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Various diseases require the selection of preferable treatment out of available alternatives. Multiple sclerosis (MS), an autoimmune inflammatory/neurodegenerative disease of the CNS, requires long-term medication with either specific disease-modifying therapy (DMT) - IFN-β or glatiramer acetate (GA) - which remain the only first-line DMTs in all countries. A significant share of MS patients are resistant to treatment with one or the other DMT; therefore, the earliest choice of preferable DMT is of particular importance. A number of conventional pharmacogenetic studies performed up to the present day have identified the treatment-sensitive genetic biomarkers that might be specific for the particular drug; however, the suitable biomarkers for selection of one or another first-line DMT are remained to be found. Comparative pharmacogenetic analysis may allow the identification of the discriminative genetic biomarkers, which may be more informative for an a priori DMT choice than those found in conventional pharmacogenetic studies. The search for discriminative markers of preferable first-line DMT, which differ in carriage between IFN-β responders and GA responders as well as between IFN-β nonresponders and GA nonresponders, has been performed in 253 IFN-β-treated MS patients and 285 GA-treated MS patients. A bioinformatics algorithm for identification of composite biomarkers (allelic sets) was applied on a unified set of immune-response genes, which are relevant for IFN-β and/or GA modes of action, and identical clinical criteria of treatment response. We found the range of discriminative markers, which include polymorphic variants of CCR5, IFNAR1, TGFB1, DRB1 or CTLA4 genes, in different combinations. Every allelic set includes the CCR5 genetic variant, which probably suggests its crucial role in the modulation of the DMT response. Special attention should be given to the (CCR5*d+ IFNAR1*G) discriminative combination, which clearly points towards IFN-β treatment choice for carriers of this combination. As a whole the comparative approach provides an option for the identification of prognostic composite biomarkers for a preferable medication among available alternatives.