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

Stage 1 GWA studies identify novel loci associated with BMI

We first conducted a meta-analysis of GWA studies of BMI and ~2.8 million imputed or genotyped SNPs using data from 46 studies including up to 123,865 individuals (Online Methods, Supplementary Fig. 1 and Supplementary Note). This stage 1 analysis revealed 19 loci associated with BMI at P < 5×10⁻⁸ (Table 1, Fig. 1a and Supplementary Table 1). These 19 loci included all ten loci from previous GWA studies of BMI^6–10, two loci previously associated with body weight¹⁰ (FAIM2 and SEC16B) and one locus previously associated with waist circumference¹⁴ (near TFAP2B). The remaining six loci, near GPRC5B, MAP2K5/LBXCOR1, TNNI3K, LRRN6C, FLJ35779/HMGCR, and PRKD1, have not previously 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 that reached genome-wide significance in stage 1 analyses, we examined SNPs representing 42 independent loci (including the 19 genome-wide significant loci) with stage 1 P < 5×10⁻⁶. Variants were considered to be independent if the pair-wise linkage disequilibrium (LD; r²) was less than 0.1 and if they were separated by at least 1 Mb. In stage 2, we examined these 42 SNPs in up to 125,931 additional individuals (79,561 newly genotyped individuals from 16 different studies and 46,370 individuals from 18 additional studies for which GWA data were available; Table 1, Supplementary Note, and Online Methods). In a joint analysis of stage 1 and stage 2 results, 32 of the 42 SNPs reached P < 5×10⁻⁸. Even after excluding SNPs within these 32 confirmed BMI loci, we still observed an excess of small P-values compared to the distribution expected under the null hypothesis (Fig. 1b), suggesting that more BMI loci remain to be uncovered.

The 32 confirmed associations included all 19 loci with P < 5×10⁻⁸ at stage 1, 12 additional 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 and 2). In all, our study increased the number of loci robustly associated with BMI from 10 to 32. Four of the 22 new loci were previously associated with body weight¹⁰ or waist circumference^14,15, whereas 18 loci had not previously associated with any obesity-related trait in the general population. Whilst we confirmed all loci previously established by large-scale GWA studies for BMI^6–10 and waist circumference^14,15, four loci identified by GWA studies for early-onset or adult morbid obesity^16,17 [at NPC1 (rs1805081; P = 0.0025), MAF (rs1424233; P = 0.25), PTER (rs10508503; P = 0.64), and TNKS/MSRA (rs473034; P = 0.23)] showed limited or no evidence of association with BMI in our study.

As expected, the effect sizes of the 18 newly discovered loci are slightly smaller, for a given minor allele frequency, than those of the previously identified variants (Table 1 and Fig. 1c). The increased sample size also brought out more signals with low minor allele frequency. The BMI-increasing allele frequencies for the 18 newly identified variants ranged from 4% to 87%, covering more of the allele frequency spectrum than 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 the 32 BMI-associated SNPs (Online Methods). We found no evidence for any such effects (P > 0.001, no significant results after correcting for multiple testing) (Supplementary Tables 1 and Supplementary Note).

Impact of 32 confirmed loci on BMI, obesity, body size, and other metabolic traits

Together, the 32 confirmed BMI loci explained 1.45% of the inter-individual variation in BMI of the stage 2 samples, with the FTO SNP accounting for the largest proportion of the variance (0.34%) (Table 1). To estimate the cumulative effect of the 32 variants on BMI, we constructed a genetic-susceptibility score that sums the number of BMI-increasing alleles weighted 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 unit increase in the genetic-susceptibility score, approximately equivalent to one additional risk allele, BMI increased by 0.17 kg/m², equivalent to a 435–551 g gain in body weight in adults of 160–180 cm in height. The difference in average BMI between individuals with a high genetic-susceptibility score (≥38 BMI-increasing alleles, 1.5% (n=124) of the ARIC sample) and those with a low genetic-susceptibility score (≤21 BMI-increasing alleles, 2.2% (n=175) of the ARIC sample) was 2.73 kg/m², equivalent to a 6.99 to 8.85 kg body weight difference in adults 160–180 cm in height (Fig. 2a). Still, we note that the predictive value for obesity risk and BMI of the 32 variants combined was modest, although statistically significant (Fig. 2b, Supplementary Fig. 4). The area under the receiver operating characteristic (ROC) curve for prediction of risk of obesity (BMI ≥ 30 kg/m²) using age, age² and sex only was 0.515 (P = 0.023 compared to AUC of 0.50), which increased to 0.575 (P < 10⁻⁵) when also the 32 confirmed SNPs were included in the model (Fig. 2b). The area under the ROC for the 32 SNPs only was 0.574 (P < 10⁻⁵).

