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. Author manuscript; available in PMC: 2014 Jun 17.
Published in final edited form as: Clin Sci (Lond). 2014 Jun;126(12):829–835. doi: 10.1042/CS20130652

Gene variations of ROCKs and risk of ischaemic stroke: the Women’s Genome Health Study

Robert Y L ZEE *,†,#, Qing-Mei WANG ‡,#, Daniel I CHASMAN *, Paul M RIDKER *, James K LIAO §
PMCID: PMC4059841  NIHMSID: NIHMS595860  PMID: 24351102

Abstract

Recent animal and human studies have demonstrated the importance of the ROCK (RhoA/Rho-associated kinase) pathway in IsST (ischaemic stroke). Whether the genetic variation within ROCK-associated genes modulates the risk of IsST remains elusive. The association between 66 tSNPs [tagging SNPs (single nucleotide polymorphisms)] of three ROCK-associated genes [ROCK1, ROCK2 and ARHGEF10 (Rho guanine-nucleotide-exchange factor 10)] and the incidence of IsST was investigated in 23 294 Caucasian female participants of the prospective WGHS (Women’s Genome Health Study). All were free of known cancer and cardiovascular disease at baseline. During a 15-year follow-up period, 323 participants developed their first ever IsST. Multivariable Cox regression analysis was performed to investigate the relationship between genotypes and risk of IsST assuming an additive genetic model. Haplotype-block analysis was also performed. A total of ten tSNPs were associated with the risk of IsST (three in ARHGEF10 and seven in ROCK1; P < 0.050). Further investigation using the haplotype-block analysis revealed a similar significant association of pre-specified haplotypes of ROCK1 with the risk of IsST (P = 0.005). If corroborated in other large prospective studies, the findings of the present study suggest that genetic variation within the ROCK-associated pathway gene loci examined, and in particular ROCK1 gene variation, may influence the risk of IsST.

Keywords: genetic epidemiology, ischaemic stroke, RhoA/Rho-associated kinase (ROCK), single nucleotide polymorphism (SNP)

INTRODUCTION

Stroke is the third most common cause of death in the United States and a leading cause of long-term disability [1]. Twin and family studies have suggested that susceptibility to stroke has a significant genetic component [2–4]. Although GWASs (genome-wide association studies) [5,6] and case-control association studies [7,8] have uncovered several susceptibility genes, the genetic basis remains largely unknown.

Many lines of evidence suggest that the ROCK (RhoA/Rho-associated kinase) pathway is involved in the pathogenesis of IsST (ischaemic stroke) [9]. RhoA is a member of the small GTP-binding proteins and acts as a molecular ‘on–off’ switch in multiple signalling pathways [10,11]. RhoA is activated by GEFs (guanine-nucleotide-exchange factors), which catalyse the exchange of GDP by GTP. ROCKs are the main downstream target of RhoA [12,13] and regulate diverse cellular functions [14], including actin cytoskeletal organization [15], cell adhesion/proliferation/migration [16], smooth muscle contraction [17], gene transcription [18,19] and platelet activation [20]. Two isoforms of ROCKs have been identified, ROCK1 and ROCK2 [21], with an overall 65% homology in amino acid sequence and a 92% homology in their kinase domains. Abnormal activation of ROCKs has been linked to many disease processes, including atherosclerosis formation [22], hypertension [23], vascular inflammation [24], and cerebral and coronary vasospasm [25,26].

Elevated ROCK activity leads to atherosclerosis formation via activating inflammatory process and smooth muscle proliferation. In ROCK1+/− haploinsufficient mice, reduced neointima formation, decreased levels of pro-inflammatory adhesion molecule expression and reduced leucocyte infiltration were observed following vascular injury [27]. In an apolipoprotein E-knockout mice model with accelerated atherosclerosis, atherosclerosis was reduced by a ROCK inhibitor (Y-27632) [28]. The elevated ROCK activity observed in hypertension and cerebral vasospasm probably contributes to increased smooth muscle contraction [23,26]. In addition, ROCKs might regulate vascular tone indirectly through inhibition of eNOS (endothelial nitric oxide synthase) expression and activity [29,30]. Furthermore, alteration of RhoA/ROCK signalling has been shown to lead to endothelial dysfunction in subjects with diabetes [31] and to aortic stiffness in aging subjects and those that smoke [32]. Treatment with a ROCK inhibitor (fasudil) improves endothelial function in human subjects with coronary artery disease [29]. Evidence from clinical studies has shown that leucocyte ROCK activity was elevated in patients with acute IsST [33]. Inhibition of ROCK activity by statins probably contributes to the non-cholesterol or ‘pleiotropic’ effects of statins in preventing IsST [34–36] and in inhibiting venous thromboembolic events [37].

