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. Author manuscript; available in PMC: 2010 Mar 1.
Published in final edited form as: Stroke. 2009 Jan 8;40(3):683–695. doi: 10.1161/STROKEAHA.108.524587

A Meta-Analysis of Candidate Gene Polymorphisms and Ischemic Stroke in Six Study Populations: Association of Lymphotoxin-alpha in Non-hypertensive Patients

Xingyu Wang 1, Suzanne Cheng 2, Victoria H Brophy 2, Henry A Erlich 2, Christine Mannhalter 3, Klaus Berger 4, Wolfgang Lalouschek 3, Warren S Browner 5, Yu Shi 1, Ernst B Ringelstein 4, Christof Kessler 6, Jan Luedemann 6, Klaus Lindpaintner 7, Lisheng Liu 1, Paul M Ridker 8, Robert YL Zee 8, Nancy R Cook, for the RMS Stroke SNP Consortium8,*
PMCID: PMC2757095  NIHMSID: NIHMS108487  PMID: 19131662

Abstract

Background and Purpose

Ischemic stroke is a multifactorial disease with a strong genetic component. Pathways including lipid metabolism, systemic chronic inflammation, coagulation, blood pressure regulation, and cellular adhesion have been implicated in stroke pathophysiology, and candidate gene polymorphisms in these pathways have been proposed as genetic risk factors.

Methods

We genotyped 105 simple deletions and single nucleotide polymorphisms from 64 candidate genes in 3550 patients and 6560 controls from six case-control association studies conducted in the United States, Europe and China. Genotyping was performed using the same immobilized probe typing system and meta-analyses were based on summary logistic regressions for each study. The primary analyses were fixed-effects meta-analyses adjusting for age and sex with additive, dominant and recessive models of inheritance.

Results

Although seven polymorphisms showed a nominal additive association, none remained statistically significant after adjustment for multiple comparisons. In contrast, after stratification for hypertension, two lymphotoxin-alpha polymorphisms which are in strong linkage disequilibrium were significantly associated among non-hypertensive individuals: for LTA 252A>G (additive model), OR=1.41 with 95% CI, 1.20 to 1.65, p=0.00002; for LTA 26Thr>Asn, OR 1.19 with 95% CI, 1.06 to 1.34, p=0.003. LTA 252A>G remained significant after adjustment for multiple testing using either the false discover rate or by permutation testing. The two SNPs showed no association in hypertensive subjects (eg, LTA 252A>G, OR=0.93; 95%CI, 0.84 to 1.03, p=0.17).

Conclusions

These observations may indicate an important role of LTA-mediated inflammatory processes in the pathogenesis of ischemic stroke.

Indexing terms: ischemic stroke, hypertension, inflammation, genetics

Ischemic stroke is a complex multi-factorial and polygenic disorder thought to reflect interactions between an individual’s genetic background and various environmental components. Previous studies have established hypertension, smoking, diabetes mellitus, body mass index and age as reliable stroke-risk predictors1,2. However, these conventional stroke risk factors do not fully account for the overall risk of stroke. Several physiological pathways, including lipid metabolism, blood pressure regulation, coagulation, and cellular adhesion are thought to play critical roles in stroke pathophysiology.

Among the known risk factors for ischemic stroke, hypertension contributes significantly to the onset of disease. Increased risk of stroke is not, however, limited to those with hypertension and the conventional stroke risk factors do not fully explain the risk among normotensives. Strategies to identify additional risk factors include stratification by hypertension3,4 and using blood pressure as a matching criterion for cases and controls5. A key role for inflammation is suggested by observations that hypertensive patients have elevated circulating levels of markers of inflammation and that some anti-hypertensive therapies reduce both levels of pro-inflammatory markers and the risk of ischemic stroke, in addition to lowering blood pressure6.

Both systemic and local inflammatory processes are implicated in the etiology of ischemic cerebrovascular disease and in the pathophysiology of cerebral ischemia7. Viral and bacterial infections are independent risk factors for ischemic stroke8 and increased levels of systemic inflammatory markers such as C-reactive protein (CRP), leukocyte count, and fibrinogen are associated with increased risk of ischemic stroke9. Moreover, many stroke-related diseases such as Alzheimer’s disease and atherosclerosis are initiated or worsened by systemic inflammation10,11. Polymorphisms in the CRP gene have been recently associated with both circulating protein levels and cardiovascular events12, demonstrating the potential impact of genetic variation. Pro-inflammatory cytokines are believed to play a pathogenic role in these diseases, and variations in cytokine genes have also been shown to influence both predisposition and penetrance by altering the transcription profile and pattern of pro-inflammatory cytokine production13. For example, polymorphism in the lymphotoxin-alpha gene can enhance transcription and susceptibility to myocardial infarction14. At the local level, migration of inflammatory cells to the vascular wall is associated with vascular changes leading to atherosclerosis, and early atherosclerotic lesions are preceded by inflammatory cell deposition in the sub-endothelial layer of major cerebral arteries and in small brain vessels15. Genetic variants influencing inflammatory processes could potentially contribute to the etiology of stroke.

The complex etiology of stroke suggests that individual genetic polymorphisms have modest effects that are difficult to detect, as has been observed to date16. Large studies are needed to assess these polymorphisms as risk factors. Here we report a six-study meta-analysis to investigate the associations of 105 simple deletions and single nucleotide polymorphisms (SNPs) in inflammatory and cardiovascular system-related genes with susceptibility to ischemic stroke. To search for genetic risk factors contributing to ischemic stroke beyond hypertension, we stratified the study cohort on hypertension status.

Materials and Methods

Study Sample Description

As part of the Roche Stroke SNP Consortium, results of six independent studies (Table 1) were pooled for this analysis. All six study samples were comprised of individuals with proven ischemic stroke status and healthy controls. All studies were approved by the local ethics committees and all participants gave informed consent. Briefly, the study subjects were recruited as follows:

Table 1.

Characteristics of the Study Subjects. Summary of population characteristics for each of the six studies used in the meta-analysis. All cases were diagnosed by CT/MRI.

PHS17,18 SOF19 Vienna23
Subjects (n) 319 cases, 2092 controls 247 cases, 559 controls 844 cases, 979 controls
Study Type prospective prospective cross-sectional
Recruitment Period 1982; follow-up through 1995 1986 – 1988; follow-up through 1998 1998 – 2001
Population Caucasians from USA Caucasians from USA Caucasians from Vienna, Austria
Cases Controls p‡ Cases Controls p‡ Cases Controls p‡
Age* (yrs) 61.1±8.3 58.8±8.5 <.0001 73.9±5.9 70.4±4.5 <.0001 66.1±14.4 48.8±13.0 <.0001
Sex (% male) 100 100 0 0 50.6 53.4 0.23
Hypertension
(% yes) 		48.6 26.8 <.0001 79.8 62.1 <.0001 72.2 34.2 <.0001
Diabetes (%yes) 10.7 2.7 <.0001 19.3 4.3 <.0001 32.8 2.5 <.0001
Smoking
(% ever) 		60.7 56.6 0.15 29.8 37.5 0.15 54.9 41.7 <.0001
BMI*,† (kg/m2) 25.6±3.3 25.0±3.0 0.002 26.9±4.6 26.8±4.9 0.83 26.7±4.6 25.3±3.9 <.0001
SBP*,† (mm Hg) 133.6±12.8 127.4±11.9 <.0001 151.9±23.1 140.4±17.5 <.0001 NA 132.3±20.7
DBP*,† (mm Hg) 82.1±7.0 79.0±7.2 <.0001 78.2±11.6 76.9±8.9 0.13 NA 82.7±11.7
Westphalia20,21 Pomerania22 SHINING5
Subjects (n) 700 cases, 757 controls 277 cases, 702 controls 1163 cases, 1471 controls
Study Type cross-sectional cross-sectional cross-sectional
Recruitment Period 2000 – 2003 1996 – 2001 1997–2000
Population Caucasians from northwestern Germany Caucasians from northeastern Germany Chinese primarily from Beijing, China
Cases Controls p‡ Cases Controls p‡ Cases§ Controls§ p‡
Age* (yrs) 61.0±16.9 58.7±9.9 0.002 62.8±13.2 62.7±11.8 0.88 59.3±10.7 61.1 ±10.7 <.0001
Sex (% male) 56.3 46.6 <.0001 55.1 50.8 0.23 60.3 60.1 0.93
Hypertension
(% yes) 		68.0 41.0 <.0001 66.8 52.9 <.0001 69.6 77.1 <.0001
Diabetes (%yes) 21 8.9 <.0001 30.1 14.5 <.0001 15.3 8.5 <.0001
Smoking
(% ever) 		NA 54.1 87.6 58.3 <.0001 39.7 32.3 <.0001
BMI*,† (kg/m2) NA NA NA NA 24.4±3.0 25.0±3.3 <.0001
SBP*,† (mm Hg) NA 145.9±21.4 NA 148.7±21.3 145.4±23.2 143.2±23.7 0.018
DBP*,† (mm Hg) NA 89.1±12.2 NA 86.1±11.1 87.0±12.8 86.1±13.0 0.087
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*

Continuous variables are given as mean ±SD.

