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.
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.
Obesity is a major and increasingly prevalent risk factor for multiple disorders,
including type 2 diabetes and cardiovascular disease1,2. While lifestyle changes have
driven its prevalence to epidemic proportions, heritability studies provide evidence for
a substantial genetic contribution (h2~40–70%) to obesity
risk3,4.
BMI is an inexpensive, non-invasive measure of obesity that predicts the risk of related
complications5. Identifying genetic
determinants of BMI could lead to a better understanding of the biological basis of
obesity.Genome-wide association (GWA) studies of BMI have previously identified ten loci
with 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 to
act in the central nervous system, highlighting a likely neuronal component to the
predisposition to obesity9. This pattern is
consistent with results in animal models and studies of monogenic human obesity, where
neuronal genes, particularly those expressed in the hypothalamus and involved in
regulation of appetite or energy balance, are known to play a major role in
susceptibility to obesity11–13.The ten previously identified loci account for only a small fraction of the
variation in BMI. Furthermore, power calculations based on the effect sizes of
established variants have suggested that increasing the sample size would likely lead to
the discovery of additional variants9. To identify
more loci associated with BMI, we expanded the GIANT (Genetic Investigation of
ANtropometric Traits) consortium GWA meta-analysis to include a total of 249,769
individuals of European ancestry.
Results
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 BMI6–10,
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.
Table 1
Stage 1 and stage 2 results of the 32 SNPs that were associated with BMI at
genome-wide significance (P < 5.10−8) levels.
Positions according to Build 36 and allele coding based on the positive
strand
Effect sizes in kg/m2 obtained from Stage 2 cohorts only
Association and eQTL data converge to affect gene expression
Biological candidate
BMI-associated variant is in strong LD (r2 ≥ 0.75) with a
missense variant in the indicated gene
CNV
Figure 1
Genome-wide association results for the BMI meta-analysis
(a) Manhattan plot showing the significance of association
between all SNPs and BMI in the stage 1 meta-analysis, highlighting SNPs
previously reported to show genome-wide significant association with BMI
(blue), weight or waist circumference (green), and the 18 new regions
described here (red). The 19 SNPs that reached genome-wide significance at
Stage 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 any
SNPs within 1 Mb of the 10 previously reported genome-wide significant hits
for BMI (blue), after additionally excluding SNPs from the four loci for
waist/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 small
P-values after excluding the 32 confirmed loci (Supplementary Fig. 3
shows full-scale Q-Q plot). The shaded region is the 95%
concentration band. (c) Plot of effect size (in inverse
normally transformed units (invBMI)) versus effect allele frequency of newly
identified 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 18
newly identified BMI loci (blue). The dotted lines represent the minimum
effect sizes that could be identified for a given effect-allele frequency
with 80% (upper line), 50% (middle line), and 10%
(lower line) power, assuming a sample size of 123,000 individuals and a
α-level of 5×10−8.
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
circumference14,15, 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 BMI6–10
and waist circumference14,15, four loci identified by GWA studies for
early-onset or adult morbid obesity16,17 [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).
Figure 2
Combined impact of risk alleles on BMI/obesity
(a) Combined effect of risk alleles on average BMI in the
population-based Atherosclerosis Risk in Communities (ARIC) study (n
= 8,120 individuals of European descent). For each individual, the
number of “best guess” replicated (n = 32) risk
alleles from imputed data (0,1,2) per SNP was weighted for their relative
effect sizes estimated from the stage 2 data. Weighted risk alleles were
summed for each individual and the overall individual sum was rounded to the
nearest integer to represent the individual’s risk allele score
(range 16–44). Along the x-axis, individuals in each risk allele
category 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 the
risk-allele scores. The histogram (y-axis on left) represents the number of
individuals in each risk-score category. (b) The area under the
ROC curve (AUC) of two different models predicting the risk of obesity (BMI
= ≥30 kg/m2) in the n = 8,120 genotyped
individuals of European descent in the ARIC Study. Model 1, represented by
the solid line, includes age, age2, and sex (AUC = 0.515,
P = 0.023 for difference from
AUCnull = 0.50). Model 2, represented by the dashed
line, includes age, age2, sex, and the n = 32 confirmed
BMI SNPs (AUC = 0.0575, P <
10−5 for difference from AUCnull =
0.50). The difference between both AUCs is significant (P
< 10−4).
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 (Nrange = 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
weight23,24, 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 MC4R12,25,
BDNF26, and
SH2B111,27. 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 obesity24,28,29. 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 obesity23,29,31.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.
