Front. Neurosci. Frontiers in Neuroscience Front. Neurosci. 1662-453X Frontiers Media S.A. 10.3389/fnins.2022.1030551 Neuroscience Original Research CYP11B1 gene polymorphisms and susceptibility to ischemic stroke in a Chinese Han population Liu Gaowen Duan Ying * Department of Critical Care Medicine, Xianyang Central Hospital, Xianyang, China

Edited by: Jun Zhang, Texas Tech University Health Sciences Center, United States

Reviewed by: Yonggang Hao, Sir Run Run Shaw Hospital, China; Yipeng Ding, Hainan General Hospital, China

 *Correspondence: Ying Duan duanying72@163.com

This article was submitted to Neurogenomics, a section of the journal Frontiers in Neuroscience

 01 12 2022 2022 16 1030551 08 09 2022 08 11 2022 Copyright © 2022 Liu and Duan. 2022 Liu and Duan

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 Objectives

Ischemic stroke (IS) is the major cause of death and disability. While previous studies confirmed that CYP11B1 is closely associated with IS, the present study aimed to analyze the impact of CYP11B1 gene polymorphisms on the IS susceptibility.

 Methods

The present study genotyped six single nucleotide polymorphisms (SNPs) (including rs4736312, rs5017238, rs5301, rs5283, rs6410, and rs4534) of CYP11B1 in peripheral blood samples from IS and control populations. Logistic regression analysis was used to analyze the association between the SNPs and IS risk. The multifactor dimensionality reduction (MDR) method was used to determine the roles of SNP–SNP interactions in IS.

 Results

The present study showed that rs5283 was associated with an increased susceptibility to IS [odds ratio (OR) 1.81, p = 0.012]. On the contrary, rs6410 had a protective influence on IS risk (OR 0.56, p = 0.020). Stratified analyses indicated that rs5283 could enhance the risk of IS in subjects aged >63 years (OR 2.41, p = 0.011), of female gender (OR 3.31, p = 0.001), that do not smoke (OR 1.64, p = 0.005), and with hypertension (OR 2.07, p = 0.003). Whereas, rs6410 was related to a lower susceptibility to IS in subjects aged >63 years (OR 0.43, p = 0.032), of female gender (OR 0.30, p = 0.006), do not smoke (OR 0.42, p = 0.017), and with hypertension (OR 0.52, p = 0.022). Besides, rs4736312 reduced the IS susceptibility in non-smokers (OR 0.69, p = 0.031). Rs4534 had a risk-decreasing impact on IS in non-drinking (OR 0.54, p = 0.016). Moreover, the results of the MDR analysis corroborate that the best prediction model for IS was rs5283.

 Conclusion

This study revealed that CYP11B1 gene polymorphisms strongly correlated with IS in the Chinese Han population.

 ischemic stroke susceptibility CYP11B1 gene polymorphisms case-control study Introduction

Stroke, a common cerebrovascular disease, is the second leading cause of death and the third leading cause of disability and death combined worldwide (Feigin et al., 2021). Ischemic stroke (IS) is the most common type of stroke, accounting for 87% of all stroke cases (Krishnamurthi et al., 2013). In China, stroke is the major cause of death among adults and accounts for a huge burden on medical resources (Wang W. et al., 2017). Stroke-related pain and complications have a phenomenal impact on the patient's quality of life (QoL). At the same time, such complications bring untold misery and burden to the patient's family besides imposing a huge burden on the society at large. Bearing in mind the enormity of treatment to be given to the afflicted patient, effective prevention and therapeutic strategies are warranted and are the need of the hour. Stroke is a multifactorial and complex neurological disorder, including in its fold and scope a host of conventional risk factors, genetic factors, and their interactions. The traditional risk factors for stroke are age, gender, smoking, hypertension, diabetes, obesity, dyslipidemia, etc. (Johnson et al., 2016; Zhang et al., 2019; Zhuo et al., 2020). Previous researches showed that there is a close link between genetic factors and the occurrence of IS (Chauhan and Debette, 2016; Georgakis et al., 2019). Epidemiological studies revealed that gene polymorphisms may be involved in the pathophysiological processes of IS and thereby play a major role in triggering pathophysiological changes occurring in IS (Gao et al., 2019). Moreover, recent studies identified many genetic susceptibility variants which have contributed to the risk of the onset of stroke, such as ACE (Goyal et al., 2021), ADH1B (Lin et al., 2021), MTHFR, MTR (Mialovytska and Nebor, 2021), IL-10 (Rui et al., 2020), CYP2J2 (Wang S. Y. et al., 2017), and CYP2C8 (Yi et al., 2017).

Cytochrome P450 family 11 subfamily B member 1 (CYP11B1 gene) is located on chromosome 8q24.3 and encodes the steroid 11 β-hydroxylase, which influences the synthesis of aldosterone and activates cellular pathways to promote hypertension and cardiovascular disease (Hussain and Awan, 2018). CYP11B1 genetic variants are involved in the occurrence and progression of important clinical abnormalities such as late-life depression (Ancelin et al., 2021), Cushing's syndrome (Valassi et al., 2017), hypertensive patients (Hussain et al., 2020), autism (Deng et al., 2016), and coronary heart disease (Huang et al., 2022). The CYP11B1 and CYP11B2 genes share 90–95% sequence identity in their non-coding and coding regions. CYP11B2 gene polymorphisms were found to be associated with the occurrence of IS (Munshi et al., 2010; Yan and Wang, 2012). Taking all these facts together, we speculated that CYP11B1 gene polymorphisms may have an important role in the development of IS. However, the role of CYP11B1 gene polymorphism in IS has not yet been reported.

In this study, we carried out a case-control study (that included 550 IS patients and 550 normal populations) to explore the correlation between CYP11B1 gene polymorphisms and IS susceptibility in the Chinese Han population. Our study provides a new biomarker for the prevention and diagnosis of IS.

 Materials and methods

The present study was approved by the Ethics Committee of the Xianyang Central Hospital. We explained to each participant the purpose of undertaking this study and also obtained an informed consent from the concerned participants before the commencement of the study. In this study, we randomly recruited 550 patients with IS and age- and sex-matched 550 healthy populations from the Xianyang Central Hospital during the same period. The inclusion criteria for the study required only those patients who were newly diagnosed as having IS and who should have been confirmed to have suffered IS by two experienced neurologists based on the test records of clinical examination, magnetic resonance imaging (MRI), cerebral scanner, and/ or computed tomography (CT) in accordance with the diagnostic guidelines for stroke (Liberman et al., 2016). Patients who possessed any of the exclusion criteria were ruled out from participation. The exclusion criteria for the cases were as follows: (1) patients with genetic disease; (2) patients with a family history of stroke; (3) patients with any type of cancer, including brain tumor; and (4) patients with neurological, cardiogenic, and autoimmune diseases. The control group included a healthy population who had undergone physical examination. The inclusion criteria stipulated for the controls were as follows: (1) controls matched to cases for age and gender and (2) controls with no family history of brain and neurological diseases. The basic characteristics of all participants were fetched from medical records and a standardized demographic questionnaire that included the participant's health details such as age, gender, smoking status, alcohol intake, hypertension status, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c).

