list the contents of the sample data directory.

```python
!ls  gwaslab-sample-data
```

--------------------------------

### Install GWASLab via pip

Source: https://github.com/cloufield/gwaslab/blob/main/README.md

Install the latest version of GWASLab using pip. This command is suitable for most users and environments.

```bash
pip install gwaslab
```

--------------------------------

### Example SSF validation failure output

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Format.md

This is an example of the output when an SSF file fails validation, indicating missing required columns.

```text
✗ SSF validation failed: Missing required columns: ['standard_error']
  - Missing required columns: ['standard_error']
```

--------------------------------

### Download Liftover Chain File

Source: https://github.com/cloufield/gwaslab/blob/main/docs/harmonization_liftover.md

Use wget to download the necessary chain file for liftover. Ensure the URL is correct for the desired genome builds.

```bash
! wget https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/chain/v1_nflo/grch38-chm13v2.chain
```

--------------------------------

### Limit Variants for Example

Source: https://github.com/cloufield/gwaslab/blob/main/examples/3_standardization/quality_control_and_filtering.ipynb

Use this command to limit the number of variants processed for examples or testing purposes. This helps in managing computational resources.

```bash
# use only 100000 variants for example
```

--------------------------------

### Load Partitioned GSF Files

Source: https://github.com/cloufield/gwaslab/blob/main/docs/GSF_Format.md

Demonstrates loading partitioned GSF files using the same API as non-partitioned files. Filtering on partition columns during loading is particularly efficient.

```python
# Load partitioned file (same API)
mysumstats = gl.load_gsf("sumstats_partitioned")

# Filtering on partition columns is especially efficient
mysumstats = gl.load_gsf("sumstats_partitioned", filters="CHR == 7")
```

--------------------------------

### Basic Format Conversion Examples

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Format.md

Demonstrates converting summary statistics to different formats like LDSC, PLINK (with CSV), and VCF (with bgzip and tabix indexing).

```python
# Convert to LDSC format
mysumstats.to_format(path="./output", fmt="ldsc")
# Output: output.ldsc.tsv.gz
```

```python
# Convert to PLINK format with CSV
mysumstats.to_format(path="./output", fmt="plink", tab_fmt="csv", gzip=False)
# Output: output.plink.csv
```

```python
# Convert to VCF with bgzip and tabix index
mysumstats.to_format(path="./output", fmt="vcf", bgzip=True, tabix=True)
# Output: output.vcf.bcf.gz and output.vcf.bcf.gz.tbi
```

--------------------------------

### Run Test Suite

Source: https://github.com/cloufield/gwaslab/blob/main/test/Upload_checklist.md

Execute the project's test suite from the root directory. Ensure all tests pass before proceeding with a release.

```bash
python test/run_all_tests.py
```

--------------------------------

### Initialize GWASLab Registry

Source: https://github.com/cloufield/gwaslab/blob/main/docs/CLI.md

Prepares the local reference registry by creating the necessary directory structure and optionally setting a data directory. Use `--recursive` to scan subdirectories and `--quiet` to reduce logging.

```bash
gwaslab init
```

```bash
gwaslab init --directory /data/refs
```

```bash
gwaslab init -d /data/refs --recursive --quiet
```

--------------------------------

### Set Default Directory and Get Path

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Download_reference.md

Configure the default directory for downloaded data and retrieve paths for registered references. Use `gl.set_default_directory()` to change the download location. `gl.options.paths["config"]` shows the current configuration file path. `gl.get_path()` resolves a reference keyword to its local path without downloading.

```python
gl.set_default_directory("/data/refs/")
gl.options.paths["config"]  # ~/.gwaslab/config.json by default
gl.get_path("1kg_eas_hg19")  # resolves from registry; does not download
```

