Skip to main content

Genetic variants in CYP11B1 influence the susceptibility to coronary heart disease



Genetic factors are important risk factors to develop coronary heart disease (CHD). In this study, we mainly explored whether CYP11B1 mutations influence CHD risk among Chinese Han population.


Six variants were genotyped using Agena MassARRAY system from 509 CHD patients and 509 healthy controls. The correlations between CYP11B1 mutations and CHD risk were assessed using odds ratio (OR) and 95% confidence interval (95% CI) by logistic regression. The haplotype analysis and were ultifactor dimensionality reduction (MDR) were conducted.


In the overall analysis, CYP11B1 polymorphisms were not correlated with CHD susceptibility. In the stratified analysis, we found that rs5283, rs6410, and rs4534 are significantly associated with susceptibility to CHD dependent on age and gender (p < 0.05). Moreover, we also observed that rs5283 and rs4534 could affect diabetes/hypertension risk among CHD patients (p < 0.05). In addition, the Crs4736312Ars5017238Crs5301Grs5283Trs6410Crs4534 haplotype of CYP11B1 reduce the susceptibility to CHD (p < 0.05).


We found that rs4534, rs6410 and rs5283 in CYP11B1 gene influence the susceptibility to CHD, which depend on age and gender.

Peer Review reports


Coronary heart disease (CHD) is a heart disease caused by coronary artery atherosclerosis that causes stenosis or occlusion of the lumen, leading to myocardial ischemia, hypoxia, or necrosis. Epidemiological research showed that more than 8.14 million people died of CHD in 2013, accounting for 50% of the total deaths from cardiovascular disease (CVD) [1]. CHD is considered to be one of the leading causes of death in people worldwide. The World health organization (WHO) predicts that CHD will account for 13.1% of all deaths by 2030 [2]. In China, CHD is the second leading cause of death from CVD, accounting for 22% of urban and 13% of rural mortality. Moreover, the medical costs for CHD are expected to increase by 100%, posing a severe socio-economic burden on individuals and society [3]. Therefore, it is urgent to explore the pathogenesis and etiology of CHD.

It is widely known that CHD is a complex disease that is influenced by environmental and genetic factors [4]. Genetic factors are important risk factors to develop CHD, accounting for as 30 ~ 60% of the variation in the risk of CHD [5]. Large-scale studies have documented that genetic polymorphisms are significantly associated with CHD at genome-wide significance [6]. Moreover, previous studies also have reported that a significant association between candidate gene polymorphism and CHD susceptibility [7,8,9]. These results underscored the crucial role of genetic factors in the pathogenesis of CHD.

The 11β-hydroxylase (CYP11B1) gene is located on chromosome 8q24.3, contains 9 exons and 8 introns, and consists of 503 amino acids [10]. It is a key enzyme responsible for the final step in cortisol biosynthesis [11]. At present, studies have found that the genetic variation of CYP11B1 was involved in the occurrence and development of various diseases. Rs6410 was significantly related to the secretion of aldosterone in the Chinese population [11]. However, rs4534, rs5283, rs4736312, rs5017238 and rs5301 in CYP11B1 have not been reported to be related to disease in the literature. The CYP11B family is involved in the synthesis of important steroid hormones. The genotypes of the CYP11B1/CYP11B2 loci are in strong linkage disequilibrium [12]. The two synthesize 11β hydroxylase and aldosterone synthase, and then synthesize steroids, which play an important role in myocardial fibrosis, hypertension and arteriosclerosis [10, 13]. Studies have shown that steroid metabolism induces the development of coronary artery disease [14]. CYP11B1 activates the pituitary-adrenal axis by synthesizing 11β hydroxylase, promoting the accumulation of adrenal cortical hormones, and affecting the production of aldosterone [11], which can damage cardiac or renal organs and induce hypertension [15]. Aldosterone synthase inhibitors can efficiently regulate aldosterone levels [16] by targeting CYP11B1 with high selectivity. In addition, hypertension and diabetes are major risk factors for cardiovascular disease [15]. Based on the above studies, we hypothesized that CYP11B1 may be play a vital role in the occurrence of CHD.

