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  • Review Article
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Genomics of hypertension: the road to precision medicine

Nature Reviews Cardiology volume 18, pages 235–250 (2021)Cite this article

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Abstract

The known genetic architecture of blood pressure now comprises >30 genes, with rare variants resulting in monogenic forms of hypertension or hypotension and >1,477 common single-nucleotide polymorphisms (SNPs) being associated with the blood pressure phenotype. Monogenic blood pressure syndromes predominantly involve the renin–angiotensin–aldosterone system and the adrenal glucocorticoid pathway, with a smaller fraction caused by neuroendocrine tumours of the sympathetic and parasympathetic nervous systems. The SNPs identified in genome-wide association studies (GWAS) as being associated with the blood pressure phenotype explain only approximately 27% of the 30–50% estimated heritability of blood pressure, and the effect of each SNP on the blood pressure phenotype is small. A paucity of SNPs from GWAS are mapped to known genes causing monogenic blood pressure syndromes. For example, a GWAS signal mapped to the gene encoding uromodulin has been shown to affect blood pressure by influencing sodium homeostasis, and the effects of another GWAS signal were mediated by endothelin. However, the majority of blood pressure-associated SNPs show pleiotropic associations. Unravelling these associations can potentially help us to understand the underlying biological pathways. In this Review, we appraise the current knowledge of blood pressure genomics, explore the causal pathways for hypertension identified in Mendelian randomization studies and highlight the opportunities for drug repurposing and pharmacogenomics for the treatment of hypertension.

Key points

  • The genetic architecture of blood pressure encompasses approximately 30 genes, with rare variants involved in blood pressure dysregulation and >1,477 common single-nucleotide polymorphisms (SNPs) associated with blood pressure.

  • Monogenic forms of blood pressure disorders involve both germline and somatic variants, with the latter predominantly seen in patients with neoplasms associated with hypertension.

  • Most of the SNPs identified in genome-wide association studies (GWAS) as being associated with blood pressure are pleiotropic and mapped to non-coding regions of the genome, which makes functional mapping challenging.

  • Although translating GWAS signals into causal mechanisms and clinical applications has been slow, Mendelian randomization studies are substantially improving our understanding of known epidemiological correlations between blood pressure and other traits.

  • Pharmacogenomic studies of drug–gene interactions might offer a route to the early clinical translation of GWAS signals, with two clinical trials involving blood pressure GWAS SNPs currently ongoing.

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Fig. 1: Blood pressure regulation.
Fig. 2: Genetic architecture of blood pressure regulation and hypertension.
Fig. 3: Pleiotropic signals from phenome-wide association studies.

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Acknowledgements

The authors thank Stefanie Lip (University of Glasgow, UK) for designing Fig. 1 for submission. S.P. is funded by the Medical Research Council (MR/M016560/1), the British Heart Foundation (PG/12/85/29925, CS/16/1/31878, RE/18/6/34217), Health Data Research UK and Chief Scientist Office, Scotland. A.F.D. acknowledges funding from UK Research and Innovation Strength in Places Fund (SIPF) 35049.

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  1. BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK

    Sandosh Padmanabhan & Anna F. Dominiczak

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  1. Sandosh Padmanabhan
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  2. Anna F. Dominiczak
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Both authors researched data for the article, discussed its content, wrote the manuscript, and reviewed and edited it before submission.

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Correspondence to Anna F. Dominiczak.

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Nature Reviews Cardiology thanks D. Arnett, M. Irvin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Comparative Toxicogenomics Database: http://ctdbase.org/

DrugBank: https://www.drugbank.com/

GWAS Catalog: https://www.ebi.ac.uk/gwas/

PhenoScanner: http://www.phenoscanner.medschl.cam.ac.uk/

Trans-Omics for Precision Medicine programme: https://www.nhlbi.nih.gov/science/trans-omics-precision-medicine-topmed-program

Glossary

Genome-wide association studies

(GWAS). Hypothesis-free methods that involve rapidly scanning hundreds of thousands of common genetic variations across the DNA of large numbers of individuals to find genetic variants that are associated with a particular disease.

Single-nucleotide polymorphisms

(SNPs). Naturally occurring, single-base substitutions in the human genome with a population frequency >1%. SNPs occur approximately once every 1,000 nucleotides throughout the genome, which means that roughly 4–5 million SNPs occur in a person’s genome.

Purifying selection

Natural selection can be of two types based on its effect on the fate of genetic variations: purifying (negative) selection and positive (Darwinian) selection. Purifying selection is the most prevalent form of selection because it leads to the constant elimination of deleterious variants that are produced in each generation.

Phenome-wide association studies

(PheWAS). Studies in which the association between single-nucleotide polymorphisms (SNPs) or other types of DNA variant is tested across a large number of different phenotypes. The direction of inference in a PheWAS is from a SNP to multiple phenotypes, whereas in genome-wide association studies it is from one phenotype to multiple SNPs.

Pleiotropy

A phenomenon whereby a genetic variant influences multiple traits and can involve a variant having effects on two or more traits via independent pathways (variants in FBN1 cause Marfan syndrome, with abnormalities in the heart, blood vessels, eyes, bones and joints) or because the effect on one trait is causally related to variation in another trait (variants that increase LDL-cholesterol levels are also associated with coronary artery disease).

Heterozygote advantage

When the heterozygous genotype has a higher relative fitness than either the homozygous dominant or homozygous recessive genotype. A classic example is sickle-cell anaemia, in which sickle-cell phenotype carriers have a heterozygote advantage over the reproductive fitness of normal homozygotes in malaria-endemic regions.

Tag SNPs

Linkage disequilibrium results in a high degree of correlation among nearby single-nucleotide polymorphisms (SNPs), whereby most SNP sites convey redundant information and can be omitted for cost-effectiveness during genotyping. Tag SNPs are used to tag a particular haplotype in a region of the genome and genome-wide association studies (GWAS) SNP arrays use a set of representative (tag) SNPs that sufficiently represent the genomic diversity in the study population.

Linkage disequilibrium

The non-random association of alleles at two or more loci in a general population. This property has multiple uses, including detecting sites of past selection in human populations. The fine-scale pattern of linkage disequilibrium shows that the human genome is composed of haplotype blocks within which most or all single-nucleotide polymorphisms (SNPs) are in high linkage disequilibrium, which led to the development of efficient designs of SNP arrays for genome-wide association studies.

Polygenic risk score

A single-value estimate of an individual’s genetic liability to a phenotype, calculated as a sum of the genome-wide genotypes weighted by corresponding genotype effect size estimates derived from genome-wide association studies (GWAS) data. Classic polygenic risk scores include a reduced set of single-nucleotide polymorphisms (SNPs), for instance, only SNPs with a GWAS P value below a specified threshold. Other methods include millions of SNPs, explicitly modelling the correlation structure between SNPs without identifying a minimal subset of SNPs for prediction.

Mendelian randomization

Studies using genetic variation as a natural experiment to investigate the causal relationships between potentially modifiable risk factors and diseases or phenotypes. The idea behind Mendelian randomization is that, because genetic variants are fixed at conception, they are not affected by confounding or reverse causation, which blight causal inferences in conventional observational studies.

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Padmanabhan, S., Dominiczak, A.F. Genomics of hypertension: the road to precision medicine. Nat Rev Cardiol 18, 235–250 (2021). https://doi.org/10.1038/s41569-020-00466-4

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