Association analyses of 249,796 individuals reveal eighteen new lociassociated with body mass index
Obesity is globally prevalent and highly heritable, but the underlyinggenetic factors remain largely elusive. To identify genetic loci forobesity-susceptibility, we examined associations between body mass index (BMI)and ~2.8 million SNPs in up to 123,865 individuals, with targeted follow-up of42 SNPs in up to 125,931 additional individuals. We confirmed 14 knownobesity-susceptibility loci and identified 18 new loci associated with BMI(P<5×10−8), one of whichincludes a copy number variant near GPRC5B. Some loci(MC4R, POMC, SH2B1, BDNF) map near key hypothalamicregulators of energy balance, and one is near GIPR, an incretinreceptor. Furthermore, genes in other newly-associated loci may provide novelinsights into human body weight regulation.
Obesity is a major and increasingly prevalent risk factor for multiple disorders,including type 2 diabetes and cardiovascular disease1,2. While lifestyle changes havedriven its prevalence to epidemic proportions, heritability studies provide evidence fora substantial genetic contribution (h2~40–70%) to obesityrisk3,4.BMI is an inexpensive, non-invasive measure of obesity that predicts the risk of relatedcomplications5. Identifying geneticdeterminants of BMI could lead to a better understanding of the biological basis ofobesity.
Genome-wide association (GWA) studies of BMI have previously identified ten lociwith genome-wide significant (P < 5×10−8)associations in or near FTO, MC4R, TMEM18, GNPDA2, BDNF, NEGR1, SH2B1, ETV5,MTCH2, and KCTD156–10. Many of these genes are expressed or known toact in the central nervous system, highlighting a likely neuronal component to thepredisposition to obesity9. This pattern isconsistent with results in animal models and studies of monogenic human obesity, whereneuronal genes, particularly those expressed in the hypothalamus and involved inregulation of appetite or energy balance, are known to play a major role insusceptibility to obesity11–13.
The ten previously identified loci account for only a small fraction of thevariation in BMI. Furthermore, power calculations based on the effect sizes ofestablished variants have suggested that increasing the sample size would likely lead tothe discovery of additional variants9. To identifymore loci associated with BMI, we expanded the GIANT (Genetic Investigation ofANtropometric Traits) consortium GWA meta-analysis to include a total of 249,769individuals of European ancestry.
Stage 1 GWA studies identify novel loci associated with BMI
We first conducted a meta-analysis of GWA studies of BMI and ~2.8 millionimputed or genotyped SNPs using data from 46 studies including up to 123,865individuals (OnlineMethods, Supplementary Fig. 1 and Supplementary Note). This stage 1analysis revealed 19 loci associated with BMI at P <5×10−8 (Table1, Fig. 1a and Supplementary Table 1). These 19loci included all ten loci from previous GWA studies of BMI6–10,two loci previously associated with body weight10 (FAIM2 and SEC16B) and one locus previouslyassociated with waist circumference14(near TFAP2B). The remaining six loci, near GPRC5B,MAP2K5/LBXCOR1, TNNI3K, LRRN6C, FLJ35779/HMGCR, and PRKD1, have notpreviously been associated with BMI or other obesity-related traits.
Stage 2 follow-up leads to additional novel loci for BMI
To identify additional BMI-associated loci and to validate the loci thatreached genome-wide significance in stage 1 analyses, we examined SNPsrepresenting 42 independent loci (including the 19 genome-wide significant loci)with stage 1 P < 5×10−6. Variantswere considered to be independent if the pair-wise linkage disequilibrium (LD;r2) was less than 0.1 and if they were separatedby at least 1 Mb. In stage 2, we examined these 42 SNPs in up to 125,931additional individuals (79,561 newly genotyped individuals from 16 differentstudies and 46,370 individuals from 18 additional studies for which GWA datawere available; Table 1, Supplementary Note, and Online Methods). In ajoint analysis of stage 1 and stage 2 results, 32 of the 42 SNPs reachedP < 5×10−8. Even afterexcluding SNPs within these 32 confirmed BMI loci, we still observed an excessof small P-values compared to the distribution expected underthe null hypothesis (Fig. 1b), suggestingthat more BMI loci remain to be uncovered.