Therefore, we explored the associations of CYP11B1 polymorphisms with CHD susceptibility. These genetic variants may provide a new screening strategy for CHD among Chinese Han population.

Materials and methods

Study population

This study recruited 509 patients with CHD and 509 healthy controls from Xi’an Hospital of Traditional Chinese Medicine. The patients were diagnosed as CHD by two experienced imaging specialists according to coronary angiography examination. CHD was defined as more than 1 ( ≥) atherosclerotic plaque in a major coronary artery (≥ 1.5 mm lumen diameter) causing ≥ 50% luminal diameter stenosis by coronary angiography examination. Patients suffered from congenital heart disease, cardiomyopathy, malignancy, chronic inflammatory disease, and liver or kidney disease were excluded. The healthy controls were selected from the same hospital during the same period. The subjects without cardiovascular disease, autoimmune disease, malignancy, and other known disease were included in this study. The study got approval of the ethics committee of the hospital and the experimental procedures were in accordance with the Declaration of Helsinki. Meanwhile, the informed consent signed by the subjects was obtained. Dyslipidemia is a key factor leading to the development of coronary heart disease, and coronary heart disease is related to various indicators such as triglycerides (TG), total cholesterol (TC), low density lipoprotein (LDL) and high density lipoprotein (HDL) in blood lipids [17]. There is an association between elevated serum UA levels and cardiovascular diseases such as coronary heart disease and stroke [18]. In addition, WBC often serves as an effective marker of inflammation, and elevated WBC counts are associated with common risk factors for coronary heart disease, including hypertension. An automatic blood analyzer was used to detect TC, TG, LDL, HDL and other indicators at 4 degrees Celsius. EDTA-treated anticoagulated samples are used to measure blood rheology parameters such as red blood cells (RBC) and white blood cells (WBC).

SNP selection and genotyping

According to the criteria of minor allele frequency (MAF) ≥ 0.05, six SNPs (rs4534, rs5283, rs6410, rs4736312, rs5017238 and rs5301) of CYP11B1 gene were selected from dbSNP database ( Genomic DNA from peripheral blood was isolated using the DNA Extraction Kit (GoldMag, Co, Ltd, Xi’an, China). The concentration and purity of DNA were measured by NanoDrop 2000 (Thermo Scientific, USA). The selected SNPs were genotyped using Agena MassARRAY system (Agena, San Diego, CA, U.S.A.) as described previously [19, 20] and data was managed using Agena Typer 4.0 software.

Statistical analysis

Student t-test and χ2 test were used to assess the difference in age and gender of study population. The Hardy–Weinberg equilibrium (HWE) in controls was evaluated by χ2 test. The associations between CYP11B1 SNPs and CHD susceptibility was analyzed using odds ratio (OR) and 95% confidence interval (CI) by logistic regression. In addition, linkage disequilibrium (LD) and haplotype analysis were evaluated by the Haploview software and the PLINK software. Multifactor dimensionality reduction (MDR) was conducted to assess the SNP-SNP interactions in the risk of CHD. The differences in clinical parameters in CHD patients were tested by one-way analysis of variance (ANOVA). Statistical power and false positive report probability (FPRP) values were calculated by the Excel spreadsheet which was offered on Wacholder’s website [21]. A p < 0.05 was used as the threshold of significance.


General characteristics of CHD patients and healthy controls

A total of 509 CHD patients (335 males and 174 females) and 509 healthy controls (335 males and 174 females) included in this study were of Han Chinese ethnicity. The basic characteristics of participants were summarized in Table 1. No significant difference was observed in terms of age (p = 0.094), gender (p = 1.000), low-density lipoprotein (LDL, p = 0.157), urea (p = 0.056) between the case and control groups, with their mean age being 62.16 ± 10.30 and 61.14 ± 9.02 years, respectively. Among all the CHD patients, 318 patients had hypertension, and 147 patients had diabetes. Compared with the normal control group, the levels of TG, TC, HDL, UA, RBC, WBC, HGB and Platelet were significantly decreased in the CHD group.

