Journal of Human Genetics (2011) 56, 647–651& 2011 The Japan Society of Human Genetics All rights reserved 1434-5161/11 $32.00
www.nature.com/jhg

ORIGINAL ARTICLE

Association of variations in the FTO, SCG3 andMTMR9 genes with metabolic syndrome in a
Japanese population
Kikuko Hotta1, Takuya Kitamoto1, Aya Kitamoto1, Seiho Mizusawa2, Tomoaki Matsuo3, Yoshio Nakata3,
Seika Kamohara4, Nobuyuki Miyatake5, Kazuaki Kotani6, Ryoya Komatsu7, Naoto Itoh8, Ikuo Mineo9,
Jun Wada10, Masato Yoneda11, Atsushi Nakajima11, Tohru Funahashi6, Shigeru Miyazaki12,
Katsuto Tokunaga13, Hiroaki Masuzaki14, Takato Ueno15, Kazuyuki Hamaguchi16, Kiyoji Tanaka3,
Kentaro Yamada17, Toshiaki Hanafusa18, Shinichi Oikawa19, Hironobu Yoshimatsu20, Toshiie Sakata20,
Yuji Matsuzawa6, Kazuwa Nakao1,21 and Akihiro Sekine1,2
Metabolic syndrome is defined as a cluster of multiple risk factors, including central obesity, dyslipidemia, hypertension andimpaired glucose tolerance, that increase cardiovascular disease morbidity and mortality. Genetic factors are important in thedevelopment of metabolic syndrome, as are environmental factors. However, the genetic background of metabolic syndrome is
not yet fully clarified. There is evidence that obesity and obesity-related phenotypes are associated with variations in several
genes, including NEGR1, SEC16B, TMEM18, ETV5, GNPDA2, BDNF, MTCH2, SH2B1, FTO, MAF, MC4R, KCTD15, SCG3,
MTMR9, TFAP2B, MSRA, LYPLAL1, GCKR and FADS1. To investigate the relationship between metabolic syndrome andvariations in these genes in the Japanese population, we genotyped 33 single-nucleotide polymorphisms (SNPs) in 19 genes
from 1096 patients with metabolic syndrome and 581 control individuals who had no risk factors for metabolic syndrome. Four
SNPs in the FTO gene were significantly related to metabolic syndrome: rs9939609 (P¼0.00013), rs8050136 (P¼0.00011),
rs1558902 (P¼6.6105) and rs1421085 (P¼7.4105). rs3764220 in the SCG3 gene (P¼0.0010) and rs2293855 in the
MTMR9 gene (P¼0.0015) were also significantly associated with metabolic syndrome. SNPs in the FTO, SCG3 and MTMR9
genes had no SNPSNP epistatic effects on metabolic syndrome. Our data suggest that genetic variations in the FTO, SCG3
and MTMR9 genes independently influence the risk of metabolic syndrome.
Journal of Human Genetics (2011) 56, 647–651; doi:10.1038/jhg.2011.74; published online 28 July 2011
Keywords: FTO; metabolic syndrome; MTMR9; SCG3INTRODUCTION
Metabolic syndrome is a common clinical phenotype that is concurrent with metabolic abnormalities, including central obesity, glucose intolerance, dyslipidemia and hypertension.1 Several different
definitions of the syndrome exist2 and there are still debates on theadequacy of the concept; however, metabolic syndrome has attracted
considerable interest. Although the pathogenesis of metabolic

