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Article

Novel Association of rs17111557(T) in PCSK9 with Higher Diastolic Blood Pressure in Northern Ghanaian Adults: Candidate Gene Analysis from an AWI-Gen Sub-Study

by
Joseph A. Aweeya
1,2,
Lord J. J. Gowans
2,
Engelbert A. Nonterah
1,3,
Victor Asoala
1,
Patrick Ansah
1,
Michele Ramsay
4 and
Godfred Agongo
5,*
1
Navrongo Health Research Centre, Ghana Health Service, Navrongo P.O. Box 114, Ghana
2
Department of Biochemistry and Biotechnology, Kwame Nkrumah University of Science and Technology, Kumasi 00233, Ghana
3
Department of Epidemiology, School of Public Health, C. K. Tedam University of Technology and Applied Sciences, Navrongo P.O. Box 24, Ghana
4
Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2050, South Africa
5
Department of Biochemistry and Forensic Sciences, Schol of Chemical and Biochemical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo P.O. Box 24, Ghana
*
Author to whom correspondence should be addressed.
BioMed 2025, 5(3), 15; https://doi.org/10.3390/biomed5030015
Submission received: 30 December 2024 / Revised: 10 February 2025 / Accepted: 12 February 2025 / Published: 22 July 2025
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Abstract

Background/Objectives: Cardiovascular diseases are a global health issue with an increasing burden and are exacerbated by hypertension. High blood pressure is partly attributed to genetic variants that are generally not well understood or extensively studied in sub-Saharan African populations. Variants linked to blood pressure have been found through genome-wide association studies (GWASs), which were mostly conducted among European ancestry populations; however, limited research has been undertaken in Africa. The current study evaluated single-nucleotide polymorphisms (SNPs) of PCSK9, ABCA1, LPL, and PON1 in relation to blood pressure measurements of 1839 Ghanaian adults. Methods: Genotypes were extracted from data generated by the H3Africa SNP array. After adjusting for sex, age, smoking, and body mass index (BMI), inferential statistics were used to investigate the relationships between SNPs and blood pressure (BP) indices. Additionally, Bonferroni correction was used to adjust for multiple testing. Results: Diastolic blood pressure (DBP) and the minor allele T of the PCSK9 variant (rs17111557) were positively associated at p = 0.006 after covariate adjustments. Although this novel DBP-associated variant is located in the 3′ untranslated region (3′ UTR) of the PCSK9 gene, in silico functional prediction suggests it is an expression quantitative trait locus (eQTL) that may change the binding site of transcription factors, potentially altering the rate of transcription and impacting DBP in this Ghanaian population. Conclusions: Our findings highlight the role of genetics in hypertension risk and the potential of discovering new therapies targeting isolated diastolic blood pressure in this rural African population.

1. Introduction

Globally, cardiovascular diseases (CVDs) are the leading cause of death. In 2019, 17.9 million CVD-related deaths (or 32% of all deaths) were recorded, with heart attacks and strokes accounting for 85% of these deaths. Most of these deaths occurred in low- and middle-income countries, or LMICs [1]. CVDs constitute one of the main causes of early mortality and rising healthcare costs. Socioeconomic, behavioral, environmental, and cardiometabolic risk factors are the main causes of CVDs [2]. One multifactorial disorder and a leading modifiable risk factor of CVDs is hypertension [3]. Over one billion people are affected by hypertension, which has become a global pandemic, and by 2025, 29.2% of adults are predicted to have hypertension [4].
Drivers of hypertension include environmental and genetic risk factors. While several studies have investigated the environmental factors contributing to high blood pressure (BP), there is a paucity of data on genetic drivers of hypertension in SSA. Identifying the genetic variants and genes that increase a person’s risk of developing hypertension is one strategy for preventing the condition and choosing the best course of therapy. This will contribute to the development of precision medicine in Africa. The genetic basis of hypertension in African populations is poorly understood. Single-nucleotide polymorphisms (SNPs) of proprotein convertase subtilisin/kexin type 9 (PCSK9) [5], paraoxonase 1 (PON1) [6], lipoprotein lipase (LPL) [7], and ATP-binding cassette 1 (ABCA1) [8] genes have been shown to be associated with BP in non-Africans.
A meta-analysis of genetic studies conducted on hypertension in Africa demonstrated 33 genes being associated with hypertension in Africans, including Ghanaians [9]. However, these studies did not investigate the genetic effect on individual blood pressure indices, whose incremental increases pose the risk of hypertension. Additionally, a genome-wide association study (GWAS) on the populations of four African countries, including our study population, investigated the genetic loci associated with blood pressure traits [10]. However, due to the more stringent significant threshold p values used in this GWAS, the possibility of SNPs with moderate effect size could be eliminated.
The objective of this study was to determine the association of variants in the PCSK9, LPL, PON1, and ABCA1 genes with BP indices, including systolic blood pressure (SBP) and diastolic blood pressure (DBP), among rural northern Ghanaian adults.

