sGWAS is a python library for performing robust GWAS using sibling pairs with a random effects model for within family phenotypic correlations
sibreg class: random effects regression model allowing for intra-class correlation
sGWAS.py: script for estimating 'within-family' and 'between-family' effects of SNPs using sibling genotypes
Documentation for the modules and scripts is at: https://sgwas.readthedocs.io/en/latest
sibreg has the following dependencies:
python 2.7
Packages:
- numpy
- scipy
- pysnptools
We highly recommend using a python distribution such as Anaconda (https://store.continuum.io/cshop/anaconda/). This will come with both numpy and scipy installed and can include an MKL-compiled distribution for optimal speed.
To install from source, clone the git repository, and in the directory containing the sibreg source code, at the shell type
'sudo python setupy.py install'
or, on the windows command prompt, type
'python setup.py install'
The tests directory contains scripts for testing the computation of the likelihoods, gradients, and maximum likelihood solutions To run these tests, a further dependency is required: numdifftools.
To run the tests, first install sibreg. Change to the tests/ directory and at the shell type
'python test.py'
Tests should run without any failures.