Table 1 Primary characteristics of the cases and controls

Association of CYP11B1 polymorphisms and CHD risk

Additional File 1: Table S1 exhibited the primary information of CYP11B1 polymorphisms. The genotype frequency distributions of the selected polymorphisms of CYP11B1 were in line with HWE (p > 0.05).

We investigated the association between CYP11B1 polymorphisms and CHD risk among Chinese Hans. As shown in Table 2, CYP11B1 polymorphisms were not correlated with CHD susceptibility in total population under five heritance models (p > 0.05).

Table 2 Association of CYP11B1 polymorphisms and CHD risk

Stratification analyses

The correlation between CYP11B1 polymorphisms and CHD risk was further analyzed in different subgroups (age, gender, hypertension and diabetes). Table 3 showed that the TC (OR = 1.80, 95% CI = 1.19–2.74, p = 0.006) and TT + TC genotypes (OR = 1.68, 95% CI = 1.13–2.50, p = 0.011) of rs4534 was related to a significantly increased risk of CHD in younger population (age ≤ 60 years). However, the T allele (OR = 0.72, 95% CI = 0.53–0.96, p = 0.026), TC genotype (OR = 0.67, 95% CI = 0.45–0.99, p = 0.042), TT + TC genotype (OR = 0.65, 95% CI = 0.45–0.94, p = 0.023) and the additive model (OR = 0.70, 95% CI = 0.52–0.95, p = 0.022) of rs6410 showed a decreased risk of CHD patients with age ≤ 60 years.

Table 3 The association between CYP11B1 polymorphisms and CHD susceptibility stratified by age and gender

The results of gender stratification were presented in Table 3. In females, except for the recessive model, rs6410 was correlated with a lower-risk of CHD in other models (allele: OR = 0.68, 95% CI = 0.51–0.92, p = 0.011; homozygote: OR = 0.44, 95% CI = 0.20–0.95, p = 0.036; heterozygote: OR = 0.67, 95% CI = 0.45–0.99, p = 0.044; dominant: OR = 0.63, 95% CI = 0.43–0.92, p = 0.017; additive: OR = 0.66, 95% CI = 0.49–0.90, p = 0.009). However, rs4583 could elevate the susceptibility to CHD in the allele (OR = 1.37, 95% CI = 1.01–1.85, p = 0.040), heterozygote (OR = 1.98, 95% CI = 1.32–2.97, p = 0.001), dominant (OR = 1.80, 95% CI = 1.23–2.64, p = 0.003), and additive model (OR = 1.40, 95% CI = 1.03–1.91, p = 0.034).

Stratified analyses by diabetes revealed that rs5283 increased the risk of diabetes in CHD patients (allele: OR = 1.45, 95% CI = 1.09–1.93, p = 0.012; heterozygote: OR = 1.79, 95% CI = 1.19–2.70, p = 0.006; dominant: OR = 1.78, 95% CI = 1.20–2.64, p = 0.004; additive: OR = 1.47, 95% CI = 1.09–1.98, p = 0.011, Table 4). While rs4534 could reduce the risk of diabetes in CHD subjects (allele: OR = 0.74, 95% CI = 0.56–0.98, p = 0.032; homozygote: OR = 0.53, 95% CI = 0.28–0.99, p = 0.048; additive: OR = 0.73, 95% CI = 0.54–0.98, p = 0.036). In addition, we found rs4534 was associated with a higher risk of hypertension in CHD patients (homozygote: OR = 1.97, 95% CI = 1.08–3.59, p = 0.026; recessive: OR = 1.84, 95% CI = 1.07–3.16, p = 0.028; additive: OR = 1.33, 95% CI = 1.01–1.75, p = 0.044). There were no significant associations between rs4736312, rs5017238, and rs5301 and CHD susceptibility.