syndrome is not fully understood, the predominant underlying risk
factor is considered to be central obesity due to an atherogenic dietand physical inactivity in the presence of some genetic background.2,3
Adipose tissue, especially visceral fat, secretes various adipocytokines.
An increase in adipose tissue mass leads to an alteration in the plasma
levels of adipocytokines, resulting in the development of dyslipidemia,
hypertension and insulin resistance.3,41EBM Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan; 2Center for Genomic Medicine, Unit of Genome Informatics, Kyoto University Graduate
School of Medicine, Kyoto, Japan; 3Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan; 4Health Science University, Yamanashi, Japan;
5Department of Hygiene, Faculty of Medicine, Kagawa University, Kagawa, Japan; 6Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, Osaka,
Japan; 7Rinku General Medical Center, Osaka, Japan; 8Toyonaka Municipal Hospital, Osaka, Japan; 9Otemae Hospital, Osaka, Japan; 10Department of Medicine and Clinical
Science, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan; 11Division of Gastroenterology, Yokohama City University
Graduate School of Medicine, Yokohama, Japan; 12Tokyo Postal Services Agency Hospital, Tokyo, Japan; 13Itami City Hospital, Hyogo, Japan; 14Division of Endocrinology andMetabolism, Second Department of Internal Medicine, University of the Ryukyus Faculty of Medicine, Okinawa, Japan; 15Research Center for Innovative Cancer Therapy, Kurume
University, Kurume, Japan; 16Department of Community Health and Gerontological Nursing, Faculty of Medicine, Oita University, Oita, Japan; 17Division of Endocrinology andMetabolism, Department of Medicine, Kurume University, Kurume, Japan; 18Department of Internal Medicine (I), Osaka Medical College, Osaka, Japan; 19Division of
Endocrinology and Metabolism, Department of Medicine, Nippon Medical School, Tokyo, Japan; 20Department of Internal Medicine I, Faculty of Medicine, Oita University, Oita,
Japan and 21Department of Medicine and Clinical Science, Kyoto University Graduate School of Medicine, Kyoto, Japan
Correspondence: Dr K Hotta, EBM Research Center, Kyoto University Graduate School of Medicine, Yoshida-Konoecho, Sakyo-ku, Kyoto 606-8501, Japan.
E-mail: kikukoh@kuhp.kyoto-u.ac.jp
Received 12 April 2011; revised 31 May 2011; accepted 8 June 2011; published online 28 July 2011

FTO, SCG3, MTMR9 and metabolic syndrome
K Hotta et al
648

Results from studies using twins and families have suggested thatgenetic and environmental factors contribute to the clustering of
metabolic abnormalities in various ethnic groups.5–10 We determinedin a previous study that four single-nucleotide polymorphisms (SNPs)(rs2294901, rs6133922, rs6077785 and rs6108572) in the McKusick–
Kaufman syndrome (MKKS) gene were significantly associated withmetabolic syndrome in the Japanese population11 by screening SNPs
in 85 obesity-related genes that had been reported as of 2005.12 We
carried out a large-scale case–control association study and foundthat secretogranin III (SCG3)13 and myotubularin-related protein 9(MTMR9)14 conferred susceptibility to the obesity phenotype in theJapanese population. Recent genome-wide association studies revealed
the SNPs associated with obesity and fat distribution (waist circumference and waist to hip ratio).15–19 We confirmed that some of these
SNPs are also associated with obesity and visceral fat area as determined by computed tomography in the Japanese population.20–22
In this study, we investigated the association between metabolic
syndrome and 33 SNPs related to obesity and the obesity-related
phenotype. We found that SNPs in the fat mass and obesity associated(FTO), SCG3 and MTMR9 genes are associated with metabolic
syndrome.

Table 1 Clinical characteristics of the subjects

MATERIALS AND METHODS
Study subjects

Data are shown as the mean±s.d. Men/women ratio was analyzed by the w2-test. P-values forquantitative traits were analyzed using the Mann–Whitney U-test.