The 32 confirmed associations included all 19 loci withP < 5×10−8 at stage 1, 12additional novel loci near RBJ/ADCY3/POMC, QPCTL/GIPR,SLC39A8, TMEM160, FANCL, CADM2, LRP1B, PTBP2, MTIF3/GTF3A, ZNF608,RPL27A/TUB, NUDT3/HMGA1, and one locus (NRXN3)previously associated with waist circumference15 (Table 1, Supplementary Table 1, Supplementary Fig. 1 and2). In all, our study increased the number of loci robustlyassociated with BMI from 10 to 32. Four of the 22 new loci were previouslyassociated with body weight10 or waistcircumference14,15, whereas 18 loci had not previously associatedwith any obesity-related trait in the general population. Whilst we confirmedall loci previously established by large-scale GWA studies for BMI6–10and waist circumference14,15, four loci identified by GWA studies forearly-onset or adult morbid obesity16,17 [atNPC1 (rs1805081; P = 0.0025),MAF (rs1424233; P = 0.25),PTER (rs10508503; P = 0.64), andTNKS/MSRA (rs473034; P =0.23)] showed limited or no evidence of association with BMI in ourstudy.
As expected, the effect sizes of the 18 newly discovered loci areslightly smaller, for a given minor allele frequency, than those of thepreviously identified variants (Table 1and Fig. 1c). The increased sample sizealso brought out more signals with low minor allele frequency. TheBMI-increasing allele frequencies for the 18 newly identified variants rangedfrom 4% to 87%, covering more of the allele frequency spectrumthan previous, smaller GWA studies of BMI (24%–83%)9,10(Table 1 and Fig. 1c).
We tested for evidence of non-additive (dominant or recessive) effects,SNP×SNP interaction effects and heterogeneity by sex or study among the32 BMI-associated SNPs (OnlineMethods). We found no evidence for any such effects(P > 0.001, no significant results after correcting formultiple testing) (Supplementary Tables 1 and Supplementary Note).
Impact of 32 confirmed loci on BMI, obesity, body size, and other metabolictraits
Together, the 32 confirmed BMI loci explained 1.45% of theinter-individual variation in BMI of the stage 2 samples, with theFTO SNP accounting for the largest proportion of thevariance (0.34%) (Table 1). Toestimate the cumulative effect of the 32 variants on BMI, we constructed agenetic-susceptibility score that sums the number of BMI-increasing allelesweighted by the overall stage 2 effect sizes in the ARIC study (N =8,120), one of our largest population-based studies (Online Methods). For each unitincrease in the genetic-susceptibility score, approximately equivalent to oneadditional risk allele, BMI increased by 0.17 kg/m2, equivalent to a435–551 g gain in body weight in adults of 160–180 cm in height.The difference in average BMI between individuals with a highgenetic-susceptibility score (≥38 BMI-increasing alleles, 1.5%(n=124) of the ARIC sample) and those with a low genetic-susceptibilityscore (≤21 BMI-increasing alleles, 2.2% (n=175) of theARIC sample) was 2.73 kg/m2, equivalent to a 6.99 to 8.85 kg bodyweight difference in adults 160–180 cm in height (Fig. 2a). Still, we note that the predictive value forobesity risk and BMI of the 32 variants combined was modest, althoughstatistically significant (Fig. 2b, Supplementary Fig. 4).The area under the receiver operating characteristic (ROC) curve for predictionof risk of obesity (BMI ≥ 30 kg/m2) using age,age2 and sex only was 0.515 (P = 0.023compared to AUC of 0.50), which increased to 0.575 (P <10−5) when also the 32 confirmed SNPs were included inthe model (Fig. 2b). The area under the ROCfor the 32 SNPs only was 0.574 (P <10−5).