SNP selection and genotyping

The detailed steps that had to be undertaken for the selection of CYP11B1 single nucleotide polymorphisms (SNPs) were as follows: (1) We obtained the physical position of the CYP11B1 gene on the chromosome 8:142872356-142879846 through the human Ensembl GRCh37 database (http://asia.ensembl.org/Homo_sapiens/Info/Index). In the VCF to PED Converter window (http://grch37.ensembl.org/Homo_sapiens/Tools/VcftoPed), we entered the gene location, selected the Chinese Han population in Beijing (CHB) population, and downloaded the PED and info file for the SNPs of CYP11B1. We obtained 31 SNPs within CYP11B1 from the database. (2) Then, we used Haploview software for quality control [minor allele frequency (MAF) > 5%, min genotype > 75%, r2 <0.8, and Hardy–Weinberg equilibrium (HWE) > 0.05] to select the tag-SNP. (3) The call rate of each SNP was >95%. Other SNPs in the CYP11B1 gene did not meet the above standards. Finally, six SNPs (including rs4736312, rs5017238, rs5301, rs5283, rs6410, and rs4534) that met the above standards were selected for investigation and further study. Genomic DNA from the peripheral blood samples was extracted using the DNA kit. The primers for polymerase chain reaction (PCR) amplification were designed by the Agena Design software. The six SNPs were detected by the Agena MassARRAY iPLEX platform following the manufacturer's protocols. In addition, the genotyping data were analyzed by the Agena Bioscience Typer software.

 Bioinformatics analysis

Haploreg (Version 4.1) online software was used to predict the possible functions of the six SNPs(https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php).

 Statistical analyses

In this study, statistical tests were analyzed by SPSS software (version 22.0), with a two-tailed test. Student's t-test was performed to detect the statistical differences in age, total cholesterol, triglycerides, HDL-c, and LDL-c between the case and control groups, respectively. Pearson's chi-squared (χ2) test was used to analyze the statistical differences in gender, smoking status, and alcohol intake. Fisher's exact test was carried out to calculate the HWE to detect the allele frequencies in normal controls. The association of CYP11B1 gene polymorphisms with IS susceptibility was evaluated by a logistic regression analysis under allele, codominant, dominant, recessive, and log-additive models. The Benjamini and Hochberg's false discovery rate (FDR) method was used to correct for multiple comparisons. Besides, the positive findings about the correlations between SNPs and IS risk were verified with the false-positive report probability (FPRP) analysis. Moreover, the MDR method was used to determine the influence of interactions among SNPs on IS susceptibility.

 Results Basic characteristics of the study population

As shown in Table 1, our study involved 550 patients with IS (341 men and 209 women) and 550 healthy subjects (326 men and 224 women). The mean age was 63.01 ± 7.44 years for the cases and 63.71 ± 10.53 years for the controls. The concentrations of total cholesterol, HDL-c, and LDL-c in patients with IS were significantly lower than those in the control group (all p < 0.001). In terms of age, gender, smoking status, alcohol intake, and triglyceride level, there was no significant difference between the two groups (p = 0.203, p = 0.355, p = 0.469, p = 0.763, and p = 0.114, respectively).

Basic characteristics of participants in this study.

Variables Cases (n = 550) Controls (n = 550) P
Age, years (mean ± SD)a 63.01 ± 7.44 63.71 ± 10.53 0.203
 >63 275 (50%) 270 (49.1%)
  ≤ 63 275 (50%) 280 (50.9%)
Genderb 0.355
 Male 341 (62%) 326 (59.3%)
 Female 209 (38%) 224 (40.7%)
Smoking statusb 0.469
 Smoker 283 (51.5%) 271 (49.3%)
 Non-smoker 267 (48.5%) 279 (50.7%)
Alcohol intakeb 0.763
 Yes 284 (51.6%) 279 (50.7%)
 No 266 (48.4%) 271 (49.3%)
Hypertension status
 No 165 (30%)
 Yes 385 (70%)
Total cholesterol (mmol/l)a 3.93 ± 0.87 4.74 ± 0.89 <0.001
Triglycerides (mmol/l)a 1.55 ± 0.92 1.62 ± 0.68 0.114
HDL-c (mmol/l)a 1.16 ± 0.26 1.25 ± 0.26 <0.001
LDL-c (mmol/l)a 2.05 ± 0.63 2.58 ± 0.62 <0.001

HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

Student's t-test is used.

Pearson's X2 test is used.

p < 0.05 indicates statistical significance.

Significant p-values are given in bold.

 The impact of <italic>CYP11B1</italic> gene polymorphisms on ischemic stroke susceptibility

We successfully investigated six SNPs (rs4736312, rs5017238, rs5301, rs5283, rs6410, and rs4534), and the allele frequency distribution and the potential function of the SNPs are listed in Table 2. The allele frequencies for each SNP in the controls were assigned in accordance with the HWE (all p > 0.05). The association of CYP11B1 polymorphisms with IS is listed in Table 3. Rs5283 was significantly associated with an increased susceptibility to IS in allele [OR 1.32, p = 0.003, p (FDR) = 0.020], codominant [GA vs. GG, OR 1.33, p = 0.026, p (FDR) = 0.153; AA vs. GG, OR 1.81, p = 0.012, p (FDR) = 0.072], dominant [OR 1.39, p = 0.007, p (FDR) = 0.040], recessive [OR 1.58, p = 0.044, p (FDR) = 0.133], and log-additive models [OR 1.34, p = 0.003, p (FDR) = 0.016]. Whereas, rs6410 had a risk-decreasing influence on IS in allele [OR 0.81, p = 0.027, p (FDR) = 0.080], codominant [TT vs. CC, OR 0.56, p = 0.020, p (FDR) = 0.061], recessive [OR 0.60, p = 0.037, p (FDR) = 0.224], and log-additive models [OR 0.80, p = 0.022, p (FDR) = 0.065].

The distribution of allele frequencies of CYP11B1 SNPs.

