Abstract

QT-interval prolongation is an electrophysiologic phenomenon associated with sudden cardiac death. The QT-interval in the general population is ∼35% heritable. In genome-wide association studies, a common variant (rs10494366T > G) within the nitric oxide synthase 1 adaptor protein (NOS1AP) gene was identified and consistently associated with QT-interval duration. Yet, the causal variant remains unclear. Therefore, we performed fine mapping of the association of the NOS1AP locus with QT-interval within the Rotterdam Study, a population-based, prospective cohort study of individuals of ≥55 years of age. First, we tested the association of single-nucleotide polymorphisms (SNPs) in or within ±100 kb of the NOS1AP gene with QT-interval duration, using sex-specific unstandardized residuals after regression on age and RR-interval, in 385 individuals using the combined set of SNPs present in the Affymetrix 500k and Illumina 550k chip arrays. Subsequently, we examined correspondence of the association signals in 4606 individuals using the Illumina 550k array. A C -to- T SNP at chromosome 1 position 160300514 (rs12143842, T-allele frequency = 24%) was associated with a QT-interval duration increase of 4.4 ms per additional T-allele ( P = 4.4 × 10 −28 ). For comparison, the most strongly associated variant to date, rs10494366T > G, was associated with a 3.5 ms increase ( P = 1.6 × 10 −23 ) per additional G-allele. None of the inferred haplotypes showed a stronger effect than the individual rs12143842C > T SNP. In conclusion, we found rs12143842 6 kb upstream distance of NOS1AP to be more strongly associated to QT-interval duration than rs10494366T > G. Functional analysis of this marker is warranted.

INTRODUCTION

Sudden cardiac death (SCD) is one of the major causes of cardiovascular mortality in developed countries. Most cases occur in individuals unrecognized to be at risk ( 1 ). Familial aggregation of SCD, independent of other risk factors, suggests a component of genetic variation in SCD risk ( 2–6 ). Owing to the relatively small size of SCD collections and etiologic heterogeneity, the statistical power to study this dichotomous trait is limited. The focus has thus been on quantitative outcomes such as endophenotypes for SCD risk. The QT-interval duration as measured on the electrocardiogram (ECG) is one of the most studied quantitative predictors of arrhythmogenesis and SCD. The QT-interval is a noninvasive measure of ventricular repolarization with high clinical relevance. The variation in the QT-interval in the general population is ∼35% heritable ( 7 , 8 ). It is known that a heterogeneous group of mutations in ion channels are responsible for Mendelian disorders in repolarization, such as long-QT syndrome (LQTS) and short-QT syndrome (SQTS) ( 9 ), with increased risk of SCD as the result of ventricular arrhythmias ( 10–13 ). But these rare disorders explain only little of the population burden of SCD. In addition, non-syndromal shortened and prolonged QT-intervals are associated with cardiovascular morbidity and mortality, including SCD ( 14–18 ), and multiple noncardiac medications are associated with ventricular arrhythmias and SCD due to QT prolongation ( 19–21 ). Therefore, QT-interval is an interesting quantitative risk factor for SCD to study. Until recently, research of genetic factors influencing SCD risk through variation in electrogenesis was limited to candidate genes known to have a role in arrhythmogenesis on the basis of their involvement in LQTS ( 22–30 ). Using genome-wide analysis, a common variant (rs10494366T > G, G-allele frequency 38%) in the nitric oxide synthase 1 adaptor protein (NOS1AP) gene was identified and consistently associated with QT-interval variation across independent replication studies, including ours ( 31–35 ). Since rs10494366T > G might not be the causative common variant, we studied the region using dense genome coverage genotype information to localize the region associated with QT-interval duration.

RESULTS

Study population

For QT-interval duration association analysis, the source population consisted of all subjects with at least one eligible QT-interval measurement ( n = 5442). The final number of subjects available for QT association analysis included 1854 men and 2752 women (total n = 4606), as not all eligible subjects had genotype data. Of the 423 female subjects additionally genotyped on the Affymetrix 500k, 385 had an ECG with a valid phenotype measurement. On average, these women were younger than the total study sample. Of all genotyped subjects with a valid phenotype 11 535 eligible ECGs were available, averaging 2.5 ECGs per individual. The QT-interval duration, RR-interval, residual QT duration and QTc presented in Table  1 are based on first eligible ECG measurements.

Table 1.

Baseline characteristics of the study population

Characteristic  Total QT sample
 
Dual platform  Illumina platform
 
    QT sample QT sample 
 Men Women Women Men Women 
n (%)  2139 (39.3) 3303 (60.7) 385 (100) 1854 (40.3) 2752 (59.7) 
Eligible ECGs [ n (%)]  5,263 (39.2) 8,155 (60.8) 1,244 (100) 4,642 (40.2) 6893 (59.8) 
Age [years, mean (SD)] 67.6 (7.9) 69.5 (9.0) 62.5 (4.3) 67.5 (7.8) 69.4 (8.9) 
QT [ms, mean (SD)] 397.7 (28.6) 399.0 (28.6) 402.0 (25.7) 397.6 (28.6) 399.0 (28.9) 
RR [ms, mean (SD)] 905.0 (154.6) 864.8 (138.5) 889.1 (136.3) 903.6 (154.5) 864.8 (140.3) 
QT residual [mean (SD)] a −0.4 (18.4) −0.7 (18.4) 1.0 (15.8) −0.3 (18.1) −0.7 (17.8) 
QTc [mean (SD)] b 420.3 (23.5) 430.8 (21.6) 428.0 (19.6) 420.5 (23.3) 430.9 (21.5) 
Characteristic  Total QT sample
 
