Genetic risk factors for hypertension may have age or gender specificity and pleiotropic effects. This study aims to measure the risk of genetic and non-genetic factors in the occurrence of hypertension and related diseases, with consideration of potential confounding factors and age-gender stratification. A discovery set of 352,228 genotyped plus 1.8 million imputed single-nucleotide polymorphisms were analyzed for 2,886 hypertensive cases and 3,440 healthy controls obtained from two community-based cohorts in Korea, and selected gene variants were replicated in the Health Examinee cohort (665 cases and 1,285 controls). Genome-wide association analyses were conducted in 12 groups stratified by age and gender after adjusting for potential covariates under three genetic models. Age, rural area residence, body mass index, family history of hypertension, male gender, current alcohol drinking status, and current smoking status were significantly associated with hypertension (P = 4 × 10−151 to 0.011). Five gene variants, rs11066280 (C12orf51), rs12229654 and rs3782889 (MYL2), rs2072134 (OAS3), rs2093395 (TREML2), and rs17249754 (ATP2B1), were found to be associated with hypertension mostly in men (P = 4.76 × 10−14 to 4.46 × 10−7 in the joint analysis); three SNPs (rs11066280, rs12229654, and rs3782889) remained significant after Bonferroni correction in an independent population. Three gene variants, rs12229654, rs17249754, and rs11066280, were significantly associated with metabolic disorders such as hyperlipidemia and diabetes (P = 0.00071 to 0.0097, respectively). Careful consideration of the potential confounding effects in future genome-wide association studies is necessary to uncover the genetic underpinnings of complex diseases.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Adzhubei IA, Schmidt S, Peshkin L et al (2010) A method and server for predicting damaging missense mutations. Nat Methods 7:248–249
Baik I, Cho NH, Kim SH, Han B-G, Shin C (2011) Genome-wide association studies identify genetic loci related to alcohol consumption in Korean men. Am J Clin Nutr 93:809–816
Carretero OA, Oparil S (2000) Essential hypertension: part I: definition and etiology. Circulation 101:329–335
Chatterjee N, Wheeler B, Sampson J, Hartge P, Chanock SJ, Park J-H (2013) Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat Genet 45:400–405
Cho YS, Go MJ, Kim YJ et al (2009) A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41:527–534
Committee for Establishing Treatment Instruction for Hyperlipidemia (2000) Guidelines for diagnosis and treatment of hyperlipidemia. Korean Society of Lipidology and Atherosclerosis, Seoul
Glorioso N, Herrera VL, Bagamasbad P, Filigheddu F, Troffa C, Argiolas G, Bulla E, Decano JL, Ruiz-Opazo N (2007) Association of ATP1A1 and dear single-nucleotide polymorphism haplotypes with essential hypertension: sex-specific and haplotype-specific effects. Circ Res 100:1522–1529
Hashiguchi M, Kobori H, Ritprajak P, Kamimura Y, Kozono H, Azuma M (2008) Triggering receptor expressed on myeloid cell-like transcript 2 (TLT-2) is a counter-receptor for B7-H3 and enhances T cell responses. Proc Natl Acad Sci USA 105:10495–10500
Hong KW, Jin HS, Lim JE, Kim S, Go MJ, Oh B (2010a) Recapitulation of two genome wide association studies on blood pressure and essential hypertension in the Korean population. J Hum Genet 55:336–341
Hong KW, Go MJ, Jin HS, Lim JE, Lee JY, Han BG, Hwang SY, Lee SH, Park HK, Cho YS, Oh B (2010b) Genetic variations in ATP2B1, CSK, ARSG and CSMD1 loci are related to blood pressure and/or hypertension in two Korean cohorts. J Hum Hypertens 24:367–372
Huang J, Johnson AD, O’Donnell CJ (2011) PRIMe: a method for characterization and evaluation of pleiotropic regions from multiple genome-wide association studies. Bioinformatics 27:1201–1206
Janssens AC, van Duijn CM (2008) Genome-based prediction of common diseases: advances and prospects. Hum Mol Genet 17:R166–R173
Kabaeva ZT, Perrot A, Wolter B, Dietz R, Cardim N, Correia JM, Schulte HD, Aldashev AA, Mirrakhimov MM, Osterziel KJ (2002) Systematic analysis of the regulatory and essential myosin light chain genes: genetic variants and mutations in hypertrophic cardiomyopathy. Eur J Hum Genet 10:741–748
Kato N, Takeuchi F, Tabara Y et al (2011) Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in East Asians. Nat Genet 43:531–538
Kim YJ, Go MJ, Hu C et al (2011) Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits. Nat Genet 43:990–995
Kobayashi Y, Hirawa N, Tabara Y et al (2012) Mice lacking hypertension candidate gene ATP2B1 in vascular smooth muscle cells show significant blood pressure elevation. Hypertension 59:854–860
Kraft P, de Andrade M (2003) Group 6: pleiotropy and multivariate analysis. Genet Epidemiol 25:S50–S56
Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4:1073–1081
Levy D, Ehret GB, Rice K et al (2009) Genome-wide association study of blood pressure and hypertension. Nat Genet 41:677–687
Li N, Wang H, Yang J, Zhou L, Hong J, Guo Y, Luo W, Chang J (2009) Genetic variation of NEDD4L is associated with essential hypertension in female Kazakh general population: a case-control study. BMC Med Genet 10:130
Liu C, Li H, Qi Q, Lu L, Gan W, Loos RJ, Lin X (2011) Common variants in or near FGF5, CYP17A1 and MTHFR genes are associated with blood pressure and hypertension in Chinese Hans. J Hypertens 29:70–75
McNamee R (2005) Regression modelling and other methods to control confounding. Occup Environ Med 62:500–506
Newton-Cheh C, Johnson T, Gateva V et al (2009) Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet 41:666–676
Pendergrass SA, Brown-Gentry K, Dudek S et al (2013) Phenome-wide association study (PheWAS) for detection of pleiotropy within the population architecture using genomics and epidemiology (PAGE) network. PLoS Genet 9:e1003087
Pirinen M, Donnelly P, Spencer CCA (2012) Including known covariates can reduce power to detect genetic effects in case-control studies. Nat Genet 44:848–851
Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, Boehnke M, Abecasis GR, Willer CJ (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26:2336–2337
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575
Ren S, Yu H, Zhang H, Liu Y, Huang Y, Ma L, Wei L, Wu H, Chen X (2011) Polymorphisms of interferon-inducible genes OAS associated with interferon-α treatment response in chronic HBV infection. Antiviral Res 89:232–237
Sandberg K, Ji H (2012) Sex differences in primary hypertension. Biol Sex Differ 3:7
Sivakumaran S, Agakov F, Theodoratou E, Prendergast JG, Zgaga L, Manolio T, Rudan I, McKeigue P, Wilson JF, Campbell H (2011) Abundant pleiotropy in human complex diseases and traits. Am J Hum Genet 89:607–618
Szklo M, Nieto FJ (2000) Epidemiology: beyond the basics, 2nd edn. Aspen, Gaithersburg, pp 150–161
Takeuchi F, Isono M, Katsuya T et al (2010) Blood pressure and hypertension are associated with 7 loci in the Japanese population. Circulation 121:2302–2309
World Health Organization (1999) Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Report of a WHO Consultation. World Health Organization, Geneva
Xi B, Shen Y, Zhao X et al (2013) Association of common variants in/near six genes (ATP2B1, CSK, MTHFR, CYP17A1, STK39 and FGF5) with blood pressure/hypertension risk in Chinese children. J Hum Hypertens. doi:10.1038/jhh.2013.50
Xing G, Xing C (2010) Adjusting for covariates in logistic regression models. Genet Epidemiol 34:769–772
Xu Z, Taylor JA (2009) SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies. Nucleic Acids Res 37:W600–W605
Hyung-Lae Kim, MD, Ph.D, of the Ewha School of Medicine at Seoul, and Bok-Ghee Han, Ph.D, of the National Institute of Health at Osong, contributed to direct the development and maintenance of the Korean Genome Epidemiology Study (KoGES). Nam Han Cho, Ph.D, of the Ajou University School of Medicine at Suwon, and Chol Shin, MD, Ph.D, of the Korea University Ansan Hospital at Ansan, created and developed the Ansung and Ansan cohorts, respectively, as part of the Korean Genome Epidemiology Study (KoGES). Daehee Kang, MD, Ph.D, of the Seoul National University College of Medicine at Seoul, developed the HEXA study. Phenotype information was collected by Haesook Min, MS, and Sung Soo Kim, Ph.D, both of the National Institute of Health at Osong. Young Jin Kim, MS, of the National Institute of Health at Osong and Yoon Shin Cho, Ph.D, of Hallym University at Chuncheon, performed genotyping experiments and assured the quality of the genotype data. We thank all who have been involved in the generation of data at each local institute. This research was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (2009-0090837) and Hallym University Research Fund, 2012 (HRF-S-2012-5). The study was performed within the Consortium for Large Scale Genome Wide Association Study III (2011E7300400), which was supported by genotyping data from the Korean Genome Analysis Project (4845-301), phenotypic data from the Korean Genome Epidemiology Study (4851-302) and an intramural grant from the Korea National Institute of Health (2012-N73002-00).
Conflict of interest
Electronic supplementary material
Below is the link to the electronic supplementary material.
About this article
Cite this article
Heo, S.G., Hwang, J., Uhmn, S. et al. Male-specific genetic effect on hypertension and metabolic disorders. Hum Genet 133, 311–319 (2014). https://doi.org/10.1007/s00439-013-1382-4
- Multiple Logistic Regression Analysis
- miRNA Binding Site
- Predict miRNA Binding Site
- Cardiac Myosin Beta
- Cardiac Myosin Beta Heavy Chain