All 32 confirmed BMI-increasing alleles showed directionally consistenteffects on risk of being overweight (BMI ≥25 kg/m2) or obese(≥30 kg/m2) in stage 2 samples, with 30 of 32 variantsachieving at least nominally significant associations. The BMI-increasingalleles increased the odds of overweight by 1.013 to 1.138-fold, and the oddsfor being obese by 1.016- to 1.203-fold (Supplementary Table 2). Inaddition, 30 of the 32 loci also showed directionally consistent effects on therisk of extreme and early-onset obesity in a meta-analysis of seven case-controlstudies of adults and children (binomial sign test P =1.3×10−7) (Supplementary Table 3). TheBMI-increasing allele observed in adults also increased the BMI in children andadolescents with directionally consistent effects observed for 23 of the 32 SNPs(binomial sign test P = 0.01). Furthermore, infamily-based studies, the BMI-increasing allele was over-transmitted to theobese offspring for 24 of the 32 SNPs (binomial sign test P= 0.004) (Supplementary Table 3). As these studies in extreme obesity cases,children and families were relatively small (Nrange = 354− 15,251) compared to the overall meta-analyses, their power was likelyinsufficient to confirm association for all 32 loci. Nevertheless, these resultsshow that the effects are unlikely to reflect population stratification and thatthey extend to BMI differences throughout the life course.
All BMI-increasing alleles were associated with increased body weight,as expected from the correlation between BMI and body weight (Supplementary Table 2). To confirman effect of the loci on adiposity rather than general body size, we testedassociation with body fat percentage, which was available in a subset of thestage 2 replication samples (n = 5,359–28,425) (Supplementary Table 2). TheBMI-increasing allele showed directionally consistent effects on body fatpercentage at 31 of the 32 confirmed loci (binomial sign test P= 1.54×10−8) (Supplementary Table 2).
We also examined the association of the BMI loci with metabolic traits(type 2 diabetes18, fasting glucose,fasting insulin, indices of beta-cell function (HOMA-B) and insulin resistance(HOMA-IR)19, and blood lipidlevels20) and with height (Supplementary Tables 2 and4). Although many nominal associations are expected because of knowncorrelations between BMI and most of these traits and because of overlap insamples, several associations stand out as possible examples of pleiotropiceffects of the BMI-associated variants. Particularly interesting is the variantin the GIPR locus where the BMI-increasing allele is alsoassociated with increased fasting glucose levels and lower 2-hour glucose levels(Supplementary Table4)19,21. The direction of the effect is opposite to whatwould be expected due to the correlation between obesity and glucoseintolerance, but is consistent with the suggested roles of GIPRin glucose and energy metabolism (see below)22. Three loci show strong associations (P <10−4) with height (MC4R,RBJ/ADCY3/POMC and MTCH2/NDUFS3). BecauseBMI is weakly correlated with height (and indeed, the BMI-associated variants asa group show no consistent effect on height), these associations are alsosuggestive of pleiotropy. Interestingly, analogous to the effects of severemutations in POMC and MC4R on height andweight23,24, the BMI-increasing alleles of the variants nearthese genes were associated with decreased (POMC) and increased(MC4R) height, respectively (Supplementary Table 2).
Potential functional roles and pathways analyses
Although associated variants typically implicate genomic regions ratherthan individual genes, we note that some of the 32 loci include candidate geneswith established connections to obesity. Several of the 10 previously identifiedloci are located in or near genes that encode neuronal regulators of appetite orenergy balance, including MC4R12,25,BDNF26, andSH2B111,27. Each of these genes has been tied toobesity, not only in animal models, but also by rare human variants that disrupteach of these genes and lead to severe obesity24,28,29. Using the automated literature search programme,Snipper (OnlineMethods), we identified various genes within the novel loci withpotential biological links to obesity-susceptibility (Supplementary Note). Among thenovel loci, the location of rs713586 near POMC provides furthersupport for a role of neuroendocrine circuits that regulate energy balance insusceptibility to obesity. POMC encodes several polypeptidesincluding α-MSH, a ligand of the MC4R gene product30, and rare mutations inPOMC also cause human obesity23,29,31.