Dual platform  Illumina platform
 
    QT sample QT sample 
 Men Women Women Men Women 
n (%)  2139 (39.3) 3303 (60.7) 385 (100) 1854 (40.3) 2752 (59.7) 
Eligible ECGs [ n (%)]  5,263 (39.2) 8,155 (60.8) 1,244 (100) 4,642 (40.2) 6893 (59.8) 
Age [years, mean (SD)] 67.6 (7.9) 69.5 (9.0) 62.5 (4.3) 67.5 (7.8) 69.4 (8.9) 
QT [ms, mean (SD)] 397.7 (28.6) 399.0 (28.6) 402.0 (25.7) 397.6 (28.6) 399.0 (28.9) 
RR [ms, mean (SD)] 905.0 (154.6) 864.8 (138.5) 889.1 (136.3) 903.6 (154.5) 864.8 (140.3) 
QT residual [mean (SD)] a −0.4 (18.4) −0.7 (18.4) 1.0 (15.8) −0.3 (18.1) −0.7 (17.8) 
QTc [mean (SD)] b 420.3 (23.5) 430.8 (21.6) 428.0 (19.6) 420.5 (23.3) 430.9 (21.5) 

Shown are first eligible ECG characteristics of all individuals with eligible ECG data, of the subset with genotype data on both genotyping platforms and of the total sample having genotype data.

a Residual QT duration in milliseconds on first eligible ECG after regression on age in years and RR-interval (in ms) in sex-specific strata using repeated measurements on all eligible ECGs.

b Calculated using Bazett's formula (QTc = QT/√RR).

NOS1AP variants and residual QT duration: dual platform sample

In this first stage, multiple variants within NOS1AP were significantly associated with QT-interval duration. All 22 single-nucleotide polymorphisms (SNPs) from both platforms associated with P -values <0.01 are presented in Table  2 . The original SNP related to QT duration as reported by Arking et al . ( 33 ) through GWAS, rs10494366T > G, a positive control for our data, reached a P -value of 8.2 × 10 −3 . The top result SNP from our analyses in these 385 women, rs10919117A > G, was genotyped on both the Affymetrix and the Illumina platform. A second signal situated 5′ of the gene was observed. Here the most significant SNPs rs12036340A > G (Affymetrix) and rs12134842C > T (Illumina) are array specific, but in high linkage disequilibrium (LD) ( r2 = 0.91, D ′ = 0.97). Several SNPs in the top results were in strong LD, and may flag the same association (Figure  1 ). These results allow to proceed fine mapping in the larger sample with the Illumina array genotypes only, since all of these associations are covered by the Illumina htSNPs.

Figure 1.

NOS1AP gene-wide association results to QT-interval of Illumina 550k and Affymetrix 500k genotype data in 385 women of the Rotterdam Study. ( A ) P -value plotted at a –log( P ) scale as a function of genomic position (NCBI Build 36). Rs10919117 is listed (blue diamond). Estimated recombination rates (HapMap) plotted to reflect the local LD structure around the associated SNPs and correlated SNPs in color (red: r2 > 0.8; orange: 0.5 < r2 < 0.8; yellow: 0.2 < r2 < 0.5; white: r2 < 0.2). Diamonds represent SNPs present on Illumina 550k platform. Circles indicate Affymetrix 500k unique SNPs. ( B ) Like (A), only for rs12143842. ( C ) Linkage blocks, corresponding to (A) and (B), of associated SNPs significantly associated to QT-interval presenting r2 values. [Regional association plots adapted from P.I.W. de Bakker ( 60 ).]

Figure 1.

NOS1AP gene-wide association results to QT-interval of Illumina 550k and Affymetrix 500k genotype data in 385 women of the Rotterdam Study. ( A ) P -value plotted at a –log( P ) scale as a function of genomic position (NCBI Build 36). Rs10919117 is listed (blue diamond). Estimated recombination rates (HapMap) plotted to reflect the local LD structure around the associated SNPs and correlated SNPs in color (red: r2 > 0.8; orange: 0.5 < r2 < 0.8; yellow: 0.2 < r2 < 0.5; white: r2 < 0.2). Diamonds represent SNPs present on Illumina 550k platform. Circles indicate Affymetrix 500k unique SNPs. ( B ) Like (A), only for rs12143842. ( C ) Linkage blocks, corresponding to (A) and (B), of associated SNPs significantly associated to QT-interval presenting r2 values. [Regional association plots adapted from P.I.W. de Bakker ( 60 ).]

Table 2.

Gene-wide results of Illumina 550k and Affymetrix 500k genotypes for association with QT-interval in 385 women from the Rotterdam Study

