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Meta-Analysis
. 2016 Oct;48(10):1151-1161.
doi: 10.1038/ng.3654. Epub 2016 Sep 12.

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

Praveen Surendran #  1 Fotios Drenos #  2   3 Robin Young #  1 Helen Warren #  4   5 James P Cook #  6   7 Alisa K Manning #  8   9   10 Niels Grarup #  11 Xueling Sim #  12   13   14 Daniel R Barnes  1 Kate Witkowska  4   5 James R Staley  1 Vinicius Tragante  15 Taru Tukiainen  8   9   16 Hanieh Yaghootkar  17 Nicholas Masca  18   19 Daniel F Freitag  1 Teresa Ferreira  20 Olga Giannakopoulou  21 Andrew Tinker  21   5 Magdalena Harakalova  15 Evelin Mihailov  22 Chunyu Liu  23 Aldi T Kraja  24   25 Sune Fallgaard Nielsen  26 Asif Rasheed  27 Maria Samuel  27 Wei Zhao  28 Lori L Bonnycastle  29 Anne U Jackson  13   12 Narisu Narisu  29 Amy J Swift  29 Lorraine Southam  30   20 Jonathan Marten  31 Jeroen R Huyghe  13   12 Alena Stančáková  32 Cristiano Fava  33   34 Therese Ohlsson  33 Angela Matchan  30 Kathleen E Stirrups  21   35 Jette Bork-Jensen  11 Anette P Gjesing  11 Jukka Kontto  36 Markus Perola  36   37   22 Susan Shaw-Hawkins  4 Aki S Havulinna  36 He Zhang  38 Louise A Donnelly  39 Christopher J Groves  40 N William Rayner  40   20   30 Matt J Neville  40   41 Neil R Robertson  20   40 Andrianos M Yiorkas  42   43 Karl-Heinz Herzig  44   45 Eero Kajantie  36   46   47 Weihua Zhang  48   49 Sara M Willems  50 Lars Lannfelt  51 Giovanni Malerba  52 Nicole Soranzo  53   35   54 Elisabetta Trabetti  52 Niek Verweij  55   9   56 Evangelos Evangelou  48   57 Alireza Moayyeri  48   58 Anne-Claire Vergnaud  48 Christopher P Nelson  18   19 Alaitz Poveda  59   60 Tibor V Varga  59 Muriel Caslake  61 Anton Jm de Craen  62   63 Stella Trompet  62   64 Jian'an Luan  50 Robert A Scott  50 Sarah E Harris  65   66 David Cm Liewald  65   67 Riccardo Marioni  65   66   68 Cristina Menni  69 Aliki-Eleni Farmaki  70 Göran Hallmans  71 Frida Renström  59   71 Jennifer E Huffman  31   23 Maija Hassinen  72 Stephen Burgess  1 Ramachandran S Vasan  23   73   74 Janine F Felix  75 CHARGE-Heart Failure ConsortiumMaria Uria-Nickelsen  76 Anders Malarstig  77 Dermot F Reily  78 Maarten Hoek  79 Thomas Vogt  79   80 Honghuang Lin  23   81 Wolfgang Lieb  82 EchoGen ConsortiumMatthew Traylor  83 Hugh F Markus  83 METASTROKE ConsortiumHeather M Highland  84 Anne E Justice  84 Eirini Marouli  21 GIANT ConsortiumJaana Lindström  36 Matti Uusitupa  85   86 Pirjo Komulainen  72 Timo A Lakka  72   87   88 Rainer Rauramaa  72   88 Ozren Polasek  89   90 Igor Rudan  89 Olov Rolandsson  91 Paul W Franks  59   91   92 George Dedoussis  70 Timothy D Spector  69 EPIC-InterAct ConsortiumPekka Jousilahti  36 Satu Männistö  36 Ian J Deary  65   67 John M Starr  65   93 Claudia Langenberg  50 Nick J Wareham  50 Morris J Brown  4 Anna F Dominiczak  94 John M Connell  39 J Wouter Jukema  64   95 Naveed Sattar  94 Ian Ford  61 Chris J Packard  61 Tõnu Esko  22   96   8   9 Reedik Mägi  22 Andres Metspalu  22   97 Rudolf A de Boer  98 Peter van der Meer  98 Pim van der Harst  98   99   100 Lifelines Cohort StudyGiovanni Gambaro  101 Erik Ingelsson  102   103 Lars Lind  102 Paul Iw de Bakker  104   105 Mattijs E Numans  106   105 Ivan Brandslund  107   108 Cramer Christensen  109 Eva Rb Petersen  110 Eeva Korpi-Hyövälti  111 Heikki Oksa  112 John C Chambers  48   49   113 Jaspal S Kooner  49   114   113 Alexandra If Blakemore  42   43 Steve Franks  115 Marjo-Riitta Jarvelin  116   117   118   119 Lise L Husemoen  120 Allan Linneberg  120   121   122 Tea Skaaby  120 Betina Thuesen  120 Fredrik Karpe  40   41 Jaakko Tuomilehto  36   123   124   125 Alex Sf Doney  39 Andrew D Morris  126 Colin Na Palmer  39 Oddgeir Lingaas Holmen  127   128 Kristian Hveem  127   129 Cristen J Willer  38   130   131 Tiinamaija Tuomi  132   133 Leif Groop  134   133 AnneMari Käräjämäki  135   136 Aarno Palotie  16   9   133   137 Samuli Ripatti  133   138   30 Veikko Salomaa  36 Dewan S Alam  139 Abdulla Al Shafi Majumder  140 Emanuele Di Angelantonio  1   54 Rajiv Chowdhury  1 Mark I McCarthy  40   41   20 Neil Poulter  141 Alice V Stanton  142 Peter Sever  141 Philippe Amouyel  143   144   145   146 Dominique Arveiler  147 Stefan Blankenberg  148   149 Jean Ferrières  150 Frank Kee  151 Kari Kuulasmaa  36 Martina Müller-Nurasyid  152   153   154 Giovanni Veronesi  155 Jarmo Virtamo  36 Panos Deloukas  21   156 Wellcome Trust Case Control ConsortiumPaul Elliott  116 Understanding Society Scientific GroupEleftheria Zeggini  30 Sekar Kathiresan  56   157   158   9 Olle Melander  33 Johanna Kuusisto  32 Markku Laakso  32 Sandosh Padmanabhan  94 David Porteous  66 Caroline Hayward  31 Generation Scotland  159 Francis S Collins  29 Karen L Mohlke  160 Torben Hansen  11 Oluf Pedersen  11 Michael Boehnke  13   12 Heather M Stringham  13   12 EPIC-CVD ConsortiumPhilippe Frossard  27 Christopher Newton-Cheh  56   157 CHARGE+ Exome Chip Blood Pressure ConsortiumMartin D Tobin  6 Børge Grønne Nordestgaard  26 T2D-GENES ConsortiumGoT2DGenes ConsortiumExomeBP ConsortiumCHD Exome+ ConsortiumMark J Caulfield  4   5 Anubha Mahajan  20 Andrew P Morris  20   7 Maciej Tomaszewski  18   19   161 Nilesh J Samani  18   19 Danish Saleheen #  28   27   1 Folkert W Asselbergs #  15   100   162 Cecilia M Lindgren #  163   9   20 John Danesh #  1   164   54 Louise V Wain #  6 Adam S Butterworth #  1   165 Joanna Mm Howson #  1 Patricia B Munroe #  4   5
Affiliations
Meta-Analysis