However, the genetic contribution of ROCK1 and ROCK2 towards the risk of IsST has not been reported. In the present study, we used a candidate gene approach to investigate the potential association of ROCK1 and ROCK2 tSNPs [tagging SNPs (single nucleotide polymorphisms)] with the risk of IsST in participants drawn from the WGHS (Women’s Genome Health Study). In addition, on the basis of a previous study which showed that a functional SNP of RhoA GEF encoded by ARHGEF10 (Rho GEF 10) was a susceptibility gene in the Japanese population [8], we also investigated the genetic variation in ARHGEF10 in the Caucasian population of the present study.

MATERIALS AND METHODS

Study design

Details of the design of the present study have been described previously [38]. In brief, participants in the WGHS, a genetic sub-study of the Women’s Health Study [39,40], included initially healthy North American women aged 45 years or older with no previous history of cardiovascular disease, cancer or other major chronic illnesses. A baseline blood sample was collected during the enrolment phase of the Women’s Health Study between 1992 and 1995. Study participants, who gave an informed consent for blood-based analyses related to risks of incident chronic diseases, were followed up for incident events that were adjudicated by an end points committee using standardized criteria and a full medical record review [39,40]. The present investigation included 23 294 participants of European ancestry of the WGHS. During a 15-year follow-up period, 323 cases of newly diagnosed IsST were identified. DNA extracted from the baseline WGHS blood samples underwent tSNP (r2 ≈ 0.80) genotyping using the genome-wide Illumina Infinium II Human HAP300 panel that was designed with an LD (linkage disequilibrium) tagging strategy to capture common variation among Caucasians, as described previously [41,42]. The Brigham and Women’s Hospital Institutional Review Board for Human Subjects Research approved the study protocol.

Statistical analysis

Genotype frequencies were compared with values predicted by the Hardy–Weinberg equilibrium using the χ2 test with one degree of freedom. HRs (hazard ratios) associated with each of the tSNPs were calculated separately by Cox regression analysis adjusting for age and smoking status, and further adjusting for BMI (body mass index), randomized treatment assignment, history of diabetes, hypertension and hyperlipidaemia, and current hormone use, assuming an additive model for genetic effects.

Haplotype estimation and inference were determined by the expectation-maximization algorithm. Haplotype blocks were defined using the software Haploview v4.1 [43]. In addition, the relationship between haplotypes and risk of IsST was evaluated by a referent (wild-homozygous) haplotype-based Cox regression analysis, adjusting for the same potential confounders/risk factors used in the single SNP analysis. All analyses were carried out using SAS v9.1 package (SAS Institute) or R software. A two-tailed (uncorrected/unadjusted for multiple testing) P value of 0.05 was considered as a statistically significant result. Genotyping call rates were >99% per SNP.

RESULTS

The baseline characteristics of the 23 294 initially healthy Caucasian women are shown in Table 1. Of the 66 SNPs evaluated, ten were not in Hardy–Weinberg equilibrium with an uncorrected/unadjusted P < 0.05 (Table 2). Results from the multivariable Cox regression analysis showed evidence for differential associations of ten SNPs (three for ARHGEF10 and seven for ROCK1) with the risk of IsST (Punadjusted<0.050; Table 2). Figure 1 shows the LD pattern of the tSNPs of ROCK1 in the sample population of the present study. The haplotype distribution (defined by Haploview v4.1) is shown in Table 3. Only one Haploview-defined haplotype block of ROCK1 (encompassing rs2127958 and rs1481280) was identified. Results from the haplotype-based analysis again showed an association of pre-specified Haploview-defined haplotype, carrying the minor alleles at both polymorphic sites of ROCK1 with the risk of IsST (Table 3; Punadjusted = 0.0049). All of the SNPs evaluated were in agreement of the proportionality of hazard assumption.

Table 1. Baseline characteristics of the study population.

Results are the medians and interquartile range for continuous variables and percentages for categorical variables.

Variable Value
n 23 294
Age (years) 52.90 (48.92–59.01)
BMI (kg/m2) 24.89 (22.46–28.32)
Smoking status
 Current (%) 11.64
 Past (%) 37.45
 Never (%) 50.91
History of diabetes (%) 2.52
History of hyperlipidaemia ≥240 mg/dl (%) 29.76
History of hypertension ≥140/90 mmHg (%) 24.61
Aspirin use (%) 49.87
β-Carotene use (%) 49.81
Vitamin E use (%) 50.08
Current hormone use (%) 43.86
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Table 2. Cox regression analysis of the incidence of IsST.