†

BMI = Body mass index, SBP = Systolic blood pressure prior to stroke, DBP =Diastolic blood pressure prior to stroke.

‡

Using chi-square test for categorical variables, t-test for continuous variables.

§

Partially matched by age and BP group during recruitment

Physician’s Health Study (PHS)

A nested case-control sample (319 cases, 2092 controls) was derived from the PHS cohort consisting of 22,071 predominantly Caucasian U.S. male physicians initially free of prior myocardial infarction, stroke, transient ischemic attack and cancer, who were enrolled in a placebo controlled trial of aspirin and beta-carotene for the primary prevention of cardiovascular disease and cancer17. DNA was isolated from baseline blood samples provided by 14,916 (68%) of the participants. Incident cases of ischemic stroke were identified during an average 13-year follow-up, and confirmed by medical record review. Controls were selected from study participants remaining free of reported cardiovascular disease, and matched to cases of any cardiovascular disease by age, smoking, and time since study entry18.

Study of Osteoporotic Fractures (SOF)

Ambulatory women were recruited from four clinical centers in Portland, Oregon; Minneapolis, Minnesota; Baltimore, Maryland; and the Monongahela Valley, Pennsylvania19. The SOF cohort consists of 9615 white women of at least 65 years of age who had not had bilateral hip replacement or earlier hip fracture at the time of recruitment. The stroke subgroup included here consists of 247 who suffered adjudicated ischemic strokes and 559 controls who remained free of stroke through the mean follow-up of 5.4 years. Individuals who died during follow-up were included in both cases and controls, avoiding survivor bias.

Westphalia, Germany

Cases (n = 700) were recruited through the regional Westphalian Stroke Register in northwestern Germany20. Standardized patient documentation included socio-demographic characteristics, comorbidities, stroke type and severity as well as details regarding the diagnostic and therapeutic procedures and complications; 96.8% had at least one CT or MRI of the brain during hospitalization. Controls (n = 757) were recruited from the population-based Dortmund Health Study, conducted in the same region21. Participants in this study were randomly drawn from the city’s registration office within 5-year age groups and stratified by sex. Medical histories were assessed in face to face interviews.

Pomerania, Germany

Cases (n = 277) were recruited with a standardized patient assessment form; 96.5% had at least one CT or MRI during hospitalization. Controls for this region were recruited from the population-based Study of Health in Pomerania (SHIP)22. Participants in SHIP were 20- to 79-year-olds randomly sampled from registration offices in the area. Face-to-face interviews with each participant included a short stroke symptom questionnaire. A random sample of 702 SHIP participants who were free of self-reported stroke and within the same age range and sex distribution as the cases, formed the control group.

Vienna Stroke Study

In the Vienna Stroke Registry, cases (n= 844) consisted of consecutive Caucasian patients submitted to one of nine stroke units within 72 hours of symptom onset of acute ischemic stroke. Patients who died on the way to the hospital or were first admitted to an intensive care unit were not included23. All patients underwent cranial CT or MRI and were documented according to a standardized protocol including stroke severity, risk factors and medical history (with particular reference to vascular diseases). Controls (n = 979) were voluntary participants in a health care program offered by the city of Vienna, were free of clinically manifest arterial vascular disease and reported no arterial vascular diseases in first degree relatives.

Stroke Hypertension Investigation in Genetics (SHINING)

Individuals were recruited from six geographical regions within China; 70% came from in and near Beijing. Cases (n = 1163) were individuals who had suffered a stroke within the previous 5 years, as diagnosed by brain CT/MRI. The original goal was to identify SNPs that predispose to stroke independent of blood pressure, thus randomly drawn population-based controls were initially individually matched to cases by sex, birth year ±3 yrs, geographic location, and blood pressure category (<140/90, ≥140/90 and ≤180/105, >180/105). Because some cases could not be matched, additional controls were recruited for a total of 1471 controls5.

Genotyping

A total of 105 polymorphisms from 64 genes were selected based on reported associations in the literature, as well as on evidence of gene product involvement in cardiovascular disease and inflammatory processes. As previously described24,25, three separate multi-locus polymerase chain reactions (PCRs) were carried out using biotinylated primer pools (Roche Molecular Systems, Inc., CA). The resulting PCR product pools were denatured and hybridized to linear arrays of immobilized, sequence-specific oligonucleotide probes. Hybridized amplicon was detected using a streptavidin-horseradish peroxidase conjugate and a chromogenic substrate. Laboratory technicians were blinded to the case-control status of each sample. Genotype assignments were made either manually and independently by two researchers or made by capturing images with a flatbed scanner and then using proprietary software developed by Roche Molecular Systems to resolve probe signals into genotypes for all polymorphisms. Discordant or ambiguous results were resolved by repeat PCR or hybridization. Twenty polymorphisms were not available for PHS and 23 polymorphisms were genotyped in only a subset of the Vienna Stroke participants; for these, genotypes were obtained for >90.5% of the individuals typed. For each of the 62 polymorphisms genotyped for all 10,110 subjects across all six studies, the final genotype database was ≥97.5% complete. For the LTA [MIM 153440] 252A>G and LTA 26Thr>Asn polymorphisms, in particular, the database contained 6090 (not available for PHS) and 10,091 genotypes (99.8% complete), respectively.

Statistical analysis

Individual level data were provided from each study site to the Coordinating Center at the Brigham and Women’s Hospital in Boston. Pre-specified inclusion criteria for the meta-analyses were: age of at least 20 years and no prior history of myocardial infarction. Cases were restricted to those experiencing an ischemic stroke and controls had no previous history of stroke.

Allele and genotype frequencies were estimated by study site among cases and controls separately using SAS GENETICS. Tests for Hardy-Weinberg equilibrium (HWE), both large-sample and exact, were conducted among cases and controls for each site (Supplementary Table 1). Results for the effect of each SNP on ischemic stroke were estimated for each study separately using logistic regression. Each analysis controlled for age and sex, and assessed genetic effects under three modes of inheritance: additive, dominant, and recessive. In addition, analyses were conducted for each site using all three genotypes, using a two-degree of freedom test.

Meta-analyses were conducted based on the summary logistic regression results for each study site26. The primary analyses were fixed effects meta-analyses adjusting for age and sex. These meta-analyses were also conducted across Caucasians only (data not shown), since these comprised the majority of participants for five of the six studies. Effects for each of the three modes of inheritance were estimated. PROC MIXED of SAS was used for effect estimation. Tests for heterogeneity of the genetic effect across sites were conducted using the Q-statistic27. For comparison, random effects models were estimated which allowed the genetic effect to vary across sites using study-specific effect estimates and PROC MIXED of SAS. To adjust for multiple comparisons, the false discovery rate (FDR)28 was computed and stepdown permutation tests were conducted for selected comparisons29.

Other pre-specified analyses adjusted for hypertension as well as age and sex. Across all studies, hypertensives were defined as having current or past anti-hypertensive medication, systolic blood pressure ≥140 mm Hg, or diastolic blood pressure ≥90 mm Hg. Additional smoking-adjusted analyses were limited to five studies due to the limited availability of smoking data for the Westphalian study participants. Subgroup analyses were conducted according to age, sex, presence of hypertension, or smoking (ever vs. never).

Results

A total of 3550 stroke patients and 6560 controls were genotyped with inflammation and cardiovascular SNP panels in six study sites with common methodology and genotyping software. The characteristics of all participants from six study sites are listed in Table 1. Two studies were drawn from prospective cohorts; the PHS study followed only male subjects and the SOF study followed only female subjects. The SHINING study was comprised of subjects of Han ethnicity, while the five other study populations were >99% Caucasian. There was a greater proportion of hypertension among cases than in controls, except in the SHINING study subset, for which blood pressure had been matched between the majority of cases and controls.

Table 2 lists the results obtained in the primary fixed-effects meta-analysis for all polymorphic sites under dominant, additive and recessive genetic modes of inheritance; similar results were observed under a random effects meta-analysis (data not shown). Nine SNPs were nominally significant (P < 0.05) under at least one mode of inheritance: ADRB3 [MIM 109691] Trp64Arg, CETP [MIM 118470] (−629)C>A, GNB3 [MIM 139130] 825C>T, IL4 [MIM 147780] (−590)C>T, LIPC [MIM 151670] (−480)C>T, LPL [MIM 609708] Ser447Ter, NOS3 [MIM 163729](−690)C>T, PON2 [MIM 602447] Ser311Cys and TGFB1 [MIM 190180] (−509)C>T. To account for multiple hypothesis testing, the false discovery rate or permutation testing was applied and none of these SNPs remained statistically significantly associated with ischemic stroke. Among Caucasian participants only, the same GNB3, LPL, NOS3, PON2 and TGFB1 SNPs were nominally significant under at least one mode of inheritance, in addition to eight others (APOB [MIM 107730] 71Ile>Thr, APOC3 [MIM 107720] 3175C>G, CCR5 [MIM 601373] (−2459)G>A, IL6 [MIM 147620] (−174)G>C, IL10 [MIM 124092] (−571)C>A , ITGA3 [MIM 192974] 873G>A, NOS2A [MIM 163730] 231C>T, TNF [MIM 191160] (−376)G>A), but none of the SNPs remained statistically significant after the false discovery rate was applied (data not shown).