2. Materials and Methods

2.1. Study Population

This study, nested within the African Wits-INDEPTH Partnership for Genomics studies (AWI-Gen) [11,12], recruited both men and women aged 40 to 60 years at the Navrongo Health and Demographic Surveillance site [13]. The study participants were selected, using a stratified random sampling strategy [14], from the north, east, south, and west zones using the Navrongo Health and Demographic Surveillance System (HDSS) database. Participants who were residents within the study area for at least ten years and who consented to be part of the study were recruited. Pregnant women and individuals whose anthropometric indices could not be accurately taken were excluded from the study [12]. Participants with cardiovascular diseases of atherosclerotic genesis (coronary heart disease, peripheral artery disease, and stroke) were not included in our data analyses.

2.2. Study Design and Sample Size Justification

This was a candidate gene study that sought to investigate the association of blood pressure with selected candidate genes previously found to be associated with BP levels in non-Africans [5,6,7,8]. A cross-sectional study design was adopted for the current study. The power was based on a fixed sample size and was determined using Quanto software version 1.2.4 [15]. Considering the rs1018148 variant in the FBN1 gene previously associated with SBP at β = 3.78 [16], our study had >80% power to detect effect sizes of at least 0.028 for a sample size of 1830 for blood pressure traits at MAF > 0.05.

2.3. Data Collection

2.3.1. Demographic, Blood Pressure, and Anthropometric Data

Data on demographic and anthropometric indices were captured via a paper questionnaire [11]. The information was entered into the REDCap (Research Electronic Data Capture) platform [17] and 10% of the entries were checked to ensure accurate data capture. Data on sociodemographics and anthropometry have been described previously [11]. Briefly, waist and hip circumference were measured using a stretch-resistant tape measure (SECA, Hamburg, Germany), while height was measured using a Harpenden stadiometer (Holtain, Wales, UK). Weight was taken using a calibrated electronic scale (to the nearest 0·1 kg; Physician Large Dial 200 kg capacity scales, Kendon Medical, South Africa). Body mass index (BMI) was calculated using the formula kg/m2, which is weight in kilograms divided by the square of the height in meters. A digital sphygmomanometer and a blood pressure (BP) cuff (Omron M6, Omron, Kyoto, Japan) were used to measure the systolic blood pressure (SBP) and diastolic blood pressure (DBP). Participants were seated on a chair with their feet firmly planted on the floor and their backs supported. They were asked to relax and neither move nor speak during this procedure. The measurement of BP was then taken 3 times at 5 min intervals using the left arm. The diastolic, systolic, and pulse rate measurements were recorded three times each, and the average of the last two readings was calculated and used in downstream analyses.

2.3.2. Genotyping and Imputation of Candidate Genes

DNA was extracted from the participants’ blood samples using the salting-out method [18] and genotyped using the H3Africa genotyping array, developed by Illumina and enriched with African common variants (http://www.illumina.com/services/sequencing-services.html, accessed on 5 July 2024). The following pre-imputation quality control steps were carried out on the entire AWI-Gen data set, of which these data are a subset. All samples with genotype missing rates higher than 0.05, SNPs with missing rates higher than 0.05, and p-values for the Hardy–Weinberg equilibrium (HWE) higher than 0.0001 were eliminated from the dataset. The positional Burrows–Wheeler transform (PBWT) was used as the default approach for imputation. Following post-imputation quality control, poorly imputed SNPs with an IMPUTE2 information score below 0.6, a MAF less than 0.01, and an HWE p-value less than 0.00001 were excluded. The final imputed data after quality control contained ~14 million SNPs. Our study data were extracted from these main data and only included variants associated with the four genes of interest. The extracted analytical dataset included 1130 SNPs.