Results are adjusted for age, BMI, current smoking, treatment assignment, history of diabetes, hypertension and hyperlipidaemia, and current hormone use. HWE, Hardy–Weinberg equilibrium; MA, minor allele; MAF, minor allele frequency.

Gene Chromosome Position MA MAF dbSNP SNP_ID HR Lower 95% CI Upper 95% CI P uncorrected HWE
ROCK2 2p24 11218182 A 0.4874 rs2271622 0.963 0.829 1.119 0.6198 0.5460
11229674 G 0.4432 rs3732103 0.988 0.850 1.149 0.8769 0.8630
11237110 A 0.4811 rs921322 0.966 0.831 1.121 0.6470 0.4143
11238327 A 0.4809 rs8996 0.980 0.843 1.140 0.7968 0.0005
11244205 A 0.4818 rs6753921 0.979 0.844 1.136 0.7803 0.3315
11276570 C 0.4665 rs9808232 1.031 0.888 1.198 0.6869 0.6720
11286528 A 0.4839 rs1515219 0.980 0.844 1.138 0.7924 0.3251
11303284 A 0.4470 rs6716817 0.940 0.808 1.094 0.4241 0.6806
11369434 G 0.1289 rs10203916 0.921 0.729 1.163 0.4891 0.3066
11377735 A 0.1843 rs6755337 0.914 0.749 1.116 0.3777 0.8616
11392895 A 0.2825 rs12622447 1.062 0.903 1.250 0.4659 0.5279
11406981 G 0.3750 rs7581184 1.072 0.917 1.254 0.3828 <0.0001
11412302 A 0.2800 rs6432187 1.054 0.894 1.244 0.5316 0.0392
11420801 C 0.2108 rs7424263 0.947 0.786 1.141 0.5675 0.5948
11422663 A 0.2758 rs4533449 0.905 0.760 1.076 0.2586 0.4120
ARHGEF10 8p23 1735572 A 0.1795 rs4875960 0.948 0.778 1.154 0.5936 0.2474
1740903 A 0.2279 rs4327894 0.909 0.758 1.090 0.3020 0.3517
1743991 G 0.4076 rs6558545 0.905 0.776 1.055 0.2026 0.4554
1752777 A 0.1041 rs6980781 1.065 0.841 1.349 0.6014 0.8330
1755410 G 0.2793 rs6558550 0.899 0.758 1.066 0.2202 0.0998
1769901 G 0.3649 rs6558551 1.044 0.894 1.219 0.5843 0.0811
1772670 A 0.4483 rs4875952 1.046 0.900 1.214 0.5589 0.5333
1776763 G 0.3855 rs11136430 1.139 0.979 1.325 0.0916 0.7295
1778839 G 0.4267 rs11136431 0.903 0.777 1.051 0.1884 0.0062
1791325 G 0.3029 rs9693412 0.978 0.831 1.150 0.7836 0.1668
1795162 G 0.3013 rs7007884 1.196 1.019 1.402 0.0283 0.4533
1799279 A 0.4970 rs13277792 0.943 0.812 1.096 0.4453 0.4012
1805367 A 0.2314 rs17829629 0.910 0.759 1.092 0.3118 0.0974
1808062 A 0.2297 rs3735866 0.903 0.752 1.083 0.2708 0.0541
1820250 C 0.3336 rs7014895 0.876 0.744 1.032 0.1135 0.0122
1821207 C 0.1492 rs9657362 1.045 0.850 1.285 0.6735 0.7961
1828103 C 0.3744 rs4242520 1.092 0.936 1.274 0.2646 <0.0001
1828619 C 0.2149 rs7826500 1.073 0.897 1.284 0.4388 0.4126
1833416 A 0.0541 rs4474061 0.855 0.594 1.230 0.3996 0.2473
1833702 G 0.1546 rs2272712 1.111 0.910 1.356 0.3011 0.9398
1844699 A 0.4609 rs2294035 0.973 0.838 1.131 0.7213 0.3979
1844997 A 0.0393 rs2294039 1.369 0.981 1.912 0.0650 0.3842
1847801 A 0.2258 rs6990129 1.020 0.854 1.218 0.8258 0.9104
1849377 G 0.1508 rs11995882 0.958 0.774 1.187 0.6962 0.2599
1853293 G 0.4765 rs7004405 1.138 0.979 1.322 0.0919 0.7227
1856248 G 0.4285 rs11136442 1.013 0.872 1.177 0.8650 0.1471
1858362 G 0.4217 rs4242549 0.953 0.818 1.110 0.5347 0.8191
1864037 A 0.1367 rs2272611 1.186 0.970 1.450 0.0971 0.1012
1864886 C 0.0714 rs17683288 1.348 1.042 1.744 0.0232 0.6925
1867936 G 0.4588 rs4242548 1.024 0.879 1.194 0.7602 0.2098
1875853 G 0.1069 rs3824141 0.822 0.630 1.071 0.1457 0.7570
1877939 A 0.0773 rs7386016 0.858 0.637 1.156 0.3150 0.4896
1880248 G 0.3712 rs999545 0.958 0.818 1.121 0.5905 0.0546
1885063 G 0.1503 rs2280823 1.178 0.968 1.433 0.1015 0.5551
1887097 G 0.3738 rs17830107 0.857 0.731 1.004 0.0563 0.3686
1891563 G 0.3943 rs4876268 0.796 0.680 0.931 0.0044 0.3508
1891670 A 0.3297 rs6999840 1.008 0.860 1.180 0.9262 0.0465
1894036 C 0.0656 rs14375 0.972 0.717 1.319 0.8569 0.7479
1895261 G 0.1009 rs6981540 1.015 0.796 1.294 0.9068 0.0249
1898064 G 0.06500 rs12547074 0.993 0.734 1.343 0.9639 0.9140
1903753 C 0.2684 rs4876265 1.054 0.892 1.245 0.5355 0.4042
1905758 A 0.2800 rs6558568 1.056 0.896 1.243 0.5177 0.0097
1907867 G 0.2389 rs4242546 1.030 0.867 1.225 0.7343 0.4710
ROCK1 18q11.1 16863577 A 0.4686 rs288980 1.036 0.892 1.202 0.6450 0.0013
16864064 A 0.0425 rs7239317 1.450 1.057 1.987 0.0210 0.3753
16907607 G 0.4289 rs2127958 0.848 0.728 0.987 0.0334 0.1989
16909448 A 0.3837 rs1481280 0.775 0.661 0.908 0.0016 0.1882
16935290 A 0.0427 rs1006881 1.416 1.030 1.946 0.0322 0.6309
16945763 A 0.0349 rs11874761 1.570 1.128 2.186 0.0075 0.3311
16954952 G 0.0346 rs10083915 1.451 1.024 2.055 0.0363 0.7653
16968988 G 0.0391 rs11873284 1.535 1.102 2.138 0.0112 0.8575
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Figure 1. LD pattern of the ROCK1 locus, generated by Haploview v4.1 using the default D’/LOD determination of the SNPs tested.

