Common SNPs in FTO gene are associated with obesity related anthropometric traits in an island population from the eastern Adriatic coast of Croatia.

Abstract

Multiple studies have provided compelling evidence that the FTO gene variants are associated with obesity measures. The objective of the study was to investigate whether FTO variants are associated with a broad range of obesity related anthropometric traits in an island population.
We examined genetic association between 29 FTO SNPs and a comprehensive set of anthropometric traits in 843 unrelated individuals from an island population in the eastern Adriatic coast of Croatia. The traits include 11 anthropometrics (height, weight, waist circumference, hip circumference, bicondilar upper arm width, upper arm circumference, and biceps, triceps, subscapular, suprailiac and abdominal skin-fold thicknesses) and two derived measures (BMI and WHR). Using single locus score tests, 15 common SNPs were found to be significantly associated with "body fatness" measures such as weight, BMI, hip and waist circumferences with P-values ranging from 0.0004 to 0.01. Similar but less significant associations were also observed between these markers and bicondilar upper arm width and upper arm circumference. Most of these significant findings could be explained by a mediating effect of "body fatness". However, one unique association signal between upper arm width and rs16952517 (P-value = 0.00156) could not be explained by this mediating effect. In addition, using a principle component analysis and conditional association tests adjusted for "body fatness", two novel association signals were identified between upper arm circumference and rs11075986 (P-value = 0.00211) and rs16945088 (P-value = 0.00203).
The current study confirmed the association of common variants of FTO gene with "body fatness" measures in an isolated island population. We also observed evidence of pleiotropic effects of FTO gene on fat-free mass, such as frame size and muscle mass assessed by bicondilar upper arm width and upper arm circumference respectively and these pleiotropic effects might be influenced by variants that are different from the ones associated with "body fatness".

Full text

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Common SNPs in FTO Gene Are Associated with Obesity
Related Anthropometric Traits in an Island Population
from the Eastern Adriatic Coast of Croatia
Ge Zhang1,2, Rebekah Karns1, Nina Smolej Narancic3, Guangyun Sun1, Hong Cheng1, Sasa Missoni3,
Zijad Durakovic3, Pavao Rudan3, Ranajit Chakraborty4, Ranjan Deka1*
1Center for Genome Information, Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio, United States of America, 2Department of Family and
Community Medicine, University of Cincinnati, Cincinnati, Ohio, United States of America, 3Institute for Anthropological Research, Zagreb, Croatia, 4Center for
Computational Genomics, University of North Texas Health Science Center, Fort Worth, Texas, United States of America
Abstract
Background: Multiple studies have provided compelling evidence that the FTO gene variants are associated with obesity
measures. The objective of the study was to investigate whether FTO variants are associated with a broad range of obesity
related anthropometric traits in an island population.
Methodology/Principal Findings: We examined genetic association between 29 FTO SNPs and a comprehensive set of
anthropometric traits in 843 unrelated individuals from an island population in the eastern Adriatic coast of Croatia. The
traits include 11 anthropometrics (height, weight, waist circumference, hip circumference, bicondilar upper arm width,
upper arm circumference, and biceps, triceps, subscapular, suprailiac and abdominal skin-fold thicknesses) and two derived
measures (BMI and WHR). Using single locus score tests, 15 common SNPs were found to be significantly associated with
‘‘body fatness’’ measures such as weight, BMI, hip and waist circumferences with P-values ranging from 0.0004 to 0.01.
Similar but less significant associations were also observed between these markers and bicondilar upper arm width and
upper arm circumference. Most of these significant findings could be explained by a mediating effect of ‘‘body fatness’’.
However, one unique association signal between upper arm width and rs16952517 (P-value=0.00156) could not be
explained by this mediating effect. In addition, using a principle component analysis and conditional association tests
adjusted for ‘‘body fatness’’, two novel association signals were identified between upper arm circumference and
rs11075986 (P-value=0.00211) and rs16945088 (P-value=0.00203).
