GWAS Tutorial
This quickstart tutorial shows how to perform genome-wide association studies using Glow.
Glow implements a distributed version of the Regenie method. Regenie’s domain of applicability falls in analyzing data with extreme case/control imbalances, rare variants and/or diverse populations. Therefore it is suited for working with population-scale biobank exome or genome sequencing data.
Tip
Other bioinformatics libraries for GWAS can be distributed using the Glow Pipe Transformer.
You can view html versions of the notebooks and download them from the bottom of this page.
The notebooks are written in Python, with some visualization in R.
Tip
We recommend running the Data Simulation notebooks first to prepare data for this tutorial before trying with your own data.
Important
Please sort phenotypes and covariates by sample ID in the same order as genotypes.
1. Quality Control
The first notebook in this series prepares data by performing standard quality control procedures on simulated genotype data.
2. Glow Whole Genome Regression (GloWGR)
GloWGR implements a distributed version of the Regenie method. Please review the Regenie paper in Nature Genetics and the Regenie Github repo before implementing this method on real data.
3. Regression
The GloWGR notebook calculated offsets that are used in the genetic association study below to control for population structure and relatedness.