In contrast, the locus near GIPR, which encodes areceptor of gastric inhibitory polypeptide (GIP), suggests a role for peripheralbiology in obesity. GIP, which is expressed in the K cell of the duodenum andintestine, is an incretin hormone that mediates incremental insulin secretion inresponse to oral intake of glucose. The variant associated with BMI is in strongLD (r2 = 0.83) with a missense SNP inGIPR (rs1800437, Glu354Gln) that has recently been shown toinfluence the glucose and insulin response to an oral glucose challenge 21. Although no human phenotype is known tobe caused by mutations in GIPR, mice with disruption ofGipr are resistant to diet-induced obesity32. The association of a variant inGIPR with BMI suggests that there may be a link betweenincretins/insulin secretion and body weight regulation in humans as well.
To systematically identify biological connections among the geneslocated near the 32 confirmed SNPs, and to potentially identify new pathwaysassociated with BMI, we performed pathway-based analyses using MAGENTA33. Specifically, we tested for enrichmentof BMI genetic associations in biological processes or molecular functions thatcontain at least one gene from the 32 confirmed BMI loci (Online Methods). Using annotationsfrom the KEGG, Ingenuity, PANTHER, and Gene Ontology databases, we foundevidence of enrichment for pathways involved in the platelet-derived growthfactor (PDGF) signaling (PANTHER, P = 0.0008, FDR = 0.0061),translation elongation (PANTHER, P = 0.0008, FDR = 0.0066),hormone or nuclear hormone receptor binding (Gene Ontology, P < 0.0005, FDR< 0.0085), homeobox transcription (PANTHER, P = 0.0001, FDR =0.011), regulation of cellular metabolism (Gene Ontology, P = 0.0002,FDR = 0.031), neurogenesis and neuron differentiation (Gene Ontology, P< 0.0002, FDR < 0.034), protein phosphorylation (PANTHER, P =0.0001, FDR = 0.045) and numerous other pathways related to growth,metabolism, immune and neuronal processes (Gene Ontology, P < 0.002, FDR <0.046) (Supplementary Table5).
Identifying possible functional variants
We used data from the 1000 Genomes Project and the HapMap Consortium toexplore whether the 32 confirmed BMI SNPs were in LD(r2 ≥ 0.75) with common missense SNPs orcopy number variants (CNVs) (Online Methods). Non-synonymous variants in LD with our signals werepresent in the BDNF, SLC39A8, FLJ35779/HMGCR, QPCTL/GIPR, MTCH2,ADCY3, and LBXCOR1 genes. In addition, thers7359397 signal was in LD with coding variants in several genes includingSH2B1, ATNX2L, APOB48R, SULT1A2, andAC138894.2 (Table 1,Fig. 3, Supplementary Table 6 and Supplementary Fig. 2).Furthermore, two SNPs tagged common CNVs. The first CNV was previouslyidentified and is a 45-kb deletion near NEGR19. The second CNV is a 21-kb deletion that lies 50kbupstream of GPRC5B; the deletion allele is tagged by theT-allele of rs12444979 (r2 = 1) (Fig. 3). Although the correlations withpotentially functional variants does not prove that these variants are indeedcausal, these provide first clues as to which genes and variants at these locimight be prioritized for fine-mapping and functional follow-up.
As many of the 32 BMI loci harbor multiple genes, we examined whethergene expression (eQTL) analyses could also direct us to positional candidates.Gene expression data were available for human brain, lymphocytes, blood,subcutaneous and visceral adipose tissue, and liver34–36(Online Methods,Table 1 and Supplementary Table 7). Significantcis-associations, defined at the tissue-specific level,were observed between 14 BMI-associated alleles and expression levels (Table 1 and Supplementary Table 7). In severalcases, the BMI-associated SNP was the most significant SNP or explained asubstantial proportion of the association with the most significant SNP for thegene transcript in conditional analyses(Padj>0.05). These significant associationsincluded NEGR1, ZC3H4, TMEM160,MTCH2, NDUFS3, GTF3A,ADCY3, APOB48R, SH2B1,TUFM, GPRC5B, IQCK,SLC39A8, SULT1A1, andSULT1A2 (Table 1 andSupplementary Table7), making these genes higher priority candidates within theassociated loci. However, we note that some BMI-associated variants werecorrelated with the expression of multiple nearby genes, making it difficult todetermine the most relevant gene.
