Loci influencing blood pressure identified using a cardiovascular gene-centric array

Ganesh, Suhas; Tragante, Vinicius; Guo, Wuxia; Guo, Yu-Han; Lanktree, Matthew B.; Smith, Elspeth; Johnson, Terrell K.; Castillo, Berta Almoguera; Barnard, John; Baumert, Jens; Chang, Y.-P. C.; Elbers, Clara C.; Farrall, Martin; Fischer, Melina; Franceschini, Nicola; Gaunt, Tom R.; Gho, Johannes M.I.H.; Gieger, Christian; Gong, Yajie; Isaacs, Adrian M.; Kleber, Marcus E.; Leach, Irene Mateo; McDonough, Caitrin W.; Meijs, Matthijs F.L.; Mellander, O; Molony, Cliona; Nolte, Ilja M.; Padmanabhan, Sriram; Price, Thomas S.; Rajagopalan, Ramakrishnan; Shaffer, John R.; Shah, Syed Asim; Shen, Hui; Soranzo, Nicole; Most, Peter J. van der; Iperen, Erik P.A. van; Setten, Jessica van; Vonk, Judith M.; Zhang, L.; Beitelshees, Amber L.; Berenson, Gerald S.; Bhatt, D.L.; Boer, Jolanda M. A.; Boerwinkle, Eric; Burkley, Ben; Burt, Austin; Chakravarti, Arnab; Chen, W.; Cooper‐DeHoff, Rhonda M.; Curtis, S. P.; Dreisbach, Annette; Duggan, David; Ehret, G.; Fabsitz, Richard R.; Fornage, Myriam; Fox, Eugene N.; Furlong, Clement E.; Gansevoort, Ronald; Hofker, M.H.; Hovingh, G. Kees; Kirkland, Susan; Kottke‐Marchant, Kandice; Kutlar, Abdullah; Lacroix, André; Langaee, Taimour; Li, Y. R.; Lin, Haiyan; Liu, K.; Maiwald, Stephanie; Malik, Rekha; Murugesan, Gurunathan; Newton‐Cheh, Christopher; O’Connell, Jeffery R.; Onland‐Moret, N. Charlotte; Ouwehand, Willem H.; Palmas, Walter; Penninx, Brenda; Pepine, Carl J.; Pettinger, Mary; Polak, Joseph F.; Ramachandran, Vishnu; Ranchalis, Jane; Redline, Susan; Ridker, Paul M.; Rose, L. M.; Scharnag, H.; Schork, Nicholas J.; Shimbo, Daichi; Shuldiner, Alan R.; Srinivasan, S. R.; Stolk, Ronald; Taylor, Heather A.; Thorand, B; Trip, Mieke D.; Duijn, Cornelia M. van; Verschuren, W M Monique; Wijmenga, C.; Winkelmann, Bernhard R.; Wyatt, Sharon B.; Young, J. Hunter; Boehm, B. O.; Caulfield, Mark J.; Chasman, Daniel I.; Davidson, Kathryn; Doevendans, P.A.F.M.; Fitzgerald, Grant; Gums, John G.; Hákonarson, Hákon; Hillege, H. L.; Illig, Thomas; Jarvik, Gail P.; Johnson, J. A.; Kastelein, J.J.P.; Köenig, Wolfgang; März, W.; Mitchell, Braxton D.; Murray, Sarah; Oldehinkel, Albertine J.; Rader, Daniel J.; Reilly, Muredach P; Reiner, Alexander P.; Schadt, E. E.; Silverstein, Roy L.; Snieder, Harold; Stanton, Alice; Uitterlinden, A. G.; Harst, Pim van der; Schouw, Yvonne T. van der; Samani, Nilesh J.; Johnson, A. D.; Munroe, Patricia B.; Bakker, Paul I. W. de; Xianliang, Zhu; Levy, Donna J.; Keating, Brendan J.; Asselbergs, Folkert W.

doi:10.1093/hmg/ddt177

Cited by 18 publications

(26 citation statements)

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“…We included up to 34,538 participants from eight cohorts that had been genotyped with the Human CVD BeadArray (Illumina), also termed the ''IBC'' or ''CardioChip'' array. 15 Several studies using this array have been published and have confirmed previously established associations and identified new associations between SNPs and several phenotypes and disease outcomes, including coronary artery disease, 16,17 plasma lipids, 18 blood pressure, 19,20 cardiomyopathy, 21 T2D, 22 and BMI. 23 The eight cohorts used in the current study, listed in Table S1 (available online), are Atherosclerosis Risk in Communities (ARIC), 24 the Cardiovascular Health Study (CHS), 25 Coronary Artery Risk Development in Young Adults (CARDIA), 26 the European Prospective Investigation into Cancer and Nutrition Netherlands (EPIC-NL), 27 the Framingham Heart Study (FHS), 28 Multinational Etoricoxib and Diclofenac Arthritis Long-term (MEDAL), 29 the Multi-Ethnic Study of Atherosclerosis (MESA), 30 and the Women's Health Initiative (WHI).…”

Section: Methodssupporting

confidence: 54%

Causal Effects of Body Mass Index on Cardiometabolic Traits and Events: A Mendelian Randomization Analysis

