Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.
Journal: 2010/November - Nature Genetics
ISSN: 1546-1718
Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.
Similar articles
Articles by the same authors
Discussion board
Nature genetics. Oct/31/2010; 42(11): 937-948
Published online Oct/9/2010

Association analyses of 249,796 individuals reveal eighteen new lociassociated with body mass index

+367 authors


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 KCTD15610. 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 obesity1113.

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 BMI610,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 BMI610and 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 liver3436(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.

Supplementary Material


Supplementary Figure 1 Study design. Stage 1 - Metaanalysis of genome-wide association data was performed in stage 1 across 46studies of white European Ancestry. A total of 42 SNPs representing the bestassociating (P < 10−6) loci (shown)were taken forward for replication. Nineteen of these SNPs (loci in bold)reached already genome-wide significance at stage 1. Stage 2 – The42 SNPs were genotyped in 16 de novo replication studies and extracted from18 in silico replication studies, all adults of Europeanancestry and were tested for association with BMI. In a joint analyses ofstage 1 and stage 2 data, 32 SNPs (loci in bold) reached genome-widesignificance (P < 5×10−8).Follow-up analyses – The 32 confirmed loci were taken forward foradditional analyses.

Supplementary Figure 2 Regional plots of the 32 confirmed BMIloci with missense and CNV variants. SNPs are plotted by positionon chromosome against association with BMI (−log10P-value). The SNP name shown on the plot was the mostsignificant SNP after stage 1 meta-analysis. Estimated recombination rates(from HapMap) are plotted in cyan to reflect the local LD structure. TheSNPs surrounding the most significant SNP are color-coded to reflect theirLD with this SNP (taken from pairwise r2 values from the HapMapCEU database, Genes, position of exons, and direction oftranscription from UCSC genome browser ( are noted.Hash-marks represent SNP positions available in the meta-analysis. Plotswere generated using LocusZoom (

Supplementary Figure 3 Quantile-quantile plot of SNPsat stage 1 GIANT meta-analysis (black) and after removing any SNPs within 1Mb of the 10 previously reported genome-wide significant hits for BMI(blue), after additionally excluding the four loci for waist/weight (green)and after excluding all 32 confirmed loci (red).

Supplementary Figure 4 Relationship between thepredicted BMI, based on the 32 confirmed BMI loci combined, and the actualBMI in the ARIC Study (N=8,120). Panel (a) shows thecomparison of the predicted BMI (grey), with the actual BMI (green) andquartile ranges (orange) in sets of 500 individuals, suggesting good averagepredictions. Panel (b) shows the comparison of the individualpredicted and observed BMI values, suggesting poor individualpredictions.

Supplementary Table 1 The 42 SNPs, associated with BMIat P < 5.10−6 at stage 1, that weretaken forward for replication in stage 2.

Supplementary Table 2 Association of 32 replicated SNPswith other anthropometric traits.

Supplementary Table 3 Association between 32 replicatedSNPs with risk of extreme obesity in children and adults, and with BMI inpopulation-based childhood studies.

Supplementary Table 4 Association of the 32 confirmedBMI SNPs with metabolic traits.

Supplementary Table 5 Gene set enrichment analysis(MAGENTA) of biological pathways with one or more genes from the 32confirmed BMI loci, using the BMI meta-analysis.

Supplementary Table 6 Non-synonymous or splice-sitevariants in linkage disequilibrium (r2 > 0.75) with leadSNPs.

Supplementary Table 7 Significant associations betweenBMI SNPs and cis gene expression(cis-eQTLs) in lymphocyte, blood, adipose and braintissues.

Supplementary Table 8 Estimated number of BMI loci foreach of the effect sizes observed in Stage 2 for the SNPs that reached agenome-wide significance of 5×10−8 in the jointanalysis of stage 1 and stage 2, given the power to detect the associationin the joint analysis of stage 1 and stage 2.

