Hypertension in African Populations: Review and Computational Insights
Hypertension (HTN), also known as high blood pressure (BP), can be classified into primary or secondary HTN with secondary HTN affecting 5–10% of hypertensive patients with the cause linked to an underlying medical condition. In contrast, primary hypertension also known as essential HTN accounts for 90–95% of HTN cases, it has no identifiable secondary root and is the most common cause of stroke and cardiovascular disease (CVD) . Accumulative evidence showed that the prevalence of HTN is 1.3 billion, and this number is projected to increase to 1.56 billion by 2030 with an estimated global economic cost of $274 billion . Despite the availability of advanced diagnostic options and the use of multiple antihypertensive medications, many studies have reported inadequate control of blood pressure among hypertensive individuals worldwide, including Africa . This inability to control blood pressure even after adherence has been referred to as treatment-resistant hypertension (TRH). According to the American heart association, TRH is defined as a BP which remains ≥140/90 mg despite the use of three or more antihypertensive drugs . Although poor control of high blood pressure may have many causes, the most likely contributor is the poor response to the prescribed antihypertensive drug . Current drugs are used to treat HTN without an in-depth understanding of the biological basis that regulates the disease or the effect of an individual’s make-up on the efficacy of the drug to control HTN . The most commonly prescribed drug classes to treat hypertension include diuretics and vasodilators designed to reduce vascular resistance. The major disadvantages are side effects (i.e., renal dysfunction) and unpredicted blood pressure responses in patients (i.e., HTN) . Drug classes developed from the knowledge of biological pathways include the angiotensin II antagonists and angiotensin-converting enzyme inhibitors. These have been developed through the understanding of the renin-angiotensin-aldosterone system (RAAS) in the regulation of blood pressure. As such, first-line therapy includes angiotensin-converting enzyme ( ACE ) inhibitors, angiotensin II receptors, and calcium-channel blockers . These have proven to be the most effective drug classes (coupled with lower side effects) when used as a combination therapy . Nonetheless, though effective, the one-drug-fits-all model has been proven to be ineffective due to an individual’s genetic make-up and variable response to several antihypertensive drugs could be attributed to the genetic variability in the candidate BP regulating genes and their pathways . Many studies have demonstrated that genetic factors are responsible not only for blood pressure elevation but also play a profound role in the interindividual variability in drug response, offering an opportunity for pharmacogenomic investigation and potential individualized drug therapy . Therefore, it has been speculated that a patient’s inter-individual genetic variation can be used to understand the pathophysiology of the disease and that this pharmacogenomics approach may have the potential of individualizing HTN drug therapy based on the patient’s genetic background . For example, Guo et al.,  and Choi et al.,  have reported successful examples of genetic polymorphisms on blood pressure response to antihypertensive therapies. Recently, Ma et al.  reported that various informative genetic variants can be utilized for the identification of subtypes of hypertension. These studies have highlighted the importance of understanding the biological basis of the disease and the contribution of genetic variations in the development and progression of the disease. Despite major advances being made over the last decade, understanding the role of genetics governing the phenotypic state of HTN is complex because on one hand there are rare monogenic hypertensive syndromes while on the other side are the cases of primary or TRH, which may occur due to a varied expression and interactions of multiple genes . However, polygenic inheritance patterns in such cases are not only complex but also enigmatic. Nonetheless, various subtle genetic variations have been identified through approaches such as genome-wide association studies, and these small variations among individuals may account for genetic susceptibility to HTN. However, these single-nucleotide polymorphisms (SNP) have been shown to vary across racial and ethnic groups and play a pivotal role during the development and progression of this phenotypic state . SNPs are changes in specific nucleotides at fixed positions in DNA sequence and occur frequently once in every 1000 base pairs. These are the most commonly occurring genetic variations causing varied inter-individual drug response which remains a major public health concern . Furthermore, it has been speculated that variable response to drugs could also be due to different subtypes of a disease phenotype in certain individuals. To exemplify, patients demonstrating better BP response with certain drugs may have different pathophysiology of HTN than others showing poor response with those drugs . Therefore, a thorough understanding of the genetic background of HTN is critical to predict an individual’s disease risk as well as to improve individualized treatment response. Despite many SNPs having no clinical significance, several key SNPs have been reported with a detailed analysis through pharmacogenomics-based studies playing an important role in antihypertensive drug response . As such, the purpose of this review was to systematically extract and discuss African evidence on the effect of genetic variations in hypertensive patients and use in silico methods to further elucidate biological processes that will aid in identifying novel drug targets for the treatment of HTN in an African population. 