Table 4 Associations of CYP11B1 polymorphisms and CHD risk stratified by diabetes and hypertension

Clinical characteristics and SNPs

We also investigated the correlation between clinical characteristics and SNPs (Additional File 1: Table S2). The results have shown that there was no significant correlation between the genetic variation of CYP11B1 and clinical parameters (p > 0.05).

FPRP analysis

FPRP and statistical power were calculated for all positive results. As shown in Additional File 1: Table S3, at the prior probability of 0.1 and FPRP threshold of 0.2, the significant results of rs5283 remained noteworthy.

Haplotype analysis and MDR analysis

The haplotype analysis of CYP11B1 polymorphisms and CHD risk was conducted. The results of Table 5 presented that the Crs4736312Ars5017238Crs5301Grs5283Trs6410Crs4534 haplotype was correlated with a decreased risk of CHD compared to Crs4736312Ars5017238Crs5301Grs5283Crs6410Trs4534 (OR = 0.72, 95%CI = 0.54–0.96, p = 0.024). And we observed an LD plot consisted of six SNPs (rs4736312, rs5017238, rs5301, rs5283, rs6410 and rs4534), as exhibited in Fig. 1.

Table 5 Haplotype analysis of CYP11B1 polymorphisms and CHD risk
Fig. 1
figure 1

Haplotype block map for SNPs in CYP11B1. Block 1 includes rs4736312, rs5017238, rs5301, rs5283, rs6410 and rs4534. The numbers inside the diamonds indicate the D’ for pairwise analyses.

Then, the SNP-SNP interaction was performed by MDR analysis. As shown in Fig. 2, the Fruchterman-Reingold (Fig. 2) described the interactions between these SNPs. The results of MDR model analysis of the SNP-SNP interactions are demonstrated in Table 6. The six-locus model including rs4736312, rs5017238, rs5301, rs5283, rs6410, rs4534 was the best model and driving the high-risk combinations for CHD (CVC = 10/10, OR = 1.51, 95% CI = 1.18–1.93, p = 0.0011).

Fig. 2
figure 2

The Fruchterman-Reingold of SNP-SNP interactions. Each SNP is reported in per cent the value of Information Gain (IG), while numbers in the connections indicate the entropy-based IG for the SNP pairs. Orange bar indicate the high-level synergies on the phenotype, while the brown indicate a medium-level interaction, green and blue connections with negative IG values indicate redundancy or lack of synergistic interactions between the markers

Table 6 MDR analysis of SNP-SNP interactions


This is the first study to explore the effect of CYP11B1 polymorphisms on CHD susceptibility. The results of overall analysis revealed that CYP11B1 polymorphisms were not correlated with CHD susceptibility. In the stratified analysis, we found that rs5283, rs6410, and rs4534 are significantly associated with susceptibility to CHD in females and individuals aged ≤ 60 years old. Moreover, we also observed that rs5283 and rs4534 could affect diabetes/hypertension risk among CHD patients. In addition, the Crs4736312Ars5017238Crs5301Grs5283Trs6410Crs4534 haplotype of CYP11B1 reduce the susceptibility to CHD. These data highlight the crucial role of CYP11B1 genetic variants in the development of CHD.

CYP11B1 gene is located on chromosome 8q24.3, containing 9 exons and 8 introns. It catalyzes the final step of cortisol biosynthesis. Some studies have documented that elevated cortisol is associated with a number of metabolic changes, such as hyperlipidaemia, diabetes, hypertension and abdominal adiposity [22, 23], which are correlated with CHD risk. Moreover, cortisol had a direct impact on the heart and blood vessels and played an important role in the process of atherogenesis and cardiovascular disease [24]. These results implied that CYP11B1 may be involved in pathophysiology of CHD through regulating cortisol. Recently, some reports have studied the role of CYP11B1 polymorphisms in disease. For example, Deng et al. indicated that rs4534 showed no significant association with autism in Chinese children [25]. Zhang et al. have shown that rs6410 was related to primary hyperaldosteronism, which is a common form of secondary hypertension [11]. Meanwhile, Wang et al. indicated that rs6410 and rs6387 haplotype is correlated with persistent postoperative hypertension in Chinese patients undergoing adrenalectomy with aldosterone-producing adenoma [13]. However, no study has investigated the effect of CYP11B1 genetic variants on CHD. Rs6410 in CYP11B1 can affect skeletal maturation through variable shearing [26] and was significantly associated with trabecular bone mineral density and cross-sectional area in Caucasian elderly men [27]. However, rs5283, rs4736312, rs5017238 and rs5301 in CYP11B1 have not been reported to be related to disease in the literature.