Table 2.

Unstratified meta-analysis for risk of ischemic stroke: all SNPs under three modes of inheritance.

Additive Dominant Recessive
Gene [MIM number] SNP rs number SNP Name n* Sites† OR (LCL,UCL)‡ p‡ Het p§ OR (LCL,UCL)‡ p‡ Het p§ Sites∥ OR (LCL,UCL)‡ p‡ Het p§
Adducin 1 (alpha) [102680] rs4961 ADD1 460Gly>Trp 9936 6 1.02 (0.95,1.10) 0.638 0.960 1.04 (0.95,1.15) 0.403 0.876 6 0.98 (0.84,1.14) 0.762 0.692
Adrenergic, beta-2-, receptor, surface [109690] rs1042713 ADRB2 16Gly>Arg 10082 6 0.94 (0.89,1.01) 0.080 0.271 0.95 (0.86,1.05) 0.303 0.437 6 0.90 (0.80,1.01) 0.064 0.502
Adrenergic, beta-2-, receptor, surface [109690] rs1042714 ADRB2 27Gln>Glu 10087 6 1.04 (0.97,1.11) 0.323 0.121 1.04 (0.94,1.15) 0.461 0.177 6 1.06 (0.93,1.22) 0.381 0.222
Adrenergic, beta-2-, receptor, surface [109690] rs1800888 ADRB2 164Thr>Ile 8535 6 1.33 (0.88,2.02) 0.175 0.308 1.29 (0.85,1.98) 0.236 0.324 Too few variant homozygotes observed
Adrenergic, beta-3-, receptor [109691] rs4994 ADRB3 64Trp>Arg 10093 6 0.91 (0.82,1.02) 0.090 0.679 0.89 (0.79,1.00) 0.046 0.803 6 1.13 (0.75,1.69) 0.570 0.265
Angiotensin II receptor type 1 [106165] rs5186 AGTR1 1166A>C 9942 6 1.04 (0.96,1.13) 0.346 0.068 1.03 (0.93,1.14) 0.546 0.015 6 1.11 (0.92,1.35) 0.284 0.734
Angiotensinogen [106150] rs699 AGT 235Met>Thr 9939 6 1.00 (0.94,1.07) 0.973 0.371 0.97 (0.87,1.08) 0.586 0.279 6 1.03 (0.93,1.15) 0.581 0.344
Angiotensin-converting enzyme [106180] rs1799752 ACE IVS16 Del>Ins 9862 6 0.99 (0.93,1.06) 0.841 0.069 1.02 (0.91,1.13) 0.758 0.473 6 0.97 (0.88,1.07) 0.556 0.029
Apolipoprotein A-IV [107690] rs675 APOA4 347Thr>Ser 10091 6 0.94 (0.85,1.03) 0.193 0.672 0.96 (0.86,1.07) 0.446 0.551 5 0.76 (0.57,1.03) 0.074 0.512
Apolipoprotein A-IV [107690] rs5110 APOA4 360Gln>His 10088 6 0.95 (0.82,1.11) 0.545 0.297 0.97 (0.83,1.14) 0.730 0.288 5 0.81 (0.32,2.03) 0.653 0.999
Apolipoprotein B [107730] rs1367117 APOB 71Thr>Ile 10091 6 1.04 (0.97,1.12) 0.259 0.807 1.02 (0.93,1.12) 0.675 0.416 6 1.18 (1.00,1.41) 0.056 0.849
Apolipoprotein B [107730] rs5742904 APOB 3500Arg>Gln 10093 3 6.11 (0.45,82.6) 0.173 0.999 6.11 (0.45,82.6) 0.173 0.999 No variant homozygotes observed
Apolipoprotein C-III [107720] rs2542052 APOC3 (−641)C>A 10064 6 1.03 (0.97,1.10) 0.303 0.864 1.04 (0.95,1.15) 0.377 0.595 6 1.05 (0.93,1.18) 0.434 0.834
Apolipoprotein C-III [107720] rs2854117 APOC3 (−482)C>T 10089 6 1.06 (0.99,1.13) 0.096 0.266 1.06 (0.97,1.16) 0.226 0.137 6 1.12 (0.97,1.28) 0.114 0.728
Apolipoprotein C-III [107720] rs2854116 APOC3 (−455)T>C 10087 6 1.04 (0.97,1.11) 0.244 0.775 1.04 (0.95,1.15) 0.372 0.544 6 1.06 (0.94,1.20) 0.311 0.871
Apolipoprotein C-III [107720] rs4520 APOC3 1100C>T 10089 6 1.02 (0.96,1.09) 0.517 0.313 1.03 (0.93,1.13) 0.609 0.244 6 1.04 (0.92,1.18) 0.562 0.111
Apolipoprotein C-III [107720] rs5128 APOC3 3175C>G 10091 6 1.01 (0.93,1.10) 0.776 0.124 1.05 (0.95,1.16) 0.351 0.153 6 0.84 (0.66,1.08) 0.173 0.389
Apolipoprotein C-III [107720] rs4225 APOC3 3206T>G 10073 6 1.04 (0.97,1.11) 0.274 0.372 1.09 (0.97,1.21) 0.137 0.606 6 1.02 (0.91,1.13) 0.780 0.405
Apolipoprotein E [107741] rs429358 APOE 112Cys>Arg 10050 6 1.04 (0.94,1.14) 0.447 0.341 1.03 (0.92,1.14) 0.624 0.469 6 1.25 (0.88,1.78) 0.206 0.223
Apolipoprotein E [107741] rs7412 APOE 158Arg>Cys 10055 6 0.96 (0.85,1.07) 0.424 0.171 0.97 (0.86,1.09) 0.570 0.221 6 0.71 (0.41,1.23) 0.223 0.531
CD14 molecule [158120] rs2569190 CD14 (−260)C>T 8536 6 1.03 (0.96,1.11) 0.397 0.474 1.02 (0.90,1.14) 0.801 0.274 6 1.06 (0.95,1.19) 0.285 0.952
Chemokine (C-C motif) ligand 11 [601156] rs4795895 CCL11 (−1328)G>A 6123 5 0.96 (0.85,1.08) 0.476 0.202 0.97 (0.84,1.11) 0.602 0.083 5 0.88 (0.58,1.33) 0.536 0.707
Chemokine (C-C motif) ligand 11 [601156] rs3744508 CCL11 23Ala>Thr 8536 6 1.08 (0.99,1.19) 0.099 0.856 1.09 (0.98,1.21) 0.113 0.985 6 1.16 (0.86,1.57) 0.341 0.224
Chemokine (C-X-C motif) ligand 12 (stromal cell-derived factor 1) [600835] rs1801157 CXCL12 (+800)G>A 6032 5 0.97 (0.88,1.06) 0.454 0.599 0.96 (0.86,1.07) 0.446 0.748 5 0.99 (0.76,1.27) 0.911 0.079
Chemokine receptor 2 [601267] rs1799864 CCR2 62Val>Ile 8532 6 1.08 (0.98,1.19) 0.118 0.661 1.07 (0.96,1.20) 0.241 0.670 6 1.28 (0.96,1.69) 0.090 0.717
Chemokine receptor 3 [601268] rs5742906 CCR3 39Pro>Leu 8542 5 0.86 (0.37,2.02) 0.730 0.565 0.86 (0.37,2.02) 0.730 0.565 No variant homozygotes observed
Chemokine receptor 5 [601373] rs1799987 CCR5 (−2459)A>G 8541 6 1.01 (0.94,1.08) 0.850 0.040 1.00 (0.89,1.12) 0.960 0.045 6 1.02 (0.91,1.15) 0.701 0.095
Chemokine receptor 5 [601373] rs333 CCR5 580Ins>Del32 8523 5 0.94 (0.81,1.08) 0.372 0.264 0.94 (0.81,1.10) 0.461 0.174 5 0.82 (0.44,1.51) 0.517 0.706
Cholesteryl ester transfer protein, plasma [118470] rs1800776 CETP (−631)C>A 10088 6 1.00 (0.87,1.15) 0.977 0.616 1.02 (0.88,1.19) 0.792 0.718 5 0.92 (0.46,1.83) 0.800 0.565
Cholesteryl ester transfer protein, plasma [118470] rs1800775 CETP (−629)C>A 10076 6 0.95 (0.90,1.01) 0.129 0.203 0.90 (0.81,1.00) 0.046 0.595 6 0.98 (0.88,1.08) 0.653 0.134
Cholesteryl ester transfer protein, plasma [118470] rs5882 CETP 405Ile>Val 10086 6 0.99 (0.92,1.05) 0.661 0.748 0.99 (0.91,1.09) 0.895 0.635 6 0.96 (0.84,1.09) 0.498 0.791
Cholesteryl ester transfer protein, plasma [118470] rs2303790 CETP 442Asp>Gly 10089 5 1.28 (0.87,1.89) 0.207 1.000 1.41 (0.93,2.13) 0.108 1.000 Too few variant homozygotes observed
Coagulation factor II (thrombin) [176930] rs1799963 F2 20210G>A 9902 5 1.01 (0.74,1.39) 0.941 0.108 1.02 (0.74,1.40) 0.928 0.099 Too few variant homozygotes observed
Coagulation factor V (proaccelerin, labile factor) [227400] rs6025 F5 506Arg>Gln 9941 6 0.96 (0.77,1.20) 0.712 0.867 0.96 (0.76,1.20) 0.698 0.848 Too few variant homozygotes observed
Coagulation factor VII (serum prothrombin conversion accelerator) [227500] rs5742910 F7 (−323) Del>Ins10 9941 6 0.99 (0.89,1.11) 0.909 0.057 1.00 (0.89,1.12) 0.984 0.072 6 1.05 (0.67,1.64) 0.832 0.908