A systematic search was performed using subject headings or primary search terms such as resistant hypertension, genetics, SNP, and pharmacogenomic under the Preferred Reporting Items for Systematic Review (PRISMA) guidelines . This was done using major search engines and databases such as PubMed, Scopus, Web of Science, African Journal Online, and PharmGKB were used for the search as shown in . 2.1. Inclusion Criteria and Data Extraction The studies were included in the systemic review based on the following criteria: (i) Investigated the association between SNPs and HTN in African-based population, (ii) Used case-control design, (iii) Published from 1984 to 2020, (iv) Studies done on humans . Studies were excluded if duplicate publications reported selectively on migrant Africans outside Africa, family studies, used linkage analysis and analyzed mixed populations of African descent without considering their country of residence. Moreover, language restriction was applied with studies conducted in languages other than English were excluded. The articles were independently assessed for compliance with the inclusion or exclusion criteria by two authors resolving disagreements and reached a consistent decision. The following information was extracted from each study: first author and year of publication; country of origin; ethnicity of the study population; the number of subjects under hypertensive cases and controls; diagnostic criteria for hypertensive cases and controls and SNPs analyzed. 2.2. Drug–Gene Interaction The bioinformatics tool iCTNET from Cytoscape (available at http://apps.cytoscape.org /apps/iCTNET (accessed on 10 March 2020) was used to search through the DrugBank (available at https://www.drugbank.ca (accessed on 10 March 2020) and comparative toxicogenomic database (CTD) (available at http://ctdbase.org/ (accessed on 10 March 2020) to identify drug–gene interactions. These were coupled with the occurring frequent and infrequent side-effects for each drug and plotted in a network. This analysis helped to identify the common drug-targets of HTN. 2.3. Co-Expression Networks Networks of co-expressed genes were generated from mathematical models which were used to predict protein–protein interactions (PPIs) using the bioinformatics tool Expression Correlation from Cytoscape  (available at http://apps.cytoscape.org/apps/Expression Correlation (accessed on 9 March 2020). The reference database Expression Correlation utilizes sample data from the human gene atlas project (available at https://www.pnas.org/content/101/16/6062 (accessed on 9 March 2020) from 158 expression experiments on healthy humans. From the input gene list, Expression Correlation computes a similarity matrix using expression levels from the reference database. Gene–gene correlation networks are then computed and visualized as an interaction network. The biological function was related to the identified co-expressed gene clusters using the gene ontology (GO) database for molecular function. This allowed for the selection of specific gene-clusters that might pose as drug targets. 2.4. Biological Functional Enrichment To perform biological functional enrichment, the R Bioconductor tool Cluster Profiler  was used to identify enriched GO, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and disease ontologies. Fisher’s exact test was performed to identify significantly enriched terms with a Benjamin-Hochberg adjusted probability value (BH adjusted p -value) of less than 0.05. This was conducted to relate biological function to the gene list related to HTN. Selecting genes as potential drug-targets with minimal influence on other biological pathways and processes was imperative to minimizing side effects in HTN patients. 2.5. Disease Ontology Enrichment To identify and link the disease state to known HTN genes, disease ontology enrichment (DO) was performed using Cluster Profiler . Disease ontology enrichment involved cross-referencing mappings to the Medical Subject Headings (MeSH) (available at https://www.nlm.nih.gov/databases (accessed on 9 March 2020), International Classification of Disease (ICD) (available at https://www.cdc.gov/nchs/icd/ (accessed on 9 March 2020), National Cancer Institute (NCI), (available at https://www.cancer.gov/research/resources (accessed on 9 March 2020), Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) (available at http://www.snomed.org (accessed on 9 March 2020) and Online Mendelian Inheritance in Man (OMIM) (available at https://www.omim.org (accessed on 9 March 2020) databases. A hypergeometric model assessed the over-representation of the selected genes and their association with a disease. A BH-adjusted p -value was calculated to identify significantly associated diseases (BH p -value less than 0.05). This was performed to further identify possible HTN genes that show no association with other diseases. This was a possible indicator that the targeting chosen genes would have minimal interference with other biological functions. 

The studies were included in the systemic review based on the following criteria: (i) Investigated the association between SNPs and HTN in African-based population, (ii) Used case-control design, (iii) Published from 1984 to 2020, (iv) Studies done on humans . Studies were excluded if duplicate publications reported selectively on migrant Africans outside Africa, family studies, used linkage analysis and analyzed mixed populations of African descent without considering their country of residence. Moreover, language restriction was applied with studies conducted in languages other than English were excluded. The articles were independently assessed for compliance with the inclusion or exclusion criteria by two authors resolving disagreements and reached a consistent decision. The following information was extracted from each study: first author and year of publication; country of origin; ethnicity of the study population; the number of subjects under hypertensive cases and controls; diagnostic criteria for hypertensive cases and controls and SNPs analyzed. 

The bioinformatics tool iCTNET from Cytoscape (available at http://apps.cytoscape.org /apps/iCTNET (accessed on 10 March 2020) was used to search through the DrugBank (available at https://www.drugbank.ca (accessed on 10 March 2020) and comparative toxicogenomic database (CTD) (available at http://ctdbase.org/ (accessed on 10 March 2020) to identify drug–gene interactions. These were coupled with the occurring frequent and infrequent side-effects for each drug and plotted in a network. This analysis helped to identify the common drug-targets of HTN. 

Networks of co-expressed genes were generated from mathematical models which were used to predict protein–protein interactions (PPIs) using the bioinformatics tool Expression Correlation from Cytoscape  (available at http://apps.cytoscape.org/apps/Expression Correlation (accessed on 9 March 2020). The reference database Expression Correlation utilizes sample data from the human gene atlas project (available at https://www.pnas.org/content/101/16/6062 (accessed on 9 March 2020) from 158 expression experiments on healthy humans. From the input gene list, Expression Correlation computes a similarity matrix using expression levels from the reference database. Gene–gene correlation networks are then computed and visualized as an interaction network. The biological function was related to the identified co-expressed gene clusters using the gene ontology (GO) database for molecular function. This allowed for the selection of specific gene-clusters that might pose as drug targets. 

To perform biological functional enrichment, the R Bioconductor tool Cluster Profiler  was used to identify enriched GO, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and disease ontologies. Fisher’s exact test was performed to identify significantly enriched terms with a Benjamin-Hochberg adjusted probability value (BH adjusted p -value) of less than 0.05. This was conducted to relate biological function to the gene list related to HTN. Selecting genes as potential drug-targets with minimal influence on other biological pathways and processes was imperative to minimizing side effects in HTN patients. 