--------------------------------

### Quick Start: Multiple Studies Meta-Analysis

Source: https://github.com/cloufield/gwaslab/blob/main/docs/MetaAnalysis.md

Shows how to load multiple summary statistics files and perform meta-analysis using `SumstatsMulti` with the pandas engine.

```python
import gwaslab as gl

# Load multiple sumstats
sumstats_list = [
    gl.Sumstats("study1.txt.gz", verbose=False),
    gl.Sumstats("study2.txt.gz", verbose=False),
    gl.Sumstats("study3.txt.gz", verbose=False)
]

# Create SumstatsMulti object
multi = gl.SumstatsMulti(sumstats_list, engine="pandas", verbose=False)

# Run meta-analysis
result = multi.run_meta_analysis(random_effects=False)
```

--------------------------------

### BED Format Conversion Example

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Format.md

Example of converting summary statistics to BED format, which uses 0-based, half-open coordinates, and enabling bgzip compression and tabix indexing.

```python
mysumstats.to_format(path="./output", fmt="bed", bgzip=True, tabix=True)
# Output: output.bed.gz (bgzipped and tabix-indexed)
```

--------------------------------

### Example of Clumping with a VCF File

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Clumping.md

This example demonstrates how to initiate clumping using a VCF file as input via the GWASLab `clump` function. The output file will be named 'clumping_plink2' by default.

```python
clumps = mysumstats.clump(vcf= "/home/yunye/.gwaslab/EAS.ALL.split_norm_af.1kgp3v5.hg19.vcf.gz")

```

--------------------------------

### Example: Saving Sumstats to Custom Paths

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Pickle.md

Demonstrates saving a Sumstats object to different locations including the home directory using `~`, the current directory, and an absolute path.

```python
# Save to home directory
mysumstats.to_pickle("~/my_analysis/sumstats.pickle")

# Save to current directory
mysumstats.to_pickle("./sumstats.pickle", overwrite=True)

# Save with absolute path
mysumstats.to_pickle("/data/analysis/sumstats.pickle")
```

--------------------------------

### Download Sample Data

Source: https://github.com/cloufield/gwaslab/blob/main/docs/visualization_miami2.md

Use wget to download sample GWAS summary statistics files for demonstration purposes.

```bash
#!wget -O ../0_sample_data/bmi_male_bbj.txt.gz http://jenger.riken.jp/2analysisresult_qtl_download/
#!wget -O ../0_sample_data/bmi_female_bbj.txt.gz http://jenger.riken.jp/4analysisresult_qtl_download/
```

--------------------------------

### Enable bgzip Compression and Create Tabix Index

Source: https://github.com/cloufield/gwaslab/blob/main/docs/CLI.md

Use `--bgzip` for block-gzipped compression and `--tabix` to create a tabix index for the output file, which is useful for large files.

```bash
gwaslab --input sumstats.tsv --fmt gwaslab --out output --to-fmt gwaslab --bgzip --tabix
```

--------------------------------

### Create Partitioned GSF Files

Source: https://github.com/cloufield/gwaslab/blob/main/docs/GSF_Format.md

Shows how to save summary statistics data into a partitioned GSF directory structure, using a specified column (e.g., 'CHR') for partitioning. This optimizes queries on partition columns.

```python
# Partition by chromosome
mysumstats.to_gsf("sumstats_partitioned", partition_cols=["CHR"])

# This creates a directory structure:
# sumstats_partitioned/
#   CHR=1/
#     part-0.parquet
#   CHR=2/
#     part-0.parquet
#   ...
```

--------------------------------

### Plot Regional (legacy chromosome)

Source: https://github.com/cloufield/gwaslab/blob/main/docs/CLI.md

Generate a regional association plot using the legacy `--chr` option for the chromosome and `--start`/`--end` for the interval. Note that `--chr` with `--start`/`--end` is treated as the plot region, not a variant filter.

```bash
gwaslab --input sumstats.tsv --fmt auto --plot regional --chr 6 --start 26000000 --end 34000000 --out region.png
```

--------------------------------

### Download Liftover Chain File

Source: https://github.com/cloufield/gwaslab/blob/main/examples/4_harmonization/harmonization_liftover.ipynb

Download the necessary chain file for liftover. Ensure the URL is correct for the desired genome builds.

```python
#https://github.com/marbl/CHM13
```

```bash
! wget https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/chain/v1_nflo/grch38-chm13v2.chain
```

