Membership of the Global BPgen Consortium is provided in the Acknowledgments.
Conceived and designed the experiments: RK BEKK MMBB NC PM AGU FR AH PTVMdJ CMvD BMP FJ GE TA LJL KDT SKI CJH JIR EB VG DSS JRV TYW. Analyzed the data: MKI SX RAJ MFC AWH MAI JJW GL FR CMvD LK CYC AVS NLG TL BM TBH XL QX TAS DAM SM NGM TLY JCB KLW SRH TA SF EGH EST TYW. Wrote the paper: MKI SX RAJ MFC AWH MAI JJW RK BEKK MMBB NC GL PM AGU AH PTVMdJ LK CYC AVS NLG TL BM BMP FJ GE TA TBH LJL KDT XL SKI QX TAS DAM SM NGM TLY CJH TA SF JA EGH RJS FMAI JIR AKM EB EST VG DSS JRV TYW.
¶ These authors also contributed equally to this work.
The authors have declared that no competing interests exist.
There is increasing evidence that the microcirculation plays an important role in the pathogenesis of cardiovascular diseases. Changes in retinal vascular caliber reflect early microvascular disease and predict incident cardiovascular events. We performed a genome-wide association study to identify genetic variants associated with retinal vascular caliber. We analyzed data from four population-based discovery cohorts with 15,358 unrelated Caucasian individuals, who are members of the Cohort for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and replicated findings in four independent Caucasian cohorts (n = 6,652). All participants had retinal photography and retinal arteriolar and venular caliber measured from computer software. In the discovery cohorts, 179 single nucleotide polymorphisms (SNP) spread across five loci were significantly associated (p<5.0×10−8) with retinal venular caliber, but none showed association with arteriolar caliber. Collectively, these five loci explain 1.0%–3.2% of the variation in retinal venular caliber. Four out of these five loci were confirmed in independent replication samples. In the combined analyses, the top SNPs at each locus were: rs2287921 (19q13; p = 1.61×10−25, within the
The microcirculation plays an important role in the development of cardiovascular diseases. Retinal vascular caliber changes reflect early microvascular disease and predict incident cardiovascular events. In order to identify genetic variants associated with retinal vascular caliber, we performed a genome-wide association study and analyzed data from four population-based discovery cohorts with 15,358 unrelated Caucasian individuals, who are members of the Cohort for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and replicated findings in four independent Caucasian cohorts (n = 6,652). We found evidence for association of four loci with retinal venular caliber: on chromosomes 19q13 within the
Although both macrovascular and microvascular pathology are associated with cardiovascular disease, including coronary artery disease and stroke
Quantitative measurement of retinal blood vessel caliber from photographs allows a non-invasive direct assessment of the human microcirculation