Coagulation factor VII (serum prothrombin conversion accelerator) [227500] rs6046 F7 353Arg>Gln 9938 6 0.96 (0.86,1.08) 0.510 0.193 0.97 (0.86,1.10) 0.638 0.163 6 0.83 (0.53,1.31) 0.427 0.736
Complement component 3 [120700] rs2230199 C3 102Arg>Gly 6083 5 0.99 (0.87,1.13) 0.891 0.488 0.96 (0.83,1.12) 0.609 0.429 5 1.18 (0.80,1.76) 0.408 0.798
Complement component 5 [120900] rs17611 C5 802Val>Ile 6117 5 1.02 (0.95,1.10) 0.604 0.087 1.07 (0.94,1.21) 0.328 0.162 5 0.99 (0.88,1.12) 0.897 0.328
Colony stimulating factor 2 (granulocyte-macrophage) [138960] rs25882 CSF2 117Ile>Thr 6127 5 1.00 (0.92,1.09) 0.961 0.610 1.03 (0.91,1.17) 0.601 0.285 5 0.96 (0.83,1.12) 0.626 0.535
Cytotoxic T-lymphocyte-associated protein 4 [123890] rs5742909 CTLA4 (−318)C>T 6119 5 0.91 (0.80,1.02) 0.100 0.688 0.91 (0.79,1.03) 0.135 0.544 5 0.81 (0.51,1.30) 0.387 0.197
Cytotoxic T-lymphocyte-associated protein 4 [123890] rs231775 CTLA4 17Thr>Ala 6128 5 1.02 (0.94,1.11) 0.580 0.139 0.99 (0.87,1.13) 0.862 0.154 5 1.07 (0.94,1.21) 0.301 0.251
Fibrinogen beta [134830] rs1800790 FGB (−455)G>A 9933 6 1.01 (0.93,1.09) 0.905 0.877 1.01 (0.92,1.11) 0.866 0.646 6 1.01 (0.81,1.25) 0.963 0.503
Group-specific component (vitamin D binding protein) [139200] rs7041 GC 416Glu>Asp 6121 5 1.02 (0.94,1.10) 0.719 0.224 0.97 (0.85,1.12) 0.718 0.306 5 1.05 (0.93,1.19) 0.395 0.336
Group-specific component (vitamin D binding protein) [139200] rs4588 GC 420Thr>Lys 6122 5 1.02 (0.94,1.11) 0.598 0.436 1.01 (0.89,1.14) 0.928 0.382 5 1.06 (0.92,1.21) 0.437 0.443
Guanine nucleotide binding protein (G protein), beta polypeptide 3 [139130] rs5443 GNB3 825C>T 9941 6 1.08 (1.01,1.15) 0.031 0.759 1.06 (0.96,1.16) 0.243 0.864 6 1.19 (1.05,1.36) 0.009 0.134
Integrin, alpha 2 (CD49B, alpha 2 subunit of VLA-2 receptor) [192974] rs1062535 ITGA2 873G>A 9941 6 1.04 (0.97,1.11) 0.268 0.157 1.04 (0.95,1.14) 0.367 0.327 6 1.06 (0.93,1.21) 0.356 0.163
Integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61) [173470] rs5918 ITGB3 33Leu>Pro 9937 6 0.98 (0.88,1.08) 0.648 0.349 0.97 (0.87,1.10) 0.662 0.423 5 1.00 (0.70,1.44) 0.993 0.375
Intercellular adhesion molecule 1 (CD54), human rhinovirus receptor [147840] rs5491 ICAM1 56Lys>Met 8531 6 1.04 (0.84,1.29) 0.717 0.928 1.05 (0.84,1.31) 0.690 0.940 3 0.98 (0.26,3.70) 0.980 0.907
Intercellular adhesion molecule 1 (CD54), human rhinovirus receptor [147840] rs1799969 ICAM1 241Gly>Arg 10088 6 1.09 (0.97,1.23) 0.139 0.466 1.09 (0.95,1.24) 0.208 0.398 6 1.31 (0.86,1.99) 0.203 0.916
Interleukin 1, alpha [147760] rs1800587 IL1A (−889)C>T 8508 6 0.93 (0.85,1.02) 0.110 0.778 0.91 (0.82,1.01) 0.083 0.834 6 0.95 (0.76,1.19) 0.659 0.686
Interleukin 1, beta [147720] rs16944 IL1B (−1418)C>T 6094 5 1.05 (0.97,1.13) 0.240 0.506 1.06 (0.95,1.19) 0.288 0.473 5 1.06 (0.92,1.23) 0.407 0.541
Interleukin 1, beta [147720] rs1143634 IL1B 4336C>T 8534 6 0.95 (0.86,1.05) 0.331 0.415 0.92 (0.82,1.04) 0.188 0.286 6 1.07 (0.81,1.41) 0.642 0.595
Interleukin 4 [147780] rs2243250 IL4 (−590)C>T 8541 6 1.05 (0.97,1.15) 0.237 0.015 1.19 (1.05,1.35) 0.007 0.101 6 0.92 (0.8,1.07) 0.275 0.081
Interleukin 4 receptor [147781] rs1805010 IL4R 75Ile>Val 8538 6 1.00 (0.93,1.07) 0.987 0.910 1.02 (0.91,1.14) 0.722 0.790 6 0.98 (0.87,1.10) 0.720 0.915
Interleukin 4 receptor [147781] rs1805015 IL4R 503Ser>Pro 6130 5 1.00 (0.89,1.12) 0.945 0.286 1.00 (0.88,1.14) 0.979 0.536 5 1.00 (0.63,1.59) 0.997 0.168
Interleukin 4 receptor [147781] rs1801275 IL4R 576Gln>Arg 8525 6 1.04 (0.95,1.13) 0.437 0.058 1.06 (0.96,1.18) 0.247 0.088 6 0.92 (0.70,1.21) 0.546 0.273
Interleukin 5 receptor alpha [147851] rs2290608 IL5RA (−80)G>A 8528 6 1.02 (0.94,1.11) 0.608 0.182 1.02 (0.92,1.12) 0.754 0.275 6 1.08 (0.87,1.34) 0.482 0.114
Interleukin 6 [147620] rs1800796 IL6 (−572)G>C 6124 5 0.92 (0.82,1.02) 0.103 0.061 0.84 (0.70,1.00) 0.052 0.113 4 0.95 (0.81,1.10) 0.480 1.000
Interleukin 6 [147620] rs1800795 IL6 (−174)G>C 8543 6 1.08 (0.99,1.18) 0.095 0.687 1.08 (0.95,1.23) 0.232 0.248 5 1.14 (0.97,1.34) 0.121 0.718
Interleukin 9 [146931] rs2069885 IL9 113Thr>Met 8544 6 1.04 (0.92,1.18) 0.559 0.543 1.10 (0.95,1.26) 0.209 0.772 5 0.73 (0.44,1.19) 0.205 0.131
Interleukin 10 [124092] rs1800872 IL10 (−571)C>A 8538 6 0.96 (0.89,1.04) 0.307 0.012 0.96 (0.86,1.07) 0.463 0.028 6 0.94 (0.82,1.08) 0.375 0.190
Interleukin 13 [147683] rs1295686 IL13 4045C>T 5366 4 1.01 (0.92,1.11) 0.894 0.751 0.99 (0.89,1.12) 0.917 0.854 4 1.07 (0.85,1.35) 0.561 0.380
Leukotriene C4 synthase [246530] rs730012 LTC4S (−444)A>C 6044 5 0.99 (0.90,1.09) 0.797 0.241 1.01 (0.91,1.13) 0.874 0.193 5 0.88 (0.69,1.13) 0.308 0.940
Lipase, hepatic [151670] rs1800588 LIPC (−480)C>T 10092 6 0.93 (0.87,1.00) 0.048 0.468 0.94 (0.86,1.03) 0.182 0.636 6 0.85 (0.71,1.00) 0.051 0.091
Lipoprotein, Lp(a) [152200] rs1853021 LPA 93C>T 10082 6 0.96 (0.88,1.04) 0.309 0.524 0.97 (0.88,1.06) 0.475 0.588 6 0.89 (0.66,1.19) 0.424 0.155
Lipoprotein, Lp(a) [152200] rs1800769 LPA 121G>A 10080 6 0.95 (0.88,1.03) 0.206 0.404 0.94 (0.85,1.03) 0.194 0.647 6 0.97 (0.81,1.15) 0.680 0.127
Lipoprotein lipase [609708] rs1800590 LPL (−93)T>G 10091 6 1.12 (0.84,1.49) 0.433 0.460 1.13 (0.83,1.54) 0.444 0.450 3 1.64 (0.42,6.36) 0.473 0.861
Lipoprotein lipase [609708] rs1801177 LPL 9Asp>Asn 10091 6 1.15 (0.80,1.65) 0.464 0.610 1.15 (0.80,1.66) 0.443 0.611 Too few variant homozygotes observed
Lipoprotein lipase [609708] rs268 LPL 291Asn>Ser 10090 6 1.24 (0.94,1.64) 0.133 0.887 1.29 (0.97,1.72) 0.080 0.925 Too few variant homozygotes observed
Lipoprotein lipase [609708] rs328 LPL 447Ser>Term 10088 6 0.89 (0.80,0.99) 0.033 0.455 0.88 (0.79,0.99) 0.033 0.542 6 0.89 (0.57,1.40) 0.623 0.244
Low density lipoprotein receptor [606945] rs5742911 LDLR NcoI +/− 10086 6 1.01 (0.94,1.08) 0.882 0.869 0.99 (0.91,1.09) 0.860 0.862 6 1.05 (0.91,1.21) 0.540 0.472
Lymphotoxin alpha; Tumor necrosis factor beta [153440] rs909253 LTA 252A>G 6090 5 1.02 (0.94,1.10) 0.682 0.404 1.07 (0.96,1.20) 0.226 0.561 5 0.93 (0.80,1.09) 0.381 0.294
