A genome-wide approach accounting for body mass …...The JMA association did not reach genome-wide significance (P= 6.55×10-6). This SNP, located on chromosome 11, is ~427 kb away

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A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Alisa K. Manning*, Marie-France Hivert*, Robert A. Scott*, Jonna L. Grimsby, Nabila Bouatia-Naji, Han Chen, Denis Rybin, Ching-Ti Liu, Lawrence F. Bielak, Inga Prokopenko, Najaf Amin, Daniel Barnes, Gemma Cadby, Jouke-Jan Hottenga, Erik Ingelsson, Anne U. Jackson, Toby Johnson, Stavroula Kanoni, Claes Ladenvall, Vasiliki Lagou, Jari Lahti, Cecile Lecoeur, Yongmei Liu, Maria Teresa Martinez-Larrad, May E. Montasser, Pau Navarro, John R. B. Perry, Laura J. Rasmussen-Torvik, Perttu Salo , Naveed Sattar, Dmitry Shungin, Rona J. Strawbridge, Toshiko Tanaka, Cornelia M. van Duijn, Ping An, Mariza de Andrade, Jeanette S. Andrews, Thor Aspelund, Mustafa Atalay, Yurii Aulchenko, Beverley Balkau, Stefania Bandinelli, Jacques S. Beckmann, John P. Beilby, Claire Bellis, Richard N. Bergman, John Blangero, Mladen Boban, Michael Boehnke, Eric Boerwinkle, Lori L. Bonnycastle, Dorret I. Boomsma, Ingrid B. Borecki, Yvonne Böttcher, Claude Bouchard, Eric Brunner, Danijela Budimir, Harry Campbell, Olga Carlson, Peter S. Chines, Robert Clarke, Francis S. Collins, Arturo Corbatón-Anchuelo, David Couper, Ulf de Faire, George V Dedoussis, Panos Deloukas, Maria Dimitriou, Josephine M Egan, Gudny Eiriksdottir, Michael R. Erdos, Johan G. Eriksson-, Elodie Eury, Luigi Ferrucci, Ian Ford, Nita G. Forouhi, Caroline S Fox, Maria Grazia Franzosi, Paul W Franks, Timothy M Frayling, Philippe Froguel, Pilar Galan, Eco de Geus, Bruna Gigante, Nicole L. Glazer, Anuj Goel, Leif Groop, Vilmundur Gudnason, Göran Hallmans, Anders Hamsten, Ola Hansson, Tamara B. Harris, Caroline Hayward, Simon Heath, Serge Hercberg, Andrew A. Hicks, Aroon Hingorani, Albert Hofman, Jennie Hui, Joseph Hung, Marjo Riitta Jarvelin, Min A. Jhun, Paul C.D. Johnson, J Wouter Jukema, Antti Jula, W.H. Kao, Jaakko Kaprio, Sharon L. R. Kardia, Sirkka Keinanen-Kiukaanniemi, Mika Kivimaki, Ivana Kolcic, Peter Kovacs, Meena Kumari, Johanna Kuusisto, Kirsten Ohm Kyvik, Markku Laakso, Timo Lakka, Lars Lannfelt, G Mark Lathrop, Lenore J. Launer, Karin Leander, Guo Li, Lars Lind, Jaana Lindstrom, Stéphane Lobbens, Ruth J. F. Loos, Jian’an Luan , Valeriya Lyssenko, Reedik Mägi, Patrik K. E. Magnusson, Michael Marmot, Pierre Meneton , Karen L. Mohlke, Vincent Mooser, Mario A. Morken, Iva Miljkovic, Narisu Narisu, Jeff O'Connell, Ken K. Ong, Ben A. Oostra, Lyle J. Palmer, Aarno Palotie, James S. Pankow, John F. Peden, Nancy L. Pedersen, Marina Pehlic, Leena Peltonen**, Brenda Penninx, Marijana Pericic, Markus Perola, Louis Perusse, Patricia A Peyser, Ozren Polasek, Peter P. Pramstaller, Michael A. Province, Katri Räikkönen, Rainer Rauramaa, Emil Rehnberg, Ken Rice, Jerome I. Rotter, Igor Rudan, Aimo Ruokonen, Timo Saaristo, Maria Sabater-Lleal, Veikko Salomaa, David B. Savage , Richa Saxena, Peter Schwarz, Udo Seedorf, Bengt Sennblad, Manuel Serrano-Rios, Alan R. Shuldiner, Eric J.G. Sijbrands, David S. Siscovick, Johannes H. Smit, Kerrin S. Small, Nicholas L. Smith, Albert Vernon Smith, Alena Stančáková , Kathleen Stirrups, Michael Stumvoll, Yan V. Sun, Amy J. Swift, Anke Tönjes, Jaakko Tuomilehto, Stella Trompet, Andre G. Uitterlinden, Matti Uusitupa, Max Vikström, Veronique Vitart, Marie-Claude Vohl, Benjamin F. Voight, Peter Vollenweider, Gerard Waeber, Dawn M Waterworth, Hugh Watkins, Eleanor Wheeler, Elisabeth Widen, Sarah H. Wild, Sara M. Willems, Gonneke Willemsen, James F. Wilson, Jacqueline C.M. Witteman, Alan F. Wright, Hanieh Yaghootkar, Diana Zelenika, Tatijana Zemunik, Lina Zgaga, DIAGRAM Consortium, The MUTHER Consortium, Nicholas J. Wareham , Mark I. McCarthy, Ines Barroso, Richard M. Watanabe, Jose C. Florez, Josée Dupuis, James B. Meigs†, Claudia Langenberg† * These authors contributed equally to this work † These authors jointly directed the work ** Deceased

Nature Genetics: doi:10.1038/ng.2274

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Table of Contents

Supplementary Note 3-27

Supplementary Results 3

Author Contributions 9

Study Acknowledgments 10

Notes on the MuTHER Consortium 24

Notes on the DIAGRAM Consortium 25

Supplementary Figures 28-30


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Supplementary Note

Supplementary Results

GENES WITH ESTABLISHED ASSOCIATIONS

Fasting Insulin

As previously reported, risk alleles of SNPs in GCKR and near IGF1 were

associated with fasting insulin3 (Supplementary Table 2). In the discovery analysis, we

also observed that SNP rs1801282 (P12A) located in PPARG was associated with fasting

insulin (JMA P=2.4×10-7), an association previously reported by Manning et al14.

PPARG encodes a key nuclear receptor in adipocyte function and the major allele has

been previously reported to be associated with increased risk of T2D79.

Fasting Glucose

In the discovery meta-analysis (including samples largely overlapping with the

previous MAGIC effort), we observed associations with fasting glucose at all originally

reported loci3 (Supplementary Table 2). At GCKR, there was a stronger effect in heavier

(β=0.0472; P=2.5×10-17) compared to leaner individuals (β=0.026; P=6.9×10-14;

interaction P between strata = 0.003). At TCF7L2, the test for interaction with

continuous BMI showed a P value of 0.004, but there was no difference in SNP effects

between BMI strata.

FUNCTIONAL EXPLORATION AROUND INDEX SNPs

Potential functional SNPs based on coding variants


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We extracted all coding SNPs within 500 kb of index SNPs to determine if any

coding variants were highly correlated with the association signals and used the online

tool, SIFT dbSNP (http://sift.jcvi.org/www/SIFT_dbSNP.html) to predict potential

damage to the protein. All results of potential functional SNPs are presented in

Supplementary Table 6.

The index SNP in UHRF1BP1 (rs4646949) is in strong LD (r2=0.724) with a SNP

(rs2293242) that causes a nonsense change in ANKS1A. The same index SNP is in

moderate LD (r2 = 0.363) with a coding SNP that results in a missense change (M1098T)

in UHRF1BP1 that is considered damaging based on analysis of homologous sequences.