SNP (rs #) BP pos Minor allele MAF (%) Platform Subjects ΔQT (ms) P -value  
rs10919117 160534447 47.4 I+A 384 −3.7  1.2 × 10 −3 
rs12143842 160300514 28.2 385 4.1  1.3 × 10 −3 
rs12036340 160282364 28.9 383 4.1  1.5 × 10 −3 
rs2880058 160281256 36.4 385 3.7  2.4 × 10 −3 
rs7546009 160297525 34.9 384 3.6  3.1 × 10 −3 
rs7547308 160294616 36.5 385 3.5  3.1 × 10 −3 
rs7550692 160295915 34.6 380 3.6  3.4 × 10 −3 
rs6670339 160322430 39.2 385 3.3  4.2 × 10 −3 
rs12046924 160560906 33.6 385 3.4  4.4 × 10 −3 
rs4657150 160368688 41.8 373 3.2  5.7 × 10 −3 
rs12048222 160560718 25.6 385 3.7  6.0 × 10 −3 
rs10919166 160550291 29.9 384 3.4  6.5 × 10 −3 
rs4656349 160316448 36.0 I+A 385 3.1  7.5 × 10 −3 
rs2819316 160545176 43.8 385 −3.1  8.0 × 10 −3 
rs4657140 160327889 40.1 I+A 385 3.1  8.2 × 10 −3 
rs1415257 160328668 40.1 I+A 385 3.1  8.2 × 10 −3 
rs1415259 160351933 40.1 385 3.1  8.2 × 10 −3 
rs10494366 a 160352309 40.1 385 3.1  8.2 × 10 −3 
rs2661810 160543056 46.1 383 3.1  8.7 × 10 −3 
rs2819318 160547349 46.4 385 3.1  8.9 × 10 −3 
rs2819322 160547652 46.4 385 3.1  8.9 × 10 −3 
rs7514121 160267056 19.4 I+A 385 3.7  9.4 × 10 −3 
SNP (rs #) BP pos Minor allele MAF (%) Platform Subjects ΔQT (ms) P -value  
rs10919117 160534447 47.4 I+A 384 −3.7  1.2 × 10 −3 
rs12143842 160300514 28.2 385 4.1  1.3 × 10 −3 
rs12036340 160282364 28.9 383 4.1  1.5 × 10 −3 
rs2880058 160281256 36.4 385 3.7  2.4 × 10 −3 
rs7546009 160297525 34.9 384 3.6  3.1 × 10 −3 
rs7547308 160294616 36.5 385 3.5  3.1 × 10 −3 
rs7550692 160295915 34.6 380 3.6  3.4 × 10 −3 
rs6670339 160322430 39.2 385 3.3  4.2 × 10 −3 
rs12046924 160560906 33.6 385 3.4  4.4 × 10 −3 
rs4657150 160368688 41.8 373 3.2  5.7 × 10 −3 
rs12048222 160560718 25.6 385 3.7  6.0 × 10 −3 
rs10919166 160550291 29.9 384 3.4  6.5 × 10 −3 
rs4656349 160316448 36.0 I+A 385 3.1  7.5 × 10 −3 
rs2819316 160545176 43.8 385 −3.1  8.0 × 10 −3 
rs4657140 160327889 40.1 I+A 385 3.1  8.2 × 10 −3 
rs1415257 160328668 40.1 I+A 385 3.1  8.2 × 10 −3 
rs1415259 160351933 40.1 385 3.1  8.2 × 10 −3 
rs10494366 a 160352309 40.1 385 3.1  8.2 × 10 −3 
rs2661810 160543056 46.1 383 3.1  8.7 × 10 −3 
rs2819318 160547349 46.4 385 3.1  8.9 × 10 −3 
rs2819322 160547652 46.4 385 3.1  8.9 × 10 −3 
rs7514121 160267056 19.4 I+A 385 3.7  9.4 × 10 −3 

SNPs with P < 1 × 10 −2 are presented ranked by P -value. Presented are: SNP identification (rs – number), base pair position on chromosome 1, minor allele, minor allele frequency (MAF, calculated within 385 subjects), presence of SNP on genotyping platform (I = Illumina, A = Affymetrix), number of subjects successfully genotyped for SNP, ΔQT (in milliseconds per additional coding allele) and P -value.

a Original reported SNP associated with QT-interval [Arking et al . ( 33 )].

NOS1AP variant and residual QT duration: illumina 550k platform

Single SNP association analysis

Of the 136 SNPs tested, we observed multiple highly significant associations in the sex pooled analysis. The top 10 hits are reported in Table  3 and a graphical representation of all SNPs is given in Figure  2 . All top hits arose from one block, corresponding to the rs12143842 linkage block also seen in the 385 women subset. The previously described rs10494366T > G reached a P -value of 1.2 × 10 −19 . There was full LD ( r2 = 1) between rs10494366 and three other SNPs (i.e. rs1415257, rs1415259 and rs4657140). Only two other SNPs showed stronger associations (i.e. rs6670339T > C and rs12143842C > T) than these. The top hit, rs12143842C > T, was associated with a 4.5 ms increase in QT duration per additional T-allele ( P = 4.9 × 10 −25 , minor allele frequency 24%). There exists some degree of LD between rs12143842C > T and rs10494366T > G, with an r2 value of 0.46 and a D ′ value of 0.91. The rs6670339T > C variant is in high, though not full, LD with rs10494366T > G ( r2 of 0.91; D ′ of 0.98).

Figure 2.

NOS1AP gene-wide association results to QT-interval of Illumina 550k genotype and imputed data in 4606 subjects of the Rotterdam Study. ( A ) P -values of directly genotyped SNPs plotted at a –log( P ) scale as a function of genomic position [NCBI Build 36). The SNP with the most significant association in the analysis is listed (rs12143842, blue diamond)]. Estimated recombination rates plotted to reflect the local LD structure around the associated SNPs and correlated SNPs in color (red: r2 > 0.8; orange: 0.5 < r2 < 0.8; yellow: 0.2< r2 < 0.5; white: r2 < 0.2). (A) Only directly genotyped SNPs (diamonds). ( B ) Detail of LD structure of gene region containing top 10 associated SNPs and the linkage structure of top 10 associated directly genotyped SNPs ( r2 values). [Regional association plots adapted from P.I.W. de Bakker ( 60 ).]

Figure 2.

NOS1AP gene-wide association results to QT-interval of Illumina 550k genotype and imputed data in 4606 subjects of the Rotterdam Study. ( A ) P -values of directly genotyped SNPs plotted at a –log( P ) scale as a function of genomic position [NCBI Build 36). The SNP with the most significant association in the analysis is listed (rs12143842, blue diamond)]. Estimated recombination rates plotted to reflect the local LD structure around the associated SNPs and correlated SNPs in color (red: r2 > 0.8; orange: 0.5 < r2 < 0.8; yellow: 0.2< r2 < 0.5; white: r2 < 0.2). (A) Only directly genotyped SNPs (diamonds). ( B ) Detail of LD structure of gene region containing top 10 associated SNPs and the linkage structure of top 10 associated directly genotyped SNPs ( r2 values). [Regional association plots adapted from P.I.W. de Bakker ( 60 ).]