Holmes

Lange

Palmer

et al. 2014

The American Journal of Human Genetics

196

149

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Elevated body mass index (BMI) associates with cardiometabolic traits on observational analysis, yet the underlying causal relationships remain unclear. We conducted Mendelian randomization analyses by using a genetic score (GS) comprising 14 BMI-associated SNPs from a recent discovery analysis to investigate the causal role of BMI in cardiometabolic traits and events. We used eight population-based cohorts, including 34,538 European-descent individuals (4,407 type 2 diabetes (T2D), 6,073 coronary heart disease (CHD), and 3,813 stroke cases). A 1 kg/m(2) genetically elevated BMI increased fasting glucose (0.18 mmol/l; 95% confidence interval (CI) = 0.12-0.24), fasting insulin (8.5%; 95% CI = 5.9-11.1), interleukin-6 (7.0%; 95% CI = 4.0-10.1), and systolic blood pressure (0.70 mmHg; 95% CI = 0.24-1.16) and reduced high-density lipoprotein cholesterol (-0.02 mmol/l; 95% CI = -0.03 to -0.01) and low-density lipoprotein cholesterol (LDL-C; -0.04 mmol/l; 95% CI = -0.07 to -0.01). Observational and causal estimates were directionally concordant, except for LDL-C. A 1 kg/m(2) genetically elevated BMI increased the odds of T2D (odds ratio [OR] = 1.27; 95% CI = 1.18-1.36) but did not alter risk of CHD (OR 1.01; 95% CI = 0.94-1.08) or stroke (OR = 1.03; 95% CI = 0.95-1.12). A meta-analysis incorporating published studies reporting 27,465 CHD events in 219,423 individuals yielded a pooled OR of 1.04 (95% CI = 0.97-1.12) per 1 kg/m(2) increase in BMI. In conclusion, we identified causal effects of BMI on several cardiometabolic traits; however, whether BMI causally impacts CHD risk requires further evidence.

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Section: Methodssupporting

confidence: 54%

Causal Effects of Body Mass Index on Cardiometabolic Traits and Events: A Mendelian Randomization Analysis

Holmes

Lange

Palmer

et al. 2014

The American Journal of Human Genetics

196

149

View full text Add to dashboard Cite

show abstract

“…Moreover, SH2B3, which is also expressed in human vascular endothelial cells where it modulates inflammation, has been associated with blood pressure and the risk of MI. [69][70][71] Our study further suggests that a regulation of platelets could also contribute to potential implication of SH2B3 in the development of cardiovascular diseases. The associated SNVs in the FADS1/FADS2 locus (rs174546 and rs174583) are eQTLs for multiple lipid-related transcripts in blood-related tissues, including TMEM258, FADS1, FADS2, and LDLR (Table S21).…”

Section: Overlap With Non-blood Cell Traitssupporting

confidence: 57%

Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals

Eicher

Chami

Kacprowski

et al. 2016

The American Journal of Human Genetics

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Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.

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“…GWAS have identifi ed several genes involved in blood pressure (SBP and DBP) and the risk of hypertension, although replications have not been consistent across efforts (Adeyemo et al, 2009;Ehret et al, 2008;Ganesh et al, 2013;Kidambi et al, 2012;Levy et al, 2009;Newton-Cheh et al, 2009;Padmanabhan et al, 2010). Moreover, previously identifi ed candidate genes such as that encoding ACE have not been replicated in GWAS using stringent p values, although meta-analysis suggests an association (Takeuchi et al, 2012).…”

Section: Blood Pressurementioning

confidence: 99%

Etiology of Individual Differences in Human Health and Longevity

Finkel¹,

Gerritsen²,

Reynolds³

et al. 2014

annu rev gerontol geriatr

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In this chapter, we review of the fi eld of gerontological genetics with respect to subjective and objective health, the role of stress on health, and fi nally frailty and longevity. For most indices of subjective and objective health, frailty, and longevity, genetic infl uences contribute only modestly to individual differences, wherein heritabilities are typically on the order of 35%-40%. Notable exceptions are the moderate to strong heritabilities for lipid measures and brain structure and function, with a remarkably increasing role of genetic infl uences for longevity with advancing age. Although candidate gene and genome-wide association studies (GWAS) studies have identifi ed gene variants associated with many subjective and objective health traits, their effect sizes are typically relatively small, as expected for complex traits. There is some evidence for gene-environment interactions, and stress may be an important moderator of genetic variance for health. For example, carrying a risk genotype for cardiovascular disease (CVD) in the angiotensin converting enzyme gene ( ACE ) may predict stress responsivity and risk of cardiovascular-related diagnoses. Moreover, the gene coding for apolipoprotein E ( APOE ) may moderate responsiveness to stress evoking experiences, impact of physical exercise, and associate with sleep characteristics in those who develop cognitive impairments. For metabolic syndrome (MetS), encompassing the co-occurrence of obesity, hypertension, hypertriglyceridemia, 190 ANNUAL REVIEW OF GERONTOLOGY AND GERIATRICS and hyperinsulinemia, promising associations exist although no single genotype or any gene clusters have been consistently associated with MetS across populations, suggesting that complex gene-environment interactions must be understood before the use of genetic markers can be realized in clinical practice. Future investigations of subjective and objective health, frailty, and longevity are needed to further identify sources of genetic and environmental contributionsand their dynamics across adulthood-to advance understanding of aging processes, prevention, and intervention avenues, and ultimately successful aging.

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Loci influencing blood pressure identified using a cardiovascular gene-centric array

Cited by 18 publications

References 0 publications

Causal Effects of Body Mass Index on Cardiometabolic Traits and Events: A Mendelian Randomization Analysis

Causal Effects of Body Mass Index on Cardiometabolic Traits and Events: A Mendelian Randomization Analysis

Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals

Etiology of Individual Differences in Human Health and Longevity

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