Advertisement

Molecular Genetics and Genomics

, Volume 292, Issue 2, pp 307–324 | Cite as

A genome-wide association study of essential hypertension in an Australian population using a DNA pooling approach

  • Javed Y. Fowdar
  • Rebecca Grealy
  • Yi Lu
  • Lyn R. GriffithsEmail author
Original Article

Abstract

Despite the success of genome-wide association studies (GWAS) in detecting genetic loci involved in complex traits, few susceptibility genes have been detected for essential hypertension (EH). We aimed to use pooled DNA GWAS approach to identify and validate novel genomic loci underlying EH susceptibility in an Australian case–control population. Blood samples and questionnaires detailing medical history, blood pressure, and prescribed medications were collected for 409 hypertensives and 409 age-, sex- and ethnicity-matched normotensive controls. Case and control DNA were pooled in quadruplicate and hybridized to Illumina 1 M-Duo arrays. Allele frequencies agreed with those reported in reference data and known EH association signals were represented in the top-ranked SNPs more frequently than expected by chance. Validation showed that pooled DNA GWAS gave reliable estimates of case and control allele frequencies. Although no markers reached Bonferroni-corrected genome-wide significance levels (5.0 × 10−8), the top marker rs34870220 near ASGR1 approached significance (p = 4.32 × 10−7), as did several candidate loci (p < 1 × 10−6) on chromosomes 2, 4, 6, 9, 12, and 17. Four markers (located in or near genes NHSL1, NKFB1, GLI2, and LRRC10) from the top ten ranked SNPs were individually genotyped in pool samples and were tested for association between cases and controls using the χ 2 test. Of these, rs1599961 (NFKB1) and rs12711538 (GLI2) showed significant difference between cases and controls (p < 0.01). Additionally, four top-ranking markers within NFKB1 were found to be in LD, suggesting a single strong association signal for this gene.

Keywords

Essential hypertension Genome-wide association study Pooled DNA NFKB1 GLI2 

Notes

Acknowledgements

We would like to thank Stuart MacGregor at QIMR for valuable advice and assistance with the analysis of GWAS data. J. Fowdar has been funded by an Endeavour International Postgraduate Research Scholarship (EIPRS) and a Griffith University Postgraduate Research Scholarship (GUPRS). Yi Lu is supported by the NHMRC early career fellowship (CJ-Martin overseas fellowship). This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors; however, sample collection was supported by grants from the National Health and Medical Research Council (NHMRC), Gemini Genomics UK Limited, Cambridge, UK, and Griffith University funding support.

Compliance with ethical standards

The study protocol was approved by the Griffith University’s Human Research Ethics Committee (HSC/18/04/HREC) and all procedures complied with the current ethical standards for human research in Australia, including the provision of informed consent to all participants.

Conflict of interest

The authors declare no conflict of interest.

Informed consent

Informed consent was obtained from all individual participants included in this study.

Supplementary material

438_2016_1274_MOESM1_ESM.pdf (171 kb)
Supplementary Material S1: Contains a table summarizing the top five markers with p ≤ 1 × 10−8 for published GWAS with systolic blood pressure (SBP) as phenotype. (PDF 171 kb)
438_2016_1274_MOESM2_ESM.pdf (170 kb)
Supplementary Material S2: Contains a table summarizing the top five markers with p ≤ 1 × 10−8 for published GWAS with diastolic blood pressure (DBP) as phenotype. (PDF 170 kb)
438_2016_1274_MOESM3_ESM.pdf (171 kb)
Supplementary Material S3: Contains a table summarizing the top five markers with p ≤ 1 × 10−8 for published GWAS with other BP traits as phenotype (i.e. pulse pressure). (PDF 170 kb)
438_2016_1274_MOESM4_ESM.pdf (84 kb)
Supplementary Material S4: Contains detailed methods of obtaining raw data files for pooled allele analysis by altering BeadArray default settings in the Illumina BeadStation program folder. (PDF 83 kb)
438_2016_1274_MOESM5_ESM.pdf (114 kb)
Supplementary Material S5: Contains detailed methods of pooled allele association analysis by estimation of the difference in proportion of A alleles between case and control pools. (PDF 114 kb)
438_2016_1274_MOESM6_ESM.pdf (177 kb)
Supplementary Material S6: Contains detailed methods of TaqMan® genotyping assays for GWAS validation, including marker information and genotyping protocols. (PDF 177 kb)
438_2016_1274_MOESM7_ESM.pdf (142 kb)
Supplementary Materials S7: Contains initial study power estimation using the CaTS GWAS power calculator and recalculation of study power with effective samples sizes for pooling-based GWAS. (PDF 142 kb)
438_2016_1274_MOESM8_ESM.pdf (151 kb)
Supplementary Material S8: Contains list of GWAS raw data quality control filters applied, as well as a summary Quality Control Processing Flow-Chart. (PDF 150 kb)
438_2016_1274_MOESM9_ESM.pdf (194 kb)
Supplementary Results S9: Contains scatterplot of EH case versus EH control pool allele frequencies. (PDF 194 kb)
438_2016_1274_MOESM10_ESM.pdf (195 kb)
Supplementary Results S10: Contains GWAS results for top SNPs with p-values lower than 1 × 10−4. (PDF 194 kb)
438_2016_1274_MOESM11_ESM.pdf (718 kb)
Supplementary Results S11: Contains LocusZoom plots for the GAB2, GLI2 and LOC100420968 loci. (PDF 718 kb)