To identify and link the disease state to known HTN genes, disease ontology enrichment (DO) was performed using Cluster Profiler . Disease ontology enrichment involved cross-referencing mappings to the Medical Subject Headings (MeSH) (available at https://www.nlm.nih.gov/databases (accessed on 9 March 2020), International Classification of Disease (ICD) (available at https://www.cdc.gov/nchs/icd/ (accessed on 9 March 2020), National Cancer Institute (NCI), (available at https://www.cancer.gov/research/resources (accessed on 9 March 2020), Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) (available at http://www.snomed.org (accessed on 9 March 2020) and Online Mendelian Inheritance in Man (OMIM) (available at https://www.omim.org (accessed on 9 March 2020) databases. A hypergeometric model assessed the over-representation of the selected genes and their association with a disease. A BH-adjusted p -value was calculated to identify significantly associated diseases (BH p -value less than 0.05). This was performed to further identify possible HTN genes that show no association with other diseases. This was a possible indicator that the targeting chosen genes would have minimal interference with other biological functions. 

3.1. Selected Studies We identified 2784 studies through data searches: PubMed ( n = 410), Web of Science ( n = 1115), Scopus ( n = 1245), PharmGKB ( n = 11), and African Journal Online ( n = 3) . After removing duplicates ( n = 1222) and studies that were conducted before molecular biology techniques ( n = 43) the full text of 1519 publications were tested for suitability . Of these, 1347 were excluded as follows; non-human studies ( n = 29), non-HTN studies ( n = 1194), non-African countries ( n = 78), reviews ( n = 46), and different study design (e.g., investigated the expression of genes in patients with HTN, clinical studies on HTN and family-based studies) ( n = 130). Finally, 42 studies were included in this review. The baseline characteristics of the included studies are summarized in . HTN was defined as systolic/diastolic BP (SBP/DBP) ≥ 140/90 mm Hg or the use of antihypertensive medications at inclusion . Other studies used auscultatory DBP > 90 mm Hg or 24-h ambulatory DBP >85 mm Hg and auscultatory DBP > 95 mm Hg, SBP/DBP > 159/80 mm Hg, SBP/DBP >139/89 mm Hg and SBP/DBP ≥ 125/80 mm Hg, 25. This review did not identify a genome-wide association study (GWAS) conducted among the African population with HTN. Northern African countries ( n = 25) appeared to have more studies carried on HTN and SNPs association sub-Saharan African countries (Eastern Africa = 2, Central Africa = 1, Western Africa = 6, Southern Africa = 8) as elaborated in . Twenty studies reported an association between HTN and genes such as ACE, AGT, AGTR1, ANP, APOA5, ARGHGAP42, ATP2B1, B2, BAG6, CABCOCO1, CACNB2, CAND1, CHIC2, CNNM2, CPS1, CSK, CYP11B2, CYP2C8, EBF1, FES, FGF5, GNB3, GOSR2, GRK4, GUCY1A1N, HFE, IGFBP3, JAG1, LEP, MECOM, MOV10, MTHFR, NOS3, PLCE1, PLEKHA7, PR3, SH2B3, SLC39A8, SLC4A7, STK39, SUB1, TBX5, ULK4, ZNF652, and ZNF831 whereas twenty-two studies did not show any association . The most studied genes were AGT, ACE, NOS3, AGTR1, MTHFR, ATP2B1, CYP2C8, GNB3, CNNM2, PLEKHA7, JAG1, FGF5 , and EBF1 as sequentially arranged from highest to the smallest number of studies (see ). Other genes reported in single studies were STK39, CDKAL1, IGF2BP2, TH, B2, CYP11B2, LEP, CLCNKB, SCNN1B, ADD1, ADRB2, SUB1, CEP83, IGFBP3, CHIC2, AGTR2, CPS1, MOV10, SLC4A7, MECOM, SLC39A8, GUCY1A1N, PR3, HFE, BAG6, CACNB2, PLCE1, CAND1, ARHGAP42, FES, GOSR2, ZNF831, ULK4, CABCOCO1, SH2B3, TBX5, CSK, and ZNF652 . 3.2. Gene–Drug Interaction and Ontology Analysis A gene list consisting of 53 genes  linked to HTN was analyzed to gain an understanding of drug interaction, biological processes, pathways and various associated diseases Food and Drug Administration (FDA) approved drug list was obtained , and used to identify drug interactions with the above-mentioned genes. Identification of potential drug–gene interactions is the first step in a translation research effort aiming to reduce the burden of a major public health problem such as CVD . Therefore, in the current study Drug Bank and CTD databases were utilized to generate a drug–gene interaction network  that identified the genes interacting with FDA approved HTN drugs. Of the 53 genes, only 14 genes ( CYP2C8, CYP11B2, AGT, AGTR1, AGTR2, ACE, ADRB2, LEP, MTHFR, NOS3, HFE, CNNM3, IGF2BP2 , and SCNN1B ) showed an interaction with marketed drugs along with their corresponding side-effects . GO enrichment analysis was performed to identify the enriched biological processes that the gene list regulates . Finding ontologies linked to biological processes provides insights into the underlying mechanisms regulated by the chosen gene list. This allows for the identification of additional drugs that have previously not treated HTN. Co-expression analysis was performed to identify co-expressed gene clusters . These were interrogated further to relate the molecular function for each gene cluster . This was performed to identify potential mechanisms in which drugs can be targeted against HTN. Genes in co-expressed gene clusters and their linked HTN drugs are summarized in to explore potential combination therapy options. 3.3. Co-Expression Ontology Analysis Similar to the GO enrichment analysis performed, KEGG pathway analysis  was performed to identify the enriched pathways regulated by the 53 HTN genes. This is performed to identify alternative pathways that interact with HTN related pathways, which can provide insights into how HTN drugs may influence various biological pathways. 3.4. Biological Pathway Analysis Disease ontology  was also performed to analyze the involvement of the 53 HTN genes in various other diseases. This analysis gives insights into how different diseases may share common gene expression patterns and how drug treatment influences several biological pathways causing undesired side effects. In the selection process of identifying genes that are only linked to hypertension and other related diseases, DO analysis was conducted. 3.5. Disease Ontology Disease ontology built from a disease-gene interaction database was plotted in a network  to visualize the interactions between genes and enriched diseases. This graph serves to identify genes that are targeted in HTN treatment however are also linked with other diseases. 