--------------------------------

### List Sample Data Files

Source: https://github.com/cloufield/gwaslab/blob/main/examples/1_main_tutorial/tutorial_v4.ipynb

Lists the files available in the cloned gwaslab-sample-data directory to verify the download.

```bash
!ls  gwaslab-sample-data 
```

--------------------------------

### Get Panel Type

Source: https://github.com/cloufield/gwaslab/blob/main/docs/PanelSystem.md

Retrieve the type of the panel using the `get_type()` method.

```python
# Get panel type
panel_type = panel.get_type()
```

--------------------------------

### Get Genomic Control (GC)

Source: https://github.com/cloufield/gwaslab/blob/main/docs/api/sumstats/downstream.md

Calculates the genomic control inflation factor.

```APIDOC
## get_gc

### Description
Calculates the genomic control (GC) inflation factor.

### Method
`get_gc()`

### Parameters
None

### Response
- **gc_value** (float) - The genomic control value.
```

--------------------------------

### Initialize gwaslab Environment (CLI)

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Download_reference.md

Initialize the gwaslab environment using the command-line interface. `gwaslab init` creates necessary directories and migrates configuration files. Use `--directory DIR` to specify a custom data directory, which will be persisted.

```bash
gwaslab init                          # create dirs, migrate config, scan default data_directory
gwaslab init --directory /data/refs   # set data_directory (persisted) and scan
gwaslab config set data_directory /data/refs
gwaslab path config                   # show registry JSON path
```

--------------------------------

### Default .get_lead() parameters

Source: https://github.com/cloufield/gwaslab/blob/main/docs/ExtractLead.md

This snippet shows the default parameters for the `.get_lead()` method. It uses MLOG10P by default and a 500kb window size.

```python
mysumstats.get_lead(
    scaled=False,
    use_p=False,
    windowsizekb=500,
    sig_level=5e-8,
    anno=False,
    build="19",
    source="ensembl",
    gtf_path=None,
    wc_correction=False,
    verbose=True,
    gls=False
)
```

--------------------------------

### Get All Panel Parameters

Source: https://github.com/cloufield/gwaslab/blob/main/docs/PanelSystem.md

Retrieve all parameters associated with the panel using the `get_kwargs()` method.

```python
# Get all parameters
kwargs = panel.get_kwargs()
```

--------------------------------

### Test Command on Small Sample

Source: https://github.com/cloufield/gwaslab/blob/main/docs/CLIWorkflowExamples.md

Tests the command on the first 1000 rows of a large sumstats file. This is useful for quick checks and debugging.

```python
gwaslab --input large_sumstats.tsv \
  --fmt auto \
  --nrows 1000 \
  --qc \
  --remove-dup \
  --out test_output \
  --to-fmt gwaslab
```

--------------------------------

### Get LD Matrix from VCF

Source: https://github.com/cloufield/gwaslab/blob/main/docs/api/sumstats/downstream.md

Retrieves the LD matrix directly from a VCF file.

```APIDOC
## get_ld_matrix_from_vcf

### Description
Retrieves the LD matrix from a VCF file.

### Method
`get_ld_matrix_from_vcf()`

### Parameters
None

### Response
- **ld_matrix_from_vcf** (numpy.ndarray) - The LD matrix derived from the VCF file.
```

--------------------------------

### Get CS Lead

Source: https://github.com/cloufield/gwaslab/blob/main/docs/api/sumstats/downstream.md

Identifies the lead SNP within each credible set (CS).

```APIDOC
## get_cs_lead

### Description
Identifies the lead SNP within each credible set.

### Method
`get_cs_lead()`

### Parameters
None

### Response
- **cs_lead_snps** (dict) - Lead SNPs for each credible set.
```

--------------------------------

### List Available Reference Data

Source: https://github.com/cloufield/gwaslab/blob/main/docs/CLI.md

Lists available reference datasets for download.

```bash
gwaslab list ref --available
```

--------------------------------

### Get Association Density

Source: https://github.com/cloufield/gwaslab/blob/main/docs/api/sumstats/downstream.md