Figure 3
Regional plots of selected replicating BMI loci with missense and CNV
variants
SNPs are plotted by position on chromosome against association with BMI
(−log10
P-value). The SNP name shown on the plot was the most
significant SNP after stage 1 meta-analysis. Estimated recombination rates
(from HapMap) are plotted in cyan to reflect the local LD structure. The
SNPs surrounding the most significant SNP are color-coded to reflect their
LD with this SNP (taken from pairwise r2 values from the HapMap
CEU database, www.hapmap.org). Genes, position of exons, and direction of
transcription from UCSC genome browser (http://genome.ucsc.edu)
are noted. Hashmarks represent SNP positions available in the meta-analysis.
(a, b, c) Missense variants noted with their amino acid
change for the gene noted above the plot. (d) Structural
haplotypes 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 associating
with BMI. Plots were generated using LocusZoom (http://csg.sph.umich.edu/locuszoom).
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 liver34–36
(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
(Padj>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.
Figure 4
Phenotypic variance explained by common variants
(a) Variance explained is higher when SNPs not reaching
genome-wide significance are included in the prediction model. The y-axis
represents the proportion of variance explained at different
P-value thresholds from stage 1 meta-analysis. Results
are given for three studies (RSII, RSIII, QIMR), which were not included in
the meta-analysis, after exclusion of all samples from The Netherlands (for
RSII and RSIII) and the United Kingdom (for QIMR) from the discovery
analysis for this sub-analysis. The dotted line represents the weighted
average 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 be
detected, by the expected percentage of phenotypic variation explained and
sample size required for a one-stage GWA study assuming a GC correction is
utilized. The projections are based on loci that achieved a significance
level of P < 5×10−8 in the
joint analysis of stage 1 and stage 2 and the distribution of their effect
sizes in stage 2. The dotted red line corresponds to the expected phenotypic
variance explained by the 22 loci that are expected to be discovered in a
one-stage GWAS with the sample size of stage 1 of this study.
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-analyses39,40.
Furthermore, mutations at the MC4R locus are known to influence
early-onset obesity24,41, supporting the notion that allelic heterogeneity
may be a frequent phenomenon in the genetic architecture of obesity.
Figure 5
Second signal at the MC4R locus contributing to BMI
SNPs are plotted by position in a 1 Mb window of chromosome 18 against
association with BMI ( log10
P-value). Panel (a) highlights the most
significant SNP in stage 1 meta-analysis, panel (b) the most
significant SNP after conditional analysis where the model included the most
strongly associated SNP from panel A as a covariate. Estimated recombination
rates (from HapMap) are plotted in cyan to reflect the local LD structure.
The SNPs surrounding the most significant SNP are color-coded to reflect
their LD with this SNP (taken from pairwise r2 values from the
HapMap CEU database, www.hapmap.org). Genes, exons,
and direction of transcription from UCSC genome browser (genome.ucsc.edu)
are noted. Hashmarks at the top of the figure represent positions of SNPs in
the meta-analysis. Regional plots were generated using LocusZoom (http://csg.sph.umich.edu/locuszoom).
Discussion
Using a two-stage genome-wide association meta-analysis of up to 249,796
individuals of European descent, we have identified 18 additional loci that are
associated with BMI at genome-wide significance, bringing the total number of such
loci to 32. We estimate that more than 250 (i.e. 284 predicted loci – 32
confirmed loci) common variant loci with effects on BMI similar to those described
here remain to be discovered, and even larger numbers of loci with smaller effects.
A substantial proportion of these loci should be identifiable through larger GWA
studies and/or by targeted follow-up of top signals selected from our stage 1
analysis. The latter approach is already being implemented through large-scale
genotyping of samples informative for BMI using a custom array (the Metabochip)
designed to support follow-up of thousands of promising variants in hundreds of
thousands of individuals.The combined effect on BMI of the associated variants at the 32 loci is
modest, and even when we try to account for as-yet-undiscovered variants with
similar properties, we estimate that these common variant signals account for only
6–11% of the genetic variation in BMI. There is a strong expectation
that additional variance and biology will be explained using complementary
approaches that capture variants not examined in the current study, such as lower
frequency variants and short insertion-deletion polymorphisms. There is good reason
to believe (based on our findings at MC4R and other loci
– POMC, BDNF, SH2B1 – which feature both common
and rare variant associations) that a proportion of such low-frequency and rare
causal variation will map to the loci already identified by GWA studies.A primary goal of human genetic discovery is to improve understanding of the
biology of conditions such as obesity42. One
particularly interesting finding in this regard is the association between BMI and
common variants near GIPR, which may indicate a causal contribution
of variation in postprandial insulin secretion to the development of obesity. In
most cases, the loci identified by the present study harbor few, if any, annotated
genes with clear connections to the biology of weight regulation. This reflects our
still limited understanding of the biology of BMI and obesity-related traits and is
in striking contrast with the results from equivalent studies of certain other
traits (such as autoimmune diseases or lipid levels). Thus, these results suggest
that much novel biology remains to be uncovered, and that GWA studies may provide an
important entry point. In particular, further examination of the associated loci
through a combination of resequencing and fine-mapping to find causal variants, and
genomic and experimental studies designed to assign function, could uncover novel
insights into the biology of obesity.In conclusion, we have performed GWA studies in large samples to identify
numerous genetic loci associated with variation in BMI, a common measure of obesity.
