Published online before print August 13, 2007, doi: 10.1161/HYPERTENSIONAHA.107.089128
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(Hypertension. 2007;50:672.)
© 2007 American Heart Association, Inc.
Original Articles |
Selective Genotyping Reveals Association Between the Epithelial Sodium Channel 
-Subunit and Systolic Blood Pressure
From the Department of Physiology, University of Melbourne, Parkville, Victoria, Australia.
Correspondence to Stephen B. Harrap, Department of Physiology, University of Melbourne, Victoria 3010, Australia. E-mail s.harrap{at}unimelb.edu.au
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Abstract |
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Systolic blood pressure is determined in large part by genes. Six independent studies have reported evidence of linkage between systolic pressure and chromosome 16p12 that incorporates SCNN1G, the gene encoding the
-subunit of the epithelial sodium channel. We undertook the first comprehensive association analysis of SCNN1G and systolic pressure. To achieve genetic contrast, we sampled unrelated subjects within the upper (mean: 166 mm Hg; n=96) and lower (mean: 98 mm Hg; n=94) 10% of the systolic pressure distribution of 2911 subjects from the Victorian Family Heart Study. We examined genotypes and haplotypes related to 26 single nucleotide polymorphisms across SCNN1G and its promoter. Each of 3 single nucleotide polymorphisms (rs13331086, rs11074553, and rs4299163) in introns 5 and 6 showed evidence of association with systolic pressure in logistic regression analyses adjusted for age, sex, and body mass index. Considered as a haplotype block, these single nucleotide polymorphisms were significantly associated with systolic pressure (haplo.score global: P=0.0001). In permutation analyses to account for multiple testing, a result such as this was observed only once in 10 000 permutations. The estimated frequency of 1 haplotype (TGC) was substantially greater in high (13.3%) than low (0.6%) systolic pressure subjects (P=0.0001). Three other haplotypes (TGG, TAC, and GGC) showed associations with high or low systolic pressure consistent with the observed associations of their composite alleles. These findings identify relatively common polymorphisms in the SCNN1G gene that are associated with high systolic blood pressure in the general Australian white population.
Key Words: blood pressure • genetics • polymorphisms
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Introduction |
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Blood pressure has been repeatedly identified as a risk factor for cardiovascular disease, with systolic blood pressure (SBP) associated with a greater risk of coronary heart disease and stroke death than diastolic blood pressure (DBP).1 Variation in SBP is, to a large extent, explained by genes. For example, in our Victorian Family Heart Study (VFHS), the heritability of SBP was estimated to be 41%,2 consistent with findings from other studies.3,4
In our VFHS,5 we detected suggestive linkage of SBP to chromosome 16p, a region also reported in 5 other independent populations.6–10 Fine mapping linkage analysis within the VFHS refined localization to chromosome 16p12,11 where there are 2 genes, SCNN1B and SCNN1G, that encode the ß- and -subunits of the epithelial sodium channel, respectively. Mutations within these genes are responsible for the Mendelian diseases characterized by high12,13 and low14,15 blood pressure.
Although the relationship between SCNN1B and SBP has been thoroughly examined in numerous populations,16–19 this is not the case for SCNN1G. Persu et al20 found no association of SCNN1G with hypertension in French Canadians. A single nucleotide polymorphism (SNP) in the promoter region of SCNN1G showed association with blood pressure in Japanese21 but not in Australians.22 These studies examined only 2 large blocks of linkage disequilibrium (LD) at either end of SCNN1G, overlooking the middle of the gene, wherein there exists limited LD. In the present study, we undertook a comprehensive gene-wide association analysis of SBP with SCNN1G in the VFHS population.
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Materials and Methods |
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The details of the recruitment and phenotypic measures have been published previously2 and are summarized in the expanded Methods in the online supplement (http://hyper.ahajournals.org). The ethics review committee of the Alfred Hospital, Melbourne, approved the study, and informed consent was obtained from all of the participants. To obtain representative estimates of SBP, we averaged the 2 lying SBP values and 2 standing SBP values for each subject. SBPs for subjects on antihypertensive medications (53% of the high SBP group) were adjusted using previously validated methods.23,24
Extreme Phenotype Selection
To maximize the potential genetic differences and statistical power for the association analyses,25–29 we selected unrelated subjects from the highest and lowest deciles for SBP from the VFHS (Figure 1) while also frequency matching for sex. Taking into account the mean SBP (122 mm Hg; SD: 14.3 mm Hg) in the VFHS, the mean SBPs of the high (166 mm Hg; SD: 12.7 mm Hg; n=96) and low (98 mm Hg; SD: 10.6 mm Hg; n=94) groups differed by 