2.4. Data Analysis

PLINK version 1.90 was utilized to examine the genotyping data [19,20]. STATA 17.0 (StataCorp, College Station, TX, 77845, USA) was utilized for any additional analysis. Sex is presented as percentages and compared between men and women using the Pearson χ2 test. Anthropometric and BP indices are presented as mean ± standard deviation and compared between men and women using Student’s t-test. Linear regression additive models were performed to estimate the possible association of gene variants with BP indices. All Bonferroni-corrected p values at 5% significance level after covariate adjustment were considered statistically significant. p < 0.05.

2.5. Determination of Minor Allele Frequencies of Associated Variants in Other Populations and Functional Analyses

The determination of the minor allele frequencies of the associated variants in other populations was performed using 1000 Genomes Project data (https://www.ncbi.nlm.nih.gov/dbvar/, accessed on 9 July 2024). In silico functional analyses were conducted using the Combined Annotation Dependent Depletion (CADD) score, ReulomeDB (RDB) v2.2 score [21], and Variant Effect Predictor [22] based on human genome build GRCh38. A CADD score > 10 suggests a possible effect on protein function. An RDB score of 1 suggests a most likely effect on transcription factor binding and gene expression, while a score of 6 suggests a non-effect on transcription binding. RDB scores are based on expression quantitative trait loci (eQTLs) and chromatin marks.

3. Results

3.1. Demographic and Anthropometric Characteristics of the Study Participants

The participants’ demographics and anthropometric indices, stratified by sex, are shown in Table 1. The sample size for this study was 1839, with 54% being women. The proportion of smokers in the study population was 31.6%, with men smoking more than women (p < 0.001). The mean age of participants in the study was 51 years old, with women being substantially older than men (p < 0.001). The BMI of all the study participants was 21.0 kg/m2, with women having a mean BMI of 22.3 kg/m2, indicating that their BMI was significantly higher than that of men (p < 0.001). The waist circumference (WC) of women was significantly greater than that of men (p <0.001). There was no significant difference in mean DBP between men (DBP: 77.0 mmHg) and women (77.2 mmHg) (p = 0.760). The mean SBP for all study participants was 124.1 mmHg, with no significant difference between that of men and women (p = 0.094). The mean pulse pressure (PP), which was the difference between SBP and DBP, for all study participants was 46.9 mmHg, with men recording a mean PP of 47.9 mmHg, which was significantly higher than that of women (46.1 mmHg) (p = 0.001). There was no significant difference in mean arterial pressure (MAP) between men and women (p = 0.533).

3.2. Association of Gene Variants with Blood Pressure Indices

The regression models showing the association of gene variants with BP indices are illustrated in Table 2. Only β values with p < 1.000 are included in the table. After adjusting for Bonferroni correction, but with no covariate adjustment, PCSK9 (rs17111557, a 3′UTR variant) was associated with DBP (β= 6.240, p = 0.003). After both Bonferroni correction and covariate (sex, age, and BMI) adjustment, PCSK9 (rs17111557) was still associated with DBP (β = 5.984, p = 0.006). The results of all genetic analyses of diastolic blood pressure that showed significant association are shown in Supplementary Table S1.

3.3. Minor Allele Frequencies of rs17111557 in PCSK9 in Other Populations

The minor allele frequency (MAF) of the T allele of the significantly associated variant (rs17111557) in PCSK9 was more common in the Kassena-Nankana population (11%). The MAF in Asians and Europeans is less than 1% in the 1000 Genome Phase 3 database. In the same database, Sub-Saharan Africans have an average MAF of 13.6%, whereas African Americans have an MAF of 6.6%, which is less than the one observed in the Kassena-Nankana population (Table 3).

