The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Genetics of blood pressure: time to curate the collection
Abstract
The genetics of blood pressure (BP) is all about discovery and understanding, but it is certainly not for the faint hearted. Despite heroic effort, the question we posed nearly 15 years ago [1] regarding the whereabouts of BP genes remains largely unanswered. It has not been an easy journey. The early candidate gene studies in rats [2] and humans [3] fell out of favor because of inconsistency between studies and the fact they were small in size or scope. The exciting leap from the gene to the genome and the advent of genome-wide association studies (GWAS) was hoped to solve the previous difficulties. Their unbiased interrogation of the genome using millions of markers in thousands of individuals was expected to have the oomph to unearth the location of the DNA variants that would explain the 40% of BP variability commonly attributed to genes (i.e. heritability). Unfortunately, the early GWAS essentially failed to locate anything of substance for BP [1,4]. The response was to build bigger and bigger conglomerates to study hundreds of thousands of individuals and use denser genome maps [5]. As a result, three lessons were learned. First, as with candidate gene analyses, giant genomic studies are not immune from inconsistency and disagreement. Second, the polygenic nature of BP was affirmed by the detection of multiple (about 80) common variants (each present in >5% and typically around 30% of the population), but each estimated to have unmeasurable (≤1 mmHg) effects on individual BP. Third and most surprising, instead of the expected 40%, all known GWAS variants together accounted for only about 3% of BP variance. It was little compensation that BP was not the only phenotype for which GWAS had failed to discover the variants to explain heritabilities and the concept of ‘missing heritability’ was born. There are many possible explanations for the mysterious missing heritability, but one fashionable explanation is that rare variants might be the missing pieces of the jigsaw [6]. This is conceivable, as rare variants slip through the net of a GWAS scan because it is designed to detect common variants. Therefore, the search for low frequency (1–5%) and rare (<1%) variants began. The first targets were the exons or protein-coding regions of genes. Early studies in and adjacent to the exons of three candidate BP genes discovered 30 different rare variants (frequency <0.05%) that were estimated to affect BP by a measurable decrement – lowering BP by about 6 mmHg compared with controls [7]. If three genes could yield 30 mutations from 3000 or so individuals [7], imagine what screening all 20 000 genes in tens and hundreds of thousands of study participants could reveal. So, moving from the exon to the exome – the 1–2% of the genome comprising all the exons – was a natural step made possible by rapidly advancing molecular technology. The study appearing in this issue by Nandakumar et al.[8] and two recent others [9,10] are among the first to put the exomes to the test for BP. Before passing judgement, it needs to be appreciated that these analyses are not the gold standard for exomic interrogation. Ideally, this would involve deep sequencing of the exomes, an approach underway for a variety of complex diseases [6]. However, costs are still relatively high and instead it is more economical for very large studies to use custom genotyping arrays known as exome chips. But the trade-off is a more limited survey of the exome. Also, the DNA variants on the array were derived from mostly European sequences and their utility in non-Europeans is less certain. Finally, the exome array comprises mostly low and rare variants, but 11% of the 250 000 chip variants are common, including almost 6000 from the GWAS catalogue. The two largest exomic analyses were conducted in populations composed mostly of European ancestry and together they studied nearly 700 000 individuals [9,10]. The sum of discovery from these efforts was 61 new BP loci – some for systolic, some diastolic, some pulse pressure, some increasing BP, and some lowering it. Yet, despite the majority of the array variants being either low frequency or rare, all but six of the 61 new loci were common variants with an average population frequency of 34% and a mean estimated BP effect of 0.25 mmHg. The remaining six loci were either low frequency or rare variants, with an average estimated BP effect of 2.2 mmHg. The overall picture looked more like another GWAS and could not have been further removed in either nature or number of variants expected from the prior screening of exons in candidate genes [7]. And frustratingly, still the vast majority of heritability remained missing. The Nandakumar et al.[8] study provides a slightly more encouraging perspective. Conducted exclusively in study participants of African ancestry (but relying on the essentially European array) the discoveries were exclusively rare variants for 10 genes, many of which are plausible candidates in a fun
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“However, the search for genetic and epigenetic contributions to obesity-induced hypertension has been disappointing, explaining only a tiny fraction of hypertension31.”
