Six new loci associated with body mass index highlight a
neuronal influence on body weight regulation
Cristen J Willer
1,77,78
, Elizabeth K Speliotes
2,3,77,78
, Ruth J F Loos
4,5,77,78
, Shengxu
Li
4,5,77,78
, Cecilia M Lindgren
6,78
, Iris M Heid
7,78
, Sonja I Berndt
8
, Amanda L Elliott
9,10
,
Anne U Jackson
1
, Claudia Lamina
7
, Guillaume Lettre
9,11
, Noha Lim
12
, Helen N Lyon
3,11
,
Steven A McCarroll
9,10
, Konstantinos Papadakis
13
, Lu Qi
14,15
, Joshua C Randall
6
, Rosa
Maria Roccasecca
16
, Serena Sanna
17
, Paul Scheet
18
, Michael N Weedon
19
, Eleanor
Wheeler
16
, Jing Hua Zhao
4,5
, Leonie C Jacobs
20
, Inga Prokopenko
6,21
, Nicole Soranzo
16,22
,
Toshiko Tanaka
23
, Nicholas J Timpson
24
, Peter Almgren
25
, Amanda Bennett
26
, Richard N
Bergman
27
, Sheila A Bingham
28,29
, Lori L Bonnycastle
30
, Morris Brown
31
, Noël P Burtt
9
,
Peter Chines
30
, Lachlan Coin
32
, Francis S Collins
30
, John M Connell
33
, Cyrus Cooper
34
,
George Davey Smith
24
, Elaine M Dennison
34
, Parimal Deodhar
30
, Paul Elliott
32
, Michael R
Erdos
30
, Karol Estrada
20
, David M Evans
24
, Lauren Gianniny
9
, Christian Gieger
7
,
Christopher J Gillson
4,5
, Candace Guiducci
9
, Rachel Hackett
9
, David Hadley
13
, Alistair S
Hall
35
, Aki S Havulinna
36
, Johannes Hebebrand
37
, Albert Hofman
38
, Bo Isomaa
39
, Kevin B
Jacobs
40
, Toby Johnson
41,42,43
, Pekka Jousilahti
36
, Zorica Jovanovic
5,44
, Kay-Tee Khaw
45
,
Peter Kraft
46
, Mikko Kuokkanen
9,47
, Johanna Kuusisto
48
, Jaana Laitinen
49
, Edward G
Lakatta
50
, Jian'an Luan
4,5
, Robert N Luben
45
, Massimo Mangino
51
, Wendy L McArdle
52
,
Thomas Meitinger
53,54
, Antonella Mulas
17
, Patricia B Munroe
55
, Narisu Narisu
30
, Andrew R
Ness
56
, Kate Northstone
52
, Stephen O'Rahilly
5,44
, Carolin Purmann
5,44
, Matthew G Rees
30
,
Martin Ridderstråle
57
, Susan M Ring
52
, Fernando Rivadeneira
20,38
, Aimo Ruokonen
58
,
Manjinder S Sandhu
4,45
, Jouko Saramies
59
, Laura J Scott
1
, Angelo Scuteri
60
, Kaisa
Silander
47
, Matthew A Sims
4,5
, Kijoung Song
12
, Jonathan Stephens
61
, Suzanne Stevens
51
,
Heather M Stringham
1
, Y C Loraine Tung
5,44
, Timo T Valle
62
, Cornelia M Van Duijn
38
,
Karani S Vimaleswaran
4,5
, Peter Vollenweider
63
, Gerard Waeber
63
, Chris Wallace
55
,
Richard M Watanabe
64
, Dawn M Waterworth
12
, Nicholas Watkins
61
, The Wellcome Trust
Case Control Consortium
76
, Jacqueline C M Witteman
38
, Eleftheria Zeggini
6
, Guangju
Zhai
22
, M Carola Zillikens
20
, David Altshuler
9,10
, Mark J Caulfield
55
, Stephen J Chanock
8
, I
Sadaf Farooqi
5,44
, Luigi Ferrucci
23
, Jack M Guralnik
65
, Andrew T Hattersley
66
, Frank B
Hu
14,15
, Marjo-Riitta Jarvelin
32
, Markku Laakso
48
, Vincent Mooser
12
, Ken K Ong
4,5
, Willem
H Ouwehand
16,61
, Veikko Salomaa
36
, Nilesh J Samani
51
, Timothy D Spector
22
, Tiinamaija
Tuomi
67,68
, Jaakko Tuomilehto
62
, Manuela Uda
17
, André G Uitterlinden
20,38
, Nicholas J
© 2009 Nature America, Inc. All rights reserved.