Figure 1

Genome-wide association results for the BMI meta-analysis

(a) Manhattan plot showing the significance of associationbetween all SNPs and BMI in the stage 1 meta-analysis, highlighting SNPspreviously reported to show genome-wide significant association with BMI(blue), weight or waist circumference (green), and the 18 new regionsdescribed here (red). The 19 SNPs that reached genome-wide significance atStage 1 (13 previously reported and 6 new) are listed in Table 1). (b) Quantile-quantile(Q-Q) plot of SNPs in stage 1 meta-analysis (black) and after removing anySNPs within 1 Mb of the 10 previously reported genome-wide significant hitsfor BMI (blue), after additionally excluding SNPs from the four loci forwaist/weight (green) and after excluding SNPs from all 32 confirmed loci(red). The plot was abridged at the Y-axis (at P <10−20) to better visualise the excess of smallP-values after excluding the 32 confirmed loci (Supplementary Fig. 3shows full-scale Q-Q plot). The shaded region is the 95%concentration band. (c) Plot of effect size (in inversenormally transformed units (invBMI)) versus effect allele frequency of newlyidentified and previously identified BMI variants after stage 1 +stage 2 analysis; including the 10 previously identified BMI loci (blue),the four previously identified waist and weight loci (green) and the 18newly identified BMI loci (blue). The dotted lines represent the minimumeffect sizes that could be identified for a given effect-allele frequencywith 80% (upper line), 50% (middle line), and 10%(lower line) power, assuming a sample size of 123,000 individuals and aα-level of 5×10−8.

Figure 2

Combined impact of risk alleles on BMI/obesity

(a) Combined effect of risk alleles on average BMI in thepopulation-based Atherosclerosis Risk in Communities (ARIC) study (n= 8,120 individuals of European descent). For each individual, thenumber of “best guess” replicated (n = 32) riskalleles from imputed data (0,1,2) per SNP was weighted for their relativeeffect sizes estimated from the stage 2 data. Weighted risk alleles weresummed for each individual and the overall individual sum was rounded to thenearest integer to represent the individual’s risk allele score(range 16–44). Along the x-axis, individuals in each risk allelecategory are shown (grouped ≤21 and ≥38 at the extremes),and the mean BMI (+/− SEM) is plotted (y axis on right),with the line representing the regression of the mean BMI values across therisk-allele scores. The histogram (y-axis on left) represents the number ofindividuals in each risk-score category. (b) The area under theROC curve (AUC) of two different models predicting the risk of obesity (BMI= ≥30 kg/m2) in the n = 8,120 genotypedindividuals of European descent in the ARIC Study. Model 1, represented bythe solid line, includes age, age2, and sex (AUC = 0.515,P = 0.023 for difference fromAUCnull = 0.50). Model 2, represented by the dashedline, includes age, age2, sex, and the n = 32 confirmedBMI SNPs (AUC = 0.0575, P <10−5 for difference from AUCnull =0.50). The difference between both AUCs is significant (P< 10−4).

Figure 3

Regional plots of selected replicating BMI loci with missense and CNVvariants

SNPs are plotted by position on chromosome against association with BMI(−log10P-value). The SNP name shown on the plot was the mostsignificant SNP after stage 1 meta-analysis. Estimated recombination rates(from HapMap) are plotted in cyan to reflect the local LD structure. TheSNPs surrounding the most significant SNP are color-coded to reflect theirLD with this SNP (taken from pairwise r2 values from the HapMapCEU database, Genes, position of exons, and direction oftranscription from UCSC genome browser ( noted. Hashmarks represent SNP positions available in the meta-analysis.(a, b, c) Missense variants noted with their amino acidchange for the gene noted above the plot. (d) Structuralhaplotypes and BMI association signal in the GPRC5B region.A 21 kb deletion polymorphism is associated with 4 SNPs(r2=1.0) that comprise the best haplogroup associatingwith BMI. Plots were generated using LocusZoom (

