Motivation: Association studies usually search for association between common genetic variants in different individuals and a given phenotype. In this work we consider two types of biological markers: genetic and metagenomic markers. The genotypic markers allow to characterise the individual by its inherited genetic information whereas the metagenomic markers are related to the environment. Both types of markers are available by millions and represent a unique signature characterizing each individual. Results: We focus on the detection of interactions between groups of metagenomic and genetic markers to better understand the complex relationship between environment and genome in the expression of a given phenotype. We propose a method that reduces the dimension of the search space by selecting a subset of supervariables in both complementary datasets. These super variables stem from a weighted group structure defined on sets of variables of different scales. A Lasso selection is then applied on each type of supervariables to obtain a subset of potential interactions that will be explored via linear model testing.