There is moderate LD (r2 = 0.404) between the index SNP in DPYSL5

(rs1371614) and a SNP (rs2384572) that causes a missense change (I116I or I20I

[depending on transcript]) in CGREF1 that is considered damaging based on analysis of

homologous and orthologous sequences. All other coding SNPs in moderate or stronger

LD (r2 > 0.1) with index SNPs were either synonymous, or were considered not

damaging to the proteins (see Supplementary Table 3).

Copy number variants in regions around index SNPs

To establish whether index SNPs were in LD with known copy number variants

(CNVs), we used a database of 7,411 SNPs that tag 3,188 CNVs from the Wellcome

Trust Case Control Consortium (WTCCC)80 in addition to a list of 422 SNPs tagging 261

deletions78.

The index SNP near IRS1, rs2943634, is in moderate LD (r2=0.438) with three

SNPs (rs1849878, rs2673148, rs2713547) that tag a CNV (CNVR1152.1). Index SNP

rs4646949 in UHRF1BP1 was in strong LD (r2 = 0.95) with two perfect proxies


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(rs2477508 and rs2814922) for a CNV (CNVR2857.1). No other CNV (or tagging SNPs

for CNVs) were identified to be in moderate or stronger LD (r2 < 0.1) with the index

SNPs.

EXPRESSION AND eQTL STUDIES

Expression in human tissues

Using available online micro-array expression data from various human tissues

(GNF Expression Atlas 2 Data from U133A and GNF1H Chips,

http://genome.ucsc.edu/index.html?org=Human&db=hg19&hgsid=190151175), we

examined expression levels of genes nearest to and within 500 kb of the index SNPs.

Several genes near fasting insulin index SNPs are expressed in skeletal muscle (e.g.

COBLL1), adipose tissue (e.g. PEPD, PDGFC, SLC30A10, PCSK1) or liver (e.g. GRB14,

CEBPA, PEPD, SLC30A10, STARD10), so we chose to pursue eQTL look-up in specific

tissues for all of the fasting insulin-associated SNPs in expression datasets.

Liver eQTL

We queried a database of gene expression data in human liver tissue38 to search

for SNPs associated with changes in gene expression (cis and transeQTL SNPs). Several

eQTL exist around the fasting insulin index SNPs in liver. However, LD between index

SNPs and eQTL SNPs were generally low, with r2 ranging from 0 to 0.334. The

strongest LD observed between liver eQTL and index SNPs was between SNP

rs12173920 and UHRF1BP1 index SNP rs4646949 (r2 = 0.334); SNP rs12173920 is

significantly associated with expression changes of STEAP4 and UHRF1BP1 (P =


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2.9×10-8 and 9.6×10-6, respectively). Supplementary Table 7 contains main findings from

eQTL lookups in the liver tissue expression database (P< 0.003).

Adipose tissue eQTL

Index SNPs associated with fasting insulin were also checked for association with

gene expression in fat tissue in the MuTHER dataset51. Index SNP rs8182584 was found

to be a cis-eQTL for PEPD (P = 10.0×10-10) in fat tissue. This may be driven by a very

strong eQTL signal for SNP rs17226118 in the same region on PEPD expression (P =

2.2×10-55), despite the weak LD between the two SNPs (r2 = 0.145). Supplementary

Table 5 contains main findings from eQTL lookups in the fat tissue expression database.

CONDITIONAL ANALYSES

Chromosome 2, near GCKR

In the fasting glucose JMA, two regions near GCKR on chromosome 2 showed

genome-wide significant associations with fasting glucose: SNPs near MRPL33, a gene

downstream of GCKR, and SNPs near DPYSL5, a gene upstream from GCKR. We

performed regression analyses conditional upon two previously reported GCKR SNPs to

determine whether the novel associations observed near MRPL33 and DPYSL5 were

independent or driven by linkage disequilibrium (LD) in this region. The JMA P-values

for the two known GCKR SNPs, rs1260326 (P446L) and rs780094, were 2.1×10-24 and

4.0×10-24, respectively. We first performed conditional analyses for the two GCKR SNPs

in the Framingham Heart Study (FHS). FHS was chosen to conduct the conditional

analyses around GCKR because it is one of the largest cohorts participating in the meta-


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analysis and because each SNP (previously identified in/near GCKR, and both newly

revealed in DPYSL5 and MRPL33) was associated with fasting glucose (p <0.05) when

tested independently. In a regression model that included terms for both GCKR SNPs,

both were nominally associated with fasting glucose (rs1260326 P = 0.03; rs780094 P =

0.02), suggesting that they represent independent signals.

The MRPL33 SNP, rs3736594, showed a genome-wide significant association

with fasting glucose in the discovery meta-analysis (JMA P = 1.3×10-11). The SNP is in

low LD with both GCKR SNPs rs1260326 (r2=0.053) and rs780094 (r2=0.046). In FHS,

the joint test of the association of MRPL33 SNP rs3736594 with fasting glucose was

nominally significant (P = 0.047). After conditioning on rs1260326 and rs780094, the

joint test was no longer nominally significant (P = 0.20), suggesting that the association

between MRPL33 SNP rs3736594 and fasting glucose was not independent of the known

GCKR association signals.

The DPYSL5 SNP, rs1371614, showed a genome-wide significant association

with fasting glucose in the discovery meta-analysis (JMA P = 2.9×10-9). This SNP is

located approximately 600 kb from GCKR. In FHS, the joint test of association for

DPYSL5 SNP rs1371614 with fasting glucose was nominally significant (P = 0.007).

After conditioning on the two known GCKR SNPs rs1260326 and rs780094, the joint test

retained nominal significance (P=0.02), suggesting that the association signal in DPYSL5

was not solely due to LD with the associated SNPs in GCKR.


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Chromosome 11, near CRY2

A SNP in CREB3L1, rs12280680, showed a genome-wide significant association

with fasting glucose in the main effects model without adjusting for BMI (P=3.28×10-8).

The JMA association did not reach genome-wide significance (P= 6.55×10-6). This SNP,

located on chromosome 11, is ~427 kb away from rs11605924 (r2=0 with rs12280680), a

SNP in the CRY2 gene that was previously shown to be associated with fasting glucose3.

The JMA and main effects association (without BMI adjustment) P values for CRY2 SNP

rs11605924 were 4.1×10-14 and 3.0×10-13, respectively. In FHS, the joint test was

nominally significant for both SNPs (rs12280680 P = 0.006, rs11605924 P = 0.02) as

were the main effect associations without BMI-adjustment (rs12280680 P = 0.004,

rs11605924 P = 0.0004). After conditioning on CRY2 SNP rs11605924, CREB3L1 SNP

rs12280680 remained nominally significant (joint test P = 0.007, main effects P =

0.0007) demonstrating that this association was independent of the association at the

CRY2 locus.

3. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nature genetics 42, 105-116 (2010).

14. Manning, A.K. et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP x environment regression coefficients. Genetic epidemiology 35, 11-18 (2011).