3.4. Functional Analysis of rs17111557 in PCSK9

The functional annotation showed that the significantly associated variant was located in the 3′ untranslated region (3′ UTR) of PCSK9 with a CADD score of 0.171, suggesting that it did not alter the protein function that usually emanates from an amino acid change. The clinical significance of the associated variant was predicted to be benign by the Variant Effect Predictor (VEP). This was probably because it was a 3′UTR variant. However, when RegulomeDB (RDB) was employed to analyze the variant, a score of 0.55436 and a rank of 1f were obtained, which indicated a possible deleterious effect on transcription factor binding sites and the regulation of transcription (Table 3). An RDB rank of 1f indicates that there are eQTL, transcription factor (TF) binding, and DNase hypersensitivity datasets to support the prediction. The further functional analysis of rs17111557, employing RegulomeDB (Figure 1), demonstrated that this SNP is situated in a transcriptionally active region (Figure 1B) that is euchromatic, and that this region may actively regulate transcription in both the left and right ventricles of the heart (Figure 1C). The analyses suggested that the region harboring rs17111557 is bound by CTCF (Figure 2A), a protein that binds at chromatin domain boundaries, enhancers, gene promoters, and inside gene bodies. It also suggests that the region is likely to be an enhancer element (Figure 2B).

4. Discussion

This study examined the association of single-nucleotide polymorphisms (SNPs) in four genes (PCSK9, ABCA1, LPL, and PON1) with blood pressure indices (SBP, DBP, MAP, and PP) among northern Ghanaian adults and identified a significant association between PCSK9 (rs17111557) and DBP. Previous findings have linked PCSK9 to blood pressure regulation, demonstrating that the PCSK9 gene product in Xenopus oocytes reduces the trafficking of the epithelial sodium channel (ENaC) protein to the cell surface by promoting proteasomal degradation. By allowing sodium to enter epithelial cells at the apical membrane, the ENaC regulates sodium absorption in the kidney and aids in blood pressure regulation [5]. Though previous studies have linked rs17111557(T) to hypercholesterolaemia [23] and increased fasting glucose [24], there is no prior evidence for the association of the variant with BP levels. Additionally, a genome-wide association study (GWAS) demonstrated that PCSK9 (rs9730100) was associated with SBP, but did not find any association with DBP [5]. Thus, our study is the first to demonstrate the association of rs17111557 (T) with DBP. Our findings suggest that PCSK9 may not only be a candidate gene for dyslipidemia therapy, but also for the treatment of isolated diastolic hypertension.
The low CADD and VEP values demonstrate that the SNP is non-coding in its functionality, with RDB analysis suggesting that the SNP is located in a transcriptionally active site and may play a regulatory role in transcription. The functional analysis further suggests that this associated SNP is located in an enhancer element that is active in the left and right ventricles of the heart. Thus, this variant may alter the level of expression of the PCSK9 gene in the heart. The minor allele frequency (MAF = 11%) of the associated variant in this study population was higher than the MAFs (<1%) in Europeans and Asians in the 1000 Genomes Phase 3 database. This suggests that this allele may be African-specific in its role in blood pressure regulation.
Our study was not without limitations. The candidate gene analysis, with the selection of only four genes for our association study, may have excluded several SNP associations with BP indices. A GWAS with a larger sample size could identify more SNP associations with small effect sizes. Additionally, the sequencing of candidate genes could have identified more allelic associations with BP levels in these genes than the genotyping array. These notwithstanding, we have demonstrated that the use of an African-common-variant-enriched array bolsters the strength of identifying African-specific SNP associations that may not be captured using Eurocentric arrays.

5. Conclusions

Our findings highlight the role of genetics in hypertension risk among middle-aged adults in the study population from rural Ghana. Further studies involving the use of sequenced data on the role of PCSK9 in hypertension risk could result in new scientific discoveries and potentially new therapies targeting isolated diastolic blood pressure.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomed5030015/s1, Table S1: Genetic association results of gene with significant variant for diastolic blood pressure.