In the present study, we found that rs6410 reduced the susceptibility to CHD individuals aged ≤ 60 years old and females. Rs5283 not only increased the susceptibility to CHD in females, but also enhanced the risk of diabetes among CHD patients. Rs4534 also correlated with increased risk of CHD subjects younger than 60 years. Besides, in CHD patients, rs4534 enhanced the susceptibility to diabetes, whereas reduced the risk of hypertension. The rs4534 polymorphism is a missense variant located in exon 9 of the CYP11B1 gene. Rs5283 and rs6410 are, synonymous nucleotide polymorphisms, located on the exon region. Missense and synonymous mutations have been widely studied in the development of disease by causing changes in protein expression, conformation and function [28,29,30,31]. Therefore, we presumed that these three polymorphisms can affect CYP11B1 gene mRNA and protein by altering translation, mRNA stability or protein folding, thereby affecting CHD susceptibility. However, it should be confirmed in further functional studies.


To sum up, we found that a missense mutation (rs4534) and two synonymous variants (rs6410 and rs5283) in CYP11B1 gene influence the susceptibility to CHD, which depend on age and gender. It indicated that CYP11B1 gene variant plays an important role in the development of CHD. These mutations may provide new ideas for exploring the pathogenesis of CHD.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the zenodo repository (



Coronary heart disease


False positive report probability


Odds ratio


Confidence intervals


Minor allele frequency


Hardy–Weinberg equilibrium


Multifactor dimensionality reduction


  1. Aboyans V. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet (London, England). 2015;385(9963):117–71.

    Article  Google Scholar 

  2. Lu WH, Zhang WQ, Zhao YJ, Gao YT, Tao N, Ma YT, et al. Case-control study on the interaction effects of rs10757278 polymorphisms at 9p21 locus and traditional risk factors on coronary heart disease in Xinjiang, China. J Cardiovasc Pharmacol. 2020;75(5):439–45.

    CAS  Article  Google Scholar 

  3. Heidenreich PA, Trogdon JG, Khavjou OA, Butler J, Dracup K, Ezekowitz MD, et al. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011;123(8):933–44.

    Article  Google Scholar 

  4. Di Angelantonio E, Thompson A, Wensley F, Danesh J. Coronary heart disease. IARC Sci Publ. 2011;163:363–86.

    Google Scholar 

  5. Lloyd-Jones DM, Nam BH, D’Agostino RB Sr, Levy D, Murabito JM, Wang TJ, et al. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA. 2004;291(18):2204–11.

    CAS  Article  Google Scholar 

  6. Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45(1):25–33.

    CAS  Article  Google Scholar 

  7. Li Q, Du Q. Associations between nine candidate genetic polymorphisms with coronary heart disease: a meta-analysis. Herz. 2020;45(Suppl 1):15–28.

    CAS  Article  Google Scholar 

  8. Lu Y, Boer JM, Barsova RM, Favorova O, Goel A, Müller M, et al. TGFB1 genetic polymorphisms and coronary heart disease risk: a meta-analysis. BMC Med Genet. 2012;13:39.

    CAS  Article  Google Scholar 

  9. Lu S, Zhong J, Zhang Y, Huang K, Wu M, Zhou Y, et al. CYP17A1 polymorphisms are linked to the risk of coronary heart disease in a case-control study. J Cardiovasc Pharmacol. 2019;74(2):98–104.