Conclusions/Significance: The current study confirmed the association of common variants of FTO gene with ‘‘body
fatness’’ measures in an isolated island population. We also observed evidence of pleiotropic effects of FTO gene on fat-free
mass, such as frame size and muscle mass assessed by bicondilar upper arm width and upper arm circumference
respectively and these pleiotropic effects might be influenced by variants that are different from the ones associated with
‘‘body fatness’’.
Citation: Zhang G, Karns R, Narancic NS, Sun G, Cheng H, et al. (2010) Common SNPs in FTO Gene Are Associated with Obesity Related Anthropometric Traits in
an Island Population from the Eastern Adriatic Coast of Croatia. PLoS ONE 5(4): e10375. doi:10.1371/journal.pone.0010375
Editor: Cuilin Zhang, National Institute of Child Health and Human Development/National Institutes of Health, United States of America
Received December 23, 2009; Accepted March 22, 2010; Published April 28, 2010
Copyright: ? 2010 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by a grant from the National Institutes of Health, USA (R01 DK069845) and grants from the Croatian Ministry of Science,
Education and Sports (196-1962766-2751 to PR, 196-1962766-2747 to NSN, 196-0342282-0291 to ZD). GZ was supported partially by a gift fund from Ellen and the
late Dr. George Rieveschl Jr. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: ranjan.deka@uc.edu
Introduction
The worldwide prevalence of obesity has reached epidemic
proportions in recent decades and is associated with increased risks
of type 2 diabetes and cardiovascular diseases. The increase in
overweight and obesityhasbeenattributed tomodernization,calorie-
rich nutritionally poor diets and sedentary lifestyles [1,2]. In addition
to environmental determinants, twin and adoption studies show that
genetic factors strongly influence the development of obesity and its
associated morbidities [3–6]. The reported heritability of common
obesity is substantially high ranging between 30 and 70% [7]. While
candidate gene studies and genome-wide linkage analysis have
identified numerousobesity related lociand someof which have been
replicated across different studies and populations, precise identifica-
tion of obesity genes has been difficult [7–9]. However, recent
genome-wide association studies (GWAS) have achieved remarkable
success inunraveling the genetic basis of common diseases.Using this
approach, common variants associated with adult and childhood
obesity have been identified in several gene regions, including
INSIG2, FTO, MC4R, BDNF, SH2B1 [10–14]. While the effect
sizes of these variants contributing to the risk of obesity are modest,
the fat mass obesity-associated (FTO) gene region has been replicated
in several studies and in multiple populations [11,14–17].
Although the aforementioned studies provide compelling
evidence that the FTO gene is involved in altering fat mass, the
reported associations are typically limited to ‘‘body fatness’’
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measures such as body weight, body mass index (BMI)
[11,12,14,16,18], hip and waist circumference [16,19]. We report
here the associations of common FTO variants with these classic
and other obesity related anthropometric measures including
bicondilar upper arm width, upper arm circumference and five
skin-fold thickness measures. In all, we tested for association of 29
SNPs, eight from previous GWAS and 21 common tagging SNPs
with 13 anthropometric traits in a sample of 843 unrelated adult
individuals from an island population of the eastern Adriatic coast
of Croatia. Our choice of the study sample for such genetic
association study was prompted by their relatively homogenous
genetic background and similar life style and dietary patterns. The
objective of this study was to replicate the association between
FTO SNPs and ‘‘body fatness’’ measures in our sampled
population and to examine whether FTO variants affected other
obesity related anthropometric measures.
Methods
Ethics Statement
This study was approved by the Institutional Review Board of
the University of Cincinnati and the Ethics Committee of the
Institute for Anthropological Research, Zagreb. Written informed
consent was obtained from each study participant.
Study population and phenotypic measurements
The sample was derived from a study on genetics of metabolic
syndrome in an isolated population from the middle Dalmatian
island of Hvar in the eastern Adriatic coast of Croatia (Figure 1).
