Permutation and parametric bootstrap tests for gene-gene and gene-environment interactions

Permutation tests are widely used in genomic research as a straightforward way to obtain reliable statistical inference without making strong distributional assumptions. However, in this paper we show that in genetic association studies it is not typically possible to construct exact permutation tests of gene-gene or gene-environment interaction hypotheses. We describe an alternative to the permutation approach in testing for interaction, a parametric bootstrap approach. Using simulations, we compare the finite-sample properties of a few often-used permutation tests and the parametric bootstrap. We consider interactions of an exposure with single and multiple polymorphisms. Finally, we address when permutation tests of interaction will be approximately valid in large samples for specific test statistics.