    CAS  Article  Google Scholar 

  10. Yuan XLL, Chen S, Jiang J, Wang X, Liu Z, Zhu H, Pan H, Lu Z. A Chinese patient with 11β-hydroxylase deficiency due to novel compound heterozygous mutation in CYP11B1 gene: a case report. BMC Endocr Disord. 2018;18(1):68.

    Article  Google Scholar 

  11. Zhang GX, Wang BJ, Ouyang JZ, Deng XY, Ma X, Li HZ, Wu Z, Liu SL, Xu H, Zhang X. Polymorphisms in CYP11B2 and CYP11B1 genes associated with primary hyperaldosteronism. Hypertens Res. 2010;33(5):478–84.

    CAS  Article  Google Scholar 

  12. Bernhardt R. The potential of targeting CYP11B. Expert Opin Ther Targets. 2016;20(8):923–34.

    CAS  Article  Google Scholar 

  13. Wang B, Zhang G, Ouyang J, Deng X, Shi T, Ma X, Li H, Ju Z, Wang C, Wu Z, Liu S. Association of DNA polymorphisms within the CYP11B2/CYP11B1 locus and postoperative hypertension risk in the patients with aldosterone-producing adenomas. Urology. 2010;76(4):1018-e1.

    Article  Google Scholar 

  14. Pott J, Bae YJ, Horn K, Teren A, Kühnapfel A, Kirsten H, Ceglarek U, Loeffler M, Thiery J, Kratzsch J, Scholz M. Genetic association study of eight steroid hormones and implications for sexual dimorphism of coronary artery disease. J Clin Endocrinol Metab. 2019;104(11):5008–23.

    Article  Google Scholar 

  15. Cerny MA, Csengery A, Schmenk J, Frederick K. Development of CYP11B1 and CYP11B2 assays utilizing homogenates of adrenal glands: Utility of monkey as a surrogate for human. J Steroid Biochem Mol Biol. 2015;154:197–205.

    CAS  Article  Google Scholar 

  16. Liu Y, Wu J, Zhou M, Chen W, Li D, Wang Z, Hornsperger B, Aebi JD, Märki H-P, Kuhn B, Wang L, Kuglstatter A, Benz J, Müller S, Hochstrasser R, Ottaviani G, Xin J, Kirchner S, Mohr S, Verry P, Riboulet W, Shen HC, Mayweg AV, Amrein K, Tan X. Discovery of 3-pyridyl isoindolin-1-one derivatives as potent, selective, and orally active aldosterone synthase inhibitors. J Med Chem 2020;63(13): 6876–97.

  17. Yang Q, Wang JH, Huang DD, Li DG, Chen B, Zhang LM, Yuan CL, Cai LJ. Clinical significance of analysis of the level of blood fat, CRP and hemorheological indicators in the diagnosis of elder coronary heart disease. Saudi J Biol Sci. 2018;25(8):1812–6.

    CAS  Article  Google Scholar 

  18. Ndrepepa G. Uric acid and cardiovascular disease. Clin Chim Acta. 2018;484:150–63.

    CAS  Article  Google Scholar 

  19. Xia P, Li B, Geng T, Deng Z, Dang C, Chang D, et al. FGFR2 gene polymorphisms are associated with breast cancer risk in the Han Chinese population. Am J Cancer Res. 2015;5(5):1854–61.

    PubMed  PubMed Central  Google Scholar 

  20. Liang J, Kang X, Halifu Y, Zeng X, Jin T, Zhang M, et al. Secreted frizzled-related protein promotors are hypermethylated in cutaneous squamous carcinoma compared with normal epidermis. BMC Cancer. 2015;15:641.

    Article  Google Scholar 

  21. Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst. 2004;96(6):434–42.

    Article  Google Scholar 

  22. Girod JP, Brotman DJ. Does altered glucocorticoid homeostasis increase cardiovascular risk? Cardiovasc Res. 2004;64(2):217–26.