The Croatian islanders are predominantly of Slavic origins who
migrated from the mainland and have remained isolated since
their last emigration in the 18thcentury [20,21]. Anthropometric
data and blood samples were collected in two field seasons of May
2007 and May 2008. A total of 843 unrelated subjects (360 male
and 483 female) between age 18 and 80 years residing in eight
villages of the Hvar island (Figure 1) were used for this study. The
anthropometric traits consist of: height (Ht), weight (Wt), waist
circumference (WC), hip circumference (HC), bicondilar upper
arm width (UAW), upper arm circumference (UAC), and biceps
(BiS), triceps (TrS), subscapular (SbS), suprailiac (SpS) and
abdominal (AbS) skin-fold thicknesses (Table 1). Anthropometric
measurements were obtained by standard techniques as described
in Weiner and Lourie [22].
SNP selection and genotyping
The FTO gene (MIM 610966) is large encompassing .410kb of
genomic region. Therefore, we adopted a targeted approach to
search for common tagging SNPs within 30kb upstream and 30kb
downstream of the original and most significantly associated SNP,
rs9939609 reported by Frayling et al. [11]. Twenty-eight SNPs
were selected by a tagging approach [23] using the Caucasian
HapMap database (www.hapmap.org) based on a pairwise r2of
$0.8 among all common SNPs with minor allele frequency of
$0.05. We also included eight significant SNPs from previous
GWAS(rs9939973, rs1421085,
rs8050136, rs3751812, rs9939609, rs7190492). These 36 SNPs
span 60kb and fall in intron 1 and intron 2 of the FTO gene.
Genotyping was performed using the SNPlex protocol (Applied
Biosystems), which is based on multiple oligonucleotide ligation/
PCR assay with a universal ZipChuteTMprobe detection for high-
throughput multiplexed SNP genotyping. Details of the SNPlex
genotyping methods were described previously [24]. To assure
genotypic quality control, negative controls and blind duplicates
were introduced in each batch of samples in the 96-well format.
The overall genotype call rate of the 36 SNPs was 96.5% and the
genotype consistency rate based on 8 internal replicates was higher
than 99.5%.
rs1121980,rs17817449,
Figure 1. Geographical location of Hvar and the 8 villages from where the samples were collected.
doi:10.1371/journal.pone.0010375.g001
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Statistical analyses
Statistical analyses were performed using R (version 2.8.0) and
the GenABEL library [25]. Pairwise LD (r2) between markers was
estimated by Haploview (version 4.1) [26]. The anthropometric
measures were adjusted for age and gender and their interaction
term by linear regression before conducting the cluster or
association analyses. Deviation of genotype frequencies from
Hardy-Weinberg equilibrium (HWE) was assessed by the exact test
[27] implemented in GenABEL. The association between each
SNP and quantitative anthropometric measures was evaluated by
the score test [28] under the additive model. Permutation test with
100,000 replications was used to access the empirical significance
adjusted for the number tests on multiple markers.
Results
Correlation structure and cluster of anthropometric
measures
Summary statistics of the 11 anthropometrics and two
derivatives (BMI and WHR) are listed in Table 1 for males
(N=360) and females (N=483) separately. Since these measures
are correlated with gender and age (data not shown), we first
adjusted the effects of gender and age and their interaction term
by linear regression. Unless otherwise mentioned, all data analyses
were performed on these adjusted measures.
The anthropometric measures we studied correlated to each
other (Supplement Table S1). Therefore, we first clustered these
phenotype measures based on their pairwise correlation structure
using hierarchical cluster approach. As expected, the measures
that reflect ‘‘body fatness’’ (Wt, HC, WC and BMI) together with
UAC clustered together (cluster 1). Likewise, a second cluster
(cluster 2) reflecting ‘‘subcutaneous obesity’’ was represented by
the five skin fold measures. Ht, WHR and UAW were outliers to
these two clusters (Figure 2).
