Next Steps for Gene Identification in Primary Hypertension Genomics

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Reference

Next Steps for Gene Identification in Primary Hypertension Genomics

EHRET, Georg Benedikt

EHRET, Georg Benedikt. Next Steps for Gene Identification in Primary Hypertension Genomics.

Hypertension , 2017, vol. 70, no. 4, p. 695-697

DOI : 10.1161/HYPERTENSIONAHA.117.09719 PMID : 28784647

Available at:

http://archive-ouverte.unige.ch/unige:127658

Disclaimer: layout of this document may differ from the published version.

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695 See related article, pp 743–750

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lood pressure (BP) genomics informs on the root origins of primary hypertension, which are still unclear. Since 2008, the field of BP genetics has changed with evidence accu- mulating from genome-wide association studies. In this issue of Hypertension, Zeller et al¹ describe a different approach to BP gene discovery by using RNA expression profiles instead of DNA variants.

Using DNA variants, 24 large genome-wide association studies have been published to date, and the number of new loci is steadily increasing with a large contribution from the latest studies with 150 000–320 000 individuals in the discovery phase (Table; Figure). In total, ≈300 variants have now been replicated to be associated with systolic BP and diastolic BP and their phenotypic derivatives (full list and references to individual studies at www.bloodpressuregenetics.org). Some consider the glass half-full and others half- empty on the new knowledge gained by BP genome-wide association studies. It is clear that novel findings have been added, but at the same time, much of the heritability is not yet captured by the variants identified. To date, only ≈3% to 4% of phenotypic variance is explained by the variants identified,² translating to ≈6% to 8% of the heritability. For the hypertension clinician new results on possible causal tissues of primary hypertension and notably on the relationship of hypertension with outcomes, such as renal function, are of particular interest.^2–4

How can progress be made in addition to ever larger sample sizes, soon to reach close to 1 million participants?

Extrapolating from Figure, an empirical number of ≈600 BP loci is expected to be apparent with 1 million samples, some- what more than the expected number based on theoretical con- siderations.⁵ Other groups concentrate on filtering the variants based on position or function which are promising avenues given the limited statistical power because of multiple-test- ing inflation, even using very large sample sizes. Additional valuable efforts concentrate on phenotypes with increased

phenotypic precision, but systolic BP and diastolic BP are hard to beat in their simplicity to ascertain, with consequently large numbers of study participants available, their evidence from clinical trials, and their predictive power for cardiovascular events.

Zeller et al¹ use RNA expression levels, without consider- ing of DNA variants, to learn more about BP, an approach that Levy and colleagues had described before⁶: RNA expression levels themselves are used to find signatures associated with BP elevation, as opposed to experiments that use gene expression linked to genotype data (eSNPs) to identify likely links between variants and genes. Zeller et al¹ make use of 4 studies with a total sample size of 4539 individuals for which whole blood RNA was assessed on microarrays.

They identify 8 transcripts that were independently replicated to be associated with BP and that explain, collectively, 13%

of the BP variance. In addition, Zeller et al¹ show that 7 of these transcripts are associated with BP changes over time, and 1 transcript (CRIP1) is associated with cardiac hyper- trophy; protein levels of CRIP1 are associated with stroke;

however, for all these latter findings, no replication effort was undertaken.

While being much more dynamic and related to function, the use of RNA has several distinct disadvantages over DNA: first, there is little possibility of distinguishing a consequence from a cause of hypertension using only these methods. The 13% variance explained by Zeller et al¹ is possibly a consequence of BP elevation rather than a cause for BP elevation. It is therefore of course not possible to directly compare the variance explained by DNA variants with variation explained by transcript levels. But all genes associated with BP elevation are candidates for genes driv- ing BP elevation, and these require confirmation by other methods. Second, the expression patterns of RNA transcripts vary greatly between individuals, per cell type, and also between cells of the same tissue because of cell–cell heterogeneity, due to mechanisms such as transcriptional bursting.⁷ Although there are few genes present exclusively in one particular tissue or with more variation between cell types than individuals,⁸ results from one cell type can only inform partly on another cell type. The causal tissue for primary hypertension is unknown, although there are some signals for cellular components of the large vessels.^2–4 As vascular cell types are only available in low numbers, it is unclear whether similar expression signatures could be identified here. Expression profiles in peripheral leucocytes, as used in the experiments of Zeller et al,¹ might be of rel- evance to primary hypertension as T cells may be involved in hypertension pathogenesis.⁹ Third, DNA genotyping The opinions expressed in this article are not necessarily those of the

editors or of the American Heart Association.

From Cardiology, Department of Specialties of Medicine, Geneva University Hospitals, Switzerland; and Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.

Correspondence to Georg Ehret, Cardiology, Department of Specialties of Medicine, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, 1211 Geneva, Switzerland. E-mail [email protected]

Next Steps for Gene Identification in Primary Hypertension Genomics

Georg Ehret

Editorial Commentary

(Hypertension. 2017;70:695-697.

