Genome-Wide Analysis of Blood Pressure Variability and Ischemic Stroke
This article has been corrected.
VIEW CORRECTIONAbstract
Background and Purpose—
Visit-to-visit variability in blood pressure (vBP) is associated with ischemic stroke. We sought to determine whether such variability has genetic causes and whether genetic variants associated with BP variability are also associated with ischemic stroke.
Methods—
A Genome Wide Association Study (GWAS) for loci influencing BP variability was undertaken in 3802 individuals from the Anglo-Scandinavian Cardiac Outcome Trial (ASCOT) study, in which long-term visit-to-visit and within-visit BP measures were available. Because BP variability is strongly associated with ischemic stroke, we genotyped the sentinel single nucleotide polymorphism in an independent ischemic stroke population comprising 8624 cases and 12 722 controls and in 3900 additional (Scandinavian) participants from the ASCOT study to replicate our findings.
Results—
The ASCOT discovery GWAS identified a cluster of 17 correlated single nucleotide polymorphisms within the NLGN1 gene (3q26.31) associated with BP variability. The strongest association was with rs976683 (P=1.4×10−8). Conditional analysis of rs976683 provided no evidence of additional independent associations at the locus. Analysis of rs976683 in patients with ischemic stroke found no association for overall stroke (odds ratio, 1.02; 95% CI, 0.97–1.07; P=0.52) or its subtypes: cardioembolic (odds ratio, 1.07; 95% CI, 0.97–1.16; P=0.17), large vessel disease (odds ratio, 0.98; 95% CI, 0.89–1.07; P=0.60), and small vessel disease (odds ratio, 1.07; 95% CI, 0.97–1.17; P=0.19). No evidence for association was found between rs976683 and BP variability in the additional (Scandinavian) ASCOT participants (P=0.18).
Conclusions—
We identified a cluster of single nucleotide polymorphisms at the NLGN1 locus showing significant association with BP variability. Follow-up analyses did not support an association with risk of ischemic stroke and its subtypes.
Introduction
Familial studies have long provided evidence of heritability (31%–68%) of blood pressure (BP).1 In recent years, substantial progress has also been made in our understanding of the genetics of various measures of BP (systolic BP [SBP], diastolic BP, mean arterial pressure, and pulse pressure).2–7 However, episodic hypertension or variability in BP remains understudied, despite evidence supporting their role as risk factors in vascular events.8 Visit-to-visit variability in SBP is a strong predictor of ischemic stroke independent of mean BP,9 with hypertensives showing the most BP variability over a series of visits and having the greatest risk of a cardiovascular event.8,10
Determining whether BP variability has a genetic basis is difficult given the lack of prospective cohorts with visit-to-visit BPs recorded and accompanying Genome Wide Association Study (GWAS) data. The Anglo-Scandinavian Cardiac Outcome Trial (ASCOT) study is a longitudinal study investigating the impact of a calcium channel blocker against a β-blocker regime in hypertensive individuals at moderate risk for a cardiovascular outcome recruited in the United Kingdom, Ireland, and Nordic countries from 1998 to 2000.11 Long-term BP variability measurements spread >5 years, and genome-wide genotyping was available for an ASCOT study subset, the ASCOT-United Kingdom-Ireland cohort (ASCOT-UK-IR), allowing a GWAS to be conducted for genetic risk variants of BP variability.
We hypothesized that because visit-to-visit BP variability is associated with the risk of ischemic stroke more than hemorrhagic stroke,8 and because hypertension is a major modifiable risk factor, any genetic variants that are associated with BP variability may also be associated with ischemic stroke. On the basis of recently published GWAS12,13 that show the genetic risk of stroke to be subtype specific, we tested the genetic variant in ischemic stroke subtypes. In an effort to replicate our findings, we also tested the genetic variant for association with BP variability in an independent set of individuals from the ASCOT Scandinavian (ASCOT study recruited in Denmark, Finland, Norway, and Sweden [ASCOT-DK-FI-NO-SE]) cohort.