The sample size of the group of Japanese subjects with metabolic syndrome was1096 (male to female ratio, 594:502; age, 53.8±12.5 years). The sample size ofthe group of Japanese controls was 581 (male to female ratio, 182:399; age,47.2±14.8 years). Metabolic syndrome was diagnosed as reported previously.11,23 In brief, metabolic syndrome is defined by the presence of two
or more metabolic abnormalities in addition to obesity (body mass index
425 kg m2). The metabolic abnormalities were as follows: (1) triglyceride
level X150 mg per 100 ml and/or high-density lipoprotein cholesterol levelo40 mg per 100 ml, or under treatment for this type of dyslipidemia; (2)systolic blood pressure X130 mm Hg and/or diastolic blood pressure bloodpressure X85 mm Hg, or under treatment for hypertension; and (3) fasting
glucose level X110 mg per 100 ml, or under treatment for diabetes. The control
group consisted of the subjects who were not obese (body mass index
o25 kg m2) and who exhibited none of the metabolic abnormalities describedabove. Clinical characteristics of subjects are summarized in Table 1. Subjects
with metabolic syndrome were recruited from outpatient clinics. Control
subjects were selected from non-obese Japanese volunteers who had undergone
a medical examination for common disease screening. Written informed
consent was obtained from each subject and the protocol was approved by
the ethics committee of each institution and by that of Kyoto University.

DNA preparation and SNP genotyping
Using Genomix (Talent Srl, Trieste, Italy), genomic DNA was extracted fromblood samples collected from each subject. We constructed Invader probes(Third Wave Technologies, Madison, WI, USA) for rs3101336, rs2568958 andrs2815752 in the neuronal growth regulator 1 (NEGR1) gene; rs10913469 in the
SEC16 homolog B (SEC16B) gene; rs6548238 and rs7561317 in the transmembrane protein 18 (TMEM18) gene; rs7647305 in the ets variant 5 (ETV5) gene;rs10938397 in the glucosamine-6-phosphate deaminase 2 (GNPDA2) gene;rs6265 and rs925946 in the brain-derived neurotrophic factor (BDNF) gene;rs10838738 in the mitochondrial carrier homolog 2 (MTCH2) gene; rs7498665
in the SH2B adaptor protein 1 (SH2B1) gene; rs9939609, rs8050136, rs6499640,
rs1121980, rs1558902 and rs1421085 in the FTO gene; rs1424233 in the v-maf
musculo-aponeurotic fibrosarcoma oncogene homolog (MAF) gene;rs17782313, rs12970134, rs489693 and rs17700144 in the melanocortin 4
receptor (MC4R) gene; rs29941 and rs11084753 in the potassium channel
tetramerization domain-containing 15 (KCTD15) gene; rs3764220 in the SCG3
gene; rs2293855 in the MTMR9 gene; rs987237 in the transcription factor AP2b (TFAP2B) gene; rs7826222 in the methionine sulfoxide reductase A (MSRA)
gene; rs2605100 in the lysophospholipase-like-1 (LYPLAL1) gene; rs780094 andJournal of Human Genetics

Gender (men/women)
Age (years)
Body mass index (kg m2)
Plasma glucose (mg per 100 ml)
Triglycerides (mg per 100 ml)
High-density lipoproteincholesterol (mg per 100 ml)
Systolic blood pressure (mm Hg)
Diastolic blood pressure (mm Hg)

Case

Control

P-value594/50253.8±12.5
32.0±5.2

182/39947.2±14.8
21.4±2.1

o0.0001
o0.0001

131.9±45.1
184.0±145.3

90.9±7.776.0±28.851.3±18.0

67.3±14.0

139.9±17.0
85.3±12.2

111.1±10.2
69.4±7.5

Prevalence of metabolic disease
Dyslipidemia

858 (78%)0Hypertension
Impaired fasting glucose

974 (89%)
770 (70%)

00Dyslipidemia
Hypertension

319
466

0
0

Diabetes379

0o0.0001
o0.0001
o0.0001
o0.0001
o0.0001
o0.0001

No. of subjects under treatment

rs1260326 in the glucokinase regulator (GCKR) gene; and rs174547 in the fatty
acid desaturase 1 (FADS1) gene. The SNPs were genotyped using Invader assays
as previously described.24 The success rate of these assays was 499.0%.