Lymphotoxin alpha; Tumor necrosis factor beta [153440] rs1041981 LTA 26Thr>Asn 10091 6 1.01 (0.95,1.08) 0.742 0.387 1.02 (0.93,1.11) 0.715 0.274 6 1.01 (0.88,1.16) 0.872 0.344
Membrane-spanning 4-domains, subfamily A, member 2 (Fc fragment of IgE, high affinity I, receptor for; beta polypeptide) [147138] rs569108 MS4A2 237Glu>Gly 8517 6 1.08 (0.95,1.23) 0.256 0.479 1.10 (0.95,1.27) 0.220 0.480 3 1.03 (0.65,1.64) 0.891 0.999
5, 10-methylenetetrahydrofolate reductase [607093] rs1801133 MTHFR 677C>T 9943 6 1.03 (0.96,1.10) 0.383 0.389 1.05 (0.95,1.15) 0.360 0.514 6 1.03 (0.91,1.16) 0.629 0.165
Natriuretic peptide precursor A [108780] rs5063 NPPA 664G>A 9940 6 0.96 (0.83,1.11) 0.555 0.016 0.94 (0.81,1.10) 0.448 0.022 5 1.54 (0.73,3.26) 0.257 0.689
Natriuretic peptide precursor A [108780] rs5065 NPPA 2238T>C 9939 6 1.03 (0.92,1.15) 0.612 0.781 1.05 (0.93,1.18) 0.452 0.924 6 0.89 (0.59,1.33) 0.560 0.453
Nitric oxide synthase 2A (inducible, hepatocytes) asp346asp [163730] rs1137933 NOS2A 231C>T 6131 5 0.91 (0.82,1.01) 0.062 0.659 0.91 (0.81,1.02) 0.111 0.439 5 0.81 (0.60,1.09) 0.166 0.516
Nitric oxide synthase 3 (endothelial cell) [163729] rs1800779 NOS3 (−922)A>G 10093 6 1.02 (0.95,1.10) 0.575 0.676 1.03 (0.93,1.14) 0.562 0.358 6 1.03 (0.88,1.20) 0.714 0.129
Nitric oxide synthase 3 (endothelial cell) [163729] rs3918226 NOS3 (−690)C>T 9936 6 1.15 (1.00,1.32) 0.047 0.479 1.16 (1.00,1.34) 0.052 0.562 5 1.30 (0.70,2.41) 0.408 0.726
Nitric oxide synthase 3 [163729] rs1799983 NOS3 298Glu>Asp 10094 6 1.05 (0.97,1.13) 0.224 0.176 1.05 (0.95,1.15) 0.327 0.145 6 1.10 (0.92,1.31) 0.293 0.569
Paraoxonase 1 [168820] rs854560 PON1 55Leu>Met 10090 6 0.97 (0.91,1.05) 0.467 0.510 0.97 (0.88,1.08) 0.584 0.495 6 0.95 (0.81,1.12) 0.554 0.157
Paraoxonase 1 [168820] rs662 PON1 192Gln>Arg 10088 6 1.05 (0.98,1.13) 0.155 0.774 1.07 (0.97,1.18) 0.167 0.463 6 1.05 (0.93,1.20) 0.412 0.268
Paraoxonase 2 [602447] rs6954345 PON2 311Ser>Cys 10092 6 1.09 (1.01,1.18) 0.025 0.793 1.09 (1.00,1.20) 0.060 0.628 6 1.22 (0.99,1.49) 0.063 0.460
Peroxisome proliferator activated-receptor gamma [601487] rs1801282 PPARG 12Pro>Ala 10093 6 1.05 (0.95,1.16) 0.362 0.702 1.07 (0.96,1.20) 0.220 0.425 6 0.88 (0.59,1.32) 0.532 0.490
Secretoglobin, family 1A, member 1 (uteroglobin) [192020] rs3741240 SCGB1A1 (+38)G>A 8538 6 0.99 (0.92,1.06) 0.797 0.103 0.98 (0.89,1.09) 0.701 0.431 6 1.01 (0.88,1.16) 0.884 0.040
Selectin E (endothelial adhesion molecule 1) [131210] rs5361 SELE 128Ser>Arg 10094 6 1.00 (0.89,1.13) 0.965 0.666 1.01 (0.89,1.15) 0.903 0.642 6 1.05 (0.61,1.81) 0.850 0.673
Selectin E (endothelial adhesion molecule 1) [131210] rs5355 SELE 554Leu>Phe 9904 6 0.91 (0.76,1.08) 0.266 0.004 0.92 (0.77,1.10) 0.362 0.005 5 0.37 (0.07,1.88) 0.228 0.998
Selectin P (granule membrane protein 140kDa, antigen CD62) [173610] rs6131 SELP 330Ser>Asn 8539 6 1.04 (0.95,1.13) 0.424 0.296 1.03 (0.93,1.14) 0.565 0.483 6 1.12 (0.88,1.43) 0.365 0.506
Selectin P (granule membrane protein 140kDa, antigen CD62) [173610] rs6133 SELP 640Val>Leu 8537 6 1.09 (0.95,1.25) 0.229 0.037 1.10 (0.94,1.28) 0.226 0.028 5 1.22 (0.71,2.12) 0.471 0.876
Serine (or cysteine) proteinase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 1 [173360] rs1799768 SERPINE1 (−675)Del>InsG 9931 6 0.99 (0.93,1.06) 0.769 0.541 1.04 (0.95,1.15) 0.387 0.525 6 0.92 (0.82,1.03) 0.126 0.669
Serine (or cysteine) proteinase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 1 [173360] rs7242 SERPINE1 11053T>G 9940 6 0.99 (0.93,1.05) 0.699 0.266 1.02 (0.92,1.12) 0.766 0.257 6 0.95 (0.84,1.06) 0.324 0.413
Sodium channel, nonvoltage-gated 1 alpha [600228] rs5742912 SCNN1A 493Trp>Arg 9940 6 0.99 (0.76,1.27) 0.907 0.004 0.97 (0.75,1.26) 0.817 0.002 4 1.73 (0.10,28.9) 0.705 1.000
Sodium channel, nonvoltage-gated 1 alpha [600228] rs2228576 SCNN1A 663Ala>Thr 9932 6 1.00 (0.93,1.07) 0.958 0.958 1.00 (0.91,1.10) 1.000 0.921 6 1.00 (0.87,1.14) 0.939 0.275
Matrix metalloproteinase 3 (stromelysin 1, progelatinase) [185250] rs3025058 MMP3 (−1171) Ins>DelA 9932 6 0.97 (0.91,1.04) 0.384 0.074 0.95 (0.85,1.05) 0.279 0.126 6 0.98 (0.87,1.11) 0.801 0.273
Transcription factor 7 (T-cell specific, HMG-box) [189908] rs244656 TCF7 (−1459)A>T 6118 5 0.96 (0.86,1.07) 0.438 0.033 0.97 (0.86,1.10) 0.674 0.037 5 0.75 (0.50,1.14) 0.174 0.610
Transcription factor 7 (T-cell specific, HMG-box) [189908] rs5742913 TCF7 19Pro>Thr 6117 5 1.05 (0.88,1.25) 0.614 0.404 1.04 (0.86,1.25) 0.724 0.498 4 1.40 (0.65,3.02) 0.388 0.570
Transforming growth factor, beta 1 [190180] rs1800469 TGFB1 (−509)C>T 8486 6 0.92 (0.86,0.99) 0.028 0.750 0.89 (0.8,0.98) 0.023 0.148 6 0.92 (0.81,1.06) 0.252 0.658
Tumor necrosis factor (TNF superfamily, member 2) [191160] rs1800750 TNF (−376)G>A 9941 6 0.72 (0.50,1.04) 0.077 0.371 0.72 (0.5,1.04) 0.081 0.363 Too few variant homozygotes observed
Tumor necrosis factor (TNF superfamily, member 2) [191160] rs1800629 TNF (−308)G>A 10096 6 1.04 (0.95,1.15) 0.395 0.253 1.04 (0.93,1.16) 0.479 0.326 6 1.14 (0.83,1.59) 0.420 0.506
Tumor necrosis factor (TNF superfamily, member 2) [191160] rs673 TNF (−244)G>A 9938 5 1.51 (0.38,6.01) 0.562 0.944 1.51 (0.38,6.01) 0.562 0.944 Too few variant homozygotes observed
Tumor necrosis factor (TNF superfamily, member 2) [191160] rs361525 TNF (−238)G>A 10050 6 1.02 (0.88,1.18) 0.815 0.231 1.02 (0.87,1.19) 0.797 0.280 6 1.04 (0.38,2.82) 0.946 0.694
Vascular cell adhesion molecule 1 [192225] rs1041163 VCAM1 (−1594)T>C 8525 6 0.98 (0.89,1.07) 0.595 0.891 0.99 (0.89,1.10) 0.805 0.871 6 0.86 (0.64,1.16) 0.325 0.967
Vitamin D (1,25- dihydroxyvitamin D3) receptor [601769] rs2228570 VDR 1Thr>Met 6104 5 1.07 (0.99,1.16) 0.078 0.424 1.12 (1.00,1.25) 0.057 0.867 5 1.07 (0.93,1.23) 0.367 0.237
Vitamin D (1,25- dihydroxyvitamin D3) receptor [601769] rs1544410 VDR 45082 G>A 6120 5 1.01 (0.92,1.12) 0.798 0.504 1.00 (0.88,1.14) 0.974 0.173 5 1.06 (0.87,1.31) 0.558 0.508
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*

