Multiple sclerosis is a common neurological disorder, characterized by increasing disability over time for the affected patient. The disease is considered an autoimmune disorder in which the immune system causes damage to nerves in the central nervous system through demyelination and inflammation. It is currently not understood what causes the disease, but both genetic susceptibility as well as environmental- and lifestyle factors are thought to contribute to disease development.In this project, data of known disease-associated risk factors were used to characterize the processes through which they may alter the risk of disease development. Modules for each risk factor was derived from experimental data, using the MODifieR disease-module inference algorithms. Of the five different risk factors included, each module was analysed using the PASCAL tool and disease specific GWAS data to evaluate the relevance towards the disease.Based on the modules derived using the Clique Sum Permutation module inference method a consensus module comprising 126 genes was identified, which proved to be significantly enriched for disease associated SNPs (single nucleotide polymorphisms). Additionally, the risk-factor consensus module was compared to disease specific consensus genes previously obtained within the research group. The comparison showed a significant overlap, which indicates that the methodology may provide means of examine the impact of risk-factors in the context of complex disease.