Identification of a genetic variant at 2q12.1 associated with blood pressure in East-Asians by genome-wide scan including gene-environment interactions

For our discovery stage and replication stage 1 study subjects were enlisted from those enrolled in the Korean Genome Epidemiology Study (KoGES) population-based cohort. We selected 7,486 subjects from the Korea Association REsource (KARE) project of KoGES [11] for the discovery stage and 3,703 subjects from the Health Examinee (HEXA) cohort [19] for replication stage 1. KARE project included the initial subjects composed of 10,038 individuals, aged 40 to 69, who were recruited from the Ansung and the Ansan regional cohorts that located in Gyeonggi province, near Seoul, the capital of Korea. Among them, only study subjects who had not been treated for hypertension, thyroid gland disease, osteoporosis, or asthma and who had not taken steroids, oral contraceptives, female sex hormone, or diuretics were included in the baseline for the discovery stage. Subjects of HEXA cohort were randomly selected from 1,200,000 participants of KoGES, aged 40 to 69, for use as a shared control in genome-wide disease association studies. More detailed explanations of both cohorts were previously described [11, 19]. All subjects provided written informed consent and this study was approved by ethical committee of the institute (Korea Centers for Disease Control and Prevention Institutional Review Board).

To validate selected SNPs identified in the discovery and replication stage 1, we performed analyses in replication stage 2 using data collected from a total of 841 subjects enrolled in two independent studies of Japanese populations, the Amagasaki and Ehime studies, described previously [8].

In discovery stage and replication stage 1, BP measurements were conducted using a standard mercury sphygmomanometer after the subjects had been in a sitting position for at least 5 min. In case of replication stage 2, BP was measured by automatic cuff-oscillometric device. The average of two measurements (left and right arm) was taken as the BP. The average of SBPs and DBPs of discovery stage, replication stage 1 and 2 are summarized in Table 1.

The obesity-related anthropometric measures BMI, height, weight and WHR, were used in this study as environmental risk factors for elevated BP. BMI was defined as weight in kg divided by the square of height in m. WHR was defined as the ratio of waist (cm) to hip (cm) circumferences. In replication stage 2, only 3 of 4 anthropometric measures were available, BMI, height, weight except WHR.

All of the 10,004 KARE study samples were genotyped using the Affymetrix Genome-Wide Human SNP array 5.0. After quality control, 8,842 subjects and 352,228 SNPs remained for analyses. For in silico replication, 4,302 individuals from the HEXA cohort were genotyped using the Affymetrix Genome-Wide Human SNP array 6.0. After quality control, 3,703 samples and 646,062 SNPs remained. Genotype calling methods and quality control criteria for samples and SNPs of both cohorts have been previously described [11, 19].

SNP imputation was conducted using the IMPUTE program [20] based on International HapMap (phase 2, release 22, NCBI build 36 and dbSNP build 126; http://​hapmap.​ncbi.​nlm.​nih.​gov/​) data from JPT and CHB populations. We used 1,573,409 SNPs for the KARE study and 1,984,393 SNPs for the HEXA cohort after excluding imputed SNPs of unsatisfactory quality for genetic analyses [19].

The results for replication stage 2 were generated from 356 individuals from the Ehime study using the Illumina Human Omni 2.5-8 BeadChip and 485 individuals from the Amagasaki study using the Illumina HumanHap 550 k Quad BeadChip.

Standardized residuals of SBP and DBP adjusted for age and sex by linear regression were used as the phenotypes for analyses in each study. To investigate the effect of interaction between SNPs and the anthropometric measures for BP, we conducted linear regression analyses with interaction terms based on the equation: Y = β₀ + β₁ × SNP + β₂ × anthropometric measures (BMI, height, weight, WHR) + β₃ × (SNP × anthropometric measures). Y is the residual of SBP or DBP, β₀ is a constant, β₁ and β₂ are the main effect of a particular SNP and a particular anthropometric measure, respectively, and β₃ is the effect of the interaction term being tested. All of analyses in each stage were conducted using the R program (version 2.15.2; http://​www.​r-project.​org/​).

SNPs were selected for replication stage 1 if the SNP’s main effect P value was less than 1 × 10^-4 in the discovery stage. SNPs that were found to be statistically significant (P _SNP < 0.05) in replication stage 1 were selected for replication stage 2. To combine association results for selected SNPs in multiple stages (discovery, replication 1 and replication 2), we performed inverse variance weighted meta-analysis for each SNP’s main and interactional effects from each stage using the rmeta package of the R program in which fixed effects were assumed.

After meta-analyses, SNPs with the accepted genome-wide significance level (P < 5 × 10^-8), which reflected testing of one million SNPs [21], were considered statistically significant. The more conservative genome-wide significance threshold is P < 3.18 × 10^-8 based on Bonferroni correction, but no SNP in this study exceeded this threshold. It should be noted that we have selected SNPs which showed the moderate signal (P _SNP < 1.0 × 10^-4) in the discovery stage, expected to be achieved by abundant genetic variants. This stage would require less correction for multiple testing than the final stage targeting at genome-wide significance [22]. From our 3-stage study design, we have discovered a genetic variation that reached genome-wide significance in the final stage.