Figure 1

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D’ is the value of D prime between the two loci. LOD is the log of the likelihood odds ratio, a measure of confidence in the value of D’.

Table 3. Haplotype-based Cox regression analysis of the incidence of IsST.

Results are adjusted for age, BMI, current smoking, treatment assignment, history of diabetes, hypertension and hyperlipidaemia, and current hormone use. Only haplotypes with a frequency greater than 1% are shown. Haplotype block was defined by Haploview v4.1. 1 denotes the major allele and 2 the minor allele. HF, haplotype frequency.

Gene Haplotype block HF HR (95% CI) P uncorrected
ROCK1 rs2127958–rs1481280
11 (AC) 0.57043 Referent
21 (GC) 0.04587 1.710 (0.918–3.183) 0.0908
22 (GA) 0.38316 0.630 (0.456–0.869) 0.0049
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DISCUSSION

Abnormal activation of ROCKs has been shown to play an important role in the pathogenesis of IsST. The results from the present study revealed that seven (out of eight) of the tSNPs evaluated in ROCK1 were associated significantly with the risk of IsST. The positive tSNPs are located at intervals of 104 924 bases apart. A total of four tSNPs are located within introns, one tSNP is located in the 5′-UTR and two tSNPs are located in the 5′ promoter region. The functional SNPs need to be identified further. In contrast, none of the tSNPs in ROCK2 were associated with the risk of IsST.