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

Praveen Surendran et al. Nat Genet. 2016 Oct.

Abstract

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to 192,763 individuals and used ∼155,063 samples for independent replication. We identified 30 new blood pressure- or hypertension-associated genetic regions in the general population, including 3 rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5 mm Hg/allele) than common variants. Multiple rare nonsense and missense variant associations were found in A2ML1, and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention.

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Conflict of interest statement

Conflict of interests N. P. has received financial support from several pharmaceutical companies that manufacture either blood pressure lowering or lipid lowering agents, or both, and consultancy fees. S. K. has received Research Grant-Merck, Bayer, Aegerion; SAB-Catabasis, Regeneron Genetics Center, Merck, Celera; Equity-San Therapeutics, Catabasis; Consulting-Novartis, Aegerion, Bristol Myers-Squibb, Sanofi, AstraZeneca, Alnylam. P. Sever has received research awards from Pfizer Inc. A. Malarstig and M. Uria-Nickelsen are full time employees of Pfizer. D. Reily and M. Hoek are full time employees of Merck and co Inc. M.J. Caulfield is Chief Scientist for Genomics England a UK Government company. The authors declare no competing financial interest.

Figures

Figure 1
Figure 1. Study design and work flow diagram of single variant discovery analyses.
EUR=European, SAS=South Asian, HIS=Hispanic, AA=African American, HTN=hypertension, BP=blood pressure, SBP=systolic blood pressure, DBP= diastolic blood pressure, PP=pulse pressure, N=sample size, MAF=minor allele frequency, P=P-value significance threshold, SNV=single-nucleotide variant, GWS=genome-wide significance *Further details of the selection criteria are provided in the methods.
Figure 2
Figure 2. Overlap of the 30 novel loci associations across SBP, DBP, PP and HTN.
The Venn diagram shows which of the 30 newly identified BP loci are associated with multiple BP traits. Only SNV-BP trait associations that were genome-wide significant (P < 5x10-8) in the combined discovery and replication meta-analyses are listed for any given BP trait, within the corresponding ancestry dataset that the given locus was validated for (see Tables 1 and 2). The association of RRAS variant with SBP was replicated in the independent samples, but did not achieve GWS in the combined discovery and replication meta-analysis and is therefore only included for SBP. HTN=hypertension, SBP=systolic blood pressure, DBP= diastolic blood pressure, PP=pulse pressure.
Figure 3
Figure 3. Study design for conditional analyses and rare variant gene-based discovery analyses.
RMW=RareMetalWorker, EUR=European, SAS = South Asian, HTN=hypertension, BP=blood pressure, SBP=systolic blood pressure, DBP= diastolic blood pressure, PP=pulse pressure. N=sample size, MAF=minor allele frequency, P=P-value significance threshold, Pcond=conditional P-value significance threshold
Figure 4
Figure 4. Locus plot for A2ML1 and secondary amino acid structure of the gene product.
(a) Locus plot for A2ML1 associated with HTN identified through gene based tests. The variants’ positions along the gene (x axis, based on human genome build 37) and the –log10(P-value of association) (y axis) are indicated. The variants are colour coded: nonsense (black), missense, predicted damaging (blue), and missense (orange). The schematic above the x-axis represents the intron / exon (black vertical bars) structure, the untranslated regions are shown as grey vertical bars. (b) The white box denotes the full-length amino acid sequence for each of the two gene products. Black numbers denote amino acid residue positions of note. Coloured boxes depict putative functional domains (see below). Coloured vertical lines indicate the amino acid substitutions corresponding to the variants depicted in the locus plots above using the same colour coding. Bold, italic indicates the SNV association with smallest P-value. Dark grey – signal peptide sequence. Brown – regions of intramolecular disulfide bonds. For simplicity only those regions coinciding with variants described were indicated. Black – bait region described to interact with proteases. Purple – thiol ester sequence region aiding in interaction with proteases. Light grey – alpha helical regions thought to mediate A2ML1 interaction with LRP1, facilitating receptor-mediated endocytosis.

Comment in

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