Figure 4

Phenotypic variance explained by common variants

(a) Variance explained is higher when SNPs not reachinggenome-wide significance are included in the prediction model. The y-axisrepresents the proportion of variance explained at differentP-value thresholds from stage 1 meta-analysis. Resultsare given for three studies (RSII, RSIII, QIMR), which were not included inthe meta-analysis, after exclusion of all samples from The Netherlands (forRSII and RSIII) and the United Kingdom (for QIMR) from the discoveryanalysis for this sub-analysis. The dotted line represents the weightedaverage of the explained variance of three validation sets. (b)Cumulative number of susceptibility loci expected to be discovered,including those we have already identified and others that have yet to bedetected, by the expected percentage of phenotypic variation explained andsample size required for a one-stage GWA study assuming a GC correction isutilized. The projections are based on loci that achieved a significancelevel of P < 5×10−8 in thejoint analysis of stage 1 and stage 2 and the distribution of their effectsizes in stage 2. The dotted red line corresponds to the expected phenotypicvariance explained by the 22 loci that are expected to be discovered in aone-stage GWAS with the sample size of stage 1 of this study.

Figure 5

Second signal at the MC4R locus contributing to BMI

SNPs are plotted by position in a 1 Mb window of chromosome 18 againstassociation with BMI ( log10P-value). Panel (a) highlights the mostsignificant SNP in stage 1 meta-analysis, panel (b) the mostsignificant SNP after conditional analysis where the model included the moststrongly associated SNP from panel A as a covariate. Estimated recombinationrates (from HapMap) are plotted in cyan to reflect the local LD structure.The SNPs surrounding the most significant SNP are color-coded to reflecttheir LD with this SNP (taken from pairwise r2 values from theHapMap CEU database, Genes, exons,and direction of transcription from UCSC genome browser ( noted. Hashmarks at the top of the figure represent positions of SNPs inthe meta-analysis. Regional plots were generated using LocusZoom (

Table 1

Stage 1 and stage 2 results of the 32 SNPs that were associated with BMI atgenome-wide significance (P < 5.10−8) levels.

Previous BMI lociPrevious waist & weight lociNewly identified BMI loci
SNPNearest geneOther nearby genes*ChrPosition** (bp)Alleles**Frequency effect allele(%)Per allele change in BMIbeta (se)***Explained variance(%)Stage 1P-valueStage 2P-valueStage 1 + 2
rs1558902FTO1652,361,075at42%0.39 (0.02)0.34%2.05E-621.007E-60192,3444.8E-120