Total number of genotypes available across all six study populations.

†

Number of studies observing sufficient polymorphism at SNP site for analysis under additive and dominant modes.

‡

OR = Odds Ratio, LCL = Lower confidence limit, UCL = Upper confidence limit, p= corresponding p-value

§

P-value for heterogeneity of the genetic effect across all studies.

∥

Number of studies observing sufficient polymorphism at SNP site for analysis under recessive mode.

The data were then stratified on age, sex, hypertension or smoking status. No statistically significant associations were observed in the age- (Supplementary Table 2A) or sex-stratified (Supplementary Table 2B) analyses, nor among those with current or past hypertension (Table 3) after adjusting for the FDR. In contrast, a large number of nominally significant associations with ischemic stroke among normotensives were observed (Table 4). The strongest associations under the additive and dominant models were for LTA 252A>G and LTA 26Thr>Asn, two SNPs in strong linkage disequilibrium, while NOS3 298Glu>Asp had the strongest association under the recessive model. After adjusting for FDR and permutation testing, only the LTA 252A>G SNP showed significant association among those without hypertension. In the additive mode, the estimated relative risk across the three LTA 252 genotypes was 1.41 (p=0.00002) in the fixed effects analysis and the FDR was 0.002, with p<0.01 in permutation testing. Results for the dominant model were similar (OR = 1.57, FDR = 0.005). In the random effects meta-analysis (data not shown), the LTA 252A>G association with stroke under the dominant model (OR = 1.56) had an FDR of 0.02. Among Caucasians only, LTA 252A>G was similarly associated with ischemic stroke among those without hypertension under additive and dominant models (OR = 1.28, p=0.016 and OR = 1.39, p=0.019, respectively). Minor allele frequencies among non-hypertensive controls are given in Table 4; frequencies among non-hypertensive cases were 0.37, 0.38, 0.31, 0.41, and 0.46 in SOF, Vienna, Westphalia, Pomerania, and SHINING, respectively.

Table 3.

Nominally significant (P<0.05) fixed-effects meta-analysis results among those with current or past hypertension

SNP Mode* # Studies OR† Lower CL† Upper CL† p FDR‡
LPA 121G>A ADD 6 0.886 0.801 0.979 0.017 0.915
TGFB1 (−509)C>T ADD 6 0.894 0.812 0.983 0.021 0.915
CTLA4 (−318)C>T ADD 5 0.851 0.730 0.993 0.040 0.915
TGFB1 (−509)C>T DOM 6 0.845 0.737 0.969 0.016 0.968
LPA 121G>A DOM 6 0.872 0.768 0.992 0.037 0.968
APOC3 3175C>G REC 6 0.708 0.514 0.975 0.034 0.824
SERPINE1 (−675)Del>InsG REC 6 0.851 0.731 0.991 0.038 0.824
LTA 252A>G REC 5 0.806 0.657 0.989 0.039 0.824
ITGA2 873G>A REC 6 1.202 1.003 1.442 0.047 0.824
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*

ADD = additive, DOM = dominant, REC = recessive mode of inheritance

†

Odds ratio (OR) and confidence limits (CL)

‡

False discovery rate

Table 4.

SNPs achieving P<0.05 for association with ischemic stroke among normotensives (1068 cases, 3390 controls) under three modes of inheritance.