We identified 2784 studies through data searches: PubMed ( n = 410), Web of Science ( n = 1115), Scopus ( n = 1245), PharmGKB ( n = 11), and African Journal Online ( n = 3) . After removing duplicates ( n = 1222) and studies that were conducted before molecular biology techniques ( n = 43) the full text of 1519 publications were tested for suitability . Of these, 1347 were excluded as follows; non-human studies ( n = 29), non-HTN studies ( n = 1194), non-African countries ( n = 78), reviews ( n = 46), and different study design (e.g., investigated the expression of genes in patients with HTN, clinical studies on HTN and family-based studies) ( n = 130). Finally, 42 studies were included in this review. The baseline characteristics of the included studies are summarized in . HTN was defined as systolic/diastolic BP (SBP/DBP) ≥ 140/90 mm Hg or the use of antihypertensive medications at inclusion . Other studies used auscultatory DBP > 90 mm Hg or 24-h ambulatory DBP >85 mm Hg and auscultatory DBP > 95 mm Hg, SBP/DBP > 159/80 mm Hg, SBP/DBP >139/89 mm Hg and SBP/DBP ≥ 125/80 mm Hg, 25. This review did not identify a genome-wide association study (GWAS) conducted among the African population with HTN. Northern African countries ( n = 25) appeared to have more studies carried on HTN and SNPs association sub-Saharan African countries (Eastern Africa = 2, Central Africa = 1, Western Africa = 6, Southern Africa = 8) as elaborated in . Twenty studies reported an association between HTN and genes such as ACE, AGT, AGTR1, ANP, APOA5, ARGHGAP42, ATP2B1, B2, BAG6, CABCOCO1, CACNB2, CAND1, CHIC2, CNNM2, CPS1, CSK, CYP11B2, CYP2C8, EBF1, FES, FGF5, GNB3, GOSR2, GRK4, GUCY1A1N, HFE, IGFBP3, JAG1, LEP, MECOM, MOV10, MTHFR, NOS3, PLCE1, PLEKHA7, PR3, SH2B3, SLC39A8, SLC4A7, STK39, SUB1, TBX5, ULK4, ZNF652, and ZNF831 whereas twenty-two studies did not show any association . The most studied genes were AGT, ACE, NOS3, AGTR1, MTHFR, ATP2B1, CYP2C8, GNB3, CNNM2, PLEKHA7, JAG1, FGF5 , and EBF1 as sequentially arranged from highest to the smallest number of studies (see ). Other genes reported in single studies were STK39, CDKAL1, IGF2BP2, TH, B2, CYP11B2, LEP, CLCNKB, SCNN1B, ADD1, ADRB2, SUB1, CEP83, IGFBP3, CHIC2, AGTR2, CPS1, MOV10, SLC4A7, MECOM, SLC39A8, GUCY1A1N, PR3, HFE, BAG6, CACNB2, PLCE1, CAND1, ARHGAP42, FES, GOSR2, ZNF831, ULK4, CABCOCO1, SH2B3, TBX5, CSK, and ZNF652 . 

A gene list consisting of 53 genes  linked to HTN was analyzed to gain an understanding of drug interaction, biological processes, pathways and various associated diseases Food and Drug Administration (FDA) approved drug list was obtained , and used to identify drug interactions with the above-mentioned genes. Identification of potential drug–gene interactions is the first step in a translation research effort aiming to reduce the burden of a major public health problem such as CVD . Therefore, in the current study Drug Bank and CTD databases were utilized to generate a drug–gene interaction network  that identified the genes interacting with FDA approved HTN drugs. Of the 53 genes, only 14 genes ( CYP2C8, CYP11B2, AGT, AGTR1, AGTR2, ACE, ADRB2, LEP, MTHFR, NOS3, HFE, CNNM3, IGF2BP2 , and SCNN1B ) showed an interaction with marketed drugs along with their corresponding side-effects . GO enrichment analysis was performed to identify the enriched biological processes that the gene list regulates . Finding ontologies linked to biological processes provides insights into the underlying mechanisms regulated by the chosen gene list. This allows for the identification of additional drugs that have previously not treated HTN. Co-expression analysis was performed to identify co-expressed gene clusters . These were interrogated further to relate the molecular function for each gene cluster . This was performed to identify potential mechanisms in which drugs can be targeted against HTN. Genes in co-expressed gene clusters and their linked HTN drugs are summarized in to explore potential combination therapy options. 

Similar to the GO enrichment analysis performed, KEGG pathway analysis  was performed to identify the enriched pathways regulated by the 53 HTN genes. This is performed to identify alternative pathways that interact with HTN related pathways, which can provide insights into how HTN drugs may influence various biological pathways. 