From Obesity, kidney dysfunction and hypertension: mechanistic links (2019) · cites this paper as [31]
Citations
Cites
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Function and information content of DNA methylation
Schübeler, D. (2015) · Nature · doi:10.1038/nature14192 · Cited by 2,060
Molecular basis of human hypertension: Role of angiotensinogen
Jeunemaı̂tre, X., Soubrier, F., Kotelevtsev, Y. V., Lifton, R. P., Williams, C. S., Charru, A., Hunt, S. C., Hopkins, P. N., Williams, R. R., Lalouel, J.-M., et al. (1992) · Cell · doi:10.1016/0092-8674(92)90275-h · Cited by 1,850
Pervasive roles of microRNAs in cardiovascular biology
Small, E. M., & Olson, E. N. (2011) · Nature · doi:10.1038/nature09783 · Cited by 1,180
Rare-Variant Association Analysis: Study Designs and Statistical Tests
Lee, S., Abecasis, G. R., Boehnke, M., & Lin, X. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.06.009 · Cited by 1,042
Rare independent mutations in renal salt handling genes contribute to blood pressure variation
Ji, W., Foo, J. N., O'Roak, B. J., Zhao, H., Larson, M. G., Simon, D. B., Newton-Cheh, C., State, M. W., Levy, D., & Lifton, R. P. (2008) · Nature Genetics · doi:10.1038/ng.118 · Cited by 812
Family-based designs in the age of large-scale gene-association studies
Laird, N. M., & Lange, C. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1839 · Cited by 459
CHARGE-Heart Failure Consortium, Surendran, P., EchoGen Consortium, METASTROKE Consortium, GIANT Consortium, EPIC-InterAct Consortium, Study, L. C., Wellcome Trust Case Control Consortium, Understanding Society Scientific Group, EPIC-CVD Consortium, et al. (2016) · Nature Genetics · doi:10.1038/ng.3654 · Cited by 312
A Genetic Polymorphism in the Renin Gene of Dahl Rats Cosegregates with Blood Pressure
Rapp, J. P., Wang, S.-M., & Dene, H. (1989) · Science · doi:10.1126/science.2563177 · Cited by 294
Liu, C., Consortia, M. I. G. A. C. E., Kraja, A. T., Smith, J. A., Brody, J. A., Franceschini, N., Bis, J. C., Rice, K. M., Morrison, A. C., Lu, Y., et al. (2016) · Nature Genetics · doi:10.1038/ng.3660 · Cited by 265
Flagged first, then by citations
Cited by
Sorted by citations
Obesity, kidney dysfunction and hypertension: mechanistic links
Hall, J. E., do Carmo, J. M., da Silva, A. A., Wang, Z., & Hall, M. E. (2019) · Nature Reviews Nephrology · doi:10.1038/s41581-019-0145-4 · Cited by 654
Metadata
- Authors
- Harrap, S. B., & Charchar, F. J.
- Affiliations
- Federation University (AU)
- Corresponding author
- Harrap, Stephen B.
- Venue
- Journal of Hypertension
- Published
- 2017-07
- Volume
- 35
- Issue
- 7
- Pages
- 1360-1362
- ISSN
- 0263-6352 (ISSN-L)
- OpenAlex
- W2594126256
- Cited by
- 2
- References
- 14
- Citations by year
- 2019: 1 · 2018: 1
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Hypertension (publishedVersion)
- PubMed (publishedVersion)
- FedUni ResearchOnline (Federation University Australia) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Blood Pressure and Hypertension Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- missing heritability, rare variants, polygenic hypertension, genome-wide association study, candidate gene association, heritability of blood pressure, low-frequency variants, exome sequencing, rare variant burden, blood pressure variability, gene-environment interaction, gene-environment moderation, gene-environment confounding
- MeSH terms
- Blood Pressure, Blood Pressure Determination, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review