Polygenic diseases are caused by the joint contribution of a number of
independently acting or interacting polymorphic genes; the individual
contribution of each gene may be small or even unnoticeable. The carriage of
certain combinations of genes can determine the occurrence of clinically
heterogeneous forms of the disease and treatment efficacy. This review describes
the approaches used in a polygenic analysis of data in medical genomics, in
particular, pharmacogenomics, aimed at identifying the cumulative effect of
genes. This effect may result from the summation of gains of different genes or
be caused by the epistatic interaction between the genes. Both cases are
undoubtedly of great interest in investigating the nature of polygenic diseases.
The means that allow one to discriminate between these two possibilities are
discussed. The methods for searching for combinations of alleles of different
genes associated with the polygenic phenotypic traits of the disease, as well as
the methods for presenting and validating the results, are described and
compared. An attempt is made to evaluate the applicability of the existing
methods to an epistasis analysis. The results obtained by the authors using the
APSampler software are described and summarized.
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.
BackgroundIn spite of progress in cardiovascular genetics, data on genetic background of myocardial infarction are still limited and contradictory. This applies as well to the genes involved in inflammation and coagulation processes, which play a crucial role in the disease etiopathogenesis.Methods and ResultsIn this study we found genetic variants of TGFB1, FGB and CRP genes associated with myocardial infarction in discovery and replication groups of Russian descent from the Moscow region and the Republic of Bashkortostan (325/185 and 220/197 samples, correspondingly). We also found and replicated biallelic combinations of TGFB1 with FGB, TGFB1 with CRP and IFNG with PTGS1 genetic variants associated with myocardial infarction providing a detectable cumulative effect. We proposed an original two-component procedure for the analysis of nonlinear (epistatic) interactions between the genes in biallelic combinations and confirmed the epistasis hypothesis for the set of alleles of IFNG with PTGS. The procedure is applicable to any pair of logical variables, e.g. carriage of two sets of alleles. The composite model that included three single gene variants and the epistatic pair has AUC of 0.66 both in discovery and replication groups.ConclusionsThe genetic impact of TGFB1, FGB, CRP, IFNG, and PTGS and/or their biallelic combinations on myocardial infarction was found and replicated in Russians. Evidence of epistatic interactions between IFNG with PTGS genes was obtained both in discovery and replication groups.
The results suggest that the influence of immune-response genes on GA-induced response has a polygenic nature. The data are interpreted as evidence of additive and epistatic influences of the genes on GA efficiency for MS treatment.
The data obtained provides evidence of the cumulative effect of immune-response genes on IFN-β treatment efficacy. This joint contribution may reflect the additive effect of independent allelic variants and epistatic interactions between some of them.
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