4.5 SDs.
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Genotyping Methods
In addition to the 5 SNPs previously analyzed,20–22 we selected 21 SNPs spaced across SCNN1G from the public dbSNP database (http://www.ncbi.nlm.nih.gov/SNP/index.html; Figure 2). We used genomic DNA extracted and purified from whole blood as described previously.2 SNP genotypes were obtained using a combination of single nucleotide primer extension and sequencing on the MegaBACE 1000 DNA Analysis Platform (GE Biosciences). Primer sequences are provided in the online supplement.
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Statistical Methods
Preliminary association testing of individual SNPs was performed using unadjusted 
2 tests (or Fisher’s exact tests where appropriate) and logistic regression models that adjusted for age, sex, and body mass index (BMI) using the statistical software R (version 2.0.1, The R Project for Statistical Computing, http://www.r-project.org). Hardy-Weinberg equilibrium (HWE) estimates (for high and low SBP groups separately) were obtained using the software JLIN.30 LD correlates between SNPs were also determined using JLIN.30
Haplotypes were estimated within the Haplo.Stats package (version 1.2.1, Mayo Clinic/Foundation, http://mayoresearch.mayo.edu/mayo/research/schaid_lab/software.cfm), and association testing was performed using the haplo.score function31 and confirmed using logistic regression analyses within the same package (haplo.glm). All of the haplotype analyses included adjustments for the covariates age, sex, and BMI.
To account for multiple testing, we used a permutation approach, in which observed results were sought in 10 000 permuted data sets to provide permutation probability values (Pperm) for comparison with the asymptotic P values from haplo.stats and to estimate empirical correlations between test statistics in the logistic regression and haplotype analyses. These methods are detailed in the online supplement.
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Results |
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Subject Characteristics
Subjects with high SBP had higher mean DBP (92 mm Hg, SD: 10.6 mm Hg versus 64 mm Hg, SD: 7.3 mm Hg; P=4.4x10–8), were older (55 years, SD: 8.3 years versus 32 years, SD: 14.5 years; P=0.0021) and tended to have a higher BMI (28.3 kg.m–2, SD 4.5 kg.m–2 versus 22.7 kg.m–2, SD 3.3 kg.m–2; P=0.094). No association was observed between SBP and sex (P=0.9394). Interactions between age and sex (P=0.016) and between sex and BMI (P=0.019) were also evident.
Genotyping
Among the 26 SNPs genotyped, all but 1 (rs5732) were polymorphic in our study population, and all 25 of the polymorphic SNPs had minor allele frequencies of 
5%. Departure from HWE was observed for 5 of the SNPs genotyped. SNPs rs5718, rs4247210, and rs4281710 diverged in the low SBP group, SNP rs13331086 diverged in the high SBP group, and SNP rs4299163 diverged in both low and high SBP groups but in opposite directions.
Individual SNP Associations
LD r2 analysis revealed 2 LD blocks at either end of the gene separated by a large region lacking LD that was bordered by the SNPs rs4302034 and rs4299163 (Figure 3). This pattern is similar to the r2 estimates seen in the CEU population of the International HapMap Project (http://www.hapmap.org/).
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We detected evidence of association of SBP with 6 SNPs (rs13331086, rs11074553, rs4299163, rs5740, rs4281710, and rs4470152) using 2 testing, 4 of which showed evidence of association with logistic regression analysis and adjustments for covariates (Table 1). Three of these 4 SNPs (rs13331086, rs11074553, and rs4299163) were in adjacent sections of introns 5 and 6, and the fourth (rs5740) was in intron 7 (Figure 2). None of these 4 SNPs displayed significant LD with any other of the 3.