Statistical analysis
For the additive model, we coded genotypes as 0, 1 or 2, depending on thenumber of copies of the risk alleles. For the dominant model, homozygosityand heterozygosity with the risk allele were coded as 1 and the other was coded
as 0. For the recessive model, homozygosity with the risk allele was coded as 1
and others were coded as 0. Additive, dominant and recessive models werechosen for each SNP, according to data from the previous reports.13–19 Odds
ratios (ORs) and P-values adjusted for age and gender were calculated using
multiple logistic regression analysis with genotypes, age and gender as theindependent variables. The Hardy–Weinberg equilibrium was assessed using
the w2-test.25 Simple comparison of the clinical data between case and control
groups was carried out using the Mann–Whitney U-test. To test SNPSNP
epistasis for case–control population-based samples, we used the logistic
regression model for each SNP1 and SNP2, and fits the model in theform of Y¼b0+b1SNP1+b2SNP2+b3SNP1SNP2+b4age+b5gender.
Statistical analysis was performed using the software R (http://www.r-project.
org/). P-values were corrected by Bonferroni adjustment and Po0.00152 (0.05/
33) was considered significant.
We examined the power (a¼0.00152) of the test for 1096 cases and 581
controls using GDesignPlus (StaGen, Tokyo, Japan). We considered the controlsof five different levels of minor allele frequencies (0.1–0.5) and four different
levels of the ORs (1.2–1.5). Obtained power was indicated in Supplementary
Table 1.

RESULTS
We used measurements of body mass index (425 kg m2) instead of
waist circumference, as there are still debates on the criteria for waist
circumference, especially in Japanese women. The prevalence of
metabolic syndrome increased with subject age and has been previously reported to be B6–7 times higher in men than in women in
Japan.23 Among our subjects, the ratio of men to women and theaverage age were both significantly higher in the case group than in thecontrol group (Table 1). To adjust for the effects of age and gender,
logistic regression analysis was performed. The most significant

FTO, SCG3, MTMR9 and metabolic syndrome
K Hotta et al
649

Table 2 Genotype and association tests
Genotype
SNP ID

NearbyAllele1/

Risk

gene

Allele2

AlleleCaseControlModel

P-value

OR (95% CI)

rs3101336

NEGR1

A/G

G

4/153/938

1/88/492a

0.55

1.09 (0.82–1.46)

rs2568958
rs2815752

NEGR1
NEGR1

A/G
A/G

A
A

932/154/4
938/154/4

490/88/1
492/88/1

a
a

0.60
0.58

1.08 (0.81–1.44)
1.09 (0.81–1.45)

rs10913469
rs6548238

SEC16B
TMEM18

T/C
T/C

C
C

596/430/69
8/169/919

331/224/26
7/101/473

a
a

0.054
0.037

1.19 (1.00–1.43)
1.32 (1.02–1.71)

rs7561317
rs7647305

TMEM18
ETV5

G/A
C/T

G
C

917/171/8
1015/77/0

472/102/7
537/39/1

a
a

0.037
0.57

1.32 (1.02–1.71)
1.13 (0.75–1.70)

rs10938397
rs6265

GNPDA2
BDNF

A/G
A/G

G
G

513/470/111
182/522/388

289/235/55
100/283/196

a
a

0.37
0.29

1.08 (0.92–1.27)
1.09 (0.93–1.26)

rs925946

BDNF

T/G

T

2/74/1020

0/44/537a

0.75

0.94 (0.63–1.40)

rs10838738
rs7498665

MTCH2
SH2B1

G/A
G/A

G
G

95/437/564
30/280/784

67/234/277
9/133/439

a
a

0.023
0.045

0.83 (0.71–0.98)
1.25 (1.01–1.55)

rs9939609
rs8050136

FTO
FTO

T/A
C/A

A
A

650/379/65
650/377/65

389/170/20
389/171/19aa0.000130.000111.45 (1.20–1.75)
1.45 (1.20–1.76)