rs2867125TMEM182612,827ct83%0.31 (0.03)0.15%2.42E-224.42E-30197,8062.77E-49
rs571312MC4RB1855,990,749ac24%0.23 (0.03)0.10%1.82E-223.19E-21203,6006.43E-42
rs10938397GNPDA2444,877,284ga43%0.18 (0.02)0.08%4.35E-171.45E-15197,0083.78E-31
rs10767664BDNFB,M1127,682,562at78%0.19 (0.03)0.07%5.53E-131.17E-14204,1584.69E-26
rs2815752NEGR1C,Q172,585,028ag61%0.13 (0.02)0.04%1.17E-142.29E-09198,3801.61E-22
rs7359397SH2B1Q,B,MAPOB48RQ,M, SULT1A2Q,M, AC138894.2M,ATXN2LM, TUFMQ1628,793,160tc40%0.15 (0.02)0.05%1.75E-107.89E-12204,3091.88E-20
rs9816226ETV53187,317,193ta82%0.14 (0.03)0.03%7.61E-141.15E-06196,2211.69E-18
rs3817334MTCH2Q,MNDUFS3Q, CUGBP1Q1147,607,569tc41%0.06 (0.02)0.01%4.79E-111.10E-03191,9431.59E-12
rs29941KCTD151939,001,372ga67%0.06 (0.02)0.00%1.31E-092.40E-02192,8723.01E-09
rs543874SEC16B1176,156,103ga19%0.22 (0.03)0.07%1.66E-132.41E-11179,4143.56E-23
rs987237TFAP2B650,911,009ga18%0.13 (0.03)0.03%5.97E-162.40E-06195,7762.90E-20
rs7138803FAIM21248,533,735ag38%0.12 (0.02)0.04%3.96E-117.82E-08200,0641.82E-17
rs10150332NRXN31479,006,717ct21%0.13 (0.03)0.02%2.03E-072.86E-05183,0222.75E-11
rs713586RBJADCY3Q, M, POMCQ,B225,011,512ct47%0.14 (0.02)0.06%1.80E-071.44E-16230,7486.17E-22
rs12444979GPRC5BC,QIQCKQ1619,841,101ct87%0.17 (0.03)0.04%4.20E-118.13E-12239,7152.91E-21
rs2241423MAP2K5LBXCOR1M1565,873,892ga78%0.13 (0.02)0.03%1.15E-101.59E-09227,9501.19E-18
rs2287019QPCTLGIPRB,M1950,894,012ct80%0.15 (0.03)0.04%3.18E-071.40E-10194,5641.88E-16
rs1514175TNNI3K174,764,232ag43%0.07 (0.02)0.02%1.36E-097.04E-06227,9008.16E-14
rs13107325SLC39A8Q,M4103,407,732tc7%0.19 (0.04)0.03%1.37E-071.93E-07245,3781.50E-13
rs2112347FLJ35779MHMGCRB575,050,998tg63%0.10 (0.02)0.02%4.76E-088.29E-07231,7292.17E-13
rs10968576LRRN6C928,404,339ga31%0.11 (0.02)0.02%1.88E-083.19E-06216,9162.65E-13
rs3810291TMEM160QZC3H4Q1952,260,843ag67%0.09 (0.02)0.02%1.04E-071.59E-06233,5121.64E-12
rs887912FANCL259,156,381tc29%0.10 (0.02)0.03%2.69E-061.72E-07242,8071.79E-12
rs13078807CADM2385,966,840ga20%0.10 (0.02)0.02%9.81E-085.32E-05237,4043.94E-11
rs11847697PRKD11429,584,863tc4%0.17 (0.05)0.01%1.11E-082.25E-04241,6675.76E-11
rs2890652LRP1B2142,676,401ct18%0.09 (0.03)0.02%2.38E-079.47E-05209,0681.35E-10
rs1555543PTBP2196,717,385ca59%0.06 (0.02)0.01%7.65E-074.48E-05243,0133.68E-10
rs4771122MTIF3GTF3AQ1326,918,180ga24%0.09 (0.03)0.02%1.20E-078.24E-04198,5779.48E-10
rs4836133ZNF6085124,360,002ac48%0.07 (0.02)0.01%7.04E-071.88E-04241,9991.97E-09
rs4929949RPL27ATUBB118,561,169ct52%0.06 (0.02)0.01%7.57E-081.00E-03249,7912.80E-09
rs206936NUDT3HMGA1B634,410,847ga21%0.06 (0.02)0.01%2.81E-067.39E-04249,7773.02E-08