Evidence for the existence of additional associated variants
Because the variants identified by this large study explain only1.45% of the variance in BMI (2–4% of genetic variancebased on an estimated heritability of 40–70%), we considered howmuch the explained phenotypic variance could be increased by including more SNPsat various degrees of significance in a polygene model using an independentvalidation set (OnlineMethods)37. We found thatincluding SNPs associated with BMI at lower significance levels (up toP > 0.05) increased the explained phenotypic variance inBMI to 2.5%, or 4% to 6% of genetic variance (Fig. 4a). In a separate analysis, weestimated the total number of independent BMI-associated variants that arelikely to exist with similar effect sizes to the 32 confirmed here (Online Methods)38. Based on the effect size and allelefrequencies of the 32 replicated loci observed in stage 2 and the power todetect association in the combined stage 1 and stage 2, we estimated that thereare 284 (95% CI: 132–510) loci with similar effect sizes as thecurrently observed ones, which together would account for 4.5%(95% CI: 3.1–6.8%) of the variation in BMI or6–11% of the genetic variation (based on an estimatedheritability of 40–70%) (Supplementary Table 8). In order todetect 95% of these loci, a sample size of approximately 730,000subjects would be needed (Fig. 4b). Thismethod does not account for the potential of loci of smaller effect than thoseidentified here to explain even more of the variance and thus provides anestimated lower bound of explained variance. These two analyses strongly suggestthat larger GWA studies will continue to identify additional novel associatedloci, but also indicate that even extremely large studies focusing on variantswith allele frequencies above 5% will not account for a large fractionof the genetic contribution to BMI.
We examined whether selecting only a single variant from each locus forfollow-up led us to underestimate the fraction of phenotypic variation explainedby the associated loci. To search for additional independent loci at each of the32 associated BMI loci, we repeated our GWA meta-analysis, conditioning on the32 confirmed SNPs. Using a significance threshold of 5 ×10−6 for SNPs at known loci, we identified one apparentlyindependent signal at the MC4R locus; rs7227255 was associatedwith BMI (P = 6.56 × 10−7)even after conditioning for the most strongly associated variant nearMC4R (rs571312) (Fig.5). Interestingly, rs7227255 is in perfect LD (r2= 1) with a relatively rare MC4R missense variant(rs2229616, V103I, minor allele frequency = 1.7%) that has beenassociated with BMI in two independent meta-analyses39,40.Furthermore, mutations at the MC4R locus are known to influenceearly-onset obesity24,41, supporting the notion that allelic heterogeneitymay be a frequent phenomenon in the genetic architecture of obesity.
Using a two-stage genome-wide association meta-analysis of up to 249,796individuals of European descent, we have identified 18 additional loci that areassociated with BMI at genome-wide significance, bringing the total number of suchloci to 32. We estimate that more than 250 (i.e. 284 predicted loci – 32confirmed loci) common variant loci with effects on BMI similar to those describedhere remain to be discovered, and even larger numbers of loci with smaller effects.A substantial proportion of these loci should be identifiable through larger GWAstudies and/or by targeted follow-up of top signals selected from our stage 1analysis. The latter approach is already being implemented through large-scalegenotyping of samples informative for BMI using a custom array (the Metabochip)designed to support follow-up of thousands of promising variants in hundreds ofthousands of individuals.
The combined effect on BMI of the associated variants at the 32 loci ismodest, and even when we try to account for as-yet-undiscovered variants withsimilar properties, we estimate that these common variant signals account for only6–11% of the genetic variation in BMI. There is a strong expectationthat additional variance and biology will be explained using complementaryapproaches that capture variants not examined in the current study, such as lowerfrequency variants and short insertion-deletion polymorphisms. There is good reasonto believe (based on our findings at MC4R and other loci– POMC, BDNF, SH2B1 – which feature both commonand rare variant associations) that a proportion of such low-frequency and rarecausal variation will map to the loci already identified by GWA studies.