38. Schadt, E.E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol 6, e107 (2008).

78. McCarroll, S.A. et al. Common deletion polymorphisms in the human genome. Nat Genet 38, 86-92 (2006).

79. Altshuler, D. et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nature genetics 26, 76-80 (2000).

80. Craddock, N. et al. Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature 464, 713-20 (2010).


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Author Contributions Individuals involved in the management of this project were: AKM, AT, AAH, AFW, AH, AH, AH, AJ, AP, ARS, BO, BP, CL, CMV, DC, DI, DMW, DSS, EB, EI, EW, FSC, G, GE, GH, GVD, GW, HW, IR, JBM, JCF, JCN, JD, JFW, JGE, JIR, JK, JP, JSB, JSP, JT, JWJ, KOK, KR, LP, LF, LG, LJL, LJP, LP, MIM, MS, MB, MB, MGF, MK, MK, MM, MP, M-RJ, MSR, NBN, NJW, NLG, NLP, NLS, NS, OP, PAP, PD, PKEM, PM, PPP, PV, RAS, RC, RNB, SHW, SLK, ST, TBH, UdF, US, VG, VM, VS, W, WK, YL. Individuals involved in the design of this project were: AKM, AR, AFW, AH, AH, AJ, AP, ARS, BFV, BO, BP, CB, CL, CMV, DC, DI, DMW, DSS, EB, EI, EW, FSC, G, GVD, GW, HC, HW, IM, IR, JBM, JCF, JCN, JD, JFW, JGE, JIR, JK, JSP, JT, KOK, KR, LP, LF, LG, LJL, LJP, MIM, MS, MB, MB, MGF, MK, MK, MM, MP, M-RJ, MSR, MTML, NJW, NLP, NLS, OP, PA, PD, PG, PKEM, PM, PPP, PV, RAS, RC, RMW, RNB, SB, SH, TBH, TMF, UdF, US, VG, VM, VS, W, WK, YA, YL. Individuals involved in sample collection and phenotyping for this project were: AR, AT, ACA, AH, AH, AJ, BB, CB, CH, CMV, CSF, DC, DI, DSS, EB, EI, EJG, G, GE, GH, HW, IK, IM, IR, JBM, JFW, JGE, JH, JK, JL, JME, JPB, KL, KOK, LF, LL, LL, LZ, MD, MGF, MK, MK, MP, MP, M-RJ, MTML, NJW, NLP, OC, OP, PK, PF, PG, PKEM, PPP, PV, RC, RS, SB, SH, SHW, SK, TBH, TMF, TZ, UdF, US, VG, VL, VS, W, YL. Individuals involved with genotyping for this project were: AAH, AG, AJS, AP, BG, BS, CB, CH, CL, CMV, CSF, DC, DI, DZ, EB, EE, EI, EW, EW, HC, IB, JB, JD, JFW, JH, JIR, JJH, JK, JP, JPB, KL, KOK, KS, LF, LJP, LLB, LP, MAM, MCV, MDA, MK, ML, MP, MP, M-RJ, MRE, MTML, NBN, NLP, NN, PK, PF, PKEM, PPP, PSC, RJS, SB, SH, SL, TMF, TZ, VV, YB, YL. Individuals involved with statistical analysis for this project were: AG, AKM, AUJ, AVS, BG, BS, CL, CLa, CTL, DB, DR, DS, EI, ER, EW, GC, GL, HC, HY, IF, IP, JD, JJH, JL, JO, JP, JRBP, JSA, KL, LFB, LJRT, LP, MAJ, MEM, MP, MSL, MTML, MV, N, NBN, NLG, PCDS, PN, PS, RM, RAS, RJS, SK, TA, TJ, TT, VL, VV, W, YA, YL, YVS. Individuals involved with writing this paper were: AKM, ACA, BB, CL, CL, IP, JG, KL, LFB, MIM, MFH, MSL, NJW, NLP, PAP, PF, RAS, RMW, SL, TBH, UdF.


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Study Acknowledgments FRAMINGHAM HEART STUDY

This research was conducted in part using data and resources from the Framingham Heart

Study of the National Heart Lung and Blood Institute of the National Institutes of Health

and Boston University School of Medicine. The analyses reflect intellectual input and

resource development from the Framingham Heart Study investigators participating in

the SNP Health Association Resource (SHARe) project. This work was partially

supported by the National Heart, Lung and Blood Institute's Framingham Heart Study

(Contract No. N01-HC-25195) and its contract with Affymetrix, Inc for genotyping

services (Contract No. N02-HL-6-4278). A portion of this research utilized the Linux

Cluster for Genetic Analysis (LinGA‐II) funded by the Robert Dawson Evans

Endowment of the Department of Medicine at Boston University School of Medicine and

Boston Medical Center. Also supported by National Institute for Diabetes and Digestive

and Kidney Diseases (NIDDK) R01 DK078616 to Drs. Meigs, Dupuis and Florez,

NIDDK K24 DK080140 to Dr. Meigs, and a Massachusetts General Hospital Physician

Scientist Development Award and a Doris Duke Charitable Foundation Clinical Scientist

Development Award to Dr. Florez.

HELSINKI BIRTH COHORT STUDY

We thank all study participants as well as everybody involved in the Helsinki Birth

Cohort Study. Helsinki Birth Cohort Study has been supported by grants from the

Academy of Finland, the Finnish Diabetes Research Society, Folkhälsan Research

Foundation, Novo Nordisk Foundation, Finska Läkaresällskapet, Signe and Ane

Gyllenberg Foundation, University of Helsinki, European Science Foundation

(EUROSTRESS), Ministry of Education, Ahokas Foundation, Emil Aaltonen

Foundation, Juho Vainio Foundation, and Wellcome Trust (grant number WT089062).


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DGI

The DGI study was supported by a grant from Novartis. The Botnia PPP study was

supported by grants from the Signe and Ane Gyllenberg Foundation, Swedish Cultural

Foundation in Finland, Finnish Diabetes Research Society, the Sigrid Juselius

Foundation, Folkhälsan Research Foundation, Foundation for Life and Health in Finland,

Jakobstad Hospital, Medical Society of Finland, Närpes Research Foundation and the

Vasa and Närpes Health centers, the European Community's Seventh Framework

Programme (FP7/2007-2013), the European Network for Genetic and Genomic

Epidemiology (ENGAGE), the Collarative European Effort to Develop Diabetes

Diagnostics (CEED/2008-2012), and the Swedish Research Council, including a Linné

grant (No.31475113580).

GLACIER

The GLACIER Study is nested within the Northern Sweden Health and Disease Study

and phenotyping was conducted as part of the Västerbotten Intervention Project. We

thank the participants and the investigators from these studies for their valuable

contributions, with specific thanks to Lars Weinehall, Åsa Agren, Kerstin Enquist, and

Thore Johansson. The GLACIER Study and part of PWF's salary were funded by grants

from the Swedish Research Council, Swedish Heart-Lung Foundation, Novo Nordisk,

Umeå Medical Research Foundation, and the Swedish Diabetes Association (to PWF).

Genotyping for this specific project was funded by the Wellcome Trust Sanger Institute.

GENOMEUTWIN

The GenomEUtwin project is supported by the European Commission under the

programme 'Quality of Life and Management of the Living Resources' of 5th Framework

Programme (no. QLG2-CT-2002-01254). FINTWIN: J.K. has been supported by the

Academy of Finland Centre of Excellence in Complex Disease Genetics (grant numbers:

213506, 129680). SWETWIN: The Swedish Twin Cohort has been financially supported

by the Swedish Research Council and Swedish Foundation for Strategic Research.

DENTWIN: The Danish twin study was supported by the Medical Research Council, The


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Danish Diabetes Foundation, The Danish Heart Foundation and the Novo Nordisk

Foundation.

AMISH

The Amish study is supported by NIH research grants U01 HL72515; R01 HL088119;

U01 GM074518, the University of Maryland General Clinical Research Center, grant

M01 RR 16500, the Mid Atlantic Nutrition and Obesity Research Center (P30

DK072488), and T32 training Grant (AG000219). We thank the staff at the Amish

Research Clinic for their outstanding efforts and our Amish research volunteers for their

long-standing partnership in research.

ARIC

The Atherosclerosis Risk in Communities Study is carried out as a collaborative study

supported by National Heart, Lung, and Blood Institute contracts

(HHSN268201100005C, HHSN268201100006C, HHSN268201100007C,

HHSN268201100008C, HHSN268201100009C, HHSN268201100010C,

HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and

R01HL086694; National Human Genome Research Institute contract U01HG004402;

and National Institutes of Health contract HHSN268200625226C. The authors thank the

staff and participants of the ARIC study for their important contributions. Infrastructure

was partly supported by Grant Number UL1RR025005, a component of the National

Institutes of Health and NIH Roadmap for Medical Research.

FENLAND

The Fenland Study is funded by the Wellcome Trust and the Medical Research Council.