Author Contributions

The study was conceived by J.A.A., L.J.J.G. and G.A. The data were acquired by J.A.A., E.A.N., V.A., P.A., M.R., L.J.J.G. and G.A. Formal analysis was performed by G.A., J.A.A. and L.J.J.G. The original draft was prepared by J.A.A. and G.A. Supervision was undertaken by L.J.J.G. and G.A. Funding was acquired by M.R. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Institutes of Health (NIH) through the H3Africa AWI-Gen project with grant number U54HG006938.

Institutional Review Board Statement

Prior to participant recruitment, ethical approval was obtained from the Navrongo Health Research Centre Institutional Review Board (NHRCIRB) with the approval number NHRCIRB178.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

The dataset used in this study is available from the European Genome-Phenome Archive (https://ega-archive.org/) on application to the H3Africa Data and Biobank Access Committee.

Acknowledgments

The authors are very grateful to all the study participants who voluntarily consented to participate in this study. We also acknowledge the tremendous support of the director and management of the Navrongo Health Research Centre (NHRC), the entire AWI-Gen team, and the laboratory technicians at the NHRC for participant recruitment, as well as data and sample collection. We acknowledge the Sydney Brenner Institute for Molecular Bioscience (SBIMB) team for playing lead roles in the processing of the samples and analyses.

Conflicts of Interest

The authors declare no conflicts of interest.

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Biomed 05 00015 g001
Figure 1. Functional analysis of rs17111557 in PCSK9, employing RegulomeDB. (A) SNPs surrounding rs17111557 on human chromosome 1; (B) DNA hypersensitivity and ChIP-Seq assays suggest that rs17111557 is located in euchromatin at the position of the vertical yellow line; (C) DNase-Seq and ATAC-Seq experiments on chromatin accessibility suggest this region is active in the right and left ventricles of the heart. Source: https://regulomedb.org/regulome-search/, accessed on 14 September 2024.
Figure 1. Functional analysis of rs17111557 in PCSK9, employing RegulomeDB. (A) SNPs surrounding rs17111557 on human chromosome 1; (B) DNA hypersensitivity and ChIP-Seq assays suggest that rs17111557 is located in euchromatin at the position of the vertical yellow line; (C) DNase-Seq and ATAC-Seq experiments on chromatin accessibility suggest this region is active in the right and left ventricles of the heart. Source: https://regulomedb.org/regulome-search/, accessed on 14 September 2024.
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Figure 2. rs17111557 in PCSK9 is located in an enhancer element. (A) Functional analyses suggest that the region harboring rs17111557 is bound by CTCF, a protein that binds at chromatin domain boundaries, enhancers, gene promoters, and inside gene bodies. (B) The region encompassing rs17111557 is likely an enhancer element, as depicted by various experiments. Source: https://regulomedb.org/regulome-search/, accessed on 14 September 2024.
Figure 2. rs17111557 in PCSK9 is located in an enhancer element. (A) Functional analyses suggest that the region harboring rs17111557 is bound by CTCF, a protein that binds at chromatin domain boundaries, enhancers, gene promoters, and inside gene bodies. (B) The region encompassing rs17111557 is likely an enhancer element, as depicted by various experiments. Source: https://regulomedb.org/regulome-search/, accessed on 14 September 2024.
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Table 1. Basic characteristics of the 1839 study participants.
Table 1. Basic characteristics of the 1839 study participants.
Variables Men Women Total * p Value
Sex/n (%)846 (46%)993 (54%)1839 (100%)0.001
Smoking status/n (%)
No298 (35.3)960 (96.7)1258 (68.4)p < 0.001
Yes548 (64.8)33 (3.2)581 (31.6)
Age/years51 ± 5.852 ± 5.851 ± 5.8<0.001
BMI/kg/m220.9 ± 3.222.3 ± 3.9 21.6 ± 3.6<0.001
WC/cm7.3 ± 0.8 7.7 ± 1.07.5 ± 0.9<0.001
DBP/mmHg77.0 ± 12.9 77.2 ± 12.6 77.1 ± 12.7 0.760
SBP/mmHg125.0 ± 20.4 123.3 ± 22.6 124.1 ± 21.6 0.094
PP/mmHg47.9 ± 10.9 46.1 ± 13.1 46.9 ± 12.2 0.001
MAP/mmHg93.0 ± 14.9 92.6 ± 15.4 92.8 ± 15.2 0.533
BMI: body mass index, WC: waist circumference, DBP: diastolic blood pressure, SBP: systolic blood pressure, PP: pulse pressure, MAP: mean arterial pressure, n: number. With the exception of sex and smoking, which we report as n (%), all values are reported as mean ± standard deviation. * The p-value was generated by comparison between men and women.
Table 2. Association of PCSK9 SNPs with diastolic blood pressure (DBP) with Bonferroni correction.
Table 2. Association of PCSK9 SNPs with diastolic blood pressure (DBP) with Bonferroni correction.
SNPGenotypeMinor
Allele		# MAFpHWEWithout Covariate Adjustment* With Covariate Adjustment
β ValueS.Ep Valueβ ValueS.Ep Value
rs17111557TCT0.1130.4026.2401.4790.0035.9841.4760.006
1:55522141GAG0.1920.3100.3111.0910.1530.2461.0900.164
rs625619AGA0.2250.7913.0351.0960.6722.9761.0930.771
DBP: diastolic blood pressure; MAF: minor allele frequency; pHWE: Hardy–Weinberg equilibrium; S.E: standard error; SNP: single-nucleotide polymorphism; * covariates adjusted for were age, sex, tobacco smoking, and BMI; # MAF is calculated in the study.
Table 3. Minor allele (T allele) frequencies in other populations and functional analysis of rs17111557 in PCSK9.
Table 3. Minor allele (T allele) frequencies in other populations and functional analysis of rs17111557 in PCSK9.
PopulationMAF
* Kassena-Nankana population0.113
# KGP Sub-Saharan Africans 0.136
KGP Europeans0.007
KGP African Americans0.066
KGP Asians0.009
Functionality ItemValue
Localization3′UTR
CADD Score0.171
RDB Score0.55436
VEP Benign
* Variants from the present study; all others are from the Thousand Genomes Project (KGP) Phase 3. # Average minor allele frequency (MAF) for ESN, GWD, LWK, MSL, and YRI. CADD: Combined Annotation Dependent Depletion, RDB: RegulomedB, ESN: Esan in Nigeria, GWD: Gambian in Western Division, The Gambia, LWK: Luhya in Webuye, Kenya, MSL: Mende in Sierra Leon, YRI: Yoruba in Ibadan, Nigeria, 3′UTR: 3′ untranslated region.
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Aweeya, J.A.; Gowans, L.J.J.; Nonterah, E.A.; Asoala, V.; Ansah, P.; Ramsay, M.; Agongo, G. Novel Association of rs17111557(T) in PCSK9 with Higher Diastolic Blood Pressure in Northern Ghanaian Adults: Candidate Gene Analysis from an AWI-Gen Sub-Study. BioMed 2025, 5, 15. https://doi.org/10.3390/biomed5030015