Materials and Methods
Study Populations
ASCOT
The ASCOT-Blood Pressure–Lowering Arm (ASCOT-BPLA) is an investigator-led multicenter trial that included >19 000 patients with hypertension, aged 40 to 79 years at baseline, with an average SBP of 140/90 mm Hg with treatment and 160/100 mm Hg without treatment.11 Patients had no history of coronory heart disease, but had ≥3 other risk factors for cardiovascular disease, such as left ventricular hypertrophy, type II diabetes mellitus, peripheral artery disease, previous stroke/transient ischemic attack, men aged ≥55 years, or cigarette smoking. The study tested the impact of a contemporary calcium channel blocker–based regimen against an older β-blocker–based regime in hypertensives at moderate risk of a cardiovascular outcome. The primary objective of the BP-lowering arm was to assess and compare the long-term effects of 2 blood pressure–lowering regimens on the combined end point of nonfatal myocardial infarction (including silent myocardial infarction) and fatal coronory heart disease. BP was measured in a seated position by a uniform automated device (Omron HEM705CP) in all participants during an average of 13 visits across 5.5 years.
The ASCOT-UK-IR GWAS population included 3802 individuals extracted from the original cohort of 19 342 hypertensives. Visit-to-visit BP variability measurements were recorded prospectively for within-visit and between-visit BP variability >5.5 years. Blood samples for DNA isolation were collected, of which 3802 individuals of European ancestry from United Kingdom and Ireland were genotyped, allowing a GWAS to be conducted for risk variants of BP variability. A subset of 3900 individuals from the ASCOT-DK-FI-NO-SE for whom DNA was available was used for replication analyses. The recruitment criterion for the Scandinavian ASCOT participants was identical to that for the United Kingdom and Irish participants, and all had BP measurements performed at similar time points to calculate BP variability. Details of ASCOT-UK-IR study population are tabulated in Table I in the online-only Data Supplement.
Ischemic Stroke
The stroke population included 8624 cases and 12 722 controls from 7 different cohorts (online-only Data Supplement): Australian Stroke Genetics Collaborative (ASGC),13,14 Bio-Repository of DNA in Stroke (BRAINS),15,16 Genetics of Early Onset Stroke (GEOS),17,18 Ischemic Stroke Genetics Study and Siblings with Ischemic Stroke Study (ISGS19/SWISS),20 Welcome Trust Case Control Consortium 2-United Kingdom (WTCCC2-UK),21 WTCCC2-Germany,21 and Vitamin Intervention for Stroke Prevention (VISP) trial.22 All participating cohorts received institutional ethical clearance and signed consent from each participating study subject. ISGS/SWISS, Genetics of Early Onset Stroke and, VISP used sex- and age-matched stroke-free controls recruited from the local population. Bio-Repository of DNA in Stroke and WTCCC2-UK used the WTCCC 1958 British Birth cohort and National Blood Service controls. WTCCC2-Germany derived controls of German Caucasian origin from the KORAgen study (www.gsf.de/kora).
Trial of Org 10172 in Acute Stroke Treatment classification23 was performed by an in-house neurologist and all stroke cases were classified into 3 categories: cardioembolic stroke, large artery disease, and small vessel disease. All cohorts except VISP provided stroke subtype data.
Details of stroke cohort study populations are tabulated in Table II in the online-only Data Supplement.
Genotyping and Imputation
The genotyping, imputation, and quality control for the ASCOT GWAS has been described previously.24 A detailed description of genotyping, imputation, and quality control methods for each participating study in the ischemic stroke analysis is provided in the Materials and Methods and Table III in the online-only Data Supplement. Single nucleotide polymorphism (SNP) genotyping of rs976683 in 3900 Scandinavian ASCOT samples was performed using the KASPAR assay at St. Bartholomew’s Hospital and the London Genome Centre. Image processing and genotype calling were performed using SDS (Applied Biosystems) and Autocaller (Applied Biosystems). Any genotypes with discrepancies between the 2 calling algorithms were manually inspected and corrected.