Table 3.

Gene-wide results of Illumina 550k genotypes for association with QT-interval in 4606 men and women from the Rotterdam Study

SNP (rs #) BP position Minor allele MAF (%)  Subjects ( n )  ΔQT (ms) P -value  r2 to rs12143842  
rs12143842 160300514 24.0 4592 4.5  4.9 × 10 −25 – 
rs6670339 160322430 35.0 4606 3.7  1.0 × 10 −21 0.50 
rs1415257 a 160328668 36.2 4606 3.5  1.0 × 10 −19 0.46 
rs1415259 a 160351933 36.2 4606 3.5  1.0 × 10 −19 0.46 
rs4657140 a 160327889 36.2 4605 3.5  1.0 × 10 −19 0.46 
rs10494366 b 160352309 36.2 4603 3.5  1.2 × 10 −19 0.46 
rs2880058 160281256 32.5 4606 3.5  8.6 × 10 −19 0.63 
rs7550692 160295915 31.2 4,490 3.4  3.6 × 10 −17 0.69 
rs1932933 160384670 37.2 4605 3.1  4.3 × 10 −16 0.42 
rs4656349 160316448 31.5 4602 3.2  5.3 × 10 −16 0.57 
SNP (rs #) BP position Minor allele MAF (%)  Subjects ( n )  ΔQT (ms) P -value  r2 to rs12143842  
rs12143842 160300514 24.0 4592 4.5  4.9 × 10 −25 – 
rs6670339 160322430 35.0 4606 3.7  1.0 × 10 −21 0.50 
rs1415257 a 160328668 36.2 4606 3.5  1.0 × 10 −19 0.46 
rs1415259 a 160351933 36.2 4606 3.5  1.0 × 10 −19 0.46 
rs4657140 a 160327889 36.2 4605 3.5  1.0 × 10 −19 0.46 
rs10494366 b 160352309 36.2 4603 3.5  1.2 × 10 −19 0.46 
rs2880058 160281256 32.5 4606 3.5  8.6 × 10 −19 0.63 
rs7550692 160295915 31.2 4,490 3.4  3.6 × 10 −17 0.69 
rs1932933 160384670 37.2 4605 3.1  4.3 × 10 −16 0.42 
rs4656349 160316448 31.5 4602 3.2  5.3 × 10 −16 0.57 

Top 10 SNPs are presented and ranked by P -value. Presented are: SNP identification (rs–number), base pair position on chromosome 1, minor allele, minor allele frequency (MAF, calculated within 4606 subjects), number of subjects successfully genotyped for SNP, ΔQT (in milliseconds per additional coding allele), P -value and extend of LD (expressed as r2 ) to rs12143842.

ar2 = 1 to rs10494366.

b Original reported SNP associated with QT-interval [Arking et al . ( 33 )].

Compared with the sex-pooled results, only some rank-order differences are observed in the sex-stratified analysis, but rs12143842C > T is consistently the strongest associated SNP in both men and women. The top result (rs10919117A > G), and other significant SNPs from within the second block from the first-stage association analysis, lost all significance in the larger sample.

Multimarker haplotype association analysis

Haplotype analysis results for rs12143842C > T | rs6670339T > C (4.62 ms; P -value 4.85 × 10 −25 for the TC haplotype, freq: 22.8%) and rs12143842C > T | rs10494366T > G (4.68 ms; P -value 1.53 × 10 −25 for the TG haplotype; freq: 22.5%) are consistent with the single rs12143842 T-allele effect. Using a sliding window, the region was scanned for haplotypes associated with QT duration. The results are displayed graphically in Figure  3 . The top two haplotypes for each window width (rs7550692T | rs12143842T and rs12143842T | rs1267209G for window width = 2; rs2880058G | rs7550692T | rs12143842T and rs7550692T | rs12143842T | rs12567029G for window width = 3) all showed associations similar to the individual rs12143842C > T effect. Finally, using the fixed marker set, the rs2880058G | rs12143842T haplotype reached the highest level of significance, but again the results were consistent with the rs12143842 T-allele effect seen in single SNP association analysis. Adding rs10494366T > G to any of the haplotypes did not change these results. An overview of haplotype effect estimates and P -values are given in Supplementary Material, Figure S1 .

Figure 3.

NOS1AP gene-wide association results of QT-interval sliding haplotype analysis. Haplotype analysis with sliding window width of two and three SNPs, respectively, in the NOS1AP gene area in 4606 subjects of the Rotterdam Study plotted at a –log( p ) scale. x -Axes ranges from 5′ − 100 kb to 3′ + 100 kb, from left to right. Scale is not representing true physical position and distances.

Figure 3.

NOS1AP gene-wide association results of QT-interval sliding haplotype analysis. Haplotype analysis with sliding window width of two and three SNPs, respectively, in the NOS1AP gene area in 4606 subjects of the Rotterdam Study plotted at a –log( p ) scale. x -Axes ranges from 5′ − 100 kb to 3′ + 100 kb, from left to right. Scale is not representing true physical position and distances.

Repeated measurement association analysis

We used all eligible ECGs in a repeated measurement analysis of rs12143842C > T and, for comparison, rs10494366G > T. The rs12143842C > T variant was associated with 4.4 ms increase in QT duration per additional T-allele (95% CI 3.6–5.1; P = 4.4 × 10 −28 ). In contrast, rs10494366T > G was associated with a 3.5 ms increase of QT duration per additional G-allele (95% CI 2.8–4.2; P = 1.6 × 10 −23 ). In genotypic analysis, heterozygous and homozygous carriage of the rs12143842 T-allele were associated with a 5.0 ms (95% CI 4.0–6.0) and a 7.3 ms (95% CI 5.3–9.4) increase in QT duration, respectively. In sex-specific analysis, the QT prolonging effect of rs12134842C > T was 13% longer in women compared with men (Table  4 A). With conditional regression analysis, independent effects were estimated for rs12143842C > T, rs6670339T > C and rs10494366T > G. Within subjects with the rs12143842-CC genotype both rs6670339T > C (+2.6 ms; P = 1.1 × 10 −5 ) and rs10494366T > G (+2.0 ms; P = 3.0 × 10 −4 ) remained associated with QT-interval. Likewise, within rs10494366-TT and rs6670339-TT genotypes, rs12143842C > T was associated to QT-interval duration (+4.0 ms; P = 1.3 × 10 −2 and +4.8 ms; P = 5.0 × 10 −3 , respectively). Both rs10494366T > G and rs6670339T > C did not show independence, which is expected since they are highly linked ( r2 = 0.91) (Table  4 B).