References

  1. Adeyemo A, Gerry N, Chen G et al (2009) A genome-wide association study of hypertension and blood pressure in African Americans. PLoS Genet 5:e1000564CrossRefPubMedPubMedCentralGoogle Scholar
  2. Barnett IJ, Lee S, Lin X (2013) Detecting rare variant effects using extreme phenotype sampling in sequencing association studies. Genet Epidemiol 37:142–151CrossRefPubMedGoogle Scholar
  3. Basson J, Simino J, Rao DC (2012) Between candidate genes and whole genomes: time for alternative approaches in blood pressure genetics. Curr Hypertens Rep 14:46–61CrossRefPubMedGoogle Scholar
  4. Bosse Y, Bacot F, Montpetit A et al (2009) Identification of susceptibility genes for complex diseases using pooling-based genome-wide association scans. Hum Genet 125:305–318CrossRefPubMedGoogle Scholar
  5. Burton PR, Clayton DG, Cardon LR et al (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3000 shared controls. Nature 447:661–678CrossRefGoogle Scholar
  6. Butcher LM, Davis OSP, Craig IW, Plomin R (2008) Genome-wide quantitative trait locus association scan of general cognitive ability using pooled DNA and 500 K single nucleotide polymorphism microarrays. Genes Brain Behav 7:435–446CrossRefPubMedPubMedCentralGoogle Scholar
  7. Chiang KM, Yang HC, Liang YJ et al (2014) A three-stage genome-wide association study combining multilocus test and gene expression analysis for young-onset hypertension in Taiwan Han Chinese. Am J Hypertens 27:819–827CrossRefPubMedGoogle Scholar
  8. Chobanian AV, Bakris GL, Black HR et al (2003) The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA 289:2560–2572CrossRefPubMedGoogle Scholar
  9. Craig DW, Huentelman MJ, Hu-Lince D et al (2005) Identification of disease causing loci using an array-based genotyping approach on pooled DNA. BMC Genom 6:138–147CrossRefGoogle Scholar
  10. Craig JE, Hewitt AW, McMellon AE et al (2009) Rapid inexpensive genome-wide association using pooled whole blood. Genome Res 19:2075–2080CrossRefPubMedPubMedCentralGoogle Scholar
  11. Davis ME, Grumbach IM, Fukai T, Cutchins A, Harrison DG (2004) Shear stress regulates endothelial nitric-oxide synthase promoter activity through nuclear factor kappaB binding. J Biol Chem 279:163–168CrossRefPubMedGoogle Scholar
  12. Docherty SJ, Butcher LM, Schalkwyk LC, Plomin R (2007) Applicability of DNA pools on 500 K SNP microarrays for cost-effective initial screens in genomewide association studies. BMC Genom 8:214–221CrossRefGoogle Scholar
  13. Earp MA, Rahmani M, Chew K, Brooks-Wilson A (2011) Estimates of array and pool-construction variance for planning efficient DNA-pooling genome wide association studies. BMC Med Genom 4:81CrossRefGoogle Scholar
  14. Ehret GB, Munroe PB, Rice KM et al (2011) Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478:103–109CrossRefPubMedGoogle Scholar
  15. Fowdar JY, Lason MV, Szvetko AL, Lea RA, Griffiths LR (2012) Investigation of homocysteine-pathway-related variants in essential hypertension. Int J Hypertens 2012:190923CrossRefPubMedPubMedCentralGoogle Scholar
  16. Grewal PK, Uchiyama S, Ditto D et al (2008) The Ashwell receptor mitigates the lethal coagulopathy of sepsis. Nat Med 14:648–655CrossRefPubMedPubMedCentralGoogle Scholar
  17. Guo Y, Tomlinson B, Chu T et al (2012) A genome-wide linkage and association scan reveals novel loci for hypertension and blood pressure traits. PLoS One 7:e31489CrossRefPubMedPubMedCentralGoogle Scholar
  18. Hiura Y, Tabara Y, Kokubo Y et al (2010) A genome-wide association study of hypertension-related phenotypes in a Japanese population. Circ J 74:2353–2359CrossRefPubMedGoogle Scholar
  19. Janicki PK, Vealey R, Liu J, Escajeda J, Postula M, Welker K (2011) Genome-wide association study using pooled DNA to identify candidate markers mediating susceptibility to postoperative nausea and vomiting. Anesthesiology 115:54–64CrossRefPubMedGoogle Scholar