Recent studies suggest that genetic factors may play a role in influencing retinal vascular caliber
The total study sample for the discovery analyses was 15,358 and for the replication analyses 6,652. Characteristics of both the discovery and replication samples are presented in
Discovery cohorts | Replication cohorts | |||||||
Original cohort | 5,764 | 15,792 | 5,888 | 7,983 | 2,235 | 8,810 | 2,579 | 3,508 |
Non-Hispanic whites in original cohort | 11,478 | 4,925 | 7,983 | 2,190 | 8,810 | 2,579 | 3,487 | |
Total number included in analyses | 2,949 | 6,317 | 1,272 | 4,820 | 1,709 | 1,132 | 2,522 | 1,310 |
Mean age (years) (SD) [range] | 76.2 (5.4)[66–94] | 60.3 (5.6)[50–72] | 78.4 (4.1)[72–95] | 68.0 (8.2)[55–99] | 22.5 (12.4)[5–90] | 58.1 (10.1)[16–81] | 60.6 (10.8)[43–86] | 66.0 (8.6)[49–93] |
Proportion female (%) | 57.5 | 52.9 | 62.9 | 59.0 | 57.0 | 97.7 | 55.8 | 58.4 |
Mean CRAE (µm) (SD) [range] | 139.7(13.4)[74.0–221.4] | 136.1 (14.3)[72.6–203.8] | 140.4 (15.7)[77.6–197.4] | 150.0 (14.4)[98.5–235.4] | 164.2 (13.6)[83.6–205.2] | 163.8 (18.1)[91.0–219.6] | 149.5 (13.7)[100.3–196.6] | 160.0 (20.2)[93.4–213.4] |
Mean CRVE (µm) (SD) [range] | 202.0(19.5)[123.8–273.0] | 199.4 (19.2)[129.3–304.1] | 196.5 (19.2)[142.5–271.7] | 226.0 (20.1)[162.5–324.3] | 248.0 (19.0)[130.5–325.7] | 253.0 (28.6)[147.0–338.0] | 230.3 (21.7)[165.9–335.1] | 238.4 (24.0)[167.6–331.1] |
Systolic blood pressure (mm Hg) (SD) [range] | 142.5(20.2)[92–253] | 122.9 (18.1)[75–226] | 134.4 (20.4)[82–241] | 138.5 (22.1)[74–250] | N/A | 130.5 (19.7)[85–210] | 130.5 (20.0)[71–248] | 146.0 (20.5)[95–240] |
Diastolic blood pressure (mm Hg) (SD) [range] | 74.1 (20.2)[92–253] | 70.8 (10.0)[32–114] | 67.9 (10.8)[15–110] | 73.7 (11.4)[24–139] | N/A | 79.5 (11.9)[50–124] | 77.4 (10.8)[44–123] | 83.6 (9.8)[50–125] |
Hypertension (%) | 80.6 | 35.3 | 48.7 | 42.3 | 3.2 | 41.7 | 35.1 | 46.1 |
Diabetes mellitus (%) | 11.4 | 12.4 | 12.2 | 10.0 | 1.0 | 1.5 | 10.3 | 7.9 |
Current smokers (%) | 12.5 | 17.3 | 6.2 | 23.6 | 11.0 | 13.8 | 21.6 | 12.4 |
Body mass index (kg/m2) (SD) [range] | 27.1 (4.4)[14.8–48.5] | 28.0 (5.2)[14.2–59.1] | 26.8 (4.3)[15.6–46.7] | 26.3 (3.7)[14.2–50.7] | N/A | 25.6 (4.3)[15.0–48.2] | 28.3 (5.2)[15–55] | 26.3 (4.4)15.2–49.2 |
AGES: Age Gene/Environment Susceptibility – Reykjavik Study; ARIC: Atherosclerosis Risk in Communities Study; CHS: Cardiovascular Health Study; RS: Rotterdam Study; BDES: Beaver Dam Eye Study; BMES: Blue Mountains Eye Study; CRAE: central retinal arteriolar equivalent; CRVE: central retinal venular equivalent; SD: standard deviation; N/A Not available.
A total of 179 single nucleotide polymorphisms (SNPs) at five loci surpassed our preset threshold (p<5.0×10−8) for genome-wide significance for retinal venular caliber. Collectively, these five independent loci explain 1.0–3.2% of the variation in retinal venular caliber within our discovery cohorts. The QQ-plots (
For (a) retinal venular caliber and (b) retinal arteriolar caliber.