Table 4.

Repeated measurement analysis for genotypic and allelic effects of rs12143842 and rs10494366 on QT-interval and conditional analysis, including 11 535 ECGs from 4606 men and women from the Rotterdam Study

SNP (rs number)  Genotypic
 
P -value   Allelic model P -value  
rs12143842C > T CC CT TT   Per T-allele  
 All subjects (ECGs), n 2643 (6604) 1697 (4227) 252 (666)   4592 (11497)  
 ΔQT, ms (95% CI) Reference 5.0 (4.0–6.0) 7.3 (5.3–9.4)  7.9 × 10 −28  4.4 (3.6–5.1)  4.4 × 10 −28 
 Men (ECGs), n 1052 (2636) 696 (1717) 103 (281)   1851 (4634)  
 ΔQT, ms (95% CI) Reference 4.7 (3.2–6.3) 6.8 (3.6–10.1)  2.5 × 10 −10  4.1 (2.9–5.3)  7.0 × 10 −11 
 Women (ECGs), n 1591 (3968) 1001 (2510) 149 (385)   2741 (6863)  
 ΔQT, ms (95% CI) Reference 5.2 (3.9–6.4) 7.7 (5.0–10.3)  2.7 × 10 −18  4.6 (3.5–5.6)  8.3 × 10 −19 
rs10494366T > G a TT TG GG   Per G-allele  
 All subjects (ECGs), n 1883 (4681) 2110 (5273) 610 (1575)   4603 (11529)  
 ΔQT, ms (95% CI) Reference 4.1 (3.1–5.1) 6.6 (5.1–8.1)  5.8 × 10 −23  3.5 (2.8–4.2)  1.6 ×10 −23 
 Men (ECGs), n 755 (1897) 848 (2083) 249 (658)   1852 (4638)  
 ΔQT, ms (95% CI) Reference 3.7 (2.1–5.3) 5.8 (3.5–8.1)  8.6 × 10 −8  3.1 (2.0–4.2)  1.9 × 10 −8 
 Women (ECGs), n 1128 (2784) 1262 (3190) 361 (917)   2751 (6891)  
 ΔQT, ms (95% CI) Reference 4.4 (3.1–5.7) 7.1 (5.2–9.0)  4.2 × 10 −16  3.8 (2.9–4.7)  8.3 × 10 −17 

 
Condition  ECGs ( n )  ΔQT, ms (95% CI)  ΔQT, ms (95% CI)  ΔQT, ms (95% CI)  
  Per rs10494366 G-allele P -value  Per rs6670339 C-allele P -value  Per rs12143842 T-allele P -value  

 
rs10494366 TT 4650   0.8 (−5.7–7.4)  8.1 × 10 −1 4.0 (0.8–7.2)  1.3 × 10 −2 
rs6670339 TT 4843 −1.0 (−4.2–2.2)  5.5 × 10 −1   4.8 (1.5–8.2)  5.0 × 10 −3 
rs12143842 CC 6602 2.0 (0.9–3.1)  3.0 × 10 −4 2.6 (1.4–3.7)  1.1 × 10 −5   
SNP (rs number)  Genotypic
 
P -value   Allelic model P -value  
rs12143842C > T CC CT TT   Per T-allele  
 All subjects (ECGs), n 2643 (6604) 1697 (4227) 252 (666)   4592 (11497)  
 ΔQT, ms (95% CI) Reference 5.0 (4.0–6.0) 7.3 (5.3–9.4)  7.9 × 10 −28  4.4 (3.6–5.1)  4.4 × 10 −28 
 Men (ECGs), n 1052 (2636) 696 (1717) 103 (281)   1851 (4634)  
 ΔQT, ms (95% CI) Reference 4.7 (3.2–6.3) 6.8 (3.6–10.1)  2.5 × 10 −10  4.1 (2.9–5.3)  7.0 × 10 −11 
 Women (ECGs), n 1591 (3968) 1001 (2510) 149 (385)   2741 (6863)  
 ΔQT, ms (95% CI) Reference 5.2 (3.9–6.4) 7.7 (5.0–10.3)  2.7 × 10 −18  4.6 (3.5–5.6)  8.3 × 10 −19 
rs10494366T > G a TT TG GG   Per G-allele  
 All subjects (ECGs), n 1883 (4681) 2110 (5273) 610 (1575)   4603 (11529)  
 ΔQT, ms (95% CI) Reference 4.1 (3.1–5.1) 6.6 (5.1–8.1)  5.8 × 10 −23  3.5 (2.8–4.2)  1.6 ×10 −23 
 Men (ECGs), n 755 (1897) 848 (2083) 249 (658)   1852 (4638)  
 ΔQT, ms (95% CI) Reference 3.7 (2.1–5.3) 5.8 (3.5–8.1)  8.6 × 10 −8  3.1 (2.0–4.2)  1.9 × 10 −8 
 Women (ECGs), n 1128 (2784) 1262 (3190) 361 (917)   2751 (6891)  
 ΔQT, ms (95% CI) Reference 4.4 (3.1–5.7) 7.1 (5.2–9.0)  4.2 × 10 −16  3.8 (2.9–4.7)  8.3 × 10 −17 