The specific functions of ROCK1 and ROCK2 remain unclear due to the lack of specific inhibitors that distinguish ROCK1 from ROCK2, as well as from other serine/threonine kinases such as PKA (protein kinase A) and PKC (protein kinase C) [44]. Because homozygous ROCK1−/− [45] and ROCK2−/− [46] knockout mice are lethal, a genetic approach using conditional or halploinsufficient ROCK1- and ROCK2-knockout mice provides a good opportunity to ascertain the function. Previous studies have shown that ROCK1+/− , but not ROCK2+/− , halploinsufficient mice have reduced neointima formation following vascular injury [27]. In addition, deficiency of ROCK1 in bone-marrow-derived cells protects against atherosclerosis [47]. Consistently, results from the present study suggest that genetic variations in ROCK1, but not ROCK2, are associated with the risk of IsST. These findings warrant further investigation of the specific role of ROCK1 and ROCK2 in IsST, and may provide an insight into the development of specific ROCK inhibitors to prevent IsST.

The present study also revealed that three tSNPs from ARHGEF10 are associated with the risk of IsST. ARHGEF10 encodes a GEF. In vitro small GTPase activity assays showed that a gene product of ARHGEF10 activated RhoA [8]. In this Japanese study, a SNP (rs4376531) affected ARHGEF10 transcriptional activity via regulating the binding affinity of Sp1 and was found to be associated with the risk of IsST [8]. The present study suggests that there are different susceptible ARHGEF10 SNPs in the Caucasian population.

The strengths of the present study are the overall sample size, the biological relevance of the polymorphisms considered, the prospective design and the complete long-term follow up. We also chose, on an a priori basis, to present all our data simultaneously rather than focusing on any one specific finding. Nonetheless, some potential limitations of the present study require discussion, including generalization and potential bias. We only examined Caucasian middle-aged and older women with a distinct socioeconomic status (health professionals), and our findings may not represent other populations with diverse ethnicity or socioeconomic backgrounds. Cautious interpretation of the findings of the present study (uncorrected/unadjusted for multiple testing may lead to chance findings) should be exercised. Furthermore, the U-shaped haplotypic relationship, as shown in Table 3, could be due partly to a phenomenon previously termed/described as heterosis with hybrids/heterozygotes displaying altered levels of growth, survival or fitness relative to their parental (homozygous) states, although the exact molecular bases for this phenomenon remain elusive [48]. Moreover, an alternative explanation is that the observed U-shaped relationship could be due to the rarity of the heterozygous haplotype, compared with the homozygous haplotype, with a wide 95% CI (confidence interval). Hence, taken altogether, confirmation of the findings of the present study is required.

In the present study, we had the ability to detect, on the basis of the sample size, assuming 80% power, at an α of 0.05, an HR of greater than 1.30 if the minor allele frequency was 0.50 and of greater than 2.80 if the minor allele frequency was 0.01, assuming a univariable-additive model. Thus we cannot rule out a low-to-modest risk of IsST associated with the tSNPs tested. Furthermore, the present investigation (decided a priori) did not examine IsST subtypes with the genetic loci evaluated; thus further subtype-specific investigation is needed.

In conclusion, the findings of the present study warrant further investigation into the involvement of the ROCK-associated pathway genes tested in the pathogenesis of IsST. More importantly, the findings of the present study require confirmation/replication in future large prospective studies.

CLINICAL PERSPECTIVES.

  • Recent animal and human studies have demonstrated the relevance of the ROCK pathway in the pathogenesis of IsST. Whether the genetic variation within the ROCK-associated genes modulates IsST risk remains elusive.

  • In the present large prospective study, we observed an association of ROCK1 gene variation with the risk of IsST.

  • In a clinical situation, the findings of the present study highlight the potential prognostic utility of ROCK-associated gene variation in the prediction of the risk of IsST.

Acknowledgments

FUNDING This work was supported by the National Institutes of Health [grant numbers HL-043851, HL-080467, HL099355 and CA-047988]. Collaborative scientific support for genotyping was provided by Amgen.

Abbreviations

ARHGEF10

Rho GEF 10

BMI

body mass index

CI

confidence interval

GEF

guanine-nucleotide-exchange factor

HR

hazard ratio

IsST

ischaemic stroke

LD

linkage disequilibrium

ROCK

RhoA/Rho-associated kinase

tSNP

tagging single nucleotide polymorphism

WGHS

Women’s Genome Health Study

Footnotes

AUTHOR CONTRIBUTION Robert Zee, Qing-Mei Wang and James Liao conceived and designed the experiments; Robert Zee and Daniel Chasman performed the experiments; Robert Zee analysed the data; all authors interpreted the data and wrote, reviewed and approved submission of the paper.

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