All 32 confirmed BMI-increasing alleles showed directionally consistent effects on risk of being overweight (BMI ≥25 kg/m²) or obese (≥30 kg/m²) in stage 2 samples, with 30 of 32 variants achieving at least nominally significant associations. The BMI-increasing alleles increased the odds of overweight by 1.013 to 1.138-fold, and the odds for being obese by 1.016- to 1.203-fold (Supplementary Table 2). In addition, 30 of the 32 loci also showed directionally consistent effects on the risk of extreme and early-onset obesity in a meta-analysis of seven case-control studies of adults and children (binomial sign test P = 1.3×10⁻⁷) (Supplementary Table 3). The BMI-increasing allele observed in adults also increased the BMI in children and adolescents with directionally consistent effects observed for 23 of the 32 SNPs (binomial sign test P = 0.01). Furthermore, in family-based studies, the BMI-increasing allele was over-transmitted to the obese 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 (N_range = 354 − 15,251) compared to the overall meta-analyses, their power was likely insufficient to confirm association for all 32 loci. Nevertheless, these results show that the effects are unlikely to reflect population stratification and that they 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 confirm an effect of the loci on adiposity rather than general body size, we tested association with body fat percentage, which was available in a subset of the stage 2 replication samples (n = 5,359–28,425) (Supplementary Table 2). The BMI-increasing allele showed directionally consistent effects on body fat percentage 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 lipid levels²⁰) and with height (Supplementary Tables 2 and 4). Although many nominal associations are expected because of known correlations between BMI and most of these traits and because of overlap in samples, several associations stand out as possible examples of pleiotropic effects of the BMI-associated variants. Particularly interesting is the variant in the GIPR locus where the BMI-increasing allele is also associated with increased fasting glucose levels and lower 2-hour glucose levels (Supplementary Table 4)^19,21. The direction of the effect is opposite to what would be expected due to the correlation between obesity and glucose intolerance, but is consistent with the suggested roles of GIPR in glucose and energy metabolism (see below)²². Three loci show strong associations (P < 10⁻⁴) with height (MC4R, RBJ/ADCY3/POMC and MTCH2/NDUFS3). Because BMI is weakly correlated with height (and indeed, the BMI-associated variants as a group show no consistent effect on height), these associations are also suggestive of pleiotropy. Interestingly, analogous to the effects of severe mutations in POMC and MC4R on height and weight^23,24, the BMI-increasing alleles of the variants near these 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 rather than individual genes, we note that some of the 32 loci include candidate genes with established connections to obesity. Several of the 10 previously identified loci are located in or near genes that encode neuronal regulators of appetite or energy balance, including MC4R^12,25, BDNF²⁶, and SH2B1^11,27. Each of these genes has been tied to obesity, not only in animal models, but also by rare human variants that disrupt each of these genes and lead to severe obesity^24,28,29. Using the automated literature search programme, Snipper (Online Methods), we identified various genes within the novel loci with potential biological links to obesity-susceptibility (Supplementary Note). Among the novel loci, the location of rs713586 near POMC provides further support for a role of neuroendocrine circuits that regulate energy balance in susceptibility to obesity. POMC encodes several polypeptides including α-MSH, a ligand of the MC4R gene product³⁰, and rare mutations in POMC also cause human obesity^23,29,31.

In contrast, the locus near GIPR, which encodes a receptor of gastric inhibitory polypeptide (GIP), suggests a role for peripheral biology in obesity. GIP, which is expressed in the K cell of the duodenum and intestine, is an incretin hormone that mediates incremental insulin secretion in response to oral intake of glucose. The variant associated with BMI is in strong LD (r² = 0.83) with a missense SNP in GIPR (rs1800437, Glu354Gln) that has recently been shown to influence the glucose and insulin response to an oral glucose challenge ²¹. Although no human phenotype is known to be caused by mutations in GIPR, mice with disruption of Gipr are resistant to diet-induced obesity³². The association of a variant in GIPR with BMI suggests that there may be a link between incretins/insulin secretion and body weight regulation in humans as well.

To systematically identify biological connections among the genes located near the 32 confirmed SNPs, and to potentially identify new pathways associated with BMI, we performed pathway-based analyses using MAGENTA³³. Specifically, we tested for enrichment of BMI genetic associations in biological processes or molecular functions that contain at least one gene from the 32 confirmed BMI loci (Online Methods). Using annotations from the KEGG, Ingenuity, PANTHER, and Gene Ontology databases, we found evidence of enrichment for pathways involved in the platelet-derived growth factor (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 Table 5).