Disease ontology  was also performed to analyze the involvement of the 53 HTN genes in various other diseases. This analysis gives insights into how different diseases may share common gene expression patterns and how drug treatment influences several biological pathways causing undesired side effects. In the selection process of identifying genes that are only linked to hypertension and other related diseases, DO analysis was conducted. 

Disease ontology built from a disease-gene interaction database was plotted in a network  to visualize the interactions between genes and enriched diseases. This graph serves to identify genes that are targeted in HTN treatment however are also linked with other diseases. 

HTN is a multifactorial disease affecting one billion individuals. It is a leading cardiovascular risk factor accounting for premature deaths and has a significant economic cost . Despite the availability of many antihypertensive drugs, less than 50% of patients have their blood pressure controlled . These disappointing outcomes could be because of medication non-adherence and/or interindividual variation . Considering studies linking interindividual variation to drug response, several studies have tested SNPs as predictors of TRH risk . Various studies have reported an association between SNPs and HTN in European , African American , and Asian population , however not much evidence has been generated on the association of specific SNP or its associated haplotypes with HTN in an African population . Therefore, we systematically extracted and discussed evidence on the African genetic variation and pharmacogenomics towards the treatment of HTN. A total of 42 studies comprising of 53 genes are included in this review, however, only 20 studies reported a significant association with the risk of HTN. highlighted candidate genes such as ACE, NOS3, ATP2B1 , and MTHFR that have repeatedly been implicated to have an association with HTN in various African populations. 4.1. Angiotensin-Converting Enzyme Gene Angiotensin-converting enzyme ( ACE ) is a metalloenzyme that cleaves angiotensin I to angiotensin II and inactivates a potent vasodilator . ACE is encoded by the ACE gene which has been mapped to chromosome 17q23, and it has 26 exons and 25 introns . Single-nucleotide polymorphism (rs1799752) on the ACE gene was investigated for its implication on high blood pressure in different sub-Saharan Africa countries . It has been showed in various studies conducted in different parts of Africa continent that rs1799752 could be a potential genetic predictor for the development of HTN and permit initiation of personalized medicine . However, other studies performed in South Africa, Tunisia, and Egypt exhibited no association of ACE gene polymorphism with the risk of developing HTN . 4.2. Nitric Oxide Synthase Gene Nitric oxide synthase ( NOS3 ) gene is another important candidate gene because of its critical role in regulating blood pressure. This gene ( NOS3 ) is on chromosome 7q35–36, comprising 26 exons. NOS3 is an integral component of vasorelaxing pathway, which is mediated by endothelium derived nitric oxide . Various studies summarized in this review showed that different populations from Africa exhibit positive association of genetic variants of NOS3 such as rs1799983 , rs2070744 , rs149868979  and rs61722009  with the risk of developing HTN. However, two studies in this review that aimed to investigate the NOS3 gene variants associated with the development of HTN showed no association with the disease . 4.3. Plasma Membrane Calcium-Transporting ATPase 1 Gene In 2009, the genome-wide association studies (GWAS) have identified the association between the Plasma membrane calcium-transporting ATPase 1 ( ATP2B1 ) polymorphisms (rs2681472, rs17249754, rs2681492) and HTN in diverse populations . This gene encodes for plasma membrane calcium dependent ATPase, which handles calcium pumping to the extracellular compartment . In this review, it was revealed that genetic variants in ATP2B1 genes in some populations from Algeria, Burkina Faso, and Uganda are associated with the risk of developing HTN. For instance, a study conducted in an Algerian population reported that the ATP2B1 variant (rs17249754) has a robust significant association with HTN . The findings were further replicated in the Burkinabe population . In another study conducted by Kayima et al.  on a Ugandan population, it was reported that rs2681492 was significantly associated with HTN in hypertensive individuals. Likewise, SNPs on ATP2B1 (rs17249754, rs2681472, and rs2681492) were reported to be in linkage disequilibrium and located in the same linkage disequilibrium block in the Chinese population . 4.4. Methylenetetrahydrofolate Reductase Gene Methylenetetrahydrofolate reductase gene ( MTHFR ) is one of the most studied genes associated with HTN in the African countries. MTHFR is on chromosome 1p36.3 and is well known to be involved in the metabolism of homocysteine and folate . Previous studies have shown that the MTHFR variant such as rs1801133 has been associated with HTN between 24–87% . Similarly, in this review, 50% of studies investigating the role of MTHFR provided a positive line of evidence linking this gene with HTN, showing that the rs1801133 polymorphism in MTHFR increases the risk of HTN . Conversely, Amrani-Midoun et al.  and Amin et al.  in Algeria and Egypt showed no association of rs1801133 in hypertensive patients. Furthermore, genes such as CYP11B2 (rs1799998) , LEP (rs7799039) , and CPS1 (rs1047891)  were also reported to be associated with HTN, however, these findings could not be replicated in the different African populations. As aforementioned, ACE and NOS3 genes have been studied for its implication on HTN in more than six published papers indexed on the different databases, of which 50% of those were able to link these genes polymorphisms with the high risk for HTN. This suggests that there was a significant association of ACE and NOS3 variants with HTN in African populations. However, AGT, AGTR1 , and AGTR2 polymorphisms in African populations showed less than 25% or no association with HTN. For example, all the SNPs located near AGT gene, only rs2004776 was demonstrated to have a strong association with HTN in Ugandan population . In a recent study by EI-Garawani et al. , two polymorphisms (rs4762 and rs699) of AGT gene were found to be associated with the risk of diabetes and hypertension in the Egyptian population. Furthermore, Farrag et al.  reported the positive association of AGTR1 , rs5186 with HTN in Egyptian population and similar findings were also observed in the Ghanaian population. However, no association has been recorded in other AGTR1 and AGTR2 polymorphisms. 