PHS (163 cs, 1522 ctrl)* SOF (50 cs, 212 ctrl)* Vienna (232 cs, 632 ctrl) Westphalia (224 cs, 444 ctrl) Pomerania (92 cs, 330 ctrl) SHINING (307 cs, 250 ctrl) Meta*
Mode SNP OR† P MAF‡ OR† P MAF‡ OR† P MAF‡ OR† P MAF‡ OR† P MAF‡ OR† P MAF‡ OR† P FDR† Het§
LTA 252A>G not genotyped 0.97 0.913 0.33 1.30 0.182 0.32 1.11 0.604 0.32 1.77 0.003 0.29 1.62 <0.001 0.36 1.41 <0.0001 0.002 *
LTA 26Thr>Asn 1.07 0.603 0.32 0.99 0.970 0.33 1.10 0.475 0.32 0.96 0.771 0.33 1.63 0.008 0.29 1.60 <0.001 0.35 1.19 0.003 0.161 0.03
ACE Del>Ins 0.83 0.107 0.45 0.86 0.508 0.49 0.93 0.518 0.47 0.69 0.003 0.55 0.98 0.884 0.50 0.97 0.826 0.65 0.86 0.007 0.244 *
CETP (−631)C>A 0.53 0.036 0.07 0.93 0.871 0.09 0.99 0.976 0.09 0.45 0.003 0.08 0.78 0.47 0.08 not polymorphic 0.00 0.71 0.009 0.244 *
SELP 640Val>Leu 1.73 <0.001 0.11 1.20 0.618 0.12 0.91 0.759 0.11 1.02 0.947 0.11 0.92 0.777 0.11 1.52 0.637 0.004 1.31 0.013 0.255 *
Additive TCF7 19 Pro>Thr not genotyped 0.90 0.788 0.11 1.94 0.024 0.07 1.03 0.922 0.10 1.91 0.023 0.08 not polymorphic 0.00 1.45 0.017 0.255 *
ADD 460Gly>Trp 1.05 0.758 0.19 1.70 0.063 0.17 0.96 0.797 0.18 1.22 0.203 0.19 1.56 0.022 0.20 1.16 0.241 0.51 1.17 0.017 0.255 *
IL10 (−571)C>A 0.65 0.006 0.24 0.53 0.034 0.26 1.15 0.508 0.23 0.78 0.249 0.26 1.02 0.915 0.22 0.93 0.548 0.63 0.84 0.020 0.255 *
AGT 235Met>Thr 0.69 0.002 0.43 1.03 0.917 0.40 1.13 0.309 0.46 0.91 0.436 0.45 0.64 0.011 0.43 0.94 0.670 0.81 0.87 0.021 0.255 0.03
PON2 311Ser>Cys 1.07 0.610 0.23 0.90 0.706 0.24 1.27 0.096 0.23 1.21 0.195 0.23 1.11 0.608 0.24 1.15 0.366 0.19 1.15 0.038 0.389 *
IL6 (−572)G>C not genotyped 1.07 0.894 0.05 0.74 0.485 0.06 0.54 0.189 0.06 1.30 0.499 0.05 0.76 0.039 0.72 0.79 0.041 0.389 *
APOB 71Thr>Ile 1.23 0.091 0.29 0.84 0.493 0.32 1.25 0.089 0.28 1.32 0.036 0.28 0.93 0.682 0.33 0.84 0.333 0.14 1.13 0.046 0.389 *
LTA 252A>G not genotyped 1.24 0.542 0.33 1.53 0.116 0.32 1.09 0.746 0.32 1.77 0.025 0.29 1.94 <0.001 0.36 1.57 <0.0001 0.005 *
LTA 26Thr>Asn 1.14 0.437 0.32 1.29 0.461 0.33 1.13 0.462 0.32 0.88 0.454 0.33 1.63 0.045 0.29 1.87 0.001 0.35 1.24 0.007 0.253 0.06
ADD1 460Gly>Trp 1.14 0.443 0.19 1.50 0.235 0.17 0.97 0.860 0.18 1.40 0.066 0.19 1.57 0.059 0.20 1.35 0.161 0.51 1.25 0.008 0.253 *
SELP 640Val>Leu 1.94 <0.001 0.11 1.10 0.815 0.12 0.98 0.936 0.11 1.01 0.983 0.11 0.96 0.896 0.11 1.52 0.637 0.004 1.36 0.011 0.253 *
CETP (−631)C>A 0.54 0.045 0.07 0.99 0.982 0.09 1.01 0.954 0.09 0.38 0.001 0.08 0.79 0.494 0.08 not polymorphic 0.00 0.71 0.011 0.253 0.08
Dominant IL4 (−590)C>T 1.45 0.031 0.17 1.16 0.682 0.14 0.86 0.570 0.18 1.00 0.993 0.15 1.35 0.245 0.15 4.44 0.002 0.80 1.30 0.015 0.275 0.07
TCF7 19 Pro>Thr not genotyped 0.96 0.920 0.11 2.14 0.017 0.07 0.96 0.888 0.10 1.84 0.038 0.08 not polymorphic 0.00 1.46 0.022 0.353 *
IL10 (−571)C>A 0.62 0.007 0.24 0.48 0.042 0.26 1.24 0.388 0.23 0.78 0.356 0.26 0.86 0.569 0.22 1.06 0.821 0.63 0.80 0.028 0.363 *
PON2 311Ser>Cys 0.99 0.948 0.23 0.89 0.720 0.24 1.31 0.116 0.23 1.38 0.073 0.23 1.12 0.646 0.24 1.27 0.184 0.19 1.19 0.031 0.363 *
AGT 235Met>Thr 0.61 0.003 0.43 1.15 0.704 0.40 1.17 0.406 0.46 0.90 0.580 0.45 0.57 0.023 0.43 1.26 0.609 0.81 0.82 0.033 0.363 0.05
ACE Del>Ins 0.86 0.390 0.45 0.66 0.241 0.49 0.949 0.778 0.47 0.74 0.149 0.55 0.93 0.794 0.50 0.73 0.249 0.65 0.83 0.046 0.413 *
APOE 112Cys>Arg 1.18 0.369 0.14 1.19 0.648 0.13 1.23 0.327 0.11 1.54 0.025 0.14 1.09 0.754 0.12 0.92 0.700 0.11 1.20 0.048 0.413 *
NOS3 298Glu>Asp 1.35 0.244 0.31 1.80 0.205 0.31 1.52 0.125 0.29 1.63 0.084 0.31 2.49 0.013 0.27 0.58 0.423 0.11 1.56 0.001 0.102 *
LTA 252A>G not genotyped 0.53 0.272 0.33 1.15 0.744 0.32 1.29 0.548 0.32 2.90 0.005 0.29 1.72 0.029 0.36 1.55 0.007 0.360 *
Recessive ACE Del>Ins 0.65 0.066 0.45 1.04 0.926 0.49 0.85 0.420 0.47 0.48 0.001 0.55 1.01 0.98 0.50 1.09 0.635 0.65 0.80 0.020 0.536 0.06
GNB3 825C>T 0.97 0.918 0.32 2.07 0.164 0.30 1.03 0.928 0.31 1.75 0.045 0.31 1.21 0.661 0.28 1.42 0.112 0.44 1.31 0.028 0.569 *
LTA 26Thr>Asn 0.96 0.882 0.32 0.51 0.254 0.33 1.10 0.737 0.32 1.20 0.533 0.33 2.50 0.013 0.29 1.80 0.017 0.35 1.31 0.032 0.569 *
APOB 71Thr>Ile 1.80 0.016 0.29 1.14 0.805 0.32 0.88 0.676 0.28 1.51 0.154 0.28 1.14 0.721 0.33 1.09 0.897 0.14 1.33 0.039 0.592 *
Open in a new tab
*

Fixed effect meta-analyses adjusted for age and sex were conducted based on the summary logistic regression results for each study site.Cs = cases, ctrl = controls.

†

OR= Odds Ratio, FDR = False Discovery Rate

‡

Minor allele frequency observed among controls. For ACE, the insertion allele was defined as the minor allele based upon the PHS population.

§

P-values <0.1 for heterogeneity of the genetic effect across six studies.

The point estimates for the OR were somewhat higher for LTA 252A>G, a polymorphism in intron 1, than for the non-synonymous polymorphism LTA 26Thr>Asn, although the confidence intervals overlapped after adjusting for age and sex (Figure1, A–D). The FDR values for the Thr>Asn polymorphism were also greater than 0.05. The associations with stroke risk for both LTA SNPs reached statistical significance among normotensives within the individual studies of SHINING and Pomerania, whereas among hypertensives, the OR point estimates were usually just below 1 and were not statistically significant (Figure 1). This trend for increased stroke risk associated with the LTA SNPs among normotensives relative to hypertensives was observed across the other studies, although none of these individual associations was statistically significant. We note that the PHS cohort was genotyped only for the LTA 26Thr>Asn polymorphism under the expectation that this coding SNP could be functional and would be an effective “tag” for LTA 252, based on the very strong LD between these two polymorphisms; furthermore, in the Vienna and Westphalia studies, some samples had missing genotypes for LTA 252A>G. When the meta-analysis was repeated with only those samples that had been genotyped for both LTA SNPs, the OR estimates were virtually identical (1.572 and 1.565 among normotensives under the dominant model for LTA 252 and LTA 26, respectively; data not shown). In addition, if the LTA 26 result for the PHS were imputed for the missing LTA 252 data, the additive result for LTA 252 would remain highly significant (OR=1.30, p=0.0001). Alternatively, if a completely null estimate for the PHS were imputed, the overall result would remain significant (OR=1.27, p=0.0004) and would continue to pass the stringent multiple comparisons testing.

Figure 1.

Figure 1

Open in a new tab

Risk of ischemic stroke associated with LTA polymorphism. Odds ratios under additive and dominant modes of inheritance for the LTA 252A>G and LTA 26Thr>Asn polymorphisms among normotensive (square) and hypertensive (diamond) subjects are plotted for each study (open squares/diamonds) and the fixed effects meta-analysis (filled squares/diamonds). Horizontal lines extend across the 95% confidence limits.

In the smoking-stratified analyses, no associations remained statistically significant after the false discovery rate was applied, although under the dominant model, CD14 (−260)C>T was suggestively associated (OR 1.24, P = 0.001, FDR = 0.058) with ischemic stroke among never-smokers (Supplementary Table 2C). Both LTA SNPs were associated with a greater risk for ischemic stroke in never-smokers than ever-smokers under the dominant model and although these associations were not statistically significant after accounting for multiple testing, this trend was consistent across five studies (data not shown); the Westphalian study was excluded due to limited smoking data.

Discussion

In this meta-analysis, we evaluated the association between 105 polymorphisms in 64 inflammation and cardiovascular-related genes and ischemic stroke in 3550 case and 6560 control subjects across six different studies. Although we could not further define subtypes of ischemic stroke, key strengths of our stroke consortium are that these analyses were not subject to publication bias and all studies used common genotyping reagents. In the primary meta-analysis, modest associations with stroke became non-significant after adjustment for multiple testing using the FDR or permutation testing. Stratification on sex or age also revealed no significant associations. Notably, subjects in two of our studies were limited to one sex and the consortium encompassed subjects recruited from different regions in Europe, North America, and China. We observed similar results among Caucasian participants only, but study population differences resulting in heterogeneity in stroke etiology could have obscured genetic associations.