SNP (chromosome position) locus | Minor allele (MAF) | Beta | SE | P-value | Beta | SE | P-value | Beta | SE | P-value | Beta | SE | P-value | Beta | SE | P-value | Genes of Interest | Other genes within 60kb | Additional SNPs at p-value<5×10−8 |
rs2287921 (53920084)19q13 | T (0.47) | −2.0 | 0.23 | 3.30×10−18 | −1.0 | 0.56 | 7.40×10−2 | −2.5 | 0.36 | 3.80×10−12 | −2.9 | 0.77 | 1.66×10−4 | −1.7 | 0.42 | 5.17×10−5 | 37 | ||
rs225717 (142589792)6q24 | C (0.23) | −1.8 | 0.27 | 5.99×10−12 | −1.9 | 0.60 | 1.54×10−3 | −2.2 | 0.40 | 7.25×10−8 | −2.0 | 0.96 | 3.70×10−2 | −1.3 | 0.50 | 9.32×10−3 | 31 | ||
rs10774625 (110394602)12q24 | A (0.48) | 1.6 | 0.23 | 1.16×10−11 | 1.3 | 0.43 | 2.50×10−3 | 1.5 | 0.35 | 1.82×10−5 | 1.9 | 0.75 | 1.10×10−2 | 1.7 | 0.43 | 7.70×10−5 | 9 | ||
rs17421627 (87883342)5q14 | G (0.08) | 2.5 | 0.43 | 5.05×10−9 | 2.2 | 1.16 | 5.80×10−2 | 1.6 | 0.63 | 1.10×10−2 | 5.3 | 1.91 | 5.52×10−3 | 3.2 | 0.71 | 6.57×10−6 | - | 28 | |
rs7824557 (11141521)8p23 | G (0.39) | 1.4 | 0.23 | 2.17×10−9 | 1.6 | 0.56 | 4.27×10−3 | 0.8 | 0.35 | 2.2×10−2 | 1.7 | 0.84 | 4.30×10−2 | 2.2 | 0.43 | 5.32×10−7 | - | 69 |
CHARGE: Cohorts for Heart and Aging Research in Genomic Epidemiology consortium; AGES: Age Gene/Environment Susceptibility – Reykjavik Study; ARIC: Atherosclerosis Risk in Communities Study; CHS: Cardiovascular Health Study; RS: Rotterdam Study; SNP: single nucleotide polymorphism; MAF: minor allele frequency; Beta: Change in retinal venular calibre for each copy of the minor allele; SE: standard error.
No genome-wide significant locus was identified for retinal arteriolar caliber and only one SNP was associated with retinal arteriolar caliber at a significance threshold between 5.0×10−8 and 1.0×10−6. The QQ-plot (
SNP (locus) | Beta | SE | P-value | Beta | SE | P-value | Beta | SE | P-value | Beta | SE | P-value | Beta | SE | P-value | Beta | SE | P-value | Beta | SE | P-value |
rs2287921(19q13) | −2.0 | 0.23 | 3.30×10−18 | −1.3 | 0.72 | 7.10×10−2 | −4.2 | 1.29 | 1.00×10−3 | −1.6 | 0.61 | 8.30×10−3 | −4.6 | 0.93 | 8.22×10−7 | −2.3 | 0.40 | 6.70×10−9 | −2.1 | 0.20 | 1.61×10−25 |
Rs225717(6q24) | −1.8 | 0.27 | 5.99×10−12 | −2.2 | 0.84 | 1.00×10−2 | −0.3 | 1.51 | 8.35×10−1 | −2.6 | 0.71 | 2.00×10−4 | −1.9 | 1.07 | 7.30×10−2 | −2.1 | 0.46 | 3.53×10−6 | −1.9 | 0.23 | 1.25×10−16 |
rs10774625(12q24) | 1.6 | 0.23 | 1.16×10−11 | 1.6 | 0.72 | 3.00×10−2 | −0.1 | 1.32 | 9.59×10−1 | 0.5 | 0.61 | 3.75×10−1 | 2.6 | 0.93 | 5.55×10−3 | 1.2 | 0.40 | 3.33×10−3 | 1.5 | 0.20 | 2.15×10−13 |
rs17421627(5q14) | 2.5 | 0.43 | 5.05×10−9 | 3.5 | 1.29 | 7.16×10−3 | 7.4 | 2.73 | 7.00×10−3 | 3.3 | 1.21 | 6.20×10−3 | 7.1 | 1.61 | 1.11×10−5 | 4.5 | 0.74 | 1.73×10−9 | 3.0 | 0.37 | 7.32×10−16 |
rs7824557(8p23) | 1.4 | 0.23 | 2.17×10−9 | −1.4 | 0.72 | 4.70×10−2 | −0.1 | 1.28 | 9.22×10−1 | 0.6 | 0.64 | 3.85×10−1 | 0.9 | 0.97 | 3.44×10−1 | −0.1 | 0.41 | 8.36×10−1 | 1.0 | 0.20 | 3.80×10−7 |
CHARGE: Cohorts for Heart and Aging Research in Genomic Epidemiology consortium; BDES: Beaver Dam Eye Study; BMES: Blue Mountains Eye Study; SNP: single nucleotide polymorphism; Beta: Change in retinal venular caliber for each copy of the minor allele; SE: standard error.