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For rs13331086, the odds ratio from logistic regression for association of the TT genotype with high SBP was 4.04 (95% CI: 1.41 to 11.58; P=0.009; Pperm=0.009), and the odds ratio for association with the T allele was 2.61 (95% CI: 1.15 to 5.95; P=0.023; Pperm=0.024). For rs11074553, the odds ratio for the association of the G allele and SBP was 2.23 (95% CI: 1.14 to 4.37; P=0.019; Pperm=0.018). The odds ratio for the rs4299163 G allele and high SBP was 2.36 (95% CI: 1.22 to 4.59; P=0.01; Pperm=0.01), and categorical models provided evidence of association between genotypes containing the G allele and high SBP (Table 1). For rs5740, both the additive (P=0.035; Pperm=0.030) and dominant (P=0.018) logistic regression modeling suggested association between genotypes incorporating the A allele and high SBP (Table 1).
The logistic regression Z statistics that provided an indication of the association between the SNP and blood pressure were correlated between SNPs much to the same extent as the LD observed between the same SNPs (data not shown). If we considered pairwise Z statistic correlations between adjacent SNPs that were >0.6 to be a single "test," one could infer that there were 14 "independent" SNP analyses. Applying a Bonferroni correction of P=0.05/14 gives a significance level of 0.0036, which is lower than any asymptotic or permutation P values observed for the SNPs individually.
Inclusion of the 3 adjacent associated SNPs (rs13331086, rs11074553, and rs4299163) as continuous variables together in a logistic regression analysis indicated that rs13331086 and rs11074553 were more strongly associated with SBP (P=0.00075 and 0.00149, respectively), whereas rs4299163 was not associated with SBP (P=0.334) when the other 2 SNPs were included in the model, indicating that the first 2 SNPs probably account for most of the association "signal" in this region. This result remained significant after Bonferroni corrections.
Haplotype Analysis
The results of the sliding window haplotype analyses for groups of 3 SNPs are shown in Figure 4 in which the horizontal bars define each haplotype block, and the vertical position of each bar defines the global significance of the association with SBP adjusted for age, sex, and BMI.
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The most strongly associated haplotype block (global P=0.0001; global Pperm=0.007) contained the 3 adjacent SNPs rs13331086, rs11074555, and rs4299163. The significance threshold was exceeded for 3 other haplotype blocks, which contained 1 of these 3 SNPs. A further 3 haplotype blocks containing the SNP rs5740 also exceeded the global significance threshold of 0.05.
Table 2 shows the estimated frequencies of the 9 specific haplotypes in the haplotype block composed of the 3 SNPs rs13331086, rs11074555, and rs4299163. The alleles associated with high SBP from the individual SNP analyses (above) are shown as italicized characters. The overall estimated frequencies of the haplotypes ranged from 0.5% (GAG) to 50% (TAC).
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The haplotype composed of the 3 alleles associated with high SBP (TGG) was estimated to be significantly more frequent in the high (0.223) compared with the low SBP group (0.125; P=0.019). The haplotype composed of 2 alleles associated with high SBP (TGC) was estimated to be 22 times more frequent in the high (0.133) than in the low SBP group (0.006; P=0.0001). The haplotype TAG was also estimated to appear more frequently in the high SBP group but was not significant (Table 2). Two haplotypes composed of 2 alleles associated with low SBP (TAC and GGC) were estimated to be more frequent in the low SBP than the high SBP groups (Table 2). Similar conclusions were obtained when logistic regression was used to make these comparisons (data not shown).
Correlation between global score statistics was moderate for adjacent haplotypes (mean: 0.57; range: 0.49 to 0.84) and very low between score statistics for more distant haplotypes (Figure 5). Considering haplotype analyses with adjacent pairwise correlations of >0.6 as a single test, we have 15 independent results. Applying a Bonferroni correction of P=0.05/15 gives a significance level of 0.0033, suggesting that if the asymptotic P values are used, haplotypes 10 and 11 are significant, whereas haplotype 19 is also close to significance at P=0.005.
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Simultaneous Adjustments for Multiple Testing
To adjust simultaneously for multiple testing arising from the use of both the SNP and haplotype analyses, we counted the number of permutations in which 3 adjacent Z statistics and the score test statistic for the haplotype including those 3 SNPs all had permutation P values <0.05. This occurred only once in the 10 000 permutations for haplotype 11, thus giving an empirical P value of 0.0001. In addition, no permuted data sets had both more extreme Z statistics and a more extreme score statistic than the real data set for the set of SNPs comprising haplotype 11 or the set of SNPs comprising haplotype 10. In the 10 000 permutations, more extreme Z and score statistics were observed only once for haplotype 12 and 16 times for haplotype 19.