A primary goal of human genetic discovery is to improve understanding of thebiology of conditions such as obesity42. Oneparticularly interesting finding in this regard is the association between BMI andcommon variants near GIPR, which may indicate a causal contributionof variation in postprandial insulin secretion to the development of obesity. Inmost cases, the loci identified by the present study harbor few, if any, annotatedgenes with clear connections to the biology of weight regulation. This reflects ourstill limited understanding of the biology of BMI and obesity-related traits and isin striking contrast with the results from equivalent studies of certain othertraits (such as autoimmune diseases or lipid levels). Thus, these results suggestthat much novel biology remains to be uncovered, and that GWA studies may provide animportant entry point. In particular, further examination of the associated locithrough a combination of resequencing and fine-mapping to find causal variants, andgenomic and experimental studies designed to assign function, could uncover novelinsights into the biology of obesity.
In conclusion, we have performed GWA studies in large samples to identifynumerous genetic loci associated with variation in BMI, a common measure of obesity.Because current lifestyle interventions are largely ineffective in addressing thechallenges of growing obesity43,44, new insights into biology are criticallyneeded to guide the development and application of future therapies andinterventions.
|SNP||Nearest gene||Other nearby genes*||Chr||Position** (bp)||Alleles**||Frequency effect allele(%)||Per allele change in BMIbeta (se)***||Explained variance(%)||Stage 1P-value||Stage 2P-value||Stage 1 + 2|
|rs7359397||SH2B1Q,B,M||APOB48RQ,M, SULT1A2Q,M, AC138894.2M,ATXN2LM, TUFMQ||16||28,793,160||t||c||40%||0.15 (0.02)||0.05%||1.75E-10||7.89E-12||204,309||1.88E-20|
|rs3817334||MTCH2Q,M||NDUFS3Q, CUGBP1Q||11||47,607,569||t||c||41%||0.06 (0.02)||0.01%||4.79E-11||1.10E-03||191,943||1.59E-12|
|rs713586||RBJ||ADCY3Q, M, POMCQ,B||2||25,011,512||c||t||47%||0.14 (0.02)||0.06%||1.80E-07||1.44E-16||230,748||6.17E-22|
*Genes within +/− 500 kb of the lead SNP
**Positions according to Build 36 and allele coding based on the positivestrand
***Effect sizes in kg/m2 obtained from Stage 2 cohorts only
QAssociation and eQTL data converge to affect gene expression
MBMI-associated variant is in strong LD (r2 ≥ 0.75) with amissense variant in the indicated gene
A full list of author contributions appears in the Supplementary Note.
Competing interests statement
The authors declare competing financial interests. A full list of competinginterests appears in the Supplementary Note.
A full list of acknowledgments appears in the Supplementary Note.
Academy of Finland (10404, 77299, 104781, 114382, 117797, 120315, 121584, 124243,126775, 126925, 127437, 129255, 129269, 129306, 129494, 129680, 130326, 209072,210595, 213225, 213506, 216374); ADA Mentor-Based Postdoctoral Fellowship; Amgen;Agency for Science, Technology and Research of Singapore (A*STAR); ALF/LUAresearch grant in Gothenburg; Althingi (the Icelandic Parliament); AstraZeneca;Augustinus Foundation; Australian National Health and Medical Research Council(241944, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 496688, 552485 and613672); Australian Research Council (ARC grant DP0770096); Becket Foundation;Biocenter (Finland); Biomedicum Helsinki Foundation, Boston Obesity NutritionResearch Center; British Diabetes Association (1192); British Heart Foundation(97020; PG/02/128); Busselton Population Medical Research Foundation; CambridgeInstitute for Medical Research; Cambridge NIHR