Genetic association analysis
Among the 36 genotyped SNPs, seven were excluded from the
analysis either due to low minor allele frequency (5 with
MAF,0.05) or deviation from HWE (2 with exact HWE test P-
value,0.01). We tested the genetic association of the remaining 29
SNPs with each of the adjusted anthropometric trait using the
single-locus score test under additive model (1df test). The
distribution and the pairwise linkage disequilibrium (LD) of these
29 SNPs are shown in Figure 3. Significant associations were
Table 1. Summary statistics of anthropometric measures.
Anthropometric
measures (unit)
Male (N=360) Female (N=483)
Mean±SE Range (min, max)NA*
Mean±SE
Range (min, max) NA*
Ht (cm)177.4260.381 (157.2, 206)0164.160.309(139, 187.2)0
Wt (kg) 89.3160.682(60.2, 142.5)074.160.578(43, 123.9)0
BMI (kg/m2)28.3360.176(19.64, 42.23)027.5560.209(16.7, 45.79)0
WC (cm) 101.8560.474(79.3, 138.5)191.9160.551(62.3, 137.4)1
HC (cm)104.4260.384 (90.8, 132)0 106.1560.476(75, 150)1
WHR0.9860.003 (0.727, 1.124)10.8760.003(0.651, 1.12)1
UAC (mm) 301.2961.333 (235, 382)0 289.1261.428 (209, 400)0
UAW (mm)73.9360.318(54, 98)064.5560.282(46, 99)0
BiS (mm)14.0260.355 (1, 37.6)1 20.4360.342(4.2, 39)0
TrS (mm) 15.0460.306(1, 44)1 25.9560.294 (4.6, 43.2)0
SbS (mm) 24.1260.354(8.8, 47)324.3960.371(7.2, 49.9)3
SpS (mm)28.0360.451(9, 48.8)230.2660.388 (7.2, 52.3)1
AbS (mm) 30.7160.483 (7.4, 58.2)2 33.3960.407 (9, 59)1
*NA: number of missing values.
doi:10.1371/journal.pone.0010375.t001
Figure 2. Hierarchical clustering of the 13 anthropometric
traits.
doi:10.1371/journal.pone.0010375.g002
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found between several SNPs and various phenotypic measures
(Table 2). Majority of these significant associations were between
the ‘‘body fatness’’ measures (Wt, HC, WC and BMI) of cluster 1
and a set of highly polymorphic SNP markers (MAF.0.3) that are
located between rs7206790 (No. 9) and rs1861867 (No. 27), which
spans approximately 50kb. For abbreviation, we refer to these 15
significant markers as ‘‘body fatness’’ markers (highlighted in
boldface in Table 2). These associations remained significant even
after multiple-testing adjustment on the number of markers by
permutation test (Table 2 and Supplement Table S2).
Interestingly, UAW was found to be significantly associated with
‘‘body fatness’’ markers, although it is less closely correlated with
the ‘‘body fatness’’ traits (Figure 2). In addition, one significant
association signal was found between UAW and rs16952517
(No. 6, P-value=0.00156, significant even with permutation test).
In contrast, ‘‘body fatness’’ markers were less significantly
associated with UAC (a member of cluster 1 traits) compared
with UAW. WHR and two skin fold measures (TrS and SbS) of
cluster 2 also showed nominally significant associations with some
of these markers, significance of which were not supported by the
permutation test.
Since multiple cluster 1 measures and UAW were found to be
associated with similar sets of SNP markers, we performed a
principle component analysis (PCA) to evaluate whether some
‘‘lower-dimensional summary’’ of these variables provide more
information about the observed associations. We identified three
major components (PC1, PC2 and PC3) that could explain up to
90% of the total variance of these measures (Supplement Table S3).