*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

BBiological candidate

MBMI-associated variant is in strong LD (r2 ≥ 0.75) with amissense variant in the indicated gene



Author contributions

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.


  • 1. Clinical Guidelines on the Identification, Evaluation, andTreatment of Overweight and Obesity in Adults--The Evidence Report. NationalInstitutes of HealthObes Res6 Suppl 251S209S1998[PubMed][Google Scholar]
  • 2. LewisCEMortality, health outcomes, and body mass index in the overweightrange: a science advisory from the American HeartAssociationCirculation1193263712009[PubMed][Google Scholar]
  • 3. StunkardAJFochTTHrubecZA twin study of human obesityJama2565141986[PubMed][Google Scholar]
  • 4. MaesHHNealeMCEavesLJGenetic and environmental factors in relative body weight andhuman adiposityBehav Genet27325511997[PubMed][Google Scholar]
  • 5. TaylorAEComparison of the associations of body mass index and measures ofcentral adiposity and fat mass with coronary heart disease, diabetes, andall-cause mortality: a study using data from 4 UK cohortsAm J Clin Nutr91547562010[PubMed][Google Scholar]
  • 6. FraylingTMA common variant in the FTO gene is associated with body massindex and predisposes to childhood and adult obesityScience316889942007[PubMed][Google Scholar]
  • 7. ScuteriAGenome-wide association scan shows genetic variants in the FTOgene are associated with obesity-related traitsPLoS Genet3e1152007[PubMed][Google Scholar]
  • 8. LoosRJCommon variants near MC4R are associated with fat mass, weightand risk of obesityNat Genet40768752008[PubMed][Google Scholar]
  • 9. WillerCJSix new loci associated with body mass index highlight a neuronalinfluence on body weight regulationNat Genet4125342009[PubMed][Google Scholar]
  • 10. ThorleifssonGGenome-wide association yields new sequence variants at sevenloci that associate with measures of obesityNat Genet4118242009[PubMed][Google Scholar]
  • 11. RenDNeuronal SH2B1 is essential for controlling energy and glucosehomeostasisJ Clin Invest1173974062007[PubMed][Google Scholar]
  • 12. HuszarDTargeted disruption of the melanocortin-4 receptor results inobesity in miceCell88131411997[PubMed][Google Scholar]
  • 13. O’RahillySFarooqiISHuman obesity as a heritable disorder of the central control ofenergy balanceInt J Obes (Lond)32 Suppl 7S55612008[PubMed][Google Scholar]
  • 14. LindgrenCMGenome-wide association scan meta-analysis identifies three Lociinfluencing adiposity and fat distributionPLoS Genet5e10005082009[PubMed][Google Scholar]
  • 15. Heard-CostaNLNRXN3 is a novel locus for waist circumference: a genome-wideassociation study from the CHARGE ConsortiumPLoS Genet5e10005392009[PubMed][Google Scholar]
  • 16. MeyreDGenome-wide association study for early-onset and morbid adultobesity identifies three new risk loci in EuropeanpopulationsNat Genet4115792009[PubMed][Google Scholar]
  • 17. ScheragATwo new Loci for body-weight regulation identified in a jointanalysis of genome-wide association studies for early-onset extreme obesityin French and german study groupsPLoS Genet6e10009162010[PubMed][Google Scholar]
  • 18. ZegginiEMeta-analysis of genome-wide association data and large-scalereplication identifies additional susceptibility loci for type 2diabetesNat Genet40638452008[PubMed][Google Scholar]
  • 19. DupuisJNew genetic loci implicated in fasting glucose homeostasis andtheir impact on type 2 diabetes riskNat Genet42105162010[PubMed][Google Scholar]
  • 20. KathiresanSCommon variants at 30 loci contribute to polygenicdyslipidemiaNat Genet4156652009[PubMed][Google Scholar]
  • 21. SaxenaRGenetic variation in GIPR influences the glucose and insulinresponses to an oral glucose challengeNat Genet4214282010[PubMed][Google Scholar]
  • 22. McIntoshCHWidenmaierSKimSJGlucose-dependent insulinotropic polypeptide (Gastric InhibitoryPolypeptide; GIP)Vitam Horm80409712009[PubMed][Google Scholar]
  • 23. FarooqiISHeterozygosity for a POMC-null mutation and increased obesityrisk in humansDiabetes552549532006[PubMed][Google Scholar]
  • 24. FarooqiISClinical spectrum of obesity and mutations in the melanocortin 4receptor geneN Engl J Med3481085952003[PubMed][Google Scholar]
  • 25. MarshDJResponse of melanocortin-4 receptor-deficient mice to anorecticand orexigenic peptidesNat Genet21119221999[PubMed][Google Scholar]