Correspondence should be addressed to J.N.H. (joelh@broad.mit.edu), G.R.A. (goncalo@umich.edu), I.B. (ib1@sanger.ac.uk), M.
Boehnke (boehnke@umich.edu) or M.I.M. (mark.mccarthy@drl.ox.ac.uk)..
AUTHOR CONTRIBUTIONS
The writing team consisted of G.R.A., I.B., M.B., I.M.H., J.N.H., S.L., C.M.L., R.J.F.L., M.I.McC., E.K.S. and C.J.W. Full author
contributions and roles are listed in the Supplementary Note.
76
A full list of members is provided in the Supplementary Note online.
77
These authors contributed equally to this work.
78
Members of the writing team.
79
These authors jointly directed the project.
80
All authors are members of the Genetic Investigation of ANthropometric Traits (GIANT) Consortium.
Note: Supplementary information is available on the Nature Genetics website.
COMPETING INTERESTS STATEMENT
The authors declare competing financial interests: details accompany the full-text HTML version of the paper at http://
www.nature.com/naturegenetics/.
Reprints and permissions information is available online at http://npg.nature.com/ reprintsandpermissions/
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Published in final edited form as:
Nat Genet
. 2009 January ; 41(1): 25–34. doi:10.1038/ng.287.
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Wareham
4,5
, Panagiotis Deloukas
16
, Timothy M Frayling
19
, Leif C Groop
25,69
, Richard B
Hayes
8
, David J Hunter
9,14,15,46
, Karen L Mohlke
70
, Leena Peltonen
9,16,71
, David
Schlessinger
72
, David P Strachan
13
, H-Erich Wichmann
7,73
, Mark I McCarthy
6,21,74,78,79
,
Michael Boehnke
1,78,79
, Inês Barroso
16,78,79
, Gonçalo R Abecasis
18,78,79
, and Joel N
Hirschhorn
3,11,75,78,79
for the GIANT Consortium
80
1
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
48109, USA.
2
Division of Gastroenterology, Massachusetts General Hospital, Boston,
Massachusetts 02114, USA.
3
Metabolism Initiative and Program in Medical and Population
Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Boston,
Massachusetts 02142, USA.
4
Medical Research Council Epidemiology Unit, Addenbrooke's
Hospital, Cambridge CB2 0QQ, UK.
5
Institute of Metabolic Science, Addenbrooke's Hospital,
Cambridge CB2 0QQ, UK.
6
Wellcome Trust Centre for Human Genetics, University of Oxford,
Oxford OX3 7BN, UK.
7
Institute of Epidemiology, Helmholtz Zentrum München, Ingolstaedter
Landstr. 1, 85764 Neuherberg, Germany.
8
Division of Cancer Epidemiology and Genetics,
National Cancer Institute, National Institutes of Health, Department of Health and Human
Services, Bethesda, Maryland 20892, USA.
9
Program in Medical and Population Genetics, Broad
Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
10
Department of Molecular
Biology, Massachusetts General Hospital, Cambridge, Massachusetts 02144, USA.
11
Program in
Genomics and Divisions of Endocrinology and Genetics, Children's Hospital, Boston,
Massachusetts 02115, USA.
12
Medical Genetics/Clinical Pharmacology and Discovery Medicine,
King of Prussia, Pennsylvania 19406, USA.
13
Division of Community Health Sciences, St.
George's, University of London, London SW17 0RE, UK.
14
Department of Nutrition, Harvard
School of Public Health, Boston, Massachusetts 02115, USA.
15
Channing Laboratory,
Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.
16
Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
17
Istituto di
Neurogenetica e Neurofarmacologia, Consiglio Nazionale delle Ricerche, Cagliari 09042, Italy.
18
Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor,
Michigan 48109, USA.
19
Genetics of Complex Traits, Peninsula Medical School, Exeter EX1 2LU,
UK.
20
Department of Internal Medicine, Erasmus MC, PO Box 2400, NL-3000-CA Rotterdam, The
Netherlands.
21
Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford,
Churchill Hospital, Oxford OX3 7LJ, UK.
22
Department of Twin Research and Genetic
Epidemiology, King's College London, London SE1 7EH, UK.
23
National Institute of Aging,
Clinical Research Branch Longitudinal Studies Section, Baltimore, Maryland 21225, USA.
24
MRC
Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine,
University of Bristol, Bristol BS8 2PR, UK.
25
Lund University Diabetes Centre, Department of
Clinical Sciences, Lund University, 20502 Malmö, Sweden.
26
DRL, OCDEM, Churchill Hospital,
Headington, Oxford OX3 7LJ, UK.
