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


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.


Essential hypertension Genome-wide association study Pooled DNA NFKB1 GLI2 



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)


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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

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