We are grateful to all the volunteers for their time and help, and to the General

Practitioners and practice staff for help with recruitment. We thank the Fenland Study

co-ordination team and the Field Epidemiology team of the MRC Epidemiology Unit for

recruitment and clinical testing. We also thank the NIHR Cambridge Biomedical

Research Centre, Cambridge, U.K. for biochemical analyses.


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INCHIANTI

We thank the Intramural Research Program of the NIH, National Institute on Aging who

are responsible for the InCHIANTI samples. We also thank the InCHIANTI participants.

The InCHIANTI study baseline (1998‐2000) was supported as a “targeted project”

(ICS110.1/RF97.71) by the Italian Ministry of Health and in part by the U.S. National

Institute on Aging (Contracts: 263 MD 9164 and 263 MD 821336); the

InCHIANTI Follow‐up 1 (2001‐2003) was funded by the U.S. National Institute on

Aging (Contracts: N.1‐AG‐1‐1 and N.1‐AG‐1‐2111); the InCHIANTI Follow

‐ups 2 and 3 studies (2004‐2010) were financed by the U.S. National Institute on

Aging (Contract: N01‐AG‐5‐0002); supported in part by the Intramural research

program of the National Institute on Aging, National Institutes of Health, Baltimore,

Maryland. JRBP is funded by a Sir Henry Wellcome Postdoctoral Fellowship

(092447/Z/10/Z).

ULSAM

Genotyping was performed by the SNP&SEQ Technology Platform in Uppsala

(www.genotyping.se). We thank Tomas Axelsson, Ann-Christine Wiman and Caisa

Pöntinen for their excellent assistance with genotyping. The SNP Technology Platform is

supported by Uppsala University, Uppsala University Hospital and the Swedish Research

Council for Infrastructures. E.I. is supported by grants from the Swedish Research

Council, the Swedish Heart-Lung Foundation, the Swedish Foundation for Strategic

Research, and the Royal Swedish Academy of Science.

PIVUS

Genotyping was performed by the SNP&SEQ Technology Platform in Uppsala

(www.genotyping.se). We thank Tomas Axelsson, Ann-Christine Wiman and Caisa

Pöntinen for their excellent assistance with genotyping. The SNP Technology Platform is

supported by Uppsala University, Uppsala University Hospital and the Swedish Research


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Council for Infrastructures. E.I. is supported by grants from the Swedish Research

Council, the Swedish Heart-Lung Foundation, the Swedish Foundation for Strategic

Research, and the Royal Swedish Academy of Science.

SWEDISH TWINS REGISTRY (STR)

This work was supported by grants from the US National Institutes of Health

(AG028555, AG08724, AG04563, AG10175, AG08861), the Swedish Research Council,

the Swedish Heart-Lung Foundation, the Swedish Foundation for Strategic Research, the

Royal Swedish Academy of Science, and ENGAGE (within the European Union Seventh

Framework Programme, HEALTH-F4-2007-201413). Genotyping was performed by the

SNP&SEQ Technology Platform in Uppsala (www.genotyping.se). We thank Tomas

Axelsson, Ann-Christine Wiman and Caisa Pöntinen for their excellent assistance with

genotyping. The SNP Technology Platform is supported by Uppsala University, Uppsala

University Hospital and the Swedish Research Council for Infrastructures.

ERF

This research was supported by grants from the Netherlands Foundation for Scientific

Research (NWO), a joint grant from NWO and the Russian Foundation for Basic

Research (RFBR), and by the Centre for Medical Systems Biology (CMSB).

BLSA

The BLSA was supported in part by the Intramural Research Program of the NIH,

National Institute on Aging. A portion of that support was through a R&D contract with

MedStar Research Institute.

SEGOVIA

Supported by grant FISS 03/1618 from Fondo de Investigaciones Sanitarias and a grant

from Instituto de Salud Carlos III, RETIC RD06 (RD06/0015/0012). This work was also

partially supported by an Educational Grant from Eli Lilly Laboratories, Spain. CIBER in

Diabetes and Associated Metabolic Disorders (ISCIII, Ministerio de Ciencia e

Innovación, Spain). We thank the study participants. We also thank Milagros Perez


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Barba, Angeles Asencio Prianes for dedicated and careful technical assistance, and Dr

Cristina Fernandez and CEGEN (Centro Nacional de Genotipado) for their assistance

with genotyping.

SUVIMAX

This research was conducted in part using funds from the Commissariat à l’Energie

Atomique, the Conservatoire National des Arts et Métiers, the Institut National de la

Recherche Agronomique and the Institut National de la Santé et de la Recherche

Médicale.

COLAUS

The CoLaus study was and is supported by research grants from GlaxoSmithKline, the

Faculty of Biology and Medicine of Lausanne, Switzerland and the Swiss National

Science Foundation (grant no: 33CSCO-122661).

WHITEHALL

The Whitehall II study has been supported by grants from the Medical Research Council;

Economic and Social Research Council; British Heart Foundation; Health and Safety

Executive; Department of Health; National Heart Lung and Blood Institute (HL36310),

US, NIH: National Institute on Aging (AG13196), US, NIH; Agency for Health Care

Policy Research (HS06516); and the John D and Catherine T MacArthur Foundation

Research Networks on Successful Midlife Development and Socio-economic Status and

Health. Whitehall II genotyping was in part supported by a MRC-GSK pilot programme

grant (ID 85374). M Kivimaki and M Kumari are supported by NIH NHLBI (R01

HL036310) and Kivimaki is additionally supported by NIA (R01 AG34454), US.

HEALTH ABC

This research was supported by NIA contracts N01AG62101, N01AG62103, and

N01AG62106, and in part by the Intramural Research Program of the NIH, National

Institute on Aging.The genome-wide association study was funded by NIA grant

1R01AG032098-01A1 to Wake Forest University Health Sciences and genotyping


16

services were provided by the Center for Inherited Disease Research (CIDR). CIDR is

fully funded through a federal contract from the National Institutes of Health to The

Johns Hopkins University, contract number HHSN268200782096C.

KORCULA

The study was funded by grants from the Medical Research Council (UK) and Republic

of Croatia Ministry of Science, Education and Sports research grants to I.R. (108-

1080315-0302). SNP genotyping was performed at the Institute of Human Genetics,

Helmholtz Zentrum München, Germany

SPLIT

The study was funded by grants from the Medical Research Council (UK) and Republic

of Croatia Ministry of Science, Education and Sports research grants to I.R. (108-

1080315-0302). SNP genotyping was performed at the Institute of Human Genetics,

Helmholtz Zentrum München, Germany

MICROS

For the MICROS study, we thank the primary care practitioners Raffaela Stocker, Stefan

Waldner, Toni Pizzecco, Josef Plangger, Ugo Marcadent, and the personnel of the

Hospital of Silandro (Department of Laboratory Medicine) for their participation and

collaboration in the research project.In South Tyrol, the study was supported by the

Ministry of Health and Department of Educational Assistance, University and Research

of the Autonomous Province of Bolzano, and the South Tyrolean Sparkasse Foundation.

HEALTH 2000

We thank the volunteers who participated in the HEALTH 2000 study. We are grateful

for the excellent laboratory work of Anne Vikman and Eija Hämäläinen. VS was

supported by the Finnish Academy, grant number 129494 and the Finnish Foundation for

Cardiovascular Research. MP was supported by the Finnish Foundation for

Cardiovascular disease, the Sigrid Jusélius Foundation, and the Finnish Academy

SALVE‐program "Pubgensense", grant number 129322.


17

GENOA

Genetic Epidemiology Network of Arteriopathy (GENOA) study is supported by the

National Institutes of Health, grant numbers HL087660 and HL100245 from National

Heart, Lung, Blood Institute. We thank Eric Boerwinkle, PhD from the Human Genetics

Center and Institute of Molecular Medicine and Division of Epidemiology, University of

Texas Health Science Center, Houston, Texas, USA and Julie Cunningham, PhD from

the Department of Health Sciences Research, Mayo Clinic College of Medicine,

Rochester, MN, USA for their help with genotyping.