The first component (PC1) explained more than 70% of the
total variance and was approximately an average of all the
included measures with similar loading coefficients (around
20.40,20.45), except for UAW (with a loading coefficient of
20.264). The association signals (P-values) observed between PC1
and the ‘‘body fatness’’ markers shared similar pattern with
individual body fatness measures (Wt, HC, WC and BMI) and the
highestassociationpeakwasfoundatrs1421085(P-value=0.000307)
(Figure4A).Therefore,weconsidered PC1asa summaryvariable for
‘‘body fatness’’ and it well captured the significant association
between individual ‘‘body fatness’’ measures and ‘‘body fatness’’
markers. To confirm this, we tested association using PC1 as
covariate to remove the effect of ‘‘body fatness’’. As expected, all the
significant signals found between ‘‘body fatness’’ measures and ‘‘body
fatness’’ markers disappeared (P-value.0.01) after the adjustment for
PC1 (Supplement Figure S1). We also performed association test
conditional on rs1421085 (the most significant marker) to investigate
whether the significant association signals between ‘‘body fatness’’
measures and ‘‘body fatness’’ markers can be explained by a single
locus.Again,afteradjustmentforrs1421085,thesignificantsignalson
other ‘‘body fatness’’ markers diminished (Supplement Figure S2).
The second component (PC2) had a prominent UAW
constituent (with a loading coefficient of 0.935) and it mainly
represented the effect of UAW with adjustments of other variables.
Not surprisingly, PC2 was not associated with ‘‘body fatness’’
markers because PC2 was independent to PC1 and the later
already absorbed most of the variance of ‘‘body fatness’’. In this
sense, PC2 could be approximately regarded as UAW adjusted for
PC1 or ‘‘body fatness’’ (UAW|PC1 in Figure 4B), which was
exemplified by the similar association pattern of these two
variables. Interestingly, unlike the ‘‘body fatness’’ markers, the
unique significant association between rs16952517 and UAW still
Figure 3. Distribution and pairwise LD (r2) map of the 29 SNP markers.
doi:10.1371/journal.pone.0010375.g003
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Table 2. Association test between SNP markers and anthropometric traits.
No
rs id$
Pos{ {
Alleles1 1
MAF
P-value of single-locus association test (1df test under additive model)*
Outliers
Body fatness measures and UAC (Cluster 1)
Subcutaneous obesity measures (Cluster 2)
Ht
WHR
UAW
Wt
HC
WC
BMI
UAC
SbS
SpS
AbS
BiS
TrS
1
rs1861869
52347682
CG
0.450
0.173
0.124
0.00511
0.0182
0.0854
0.021
0.0665
0.0296
0.147
0.143
0.179
0.998
0.235
2
rs1077128
52349154
GT
0.208
0.559
0.956
0.066
0.149
0.258
0.359
0.169
0.0792
0.0103
0.12
0.111
0.393
0.93
3
rs11643744
52349299
AG
0.280
0.254
0.116
0.264
0.172
0.362
0.0967
0.421
0.244
0.808
0.372
0.425
0.621
0.204
4
rs7186521
52350423
GA
0.496
0.147
0.584
0.00644
0.0161
0.0477
0.0555
0.0701
0.0159
0.11
0.0991
0.15
0.596
0.392
5
rs13334933
52353137
AG
0.195
0.311
0.799
0.0196
0.0628
0.135
0.292
0.11
0.108
0.0344
0.273
0.131
0.377
0.985
6
rs16952517
52354558
GA
0.134
0.922
0.174
0.00156
0.343
0.213
0.852
0.274
0.665
0.611
0.646
0.69