rs6499640
rs1121980

FTO
FTO

A/G
A/G

A
A

33/248/814
74/404/614

9/134/438
25/196/357

a
a

0.35
0.0028

1.11 (0.89–1.37)
1.32 (1.10–1.58)

rs1558902
rs1421085

FTO
FTO

A/T
C/T

A
C

63/381/648
63/380/648

19/170/391
19/170/390aa6.6105
7.41051.47 (1.22–1.78)
1.47 (1.21–1.78)

rs1424233
rs17782313

MAF
MC4R

C/G
T/C

C
C

644/368/83
638/392/66

329/211/40
348/200/33

a
a

0.63
0.25

1.04 (0.88–1.23)
1.11 (0.93–1.32)

rs12970134
rs489693

MC4R
MC4R

A/G
C/A

A
A

21/304/762
682/367/45

16/157/404
365/191/24

a
a

0.68
0.43

1.04 (0.85–1.28)
1.08 (0.89–1.30)

rs17700144
rs29941

MC4R
KCTD15

A/G
T/C

A
C

2/67/1025
672/378/45

1/31/546
349/209/22

a
a

0.21
0.62

1.32 (0.85–2.05)
1.05 (0.87–1.26)

rs11084753
rs3764220

KCTD15
SCG3

G/A
A/G

G
A

96/472/528
844/240/11

56/243/282
438/120/20

a
d

0.60
0.00101.04 (0.89–1.23)
3.74 (1.70–8.23)

rs2293855
rs987237

MTMR9
TFAP2B

A/G
A/G

A
G

482/466/146
664/382/48

215/282/83
363/188/27

ra0.00150.48

1.43 (1.15–1.78)
1.07 (0.89–1.28)

rs7826222
rs2605100

MSRA
LYPLAL1

G/C
A/G

C
G

423/511/162
39/315/740

232/269/79
19/159/399

a
a

0.66
0.67

1.04 (0.89–1.21)
0.96 (0.79–1.17)

rs780094
rs1260326

GCKR
GCKR

T/C
T/C

C
T

213/510/368
364/519/211

124/287/169
160/297/124

a
a

0.17
0.072

1.11 (0.96–1.28)
1.15 (0.99–1.33)

rs174547

FADS1

T/C

C

426/537/133

232/262/87

a

0.48

0.95 (0.81–1.11)

Abbreviations: a, additive model; CI, confidence interval; d, dominant model; OR, odds ratio; r, recessive model; SNP, single-nucleotide polymorphism.
The OR for each SNP was adjusted simultaneously for age and gender.

associations were observed for rs1558902 (P¼6.6105, allele-specific
OR (95% confidence interval (CI) adjusted for age and gender)¼1.47(1.22–1.78)) and rs1421085 (P¼7.4105, allele-specific OR (95% CI
adjusted for age and gender)¼1.47 (1.21–1.77)) in the FTO gene
(Table 2). These two SNPs were previously reported as variations
associated with waist circumference18,19 and associated with visceralfat area as determined by computed tomography.22 Two other SNPs
(rs9939609 and rs8050136) in the FTO gene were also significantly
associated with metabolic syndrome even when the conservative
Bonferroni’s correction was applied (Po0.00152). Significant associations were also observed between metabolic syndrome and rs3764220
in the SCG3 gene (P¼0.0010) and rs2293855 in the MTMR9 gene
(P¼0.00146). SNPs, rs6548238 and rs7561317 in the TMEM18 gene,
rs7498665 in the SH2B1 gene and rs1121980 in the FTO gene, were
marginally associated with metabolic syndrome (Po0.05). SNP
rs10838738 in the MTCH2 gene was also marginally associated withmetabolic syndrome, although presence of the G-allele, which is a risk
allele for obesity,15,16 was associated with a reduced risk of metabolic
syndrome. Other SNPs did not show any significant associations withmetabolic syndrome. The lack of significant association of these SNPs
is most likely due to the relatively lower power of this study

Table 3 Results for SNP pairs for epistasis in case–control analysis
SNP1

SNP2

OR (95% CI)