The regional association plots for these four loci are presented in
(a) Chromosome 19q13, (b) chromosome 6q24, (c) chromosome 12q24, and (d) chromosome 5q14. The blue diamonds show stage 1 p-values (discovery phase) for the top SNP at each locus, whereas the grey diamonds show the p-values following stage 2 meta-analysis including the replication cohorts for that top SNP. P-values from stage 1 for additional SNPs at each locus are colour-coded according to their linkage disequilibrium with the top SNP as follows: r2<0.2 white, 0.2<r2<0.5 yellow, 0.5<r2<orange-red, r2>0.8 red.
DIAGRAM+(DM) | ||||||
12q24(rs10774625)M.A.: A | Beta: 1.6(SE 0.23)P = 1.16×10−11 | OR: 1.03(0.89; 1.20)P = 0.66 | OR: 1.05(0.94; 1.18)P = 0.39 | OR: 1.02(0.98; 1.06)P = 0.36 | ||
19q13 (rs2287921)M.A.: T | Beta: −2.0(SE 0.23)P = 3.30×10−18 | OR: 0.95(0.87; 1.05)P = 0.303 | OR: 0.90(0.77; 1.06)P = 0.20 | OR: 0.91(0.81; 1.03)P = 0.13 | OR: 1.01(0.96; 1.07)P = 0.67 | OR: 1.01(0.97; 1.05)P = 0.72 |
6q24(rs225717)M.A.: C | Beta: −1.8(SE 0.27)P = 5.99×10−12 | OR: 0.98(0.89; 1.08)P = 0.65 | OR: 1.11(0.93; 1.32)P = 0.24 | OR: 1.12(0.98; 1.27)P = 0.11 | OR: 0.98(0.93; 1.04)P = 0.55 | OR: 0.98(0.93; 1.02)P = 0.33 |
5q14(rs17421627)M.A.: G | Beta: 2.5(SE 0.43)P = 5.05×10−9 | OR: 1.02(0.88; 1.18)P = 0.81 | OR: 1.15(0.83; 1.59)P = 0.39 | OR: 1.02(0.79; 1.31)P = 0.89 | OR: 1.07(0.98; 1.17)P = 0.14 | OR: 0.98(0.91; 1.05)P = 0.60 |
CHARGE: Cohort for Heart and Aging Research in Genomic Epidemiology Consortium CRVE: central retinal venular equivalent (CRVE), SE: standard error; WTCCC: Wellcome Trust Case Control Consortium; CAD: coronary artery disease; HVH: Heart and Vascular Health Study; MI: myocardial infarction; Global BPgen: Global Blood Pressure Genetics Consortium; HTN: hypertension; DIAGRAM+: Diabetes Genetics Replication and Meta-analysis+; DM: diabetes mellitus; M.A.: Minor allele within CHARGE; OR: odds ratio (with corresponding 95% confidence interval) per copy of the minor allele.
In this meta-analysis of GWAS data from four populations on retinal microcirculation and subsequent replication in four independent cohorts, we identified four novel loci on chromosomes 19q13, 6q24, 12q24 and 5q14 that were consistently associated with retinal venular caliber in persons of Caucasian descent at genome-wide significance of <5.0×10−8. The most significant SNPs at each of the four loci were associated with an approximate 2.0µm change in retinal venular caliber for each copy of the minor allele. Locus 12q24 was also associated with coronary heart disease and hypertension. We did not find any loci that reached genome-wide significance for retinal arteriolar caliber, and only one SNP reached highly suggestive levels.