SNP ID Chromosome position Role Alleles (A/B) Callrate MAF p-HWE HaploReg v4.1
Case Control
rs4736312 chr8: 142872521 3′-UTR A/C 99.9% 0.154 0.170 0.652 DNAse, Motifs changed, Selected eQTL hits
rs5017238 chr8: 142873353 3′-UTR G/A 99.9% 0.158 0.171 1.000 Promoter histone marks, DNAse, Motifs changed, Selected eQTL hits
rs5301 chr8: 142873857 3′-UTR T/C 100% 0.160 0.171 0.652 Promoter histone marks, Motifs changed, GRASP QTL hits, Selected eQTL hits
rs5283 chr8: 142879181 Synonymous A/G 99.7% 0.326 0.269 0.385 DNAse, Motifs changed, Selected eQTL hits
rs6410 chr8: 142879589 Synonymous T/C 100% 0.265 0.308 0.319 Promoter histone marks, Enhancer histone marks, Motifs changed, GRASP QTL hits, Selected eQTL hits
rs4534 chr8: 142879686 Missense T/C 99.9% 0.400 0.419 1.000 Promoter histone marks, Enhancer histone marks, DNAse, Motifs changed

SNPs, single nucleotide polymorphisms; A, minor allele; B, major allele; MAF, minor allele frequency; HWE, Hardy–Weinberg equilibrium.

p < 0.05 are excluded.

Association between CYP11B1 gene polymorphisms and ischemic stroke susceptibility.

SNP ID Model Allele/Genotype Case N Control N OR (95% CI) p p (FDR)
rs4736312 Allele C 931 911 1
A 169 187 0.88 (0.70–1.11) 0.289 0.577
Codominant AC 153 159 0.92 (0.71–1.20) 0.530 1.060
AA 8 14 0.55 (0.23–1.34) 0.191 0.382
CC 389 376 1
Dominant AC-AA 161 173 0.89 (0.69–1.15) 0.373 0.747
Recessive CC-AC 542 535 1
AA 8 14 0.57 (0.24–1.37) 0.208 0.312
Log-additive – – – 0.87 (0.69–1.10) 0.248 0.497
rs5017238 Allele A 925 912 1
G 173 188 0.91 (0.72–1.14) 0.398 0.478
Codominant AG 149 156 0.92 (0.70–1.20) 0.536 0.804
GG 12 16 0.73 (0.34–1.56) 0.417 0.417
AA 388 378 1
Dominant AG-GG 161 172 0.90 (0.70–1.17) 0.432 0.648
Recessive AA-AG 537 534 1
GG 12 16 0.75 (0.35–1.60) 0.451 0.451
Log-additive – – – 0.90 (0.72–1.13) 0.357 0.428
rs5301 Allele C 924 912 1
T 176 188 0.92 (0.74–1.16) 0.491 0.491
Codominant TC 158 160 0.96 (0.74–1.25) 0.747 0.747
TT 9 14 0.63 (0.27–1.48) 0.289 0.346
CC 383 376 1
Dominant TC-TT 167 174 0.93 (0.72–1.20) 0.587 0.704
Recessive CC-TC 541 536 1
TT 9 14 0.64 (0.27–1.49) 0.300 0.360
Log-additive – – – 0.91 (0.72–1.15) 0.435 0.435
rs5283 Allele G 740 803 1
A 358 295 1.32 (1.10–1.58) 0.003 0.020
Codominant GA 252 225 1.33 (1.04–1.70) 0.026 0.153
AA 53 35 1.81 (1.14–2.86) 0.012 0.072
GG 244 289 1
Dominant GA-AA 305 260 1.39 (1.10–1.77) 0.007 0.040
Recessive GG-GA 496 514 1
AA 53 35 1.58 (1.01–2.47) 0.044 0.133
Log-additive – – – 1.34 (1.11–1.61) 0.003 0.016
rs6410 Allele C 808 761 1
T 292 339 0.81 (0.67–0.98) 0.027 0.080
Codominant TC 234 245 0.85 (0.67–1.09) 0.207 0.620
TT 29 47 0.56 (0.34–0.91) 0.020 0.061
CC 287 258 1
Dominant TC-TT 263 292 0.81 (0.64–1.02) 0.075 0.224
Recessive CC-TC 521 503 1
TT 29 47 0.60 (0.37–0.97) 0.037 0.224
Log-additive – – – 0.80 (0.66–0.97) 0.022 0.065
rs4534 Allele C 660 639 1
T 440 461 0.92 (0.78–1.10) 0.363 0.544
Codominant TC 288 269 1.06 (0.81–1.37) 0.683 0.820
TT 76 96 0.79 (0.55–1.14) 0.206 0.308
CC 186 185 1
Dominant TC-TT 364 365 0.99 (0.77–1.27) 0.916 0.916
Recessive CC-TC 474 454 1
TT 76 96 0.76 (0.55–1.06) 0.109 0.219
Log-additive – – – 0.92 (0.77–1.10) 0.352 0.528

SNP: single nucleotide polymorphism; OR, odds ratio, 95% CI; 95% confidence intervals; FDR, false discovery rate.

p-values were calculated by unconditional logistic regression analysis with adjustment for age, gender, smoking, and drinking.

A p-value of < 0.05 indicates statistical significance.

Significant p-values are given in bold.

 <italic>CYP11B1</italic> SNPs associated with risk factors for ischemic stroke

We further analyzed the impact of CYP11B1 SNPs on risk factors (gender, age, alcohol intake, smoking, and hypertension) for patients with IS. As shown in Table 4, age-stratified analysis indicated that rs5283 was related to an increased risk of IS in people aged >63 years [allele: OR 1.38, p = 0.015, p (FDR) = 0.089; codominant: GA vs. GG, OR 1.58, p = 0.018, p (FDR) = 0.108, and AA vs. GG, OR 2.41, p = 0.011, p (FDR) = 0.032; dominant: OR 1.69, p = 0.005, p (FDR) = 0.028; recessive: OR 1.93, p = 0.047, p (FDR) = 0.282; log-additive: OR 1.56, p = 0.002, p (FDR) = 0.013]. Rs6410 was related to a lower risk in ischemic stroke aged >63 years under codominant [TT vs. CC, OR 0.43, p = 0.032, p (FDR) = 0.194] and log-additive models [OR 0.72, p = 0.031, p (FDR) = 0.094]. When stratified by gender, it was found that rs5283 showed an enhanced susceptibility to IS in women [allele: OR 1.87, p < 0.001, p (FDR) < 0.001; codominant: GA vs. GG, OR 2.24, p < 0.001, p (FDR) = 0.001, and AA vs. GG, OR 3.31, p = 0.001, p (FDR) = 0.009; dominant: OR 2.39, p < 0.001, p (FDR) <0.001; recessive: OR 2.25, p = 0.024, p (FDR) = 0.073; log-additive: OR 1.99, p < 0.001, p (FDR) < 0.001]. However, rs6410 [allele: OR 0.64, p = 0.003, p (FDR) = 0.009; codominant: TC vs. CC, OR 0.67, p = 0.045, p (FDR) = 0.136, and TT vs. CC, OR 0.30, p = 0.006, p (FDR) = 0.017; dominant: OR 0.60, p = 0.009, p (FDR) = 0.028; recessive: OR 0.36, p = 0.017, p (FDR) = 0.103; log-additive: OR 0.61, p = 0.002, p (FDR) = 0.006] and rs4534 [recessive: OR 0.58, p = 0.046, p (FDR) = 0.093] had a protective impact on the risk of IS in women.