Data Analysis
In the ASCOT study, BP was measured in all participants during an average of 13 visits across 5.5 years. Measurements during the first 6 months after starting therapy were excluded because this was a period of forced medication titration and any differential medication effects could have acted as a confounder. Data simulations demonstrated that the combination of within-visit BP variability and visit-to-visit BP variability allowed the use of more BP measurements. Within-individual visit-to-visit BP variability phenotype was expressed as mean (±SD) and coefficient of variation (SD/mean) using the second and third readings for every visit for ASCOT-BPLA cohort. The variance independent of mean transformation was applied if there was a correlation between the mean SBP and coefficient of variation.10 The SBP variance independent of mean was derived for all on-treatment SBP values, analyzing total variability (within-visit and between-visit variability) using a coefficient of variation (SD/meank), with k determined from curve fitting.10 Analysis also included the use of residual SD for effect size estimates, which is the square root of the total squared deviation of data points from a linear regression of BP values against time, divided by (n–2), with n being the number of readings.10 All analyses were adjusted for age, sex, sex-age (sex×age, with gender coded as 1 [men] or 2 [women]), SBP mean, and the first 10 principal components (from decomposition of the genotype matrix).
For the stroke meta-analysis, the candidate SNPs were extracted from the genome-wide data, and site-specific logistic regression analysis was performed to test the association of top SNP with overall ischemic stroke and its major subtypes (large artery disease, cardioembolic stroke, and small vessel disease) under an additive genetic model. Age and sex were used as covariates. Beta coefficients, SEs, and P values from different studies were pooled via inverse variance meta-analysis using a fixed effects model. Meta-analysis was performed for overall ischemic stroke and its subtypes on the basis of Trial of Org 10172 in Acute Stroke Treatment criteria23 using METAL software.25 Pooled odds ratios (ORs) were calculated using estimated effect size of the SNP and SE of the effect size estimate. The 95% CIs were calculated using ORs and SE. A detailed description of the statistical analysis methods for each participating study is provided in Table IV in the online-only Data Supplement.
Power for the stroke meta-analysis was calculated using the CATS genetic power calculator.26 The following parameters were used to calculate the power for the replication of SNPs rs976683 in the ischemic stroke population using an additive genetic model: n (cases): 8624, n (controls): 12 722, stroke prevalence: 7.2%,27 rs976683 minor allele frequency: 0.25, and significance level: 0.05. The sample size provided sufficient power to detect modest effect sizes ranging from 1.1 to 1.4 for overall ischemic stroke but had reduced power for subtypes.
Results
ASCOT GWAS
The ASCOT GWAS population consisting of 3802 subjects comprised primarily men (82.3%) with a mean age of 63.7 (±8.1) years. Mean baseline SBP, mean baseline DBP, and mean variance independent of mean were 161.6 mm Hg (±17.6), 92.4 mm Hg (±9.9), and 0.004 mm Hg (±0.001), respectively. Details of the ASCOT-UK-IR study population are tabulated in Table I in the online-only Data Supplement.
GWAS for BP variability identified a cluster of 17 correlated SNPs within the Neuroligin-1 (NLGN1) gene on 3q26.31 (ENCODE ID: ENSG00000169760.13; Figure 1; Table V in the online-only Data Supplement). Within the cluster, 12 SNPs were directly genotyped and 5 were imputed. Seven SNPs (3 imputed and 4 genotyped) reached genome-wide significance (P≤5×10–8), with the strongest association at the imputed SNP rs976683 (P=1.4×10–8; Figure 2A and 2B). The effect size for SNP rs976683 association was small (β=0.000179), corresponding to a 0.01% mm Hg change in BP variability per copy of the risk allele. Conditional analysis using rs976683 provided no evidence of an independent signal at this locus (P=0.18).