    CAS  Article  Google Scholar 

  23. Whitworth JA, Williamson PM, Mangos G, Kelly JJ. Cardiovascular consequences of cortisol excess. Vasc Health Risk Manag. 2005;1(4):291–9.

    CAS  Article  Google Scholar 

  24. van der Sluis RJ, Hoekstra M. Glucocorticoids are active players and therapeutic targets in atherosclerotic cardiovascular disease. Mol Cell Endocrinol. 2020;504:110728.

    Article  Google Scholar 

  25. Deng HZ, You C, Xing Y, Chen KY, Zou XB. A family-based association study of CYP11A1 and CYP11B1 gene polymorphisms with autism in Chinese trios. J Child Neurol. 2016;31(6):733–7.

    Article  Google Scholar 

  26. Grgic OGM, Chesi A, Medina-Gomez C, Cousminer DL, Mitchell JA, Prijatelj V, de Vries J, Shevroja E, McCormack SE, Kalkwarf HJ, Lappe JM, Gilsanz V, Oberfield SE, Shepherd JA, Kelly A, Mahboubi S, Faucz FR, Feelders RA, de Jong FH, Uitterlinden AG, Visser JA, Ghanem LR, Wolvius EB, Hofland LJ, Stratakis CA, Zemel BS, Barash Y, Grant SFA, Rivadeneira F. CYP11B1 variants influence skeletal maturation via alternative splicing. Commun Biol. 2021;4(1):1274.

    CAS  Article  Google Scholar 

  27. Zmuda JM, Yerges-Armstrong LM, Moffett SP, Klei L, Kammerer CM, Roeder K, Cauley JA, Kuipers A, Ensrud KE, Nestlerode CS, Hoffman AR. Genetic analysis of vertebral trabecular bone density and cross-sectional area in older men. Osteoporos Int. 2011;22(4):1079–90.

    CAS  Article  Google Scholar 

  28. Sauna ZE, Kimchi-Sarfaty C. Understanding the contribution of synonymous mutations to human disease. Nat Rev Genet. 2011;12(10):683–91.

    CAS  Article  Google Scholar 

  29. Kristofich J, Morgenthaler AB, Kinney WR, Ebmeier CC, Snyder DJ, Old WM, et al. Synonymous mutations make dramatic contributions to fitness when growth is limited by a weak-link enzyme. PLoS Genet. 2018;14(8):e1007615.

    Article  Google Scholar 

  30. Mao Z, Wang Y, Peng H, He F, Zhu L, Huang H, et al. A newly identified missense mutation in CLCA2 is associated with autosomal dominant cardiac conduction block. Gene. 2019;714:143990.

    CAS  Article  Google Scholar 

  31. Han L, Chen M, Wang Y, Wu H, Quan Y, Bai T, et al. Pathogenic missense mutation pattern of forkhead box genes in neurodevelopmental disorders. Mol Genet Genomic Med. 2019;7(7):e00789.

    Article  Google Scholar 

Download references


We sincerely thank all participators in this study.


Not applicable.

Author information

Authors and Affiliations



NW designed, conceived, and supervised the study; XH drafted the manuscript; XH performed the DNA extraction and genotyping; YC performed the data analysis. NW polished the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Na Wang.

Ethics declarations

Ethics approval and consent to participate

This research received approval from the Ethics committee of Xi’an Hospital of Traditional Chinese Medicine, and conformed to the Declaration of Helsinki. Informed consent was acquired from each participant at recruitment after fully describing our research to them.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

 Basic information on SNPs of CYP11B1, clinical characteristics of participants with different SNPs, and false-positive reporting probability of susceptibility.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Huang, X., Cheng, Y. & Wang, N. Genetic variants in CYP11B1 influence the susceptibility to coronary heart disease. BMC Med Genomics 15, 158 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Coronary heart disease
  • CYP11B1
  • Polymorphisms
  • Susceptibility
  • Gene