27
Physiology and Biophysics, University of Southern California
School of Medicine, Los Angeles, California 90033, USA.
28
MRC Dunn Human Nutrition Unit,
Wellcome Trust/MRC Building, Cambridge CB2 0XY, UK.
29
MRC Centre for Nutritional
Epidemiology in Cancer Prevention and Survival, Cambridge CB1 8RN, UK.
30
National Human
Genome Research Institute, Bethesda, Maryland 20892, USA.
31
Clinical Pharmacology Unit,
University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.
32
Department of
Epidemiology and Public Health, Imperial College London, St. Mary's Campus, Norfolk Place,
London W2 1PG, UK.
33
British Heart Foundation Glasgow Cardiovascular Research Centre,
Faculty of Medicine, University of Glasgow, Glasgow G12 8TA, UK.
34
MRC Epidemiology
Resource Centre, University of Southampton, Southampton General Hospital, Southampton
SO16 6YD, UK.
35
Yorkshire Heart Centre, Leeds General Infirmary, Leeds LS1 3EX, UK.
36
KTL-
National Public Health Institute, FI-00300 Helsinki, Finland.
37
Department of Child and Adolescent
Psychiatry, University of Duisburg-Essen, Virchowstr. 174, 45147 Essen, Germany.
38
Department
of Epidemiology, Erasmus MC, PO Box 2400, NL-3000-CA Rotterdam, The Netherlands.
39
Folkhalsan Research Center, Malmska Municipal Health Center and Hospital, FIN-00014
Willer et al.
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Jakobstad, Finland.
40
Bioinformed Consulting Services, Gaithersburg, Maryland 20877, USA.
41
Department of Medical Genetics, University of Lausanne, CH-1005 Lausanne, Switzerland.
42
University Institute for Social and Preventative Medicine, Centre Hospitalier Universitaire
Vaudois (CHUV), CH-1005 Lausanne, Switzerland.
43
Swiss Institute of Bioinformatics, CH-1005
Lausanne, Switzerland.
44
University of Cambridge Metabolic Research Laboratories,
Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.
45
Department of Public Health and Primary
Care, Institute of Public Health, University of Cambridge, Cambridge CB2 0SR, UK.
46
Program in
Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts
02115, USA.
47
Department of Molecular Medicine, National Public Health Institute, FIN-00300
Helsinki, Finland.
48
Department of Medicine, University of Kuopio, 70210 Kuopio, Finland.
49
Finnish Institute of Occupational Health, Aapistie 1, Fin-90220 Oulu, Finland.
50
Laboratory of
Cardiovascular Science, Gerontology Research Center, National Institute on Aging, Baltimore,
Maryland 21224, USA.
51
Department of Cardiovascular Sciences, University of Leicester, Clinical
Sciences, Glenfield General Hospital, Leicester LE3 9QP, UK.
52
Avon Longitudinal Study of
Parents and Children (ALSPAC), Department of Social Medicine, University of Bristol, Bristol BS8
1TQ, UK.
53
Institute of Human Genetics, Helmholtz Zentrum München, Ingolstaedter Landstr. 1,
85764 Neuherberg, Germany.
54
Institute of Human Genetics, Technical University Munich,
D-81765, Munich, Germany.
55
Clinical Pharmacology, The William Harvey Research Institute,
Bart's and The London, Queen Mary's School of Medicine and Dentistry, Charterhouse Square,
London EC1M 6BQ, UK.
56
Department of Oral & Dental Science, University of Bristol, Bristol BS1
2LY, UK.
57
Department of Clinical Sciences, Lund University, 20502 Malmö, Sweden.
58
Department of Clinical Chemistry, University of Oulu, Fin-90220 Oulu, Finland.
59
Savitaipale
Health Center, FIN-54800 Savitaipale, Finland.
60
Unitá Operativa Geriatria, Istituto Nazionale
Ricovero e Cura Anziani, Rome 00189, Italy.
61
Department of Haematology, University of
Cambridge/NHS Blood & Transplant, Cambridge CB2 2PR, UK.
62
National Public Health Institute,
Department of Epidemiology and Health Promotion, Mannerheimintie 166, FIN-00300 Helsinki,
Finland.
63
Department of Internal Medicine, BH-10 Centre Hospitalier Universitaire Vaudois
(CHUV), 1011 Lausanne, Switzerland.
64
Department of Preventive Medicine, Division of
Biostatistics, Keck School of Medicine, University of Southern California, CHP-220, Los Angeles,
California 90089, USA.
65
Laboratory of Epidemiology, Demography, and Biometry; Gerontology
Research Center, National Institute on Aging, Bethesda, Maryland 20892, USA.