Correlation of CYP11B1 polymorphisms and ischemic stroke susceptibility stratified by age and gender.

SNP ID Model Allele/ Genotype >63 years ≤ 63 years Men Women
OR (95% CI) pa p (FDR) OR (95% CI) pa p (FDR) OR (95% CI) pb p (FDR) OR (95% CI) pb p (FDR)
rs4736312 Allele C 1 1 1 1
A 0.86 (0.62–1.18) 0.345 0.517 0.91 (0.6–1.26) 0.575 1.725 0.85 (0.64–1.13) 0.260 1.558 0.94 (0.65–1.36) 0.746 1.119
Codominant AC 0.82 (0.56–1.22) 0.334 0.668 0.99 (0.67–1.46) 0.946 1.135 0.93 (0.66–1.30) 0.653 1.305 0.89 (0.57–1.37) 0.583 0.874
AA 0.38 (0.08–1.67) 0.198 0.297 0.63 (0.20–2.00) 0.428 0.856 0.40 (0.12–1.31) 0.131 0.785 1.03 (0.25–4.23) 0.968 0.968
CC 1 1 1 1
Dominant AC-AA 0.79 (0.54–1.16) 0.234 0.467 0.95 (0.65–1.39) 0.796 1.591 0.88 (0.63–1.22) 0.439 2.636 0.89 (0.59–1.37) 0.605 0.907
Recessive CC-AC 1 1 1 1
AA 0.40 (0.09–1.75) 0.223 0.335 0.63 (0.20–1.99) 0.430 0.860 0.41 (0.13–1.34) 0.139 0.835 1.06 (0.26–4.35) 0.932 1.118
Log-additive – 0.78 (0.54–1.11) 0.159 0.239 0.92 (0.66–1.29) 0.641 1.282 0.84 (0.63–1.14) 0.263 1.576 0.92 (0.63–1.34) 0.656 0.984
rs5017238 Allele A 1 1 1 1
G 0.89 (0.65–1.22) 0.451 0.541 0.93 (0.67–1.28) 0.655 1.310 0.86 (0.65–1.15) 0.316 0.947 0.97 (0.67–1.41) 0.893 0.893
Codominant AG 0.83 (0.56–1.24) 0.364 0.546 0.98 (0.66–1.46) 0.931 1.396 0.92 (0.66–1.30) 0.648 1.943 0.88 (0.57–1.37) 0.578 1.155
GG 0.55 (0.16–1.91) 0.344 0.413 0.75 (0.27–2.11) 0.590 0.708 0.57 (0.22–1.48) 0.248 0.496 1.36 (0.35–5.21) 0.655 0.983
AA 1 1 1 1
Dominant AG-GG 0.81 (0.55–1.19) 0.282 0.422 0.96 (0.65–1.40) 0.819 1.229 0.88 (0.64–1.23) 0.467 1.400 0.91 (0.60–1.39) 0.671 0.671
Recessive AA-AG 1 1 1 1
GG 0.58 (0.17–2.00) 0.388 0.388 0.76 (0.27–2.11) 0.594 0.713 0.58 (0.22–1.51) 0.265 0.531 1.40 (0.37–5.36) 0.621 0.931
Log-additive – 0.81 (0.57–1.14) 0.225 0.270 0.94 (0.68–1.31) 0.712 0.854 0.86 (0.65–1.15) 0.323 0.970 0.96 (0.65–1.40) 0.812 0.812
rs5301 Allele C 1 1 1 1
T 0.92 (0.67–1.26) 0.590 0.590 0.93 (0.67–1.28) 0.658 0.986 0.91 (0.68–1.21) 0.506 1.012 0.94 (0.65–1.36) 0.752 0.903
Codominant TC 0.87 (0.59–1.28) 0.473 0.568 1.01 (0.68–1.48) 0.976 0.976 0.99 (0.70–1.38) 0.937 1.406 0.89 (0.58–1.37) 0.608 0.729
TT 0.52 (0.14–2.02) 0.346 0.346 0.63 (0.20–2.02) 0.440 0.660 0.51 (0.17–1.52) 0.223 0.669 1.03 (0.25–4.24) 0.966 1.159
CC 1 1 1 1
Dominant TC-TT 0.84 (0.57–1.23) 0.377 0.452 0.97 (0.67–1.41) 0.875 0.875 0.94 (0.68–1.31) 0.725 1.450 0.90 (0.59–1.37) 0.629 0.755
Recessive CC-TC 1 1 1 1
TT 0.55 (0.14–2.09) 0.377 0.452 0.63 (0.20–2.01) 0.436 0.654 0.51 (0.17–1.51) 0.224 0.672 1.06 (0.26–4.35) 0.932 1.118
Log-additive – 0.83 (0.59–1.18) 0.298 0.298 0.94 (0.67–1.31) 0.710 1.065 0.90 (0.67–1.21) 0.504 1.008 0.92 (0.63–1.35) 0.679 0.814
rs5283 Allele G 1 1 1 1
A 1.38 (1.07–1.79) 0.015 0.089 1.26 (0.97–1.63) 1.000 1.000 1.05 (0.83–1.33) 0.692 0.831 1.87 (1.40–2.52) <0.001 <0.001
Codominant GA 1.58 (1.08–2.30) 0.018 0.108 1.22 (0.86–1.74) 0.271 0.814 1.01 (0.73–1.39) 0.955 1.146 2.24 (1.48–3.37) <0.001 0.001
AA 2.41 (1.23–4.73) 0.011 0.032 2.00 (1.00–3.99) 0.051 0.303 1.22 (0.66–2.23) 0.527 0.632 3.31 (1.58–6.90) 0.001 0.009
GG 1 1 1 1
Dominant GA-AA 1.69 (1.18–2.42) 0.005 0.028 1.31 (0.93–1.84) 0.123 0.739 1.04 (0.76–1.41) 0.818 1.227 2.39 (1.61–3.53) <0.001 <0.001
Recessive GG-GA 1 1 1 1
AA 1.93 (1.01–3.68) 0.047 0.282 1.82 (0.93–3.56) 0.082 0.489 1.21 (0.67–2.18) 0.523 0.784 2.25 (1.11–4.56) 0.024 0.073
Log-additive – 1.56 (1.18–2.08) 0.002 0.013 1.32 (1.00–1.74) 0.050 0.297 1.06 (0.83–1.35) 0.651 0.976 1.99 (1.46–2.72) <0.001 <0.001
rs6410 Allele C 1 1 1 1
T 0.81 (0.63–1.06) 0.126 0.378 0.81 (0.62–1.05) 0.109 0.652 0.95 (0.75–1.20) 0.645 0.967 0.64 (0.47–0.86) 0.003 0.009
Codominant TC 0.79 (0.55–1.15) 0.218 0.655 0.80 (0.56–1.15) 0.224 1.344 1.00 (0.73–1.37) 0.986 0.986 0.67 (0.45–0.99) 0.045 0.136
TT 0.43 (0.20–0.93) 0.032 0.194 0.59 (0.30–1.16) 0.123 0.369 0.83 (0.44–1.54) 0.550 0.824 0.30 (0.13–0.70) 0.006 0.017
CC 1 1 1 1
Dominant TC-TT 0.73 (0.51–1.05) 0.092 0.277 0.76 (0.54–1.08) 0.124 0.372 0.97 (0.72–1.32) 0.855 0.855 0.60 (0.41–0.88) 0.009 0.028
Recessive CC-TC 1 1 1 1
TT 0.48 (0.23–1.02) 0.056 0.169 0.65 (0.34–1.25) 0.198 0.593 0.83 (0.45–1.52) 0.541 0.541 0.36 (0.16–0.83) 0.017 0.103
Log-additive – 0.72 (0.54–0.97) 0.031 0.094 0.78 (0.60–1.03) 0.078 0.234 0.95 (0.74–1.22) 0.690 0.828 0.61 (0.44–0.83) 0.002 0.006
rs4534 Allele C 1 1 1 1
T 0.85 (0.67–1.09) 0.194 0.388 1.00 (0.79–1.27) 0.993 1.192 0.99 (0.80–1.23) 0.940 0.940 0.83 (0.63–1.09) 0.170 0.341
Codominant TC 0.96 (0.65–1.42) 0.835 0.835 1.07 (0.73–1.57) 0.714 1.428 1.07 (0.76–1.51) 0.684 4.106 1.01 (0.66–1.54) 0.963 0.963
TT 0.62 (0.35–1.10) 0.103 0.206 0.95 (0.58–1.58) 0.857 0.857 0.91 (0.57–1.47) 0.708 0.708 0.59 (0.33–1.05) 0.074 0.148
CC 1 1 1 1
Dominant TC-TT 0.87 (0.60–1.28) 0.487 0.487 1.04 (0.73–1.49) 0.824 0.989 1.03 (0.75–1.43) 0.839 1.006 0.89 (0.59–1.32) 0.552 1.103
Recessive CC-TC 1 1 1 1
TT 0.64 (0.38–1.07) 0.088 0.177 0.92 (0.58–1.44) 0.703 0.703 0.87 (0.57–1.34) 0.537 0.645 0.58 (0.34–0.99) 0.046 0.093
Log-additive – 0.83 (0.63–1.08) 0.163 0.326 0.99 (0.78–1.27) 0.956 0.956 0.98 (0.78–1.23) 0.850 0.850 0.81 (0.61–1.07) 0.139 0.278