Epistasis P-value

rs2293855
rs2293855

rs3764220
rs1421085

0.07 (0.01–0.70)
0.80 (0.55–1.18)

0.023
0.27

rs2293855
rs2293855

rs1558902
rs8050136

0.81 (0.55–1.19)
0.84 (0.57–1.24)

0.29
0.38

rs2293855
rs3764220

rs9939609
rs1421085

0.82 (0.56–1.21)
1.60 (0.44–5.82)

0.32
0.48

rs3764220

rs1558902

1.60 (0.44–5.84)

0.47

rs3764220
rs3764220

rs8050136
rs9939609

1.58 (0.43–5.74)
1.57 (0.43–5.71)

0.49
0.49

Abbreviations: CI, confidence interval; OR, odds ratio; SNP, single-nucleotide polymorphism.

(Supplementary Table 1). All SNPs were in Hardy–Weinberg equilibrium (P40.05), with the exception of rs6499640 (P¼0.0099) andrs1424233 (P¼0.0034) in the case group and rs3764220 in the control
subjects (P¼0.0018).
As six SNPs were significantly associated with metabolic syndrome,
we tested SNPSNP epistasis. Four SNPs in the FTO gene were in a
Journal of Human Genetics

FTO, SCG3, MTMR9 and metabolic syndrome
K Hotta et al
650

Table 4 Genotype and association tests of dyslipidemia, hypertension and impaired fasting glucose
Dyslipidemia (n¼858)

Hypertension (n¼974)

Impaired fasting glucose (n¼770)

Control
Genotype

Genotype

P-value

OR (95% CI)

Genotype

P-value

OR (95% CI)

Genotype

P-value

OR (95% CI)

rs9939609 389/170/20

511/297/48

0.00051 1.42 (1.17–1.73)

575/342/57

9.51051.47 (1.21–1.79)

458/266/44

0.00024 1.46 (1.19–1.79)

rs8050136 389/171/19
rs1558902 19/170/391

511/295/48
47/298/510

0.00045 1.43 (1.17–1.74)
0.00024 1.45 (1.19–1.77)

575/339/57
55/344/573

8.9105 1.48 (1.22–1.80)
4.8105 1.50 (1.23–1.83)

458/265/44
43/268/456

0.00020 1.47 (1.20–1.80)
0.00011 1.49 (1.22–1.83)

rs1421085 19/170/390
rs3764220 438/120/20

47/297/509
658/190/9

0.00026 1.45 (1.19–1.77)
0.0042 3.37 (1.47–7.76)

55/343/573
752/210/11

5.4105 1.50 (1.23–1.82)
0.0028 3.34 (1.52–7.38)

43/267/456
592/170/8

0.00012 1.49 (1.22–1.83)
0.0026 3.81 (1.60–9.10)

rs2293855 215/282/83 366/372/118 0.0098

1.35 (1.08–1.70) 430/415/127

0.00098

1.46 (1.17–1.83) 351/315/104 0.00039 1.53 (1.21–1.93)

Abbreviations: CI, confidence interval; OR, odds ratio.
The OR for each single-nucleotide polymorphism was adjusted simultaneously for age and gender.

