# Introduction

`genal` is a Python toolkit for common GWAS-derived workflows:

- **Preprocess** GWAS summary statistics into a consistent SNP table (column validation, allele checks, optional filling of missing `SNP`/`CHR`/`POS`/`EA`/`NEA`/`SE`/`P` using reference data, and computation of per-variant F-statistic `FSTAT`).
- **Select instruments** via LD clumping (PLINK 2).
- **Compute PRS** on individual-level genotype data (PLINK 2), with optional **proxy SNP** support.
- **Run two-sample MR** (multiple estimators + sensitivity analyses), with plotting helpers.
- **MR-PRESSO** (parallel implementation) for outlier detection and distortion testing.
- **Colocalization** using approximate Bayes factors (ABF) to assess whether two signals likely share a causal variant.
- **Utilities**: liftover between builds, GWAS Catalog annotation, gene-window filtering, allele-frequency updates from a reference panel.

`genal` is centered around a single class, {py:class}`genal.Geno`, which wraps a `pandas.DataFrame` of SNP-level data and provides end-to-end workflows.

```{figure} Images/Genal_flowchart.png
:alt: Genal flowchart
:width: 95%

High-level pipeline (GWAS → instruments → PRS/MR).
```

## Design notes

- `Geno.data` is always the “source table”. Most methods either **modify** it in place or **return a new** `Geno` (see {doc}`concepts`).
- Operations that require LD or genotypes delegate to **PLINK 2** (clumping, proxy search, scoring, association testing, allele-frequency updates).
- Reference data is cached under `~/.genal/` by default (config + downloaded panels). See {doc}`setup`.

## When to use genal (and when not)

Use `genal` when you already have:
- GWAS summary statistics (exposure and/or outcome), and/or
- PLINK genotype files for a target cohort.

`genal` does not try to replace a full genotype QC pipeline; it assumes you provide reasonable inputs (or you call `preprocess_data()` to enforce basic validity).