OR, odds ratio, 95% CI; 95% confidence intervals; FDR, false discovery rate.

The pa values were calculated by logistic regression analysis adjusted by gender, smoking, and drinking. The pb values were calculated by logistic regression analysis adjusted by age, smoking, and drinking.

A p-value of < 0.05 indicates statistical significance.

Significant p-values are given in bold.

After stratified by smoking and alcohol intake (Table 5), we found that rs4736312 [allele: OR 0.72, p = 0.045, p (FDR) = 0.091; log-additive: OR 0.69, p = 0.031, p (FDR) = 0.063] and rs6410 [OR 0.74, p = 0.027, p (FDR) = 0.082; codominant: OR 0.42, p = 0.017, p (FDR) = 0.104; recessive: OR 0.46, p = 0.029, p (FDR) = 0.172; log-additive: OR 0.73, p = 0.024, p (FDR) = 0.073] could decrease the risk of patients that do not smoke. In addition, rs5283 [allele: OR 1.40, p = 0.012, p (FDR) = 0.070; codominant: GA vs. GG, OR 1.62, p = 0.008, p (FDR) = 0.051; dominant: OR 1.64, p = 0.005, p (FDR) = 0.029; log-additive: OR 1.45, p = 0.008, p (FDR) = 0.047] could increase the susceptibility of IS in patients that do not smoke. Rs4534 was related to decreased susceptibility in patients that do not consume alcohol under codominant [TT vs. CC, OR 0.57, p = 0.041, p (FDR) = 0.245] and recessive models [OR 0.54, p = 0.016, p (FDR) = 1.977]. We further evaluated the correlations between SNPs and IS complicated with hypertension. As summarized in Table 6, rs5283 significantly increased the risk of IS complicated with hypertension in allele [OR 1.34, p = 0.004, p (FDR) = 0.026], codominant [AA vs. GG, OR 2.07, p = 0.003, p (FDR) = 0.020], dominant [OR 1.35, p = 0.024, p (FDR) = 0.143], recessive [OR 1.87, p = 0.009, p (FDR) = 0.233], and log-additive models [OR 1.35, p = 0.004, p (FDR) = 0.022]. Rs6410 had a lower susceptibility to IS complicated with hypertension [allele: OR 0.80, p = 0.031, p (FDR) = 0.094, codominant: OR 0.52, p = 0.022, p (FDR) = 0.067, recessive: OR 0.56, p = 0.039, p (FDR) = 0.875, and log-additive: OR 0.78, p = 0.025, p (FDR) = 0.074].

Correlation of CYP11B1 polymorphisms and ischemic stroke susceptibility stratified by smoking and drinking.