DOI: 10.1161/HYPERTENSIONAHA.117.09719.)

Hypertension is available at http://hyper.ahajournals.org DOI: 10.1161/HYPERTENSIONAHA.117.09719

Downloaded from http://ahajournals.org by on December 10, 2019

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696 Hypertension October 2017

is remarkably accurate using modern methods. For RNA expression measures, there is much greater variability, and there are substantial differences between methods with an apparent advantage when RNA sequencing is used, but at significantly higher pricing.¹⁰

In summary, the article of Zeller et al¹ outlines one possible step toward a different approach to unravel more of BP genetics. There are limitations in the method, but the approach is interesting, and future experiments, run in larger sample sizes and different tissues, would likely yield additional infor- mation. RNA expression profiles in many tissues at large numbers, in addition to those existing, can be used for various additional kinds of experiments and would be very valuable in the endeavor to explain primary hypertension.

Sources of Funding

This work was supported by grants 5R01HL086694, 5R01HL128782-03, and Fondation pour Recherches Médicales.

Disclosures

None.

References

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Figure. Number of blood pressure (BP) loci discovered in the large genome-wide association studies (GWAS) studies (Table) as a function of sample size. The study name is indicated in the plot for the largest studies. The line represents the result of a linear regression of y on x.

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Ehret Gene Identification in Primary Hypertension Genomics 697

Table. Large BP gw, gc, cm, and ex Association Analyses

Phenotype(s) Consortium Ancestry

Discovery Sample Size

No. of New Loci

Total No. of Loci

Published Year

SBP, DBP, HTN (gw) CHARGE-BP European 29 136 9 9 2009

SBP, DBP (gw) Global BP-Gen European 34 433 9 9 2009

SBP, DBP (gw) Amish European 7125 1 1 2009

HTN (gw) Global BP-Gen European 3320 1 1 2010

SBP, DBP, HTN (gw) Amagasaki Asian 1526 0 7 2010

SBP, DBP (gw) AGEN-BP Asian 19 608 4 11 2011

SBP, DBP (gw) ICBP European 69 395 14 28 2011

MAP, PP (gw) ICBP European 74 064 3 12 2011

SBP, DBP (gw) CARe African and AD 8591 1 1 2011

SBP, DBP, PP, MAP (gw) Global BP-Gen European 25 118 4 8 2011

SBP, DBP (gw) COGENT African and AD 29 378 3 5 2013

SBP, DBP, PP, MAP (gc) CARe European 61 619 2 13 2013

SBP, DBP, PP, MAP LTA (gw) ICBP European 46 629 3 19 2014

SBP, DBP, PP, MAP age-effect (gw) ICBP European 55 796 2 20 2014

SBP, DBP, PP, MAP (gc) IBC-BP European 87 736 11 38 2014

SBP, DBP, PP, MAP (gw) AGEN-BP Asian and European 99 994 12 35 2015

SBP, DBP, HTN pleiotropy (gw) COGENT African and AD 29 378 1 4 2015

SBP, DBP kids and adolescence (gw) EAGLE European 23 689 2 2 2016

SBP, DBP (cm) ICBP European 201 000 17 66 2016

SBP, DBP, PP, HTN (ex) CHARGE+ Transethnic 146 562 31 70 2016

SBP, DBP, PP, MAP, HTN (ex) Exome BP Transethnic 192 763 30 51 2016

SBP, DBP, PP, HTN (gw) KAISER+ Transethnic 321 262 44 241* 2017

SBP, DBP, PP, HTN (gw) UKB/ICBP European 152 259 32 102 2017

SBP, DBP, MAP, PP LTA (gw) AGEN-BP Asian 18 422 1 4 2017

Full references and a list of loci are given in www.bloodpressuregenetics.org. AD indicates African diaspora; AGEN, Asian Genetic Epidemiology Network; BP, blood pressure; BP-Gen, Global Blood Pressure Genetics Consortium; CARe, Candidate Gene Association Resource Consortium; CHARGE, Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium; cm, CardioMetabo-Chip wide; COGENT, Continental Origins and Genetic Epidemiology Network; DBP, diastolic BP; EAGLE, Early Genetics and Lifecourse Epidemiology Consortium; eSNPs, expression single-nucleotide polymorphisms; ex, exome wide; gc, gene centric; gw, genome wide; HTN, hypertension; IBC, ITMAT-Broad-CARe array consortium; ICBP, International Consortium for Blood Pressure GWAS; KAISER, KAISER Permanente Resource for Genetic Epidemiology Research on Adult Health and Aging; LTA, long term average; MAP, mean arterial pressure; PP, pulse pressure; SBP, systolic BP; and UKB, UK Biobank.

*No replication available.