Risk Effects of rs1799945 Polymorphism of the HFE Gene and Intergenic Interactions of GWAS-Significant Loci for Arterial Hypertension in the Caucasian Population of Central Russia

Abstract

The aim of this case-control replicative study was to investigate the link between GWAS-impact for arterial hypertension (AH) and/or blood pressure (BP) gene polymorphisms and AH risk in Russian subjects (Caucasian population of Central Russia). AH (n = 939) and control (n = 466) cohorts were examined for ten GWAS AH/BP risk loci. The genotypes/alleles of these SNP and their combinations (SNP–SNP interactions) were tested for their association with the AH development using a logistic regression statistical procedure. The genotype GG of the SNP rs1799945 (C/G) HFE was strongly linked with an increased AH risk (ORrecGG = 2.53; 95%CIrecGG1.03–6.23; ppermGG = 0.045). The seven SNPs such as rs1173771 (G/A) AC026703.1, rs1799945 (C/G) HFE, rs805303 (G/A) BAG6, rs932764 (A/G) PLCE1, rs4387287 (C/A) OBFC1, rs7302981 (G/A) CERS5, rs167479 (T/G) RGL3, out of ten regarded loci, were related with AH within eight SNP–SNP interaction models (<0.001 ≤ pperm-interaction ≤ 0.047). Three polymorphisms such as rs8068318 (T/C) TBX2, rs633185 (C/G) ARHGAP42, and rs2681472 (A/G) ATP2B1 were not linked with AH. The pairwise rs805303 (G/A) BAG6–rs7302981 (G/A) CERS5 combination was a priority in determining the susceptibility to AH (included in six out of eight SNP–SNP interaction models [75%] and described 0.82% AH entropy). AH-associated variants are conjecturally functional for 101 genes involved in processes related to the immune system (major histocompatibility complex protein, processing/presentation of antigens, immune system process regulation, etc.). In conclusion, the rs1799945 polymorphism of the HFE gene and intergenic interactions of BAG6, CERS5, AC026703.1, HFE, PLCE1, OBFC1, RGL3 have been linked with AH risky in the Caucasian population of Central Russia.

Obesity-Dependent Association of the rs10454142 PPP1R21 with Breast Cancer

“…As an indicator "denoting" reliable data, we adopted p perm < 0.025. (The Bonferroni correction was imposed equal to 2 based on the number of pairs being compared, with and without obesity [59]). The power of the identified SNP-BC associative links was evaluated in the Quanto program (v. 1.2.4) [60].…”

Maternal Age at Menarche Gene Polymorphisms Are Associated with Offspring Birth Weight

“…For genetic computations, transformed BW values were used (real BW values with non-normal distribution were reduced to a normal distribution by QQ-plot function in the R program [24]). The calculations used covariates (age at menarche, pre-pregnancy BMI, presence in the anamnesis of hypertension and fetal growth restriction, presence of current pregnancy complications (fetal growth restriction, preeclampsia and a combination of fetal growth restriction with preeclampsia; date from Table 1) (covariates were included as independent variables in the regression analyses) and procedures that minimize the risk of false positive results (permutation test was applied [63,64]). Importantly, when selecting BW-significant interlocus interaction models for permutation procedures, we introduced additional Bonferroni corrections (the quantity of possible combinations of 49 SNPs was taken into account) and as a result, values of the significance level such as p < 4 × 10 −5 (<0.05/1176; two-locus models), p < 3 × 10 −6 (<0.05/18424; three-locus models) and p < 2 × 10 −7 (<0.05/211876; four-locus models) were set as the "threshold".…”

Maternal Age at Menarche Genes Determines Fetal Growth Restriction Risk

“…FGR-significant locus/gene probable functions. We explored FGR-correlated loci and LD SNPs (r 2 was not less than 0.80 [ 142 , 143 ]) from the standpoint of their possible functionality [ 144 , 145 , 146 ]. For the purpose of in-depth/comprehensive analysis of materials on this issue, based on the positively proven in extensive (including GWAS) genetic research with an in silico approach [ 147 , 148 , 149 , 150 ], six different contemporary bioinformatic programs/resources were utilized, such as (a) GTE Consortium data [ 151 ], (b) HaploReg [ 128 ], (c) GeneOntology knowledge base [ 152 ], (d) STRING [ 153 ], (e) Blood eQTL resource [ 154 ], and (f) SIFT [ 155 ].…”