NTR/NESDA/NTR2

The Netherlands Study of Depression and Anxiety/ Netherlands Twin Register

acknowledge funding from the Netherlands Organization for Scientific Research (NWO:

MagW/ZonMW grants 904-61-090, 985-10-002,904-61-193,480-04-004, 400-05-717,

912-100-20, Spinozapremie 56-464-14192, Geestkracht program [grant 10-000-1002]);

the Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking and

Biomolecular Resources Research Infrastructure (BBMRI –NL), VU University’s

institutes for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam

(NCA), NBIC/BioAssist/RK(2008.024), the European Science Foundation (ESF,

EU/QLRT-2001-01254), the European Community's Seventh Framework Program

(FP7/2007-2013), ENGAGE (HEALTH-F4-2007-201413); the European Science

Council (ERC, 230374). Genotyping was funded in part by the Genetic Association

Information Network (GAIN) of the Foundation for the US National Institutes of Health.

BUSSELTON

The Busselton Health Study acknowledges the generous support for the 1994/5 follow-up

study from Healthway, Western Australia and the numerous Busselton community

volunteers who assisted with data collection and the study participants from the Shire of

Busselton. The Busselton Health Study is supported by The Great Wine Estates of the

Margaret River region of Western Australia. The BHS gratefully acknowledges the

assistance of the Western Australian DNA Bank (NHMRC Enabling Facility) with DNA


18

samples and the support provided by the Western Australian Genetic Epidemiology

Resource (NHMRC Enabling Facility) for this study.

FAMHS

The Family Heart Study (FamHS) work was supported in part by NIH grants

5R01HL08770003, 5R01HL08821502 (Michael A. Province) from NHLBI and

5R01DK07568102, 5R01DK06833603 from NIDDK (Ingrid B. Borecki).

FRENCH CONTROLS

French genetic studies were supported in part by the “Conseil Regional Nord-Pas-de-

Calais: Fonds européen de développement économique et regional”, Genome Quebec-

Genome Canada and the British Medical Research Council. We acknowledge the Inserm,

NB-N' employer.

DESIR

The D.E.S.I.R. study has been supported by INSERM, CNAMTS, Lilly, Novartis Pharma

and Sanofi‐Aventis, the Association Diabète Risque Vasculaire, the Fédération

Française de Cardiologie, La Fondation de France, ALFEDIAM, ONIVINS, Ardix

Medical, Bayer Diagnostics, Becton Dickinson, Cardionics, Merck Santé, Novo Nordisk,

Pierre Fabre, Roche, Topcon. Geneotyping is supported by "Conseil Regional Nord-Pas-

de-Calais: Fonds européen de développement économique et regional” Axe cardio-

diabète grant 2009-2011.

ROTTERDAM STUDY

The generation and management of GWAS genotype data for the Rotterdam Study is

supported by the Netherlands Organisation of Scientific Research NWO Investments (nr.

175.010.2005.011, 911-03-012). This study is funded by the Research Institute for

Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative

(NGI)/Netherlands Organisation for Scientific Research (NWO) project nr. 050-060-810.

We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein


19

Peters for their help in creating the GWAS database, and Karol Estrada and Maksim V.

Struchalin for their support in creation and analysis of imputed data.

The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University,

Rotterdam, Netherlands Organization for the Health Research and Development

(ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of

Education, Culture and Science, the Ministry for Health, Welfare and Sports, the

European Commission (DG XII), and the Municipality of Rotterdam. The authors are

grateful to the study participants, the staff from the Rotterdam Study and the participating

general practitioners and pharmacists.

SCARFSHEEP

The SCARF-SHEEP study was funded by the Swedish Heart-Lung Foundation, the

Swedish Research Council, the Knut and Alice Wallenberg Foundation, the Torsten and

Ragnar Söderberg Foundation, the European Commission (LSHM-CT- 2007- 037273),

the Strategic Cardiovascular and Diabetes Programs of Karolinska Institutet and

Stockholm County Council, the Foundation for Strategic Research, Astra Zeneca and the

Stockholm County Council (560283).

PROCARDIS

The PROCARDIS study was supported by the European Community Sixth Framework

Program (LSHM-CT- 2007-037273), AstraZeneca, the British Heart Foundation, the

Wellcome Trust (075491/Z/04), the Oxford BHF Centre of Research Excellence, the

Swedish Research Council, the Knut and Alice Wallenberg Foundation, the Swedish

Heart-Lung Foundation, the Torsten and Ragnar Söderberg Foundation, the Strategic

Cardiovascular Program of Karolinska Institutet and Stockholm County Council, the

Foundation for Strategic Research and the Stockholm County Council (560283).

CHS

The CHS research reported in this article was supported by NHLBI contract N01-HC-

85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-

HC-75150, N01-HC-45133, and grants HL075366, HL080295, HL087652, and


20

HL105756 with additional contribution from the NINDS. Additional support from the

NIA was provided through AG-023269, AG-15928, AG-20098, and AG-027058. A full

list of principal CHS investigators and institutions can be found at http://www.chs-

nhlbi.org/pi.htm. DNA handling and genotyping was supported in part by CTSI grant

UL1RR033176 to UCLA and Cedars-Sinai Genotyping Core and National Institute of

Diabetes and Digestive and Kidney Diseases grant DK063491 to the Southern California

Diabetes Endocrinology Research Center. Additional support from Cedars-Sinai Board of

Governors' Chair in Medical Genetics (JIR)

AGES

This study has been funded by NIH contract N01-AG-1-2100, the NIA Intramural

Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the

Icelandic Parliament). The study is approved by the Icelandic National Bioethics

Committee, VSN: 00-063. The researchers are indebted to the participants for their

willingness to participate in the study.

QUEBEC FAMILY STUDY

The Quebec Family Study was funded by multiple grants from the Medical Research

Council of Canada and the Canadian Institutes for Health Research. This work was

supported by a team grant from the Canadian Institutes for Health Research (FRN-CCT-

83028)

CROATIA-VIS

The study was funded by grants from the Medical Research Council (UK), European

Commission Framework 6 project EUROSPAN (Contract No. LSHG-CT-2006-018947)

and Republic of Croatia Ministry of Science, Education and Sports research grants to I.R.

(108-1080315-0302). The CROATIA-Vis study would like to acknowledge the staff of

several institutions in Croatia that supported the field work, including but not limited to

The University of Split and Zagreb Medical Schools, Institute for Anthropological


21

Research in Zagreb and Croatian Institute for Public Health. SNP genotyping was

performed at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland

ORCADES (Orkney)

The Orkney Complex Disease Study (ORCADES) was supported by the Chief Scientist

Office of the Scottish Government, the Royal Society and the European Union

framework program 6 EUROSPAN project (contract no. LSHG-CT-2006-018947). DNA

extractions were performed at the Wellcome Trust Clinical Research Facility in

Edinburgh. We would like to acknowledge the invaluable contributions of Lorraine

Anderson and the research nurses in Orkney, the administrative team in Edinburgh and

the people of Orkney.

FUSION

We would like to thank the many Finnish volunteers who generously participated in the

FUSION, D2D, Health 2000, Finrisk 1987, Finrisk 2002, and Savitaipale studies from

which we chose our FUSION GWA and replication cohorts (no overlap with Health 2000

replication cohort). We also thank Terry Gliedt and Peggy White for informatics and

analysis support. The Center for Inherited Disease Research performed the GWA

genotyping. Support for this study was provided by the following: NIH grants DK069922

(R.M.W.), U54 DA021519 (R.M.W.), DK062370 (M.Boe.), and DK072193 (K.L.M.).

Additional support comes from the National Human Genome Research Institute

intramural project number 1 Z01 HG000024 (F.S.C.).

METSIM

The METabolic Syndrome In Men Study (METSIM) was supported by grants to M.