4.5. Computational Insights Furthermore, we attempted to shortlist key target genes out of the 53 identified genes in this review by using bioinformatics methods on gene-specific data available in various public databases. The bioinformatics analysis investigated various parameters associated with the 53 HTN genes such as drug interactions, co-expression of genes and disease ontology, and linked pathways. Only 14 genes ( CYP2C8, CYP11B2, AGT, AGTR1, AGTR2, ACE, ADRB2, LEP, MTHFR, NOS3, HFE, CNNM3, IGF2BP2 , and SCNN1B ) were identified to have an interaction with FDA approved marketed HTN drugs ( and ). As evident from , the various antihypertensive drugs such as losartan, acebutolol, atenolol, metoprolol, ramipril, lisinopril, labetalol, and amlodipine show distinct side-effects. It has also been reported in the literature  that 20–97% of patients taking antihypertensive drugs suffer from various drug-related side effects. Furthermore, these side effects may potentially also account for non-adherence to antihypertensive drugs . Losartan, an angiotensin II antagonist and atenolol, a β -blocker showed the most side-effects related to respiratory impairments and infection. Prior studies have also documented a higher frequency of side-effects and lower adherence among patients taking diuretics and β -blockers . In hypertensive patients of African ancestry, diuretics and or calcium channel blockers (amlodipine) are preferably recommended  and patients who remain concerned about the adverse health effects of antihypertensive drugs are less likely to be adherent to their medications . The co-expression analysis identified three clusters, including cluster 1 ( ACE, AGT, AGTR1, AGTR2, and NOS3), cluster 2 (MOV10, CAN1 , and IGF2BP2 ), and cluster 3 ( CSK and ADRB2 ). Upon further analysis, from cluster 1, AGTR1, AGTR2, AGT , and ACE ; cluster 2, IGFBP2 and cluster 3 ADRB2 genes are shown to be mapped to HTN drugs shown in and . Interestingly, the protein-coding genes AGTR1, AGTR2, AGT , and ACE are all involved in the RAAS cascade which regulates blood pressure and vascular resistance. As such, the most widely used HTN drug classes include ACE inhibitors, angiotensin II antagonists and direct renin inhibitors which target the RAAS cascade . Whereas, in the second and third cluster, the genes IGFBP2 (targeted by vasodilators) and ADRB2 (targeted by β -blockers), respectively, have been recently linked with increased risk of HTN and have been recently proposed as potential diagnostic biomarkers for HTN. For example, in a study by Yang et al.  the overexpression of the gene IGFBP2 which is linked to the regulation of vascularization and angiogenesis is associated with the increased severity of HTN. Additionally, the gene ADRB2 has been shown to interact with the gene NOS3 . ADRB2 may indirectly interact with the RAAS cascade via the NOS3 forming part of the gene cluster of genes associated with the RAAS cascade . This is further illustrated in where ADRB2 shows interaction with RAAS associated genes by regulating common biological pathways including renin secretion and adrenergic signaling. Lastly, the observations in , and highlight the genes ADRB2 , ACE, AGT , and NOS3 to have an association with the metabolic syndrome (includes hypertension, type II diabetes, excess lipids and abnormal cholesterol levels). Although further validation is required, the above-mentioned genes exhibit characteristics of being key regulators in metabolic syndrome. This is also evident from the biological processes and pathways showed enrichment of HTN (by vasoconstriction and RAAS) and diabetic-related terms (see and ). NOS3 (mapped to drug–gene interaction network) and PLCE1 showed no interaction with HTN related KEGG pathways but exclusive to AGE-RAGE signaling pathways in diabetic complications. As such, using drugs targeted against HTN may influence other signaling pathways. In this case, NOS3 interacts with 3 HTN drugs (Angiotensin II antagonists, calcium channel blockers, and β-blockers). It is also important to understand the roles that genes play in different sub-types of HTN. shows that certain genes are shared among different sub-types of HTN and there are genes which contribute to a specific sub-type of the disease. For example, CLCNKB is only linked to TRH , therefore, a drug targeting this gene will be effective in treating salt sensitive HTN, since a polymorphism in this gene leads to increased salt retention in the bloodstream. Similarly, the gene such as ACE is linked to almost all categories of diseases shown in , thus pointing towards its universal role in HTN related diseases. From the DO analysis, the following potential gene targets have been selected, these include CLCNKB, CYPB11B2, SH2B2, STK9 , and TBX5 as they show interactions exclusively with hypertension-related pathways (see ). Furthermore, targeting co-regulated gene clusters as opposed to single-gene targeting increases the likelihood of more effective drug treatment for HTN. Cluster 1 ( ACE, AGT, AGTR1, AGTR2 , and NOS3 ) and cluster 3 ( CSK and ADRG1 ) showed enrichment of G-coupled receptor binding activity (a classical target of drug discovery) and protein tyrosine kinase receptors (see ), thus demonstrates that these gene clusters can be targeted during future drug design. Gene–gene interactions among regulatory variants play a critical role in the drug–response phenotype . Based on the previous research, and findings in the current research paper it has been suggested that all genes within a cluster should be screened and molecular mechanisms supporting various gene–gene interactions should be explored that may enable identification of potential pharmacogenomic biomarkers to guide personalized medicine approaches for HTN. Therefore, we anticipate that these potential genes including their SNPs need to be investigated further in African populations. While several studies in this review support an important contribution of genetic polymorphisms in HTN, some studies have failed to detect significant effects or replicate previous findings. These inconsistencies may be linked to the African population’s high genetic diversity and low levels of linkage disequilibrium among loci when compared to populations from other countries outside Africa . Furthermore, studies suggest that from a genetic standpoint, there is no SNP-database that can be used to personalize treatments for HTN in an African population . Inferring that the lack of genetic information with robust allele frequency distributions serves as a hurdle to implement corrective treatment and this may have important medical implications . Providing a more accurate reference foundation on which to support future disease research in Africa is thus of utmost importance . 