Identifying possible functional variants

We used data from the 1000 Genomes Project and the HapMap Consortium to explore whether the 32 confirmed BMI SNPs were in LD (r² ≥ 0.75) with common missense SNPs or copy number variants (CNVs) (Online Methods). Non-synonymous variants in LD with our signals were present in the BDNF, SLC39A8, FLJ35779/HMGCR, QPCTL/GIPR, MTCH2, ADCY3, and LBXCOR1 genes. In addition, the rs7359397 signal was in LD with coding variants in several genes including SH2B1, ATNX2L, APOB48R, SULT1A2, and AC138894.2 (Table 1, Fig. 3, Supplementary Table 6 and Supplementary Fig. 2). Furthermore, two SNPs tagged common CNVs. The first CNV was previously identified and is a 45-kb deletion near NEGR1⁹. The second CNV is a 21-kb deletion that lies 50kb upstream of GPRC5B; the deletion allele is tagged by the T-allele of rs12444979 (r² = 1) (Fig. 3). Although the correlations with potentially functional variants does not prove that these variants are indeed causal, these provide first clues as to which genes and variants at these loci might be prioritized for fine-mapping and functional follow-up.

As many of the 32 BMI loci harbor multiple genes, we examined whether gene 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 liver^34–36 (Online Methods, Table 1 and Supplementary Table 7). Significant cis-associations, defined at the tissue-specific level, were observed between 14 BMI-associated alleles and expression levels (Table 1 and Supplementary Table 7). In several cases, the BMI-associated SNP was the most significant SNP or explained a substantial proportion of the association with the most significant SNP for the gene transcript in conditional analyses (P_adj>0.05). These significant associations included NEGR1, ZC3H4, TMEM160, MTCH2, NDUFS3, GTF3A, ADCY3, APOB48R, SH2B1, TUFM, GPRC5B, IQCK, SLC39A8, SULT1A1, and SULT1A2 (Table 1 and Supplementary Table 7), making these genes higher priority candidates within the associated loci. However, we note that some BMI-associated variants were correlated with the expression of multiple nearby genes, making it difficult to determine the most relevant gene.

Evidence for the existence of additional associated variants

Because the variants identified by this large study explain only 1.45% of the variance in BMI (2–4% of genetic variance based on an estimated heritability of 40–70%), we considered how much the explained phenotypic variance could be increased by including more SNPs at various degrees of significance in a polygene model using an independent validation set (Online Methods)³⁷. We found that including SNPs associated with BMI at lower significance levels (up to P > 0.05) increased the explained phenotypic variance in BMI to 2.5%, or 4% to 6% of genetic variance (Fig. 4a). In a separate analysis, we estimated the total number of independent BMI-associated variants that are likely to exist with similar effect sizes to the 32 confirmed here (Online Methods)³⁸. Based on the effect size and allele frequencies of the 32 replicated loci observed in stage 2 and the power to detect association in the combined stage 1 and stage 2, we estimated that there are 284 (95% CI: 132–510) loci with similar effect sizes as the currently observed ones, which together would account for 4.5% (95% CI: 3.1–6.8%) of the variation in BMI or 6–11% of the genetic variation (based on an estimated heritability of 40–70%) (Supplementary Table 8). In order to detect 95% of these loci, a sample size of approximately 730,000 subjects would be needed (Fig. 4b). This method does not account for the potential of loci of smaller effect than those identified here to explain even more of the variance and thus provides an estimated lower bound of explained variance. These two analyses strongly suggest that larger GWA studies will continue to identify additional novel associated loci, but also indicate that even extremely large studies focusing on variants with allele frequencies above 5% will not account for a large fraction of the genetic contribution to BMI.

We examined whether selecting only a single variant from each locus for follow-up led us to underestimate the fraction of phenotypic variation explained by the associated loci. To search for additional independent loci at each of the 32 associated BMI loci, we repeated our GWA meta-analysis, conditioning on the 32 confirmed SNPs. Using a significance threshold of 5 × 10⁻⁶ for SNPs at known loci, we identified one apparently independent signal at the MC4R locus; rs7227255 was associated with BMI (P = 6.56 × 10⁻⁷) even after conditioning for the most strongly associated variant near MC4R (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 been associated with BMI in two independent meta-analyses^39,40. Furthermore, mutations at the MC4R locus are known to influence early-onset obesity^24,41, supporting the notion that allelic heterogeneity may be a frequent phenomenon in the genetic architecture of obesity.