AMA Style

Aweeya JA, Gowans LJJ, Nonterah EA, Asoala V, Ansah P, Ramsay M, Agongo G. Novel Association of rs17111557(T) in PCSK9 with Higher Diastolic Blood Pressure in Northern Ghanaian Adults: Candidate Gene Analysis from an AWI-Gen Sub-Study. BioMed. 2025; 5(3):15. https://doi.org/10.3390/biomed5030015

Chicago/Turabian Style

Aweeya, Joseph A., Lord J. J. Gowans, Engelbert A. Nonterah, Victor Asoala, Patrick Ansah, Michele Ramsay, and Godfred Agongo. 2025. "Novel Association of rs17111557(T) in PCSK9 with Higher Diastolic Blood Pressure in Northern Ghanaian Adults: Candidate Gene Analysis from an AWI-Gen Sub-Study" BioMed 5, no. 3: 15. https://doi.org/10.3390/biomed5030015

APA Style

Aweeya, J. A., Gowans, L. J. J., Nonterah, E. A., Asoala, V., Ansah, P., Ramsay, M., & Agongo, G. (2025). Novel Association of rs17111557(T) in PCSK9 with Higher Diastolic Blood Pressure in Northern Ghanaian Adults: Candidate Gene Analysis from an AWI-Gen Sub-Study. BioMed, 5(3), 15. https://doi.org/10.3390/biomed5030015

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