pygwasvcf 0.0.4
A package for reading GWAS summary statistics stored in VCF/BCF format
Requires Python: >=3.6

# GWAS-VCF Python parser

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The package provides a thin wrapper around [pysam](https://pysam.readthedocs.io/en/latest/index.html) and [rsidx](https://github.com/bioforensics/rsidx) to parse VCF files containing GWAS summary statistics and trait metadata. See also [gwasvcf](https://github.com/MRCIEU/gwasvcf) an R package for parsing GWAS-VCF files.

Parses GWAS-VCF with version 1.0 of the [specification](https://github.com/MRCIEU/gwas-vcf-specification/releases/tag/1.0.0)

## Install

```shell script
pip install git+https://github.com/mrcieu/pygwasvcf
```

## Data

Download over 10,000 GWAS-VCF files from  <https://gwas.mrcieu.ac.uk>

## Examples

Read GWAS trait/study metadata

```python
import pygwasvcf
with pygwasvcf.GwasVcf("/path/to/gwas.vcf.gz") as g:
    # print dictionary of GWAS metadata
    print(g.get_metadata())
```

Query variant-trait association(s) by chromosome and position location

```python
import pygwasvcf
with pygwasvcf.GwasVcf("/path/to/gwas.vcf.gz") as g:
    # query by chromosome and position interval
    for variant in g.query(contig="1", start=1, stop=1):
        print(variant)
```

Query variant-trait association(s) by dbSNP rsID

```python
import pygwasvcf
with pygwasvcf.GwasVcf("/path/to/gwas.vcf.gz") as g:
    # index on dbSNP identifier
    # based on [rsidx](https://github.com/bioforensics/rsidx)
    # only need to do this once and then provide the index path to the constructor
    # i.e. GwasVcf("/path/to/gwas.vcf.gz", rsidx_path="/path/to/gwas.vcf.gz.rsidx")
    g.index_rsid()

    # rapid query by rsID  
    for variant in g.query(variant_id="rs1245"):
        print(variant)
```

Extract summary statistics from a variant object

```python
import pygwasvcf
with pygwasvcf.GwasVcf("/path/to/gwas.vcf.gz") as g:
    # query by chromosome and position interval
    for variant in g.query(contig="1", start=1, stop=1):
        # print variant-trait P value
        print(pygwasvcf.VariantRecordGwasFuns.get_pval(variant, "trait_name"))
        # print variant-trait SE
        print(pygwasvcf.VariantRecordGwasFuns.get_se(variant, "trait_name"))
        # print variant-trait beta
        print(pygwasvcf.VariantRecordGwasFuns.get_beta(variant, "trait_name"))
        # print variant-trait allele frequency
        print(pygwasvcf.VariantRecordGwasFuns.get_af(variant, "trait_name"))
        # print variant-trait ID
        print(pygwasvcf.VariantRecordGwasFuns.get_id(variant, "trait_name"))
        # create and print ID on-the-fly if missing
        print(pygwasvcf.VariantRecordGwasFuns.get_id(variant, "trait_name", create_if_missing=True))
        # print variant-trait sample size
        print(pygwasvcf.VariantRecordGwasFuns.get_ss(variant, "trait_name"))
        # print variant-trait total sample size from header if per-variant is missing
        print(pygwasvcf.VariantRecordGwasFuns.get_ss(variant, "trait_name", g.get_metadata()))
        # print variant-trait number of cases
        print(pygwasvcf.VariantRecordGwasFuns.get_nc(variant, "trait_name"))
        # print variant-trait total cases from header if per-variant is missing
        print(pygwasvcf.VariantRecordGwasFuns.get_nc(variant, "trait_name", g.get_metadata()))
```

## Documentation

API documentation available from <https://mrcieu.github.io/pygwasvcf>

## Citation

```
The variant call format provides efficient and robust storage of GWAS summary statistics. Lyon MS, Andrews SJ, Elsworth B, Gaunt TR, Hemani G, Marcora E. Genome Biol. 22, 32 (2021).
```