Use Hail on Google Dataproc
Start a dataproc cluster named “my-first-cluster”. Cluster names may only contain a mix lowercase letters and dashes. Starting a cluster can take as long as two minutes.
hailctl dataproc start my-first-cluster
Create a file called “hail-script.py” and place the following analysis of a randomly generated dataset with five-hundred samples and half-a-million variants.
import hail as hl mt = hl.balding_nichols_model(n_populations=3, n_samples=500, n_variants=500_000, n_partitions=32) mt = mt.annotate_cols(drinks_coffee = hl.rand_bool(0.33)) gwas = hl.linear_regression_rows(y=mt.drinks_coffee, x=mt.GT.n_alt_alleles(), covariates=[1.0]) gwas.order_by(gwas.p_value).show(25)
Submit the analysis to the cluster and wait for the results. You should not have to wait more than a minute.
hailctl dataproc submit my-first-cluster hail-script.py
When the script is done running you’ll see 25 rows of variant association results.
You can also start a Jupyter Notebook running on the cluster:
hailctl dataproc connect my-first-cluster notebook
When you are finished with the cluster stop it:
hailctl dataproc stop my-first-cluster