Laakso from the Academy Finland (grants 77299 and 124243), Finnish Diabetes

Research Foundation, Finnish Foundation for Cardiovascular Research, University of

Eastern Finland, and the Kuopio University Hospital (EVO grant 5207).


22

NFBC66

We thank Professor Paula Rantakallio (launch of NFBC1966 and initial data collection),

Ms Tuula Ylitalo (administration), Mr Markku Koiranen (data management), Ms Outi

Tornwall and Ms Minttu Jussila (DNA biobanking). Financial support was received from

the Academy of Finland (project grants 104781, 120315 and Center of Excellence in

Complex Disease Genetics), University Hospital Oulu, Biocenter, University of Oulu,

Finland, NHLBI grant 5R01HL087679-02 through the STAMPEED program

(1RL1MH083268-01), ENGAGE project and grant agreement HEALTH-F4-2007-

201413, the Medical Research Council (studentship grant G0500539, centre grant

G0600705), the Wellcome Trust (project grant GR069224), UK, and the Research

Council UK fellowship. The DNA extractions, sample quality controls, biobank up-

keeping and aliquotting was performed in the national Public Health Institute,

Biomedicum Helsinki, Finland and supported financially by the Academy of Finland and

Biocentrum Helsinki. The research of Inga Prokopenko and Valisiki Lagou is funded in

part through the European Community's Seventh Framework Programme (FP7/2007-

2013), ENGAGE project, grant agreement HEALTH-F4-2007- 201413. Reedik Mägi is

funded by the European Commission under the Marie Curie Intra-European Fellowship.

Reedik Mägi was supported by the Development Fund of the University of Tartu.

SORBS

Dr Knut Krohn, Microarray Core Facility of the Interdisciplinary Centre for Clinical

Research, University of Leipzig, Germany; German Research Council KFO-152 (to

Michael Stumvoll); IZKF B27 (to Michael Stumvoll, Peter Kovacs and Anke Tönjes).

The research of Inga Prokopenko and Valisiki Lagou is funded in part through the

European Community's Seventh Framework Programme (FP7/2007-2013), ENGAGE

project, grant agreement HEALTH-F4-2007- 201413. Reedik Mägi is funded by the

European Commission under the Marie Curie Intra-European Fellowship.

PROSPER

The research leading to PROSPER results has received funding from the European

Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement n°


23

HEALTH-F2-2009-223004. The original PROSPER study was supported by an

unrestricted, investigator initiated grant from Bristol-Myers Squibb, USA.

DRsExtra

The DR.s EXTRA Study was supported by grants to R. Rauramaa by the

Ministry of Education and Culture of Finland (627;2004-2011), Academy of

Finland (102318; 123885), Kuopio University Hospital , Finnish Diabetes

Association, Finnish Heart Association, Pivikki and Sakari Sohlberg

Foundation and by grants from European Commission FP6 Integrated Project

(EXGENESIS); LSHM-CT-2004-005272, City of Kuopio and Social Insurance

Institution of Finland (4/26/2010).

THISEAS

Recruitment for THISEAS was partially funded by a research grant (PENED 2003) from

the Greek General Secretary of Research and Technology; we thank all the dieticians and

clinicians for their contribution to the project. We thank the members of the Wellcome

Trust Sanger Institute's Genotyping Facility for genotyping WTCCC, AMC-PAS,

THISEAS, and PROMIS. Funding: Wellcome Trust grants 083948/B/07/Z and

077016/Z/05/Z

FINNISH DIABETES PREVENTION STUDY

DPS was funded by Ministry of Education, the Academy of Finland, Sigrid Juselius

Foundation, EVO Funding from Kuopio University Hospital and Finnish Diabetes

Research Foundation.


24

Notes on the MuTHER Consortium Members of the MuTHER (Multiple Tissue Human Expression Resource) Consortium Kourosh R. Ahmadi1, Chrysanthi Ainali2, Amy Barrett3, Veronique Bataille1, Jordana T. Bell1,4, Alfonso Buil5, Panos Deloukas6, Emmanouil T. Dermitzakis5, Antigone S. Dimas4,5, Richard Durbin6, Daniel Glass1, Elin Grundberg1,6, Neelam Hassanali3, Åsa K. Hedman4, Catherine Ingle6, David Knowles7, Maria Krestyaninova8, Cecilia M. Lindgren4, Christopher E. Lowe9,10, Mark I. McCarthy3,4,11, Eshwar Meduri1,6, Paola di Meglio12, Josine L. Min4, Stephen B. Montgomery5, Frank O. Nestle12, Alexandra C. Nica5, James Nisbet6, Stephen O’Rahilly9,10, Leopold Parts6, Simon Potter6, Magdalena Sekowska6, So-Youn Shin6, Kerrin S, Small1, 6, Nicole Soranzo6, 1, Tim D. Spector1, Gabriela Surdulescu1, Mary E. Travers3, Loukia Tsaprouni6, Sophia Tsoka2, Alicja Wilk6, Tsun-Po Yang6, Krina T. Zondervan4 Affiliations 1.Department of Twin Research and Genetic Epidemiology, King's College London, London, UK 2. Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, Strand, London WC2R 2LS. 3. Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Churchill Hospital, Oxford, UK 4. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. 5. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland 6.Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK 7. University of Cambridge, Cambridge, UK 8. European Bioinformatics Institute, Hinxton, UK 9. University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke’s Hospital Cambridge, UK 10. Cambridge NIHR Biomedical Research Centre, Addenbrooke’s Hospital, Cambridge, UK 11. Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK 12. St. John's Institute of Dermatology, King's College London, London, UK Methods The MuTHER resource includes LCLs, skin and adipose tissue derived simultaneously from a subset of well-phenotyped healthy female twins. Whole-genome expression profiling of the samples, each with either two or three technical replicates, were performed using the Illumina Human HT-12 V3 BeadChips (Illumina Inc) according to the protocol supplied by the manufacturer. Log2 transformed expression signals were normalized separately per tissue as follows: quantile normalization was performed across technical replicates of each individual followed by quantile normalization across all individuals. Genotyping was done with a combination of Illumina arrays (HumanHap300, HumanHap610Q, 1M‐Duo and 1.2MDuo 1M. Untyped HapMap2 SNPs were imputed using the IMPUTE software package (v2). The number of samples with genotypes and expression values per tissue is 778 LCL, 667 skin and 776 adipose, respectively. Association between all SNPs (MAF>5%, IMPUTE info >0.8) within a gene or within 1MB of the gene transcription start or end site and normalized expression values were performed with the GenABEL/ProbABEL packages using the polygenic linear model incorporating a kinship matrix in GenABEL followed by the ProbABEL mmscore score test with imputed genotypes. Age and experimental batch were included as cofactors in the adipose and LCL analysis, while age, experimental batch and concentration were included as cofactors in the skin analysis.