0.603
0.525
7
rs6499643
52355019
TC
0.152
0.12
0.879
0.991
0.435
0.373
0.656
0.926
0.833
0.446
0.943
0.71
0.399
0.657
8
rs4784323
52355066
GA
0.307
0.953
0.205
0.178
0.211
0.252
0.122
0.141
0.00993
0.288
0.95
0.84
0.569
0.374
9
rs7206790
52355409
GC
0.469
0.851
0.00329
0.0181
0.00713
0.0694
0.000902
0.00651
0.00794
0.00702
0.116
0.288
0.716
0.0264
10
rs9939973
52358069
GA
0.493
0.369
0.0304
0.0078
0.00243
0.0139
0.000861
0.00472
0.00951
0.0404
0.204
0.088
0.583
0.022
11
rs1421085
52358455
TC
0.465
0.609
0.127
0.00707
0.0013
0.000997
0.000403
0.000969
0.0155
0.042
0.359
0.246
0.635
0.0201
12
rs10852521
52362466
CT
0.450
0.815
0.0233
0.0191
0.0203
0.059
0.00338
0.0172
0.00572
0.0125
0.173
0.435
0.795
0.0493
13
rs11075986
52362845
CG
0.088
0.596
0.534
0.425
0.0622
0.0431
0.223
0.0684
0.632
0.736
0.991
0.245
0.645
0.416
14
rs16952522
52364999
CG
0.057
0.0641
0.307
0.192
0.255
0.166
0.0802
0.0218
0.026
0.0212
0.287
0.0732
0.311
0.0124
15
rs17817288
52365265
GA
0.455
0.667
0.0119
0.00579
0.0183
0.0445
0.00165
0.0166
0.00767
0.00638
0.0994
0.276
0.583
0.0614
16
rs1121980
52366748
CT
0.470
0.471
0.117
0.0038
0.00103
0.00218
0.000621
0.00126
0.0175
0.0461
0.216
0.145
0.632
0.0157
17
rs16945088
52370025
AG
0.075
0.472
0.233
0.175
0.131
0.0353
0.368
0.146
0.551
0.562
0.782
0.78
0.733
0.49
18
rs17817449
52370868
TG
0.437
0.258
0.146
0.004
0.000906
0.00206
0.000888
0.00249
0.055
0.0542
0.0937
0.0937
0.363
0.0153
19
rs8050136
52373776
CA
0.438
0.183
0.152
0.00708
0.00503
0.00777
0.00262
0.0178
0.0972
0.267
0.3
0.273
0.927
0.204
20
rs9935401
52374339
GA
0.438
0.309
0.324
0.00395
0.00188
0.00331
0.00336
0.00475
0.0675
0.133
0.152
0.144
0.517
0.0227
21
rs3751812
52375961
GT
0.436
0.341
0.231
0.00879
0.00291
0.00293
0.00201
0.00637
0.0501
0.0977
0.169
0.132
0.541
0.0179
22
rs9939609
52378028
TA
0.432
0.26
0.103
0.00407
0.00181
0.00624
0.00156
0.0043
0.0835
0.0757
0.102
0.0879
0.434
0.0638
23
rs7190492
52386253
GA
0.328
0.232
0.0228
0.0144
0.00538
0.011
0.000892
0.013
0.00576
0.101
0.105
0.351
0.753
0.489
24
rs9930501
52387953
AG
0.470
0.589
0.16
0.00687
0.0128
0.025
0.00791
0.0171
0.0452
0.329
0.2
0.148
0.693
0.0288
25
rs17218700
52402080
GA
0.144
0.839
0.481
0.865
0.275
0.259
0.23
0.236
0.201
0.825
0.653
0.825
0.904
0.353
26
rs11642841
52402988
CA
0.473
0.469
0.0189
0.0858
0.0499
0.143
0.00938
0.0766
0.191
0.553
0.717
0.724
0.842
0.269
27
rs1861867
52406062
CT
0.350
0.532
0.0556
0.0235
0.00564
0.00664
0.00134
0.00478
0.0125
0.426
0.221
0.379
0.821
0.356
28
rs11075994
52407580
GA
0.295
0.112
0.0244
0.789
0.382
0.265
0.0233
0.0844
0.0711
0.164
0.245
0.256
0.58
0.201
29
rs1421090
52407671
TC
0.228
0.346
0.164
0.502
0.67
0.941
0.329
0.892
0.572
0.197
0.475
0.125
0.867
0.831
*Nominal significant P values (,0.05) were shown in bold font. P values that can pass permutation test were underlined.
$Markers significantly associated with multiple body fatness measures (‘‘body fatness’’ markers) were bolded.
{Chromosome positions of the SNPs are based on Human Reference Genome Sequence Build 36.
1The alleles were shown as major allele/minor allele.
doi:10.1371/journal.pone.0010375.t002
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