Identifying the genetic architecture of complex traits requires analytic strategies that move beyond single-variant association tests. Multivariate analyses such as gene-based, gene-by-gene interaction (GxG), gene-by-environment interaction (GxE), gene-set, and pathway analyses are now commonly implemented,1–5 yet, one rarely sees GxG or GxE explicitly accounted for within gene-set and pathway analyses.6 The specification of interacting variables that probably differ between genes in gene sets and pathways is not straightforward. Interacting exposure variables (termed E hereafter) could include contributing environmental factors or SNPs or haplotypes from the same region or at other susceptibility loci. Missing or incorrect information on interacting factors, as well as associated computational burden, might also limit more comprehensive surveys of the whole genome for disease association. Here we provide an easy-to-implement, straightforward solution to exploit potential GxG and GxE in gene-set and pathway analyses, and we illustrate the power of such an approach in a gene-modifier study of lung disease in cystic fibrosis (CF [MIM: 219700]).