Angiotensin-converting enzyme ( ACE ) is a metalloenzyme that cleaves angiotensin I to angiotensin II and inactivates a potent vasodilator . ACE is encoded by the ACE gene which has been mapped to chromosome 17q23, and it has 26 exons and 25 introns . Single-nucleotide polymorphism (rs1799752) on the ACE gene was investigated for its implication on high blood pressure in different sub-Saharan Africa countries . It has been showed in various studies conducted in different parts of Africa continent that rs1799752 could be a potential genetic predictor for the development of HTN and permit initiation of personalized medicine . However, other studies performed in South Africa, Tunisia, and Egypt exhibited no association of ACE gene polymorphism with the risk of developing HTN . 

Nitric oxide synthase ( NOS3 ) gene is another important candidate gene because of its critical role in regulating blood pressure. This gene ( NOS3 ) is on chromosome 7q35–36, comprising 26 exons. NOS3 is an integral component of vasorelaxing pathway, which is mediated by endothelium derived nitric oxide . Various studies summarized in this review showed that different populations from Africa exhibit positive association of genetic variants of NOS3 such as rs1799983 , rs2070744 , rs149868979  and rs61722009  with the risk of developing HTN. However, two studies in this review that aimed to investigate the NOS3 gene variants associated with the development of HTN showed no association with the disease . 

In 2009, the genome-wide association studies (GWAS) have identified the association between the Plasma membrane calcium-transporting ATPase 1 ( ATP2B1 ) polymorphisms (rs2681472, rs17249754, rs2681492) and HTN in diverse populations . This gene encodes for plasma membrane calcium dependent ATPase, which handles calcium pumping to the extracellular compartment . In this review, it was revealed that genetic variants in ATP2B1 genes in some populations from Algeria, Burkina Faso, and Uganda are associated with the risk of developing HTN. For instance, a study conducted in an Algerian population reported that the ATP2B1 variant (rs17249754) has a robust significant association with HTN . The findings were further replicated in the Burkinabe population . In another study conducted by Kayima et al.  on a Ugandan population, it was reported that rs2681492 was significantly associated with HTN in hypertensive individuals. Likewise, SNPs on ATP2B1 (rs17249754, rs2681472, and rs2681492) were reported to be in linkage disequilibrium and located in the same linkage disequilibrium block in the Chinese population . 

Methylenetetrahydrofolate reductase gene ( MTHFR ) is one of the most studied genes associated with HTN in the African countries. MTHFR is on chromosome 1p36.3 and is well known to be involved in the metabolism of homocysteine and folate . Previous studies have shown that the MTHFR variant such as rs1801133 has been associated with HTN between 24–87% . Similarly, in this review, 50% of studies investigating the role of MTHFR provided a positive line of evidence linking this gene with HTN, showing that the rs1801133 polymorphism in MTHFR increases the risk of HTN . Conversely, Amrani-Midoun et al.  and Amin et al.  in Algeria and Egypt showed no association of rs1801133 in hypertensive patients. Furthermore, genes such as CYP11B2 (rs1799998) , LEP (rs7799039) , and CPS1 (rs1047891)  were also reported to be associated with HTN, however, these findings could not be replicated in the different African populations. As aforementioned, ACE and NOS3 genes have been studied for its implication on HTN in more than six published papers indexed on the different databases, of which 50% of those were able to link these genes polymorphisms with the high risk for HTN. This suggests that there was a significant association of ACE and NOS3 variants with HTN in African populations. However, AGT, AGTR1 , and AGTR2 polymorphisms in African populations showed less than 25% or no association with HTN. For example, all the SNPs located near AGT gene, only rs2004776 was demonstrated to have a strong association with HTN in Ugandan population . In a recent study by EI-Garawani et al. , two polymorphisms (rs4762 and rs699) of AGT gene were found to be associated with the risk of diabetes and hypertension in the Egyptian population. Furthermore, Farrag et al.  reported the positive association of AGTR1 , rs5186 with HTN in Egyptian population and similar findings were also observed in the Ghanaian population. However, no association has been recorded in other AGTR1 and AGTR2 polymorphisms. 