Ischemic Stroke Population Demographics
A total of 8624 cases and 12 722 controls of European descent from 7 studies spread across Europe, America, and Australia (ASGC, BRAINS [European arm], GEOS, ISGS/SWISS, VISP, WTCCC2-UK, and WTCCC2-Germany) were available. The mean age of study participants ranged from 41.0±7.0 to 72.87±13.16 years for stroke cases and 39.5±6.7 to 66.28±7.54 years for controls. The male:female ratio was ≈50:50. The 3 main ischemic stroke subtypes, cardioembolic, large vessel disease, and small vessel disease, accounted for 1523, 1639, and 1254 cases, respectively. The demographic data, such as age, sex distribution, and stroke subtype frequencies for each population, are summarized in Table II in the online-only Data Supplement.
Association With Overall Ischemic Stroke and Subtypes
SNP rs976683 was directly genotyped in all 7 cohorts with an average minor allele frequency of 0.26 (Table VI in the online-only Data Supplement) and was not significantly associated (P≤0.05) with the increased risk of ischemic stroke or its subtypes. Pooled ORs were as follows: overall ischemic stroke (OR, 1.02; 95% CI, 0.97–1.07; P=0.52), cardioembolic (OR, 1.07; 95% CI, 0.97–1.16; P=0.17), large vessel disease (OR, 0.98; 95% CI, 0.89–1.07; P=0.60), and small vessel disease (OR, 1.07; 95% CI, 0.97–1.17; P=0.19). There was no significant heterogeneity between studies (Table VII in the online-only Data Supplement).
Despite no evidence of an additional signal from the conditional analysis, the genotyped SNP rs9830510 was also tested for association in the ischemic stroke cohort to ensure that the association result of imputed SNP rs976683 was not an imputation artifact. rs9830510 was directly genotyped in all 7 cohorts with an average minor allele frequency of 0.15 (Table VI in the online-only Data Supplement). Association with increased risk of ischemic stroke or its subtypes was not significant (P≤0.05), with pooled ORs as follows: overall ischemic stroke (OR, 0.96; 95% CI, 0.90–1.02; P=0.54), cardioembolic (OR, 1.03; 95% CI, 0.91–1.15; P=0.83), large vessel disease (OR, 0.76; 95% CI, 0.66–0.80; P=0.03), and small vessel disease (OR, 1.01; 95% CI, 0.89–1.14; P=0.92). There was no significant heterogeneity between studies (Table VIII in the online-only Data Supplement).
ASCOT BP Variability Follow-Up
Association testing of rs976683 with BP variability in the ASCOT Scandinavian arm provided no evidence of association (P=0.18).
Discussion
We provide evidence supporting a role of genetic variants at the Neuroligin-1 (NLGN1) locus with BP variability, but we were unable to demonstrate association between this locus and ischemic stroke and BP variability in an independent Scandinavian sample. A GWAS for BP variability in the UK-IR discovery cohort identified a cluster of 17 correlated SNPs within the NLGN1 gene that encode a neuronal cell surface protein implicated in the growth and remodeling of the vascular system.28 The strongest association reaching genome-wide significance was at imputed SNP rs976683 (P=1.4×10−8) and a correlated genotyped SNP rs9830510 (P=1.7×10−8), which represents a novel locus for BP variability in hypertensives and has not been detected in any previous BP GWAS. The effect size for the sentinel association was extremely small (β=0.000179), corresponding to a 0.01% unit change in BP variability per copy of the risk allele. Similar observations have been made in GWAS of other measures of BP, in which effect sizes were also very small (1 mm Hg SBP and 0.5 mm Hg diastolic BP) but could have the potential to significantly alter the outcomes at a population level. This evidence leads us to believe that the observed effect (albeit small) may be part of a battery of unrelated and common gene loci that exert independent but small effects that compound to cause the disease. However, this hypothesis can only be confirmed via large prospective GWAS.