25

Notes on the DIAGRAM Consortium Members of the DIAGRAM (DIAbetes Genetics Replication and Meta-analysis) Consortium Benjamin F Voight1,2,3, Laura J Scott4, Valgerdur Steinthorsdottir5, Andrew P Morris6, Christian Dina7,8, Ryan P Welch9, Eleftheria Zeggini6,10, Cornelia Huth11,12, Yurii S Aulchenko13, Gudmar Thorleifsson5, Laura J McCulloch14, Teresa Ferreira6, Harald Grallert11,12, Najaf Amin13, Guanming Wu15, Cristen J Willer4, Soumya Raychaudhuri1,2,16, Steve A McCarroll1,17, Claudia Langenberg18, Oliver M Hofmann19, Josée Dupuis20,21, Lu Qi22-24, Ayellet V Segrè1,2,17, Mandy van Hoek25, Pau Navarro26, Kristin Ardlie1, Beverley Balkau27,28, Rafn Benediktsson29,30, Amanda J Bennett14, Roza Blagieva31, Eric Boerwinkle32, Lori L Bonnycastle33, Kristina Bengtsson Boström34, Bert Bravenboer35, Suzannah Bumpstead10, Noël P Burtt1, Guillaume Charpentier36, Peter S Chines33, Marilyn Cornelis24, David J Couper37, Gabe Crawford1, Alex SF Doney38,39, Katherine S Elliott6, Amanda L Elliott1,17,40, Michael R Erdos33, Caroline S Fox21,41, Christopher S Franklin42, Martha Ganser4, Christian Gieger11, Niels Grarup43, Todd Green1,2, Simon Griffin18, Christopher J Groves14, Candace Guiducci1, Samy Hadjadj44, Neelam Hassanali14, Christian Herder45, Bo Isomaa46,47, Anne U Jackson4, Paul RV Johnson48, Torben Jørgensen49,50, Wen HL Kao51,52, Norman Klopp11, Augustine Kong5, Peter Kraft22,23, Johanna Kuusisto53, Torsten Lauritzen54, Man Li51, Aloysius Lieverse55, Cecilia M Lindgren6, Valeriya Lyssenko56, Michel Marre57,58, Thomas Meitinger59,60, Kristian Midthjell61, Mario A Morken33, Narisu Narisu33, Peter Nilsson56, Katharine R Owen14, Felicity Payne10, John RB Perry62,63, Ann-Kristin Petersen11, Carl Platou61, Christine Proença7, Inga Prokopenko6,14, Wolfgang Rathmann64, N William Rayner6,14, Neil R Robertson6,14, Ghislain Rocheleau65-67, Michael Roden45,68, Michael J Sampson69, Richa Saxena1,2,40, Beverley M Shields62,63, Peter Shrader3,70, Gunnar Sigurdsson29,30, Thomas Sparsø43, Klaus Strassburger64, Heather M Stringham4, Qi Sun22,23, Amy J Swift33, Barbara Thorand11, Jean Tichet71, Tiinamaija Tuomi46,72, Rob M van Dam24, Timon W van Haeften73, Thijs van Herpt25,55, Jana V van Vliet-Ostaptchouk74, G Bragi Walters5, Michael N Weedon62,63, Cisca Wijmenga75, Jacqueline Witteman13, Richard N Bergman76, Stephane Cauchi7, Francis S Collins77, Anna L Gloyn14, Ulf Gyllensten78, Torben Hansen43,79, Winston A Hide19, Graham A Hitman80, Albert Hofman13, David J Hunter22,23, Kristian Hveem61,81, Markku Laakso53, Karen L Mohlke82, Andrew D Morris38,39, Colin NA Palmer38,39, Peter P Pramstaller83, Igor Rudan42,84,85, Eric Sijbrands25, Lincoln D Stein15, Jaakko Tuomilehto86, Andre Uitterlinden25, Mark Walker87, Nicholas J Wareham18, Richard M Watanabe76,88, Goncalo R Abecasis4, Bernhard O Boehm31, Harry Campbell42, Mark J Daly1,2, Andrew T Hattersley62,63, Frank B Hu22-24, James B Meigs3,70, James S Pankow89, Oluf Pedersen43,90,91, H.-Erich Wichmann11,12,92, Inês Barroso10, Jose C Florez1,2,3,93, Timothy M Frayling62,63, Leif Groop56,72, Rob Sladek65-67, Unnur Thorsteinsdottir5,94, James F Wilson42, Thomas Illig11, Philippe Froguel7,95, Cornelia M van Duijn13, Kari Stefansson5,94, David Altshuler1,2,3,17,40,93, Michael Boehnke4, Mark I McCarthy6,14,96.

Affiliations 1. Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02142, USA 2. Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, Boston, Massachusetts 02114,

USA 3. Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA 4. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA 5. deCODE Genetics, 101 Reykjavik, Iceland 6. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK 7. CNRS-UMR-8090, Institute of Biology and Lille 2 University, Pasteur Institute, F-59019 Lille, France 8. INSERM UMR915 CNRS ERL3147 F-44007 Nantes, France 9. Bioinformatics Program, University of Michigan, Ann Arbor MI USA 48109 10. Wellcome Trust Sanger Institute, Hinxton, CB10 1HH, UK 11. Institute of Epidemiology, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany 12. Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, 81377 Munich, Germany 13. Department of Epidemiology, Erasmus University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands. 14. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, OX3 7LJ, UK 15. Ontario Institute for Cancer Research, 101 College Street, Suite 800, Toronto, Ontario M5G 0A3, Canada 16. Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston,

Massachusetts 02115, USA 17. Department of Molecular Biology, Harvard Medical School, Boston, Massachusetts 02115, USA 18. MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK


26

19. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA 20. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA 21. National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts 01702, USA 22. Department of Nutrition, Harvard School of Public Health, 665 Huntington Ave, Boston, MA 02115, USA 23. Department of Epidemiology, Harvard School of Public Health, 665 Huntington Ave, Boston, MA 02115, USA 24. Channing Laboratory, Dept. of Medicine, Brigham and Women's Hospital and Harvard Medical School, 181 Longwood Ave,

Boston, MA 02115, USA 25. Department of Internal Medicine, Erasmus University Medical Centre, PO-Box 2040, 3000 CA Rotterdam, The Netherlands 26. MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, EH4 2XU,

UK 27. INSERM U780, F-94807 Villejuif. France 28. University Paris-Sud, F-91405 Orsay, France 29. Landspitali University Hospital, 101 Reykjavik, Iceland 30. Icelandic Heart Association, 201 Kopavogur, Iceland 31. Division of Endocrinology, Diabetes and Metabolism, Ulm University, 89081 Ulm, Germany 32. The Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center, Houston,

Texas 77030, USA 33. National Human Genome Research Institute, National Institute of Health, Bethesda, Maryland 20892, USA 34. R&D Centre, Skaraborg Primary Care, 541 30 Skövde, Sweden 35. Department of Internal Medicine, Catharina Hospital, PO-Box 1350, 5602 ZA Eindhoven, The Netherlands 36. Endocrinology-Diabetology Unit, Corbeil-Essonnes Hospital, F-91100 Corbeil-Essonnes, France 37. Department of Biostatistics and Collaborative Studies Coordinating Center, University of North Carolina at Chapel Hill,

Chapel Hill, North Carolina, 27599, USA 38. Diabetes Research Centre, Biomedical Research Institute, University of Dundee, Ninewells Hospital, Dundee DD1 9SY, UK 39. Pharmacogenomics Centre, Biomedical Research Institute, University of Dundee, Ninewells Hospital, Dundee DD1 9SY, UK 40. Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA 41. Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston,

Massachusetts 02115, USA 42. Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK 43. Hagedorn Research Institute, DK-2820 Gentofte, Denmark 44. Centre Hospitalier Universitaire de Poitiers, Endocrinologie Diabetologie, CIC INSERM 0801, INSERM U927, Université

de Poitiers, UFR, Médecine Pharmacie, 86021 Poitiers Cedex, France 45. Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine

University Düsseldorf, 40225 Düsseldorf, Germany 46. Folkhälsan Research Center, FIN-00014 Helsinki, Finland 47. Malmska Municipal Health Center and Hospital, 68601 Jakobstad, Finland 48. Diabetes Research and Wellness Foundation Human Islet Isolation Facility and Oxford Islet Transplant Programme,

University of Oxford, Old Road, Headington, Oxford, OX3 7LJ, UK 49. Research Centre for Prevention and Health, Glostrup University Hospital, DK-2600 Glostrup, Denmark 50. Faculty of Health Science, University of Copenhagen, 2200 Copenhagen, Denmark 51. Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland 21287, USA 52. Department of Medicine, and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University,

Baltimore, Maryland 21287, USA 53. Department of Medicine, University of Kuopio and Kuopio University Hospital, FIN-70211 Kuopio, Finland 54. Department of General Medical Practice, University of Aarhus, DK-8000 Aarhus, Denmark 55. Department of Internal Medicine, Maxima MC, PO-Box 90052, 5600 PD Eindhoven, The Netherlands 56. Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University,

