Abstract
Multiple sclerosis (MS) is a common disease of the central nervous system characterized by inflammation, myelin loss, gliosis, varying degrees of axonal pathology, and progressive neurological dysfunction. Multiple sclerosis exhibits many of the characteristics that distinguish complex genetic disorders including polygenic inheritance and environmental exposure risks. Here, we used a highly efficient multilocus genotyping assay representing variation in 34 genes associated with inflammatory pathways to explore gene–gene interactions and disease susceptibility in a well-characterized African-American case–control MS data set. We applied the multifactor dimensionality reduction (MDR) test to detect epistasis, and identified single-IL4R(Q576R)- and three-IL4R(Q576R), IL5RA(-80), CD14(-260)- locus association models that predict MS risk with 75–76% accuracy (P < 0.01). These results demonstrate the importance of exploring both main effects and gene–gene interactions in the study of complex diseases.