SNP ID Model Allele/Genotype Smoking Non-smoking Drinking Non-drinking
OR (95% CI) pa p (FDR) OR (95% CI) pa p (FDR) OR (95% CI) pb p (FDR) OR (95% CI) pb p (FDR)
rs4736312 Allele C 1 1 1 1
A 1.09 (0.79–1.50) 0.615 0.615 0.72 (0.52–0.99) 0.045 0.091 0.95 (0.70–1.30) 0.769 1.538 0.81 (0.58–1.13) 0.207 0.311
Codominant AC 1.05 (0.72–1.52) 0.813 0.813 0.74 (0.50–1.09) 0.128 0.257 1.02 (0.70–1.47) 0.934 1.120 0.75 (0.51–1.12) 0.163 0.326
AA 1.40 (0.29–6.67) 0.672 0.672 0.36 (0.11–1.16) 0.086 0.258 0.24 (0.05–1.22) 0.085 0.254 0.88 (0.29–2.71) 0.829 0.994
CC 1 1 1 1
Dominant AC-AA 1.06 (0.73–1.53) 0.763 0.763 0.70 (0.48–1.01) 0.059 0.176 0.96 (0.67–1.37) 0.802 2.406 0.76 (0.52–1.12) 0.170 0.511
Recessive CC-AC 1 1 1 1
AA 1.38 (0.29–6.56) 0.684 0.820 0.39 (0.12–1.25) 0.113 0.339 0.24 (0.05–1.20) 0.083 0.248 0.95 (0.31–2.90) 0.928 0.820
Log-additive – 1.07 (0.76–1.51) 0.708 0.708 0.69 (0.50–0.97) 0.031 0.063 0.89 (0.64–1.24) 0.490 0.980 0.81 (0.58–1.13) 0.218 0.327
rs5017238 Allele A 1 1 1 1
G 1.11 (0.81–1.53) 0.513 0.616 0.74 (0.53–1.02) 0.063 0.076 0.97 (0.71–1.32) 0.829 1.244 0.84 (0.60–1.17) 0.302 0.362
Codominant AG 1.05 (0.72–1.54) 0.796 1.195 0.73 (0.50–1.08) 0.120 0.359 1.03 (0.71–1.50) 0.863 1.725 0.74 (0.49–1.10) 0.138 0.415
GG 1.41 (0.43–4.65) 0.569 0.854 0.46 (0.16–1.37) 0.165 0.198 0.40 (0.12–1.36) 0.144 0.287 1.15 (0.40–3.27) 0.793 1.190
AA 1 1 1 1
Dominant AG-GG 1.07 (0.74–1.56) 0.708 1.062 0.70 (0.48–1.02) 0.066 0.132 0.97 (0.68–1.39) 0.870 1.305 0.77 (0.53–1.14) 0.189 0.378
Recessive AA-AG 1 1 1 1
GG 1.39 (0.43–4.56) 0.584 0.876 0.50 (0.17–1.49) 0.214 0.320 0.40 (0.12–1.34) 0.137 0.274 1.24 (0.44–3.51) 0.683 1.403
Log-additive – 1.09 (0.78–1.52) 0.623 0.747 0.72 (0.51–1.00) 0.047 0.070 0.91 (0.66–1.26) 0.558 0.837 0.84 (0.60–1.18) 0.315 0.378
rs5301 Allele C 1 1 1 1
T 1.16 (0.84–1.59) 0.372 0.558 0.74 (0.53–1.02) 0.061 0.091 0.98 (0.72–1.33) 0.891 1.069 0.86 (0.62–1.20) 0.381 0.381
Codominant TC 1.10 (0.76–1.60) 0.609 1.217 0.77 (0.52–1.13) 0.184 0.276 1.02 (0.71–1.48) 0.901 1.352 0.83 (0.56–1.22) 0.336 0.504
TT 1.75 (0.39–7.75) 0.462 0.923 0.36 (0.11–1.17) 0.090 0.135 0.37 (0.09–1.49) 0.161 0.241 0.91 (0.30–2.78) 0.864 0.864
CC 1 1 1 1
Dominant TC-TT 1.13 (0.78–1.63) 0.530 1.060 0.72 (0.50–1.05) 0.089 0.134 0.97 (0.68–1.39) 0.877 1.052 0.83 (0.57–1.22) 0.341 0.409
Recessive CC-TC 1 1 1 1
TT 1.70 (0.38–7.49) 0.485 0.970 0.39 (0.12–1.26) 0.114 0.229 0.37 (0.09–1.47) 0.156 0.031 0.95 (0.31–2.91) 0.935 0.540
Log-additive – 1.14 (0.81–1.61) 0.452 0.678 0.72 (0.51–1.00) 0.047 0.056 0.92 (0.66–1.28) 0.603 0.723 0.86 (0.62–1.21) 0.388 0.388
rs5283 Allele G 1 1 1 1
A 1.25 (0.96–1.62) 0.098 0.586 1.40 (1.08–1.81) 0.012 0.070 1.15 (0.89–1.49) 0.286 0.859 1.51 (1.17–1.96) 0.002 0.011
Codominant GA 1.14 (0.80–1.63) 0.460 1.379 1.62 (1.13–2.32) 0.008 0.051 0.91 (0.64–1.30) 0.617 1.852 2.00 (1.39–2.89) <0.001 0.001
AA 1.90 (0.95–3.82) 0.071 0.424 1.76 (0.92–3.34) 0.086 0.172 2.20 (1.07–4.52) 0.032 0.190 1.69 (0.90–3.19) 0.102 0.204
GG 1 1 1 1
Dominant GA-AA 1.23 (0.87–1.73) 0.238 1.426 1.64 (1.16–2.32) 0.005 0.029 1.03 (0.74–1.44) 0.862 1.723 1.95 (1.37–2.76) <0.001 0.001
Recessive GG-GA 1 1 1 1
AA 1.79 (0.91–3.51) 0.093 0.280 1.39 (0.75–2.58) 0.296 0.355 2.29 (1.14–4.62) 0.020 1.413 1.21 (0.66–2.21) 0.540 0.179
Log-additive – 1.26 (0.96–1.66) 0.099 0.593 1.45 (1.10–1.90) 0.008 0.047 1.17 (0.89–1.53) 0.266 0.798 1.55 (1.18–2.04) 0.002 0.010
rs6410 Allele C 1 1 1 1
T 0.88 (0.68–1.15) 0.357 0.714 0.74 (0.57–0.97) 0.027 0.082 0.84 (0.65–1.08) 0.171 1.024 0.78 (0.60–1.03) 0.075 0.225
Codominant TC 0.88 (0.62–1.26) 0.482 2.891 0.82 (0.58–1.17) 0.281 0.337 0.84 (0.59–1.20) 0.336 2.014 0.87 (0.61–1.24) 0.441 0.530
TT 0.83 (0.41–1.72) 0.622 0.747 0.42 (0.21–0.86) 0.017 0.104 0.61 (0.31–1.23) 0.166 0.200 0.51 (0.25–1.05) 0.068 0.203
CC 1 1 1 1
Dominant TC-TT 0.87 (0.62–1.23) 0.443 1.329 0.75 (0.53–1.05) 0.096 0.115 0.81 (0.58–1.13) 0.214 1.283 0.81 (0.57–1.14) 0.224 0.336
Recessive CC-TC 1 1 1 1
TT 0.89 (0.44–1.79) 0.735 0.735 0.46 (0.23–0.92) 0.029 0.172 0.67 (0.34–1.31) 0.236 1.080 0.54 (0.27–1.10) 0.090 0.048
Log-additive – 0.90 (0.68–1.19) 0.445 0.890 0.73 (0.55–0.96) 0.024 0.073 0.81 (0.62–1.07) 0.135 0.811 0.79 (0.60–1.04) 0.093 0.279
rs4534 Allele C 1 1 1 1
T 0.85 (0.69–1.12) 0.299 0.896 0.97 (0.76–1.23) 0.785 0.785 1.01 (0.79–1.28) 0.960 0.960 0.84 (0.66–1.08) 0.171 0.343
Codominant TC 1.05 (0.71–1.54) 0.807 0.968 1.02 (0.70–1.48) 0.929 0.929 1.01 (0.69–1.47) 0.979 0.979 1.07 (0.73–1.57) 0.715 0.715
TT 0.68 (0.40–1.14) 0.143 0.428 0.88 (0.52–1.48) 0.622 0.622 1.03 (0.62–1.72) 0.901 0.901 0.57 (0.33–0.98) 0.041 0.245
CC 1 1 1 1
Dominant TC-TT 0.94 (0.66–1.36) 0.759 0.911 0.98 (0.69–1.40) 0.924 0.924 1.01 (0.71–1.45) 0.949 0.949 0.93 (0.65–1.34) 0.696 0.696
Recessive CC-TC 1 1 1 1
TT 0.66 (0.41–1.05) 0.078 0.465 0.87 (0.54–1.40) 0.560 0.560 1.03 (0.65–1.62) 0.900 0.928 0.54 (0.33–0.89) 0.016 1.977
Log-additive – 0.86 (0.67–1.11) 0.240 0.719 0.95 (0.74–1.23) 0.709 0.709 1.01 (0.79–1.30) 0.911 0.911 0.82 (0.63–1.05) 0.120 0.239