66
Peninsula
Medical School, Exeter EX5 2DW, UK.
67
Department of Medicine, Helsinki University Central
Hospital, FIN-00290 Helsinki, Finland.
68
Research Program of Molecular Medicine, University of
Helsinki, FIN-00014 Helsinki, Finland.
69
Department of Medicine, Helsinki University, FIN-00029
Helsinki, Finland.
70
Department of Genetics, University of North Carolina, CB #7264, Chapel Hill,
North Carolina 27599, USA.
71
Institute of Molecular Medicine, University of Helsinki, FIN-00014
Helsinki, Finland.
72
Laboratory of Genetics, US National Institutes of Health Biomedical Research
Center, National Institute on Aging, Baltimore, Maryland 21224, USA.
73
Institute of Medical
Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-University München,
Marchioninistr. 15, 81377 München, Germany.
74
National Institute for Health Research, Oxford
Biomedical Research Centre, University of Oxford, Old Road, Headington, Oxford OX3 7LJ, UK.
75
Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.
Abstract
Common variants at only two loci,
FTO
and
MC4R
, have been reproducibly associated with body
mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15
genome-wide association studies for BMI (
n
> 32,000) and followed up top signals in 14
additional cohorts (
n
> 59,000). We strongly confirm
FTO
and
MC4R
and identify six additional
loci (
P
< 5 × 10
−8
):
TMEM18
,
KCTD15
,
GNPDA2
,
SH2B1
,
MTCH2
and
NEGR1
(where a 45-kb
deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly
Willer et al.
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expressed or known to act in the central nervous system (CNS), emphasizing, as in rare
monogenic forms of obesity, the role of the CNS in predisposition to obesity.
Obesity is a major public health problem, resulting in increased morbidity and mortality and
severe economic burdens on health-care systems1,2. Excessive energy intake and
diminished physical activity contribute to the increasing prevalence of obesity, but genetic
factors strongly modulate the impact of the modern environment on each individual. Indeed,
family and twin studies have shown that genetic factors account for 40–70% of the
population variation in BMI3,4. BMI is the most commonly used quantitative measure of
adiposity, and adults with high values of BMI (>30 kg/m
2
) are termed obese.
Until recently, genetic variants known to influence BMI were largely restricted to mutations
in several genes that cause rare, often severe monogenic syndromes with obesity as the main
feature5. Mutations in these genes are thought to act through the CNS, and in particular the
hypothalamus, to influence energy balance and appetite, thereby leading to obesity.
However, it is not known whether genetic variation in similar pathways is also relevant to
the common form of obesity and population variation in BMI.
In the past year, large-scale searches for genetic determinants of BMI revealed previously
unreported associations with common variants at two loci,
FTO
and
MC4R6-10
. Common
variants at these loci are associated with modest effects on BMI (0.2–0.4 kg/m
2
per allele)
that translate into odds ratios of 1.1–1.3 for obesity (defined as BMI ≥ 30 kg/m
2
)
6-10
.
Common variation in
PCSK1
has been strongly associated with the risk of extreme
obesity11, but this association has not yet been independently replicated.
Together, common variants at
FTO
and
MC4R
and rare variants known to cause obesity
explain only a small fraction of the inherited contribution to population variation in BMI. To
expedite the identification of alleles associated with variation in BMI, obesity and other
anthropometric traits, we formed the GIANT (Genetic Investigation of ANthropometric
Traits) consortium to facilitate large-scale meta-analysis of data from multiple genome-wide
association studies (GWAS). Here, we report a meta-analysis of 15 GWAS totaling 32,387
individuals and test for association between BMI and ~2.4 million genotyped or imputed
SNPs. We then follow up 35 SNPs drawn from the most significantly associated loci by a
combination of
de novo
genotyping in up to 45,018 additional individuals and analysis of
these SNPs in another 14,064 individuals already genotyped as part of other GWAS. These
studies show that variants at six previously unreported loci in or near
TMEM18
,
KCTD15
,
SH2B1
,
MTCH2
,
GNPDA2
and
NEGR1
are reproducibly associated with BMI.
RESULTS
Initial meta-analysis of GWAS studies of BMI (stage 1)
We carried out a GWA meta-analysis of a total of 32,387 individuals of European ancestry
from 15 cohorts of 1,094 to 5,433 individuals using two parallel analytic strategies
(Supplementary Fig. 1 and Supplementary Tables 1–3 online). First, we carried out a
weighted
z
-score–based meta-analysis combining
P
values from cohort-specific analysis
strategies. Second, we also performed an inverse-variance meta-analysis using regression
coefficients and their standard errors obtained by applying a uniform analysis strategy across
all studies. The results for these two strategies were highly congruent (Supplementary Fig. 2
online). Here we report results of the weighted P value analysis, as it was completed first
and used to select SNPs for follow-up genotyping.