 
Condition  ECGs ( n )  ΔQT, ms (95% CI)  ΔQT, ms (95% CI)  ΔQT, ms (95% CI)  
  Per rs10494366 G-allele P -value  Per rs6670339 C-allele P -value  Per rs12143842 T-allele P -value  

 
rs10494366 TT 4650   0.8 (−5.7–7.4)  8.1 × 10 −1 4.0 (0.8–7.2)  1.3 × 10 −2 
rs6670339 TT 4843 −1.0 (−4.2–2.2)  5.5 × 10 −1   4.8 (1.5–8.2)  5.0 × 10 −3 
rs12143842 CC 6602 2.0 (0.9–3.1)  3.0 × 10 −4 2.6 (1.4–3.7)  1.1 × 10 −5   

Presented are: (A) Genotypic effect and allelic effects of rs12143842 and rs10494366. Both pooled and sex-specific effect estimates are presented with corresponding 95% confidence intervals and P- values. a Original reported SNP associated with QT-interval [Arking et al . ( 33 )]. (B) Allelic effect within strata of homozygous nonvariant carriers of the alternate SNP, testing statistical independence.

DISCUSSION

Main findings and considerations

Using high-density genotype data of the NOS1AP gene, consisting of common variants selected on the basis of LD structure (Illumina 550k), enriched with genotype data of randomly selected common variants (Affymetrix 500k) in a limited number of subjects, we observed a novel strong association of the common variant rs12143842C > T (+4.4 ms per additional allele, P -value 4.4 × 10 −28 ) with QT-interval duration. Compared with the previously described rs10494366G > T (+3.5 ms per additional allele; P -value 1.6 × 10 −23 ) variant, both the strength of association and the magnitude of effect are considerably larger. Given the certain extent of LD between rs12143842 and rs10494366 ( r2 = 0.46) and a minor allele frequency of 36.2% for rs10494366 compared with 24.0% for rs12143842, it is possible that the strong association of rs10494366 is largely explained by the much stronger association of the phenotype with rs12143842C > T, since the P -value is a function of effect size, sample size (power), SNP minor allele frequency and LD with the causative SNP. Therefore, LD in the human genome allows the identification of functional loci without directly genotyping the causative allele, but makes discrimination between the functional variant and all correlated variants difficult. In line with this argument it is very well possible that also rs12143842 is not the causal SNP. Two other common variants in the HapMap CEU database are in considerable LD ( r2 > 0.8) with rs12143842, namely rs12036340 (genotyped on the Affymetrix platform which showed equal effect sizes and rs16847548T > C (not genotyped). None of the inferred haplotypes showed stronger associations than rs12143842 alone. However, in a conditional regression analysis of these SNPs, statistical independence between rs12143842C > T and rs10494366T > G/rs6670339T > C was shown, which might indicate the presence of another (causal) SNP linked to both SNPs. Both rs12143842C > T and rs10494366T > G variant alleles might have, to a certain extent, the ability to predict this variant in the absence of one another. However, the presence of two independent causal variants at the NOS1AP locus cannot be ruled out completely. It can be noted that the rs6670339 C-allele is a better predictor of QT-interval duration than the rs10494366 G-allele in subjects with an rs12143842 CC-genotype.

The rs12143842C > T common variant

The rs12143842C > T common variant is located in the 5′ region of NOS1AP. Remarkably, the first fine map attempt in the original paper describing the association between NOS1AP and QT-interval duration already suggested that this region harbors a potential variant more strongly related to QT-interval duration ( 33 ). This strengthens the validity of our results. The rs12143842 T-allele frequency reported in the HapMap CEU sample (15.8%) was lower than that observed in our population (24.0%). These differences might be due to the lower number of subjects in the HapMap sample or to differences in ancestry, which are also reflected in different r2 values for rs12143842 | rs10494366 in the HapMap sample ( r2 = 0.11) compared with our sample ( r2 = 0.46).

The functionality of this SNP is not clear. Given its location in a potential regulatory region, we looked for potential transcription factors binding sites at this position using the TESS web tool ( http://www.cbil.upenn.edu/cgi-bin/tess/tess ) ( 36 ). For rs12143842, the C-to-T substitution resulted in two changes in predicted transcription factor binding sites. First, a loss of c-Myb as a potential transcription factor known to play a role in development of malignancies and in normal haematopoietic regulation ( 37–39 ). Secondly, more interestingly, the C-to-T substitution results in gaining the potential binding of myocyte-specific enhancer factor 2 (MEF2), alternative-myocyte-specific enhancer factor 2 (aMEF2) and related to serum response factor C4 (RSRFC4) transcription factors that are the product from the MEF2 gene and bind conserved sequences found in growth factor-inducible and muscle-specific promoters with preferential expression in the skeletal muscle, the heart and the brain ( 40 , 41 ) (Fig.  4 ). We found no evidence that the highly linked rs12036340 had functional properties over possible human transcription factor binding sites. Another linked SNP, which was not directly genotyped in our study on either platform, rs16847548T > C (HapMap: minor allele frequency 12.7%; r2 to rs12143842 of 0.82) is also situated in the 5′ region of NOS1AP but does not seem to result in a change in the potential to bind a transcription factor. Any additional conclusions on functionality derived from in silico information needs to be tested by functional experiments in order to prove that NOS1AP is truly related to QT-interval prolongation through transcription factor binding differences caused by the rs12143842C > T polymorphism. The identification of the causative variant is difficult and requires laborious and resource intensive methods. Future experiments that can be thought of include zebra fish and mice models for cardiac repolarization, which has been successful in the past for QT-interval research, where the polymorphism can be knocked in to observe the effect on the phenotype ( 42–44 ). Other methods used for addressing effects of common variation on transcription factor binding sites include electrophoretic mobility shift assays in transgenic mice, chromatin immunoprecipitation assays in keratinocyte cell lines and luciferase reporter assays ( 45 ).