205 02 Malmö, Sweden 57. Department of Endocrinology, Diabetology and Nutrition, Bichat-Claude Bernard University Hospital, Assistance Publique

des Hôpitaux de Paris, 75870 Paris Cedex 18, France 58. INSERM U695, Université Paris 7, 75018 Paris , France 59. Institute of Human Genetics, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany 60. Institute of Human Genetics, Klinikum rechts der Isar, Technische Universität München, 81675 Muenchen, Germany 61. Nord-Trøndelag Health Study (HUNT) Research Center, Department of Community Medicine and General Practice,

Norwegian University of Science and Technology, NO-7491 Trondheim, Norway 62. Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, University of Exeter,

Magdalen Road, Exeter EX1 2LU, UK 63. Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula Medical School, University of Exeter, Barrack

Road, Exeter EX2 5DW, UK 64. Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine

University Düsseldorf, 40225 Düsseldorf, Germany 65. Department of Human Genetics, McGill University, Montreal H3H 1P3, Canada 66. Department of Medicine, Faculty of Medicine, McGill University, Montreal, H3A 1A4, Canada 67. McGill University and Genome Quebec Innovation Centre, Montreal, H3A 1A4. Canada 68. Department of Metabolic Diseases, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany 69. Department of Endocrinology and Diabetes, Norfolk and Norwich University Hospital NHS Trust , Norwich, NR1 7UY, UK. 70. General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, USA 71. Institut interrégional pour la Santé (IRSA), F-37521 La Riche, France 72. Department of Medicine, Helsinki University Hospital, University of Helsinki, FIN-00290 Helsinki, Finland 73. Department of Internal Medicine, University Medical Center Utrecht, 3584 CG Utrecht,The Netherlands


27

74. Molecular Genetics, Medical Biology Section, Department of Pathology and Medical Biology, University Medical Center Groningen and University of Groningen, 9700 RB Groningen, The Netherlands

75. Department of Genetics, University Medical Center Groningen and University of Groningen, 9713 EX Groningen, The Netherlands

76. Department of Physiology and Biophysics, University of Southern California School of Medicine, Los Angeles, California 90033, USA

77. National Institute of Health, Bethesda, Maryland 20892, USA 78. Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, S-751 85 Uppsala, Sweden. 79. University of Southern Denmark, DK-5230 Odense, Denmark 80. Centre for Diabetes, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London

E1 2AT, UK 81. Department of Medicine, The Hospital of Levanger, N-7600 Levanger, Norway 82. Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA 83. Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Viale Druso 1, 39100 Bolzano, Italy 84. Croatian Centre for Global Health, Faculty of Medicine, University of Split, Soltanska 2, 21000 Split, Croatia 85. Institute for Clinical Medical Research, University Hospital "Sestre Milosrdnice", Vinogradska 29, 10000 Zagreb, Croatia 86. Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki FIN-00300, Finland, 87. Diabetes Research Group, Institute of Cellular Medicine, Newcastle University, Framlington Place, Newcastle upon Tyne

NE2 4HH, UK 88. Department of Preventitive Medicine, Keck Medical School, University of Southern California, Los Angeles, CA, 90089-

9001, USA 89. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota 55454, USA 90. Department of Biomedical Science, Panum, Faculty of Health Science, University of Copenhagen, 2200 Copenhagen,

Denmark 91. Faculty of Health Science, University of Aarhus, DK–8000 Aarhus, Denmark 92. Klinikum Grosshadern, 81377 Munich, Germany 93. Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts 02144, USA 94. Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland 95. Genomic Medicine, Imperial College London, Hammersmith Hospital, W12 0NN, London, UK 96. Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Old Road Headington,

Oxford, OX3 7LJ, UK


28

Supplementary Figures Fasting Insulin

Fasting Glucose

Supplementary Figure 1: QQ plots for the Joint Meta-Analysis of SNP and SNP×BMI Interaction effects for fasting insulin (top plot) and fasting glucose (bottom plot).


29

PPP1R3B ( rs4841132 )

Chromosome 8 position (kb)9000 9200 9400

0

5

10

15

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 1.37 10 7

PPP1R3B

P 1.72 10 10

UHRF1BP1 ( rs4646949 )


0

5

10

15

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 5.94 10 8

C6ORF106SNRPC

UHRF1BP1

TAF11ANKS1A

TCP11

P 3.66 10 8

PDGFC ( rs4691380 )


0

5

10

15

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 1.52 10 8

PDGFC

P 5.29 10 9

LYPLAL1 ( rs2785980 )


0

5

10

15

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 7.74 10 8

LYPLAL1

P 2.01 10 8

ARAP1 ( rs11603334 )


0

5

10

15

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 4.37 10 11

CLPBPDE2A

ARAP1

STARD10ATG16L2

FCHSD2

P 2.42 10 14

FOXA2 ( rs6048205 )


0

5

10

15

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 7.05 10 10

FOXA2

P 1.58 10 12

PPP1R3B ( rs4841132 )


0

5

10

15

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 7.87 10 7

PPP1R3B

P 7.61 10 9

DPYSL5 ( rs1371614 )


0

5

10

15

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 2.91 10 9

KCNK3C2ORF18

CENPA

DPYSL5MAPRE3

TMEM214

AGBL5EMILIN1

KHK

CGREF1ABHD1

PREB

C2ORF53TCF23

P 2.3 10 12

PDX1 ( rs2293941 )


0

5

10

15

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 1.25 10 8

GSX1PDX1

CDX2

PRHOXNBFLT3

P 5.34 10 10

Supplementary Figure 2: Regional association plots from the genomic loci showing genome-wide significance with the Joint Meta-Analysis (JMA), excluding the loci presented in Figure 2 and Supplementary Figure 3. Each plot shows the discovery JMA P values in the background and for the SNP taken forward to the follow-up analyses (P<10-6), the P values of the discovery JMA (dark red) and the combined discovery and follow-up JMA (light red), main effects adjusting for BMI (orange), interaction with continuous BMI (green) and interaction with dichotomous BMI (blue).


30

IRS1 ( rs2943634 )


02468

10121416182022

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 1.71 10 12

KIAA1486IRS1

RHBDD1COL4A4

COL4A3

a

P 2.49 10 14

0.01

0.03

0.05

Increase per risk allele

Effe

ct

BMI<28 BMI>28

PCSK1 ( rs13179048 )


0

2

4

6

8

10

12

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 1.59 10 9

RHOBTB3GLRX

ELL2PCSK1

CAST

bP 1.64 10 10

0.01

0.03

0.05

Increase per risk alleleEf

fect

BMI<28 BMI>28

OR4S1 ( rs1483121 )


0

2

4

6

8

10

12

log 1

0P

valu

e

0

20

40

60

Rec

ombi

natio

n ra

te (c

M/M

b)

P 6.51 10 8

PTPRJOR4B1

OR4X2OR4X1

OR4S1OR4C3

OR4C45OR4A47

c

P 1.61 10 8

0.01

0.03

0.05

Increase per risk allele

Effe

ct

BMI<28 BMI>28

Supplementary Figure 3: Regional plot and and BMI strata-specific SNP effect estimates of three genomic loci: (a) IRS1 (b) PCSK1 and (c) OR4S1. The left plot shows the discovery JMA P values in the background and for the SNP taken forward to the follow-up analyses, the P values of the discovery JMA (dark red) and the combined discovery and follow-up JMA (light red), main effects adjusting for BMI (orange), interaction with continuous BMI (green) and interaction with dichotomous BMI (blue). The right plots shows the beta estimates of the increasing allele in the two BMI strata: BMI<28 and BMI≥28 kg/m2. Effect = pmol/l change in FI or mmol/L change in FG per allele.


Documents

A genome-wide approach accounting for body mass …...The JMA association did not reach genome-wide significance (P= 6.55×10-6). This SNP, located on chromosome 11, is ~427 kb away