DNA Sequencing
We were interested to search for novel polymorphisms that might have existed in and around exon 6 that might be in LD with the 3 associated SNPs. We sequenced all of the individuals within this VFHS subset but we found no deviations from the published sequence in exon 6 or its flanking intron-exon boundaries.
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Discussion |
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Despite the number of independent linkage studies pointing to the region containing the SCNN1G gene, ours is the first gene-wide association study. In this study we undertook selective genotyping29 in subjects with very large contrasts in SBP from a population sample of
3000 individuals. We examined 26 SNPs found in the SCNN1G promoter, intron, and exon regions, and we detected association between SBP and 4 SNPs in introns 5, 6, and 7. Three of these SNPs in introns 5 and 6 (rs13331086, rs11074553, and rs4299163) were of particular interest because they showed consistent and independent evidence of association with SBP. There was limited LD between these SNPs, and evidence for independent effects of each SNP was strengthened by the observation that, combined as haplotypes, they showed stronger global association than as individual SNPs. Our permutation analyses suggested that we were unlikely to see 3 adjacent SNPs associated in this way more than once in 10 000 permuted data sets, suggesting that, on empirical grounds, these findings are most unlikely to have arisen by chance in our study. Specific haplotypes composed of alleles of these 3 SNPs were found to be significantly associated with SBP and were estimated to be relatively common. For example, the haplotype TGG composed of alleles that were individually associated with high SBP (italicized) was estimated to have frequencies of 22.3% and 12.5% in the high and low SBP groups, respectively. The haplotype TGC was estimated to be more frequent in the high compared with low SBP groups (13.3% and 0.6%, respectively). On the other hand, haplotypes such as TAC and GGC (composed of, predominantly, alleles associated individually with low SBP) were estimated to be more commonly observed in subjects with low SBP and also estimated to be relatively common overall (50.0% and 14.5%, respectively). In fact, the haplotype composed of all 3 alleles that were individually associated with low SBP (GAC) was estimated not to occur in the high SBP subjects. Our observations are consistent with independent effects on SBP of these 3 SNPs with the influence of their specific haplotypes being consistent largely with their anticipated combined effects.
The relative lack of LD among the 3 SNPs (see Figure 3) suggests that each of the SNPs arose at different times. It also raises the possibility that there exists an unrecognized polymorphism(s) in LD with these 3 SNPs that might explain the apparent functional effects. However, sequencing of exon 6 and its flanking intron-exon boundaries did not reveal any polymorphisms that might account for the association of SBP with the intron 5 and 6 SNPs. Therefore, it would seem that the explanation for the observed association does not lie in changes in amino acid sequence of the -subunit of the epithelial sodium channel in this region.
Although the role of the intronic sequences in which the 3 associated SNPs are located is unknown, the importance of noncoding DNA in explaining the sophisticated control of the human genome is becoming increasingly well recognized.32 Few polygenic conditions have reported functional polymorphisms in coding regions, and it is entirely possible that the 3 SNPs in the noncoding sequences are themselves key to some, as yet unrecognized, functional control over gene expression.
As is evident from the LD blocks derived from HapMap and estimates of LD in the present study, SCNN1G displays relatively short regions of LD, with the region encompassing introns 2 to 6 displaying little or no LD (by r2). This is relevant to considering our findings in relation to those of the 3 previous studies that examined markers in SCNN1G. It is important to note that none of the 3 previous studies have tested SNPs other than those found in the 2 blocks of LD at either end of the gene (see Figure 3). These blocks are not in LD with the 3 SNPs we found to be associated with SBP, and so none of the previous studies provides information about these middle and apparently important regions of SCNN1G.
For example, Persu et al20 studied SNPs in exons 3 and 13, and, consistent with our observations, were unable to detect association with blood pressure. The absence of association between an SNP studied by Iwai et al21 (rs5735) in a Japanese population was also consistent with our own observations, as was the lack of association for SNP rs5718 in the promoter region reported by Morris et al22 in an independent sample of white Australians. However, Iwai et al21 reported an association between rs5718 and low SBP, a finding not replicated in either our study or that of Morris et al.22 This discrepancy might result from differences in genetic architecture and LD patterns between Japanese and white subjects.