We attempted to replicate our findings with BP variability in 2 ways: first, testing the top SNPs for association with ischemic stroke in an independent population comprising 8624 cases and 12 722 controls from 7 cohorts. This is a common exploratory approach used to study candidate genes that may be associated with different vascular disorders, such as MI and stroke, through their effect on shared risk factors, such as hypertension, diabetes mellitus, and smoking.29 Our sample size provided sufficient power to detect modest effect sizes ranging from 1.1 to 1.4 for overall ischemic stroke; however, as with other studies, it had reduced power for subtypes because of small sample size. SNPs rs976683 and rs9830510 were not significantly associated (P≤0.05) with the risk of overall stroke or its subtypes, with the estimated pooled ORs ranging from 1.02 to 0.96 for overall ischemic stroke, 1.07 to 1.03 for cardioembolic, 0.98 to 0.76 for large vessel disease, and 1.07 to 1.01 for small vessel disease.
The failure to detect an association with overall stroke could be because of several reasons. Genes affecting multifactorial diseases, such as stroke, usually have small effect sizes and are difficult to identify in modestly sized study populations. Insufficient statistical power, given the small observed effect size for rs976683 on BP variability, is the most likely cause of an undetectable association with ischemic stroke. Another reason could be the clinical phenotypic heterogeneity introduced because of the diverse pathogenesis of ischemic stroke, which makes it difficult to differentiate true signals from noise. Large studies, such as the recent METASTROKE30 meta-analysis that included 15 stroke cohorts comprising 12 000 cases and 60 000 controls, also failed to identify any new genetic risk variants and only validated previous findings of variants within genes PITX2, ZFHX3, and HDAC9. Despite the large study population, the observed effect sizes were small (OR, 1.39–0.96), suggesting that a combined burden of risk alleles carried by an individual is the likely cause, as shown in hemorrhagic stroke.31 These studies have also highlighted the subtype-specific nature of the risk, which lends support to the fact that true associations may be hidden under the multifactorial pathogenesis of stroke. Our work also has a number of limitations, which include possible inaccuracy of the Trial of Org 10172 in Acute Stroke Treatment classification into stroke subtypes. The case–control study design of our meta-analysis may be another limitation because some studies can induce survival bias by including recurrent stroke, thus allowing selection of milder forms of strokes.
The second attempt to replicate our findings included testing for association of rs976683 with BP variability in 3900 individuals from the Scandinavian arm of the ASCOT study. Although not an ideal resource for follow-up of our original observation, it was the only available replication population in which individuals were selected using identical recruitment criteria as the ASCOT-UK-IR cohort and BP measurements were performed at the same time points, allowing identical analysis of BP variability. However, replication analysis in this population provided no evidence of association between NLGN1 and BP variability (P=0.18). Failure to replicate this association may, in part, be because of population stratification induced by Anglo-Scandinavian differences, such as admixture of Finnish and central European ancestry32 and recruitment of the ASCOT-SE samples in Sweden. There are considerable genetic differences among Europeans, and studies have demonstrated autosomal substructure in the Finnish and Swedish populations, warning researchers against making assumptions of genetic homogeneity in isolated European populations.33–35 These studies have also shown that the British population is genetically less differentiated as compared with the Scandinavian populations.33 Such findings have an impact on the choice of study participants for a GWAS because undetected population substructure is known to introduce bias in GWAS.36 Furthermore, it is also possible that the genetic effect is confined to specific subpopulations of smokers, alcohol consumers, and furosemide-exposed individuals within the ASCOT-UK-IR cohort. Identified SNPs from the ASCOT-UK-IR GWAS could also be artifactual.