Our study is the first large study to evaluate common genetic variants of the microcirculation, increasingly thought to play a substantial role in the pathogenesis and development of clinical cardiovascular diseases, including coronary heart disease and stroke. The retinal vasculature provides a non-invasive direct view of the human microcirculation. Retinal venular caliber has been shown to predict a range of subclinical
The most significant SNP associated with retinal venular caliber was in the
On chromosome 6q24, the top SNPs were located in or adjacent to
The signals for association on chromosome 12q24 were spread across a large 1 Mb LD block, including genes such as
The most significant SNPs at the 5q14 locus were located in an intergenic region. The closest gene in this region is
We did not find any loci that reached genome-wide significance for retinal arteriolar caliber. It is possible that genetic factors play a smaller role in arteriolar caliber, which is strongly associated with increasing age and blood pressure
While we have identified four loci associated with retinal venular caliber, the identified SNPs may not represent the causal variants but could be in high linkage disequilibrium (LD) with the causal variants, which remain to be discovered. Further fine mapping of this genomic region will be required to facilitate expression and translational studies. Our study suggests that the effect of common genetic variants on retinal vascular caliber is small, and explain only a small proportion of the heritability of these traits
To conclude, our population-based GWAS demonstrate four novel loci on chromosomes 19q13 (within the
Each cohort secured approval from their respective institutional review boards, and all participants provided written informed consent in accordance with the Declaration of Helsinki.
The CHARGE consortium included large prospective community-based cohort studies that have genome-wide marker data and extensive data on multiple phenotypes
Details of cohort selection, risk factor assessment and retinal vascular caliber measurements in the four studies have been described in
The AGES and Rotterdam cohorts consisted predominantly of Caucasian whites. Only non-Hispanic white participants were included from the ARIC and CHS. Retinal photographs were obtained from participants at the third examination in ARIC and the tenth in CHS. Participants were excluded if their photographs could not be graded (due to cataract, corneal opacities or poor focus) or if genotyping data were unavailable (
Retinal vascular caliber was measured using standardized protocols and software that were developed initially at the University of Wisconsin and used in the ARIC study and the CHS, and following slight modifications, also in the Rotterdam and AGES studies (
The consortium was formed after the individual studies had finalized their GWAS platform selection. The four studies included used different platforms: the Affymetrix GeneChip SNP Array 6.0 for the ARIC study, Illumina HumanCNV370-Duo for the AGES study and the CHS and the Illumina Infinium HumanHap550-chip v3.0 for the Rotterdam Study. All studies used their genotype data to impute to the 2.2 million non-monomorphic, autosomal, SNPs identified in HapMap (CEU population). Extensive QC analyses were performed in each cohort (
Based on an
We conducted a meta-analysis of the beta estimates obtained from the linear regression models from the four cohorts using an inverse-variance weighting using the R software (MetABEL) (
The genome-wide significant SNPs for each locus from the discovery phase were examined in four replication cohorts. The four replication sample sets included 1,709 participants from the Australian Twins Study, 1,132 from the UK Twins Study, 2,501 from the BDES and 1,310 from the BMES. Retinal vascular caliber measurements used the same methodology and formulas as in the CHARGE cohorts. Details of this and the procedures for genotyping are described in the
In order to examine the association between SNPs that were successfully replicated in the current study and cardiovascular diseases, we obtained association statistics for each of these SNPs from several GWA studies. We obtained these data from the WTCCC on 2000 cases with coronary artery disease and 3000 controls
Quantile-quantile (QQ)-plot showing the minus log-transformed observed versus the expected p-values after meta-analysis for (A) retinal venular and (B) arteriolar caliber.
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The association between the top SNPs per genome-wide significant locus and retinal vascular caliber additionally adjusted for diabetes mellitus and hypertension.
(0.05 MB DOC)
Sample selection, retinal vascular caliber measurements, genotyping quality control filters and imputation, screening for latent population substructure, meta-analysis techniques, analyses with cardiovascular diseases, reference list.
(0.09 MB DOC)
This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from
We would also like to acknowledge the contributions of the Diabetes Genetics Replication and Meta-analysis+(DIAGRAM+) Consortium, which looked up the associations with type 2 diabetes mellitus for the SNPs associated with retinal venular caliber.