Stratification on hypertension status did, however, reveal a statistically significant association for LTA 252A>G that remained after adjustment for multiple testing. Across four Caucasian populations and one Chinese population, the odds ratio for LTA 252G was consistently greater among normotensive than hypertensive subjects. LTA 26Thr>Asn yielded similar results among study participants with genotypes at both sites, as expected, given the strong linkage disequilibrium between these two LTA SNPs. Although a recent Japanese study observed no association of these SNPs with any subtype of ischemic stroke30, a smaller Hungarian study had previously reported LTA 252G as a risk factor for large-vessel ischemic stroke31 and an earlier Korean study had identified the LTA 252AA genotype as a risk factor for cerebral infarction32. We were unable to analyze ischemic stroke subtypes, but there is some evidence that subtypes may differ depending upon hypertensive status33. Although the number of non-hypertensives cases was limited to 1068, stratification by hypertension across our six populations may have reduced heterogeneity and thus enabled us to discern the modest risk associated with LTA polymorphism.

A role for LTA in chronic inflammation has been suggested by its ability to induce expression of ICAM-1 and VCAM-1 on endothelial cells in vitro34,35. LTA expression results in a localized infiltrate consisting of T cells, B cells, follicular and interdigitating dendritic cells and macrophages36. A recent mouse model study indicated that LTA was expressed in atherosclerotic lesions whose size correlated with concentration. Moreover, loss of the adjacent gene TNF did not affect development of lesions in mice fed an atherogenic diet37. The A252G site is intronic, but has been associated with higher transcriptional activity in a luciferase assay, while the variant protein bearing the LTA 26 threonine to asparagine substitution has been observed to induce greater expression of VCAM1 and SELE mRNA in vascular smooth-muscle cells. Since these two LTA SNPs are in almost complete LD, the variant protein level was estimated to be 1.5-fold higher than wildtype10. An increased level of the variant protein may contribute to the increased risk for ischemic stroke through inflammatory processes. Although the mechanism by which LTA polymorphisms influence inflammatory pathways is not clear, the meta-analysis presented here indicated that these LTA variants were associated with ischemic stroke in non-hypertensive patients.

It is believed that subjects with hypertension tend to develop chronic, low-grade systemic inflammation38–40. Severity of inflammation caused by genetic variation could independently modify predisposition to ischemic stroke. Recent reports on the association of PDE4D variants with ischemic stroke among normotensives3,4 are consistent with the hypothesis that hypertension may obscure or mask the effect of inflammation-related genetic variants and that such genetic effects can be most readily observed in the absence of this major risk factor.

Smoking, like hypertension, can elicit an inflammatory response41. In our study, the effect of LTA variation on stroke was more discernable among never-smokers than ever-smokers. Whether pro-inflammatory risks for ischemic stroke caused by hypertension, smoking, or carrying a risk allele are additive remains to be addressed by a carefully designed study.

Summary

Our six-study analysis surveyed inflammatory and cardiovascular gene polymorphisms in examining the risk for ischemic stroke. Our results indicate that the LTA 252A>G and LTA 26Thr>Asn polymorphisms have significant effects on the risk for ischemic stroke in non-hypertensive subjects. We cannot rule out the possible importance of these polymorphisms in hypertensive subjects, but a much larger cohort may be needed to clarify the interaction of hypertension and inflammation in the etiology of ischemic stroke.

Supplementary Material

Supplement

On-line Supplementary Table 1. Frequencies of the less common or “variant” allele among the (A) control and ischemic stroke case (B) subgroups for each study. P values for large-sample and exact tests for Hardy-Weinberg equilibrium (HWE).

Online Supplementary Table 2. Nominally significant (P<0.05) fixed-effects meta-analysis results with (A) age, (B) sex, or (C) smoking-status stratification.

Acknowledgments

The RMS Stroke SNP Consortium (see Appendix for full list of investigators) thanks the staff and many participants of the Physician’s Health Study, Study of Osteoporotic Fractures, Vienna Stroke Registry, Westphalian Stroke Register, Dortmund Health Study, Pomeranian Stroke Study, Study of Health in Pomerania, and SHINING for their dedication and cooperation. We thank the RMS CVD project team for their expertise in developing the genotyping reagents used for these studies, the RMS High-Throughput Genotyping Group for their expert technical assistance in genotyping the SOF samples and Michael Janisiw for assistance in genotyping the Viennese samples.

The Physician’s Health Study (PHS) is supported by grants from the National Heart Lung and Blood Institute (HL-58755, and HL-63293), the Doris Duke Charitable Foundation, the American Heart Association, and the Donald W. Reynolds Foundation, Las Vegas, Nevada. Additional funding provided by the Fondation Leducq, Paris FR (Dr Ridker).

The Study of Osteoporotic Fractures (SOF) is supported by National Institutes of Health funding. The following institutes provide support: the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) and the National Institute on Aging (NIA) under the following grant numbers: AG05407, AR35582, AG05394, AR35584, AR35583, R01 AG005407, R01 AG027576-22, 2 R01 AG005394-22A1, and 2 R01 AG027574-22A1.

Case assessments in the Westphalian and Pomeranian studies are part of the German ‘Competence Net Stroke’ which is supported by the German Federal Ministry of Education and Research (01GI9909/3). Data collection in the Dortmund Health Study was funded by the German Migraine- and Headache Society and unrestricted grants of equal share of a consortium of 7 pharmaceutical companies. The Study of Health in Pomerania (SHIP) is funded by grants from the German Federal Ministry of Education and Research (BMBF,01ZZ96030), and from the Ministry for Education, Research and Cultural Affairs and the Ministry for Social Affairs of the Federal State of Mecklenburg-Vorpommern.

The Vienna Stroke Registry (VSR) is supported by research grants of the Medizinisch-Wissenschaftlicher Fonds des Bürgermeisters der Bundeshauptstadt Wien (project numbers 1540, 1829, 1970), of the Jubiläumsfonds der Oesterreichischen Nationalbank (project numbers 6866, 7115, 8281,9344), and the Austrian Science Foundation (P13902-MED). The VSR is also supported by the Wiener Krankenanstaltenverbund.

SHINING was funded through the Beijing Hypertension League Institute, in part through the National Infrastructure Program of Chinese Genetic Resource (2005DKA21300) and an unrestricted educational grant from F. Hoffmann-La Roche.

Design and data acquisition for initial individual genetic association studies: XW, SC, HAE, CM, KB, WL, WSB, YS, ERB, CK, JL, KL, LL, PMR, RYLZ, NRC

Design and interpretation of meta-analysis: XW, SC, VHB, HAE, CM, KB, WL, PMR, RYLZ, NRC

Statistical analysis: NRC

Manuscript writing and approval: all

Conflicts of Interest Disclosures: SC, VHB and HAE are employees of Roche Molecular Systems, Inc, which provided reagents and support for genotyping to all study sites under research collaborations and partial funding for the meta-analysis. KL is an employee of F. Hoffmann-La Roche, Ltd, which provided an unrestricted educational grant to the Beijing Hypertension League Institute.

Acknowledgments Appendix

RMS Stroke SNP Consortium Investigators:

Physicians’ Health Study (PHS)

Nancy R. Cook, Paul M Ridker and Robert Y. L. Zee from the Center for Cardiovascular Disease Prevention and Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA

Study of Osteoporotic Fractures (SOF)

Warren S. Browner, Steve R. Cummings and Li-Yung Lui from the S.F. Coordinating Center, California Pacific Medical Center, Research Institute, San Francisco, CA, USA

Vienna Stroke Study

Georg Endler and Christine Mannhalter from the Department of Medical and Chemical Laboratory Diagnostics, Medical University Vienna, Vienna, Austria; Stefan Greisenegger and

Wolfgang Lalouschek from the University Clinic of Neurology, Medical University Vienna, Vienna, Austria

Pomerania and Westphalia Studies

Klaus Berger, Institute of Epidemiology and Social Medicine, University of Muenster, Germany; Harald Funke, Department of Molecular Haemostaseology, University of Jena, Germany; E. Bernd Ringelstein, Department of Neurology, University of Muenster, Germany; Florian Stoegbauer, Department of Neurology, University of Muenster and Klinikum Osnabrück, Germany; Jan Luedemann, Institutes of Clinical Chemistry and Laboratory Medicine, University of Greifswald, Germany; Christoph Kessler, Department of Neurology, University of Greifswald, Germany

SHINING

Lisheng Liu, Yu Shi and Xingyu Wang from the Laboratory of Human Genetics, Beijing Hypertension League Institute, Beijing, China

Roche Molecular Systems (RMS)

Victoria H. Brophy, Suzanne Cheng, Henry A. Erlich, Andrea M. Johnson and Brian K. Rhees from the Department of Human Genetics, Roche Molecular Systems, Inc., Pleasanton, CA, USA

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Supplementary Materials

Supplement

On-line Supplementary Table 1. Frequencies of the less common or “variant” allele among the (A) control and ischemic stroke case (B) subgroups for each study. P values for large-sample and exact tests for Hardy-Weinberg equilibrium (HWE).

Online Supplementary Table 2. Nominally significant (P<0.05) fixed-effects meta-analysis results with (A) age, (B) sex, or (C) smoking-status stratification.

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