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Sick sinus syndrome (SSS) encompasses a group of conduction disorders characterized by the inability of sinoatrial node to perform its pacemaker function. Our aim was to identify genetic predictors of SSS in a prospective cohort of patients admitted to the clinic for pacemaker implantation using single-locus and multilocus approaches. We performed genotyping for polymorphic markers of CLCNKA (rs10927887), SCN10A (rs6795970), FNDC3B (rs9647379), MIR146A (rs2910164), SYT10 (rs7980799), MYH6 (rs365990), and KCNE1 (rs1805127) genes in the group of 284 patients with SSS and 243 healthy individuals. Associations between the studied loci and SSS were tested using logistic regression under recessive genetic model using sex and age as covariates. Multilocus analysis was performed using Markov chain Monte Carlo method implemented in the APSampler program. Correction for multiple testing was performed using Benjamini–Hochberg procedure. We detected an individual association between KCNE1 rs1805127*A allele and SSS in the total study group (OR 0.43, PFDR = 0.028) and in the subgroup of patients with 2nd or 3rd degree sinoatrial block (OR 0.17, PFDR = 0.033), and identified seven allelic patterns associated with the disease. SCN10A rs6795970*T and MIR146A rs2910164*C alleles were present in all seven combinations associated with SSS. The highest risk of SSS was conferred by the combination SCN10A rs6795970*T+FNDC3B rs9647379*C+MIR146A rs2910164*C+SYT10 rs7980799*C+KCNE1 rs1805127*G (OR 2.98, CI 1.77–5.00, P = 1.27 × 10–5, PFDR = 0.022). Our findings suggest that KCNE1 rs1805127 polymorphism may play a role in susceptibility to sinoatrial node dysfunction, particularly presenting as 2nd or 3rd degree sinoatrial block, and the risk-modifying effect of other studied loci is better detected using multilocus approach.

Background Genome-wide association studies have captured a large proportion of genetic variation related to type 1 diabetes mellitus (T1D). However, most of these studies are performed in populations of European ancestry and therefore the disease risk estimations can be inaccurate when extrapolated to other world populations. Methods We conducted a case-control study in 1866 individuals from the three major populations of the Republic of Bashkortostan (Russians, Tatars, and Bashkirs) in Russian Federation, using single-locus and multilocus approach to identify genetic predictors of T1D. Results We found that LTA rs909253 and TNF rs1800629 polymorphisms were associated with T1D in the group of Tatars. Meta-analysis of the association study results in the three ethnic groups has confirmed the association between the T1D risk and LTA rs909253 genetic variant. LTA rs909253 and TNF rs1800629 loci were also featured in combinations most significantly associated with T1D. Conclusion Our findings suggest that LTA rs909253 and TNF rs1800629 polymorphisms are associated with the risk of T1D both independently and in combination with polymorphic markers in other inflammatory genes, and the analysis of multi-allelic combinations provides valuable insight in the study of polygenic traits.

Background Genome-wide association studies identified numerous susceptibility loci for multiple sclerosis in populations of European ancestry, but the associations are not always reproducible in other populations due to admixture and different linkage disequilibrium patterns obscuring true association signals. Objective Our aim was to identify genetic predictors of multiple sclerosis in three ethnically homogenous populations from the Volga-Ural region of Russian Federation. Methods In the largest to date study of multiple sclerosis in Russian population, involving 2048 participants from the Republic of Bashkortostan, Russian Federation (641 patients with multiple sclerosis and 1407 unaffected individuals), we performed replication analysis of previously identified genome-wide signals for multiple sclerosis. Associations were tested using logistic regression analysis under additive genetic model adjusted for sex. Meta-analysis of the study results in three populations was performed under fixed effects and random effects models. Results We demonstrate the association with multiple sclerosis of the five variants (INAVA rs7522462, EOMES rs11129295, C6orf10 rs3129934, CD86 rs9282641, and GPR65 rs2119704). The strongest association (OR = 2.16, CI:1.85–2.74, P = 2.53x10−13) was detected for rs3129934 polymorphism in the major histocompatibility region. Multilocus analysis has revealed 322 and 27 allelic patterns associated with multiple sclerosis in women and men, respectively. In women, the highest risk of MS was conferred by C6orf10 rs3129934*T/T + STAT3 rs744166*T combination (OR = 11.87), in men – by C6orf10 rs3129934*T + EOMES rs11129295*C + RPS6KB1 rs180515*C combination (OR = 3.25). Conclusion We confirm five associations with multiple sclerosis previously reported in genome-wide scans in Europeans in three ethnic groups from the Volga-Ural region of Russia.