OR, odds ratio, 95% CI; 95% confidence intervals; FDR, false discovery rate.

The pa-values were calculated by logistic regression analysis adjusted by age, gender, and drinking. The pb-values were calculated by logistic regression analysis adjusted by age, gender, and smoking.

A p < 0.05 indicates statistical significance.

Significant p-values are given in bold.

The relationship between CYP11B1 polymorphisms and the risk of ischemic stroke complicated with hypertension.

SNP ID Model Allele/Genotype Case N Control N OR (95% CI) p p (FDR)
rs4736312 Allele C 658 911 1
A 112 187 0.83 (0.64–1.07) 0.149 0.299
Codominant AC 100 159 0.84 (0.62–1.12) 0.235 0.469
AA 6 14 0.59 (0.22–1.55) 0.283 0.566
CC 279 376 1
Dominant AC-AA 106 173 0.82 (0.61–1.09) 0.167 0.334
Recessive CC-AC 379 535 1
AA 6 14 0.62 (0.23–1.63) 0.330 0.672
Log-additive – – – 0.82 (0.63–1.06) 0.129 0.259
rs5017238 Allele A 654 912 1
G 114 188 0.85 (0.66–1.09) 0.194 0.291
Codominant AG 98 156 0.84 (0.63–1.14) 0.263 0.394
GG 8 16 0.68 (0.29–1.62) 0.388 0.466
AA 278 378 1
Dominant AG-GG 106 172 0.83 (0.62–1.11) 0.202 0.303
Recessive AA-AG 376 534 1
GG 8 16 0.72 (0.30–1.69) 0.448 0.580
Log-additive – – – 0.84 (0.65–1.08) 0.176 0.264
rs5301 Allele C 653 912 1
T 117 188 0.87 (0.68–1.12) 0.275 0.330
Codominant TC 103 160 0.87 (0.65–1.16) 0.342 0.411
TT 7 14 0.69 (0.27–1.74) 0.433 0.433
CC 275 376 1
Dominant TC-TT 110 174 0.85 (0.64–1.14) 0.278 0.333
Recessive CC-TC 378 536 1
TT 7 14 0.72 (0.29–1.81) 0.484 0.009
Log-additive – – – 0.86 (0.66–1.11) 0.241 0.289
rs5283 Allele G 516 803 1
A 254 295 1.34 (1.10–1.64) 0.004 0.026
Codominant GA 168 225 1.24 (0.94–1.64) 0.122 0.733
AA 43 35 2.07 (1.27–3.36) 0.003 0.020
GG 174 289 1
Dominant GA-AA 211 260 1.35 (1.04–1.76) 0.024 0.143
Recessive GG-GA 342 514 1
AA 43 35 1.87 (1.17–2.98) 0.009 0.233
Log-additive – – – 1.35 (1.10–1.66) 0.004 0.022
rs6410 Allele C 568 761 1
T 202 339 0.80 (0.65–0.98) 0.031 0.094
Codominant TC 164 245 0.85 (0.65–1.11) 0.231 0.692
TT 19 47 0.52 (0.29–0.91) 0.022 0.067
CC 202 258 1
Dominant TC-TT 183 292 0.79 (0.61–1.03) 0.085 0.255
Recessive CC-TC 366 503 1
TT 19 47 0.56 (0.32–0.97) 0.039 0.875
Log-additive – – – 0.78 (0.63–0.97) 0.025 0.074
rs4534 Allele C 464 639 1
T 306 461 0.91 (0.76–1.10) 0.348 0.348
Codominant TC 192 269 0.96 (0.72–1.29) 0.804 0.804
TT 57 96 0.81 (0.54–1.20) 0.290 0.434
CC 136 185 1
Dominant TC-TT 249 365 0.92 (0.70–1.21) 0.566 0.566
Recessive CC-TC 328 454 1
TT 57 96 0.82 (0.58–1.18) 0.292 0.599
Log-additive – – – 0.91 (0.75–1.10) 0.337 0.337

OR, odds ratio, 95% CI; 95% confidence intervals; FDR, false discovery rate.

The p-values were calculated by logistic regression analysis adjusted by age, gender, smoking, and drinking.

p < 0.05 indicates statistical significance.

 FRPR results

We performed the FPRP analysis to verify the positive data in the study. As shown in Supplementary Table S1, it was found that the associations between CYP11B1 gene polymorphism and IS in the total group and subgroup, almost all of them, were significant (FPRP < 0.2).