Calculates the density of associations across the genome or specific regions.

```APIDOC
## get_density

### Description
Calculates the density of associations.

### Method
`get_density()`

### Parameters
None

### Response
- **association_density** (dict) - A dictionary representing association density.
```

--------------------------------

### Multi-step Processing Pipeline

Source: https://github.com/cloufield/gwaslab/blob/main/docs/CLIWorkflowExamples.md

Demonstrates a multi-step processing pipeline involving QC, harmonization, and final formatting for a specific tool. Each step builds upon the output of the previous one.

```python
gwaslab --input raw.tsv --fmt auto --qc --remove-dup --out step1_qc --to-fmt gwaslab

gwaslab --input step1_qc.gwaslab.tsv.gz --fmt gwaslab --harmonize \
  --ref-seq ref.fasta --out step2_harmonized --to-fmt gwaslab

gwaslab --input step2_harmonized.gwaslab.tsv.gz --fmt gwaslab \
  --out final --to-fmt plink --tab-fmt tsv
```

--------------------------------

### get_region_start_and_end

Source: https://github.com/cloufield/gwaslab/blob/main/docs/api/sumstats/filter.md

Determines the start and end positions of the genomic region covered by the current sumstats data.

```APIDOC
## get_region_start_and_end

### Description
Determines the start and end positions of the genomic region covered by the current sumstats data.

### Method
`get_region_start_and_end()`

### Request Example
```python
start, end = sumstats.get_region_start_and_end()
```

### Response
#### Success Response (200)
- **Tuple[int, int]** - A tuple containing the start and end positions of the genomic region.
```

--------------------------------

### Quick Start: Two Studies Meta-Analysis

Source: https://github.com/cloufield/gwaslab/blob/main/docs/MetaAnalysis.md

Demonstrates loading two summary statistics files and performing both fixed-effects and random-effects meta-analysis using `SumstatsPair`.

```python
import gwaslab as gl

# Load two sumstats
sumstats1 = gl.Sumstats("study1.txt.gz", verbose=False)
sumstats2 = gl.Sumstats("study2.txt.gz", verbose=False)

# Create SumstatsPair object
pair = gl.SumstatsPair(sumstats1, sumstats2, keep_all_variants=False)

# Run fixed-effects meta-analysis
result = pair.run_meta_analysis(random_effects=False)

# Run random-effects meta-analysis
result = pair.run_meta_analysis(random_effects=True)
```

--------------------------------

### Get Effective Sample Size (ESS)

Source: https://github.com/cloufield/gwaslab/blob/main/docs/api/sumstats/downstream.md

Calculates the effective sample size for association signals.

```APIDOC
## get_ess

### Description
Calculates the effective sample size (ESS).

### Method
`get_ess()`

### Parameters
None

### Response
- **ess_values** (dict) - Effective sample sizes.
```

--------------------------------

### Example of Nested Function Calls with Logging

Source: https://github.com/cloufield/gwaslab/blob/main/docs/Logging.md

Illustrates how logging methods can be used within nested function calls to track complex processes, with `indent` parameter managing the hierarchical output.

```python
def process_data(log, verbose=True):
    log.log_operation_start("process data", verbose=verbose)
    log.log_dataframe_shape(sumstats, verbose=verbose)
    
    # Call inner function
    filter_variants(sumstats, log, verbose=verbose, indent=1)
    
    log.log_operation_finish("processing data", verbose=verbose)

def filter_variants(sumstats, log, verbose=True, indent=0):
    log.log_operation_start("filter variants", indent=indent, verbose=verbose)
    
    # Nested operations
    log.log_variants_filtered(150, reason="with P > 0.05", indent=indent+1, verbose=verbose)
    log.log_variants_removed(25, reason="duplicates", indent=indent+1, verbose=verbose)
    
    log.log_operation_finish("filtering variants", indent=indent, verbose=verbose)
```

```python
Start to process data ...
 -Current Dataframe shape : 1000 x 20 ; Memory usage: 19.95 MB
  Start to filter variants ...
    -Filtered out 150 variants with P > 0.05
    -Removed 25 variants duplicates
  Finished filtering variants.
Finished processing data.
```