  20. Jawaid A, Bader JS, Purcell S, Cherny SS, Sham P (2002) Optimal selection strategies for QTL mapping using pooled DNA samples. Eur J Hum Genet 10:125–132CrossRefPubMedGoogle Scholar
  21. 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 43:531–538CrossRefPubMedPubMedCentralGoogle Scholar
  22. Kirov G, Nikolov I, Georgieva L, Moskvina V, Owen MJ, O’Donovan MC (2006) Pooled DNA genotyping on Affymetrix SNP genotyping arrays. BMC Genom 7:27–37CrossRefGoogle Scholar
  23. Klein RJ (2007) Power analysis for genome-wide association studies. BMC Genet 8:58CrossRefPubMedPubMedCentralGoogle Scholar
  24. Lanktree MB, Hegele RA, Schork NJ, Spence JD (2010) Extremes of unexplained variation as a phenotype: an efficient approach for genome-wide association studies of cardiovascular disease. Circ Cardiovasc Genet 3:215–221CrossRefPubMedPubMedCentralGoogle Scholar
  25. Lettre G, Palmer CD, Young T et al (2011) Genome-wide association study of coronary heart disease and its risk factors in 8,090 African Americans: the NHLBI CARe Project. PLoS Genet 7:e1001300CrossRefPubMedPubMedCentralGoogle Scholar
  26. Levy D, Larson MG, Benjamin EJ et al (2007) Framingham heart study 100 K project: genome-wide associations for blood pressure and arterial stiffness. BMC Med Genet 8(Suppl 1):S3CrossRefPubMedPubMedCentralGoogle Scholar
  27. Levy D, Ehret GB, Rice K et al (2009) Genome-wide association study of blood pressure and hypertension. Nat Genet 41:677–687CrossRefPubMedPubMedCentralGoogle Scholar
  28. Li F, Duman-Scheel M, Yang D et al (2010) Sonic hedgehog signaling induces vascular smooth muscle cell proliferation via induction of the G1 cyclin-retinoblastoma axis. Arterioscler Thromb Vasc Biol 30:1787–1794CrossRefPubMedGoogle Scholar
  29. Lip GY, Edmunds E, Martin SC, Jones AF, Blann AD, Beevers DG (2001) A pilot study of homocyst(e)ine levels in essential hypertension: relationship to von Willebrand factor, an index of endothelial damage. Am J Hypertens 14:627–631CrossRefPubMedGoogle Scholar
  30. Lu X, Wang L, Lin X et al (2015) Genome-wide association study in Chinese identifies novel loci for blood pressure and hypertension. Hum Mol Genet 24:865–874CrossRefPubMedGoogle Scholar
  31. Macgregor S (2007) Most pooling variation in array-based DNA pooling is attributable to array error rather than pool construction error. Eur J Hum Genet 15:501–504CrossRefPubMedGoogle Scholar
  32. Macgregor S, Zhao ZZ, Henders A, Nicholas MG, Montgomery GW, Visscher PM (2008) Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays. Nucleic Acids Res 36:35–43CrossRefGoogle Scholar
  33. Maresso K, Broeckel U (2008) Genotyping platforms for mass-throughput genotyping with SNPs, including human genome-wide scans. Adv Genet 60:107–139PubMedGoogle Scholar
  34. McEvoy BP, Montgomery GW, McRae AF et al (2009) Geographical structure and differential natural selection among North European populations. Genome Res 19:804–814CrossRefPubMedPubMedCentralGoogle Scholar
  35. Meaburn E, Butcher LM, Liu L et al (2005) Genotyping DNA pools on microarrays: tackling the QTL problem of large samples and large numbers of SNPs. BMC Genom 6:52–60CrossRefGoogle Scholar
  36. Meaburn E, Butcher LM, Schalkwyk LC, Plomin R (2006) Genotyping pooled DNA using 100 K SNP microarrays: a step towards genomewide association scans. Nucleic Acids Res 34:28–36CrossRefGoogle Scholar
  37. Miller SA, Dykes DD, Polesky HF (1988) A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 16:1215CrossRefPubMedPubMedCentralGoogle Scholar
  38. Moore DD, Dowhan D (2003) Preparation and Analysis of DNA. In: Ausubel FM (ed) Current protocols in molecular biology. Wiley, New York, pp 2.0.1–2.1.10Google Scholar
  39. Org E, Eyheramendy S, Juhanson P et al (2009) Genome-wide scan identifies CDH13 as a novel susceptibility locus contributing to blood pressure determination in two European populations. Hum Mol Genet 18:2288–2296CrossRefPubMedPubMedCentralGoogle Scholar