 SNP–SNP interactions influenced ischemic stroke risk

The MDR method was used to analyze the correlation between SNP–SNP interactions and IS. As presented in Table 7, rs5283 was the best predictive model for IS (OR 1.39, p = 0.007), with the highest testing accuracy (0.5400) and perfect cross-validation consistently (CVC) (10/10). The interaction map showed that rs4534 and rs6410 had a positive synergistic interaction (0.05%), and the interaction map with negative percent entropy indicated the redundancy or independence of each pairwise combination of SNPs (Figure 1).

Best models to predict ischemic stroke by MDR.

Model Testing bal. acc. CVC OR (95% CI) p
rs5283 0.5400 10/10 1.39 (1.10–1.76) 0.007
rs6410, rs4534 0.5327 10/10 1.46 (1.15–1.85) 0.002
rs5017238, rs5301, rs5283 0.5036 5/10 1.51 (1.19–1.91) <0.001
rs5017238, rs5283, rs6410, rs4534 0.4982 5/10 1.56 (1.23–1.99) <0.001
rs5017238, rs5301, rs5283, rs6410, rs4534 0.5055 7/10 1.58 (1.24–2.01) <0.001
rs4736312, rs5017238, rs5301, rs5283, rs6410, rs4534 0.5055 10/10 1.58 (1.24–2.01) <0.001

The model with the maximum testing accuracy and maximum CVC was considered the best model.

The p-values were validated by 1,000 permutation tests. Significant p-values are given in bold.

Bal. Acc., balanced accuracy; CVC, cross-validation consistently; MDR, multifactor dimensionality reduction.

The SNP–SNP interaction map. Values in nodes represent the information gains of the individual attributes (main effects). Values between nodes are information gains of each pair of attributes (interaction effects). Orange with positive percent entropy indicates a strong synergistic interaction. Light range, blue, and green with negative percent entropy indicate redundancy or independence.

 Discussion

We studied the impact of CYP11B1 SNPs on the IS risk. We found that rs5283 and rs6410 were closely related to the risk of IS. To the best of our knowledge, this study is the first of its kind to reveal an association between the CYP11B1 gene polymorphisms and the risk of stroke in the Han population.

Rs5283, rs6410, rs4736312, and rs4534 are located on the second exon, the first exon, the 3′ UTR, and the first exon of the CYP11B1 gene, respectively. Our study showed that rs5283 increases the risk of IS significantly, whereas rs6410 played a protective role in IS. However, Zhang et al. (2010) reported that rs6410 was associated with an increased risk of primary hyperaldosteronism. This difference may be caused by the type of disease. The incidence of stroke is proportional to age, with ~75% of stroke occurring in patients above 64 years (Mackay et al., 2004). The average age of the participants was 63 years; thus, we stratified the age group by 63 years. We found that rs5283 was associated with an increased susceptibility to IS in people aged >63 years. On the contrary, rs6410 decreased susceptibility to IS in people aged >63 years. Yang et al. (2020) reported that rs6068816 enhanced the IS risk in people aged >64 years. Cai et al. (2020) showed that rs4646 could increase the IS risk in people aged >64 years. Besides, rs2074633 and rs28688791 enhanced the risk of stroke in people aged <60 years (Wang et al., 2019). Taking various points from the above, we speculate that the association of CYP11B1 gene polymorphisms with IS susceptibility may rely on age. In addition, we observed that rs5283 and rs6410 were closely related to IS risk in women. Some studies showed that SNPs were related to IS susceptibility but influenced by gender (Xu et al., 2017; Gu et al., 2018; Yuan et al., 2021). Besides, sex differences are very important to influence the occurrence of IS (Bushnell et al., 2018). Thus, we guess that CYP11B1 genetic variants impact on the risk of IS relying on gender. Smoking and hypertension are risk factors for IS. We also analyzed the correlation between CYP11B1 polymorphisms and IS risk stratified by smoking and hypertension. We observed that rs5283 could enhance the risk of IS in patients that do not smoke and those with hypertension. Rs6410 was related to decreased susceptibility to IS risk in patients that do not smoke and those with hypertension. In addition, rs473631 polymorphism reduced IS risk in patients that do not smoke. Similar to our results, Aysun et al. revealed that genetic variants determine IS risk influenced by hypertension and smoking (Türkanoglu Özçelik et al., 2018). Tu, Yang, and Diakite showed that SNPs were related to the susceptibility of IS with hypertension (Diakite et al., 2016; Tu et al., 2020; Yang et al., 2020). Cheng et al. (2017) showed that the interactions between SNPs and smoking turned out to be significant in IS. Based on the above, we concluded that gene polymorphisms together with age, gender, smoking, and hypertension are very significant risk factors for IS.

Function prediction found that rs5283 and rs4736312 were related to the regulation of deoxyribonuclease (DNAse), motifs changed, and selected expression quantitative trait loci (eQTL) hits. Rs6410 influences the regulation of promoter histone marks, enhancer histone marks, motifs changed, GRASP (Genome-Wide Repository of Associations Between SNPs and Phenotypes) QTL hits, and selected eQTL hits. Besides, rs4534 contributed to the regulation of promoter histone marks, DNAse, motifs changed, and enhancer histone marks, which have given that these SNPs had some molecular functions in IS. Studies showed that SNPs participate in the occurrence of human diseases by regulating the expression of the gene (Alvarez-Madrazo et al., 2013; Song et al., 2020). We estimate that CYP11B1 gene polymorphism may affect the occurrence of the disease by regulating its expression, and molecular experiments have to be carried out to verify it.

There are a few disadvantages in our study. First, SNPs in CYP11B1 may influence the occurrence of IS by regulating the expression of CYP11B1, but we have not detected a similar trait in the current study, so, this aspect merits future investigation. Second, the functional experiments of CYP11B1 gene polymorphisms in patients with IS are still lacking in clarity and require further investigation and an in-depth study. Despite these limitations, our study is the first to explore the roles of CYP11B1 gene polymorphisms in patients with IS.

 Conclusion

In summary, our study provided evidence that CYP11B1 gene polymorphisms influence IS susceptibility in the Chinese Han population, which has given a new biomarker for the diagnosis and prevention of IS.

 Data availability statement

The original contributions presented in the study are publicly available. This data can be found here: https://doi.org/10.5281/zenodo.7344449.

 Ethics statement

The studies involving human participants were reviewed and approved by Xianyang Central Hospital. The patients/participants provided their written informed consent to participate in this study.

 Author contributions

YD conceived, designed the experiments, and revised the manuscript. GL performed the experiment, analyzed the data, and wrote the manuscript. Both authors read and approved the final manuscript.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

The authors thank all participants and volunteers in this study.

 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2022.1030551/full#supplementary-material

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