Furthermore, we attempted to shortlist key target genes out of the 53 identified genes in this review by using bioinformatics methods on gene-specific data available in various public databases. The bioinformatics analysis investigated various parameters associated with the 53 HTN genes such as drug interactions, co-expression of genes and disease ontology, and linked pathways. Only 14 genes ( CYP2C8, CYP11B2, AGT, AGTR1, AGTR2, ACE, ADRB2, LEP, MTHFR, NOS3, HFE, CNNM3, IGF2BP2 , and SCNN1B ) were identified to have an interaction with FDA approved marketed HTN drugs ( and ). As evident from , the various antihypertensive drugs such as losartan, acebutolol, atenolol, metoprolol, ramipril, lisinopril, labetalol, and amlodipine show distinct side-effects. It has also been reported in the literature  that 20–97% of patients taking antihypertensive drugs suffer from various drug-related side effects. Furthermore, these side effects may potentially also account for non-adherence to antihypertensive drugs . Losartan, an angiotensin II antagonist and atenolol, a β -blocker showed the most side-effects related to respiratory impairments and infection. Prior studies have also documented a higher frequency of side-effects and lower adherence among patients taking diuretics and β -blockers . In hypertensive patients of African ancestry, diuretics and or calcium channel blockers (amlodipine) are preferably recommended  and patients who remain concerned about the adverse health effects of antihypertensive drugs are less likely to be adherent to their medications . The co-expression analysis identified three clusters, including cluster 1 ( ACE, AGT, AGTR1, AGTR2, and NOS3), cluster 2 (MOV10, CAN1 , and IGF2BP2 ), and cluster 3 ( CSK and ADRB2 ). Upon further analysis, from cluster 1, AGTR1, AGTR2, AGT , and ACE ; cluster 2, IGFBP2 and cluster 3 ADRB2 genes are shown to be mapped to HTN drugs shown in and . Interestingly, the protein-coding genes AGTR1, AGTR2, AGT , and ACE are all involved in the RAAS cascade which regulates blood pressure and vascular resistance. As such, the most widely used HTN drug classes include ACE inhibitors, angiotensin II antagonists and direct renin inhibitors which target the RAAS cascade . Whereas, in the second and third cluster, the genes IGFBP2 (targeted by vasodilators) and ADRB2 (targeted by β -blockers), respectively, have been recently linked with increased risk of HTN and have been recently proposed as potential diagnostic biomarkers for HTN. For example, in a study by Yang et al.  the overexpression of the gene IGFBP2 which is linked to the regulation of vascularization and angiogenesis is associated with the increased severity of HTN. Additionally, the gene ADRB2 has been shown to interact with the gene NOS3 . ADRB2 may indirectly interact with the RAAS cascade via the NOS3 forming part of the gene cluster of genes associated with the RAAS cascade . This is further illustrated in where ADRB2 shows interaction with RAAS associated genes by regulating common biological pathways including renin secretion and adrenergic signaling. Lastly, the observations in , and highlight the genes ADRB2 , ACE, AGT , and NOS3 to have an association with the metabolic syndrome (includes hypertension, type II diabetes, excess lipids and abnormal cholesterol levels). Although further validation is required, the above-mentioned genes exhibit characteristics of being key regulators in metabolic syndrome. This is also evident from the biological processes and pathways showed enrichment of HTN (by vasoconstriction and RAAS) and diabetic-related terms (see and ). NOS3 (mapped to drug–gene interaction network) and PLCE1 showed no interaction with HTN related KEGG pathways but exclusive to AGE-RAGE signaling pathways in diabetic complications. As such, using drugs targeted against HTN may influence other signaling pathways. In this case, NOS3 interacts with 3 HTN drugs (Angiotensin II antagonists, calcium channel blockers, and β-blockers). It is also important to understand the roles that genes play in different sub-types of HTN. shows that certain genes are shared among different sub-types of HTN and there are genes which contribute to a specific sub-type of the disease. For example, CLCNKB is only linked to TRH , therefore, a drug targeting this gene will be effective in treating salt sensitive HTN, since a polymorphism in this gene leads to increased salt retention in the bloodstream. Similarly, the gene such as ACE is linked to almost all categories of diseases shown in , thus pointing towards its universal role in HTN related diseases. From the DO analysis, the following potential gene targets have been selected, these include CLCNKB, CYPB11B2, SH2B2, STK9 , and TBX5 as they show interactions exclusively with hypertension-related pathways (see ). Furthermore, targeting co-regulated gene clusters as opposed to single-gene targeting increases the likelihood of more effective drug treatment for HTN. Cluster 1 ( ACE, AGT, AGTR1, AGTR2 , and NOS3 ) and cluster 3 ( CSK and ADRG1 ) showed enrichment of G-coupled receptor binding activity (a classical target of drug discovery) and protein tyrosine kinase receptors (see ), thus demonstrates that these gene clusters can be targeted during future drug design. Gene–gene interactions among regulatory variants play a critical role in the drug–response phenotype . Based on the previous research, and findings in the current research paper it has been suggested that all genes within a cluster should be screened and molecular mechanisms supporting various gene–gene interactions should be explored that may enable identification of potential pharmacogenomic biomarkers to guide personalized medicine approaches for HTN. Therefore, we anticipate that these potential genes including their SNPs need to be investigated further in African populations. While several studies in this review support an important contribution of genetic polymorphisms in HTN, some studies have failed to detect significant effects or replicate previous findings. These inconsistencies may be linked to the African population’s high genetic diversity and low levels of linkage disequilibrium among loci when compared to populations from other countries outside Africa . Furthermore, studies suggest that from a genetic standpoint, there is no SNP-database that can be used to personalize treatments for HTN in an African population . Inferring that the lack of genetic information with robust allele frequency distributions serves as a hurdle to implement corrective treatment and this may have important medical implications . Providing a more accurate reference foundation on which to support future disease research in Africa is thus of utmost importance . 

In conclusion, the present study reported on the susceptibility of inter-individual genetic variation to HTN in African population. Large-scale genetic studies are needed to better understand the susceptibility of African population-based inter-individual genetic variation and their effect on the hypertensive drug response, which will aid in the development of effective African based individualized antihypertensive medicine. 