  40. Padmanabhan S, Melander O, Johnson T et al (2010) Genome-wide association study of blood pressure extremes identifies variant near UMOD associated with hypertension. PLoS Genet 6:e1001177CrossRefPubMedPubMedCentralGoogle Scholar
  41. Park JY, Farrance IK, Fenty NM et al (2007) NFKB1 promoter variation implicates shear-induced NOS3 gene expression and endothelial function in prehypertensives and stage I hypertensives. Am J Physiol Heart Circ Physiol 293:H2320–H2327CrossRefPubMedPubMedCentralGoogle Scholar
  42. Pruim RJ, Welch RP, Sanna S et al (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26:2336–2337CrossRefPubMedPubMedCentralGoogle Scholar
  43. Ricci G, Astolfi A, Remondini D et al (2011) Pooled genome-wide analysis to identify novel risk loci for pediatric allergic asthma. PLoS One 6:e16912CrossRefPubMedPubMedCentralGoogle Scholar
  44. Robinson JT, Thorvaldsdottir H, Winckler W et al (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26CrossRefPubMedPubMedCentralGoogle Scholar
  45. Sebastiani P, Zhao Z, Abad-Grau MM et al (2008) A hierarchical and modular approach to the discovery of robust associations in genome-wide association studies from pooled DNA samples. BMC Genet 9:6CrossRefPubMedPubMedCentralGoogle Scholar
  46. Sham PC, Curtis D (1995) Monte Carlo tests for associations between disease and alleles at highly polymorphic loci. Ann Hum Genet 59:97–105CrossRefPubMedGoogle Scholar
  47. Skol AD, Scott LJ, Abecasis GR, Boehnke M (2006) Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 38:209–213CrossRefPubMedGoogle Scholar
  48. Slavin TP, Feng T, Schnell A, Zhu X, Elston RC (2011) Two-marker association tests yield new disease associations for coronary artery disease and hypertension. Hum Genet 130:725–733CrossRefPubMedPubMedCentralGoogle Scholar
  49. Stelnicki EJ, Arbeit J, Cass DL, Saner C, Harrison M, Largman C (1998) Modulation of the human homeobox genes PRX-2 and HOXB13 in scarless fetal wounds. J Invest Dermatol 111:57–63CrossRefPubMedGoogle Scholar
  50. Thongboonkerd V (2005) Genomics, proteomics and integrative “omics” in hypertension research. Curr Opin Nephrol Hypertens 14:133–139CrossRefPubMedGoogle Scholar
  51. Voronova A, Al Madhoun A, Fischer A, Shelton M, Karamboulas C, Skerjanc IS (2012) Gli2 and MEF2C activate each other’s expression and function synergistically during cardiomyogenesis in vitro. Nucleic Acids Res 40:3329–3347CrossRefPubMedGoogle Scholar
  52. Wain LV, Verwoert GC, O’Reilly PF et al (2011) Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat Genet 43:1005–1011CrossRefPubMedPubMedCentralGoogle Scholar
  53. Yang HC, Liang YJ, Wu YL et al (2009) Genome-wide association study of young-onset hypertension in the Han Chinese population of Taiwan. PLoS One 4:e5459CrossRefPubMedPubMedCentralGoogle Scholar
  54. Yang HC, Liang YJ, Chen JW et al (2012) Identification of IGF1, SLC4A4, WWOX, and SFMBT1 as hypertension susceptibility genes in Han Chinese with a genome-wide gene-based association study. PLoS One 7:e32907CrossRefPubMedPubMedCentralGoogle Scholar
  55. Yin B-C, Li H, Ye B-C (2008) Microarray-based estimation of SNP Allele-frequency in pooled DNA using the Langmuir Kinetic model. BMC Genom 9:605–630CrossRefGoogle Scholar
  56. Zhou B, Rao L, Peng Y et al (2009) Functional polymorphism of the NFKB1 gene promoter is related to the risk of dilated cardiomyopathy. BMC Med Genet 10:47CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Javed Y. Fowdar
    • 1
  • Rebecca Grealy
    • 1
  • Yi Lu
    • 2
  • Lyn R. Griffiths
    • 3
    Email author
  1. 1.School of Medical ScienceGriffith UniversityGold CoastAustralia
  2. 2.Genetic Epidemiology DepartmentQueensland Institute of Medical ResearchBrisbaneAustralia
  3. 3.Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical SciencesQueensland University of TechnologyBrisbaneAustralia

Personalised recommendations