Figure 4.

Change in transcription factor binding potential due to rs12143842 C-to-T base pair change at position 160300514 on chromosome 1 in the 5′ upstream region of NOS1AP. The C-to-T base pair substitution results in loss of c-Myb binding potential and gain in aMEF-2, MEF-2 and RSRCF4 binding potential. Other changes are in nonhuman transcription factors. (Source: TESS.)

Figure 4.

Change in transcription factor binding potential due to rs12143842 C-to-T base pair change at position 160300514 on chromosome 1 in the 5′ upstream region of NOS1AP. The C-to-T base pair substitution results in loss of c-Myb binding potential and gain in aMEF-2, MEF-2 and RSRCF4 binding potential. Other changes are in nonhuman transcription factors. (Source: TESS.)

Study strengths and study limitations

Major advantages of our study are the large, community-based sample size, with up to four ECGs per subject, increasing the number of eligible subjects and ECGs available for analysis. Furthermore, the use of digital ECG recordings processed by the Modular ECG Analysis System (MEANS) system reduces information bias and inter-observer differential assessment of QT-interval duration. Detailed pharmacy data on day-to-day drug exposure allowed exclusion of ECGs taken during administration of QT-interval prolonging or shortening drugs. With respect to genotype data, we had the advantage of having additional data from the Affymetrix 500k array in a subset of our total study population, allowing for enrichment of genotyped SNP density. This increased the coverage of genome variability in this region, reducing the chance of missing a strong association arising from variants in low LD with the Illumina SNPs. However, even if coverage is high, difficulty in discrimination between signals remains a problem that cannot be solved solely by increasing the genome coverage. This raises the limitation that our results are restricted to a European ancestry population. The rs12143842 common variant shows LD with other SNPs at the locus in the HapMap CEU sample. Since LD at this locus is less an issue in the HapMap Yoruban sample (e.g. rs12036340 | rs12143842: YRI r2 = 0.45, CEU r2 = 0.94, 385 women sample r2 = 0.91), confounded results due to LD are less likely if common variants in the NOS1AP locus are tested for association with QT-interval duration in a population of African ancestry. In theory such admixture mapping would lead to narrowing the area of association further, given that this locus is of relevance for QT-interval duration in non-Caucasians.

Conclusion

We have successfully fine mapped the prior association of rs10494366T > G at the NOS1AP locus with QT-interval duration by newly identifying rs12143842C > T in the 5′ region of NOS1AP to be strongly associated. This SNP is promising in silico regarding transcription factor binding abilities. However, we have also showed that rs6670339C > T remained an independent predictor of QT-interval within rs12143842 CC genotype carriers. Therefore, the presence of another causal SNP linked to both rs12134842 and rs6670339 or multiple causal variants at his locus remain possible and should be explored in future research. Until the next step is made, rs12143842C > T is the best indicator in a Caucasian population for the effect of common variants in the NOS1AP locus on QT-interval duration.

MATERIALS AND METHODS

Setting and design

The Rotterdam Study is an ongoing prospective population-based cohort study of chronic diseases in Caucasian elderly, which started in 1990. All inhabitants of Ommoord, a Rotterdam suburb in the Netherlands, aged 55 years and over ( n = 10 278) were invited to participate. Of them, 78% ( n = 7983) gave their written informed consent for participation, including retrieval of medical records, use of blood and DNA for research purposes and publication of results. Baseline examinations took place from March 1990 through July 1993. Follow up examinations are carried out periodically. Furthermore, exposure to medication is continuously monitored since 1 January 1991, through computerized pharmacy records of the pharmacies in the Ommoord district. The pharmacy data include the Anatomical Therapeutical Chemical (ATC)-code, the dispensing date, the total amount of drug units per prescription, the prescribed daily number of units and product name of the drugs. This provides us with information on start and duration of use of all prescribed medication. Detailed information on design, objectives and methods of the Rotterdam Study was described elsewhere ( 46 , 47 ). The Medical Ethics Committee of the Erasmus University approved the study.

Phenotype: assessment of QT-interval, other electrocardiographic measurements and phenotype modeling

As described in earlier studies on QT duration in the Rotterdam Study ( 18 ), we used a 10 s resting 12-lead ECG (average 8–10 beats), which was recorded on an ACTA ECG (ESAOTE, Florence, Italy) at a sampling frequency of 500 Hz and stored digitally. All ECGs were processed by the MEANS to obtain ECG measurements, in agreement with the FDA guidance for clinical evaluation of QT/QTc interval prolongation ( http://www.fda.gov/cder/guidance/6922fnl.pdf ). The MEANS program determines the QT duration from the start of the QRS complex until the end of the T-wave. MEANS also determines the presence of right or left bundle-branch block and QRS duration. ECGs with right or left bundle-branch block, QRS duration of > 120 ms, atrial fibrillation or missing data were excluded from the analysis. In addition, all ECGs taken while under QT prolonging drugs were excluded to minimize confounding by nongenetic factors. Drugs were considered as potentially QT-interval prolonging if they appeared on any of the list 1–4 at the QT Drugs website ( http://www.qtdrugs.org/ ). We also excluded ECGs if subjects used flupentixol, levomepromazine, mefloquine, olanzapine or sertindole, drugs which may all prolong QT-interval ( 48–51 ). In addition, we excluded those ECGs taken under exposure to digoxin, which may shorten QT-interval ( 52 , 53 ). An ECG was considered taken during drug exposure if a prescription of any of above-mentioned drugs overlapped with the date of ECG measurement. The phenotype of interest was the residual QT-interval duration in milliseconds estimated from the regression on age (in years) and heart rate (RR-interval) in sex-specific strata. This was done to allow for different effects for age and RR-interval in men and women. Up to four ECGs per individual were available for QT phenotype modeling. Phenotype modeling was performed on all eligible ECGs using PROC MIXED in SAS 9.1.3 (SAS Institute Inc., Cary, NC, USA) for repeated measurements to enhance precision of adjustment.