  • 26. UngerTJCalderonGABradleyLCSena-EstevesMRiosMSelective deletion of Bdnf in the ventromedial and dorsomedialhypothalamus of adult mice results in hyperphagic behavior andobesityJ Neurosci2714265742007[PubMed][Google Scholar]
  • 27. LiZZhouYCarter-SuCMyersMGJrRuiLSH2B1 enhances leptin signaling by both Janus kinase 2 Tyr813phosphorylation-dependent and -independent mechanismsMol Endocrinol212270812007[PubMed][Google Scholar]
  • 28. GrayJHyperphagia, severe obesity, impaired cognitive function, andhyperactivity associated with functional loss of one copy of thebrain-derived neurotrophic factor (BDNF) geneDiabetes553366712006[PubMed][Google Scholar]
  • 29. BochukovaEGLarge, rare chromosomal deletions associated with severeearly-onset obesityNature463666702010[PubMed][Google Scholar]
  • 30. CollAPLoraine TungYCPro-opiomelanocortin (POMC)-derived peptides and the regulationof energy homeostasisMol Cell Endocrinol300147512009[PubMed][Google Scholar]
  • 31. KrudeHObesity due to proopiomelanocortin deficiency: three new casesand treatment trials with thyroid hormone andACTH4–10J Clin Endocrinol Metab884633402003[PubMed][Google Scholar]
  • 32. MiyawakiKInhibition of gastric inhibitory polypeptide signaling preventsobesityNat Med8738422002[PubMed][Google Scholar]
  • 33. SegrèAVCommon Inherited Variation in Mitochondrial Genes is not Enrichedfor Associations with Type 2 Diabetes or Related GlycemicTraitsPLoS Geneticsin press2010[Google Scholar]
  • 34. EmilssonVGenetics of gene expression and its effect ondiseaseNature45242382008[PubMed][Google Scholar]
  • 35. MyersAJA survey of genetic human cortical geneexpressionNat Genet39149492007[PubMed][Google Scholar]
  • 36. DixonALA genome-wide association study of global geneexpressionNat Genet39120272007[PubMed][Google Scholar]
  • 37. PurcellSMCommon polygenic variation contributes to risk of schizophreniaand bipolar disorderNature460748522009[PubMed][Google Scholar]
  • 38. ParkJ-HEstimation of effect size distribution from genome-wideassociation studies and implications for future discoveriesNat Genetaccepted2010[Google Scholar]
  • 39. YoungEHThe V103I polymorphism of the MC4R gene and obesity: populationbased studies and meta-analysis of 29 563 individualsInt J Obes (Lond)311437412007[PubMed][Google Scholar]
  • 40. StutzmannFNon-synonymous polymorphisms in melanocortin-4 receptor protectagainst obesity: the two facets of a Janus obesity geneHum Mol Genet161837442007[PubMed][Google Scholar]
  • 41. YeoGSMutations in the human melanocortin-4 receptor gene associatedwith severe familial obesity disrupts receptor function through multiplemolecular mechanismsHum Mol Genet12561742003[PubMed][Google Scholar]
  • 42. HirschhornJNGenomewide association studies--illuminating biologicpathwaysN Engl J Med36016997012009[PubMed][Google Scholar]
  • 43. LemmensVEOenemaAKleppKIHenriksenHBBrugJA systematic review of the evidence regarding efficacy of obesityprevention interventions among adultsObes Rev9446552008[PubMed][Google Scholar]
  • 44. AndersonJWKonzECFrederichRCWoodCLLong-term weight-loss maintenance: a meta-analysis of USstudiesAm J Clin Nutr74579842001[PubMed][Google Scholar]
  • 45. LiYWillerCSannaSAbecasisGGenotype imputationAnnu Rev Genomics Hum Genet103874062009[PubMed][Google Scholar]
  • 46. MarchiniJHowieBMyersSMcVeanGDonnellyPA new multipoint method for genome-wide association studies byimputation of genotypesNat Genet39906132007[PubMed][Google Scholar]
  • 47. GuanYStephensMPractical issues in imputation-based associationmappingPLoS Genet4e10002792008[PubMed][Google Scholar]
  • 48. AbecasisGRWiggintonJEHandling marker-marker linkage disequilibrium: pedigree analysiswith clustered markersAm J Hum Genet77754672005[PubMed][Google Scholar]
  • 49. AulchenkoYSStruchalinMVvan DuijnCMProbABEL package for genome-wide association analysis of imputeddataBMC Bioinformatics11134[PubMed][Google Scholar]
  • 50. AulchenkoYSRipkeSIsaacsAvan DuijnCMGenABEL: an R library for genome-wide associationanalysisBioinformatics23129462007[PubMed][Google Scholar]
  • 51. PurcellSPLINK: a tool set for whole-genome association andpopulation-based linkage analysesAm J Hum Genet81559752007[PubMed][Google Scholar]
  • 52. ZhongHYangXKaplanLMMolonyCSchadtEEIntegrating pathway analysis and genetics of gene expression forgenome-wide association studiesAm J Hum Genet86581912010[PubMed][Google Scholar]
Collaboration tool especially designed for Life Science professionals.Drag-and-drop any entity to your messages.