Comprehensive Biomedical ResearchCentre; CamStrad (UK); Cancer Research UK; Centre for Medical Systems Biology (TheNetherlands); Centre for Neurogenomics and Cognitive Research (The Netherlands);Chief Scientist Office of the Scottish Government; Contrat Plan Etat Région(France); Danish Centre for Health Technology Assessment; Danish DiabetesAssociation; Danish Heart Foundation; Danish Pharmaceutical Association; DanishResearch Council; Deutsche Forschungsgemeinschaft (DFG; HE 1446/4-1); Department ofHealth (UK); Diabetes UK; Diabetes & Inflammation Laboratory; Donald W. ReynoldsFoundation; Dresden University of Technology Funding Grant; Emil and Vera CornellFoundation; Erasmus Medical Center (Rotterdam); Erasmus University (Rotterdam);European Commission (DG XII; QLG1-CT-2000-01643, QLG2-CT-2002-01254, LSHC-CT-2005,LSHG-CT-2006-018947, LSHG-CT-2004-518153, LSH-2006-037593, LSHM-CT-2007-037273,HEALTH-F2-2008-ENGAGE, HEALTH-F4-2007-201413, HEALTH-F4-2007-201550, FP7/2007-2013,205419, 212111, 245536, SOC 95201408 05F02, WLRT-2001-01254); Federal Ministry ofEducation and Research (Germany) (01AK803, 01EA9401, 01GI0823, 01GI0826, 01GP0209,01GP0259, 01GS0820, 01GS0823, 01GS0824, 01GS0825, 01GS0830, 01GS0831, 01IG07015,01KU0903, 01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012); Federal State of Mecklenburg-WestPomerania; European Social Fund; Eve Appeal; Finnish Diabetes Research Foundation;Finnish Foundation for Cardiovascular Research; Finnish Foundation for PediatricResearch, Finnish Medical Society; Finska Läkaresällskapet,Päivikki and Sakari Sohlberg Foundation, Folkhalsan Research Foundation;Fond Européen pour le Développement Régional (France);Fondation LeDucq (Paris, France); Foundation for Life and Health in Finland;Foundation for Strategic Research (Sweden); Genetic Association Information Network;German Research Council (KFO-152) German National Genome Research Net‘NGFNplus’ (FKZ 01GS0823); German Research Center for EnvironmentalHealth; Giorgi-Cavaglieri Foundation; GlaxoSmithKline; Göteborg MedicalSociety; Great Wine Estates Auctions; Gyllenberg Foundation; Health Care Centers inVasa, Närpes and Korsholm; Healthway, Western Australia; Helmholtz CenterMunich; Helsinki University Central Hospital, Hjartavernd (the Icelandic HeartAssociation); INSERM (France); Ib Henriksen Foundation; IZKF (B27); Jalmari andRauha Ahokas Foundation; Juho Vainio Foundation; Juvenile Diabetes ResearchFoundation International (JDRF); Karolinska Institute; Knut and Alice WallenbergFoundation; Leenaards Foundation; Lundbeck Foundation Centre of Applied MedicalGenomics for Personalized Disease Prediction, Prevention and Care (LUCAMP); LundbergFoundation; Marie Curie Intra-European Fellowship; Medical Research Council (UK)(G0000649, G0000934, G9521010D, G0500539, G0600331, G0601261, PrevMetSyn); Ministryof Cultural Affairs and Social Ministry of the Federal State of Mecklenburg-WestPomerania; Ministry for Health, Welfare and Sports (Netherlands); Ministry ofEducation (Finland); Ministry of Education, Culture and Science (Netherlands);Ministry of Internal Affairs and Health (Denmark); Ministry of Science, Educationand Sport of the Republic of Croatia (216-1080315-0302); Ministry of Science,Research and the Arts Baden-Württemberg; Montreal Heart InstituteFoundation; Municipal Health Care Center and Hospital in Jakobstad; Municipality ofRotterdam; Närpes Health Care Foundation; National Cancer Institute;National Health and Medical Research Council of Australia; National Institute forHealth Research Cambridge Biomedical Research Centre; National Institute for HealthResearch Oxford Biomedical Research Centre; National Institute for Health