The power to detect the effect size of a genetic risk variant is dependent on its minor allele frequency.37 It is interesting that the minor allele frequency of SNPs rs976683 and rs9830510 in both study populations was similar (0.25 in ASCOT-UK-IR and 0.15 in ischemic stroke cohorts). However, even though the point estimates of the effect sizes observed for stroke were larger than BP variability, no comparative conclusions could be drawn from this because neither SNP was significantly associated with the increased risk of stroke. SNPs rs976683 and rs9830510 are intronic, and an in silico–regulatory SNP detection framework38 predicts that these SNPs alter transcription factor–binding sites for >150 cellular transcription factors. Because of the lack of association with any phenotypic trait at genome-wide significance, information regarding expression quantitative trait loci, tissue-specific expression, and histone marks remains scarce through conventional data mining resources. NLGN1 gene may play a role in BP variability via processes involving the growth and remodeling of the vascular system.28 The NLGN1 protein ubiquitously produced outside the central nervous system and expression of its α- and β-protein isoforms in the blood vessel walls and pancreatic β-cells39 support roles in atherosclerosis and insulin regulation, respectively, cellular processes that may play a role in stroke. The widespread impact of the misfiring NLGN1 gene is demonstrated in its association with type II granular corneal dystrophy40 and autism,41 which suggests that its effects can be mediated through varied cellular processes. To date, SNP rs976683 has only been suggestively implicated in Parkinson disease.42
Our findings implicate SNPs at the NLGN1 locus are associated with BP variability but not ischemic stroke, although a suitable replication cohort could not be found to confirm our results. To understand the true relationship between visit-to-visit BP variability and risk of stroke, large prospective longitudinal studies after healthy cohorts for stroke occurrence are required. There is a need for international guidelines for clinical monitoring of BP variability that advocate diagnosis and assessment of treatment response in hypertension to be based on the average of a series of BP measures. Calibration of measuring devices is also needed to avoid phenotypic bias.
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On the cover: The image is taken from an article in this issue, “Geometric, Hemodynamic, and Pathological Study of a Distal Internal Carotid Artery Aneurysm Model in Dogs” by Yan et al (Stroke. 2013;44:2926–2929). The image is Figure 2D1–D4.
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© 2013 American Heart Association, Inc.
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History
Received: 16 May 2013
Revision received: 28 June 2013
Accepted: 3 July 2013
Published online: 8 August 2013
Published in print: October 2013
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Anglo-Scandinavian Cardiac Outcome Trial was supported by Pfizer (NY, Servier Research Group, Paris, France) and Leo Laboratories (Copenhagen, Denmark). Australian Stroke Genetics Collaborative was funded through grants from the Hunter Medical Research Institute and the National health, Medical Research Council, University of Newcastle, and the Vincent Fairfax Family Foundation. Bio-Repository of DNA in Stroke received support from Department of Health (United Kingdom), Henry Smith Charity, British Council, United Kingdom-India Education and Research Institute (UKIERI), and the Qatar National Research Foundation (www.BrainsGenetics.com). Genetics of Early Onset Stroke was supported by the following grants: National Institutes of Health (NIH) Genes, Environment, and Health Initiative (GEI) Grant U01 HG004436; NIH grant HHSN268200782096C; GENEVA Coordinating Center grant U01 HG 004446; Office of Research and Development, Medical Research Service, Baltimore Geriatrics Research, Education, and Clinical Centre of the Department of Veterans Affairs; National Institute of Neurological Disorders and Stroke (NINDS), NIH Office of Research on Women’s Health (R01 NS45012, U01 NS069208-01); and Department of Veterans Affairs BLR&D Career Development Award (CDA-2). Ischemic Stroke Genetics Study and Siblings with Ischemic Stroke Study was supported in part by the Intramural Research Program of the National Institute on Aging (NIH project Z01 AG-000954-06 and NIH project Z01 AG-000015-50), NIH-NINDS grant R01 NS-42733 (ISGS, Dr Meschia), and NIH-NINDS grant R01 NS-39987 (SWISS, Dr Meschia). Vitamin Intervention for Stroke Prevention was funded by the NINDS/NIH (R01 NS34447) and National Human Genome Research Institute (grant U01 HG005160). Welcome Trust Case Control Consortium-Germany (WTCCC) and WTCCC-UK were funded by the Wellcome Trust as part of the WTCCC2 project (085475/B/08/Z, 085475/Z/08/Z, and WT084724MA). The other authors report no conflicts.
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