We also performed stratified analyses to identify the combined effect of BMI and SNP on BP. We tested the association between BMI and BP in each genotype of SNP (ex: GG, GA, AA) and the association between SNP and BP in each BMI sub-group (BMI < 18.5, 18.5 ≤ BMI < 25, 25 ≤ BMI < 30, BMI ≥ 30).

To elucidate the biological meaning of variant, we examined the cluster scores of transcription factor binding sites (TFBS) nearby SNP based on data from all five ENCODE (The Encyclopedia of DNA Elements Consortium) TFBS ChIP-seq production groups via UCSC Genome Browser (http://​genome.​ucsc.​edu/​). The UCSC Genome Browser has released a track containing 690 datasets of transcription factor ChIP-seq peaks.

Table 1 provides the descriptive characteristics of the 12,030 study subjects enlisted for the discovery stage of our analysis from the Korea Association REsource (KARE) project of the Korean Genome Epidemiological Study (KoGES), for replication stage 1 from the Health Examinee cohort (HEXA) of KoGES, and for replication stage 2 from Japanese populations enrolled in the Ehime and Amagasaki studies [8, 11, 19]. The HEXA group had a higher proportion of women (56.6%) than the KARE group (50.0%), and the average SBP was higher in the HEXA subjects (121.7 ± 14.4) than in the KARE subjects (119.6 ± 17.4). In contrast, the average DBP was higher in the KARE subjects (79.3 ± 11.1) than in the HEXA subjects (77.1 ± 9.89). All but one (weight) of variables in two groups showed statistically significant differences (P < 0.0001). The Japanese populations examined in replication stage 2 tended to be older and to have higher SBP than the HEXA and KARE groups. The correlations between BP and anthropometric measures are summarized in Additional file 1: Table S1. There were moderate to low correlations between BP and anthropometric measures across the KARE and HEXA subjects (Pearson’s r = |0.1 ~ 0.3|). But, most of the differences between two independent correlation coefficients were statistically significant (Additional file 1: Table S1).

We performed genome-wide association analyses on SBP and DBP in the 7,486 KARE subjects with consideration of interaction between approximately 1.6 million SNPs and four obesity-related anthropometric measures using four linear regression models with the following interaction terms: SNP × BMI, height, weight, or WHR (See Figure 1 for the overall study scheme). Genomic inflation factors (λ_gc) of each SNP’s main and interactional effects were calculated for each model. All of the λ_gc values were ≤ 1.057 (Additional file 1: Table S2). The quantile-quantile plots for SBP and DBP in each model with interaction terms are presented in Additional file 1: Figure S1.

In discovery stage, we identified several BP associated genetic loci with moderate significance (P _SNP < 1.0 × 10^-4) including some that had not been detected in previous BP-associated GWASs which considered only the SNP’s main effects when analyzed (Additional file 1: Table S3). To verify these loci in subsequent stage, we selected the 317 SNPs exceeding P _SNP < 1.0 × 10^-4 that showed the most significant signals at each locus considering Linkage Disequilibrium block (LD, r² > 0.2) for replication stage 1 with 3,703 HEXA subjects, 146 SNPs associated with SBP only, 150 SNPs associated with DBP only and 21 associated with both SBP and DBP (Additional file 1: Table S3).

In replication stage 1, we found six SNPs that showed statistical significance (P _SNP < 0.05) in the SNP’s main effect with the concordant direction in discovery stage’s. These six SNPs were also tested in Japanese populations (n = 841) of replication stage 2 (Additional file 1: Table S4).

In inverse variance meta-analyses combining the results from the three stages for the six SNPs (Additional file 1: Table S4), there was one SNP that reached genome-wide significance (P < 5 × 10^-8). SNP rs13390641, located in the intergenic region on 2q12.1 near the transmembrane protein 182 gene (TMEM182), was found to be associated with SBP when the interaction between SNP and BMI was considered in the analyses (combined P _SNP = 3.83 × 10^-8 and P _INT = 5.28 × 10^-8; Table 2). For rs13390641, there was no evidence for heterogeneity across the three stages (heterogeneity P = 0.47; Table 2).

We compared the effect estimates for BMI on BP among three genotype classes of rs13390641 by linear regression analyses of the KARE and HEXA subjects (n = 11,189). The strongest effect of BMI was for SBP in subjects carrying the homozygous A allele of rs13390641 (Beta = 1.58, P = 9.68 × 10^-4). In the case of DBP, the effect of BMI was also highest in subjects carrying the homozygous A allele (Beta = 1.35, P = 3.26 × 10^-5; Table 3 and Figure 2).

We also calculated effect estimates for rs13390641 on BP in four BMI sub-groups. In the highest BMI sub-group (BMI ≥ 30), rs13390641 showed a strong increasing effect on SBP (Beta = 5.35, P = 3.22 × 10^-3). For DBP, the effect of rs13390641 also increased significantly in the highest BMI sub-group (Beta = 3.50, P = 3.24 × 10^-3; Table 4 and Figure 2). It is enough to say that the subjects in the highest BMI sub-group with carrying A allele may have a higher risk of increasing BP significantly.