Genotyping, data cleaning and SNP extraction

Genomic DNA was extracted from whole blood samples using the salting out method ( 54 ). Microarray genotyping was performed in the whole original Rotterdam Study cohort with proper quality DNA samples ( n = 6449) using the Infinium II HumanHap550K Genotyping BeadChip ® version 3 (Illumina). Any samples with a call rate of <97.5% ( n = 209), excess autosomal heterozygosity of > 0.336 (FDR < 0.1%) ( n = 21), mismatch between called and phenotypic gender ( n = 36), or if there were outliers identified by the IBS clustering analysis (see below) clustering > 3 standard deviations away from the population mean ( n = 102) or IBS probabilities > 97% ( n = 129) were excluded from the analysis. In total, 5974 samples met quality control inclusion criteria.

The GeneChip ® Human Mapping 500k Duo Array Set (Affymetrix) was used in a subset of 509 women selected for a pilot study on quantitative traits on the basis of absence of major chronic diseases and/or use of medication. Genotyping procedures were followed according to Illumina and Affymetrix manufacturer's protocols, respectively. Any samples with a call rate of <95% ( n = 15), mismatch between called and phenotypic gender ( n = 3), or if there were outliers identified by the IBS clustering analysis clustering > 3 standard deviations away from the population mean ( n = 10), or if either of the NspI or StyI arrays was missing ( n = 22) were excluded from the analysis; in total, 470 samples were successfully genotyped as described previously ( 55 ). A subset of 423 selected healthy women from the pilot study had complete genotyping for both the Illumina and Affymetrix platforms.

All SNPs present within the NOS1AP gene area (Chr. 1; base pair 160306205–160604864; NCBI build 36.2) ±100 kb were extracted from both data sets and merged into one data set. This allowed for a higher SNP density by combining SNPs genotyped on either platform, considering that Illumina and Affymetrix use different SNP selection criteria. Illumina-selected haplotype tagging SNPs based on HapMap Phase II LD structure determined in the Caucasian (CEU) population. In contrast, Affymetrix selected SNPs randomly across the human genome. The combination of SNPs across platforms yielded 314 unique SNPs in a 500 kb region containing NOS1AP. With the dual platform data, we cover 72, 91 and 95% of the common variation as present in the Caucasian HapMap reference population at r2 thresholds of 1, ≥0.7 and ≥0.5, respectively. For the Illumina 550k platform only, there were 156 htSNPs present in this region. Markers were excluded if they deviated significantly from Hardy–Weinberg equilibrium ( P < 1 × 10 −4 , n = 1 SNPs for the combined data set and n = 0 SNPs for the Illumina data set), if they had a minor allele frequency <5% ( n = 59 and 19 SNPs, respectively), or if they had an SNP call rate of <95% within the samples ( n = 14 and one SNP, respectively). This resulted in 242 SNPs in the healthy women subset and 136 SNPs in the full Rotterdam Study population available for association analysis.

Association analysis

For the SNP association analyses, we followed a two-stage approach. In the first stage, we estimated the effect and P -values for the association between the selected common variants and residual QT-interval duration in the subset of women with dual platform, high density and genotype data using additive linear regression models. This allowed for evaluation of coverage by Illumina genotyped htSNPs compared with the genotype data enriched by nontagging SNPs genotyped on the Affymetrix platform. In the second stage, the same analysis methods were applied in the total study population genotyped on the Illumina platform. Additional analyses were performed, such as sex-stratified analyses and haplotype analysis. Haplotype analysis was performed using several approaches. First, a sliding window haplotype analysis using the expectation/maximization algorithm was used to estimate haplotype effects on QT-interval duration ( 56 , 57 ). Secondly, a fixed marker set based on the CEU Phase 2 HapMap r2 was used ( 58 ). Additional haplotype analyses were performed for combinations of top hits from single SNP analysis. For the gene-wide association analysis and all other haplotype analyses, we used PLINK v.1.01 ( 59 ). Since the software used does not allow for repeated measurements, we selected the first eligible ECG for association testing.

Finally, for the most significant single SNP result and the earlier reported rs10494366T > G, we performed linear regression analysis for multiple measurements using the PROC MIXED procedure in SAS 9.1.3 to make full benefit of all available ECG data. This analysis included general genotypic and allelic models. Again gender-stratified analyses were performed to assess differential effects between men and women. To test statistical independence of the most significant single SNPs and rs10494366, conditional regression analysis was performed by assessing the effect of each SNP within strata of homozygous reference allele carriers of the other SNP. The results are presented as ΔQT in milliseconds when compared with the reference allele with corresponding 95% confidence intervals and P -values.

SUPPLEMENTARY MATERIAL

Supplementary Material is available at HMG Online .

FUNDING

This work is supported by the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) [050.060.810]; the Netherlands Organization for Scientific Research (NWO) [175.010.2005.011] and the Netherlands Heart Foundation (NHS) [2007B221 to M.E.]. The Rotterdam Study is supported by the Erasmus Medical Center and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research; the Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly; the Ministry of Education, Culture and Science; the Ministry of Health Welfare and Sports; the European Commission; and the Municipality of Rotterdam.

ACKNOWLEDGEMENTS

We thank all participants in the Rotterdam Study, local healthcare centers and the municipality for making this study possible. Pascal Arp and Mila Jamai are gratefully acknowledged for genotyping. Michael Moorhouse and Marijn Verkerk are gratefully acknowledged for database management.

Conflict of Interest statement . None declared.

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