Researchcomprehensive Biomedical Research Centre; National Institutes of Health(263-MA-410953, AA07535, AA10248, AA014041, AA13320, AA13321, AA13326, CA047988,CA65725, CA87969, CA49449, CA67262, CA50385, DA12854, DK58845, DK46200, DK062370,DK063491, DK072193, HG002651, HL084729, HHSN268200625226C, HL71981, K23-DK080145,K99-HL094535, M01-RR00425, MH084698, N01-AG12100, NO1-AG12109, N01-HC15103,N01-HC25195, N01-HC35129, N01-HC45133, N01-HC55015, N01-HC55016, N01-HC55018,N01-HC55019, N01-HC55020, N01-N01HC-55021, N01-HC55022, N01-HC55222, N01-HC75150,N01-HC85079, N01-HC85080, N01-HG-65403, N01-HC85081, N01-HC85082; N01-HC85083;N01-HC85084; N01-HC85085; N01-HC85086, N02-HL64278, P30-DK072488, R01-AG031890,R01-DK073490, R01-DK075787, R01DK068336, R01DK075681, R01-HL59367, R01-HL086694,R01-HL087641, R01-HL087647, R01-HL087652, R01-HL087676, R01-HL087679, R01-HL087700,R01-HL088119, R01-MH59160, R01-MH59565, R01-MH59566, R01-MH59571, R01-MH59586,R01-MH59587, R01-MH59588, R01-MH60870, R01-MH60879, R01-MH61675, R01-MH63706,R01-MH67257, R01-MH79469, R01-MH79470, R01-MH81800, RL1-MH083268, UO1-CA098233,U01-DK062418, U01-GM074518, U01-HG004402, U01-HG004399, U01-HL72515, U01-HL080295,U01-HL084756, U54-RR020278, T32-HG00040, UL1-RR025005, Z01-HG000024); NationalAlliance for Research on Schizophrenia and Depression (NARSAD); Netherlands GenomicsInitiative/Netherlands Consortium for Healthy Aging (050-060-810); NetherlandsOrganisation for Scientific Research (NWO) (904-61-090, 904-61-193, 480-04-004,400-05-717, SPI 56-464-1419, 175.010.2005.011, 911-03-012); Nord-TrøndelagCounty Council; Nordic Center of Excellence in Disease Genetics; Novo NordiskFoundation; Norwegian Institute of Public Health; Ollqvist Foundation; Oxford NIHRBiomedical Research Centre; Organization for the Health Research and Development(10-000-1002); Paavo Nurmi Foundation; Paul Michael Donovan Charitable Foundation;Perklén Foundation; Petrus and Augusta Hedlunds Foundation; Pew Scholar forthe Biomedical Sciences; Public Health and Risk Assessment, Health & ConsumerProtection (2004310); Research Foundation of Copenhagen County; Research Institutefor Diseases in the Elderly (014-93-015; RIDE2); Robert Dawson Evans Endowment;Royal Society (UK); Royal Swedish Academy of Science; Sahlgrenska Center forCardiovascular and Metabolic Research (CMR, no. A305: 188); Siemens Healthcare,Erlangen, Germany; Sigrid Juselius Foundation; Signe and Ane Gyllenberg Foundation;Science Funding programme (UK); Social Insurance Institution of Finland;Söderberg’s Foundation; South Tyrol Ministry of Health; SouthTyrolean Sparkasse Foundation; State of Bavaria; Stockholm County Council (560183);Susan G. Komen Breast Cancer Foundation; Swedish Cancer Society; Swedish CulturalFoundation in Finland; Swedish Foundation for Strategic Research; Swedish Heart-LungFoundation; Swedish Medical Research Council (8691, K2007-66X-20270-01-3,K2010-54X-09894-19-3, K2010-54X-09894-19-3, 2006-3832); Swedish Research Council;Swedish Society of Medicine; Swiss National Science Foundation (33CSCO-122661,310000-112552, 3100AO-116323/1); Torsten and Ragnar Söderberg’sFoundation; Université Henri Poincaré-Nancy 1, RégionLorraine, Communauté Urbaine du Grand Nancy; University Hospital Medicalfunds to Tampere; University Hospital Oulu; University of Oulu, Finland (75617);Västra Götaland Foundation; Walter E. Nichols, M.D., and EleanorNichols endowments; Wellcome Trust (068545, 072960, 075491, 076113, 077016, 079557,079895, 081682, 083270, 085301, 086596); Western Australian DNA Bank; WesternAustralian Genetic Epidemiology Resource; Yrjö Jahnsson Foundation.
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