| Name | snipar JSON |
| Version |
0.0.18
JSON |
| download |
| home_page | http://github.com/alexTISYoung/snipar |
| Summary | Library and command line scripts for inferring identity-by-descent (IBD) segments shared between siblings, imputing missing parental genotypes, and for performing family based genome-wide association and polygenic score analyses. |
| upload_time | 2023-07-11 18:30:28 |
| maintainer | |
| docs_url | None |
| author | Alexander I. Young, Moeen Nehzati |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
statistics
genetics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# snipar
*snipar* (single nucleotide imputation of parents) is a Python package for inferring identity-by-descent (IBD) segments shared between siblings, imputing missing parental genotypes, and for performing
family based genome-wide association and polygenic score analyses using observed and/or imputed parental genotypes.
The imputation method and the family-based GWAS and polygenic score models are described in [Young et al. 2022](https://www.nature.com/articles/s41588-022-01085-0).
# Main features:
Infer identity-by-descent segments shared between siblings (ibd.py).
Impute missing parental genotypes given the observed genotypes in a nuclear family (impute.py).
Perform family based GWAS using observed and imputed parental genotypes (gwas.py).
Compute polygenic scores for probands, siblings, and parents from SNP weights using observed/imputed parental genotypes, and perform family
based analysis of polygenic scores (pgs.py script).
Compute genome-wide correlations between different effects estimated by gwas.py (correlate.py).
# Documentation
Documentation: https://snipar.rtfd.io/
It is recommended to read the guide: https://snipar.rtfd.io/en/latest/guide.html
And to work through the tutorial: https://snipar.rtfd.io/en/latest/tutorial.html
# Installing Using pip
*snipar* currently supports Python 3.7-3.9 on Linux, Windows, and Mac OSX. We recommend using a python distribution such as Anaconda 3 (https://store.continuum.io/cshop/anaconda/).
The easiest way to install is using pip:
pip install snipar
Sometimes this may not work because the pip in the system is outdated. You can upgrade your pip using:
pip install --upgrade pip
# Virtual Environment
You may encounter problems with the installation due to Python version incompatability or package conflicts with your existing Python environment. To overcome this, you can try installing in a virtual environment. In a bash shell, this could be done by using the following commands in your directory of choice:
python -m venv path-to-where-you-want-the-virtual-environment-to-be
You can activate and use the environment using
source path-to-where-you-want-the-virtual-environment-to-be/bin/activate
# Installing From Source
To install from source, clone the git repository, and in the directory
containing the *snipar* source code, at the shell type:
pip install .
# Python version incompatibility
*snipar* does not currently support Python 3.10 or higher due to version incompatibilities of dependencies.
To overcome this, create a Python3.9 environment using conda and install using pip in the conda environment:
conda create -n myenv python=3.9
conda activate myenv
pip install snipar
# Apple ARM processor machines
There can be difficulties install *snipar* on Apple ARM processor machines due to lack of available versions of scientific computing software made for these processors' architectures. A workaround for this is to use *snipar* in a docker (https://docs.docker.com/desktop/install/mac-install/) image. To create an appropriate docker image, use this command in the terminal:
docker run -it amd64/python:3.9.9-slim-buster /bin/bash
# Running tests
To check that the code is working properly and that the C modules have been compiled, you can run the tests using this command:
python -m unittest snipar.tests
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