GWAS

Inputs

Data

For this workflow, both bed and bgen genotypes should be available. They will likely be the respective outputs of the Combining Datasets and Genotypes Imputation workflows, optionally merged with Combining with UK Biobank depending on your project of interest.

GWAS Variables

The variables to be used for the GWAS are specified in a YAML file like so.

group: ["ANCESTRY"]
phenotype: "SEVERE_COVID_19"
covariates: ["AGE", "SEX", "AGE_x_SEX", "AGE_x_AGE"]

where:

• A GWAS will be run independently for each group defined by group
• The phenotype is the trait of interest, currently only SEVERE_COVID_19
• The covariates are additional covariates that will enter the GWAS to improve precision

Workflow Parameters

• GENOTYPES_PREFIX: Prefix to plink .bed genotypes.
• BGEN_GENOTYPES_PREFIX = Prefix to imputed .bgen genotypes.
• COVARIATES: Path to covariate file (likely the output of Combining Datasets)
• N_PCS (default: 10): Number of principal components to compute.
• PCA_APPROX (default: true): Whether PCA is performed via approximation see
• MIN_GROUP_SIZE (default: 100): Minimum number of samples in a group to proceed to effect size estimation.
• VARIABLES_CONFIG (default: assets/variables.yaml): File containing the declaration of groups, phenotypes and covariates for the GWAS (see GWAS Variables).
• REGENIE_MAF (default: 0.01): Minor allele frequency for a variant to enter the GWAS.
• REGENIE_MAC (default: 10): Minor allele count for a variant to enter the GWAS.
• REGENIE_BSIZE (default: 1000): Regenie's block size (see the regenie docs)

Running The Workflow

If the previous steps have been completed successfully you can run:

./run.sh GWAS

Outputs

All outputs are produced in PUBLISH_DIR, the main outputs of the workflow are:

DAG