IFN-beta is widely used as first-line immunomodulatory treatment for multiple sclerosis. Response to treatment is variable (30-50% of patients are nonresponders) and requires a long treatment duration for accurate assessment to be possible. Information about genetic variations that predict responsiveness would allow appropriate treatment selection early after diagnosis, improve patient care, with time saving consequences and more efficient use of resources.We analyzed 61 SNPs in 34 candidate genes as possible determinants of IFN-beta response in Irish multiple sclerosis patients. Particular emphasis was placed on the exploration of combinations of allelic variants associated with response to therapy by means of a Markov chain Monte Carlo-based approach (APSampler).The most significant allelic combinations, which differed in frequency between responders and nonresponders, included JAK2-IL10RB-GBP1-PIAS1 (permutation p-value was p(perm) = 0.0008), followed by JAK2-IL10-CASP3 (p(perm) = 0.001).The genetic mechanism of response to IFN-beta is complex and as yet poorly understood. Data mining algorithms may help in uncovering hidden allele combinations involved in drug response versus nonresponse.