Christopher Newton-Cheh1,2,3, Toby Johnson4,5,6, Vesela Gateva7, Martin D Tobin8, Murielle Bochud5, Lachlan Coin9, Samer S Najjar10, Jing Hua Zhao11,12, Simon C Heath13, Susana Eyheramendy14,15, Konstantinos Papadakis16, Benjamin F Voight1,3, Laura J Scott7, Feng Zhang17, Martin Farrall18,19, Toshiko Tanaka20,21, Chris Wallace22,23, John C Chambers9, Kay-Tee Khaw12,24, Peter Nilsson25, Pim van der Harst26, Silvia Polidoro27, Diederick E Grobbee28, N Charlotte Onland-Moret28,29, Michiel L Bots28, Louise V Wain8, Katherine S Elliott19, Alexander Teumer30, Jian'an Luan11, Gavin Lucas31, Johanna Kuusisto32, Paul R Burton8, David Hadley16, Wendy L McArdle33, Wellcome Trust Case Control Consortium34, Morris Brown35, Anna Dominiczak36, Stephen J Newhouse22, Nilesh J Samani37, John Webster38, Eleftheria Zeggini19,39, Jacques S Beckmann4,40, Sven Bergmann4,6, Noha Lim41, Kijoung Song41, Peter Vollenweider42, Gerard Waeber42, Dawn M Waterworth41, Xin Yuan41, Leif Groop43,44, Marju Orho-Melander25, Alessandra Allione27, Alessandra Di Gregorio27,45, Simonetta Guarrera27, Salvatore Panico46, Fulvio Ricceri27, Valeria Romanazzi27,45, Carlotta Sacerdote47, Paolo Vineis9,27, Inês Barroso12,39, Manjinder S Sandhu11,12,24, Robert N Luben12,24, Gabriel J. Crawford3, Pekka Jousilahti48, Markus Perola48,49, Michael Boehnke7, Lori L Bonnycastle50, Francis S Collins50, Anne U Jackson7, Karen L Mohlke51, Heather M Stringham7, Timo T Valle52, Cristen J Willer7, Richard N Bergman53, Mario A Morken50, Angela Döring15, Christian Gieger15, Thomas Illig15, Thomas Meitinger54,55, Elin Org56, Arne Pfeufer54, H Erich Wichmann15,57, Sekar Kathiresan1,2,3, Jaume Marrugat31, Christopher J O'Donnell58,59, Stephen M Schwartz60,61, David S Siscovick60,61, Isaac Subirana31,62, Nelson B Freimer63, Anna-Liisa Hartikainen64, Mark I McCarthy19,65,66, Paul F O'Reilly9, Leena Peltonen39,49, Anneli Pouta64,67, Paul E de Jong68, Harold Snieder69, Wiek H van Gilst26, Robert Clarke70, Anuj Goel18,19, Anders Hamsten71, John F Peden18,19, Udo Seedorf72, Ann-Christine Syvänen73, Giovanni Tognoni74, Edward G Lakatta10, Serena Sanna75, Paul Scheet76, David Schlessinger77, Angelo Scuteri78, Marcus Dörr79, Florian Ernst30, Stephan B Felix79, Georg Homuth30, Roberto Lorbeer80, Thorsten Reffelmann79, Rainer Rettig81, Uwe Völker30, Pilar Galan82, Ivo G Gut13, Serge Hercberg82, G Mark Lathrop13, Diana Zeleneka13, Panos Deloukas12,39, Nicole Soranzo17,39, Frances M Williams17, Guangju Zhai17, Veikko Salomaa48, Markku Laakso32, Roberto Elosua31,62, Nita G Forouhi11, Henry Völzke80, Cuno S Uiterwaal28, Yvonne T van der Schouw28, Mattijs E Numans28, Giuseppe Matullo27,45, Gerjan Navis68, Göran Berglund25, Sheila A Bingham12,83, Jaspal S Kooner84, John M Connell36, Stefania Bandinelli85, Luigi Ferrucci21, Hugh Watkins18,19, Tim D Spector17, Jaakko Tuomilehto52,86,87, David Altshuler1,3,88,89, David P Strachan16, Maris Laan56, Pierre Meneton90, Nicholas J Wareham11,12, Manuela Uda75, Marjo-Riitta Jarvelin9,67,91, Vincent Mooser41, Olle Melander25, Ruth JF Loos11,12, Paul Elliott9, Gonçalo R Abecasis92, Mark Caulfield22, Patricia B Munroe22
1. Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
2. Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
3. Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, 02142, USA
4. Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland
5. University Institute for Social and Preventative Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, 1005 Lausanne, Switzerland
6. Swiss Institute of Bioinformatics, Switzerland
7. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
8. Departments of Health Sciences & Genetics, Adrian Building, University of Leicester, University Road, Leicester LE1 7RH
9. Department of Epidemiology and Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK
10. Laboratory of Cardiovascular Science, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA 21224
11. MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
12. Cambridge - Genetics of Energy Metabolism (GEM) Consortium, Cambridge, UK
13. Centre National de Génotypage, 2 rue Gaston Crémieux, CP 5721, 91 057 Evry Cedex, France
14. Pontificia Universidad Catolica de Chile, Vicuña Mackenna 4860, Facultad de Matematicas, Casilla 306, Santiago 22, Chile, 7820436
15. Institute of Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, 85764 Neuherberg, Germany
16. Division of Community Health Sciences, St George's, University of London, London SW17 0RE, UK
17. Department of Twin Research & Genetic Epidemiology, King's College London, London SE1 7EH
18. Department of Cardiovascular Medicine, University of Oxford
19. The Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
20. Medstar Research Institute, 3001 S. Hanover Street, Baltimore, MD 21250, USA
21. Clinical Research Branch, National Institute on Aging, Baltimore, MD, 21250 USA
22. Clinical Pharmacology and The Genome Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ
23. JDRF/WT Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research University of Cambridge, Wellcome Trust/MRC Building, Addenbrooke's Hospital Cambridge, CB2 0XY
24. Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge CB2 2SR, UK
25. Department of Clinical Sciences, Lund University, Malmö University Hospital, SE-20502 Malmö, Sweden
26. Department of Cardiology University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
27. ISI Foundation (Institute for Scientific Interchange), Villa Gualino, Torino, 10133, Italy
28. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, STR 6.131, PO Box 85500, 3508 GA Utrecht, The Netherlands
29. Complex Genetics Section, Department of Medical Genetics - DBG, University Medical Center Utrecht, STR 2.2112, PO Box 85500, 3508 GA Utrecht, The Netherlands.
30. Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany
31. Cardiovascular Epidemiology and Genetics, Institut Municipal d'Investigació Mèdica, Barcelona, Spain
32. Department of Medicine University of Kuopio 70210 Kuopio, Finland
33. ALSPAC Laboratory, Department of Social Medicine, University of Bristol, BS8 2BN, UK
34. A full list of authors is provided in the supplementary methods online.
35. Clinical Pharmacology Unit, University of Cambridge, Addenbrookes Hospital, Cambridge, UK CB2 2QQ
36. BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK G12 8TA
37. Department of Cardiovascular Science, University of Leicester, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK
38. Aberdeen Royal Infirmary, Aberdeen, UK
39. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
40. Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, 1011, Switzerland
41. Genetics Division, GlaxoSmithKline, King of Prussia, PA 19406, USA
42. Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) 1011 Lausanne, Switzerland
43. Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital, Malmö
44. Lund University, Malmö S-205 02, Sweden
45. Department of Genetics, Biology and Biochemistry, University of Torino, Torino, 10126, Italy
46. Department of Clinical and Experimental Medicine, Federico II University, Naples, 80100, Italy
47. Unit of Cancer Epidemiology, University of Turin and Centre for Cancer Epidemiology and Prevention (CPO Piemonte), Turin, 10126, Italy
48. National Institute for Welfare and Health P.O. Box 30, FI-00271 Helsinki, Finland
49. Institute for Molecular Medicine Finland FIMM, University of Helsinki and National Public Health Institute
50. Genome Technology Branch, National Human Genome Research Institute, Bethesda, MD 20892, USA
51. Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
52. Diabetes Unit, Department of Epidemiology and Health Promotion, National Public Health Institute, 00300 Helsinki, Finland
53. Physiology and Biophysics USC School of Medicine 1333 San Pablo Street, MMR 626 Los Angeles, California 90033
54. Institute of Human Genetics, Helmholtz Zentrum München, German Research Centre for Environmental Health, 85764 Neuherberg, Germany
55. Institute of Human Genetics, Technische Universität München, 81675 Munich, Germany
56. Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia
57. Ludwig Maximilians University, IBE, Chair of Epidemiology, Munich
58. Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
59. Framingham Heart Study and National, Heart, Lung, and Blood Institute, Framingham, Massachusetts 01702, USA
60. Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington, 98101 USA
61. Department of Epidemiology, University of Washington, Seattle, Washington, 98195 USA
62. CIBER Epidemiología y Salud Pública, Barcelona, Spain
63. Center for Neurobehavioral Genetics, Gonda Center, Room 3506, 695 Charles E Young Drive South, Box 951761, UCLA, Los Angeles, CA 90095.
64. Department of Clinical Sciences/Obstetrics and Gynecology, P.O. Box 5000 Fin-90014, University of Oulu, Finland
65. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford OX3 7LJ, UK
66. Oxford NIHR Biomedical Research Centre, Churchill Hospital, Old Road, Headington, Oxford, UK OX3 7LJ
67. Department of Child and Adolescent Health, National Public Health Institute (KTL), Aapistie 1, P.O. Box 310, FIN-90101 Oulu, Finland
68. Division of Nephrology, Department of Medicine University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
69. Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
70. Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), University of Oxford, Richard Doll Building, Roosevelt Drive, Oxford, OX3 7LF, UK
71. Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Solna, Building L8:03, S-17176 Stockholm, Sweden
72. Leibniz-Institut für Arterioskleroseforschung an der Universität Münster, Domagkstr. 3, D-48149, Münster, Germany
73. Molecular Medicine, Department of Medical Sciences, Uppsala University, SE-751 85 Uppsala, Sweden
74. Consorzio Mario Negri Sud, Via Nazionale, 66030 Santa Maria Imbaro (Chieti), Italy
75. Istituto di Neurogenetica e Neurofarmacologia, CNR, Monserrato, 09042 Cagliari, Italy
76. Department of Epidemiology, Univ. of Texas M. D. Anderson Cancer Center, Houston, TX 77030
77. Laboratory of Genetics, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA 21224
78. Unitá Operativa Geriatria, Istituto Nazionale Ricovero e Cura per Anziani (INRCA) IRCCS, Rome, Italy
79. Department of Internal Medicine B, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany
80. Institute for Community Medicine, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany
81. Institute of Physiology, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany
82. U557 Institut National de la Santé et de la Recherche Médicale, U1125 Institut National de la Recherche Agronomique, Université Paris 13, 74 rue Marcel Cachin, 93017 Bobigny Cedex, France
83. MRC Dunn Human Nutrition Unit, Wellcome Trust/MRC Building, Cambridge CB2 0XY, U.K
84. National Heart and Lung Institute, Imperial College London SW7 2AZ
85. Geriatric Rehabilitation Unit, Azienda Sanitaria Firenze (ASF), 50125, Florence, Italy
86. Department of Public Health, University of Helsinki, 00014 Helsinki, Finland
87. South Ostrobothnia Central Hospital, 60220 Seinäjoki, Finland
88. Department of Medicine and Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
89. Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
90. U872 Institut National de la Santé et de la Recherche Médicale, Faculté de Médecine Paris Descartes, 15 rue de l'Ecole de Médecine, 75270 Paris Cedex, France
91. Institute of Health Sciences and Biocenter Oulu, Aapistie 1, FIN-90101, University of Oulu, Finland
92. Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109 USA