It merits comment that deviation from HWE of 5 of the SNPs analyzed is consistent with the association of SCNN1G with SBP given that subjects in the present study were selected for the extremes of SBP. Departure from HWE in disease cases can be interpreted as an indication of association of particular genes with disease.26,33 Under these circumstances, the lack of HWE arises as a result of selection according to the phenotype that results in allele and genotype distributions that are nonrandom. In a study such as ours, where phenotypes are sampled from the extremes, this effect is more likely for polymorphisms associated with the phenotype.
In this study we undertook a selective genotyping approach to take advantage of a case-control design that augments power to detect association by sampling from the extremes of the population SBP distribution.25,27,29,34 This approach, originally suggested by Lander and Botstein35 in the context of linkage studies, has been recommended as an efficient and effective means of detecting association with quantitative traits in large groups of subjects.25–29 The power of this method depends on a number of factors, including the size of the original group, the cutoff points for selecting from the upper and lower ends of the phenotype distribution, the allele frequencies, the mode of inheritance, and the proportion of phenotypic variance explained by the polymorphism. For example, sampling 100 subjects from the upper and lower deciles of a population is equivalent to sampling all subjects in these deciles from a population of 1000 subjects (although we estimate36 that our effective sample size was 1714 subjects). For a population of 1000, comparisons between the upper and lower deciles for an allele that explains 1% of phenotypic variance and has a frequency between 0.1 and 0.4 has been estimated to provide 70% power if dominant and 63% power if additive. For alleles accounting for 5% of phenotypic variance, power is 99% for both dominant and additive models.29
A potential limitation of this study is that measures of phenotypes related to sodium and the renin-angiotensin system are not available for our subject population. One might argue that the blood pressure effects of SCNN1G variants will depend on dietary intake of sodium. However, given the identification and involvement of epithelial sodium channel in the central neural control of BP,37 the arterial baroreceptors,38,39 and the general arterial myogenic response,40,41 one cannot assume that the intermediate phenotypes relevant to the polymorphisms will necessarily be related to fluid and electrolyte balance. Considering the wide distribution of tissue epithelial sodium channel expression, it is pertinent that the associated polymorphisms exist in intronic sequences. Such noncoding regions are known to contribute to tissue-specific expression.32 The polymorphisms that we report might result in differential expression of SCNN1G in the brain, the baroreceptors, the resistance arteries, or the kidney.
If our observed associations are related to renal function, it is important to recognize that the selection of subjects was based on blood pressure, not on SCNN1G genotypes. If we had sampled our study population by genotype, then controlling for sodium balance might be relevant. However, we sampled by blood pressure, and we know from the INTERSALT Study that within populations there is a diminishingly small association between sodium intake and blood pressure.42 Therefore, we would not expect there to be substantial average differences in the sodium intakes between our high and low blood pressure groups. In any case, it is evident that, even in the presence of individual variation in sodium intake, we have been able to detect associations between SCNN1G and blood pressure. Certainly, more focused studies of the phenotypic correlates of the SNPs and haplotypes associated with SBP in our study will be important components of replication studies in independent populations.
Perspectives
The present study suggests an important role for SCNN1G in explaining deviation of SBP from the mean within a general population. SCNN1G appears to be an example of a gene in which major mutations in coding regions have established Mendelian effects on blood pressure and is also able to exert quantitative effects on blood pressure through noncoding variations. Our study provides stimulus for larger, independent population-based studies to attempt to replicate these findings and quantify the SBP effect. There is also impetus for further studies to characterize the functional significance of the associated SNPs, because these may reveal novel genomic mechanisms involving introns and functional physiological effects that lead to long-term perturbations of blood pressure.
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Acknowledgments |
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We thank Margaret Stebbing, the general practitioners, and the research nurses for their contributions to subject recruitment. We acknowledge the support and guidance provided by Dr Zilla Wong and Angela Lamantia. We also thank Dr Lyle Gurrin for helpful comments, advice, and discussion on earlier drafts of this article.
Sources of Funding
This work was supported by the Victorian Health Promotion Foundation, the National Health and Medical Research Council of Australia, and the National Heart Foundation.
Disclosures
None.
Received February 11, 2007; first decision March 5, 2007; accepted July 25, 2007.
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