Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk.
Warren HR et al; International Consortium of Blood Pressure (ICBP) 1000G Analyses; BIOS Consortium; Lifelines Cohort Study; Understanding Society Scientific group; CHD Exome+ Consortium; ExomeBP Consortium; T2D-GENES Consortium; GoT2DGenes Consortium; Cohorts for Heart and Ageing Research in Genome Epidemiology (CHARGE) BP Exome Consortium; International Genomics of Blood Pressure (iGEN-BP) Consortium; UK Biobank CardioMetabolic Consortium BP working group. Nat Genet. 2017 Mar;49(3):403-415. PMID: 28135244
Helen Warren (UK)
The UK Biobank Cardio-metabolic Traits Consortium Blood Pressure Working Group.
- Corresponding authors: Paul Elliott (firstname.lastname@example.org) and Mark Caulfield (email@example.com)
- Joint first author Helen Warren (firstname.lastname@example.org )
Lead departments at Imperial and QMUL
i. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
ii. National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK.
iii. MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK.
1) Summarize your work in one sentence.
Using a discovery sample of ~140,000 individuals of European ancestry from the UK Biobank cohort, we tested ~9.8 million genetic variants across the genome for association with blood pressure, followed by replication in large independent data sets.
2) Summarize your findings in one sentence.
We identified and robustly validated 107 independent genetic signals associated with blood pressure, not previously reported, which together highlight new biological pathways for blood pressure regulation and suggest potential new drug targets for the treatment of hypertension.
3) Which were the more important methods you used in this work? If it is not a traditional method you can briefly explain the concept of that methodology.
The primary genetic association analysis used linear regression (after restricting data to a subset of unrelated European individuals) within SNPTEST software, taking into account the uncertainty of the imputation of all the genetic variants. By performing genome-wide association studies, we are trying to uncover the genetic architecture of diseases, like Hypertension, by looking for association across the whole genome.
We also carried out a range of computer-based bioinformatics analyses and laboratoryexperiments to better understand the functional nature of our novel findings, e.g.: pathway analyses to identify the most important biological pathways related to blood pressure; and gene expression testing which showed that blood pressure genes are most strongly expressed in the vascular tissues.
4) What did you learn from this paper, what was your take-home message?
Combining all the genetic variants associated with blood pressure into a “genetic risk score”, we show that altogether they increase systolic blood pressure by 10 mm Hg in adults over fifty years old. This is comparable to the reduction in blood pressure that can be achieved by improving lifestyle factors alone. Hence the possibility of a “precision medicine” approach for adopting early lifestyle intervention amongst individuals at high genetic risk, to offset the impact of blood pressure-raising genetic variants on future risk of cardiovascular disease, warrants further study.
Furthermore our analysis shows the great benefits of using data from cohorts like UK Biobank, for enabling the discovery of many new genetic signals associated with diseases, for example, by having (i) very large sample sizes within one single cohort, (ii) standardised phenotyping where all participants have had blood pressure measured in exactly the same way, and (iii) high quality genetic data using modern genetic imputation approaches to cover millions of genetic variants.