Guide

Introduction

sGWAS is a python library for performing regression with correlated observations within-class.

In the sGWAS/bin there is a script: ‘sGWAS.py’ (Documentation for sGWAS.py script). This script performs GWAS that fits both within and between family effects.

The core model is the sibreg model (sibreg.model), which consists of a linear model for the mean along with a vector of class labels that allows for correlations within-class. (The correlations within-class result from modelling mean differences between classes as independent, normally distributed random effects.) For the application in the script ‘sGWAS.py’, the classes are the families, so correlations are modelled between all siblings in a family.

The documentation for the sibreg module (Documentation for ‘sibreg’ module) contains information on how to define a sibreg.model, how to optimise a sibreg.model, how to predict from a sibreg.model, and how to simulate a sibreg.model.

Running tests

To check that the code is working properly and computing likelihoods and gradients accurately, you can run tests. In the sGWAS/tests subdirectory, type

python tests.py

The output should say

Ran 4 tests in...

OK