SNPs that reached
P
< 5 × 10
−8
(a threshold that corresponds to
P
< 0.05 after adjusting for
~1 million independent tests) in this stage 1 analysis all mapped within the
FTO
gene
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(association peak at rs1421085,
P
= 2.6 × 10
−19
), were in linkage disequilibrium (LD) with
each other (
r
2
> 0.51), and strongly confirm previous reports of association at this locus6-8.
A locus located near
MC4R
(rs17782313,
P
= 3.9 × 10
−7
) and recently associated with
BMI9,10 was the fourth most significant region in the stage 1 data (Fig. 1). Even after
excluding SNPs in these two established BMI loci, we observed an excess of SNPs with
small
P
values compared to chance expectations, suggesting that some of the remaining loci
with strong but not definitive evidence of association in stage 1 are truly associated with
BMI (Fig. 1b).
Additional analysis of the strongest associations (stage 2)
To validate potential associations with BMI, we designed a pool of 35 variants for further
genotyping, drawn from among the most strongly associated independent loci (for technical
reasons, these SNPs do not correspond perfectly to the top 35 loci; see Methods). We
genotyped these SNPs in up to 45,018 additional individuals of European ancestry from nine
stage 2 samples (Supplementary Fig. 1, Supplementary Tables 1 and 4 and Supplementary
Note online). We also obtained
in silico
association results for these SNPs from five BMI
GWAS on 14,064 additional individuals of European ancestry (Supplementary Fig. 1,
Supplementary Tables 1 and 4 and Supplementary Note). Meta-analysis of these stage 2
results combined with stage 1 data revealed SNPs from five previously unreported loci near
TMEM18
,
KCTD15
,
SH2B1
,
MTCH2
and
GNPDA2
that are strongly associated with BMI
(
P
< 5 × 10
−8
; Table 1, Fig. 2 and Supplementary Table 5 online). Two additional loci,
represented by rs2815752 (near
NEGR1
) and rs10769908 (near
STK33
) had supporting
evidence in stage 2 samples but did not reach the
P
< 5 × 10
−8
threshold (
P
= 6.0 × 10
−8
and
P
= 1.3 × 10
−6
, respectively). Among these two, rs2815752 also showed a highly significant
independent association with severe obesity in a pediatric cohort (
P
= 2.2 × 10
−7
;
Supplementary Table 6 online), strongly suggesting that this variant represents a sixth newly
discovered locus influencing BMI. For each of the six loci, multiple SNPs showed highly
significant association in the stage 1 data (Fig. 2), and the associations were observed across
multiple cohorts genotyped on different platforms (Supplementary Table 7 online),
suggesting that idiosyncratic genotyping artifacts are unlikely to explain our results.
Furthermore, the consistent association signals across different European-ancestry samples,
each with low genomic control inflation factors (Supplementary Table 3), also suggest that
population structure is unlikely to account for these associations. Finally, five of the six
associated variants (near
TMEM18
,
KCTD15
,
SH2B1
,
MTCH2
and
NEGR1
, but not
GNPDA
2) had Illumina proxies in high LD (
r
2
> 0.66) with our best SNPs that were
included in an independent GWAS by Thorleifsson
et al.12
; for all five, they observed
confirmatory evidence of association with BMI (Table 1), providing strong validation of
these newly discovered associations.
Of the variants showing strong association with BMI, only rs9939609 (in
FTO
) showed
nominally significant evidence of heterogeneity across cohorts (
P
= 0.02, Supplementary
Table 5), and none of the associations showed significantly different effects by sex (
P
>
0.16, Supplementary Table 5). We did not observe any significant evidence supporting the
recently reported BMI associations with SNPs near
INSIG2
(rs7566605,
P
= 0.98) and
CTNNBL1
(rs6013029,
P
= 0.34)13,14. We did observe modest evidence for association
between BMI and variation in
PCSK1
(rs6232,
P
= 0.03 in the appropriate direction), which
has previously been associated with severe obesity11.
Impact on BMI, obesity, related traits and complications
The effects of the associated variants on BMI were estimated using data solely from
genotyped stage 2 samples, to lessen the impact of the ‘winner's curse’; they ranged from
0.06 kg/m
2
to 0.33 kg/m
2
per allele, corresponding to a change of 173–954 g in weight per
Willer et al.
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