Because current lifestyle interventions are largely ineffective in addressing the
challenges of growing obesity43,44, new insights into biology are critically
needed to guide the development and application of future therapies and
interventions.Supplementary Figure 1 Study design. Stage 1 - Meta
analysis of genome-wide association data was performed in stage 1 across 46
studies of white European Ancestry. A total of 42 SNPs representing the best
associating (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 – The
42 SNPs were genotyped in 16 de novo replication studies and extracted from
18 in silico replication studies, all adults of European
ancestry and were tested for association with BMI. In a joint analyses of
stage 1 and stage 2 data, 32 SNPs (loci in bold) reached genome-wide
significance (P < 5×10−8).
Follow-up analyses – The 32 confirmed loci were taken forward for
additional analyses.Supplementary Figure 2 Regional plots of the 32 confirmed BMI
loci with missense and CNV variants. SNPs are plotted by position
on chromosome against association with BMI (−log10
P-value). The SNP name shown on the plot was the most
significant SNP after stage 1 meta-analysis. Estimated recombination rates
(from HapMap) are plotted in cyan to reflect the local LD structure. The
SNPs surrounding the most significant SNP are color-coded to reflect their
LD with this SNP (taken from pairwise r2 values from the HapMap
CEU database, www.hapmap.org). Genes, position of exons, and direction of
transcription from UCSC genome browser (genome.ucsc.edu) are noted.
Hash-marks represent SNP positions available in the meta-analysis. Plots
were generated using LocusZoom (http://csg.sph.umich.edu/locuszoom).Supplementary Figure 3 Quantile-quantile plot of SNPs
at stage 1 GIANT meta-analysis (black) and after removing any SNPs within 1
Mb 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 the
predicted BMI, based on the 32 confirmed BMI loci combined, and the actual
BMI in the ARIC Study (N=8,120). Panel (a) shows the
comparison of the predicted BMI (grey), with the actual BMI (green) and
quartile ranges (orange) in sets of 500 individuals, suggesting good average
predictions. Panel (b) shows the comparison of the individual
predicted and observed BMI values, suggesting poor individual
predictions.Supplementary Table 1 The 42 SNPs, associated with BMI
at P < 5.10−6 at stage 1, that were
taken forward for replication in stage 2.Supplementary Table 2 Association of 32 replicated SNPs
with other anthropometric traits.Supplementary Table 3 Association between 32 replicated
SNPs with risk of extreme obesity in children and adults, and with BMI in
population-based childhood studies.Supplementary Table 4 Association of the 32 confirmed
BMI SNPs with metabolic traits.Supplementary Table 5 Gene set enrichment analysis
(MAGENTA) of biological pathways with one or more genes from the 32
confirmed BMI loci, using the BMI meta-analysis.Supplementary Table 6 Non-synonymous or splice-site
variants in linkage disequilibrium (r2 > 0.75) with lead
SNPs.Supplementary Table 7 Significant associations between
BMI SNPs and cis gene expression
(cis-eQTLs) in lymphocyte, blood, adipose and brain
tissues.Supplementary Table 8 Estimated number of BMI loci for
each of the effect sizes observed in Stage 2 for the SNPs that reached a
genome-wide significance of 5×10−8 in the joint
analysis of stage 1 and stage 2, given the power to detect the association
in the joint analysis of stage 1 and stage 2.
Authors: Anna L Dixon; Liming Liang; Miriam F Moffatt; Wei Chen; Simon Heath; Kenny C C Wong; Jenny Taylor; Edward Burnett; Ivo Gut; Martin Farrall; G Mark Lathrop; Gonçalo R Abecasis; William O C Cookson Journal: Nat Genet Date: 2007-09-16 Impact factor: 38.330
Authors: Cora E Lewis; Kathleen M McTigue; Lora E Burke; Paul Poirier; Robert H Eckel; Barbara V Howard; David B Allison; Shiriki Kumanyika; F Xavier Pi-Sunyer Journal: Circulation Date: 2009-06-08 Impact factor: 29.690
Authors: Shaun M Purcell; Naomi R Wray; Jennifer L Stone; Peter M Visscher; Michael C O'Donovan; Patrick F Sullivan; Pamela Sklar Journal: Nature Date: 2009-07-01 Impact factor: 49.962
Authors: Melissa C del Rosario; Vicky Ossowski; William C Knowler; Clifton Bogardus; Leslie J Baier; Robert L Hanson Journal: Metabolism Date: 2014-01-21 Impact factor: 8.694
Figure 1
Figure 2
Figure 3
Figure 4