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This workflow implements the GEM (Gene-Environment interaction analysis for Millions of samples) tool (https://github.com/large-scale-gxe-methods/GEM). GEM conducts genome-wide gene-environment interaction tests in unrelated individuals while allowing for multiple exposures, control for genotype-covariante interactions, and robust inference.

Author: Kenny Westerman ([email protected])

GEM tool information:

Workflow steps:

  • Run GEM (scattered across an array of input files, usually chromosomes)
  • Concatenate the outputs into a single summary statistics file

Inputs:

See the "parameter_meta" section of the .wdl script.

Outputs:

  • A summary statistics file containing estimates for genetic main effects and interaction effects as well as p-values for these along with a joint test of genetic main and interaction effects.

Cost estimation:

  • Example analysis for reference
    • Platform: Terra
    • Dataset: UK Biobank (N ~ 350k)
    • Analysis parameters: binary phenotype, single interaction term, 6 covariates
    • Computational resources requested: 4 CPUs, 10GB memory, 250GB disk
  • The above analysis completed for chromosome 2 (~1M variants) in about 9 hours, which translates to a cost of approximately $2 based on typical Terra costs assuming non-preemptible machines are used.
  • To extrapolate from the above estimates: Runtime and cost should scale linearly with the sample size and number of variants and will be inversely proportional to the number of cores used.