linkage disequilibrium (r240.97), thus, pair of these SNPs were notanalyzed. Any pairs of SNPs did not show significant epistatic effect on
metabolic syndrome (Table 3). We performed the multiple logistic
regression analysis with three genotypes (rs1558902, rs3764220 andrs2293855), age and gender as the independent variables and foundthat effects of these SNPs in the three genes on metabolic syndrome
were additive: rs1558902 (additive model), P¼4.1105, OR (95%CI)¼1.50 (1.23–1.81); rs3764220 (dominant model), P¼0.00018, OR(95% CI)¼4.77 (2.11–10.79); rs2293855 (recessive model),P¼0.00090, OR (95% CI)¼1.46 (1.17–1.82). Similar results wereobtained using rs9939609, rs8050136 and rs1421085, instead of
rs1558902.
Next, we examined the effects of significant SNPs on each metabolic
disorder (dyslipidemia, hypertension and impaired fasting glucose).
SNPs (rs9939609, rs8050136, rs1558902 and rs1421085) in the FTO
gene were significantly associated with dyslipidemia, hypertension andimpaired fasting glucose (Table 4). rs2293855 in the MTMR9 gene wassignificantly associated with hypertension and impaired fasting glucose, and was marginally associated with dyslipidemia. rs3764220 in
the SCG3 gene was marginally associated with all three metabolic
disorders. There were no obvious differences among the effects (OR)on each metabolic disorder.
DISCUSSIONThe SNPs that were most significantly associated with metabolic
syndrome (rs1558902 and rs1421085) exist in the FTO gene. Visceralfat accumulation is the most predominant factor for the developmentof metabolic syndrome. SNPs (rs1558902 and rs1421085) werereported to be associated with waist circumference and visceral fat
area as measured by computed tomography.18,19,22 SNPs (rs9939609,
rs8050136, rs1558902 and rs1421085) were also associated withobesity and type 2 diabetes.15–17 We have previously reported that
rs3764220 in the SCG3 gene was associated with obesity and subcutaneous fat area as measured by computed tomography13 and that
rs2293855 in the MTMR9 gene was associated with obesity andhypertension.14 Therefore, it is likely that rs9939609, rs8050136,
rs1558902 and rs1421085 in the FTO, rs3764220 in the SCG3 andrs2293855 in the MTMR9 genes would be susceptible for metabolic
syndrome. Epistasis, or gene–gene interaction, has recently received
much attention in human genetics.26 In this study, effect of these SNPs
on metabolic syndrome was independent, and epistatic effect was notobserved.
In the simulation study, the power of this test was 0.317 in thefollowing condition; the sizes of the case and the control groups are
1096 and 581, respectively, OR is 1.4, risk allele frequency is 0.2 andthe model of inheritance is additive. Therefore, further studies would
Journal of Human Genetics

be necessary to elucidate the association between SNPs and metabolic
syndrome.
SNPs (rs780094 and rs1260326) in the GCKR gene were previously
reported to be associated with metabolic disorders;27–29 however, thepresent study did not show the association with metabolic syndrome.
The C-allele of rs780094 was reported to be associated with increased
fasting plasma glucose and lower triglyceride levels. The risk allele of
rs780094 had an antagonistic affect on plasma glucose and triglycerides levels. SNP rs174547 in the FADS1 gene, which is in almost
complete linkage disequilibrium with rs174550, is associated withdyslipidemia and type 2 diabetes.28,30 Similar to rs780094, the effectsof the risk allele of rs174547 on type 2 diabetes, hypertension anddyslipidemia are not the same. Thus, rs780094 and rs174550 are not
likely to be important in the development of metabolic syndrome.
SNPs (rs6548238 and rs7561317) in the TMEM18 gene were
marginally associated with metabolic syndrome. We have reported
that these SNPs are associated with obesity.21 Takeuchi et al. reported
that rs48454344, which is in almost complete linkage disequilibriumwith rs6548238 and rs7561317,21 was associated with body mass index
and type 2 diabetes.31 Therefore, rs6548238 and rs7561317 in the
TMEM18 gene might be susceptible for the development of metabolic
syndrome.
The G-allele of rs10838738 in the MTCH2 gene, which is a risk allele
for obesity,16 reduced the risk of metabolic syndrome. Takeuchi et al.31
reported that the G-allele is a risk factor for obesity and is protective
for type 2 diabetes. Although further investigations would be necessary, there is a possibility that there are some variations that are
specifically associated with components of metabolic syndrome, apartfrom obesity.
Our approach effectively indicated that SNPs in the FTO, SCG3 andMTMR9 genes are associated with metabolic syndrome in the Japanese
population, in addition to the previously reported SNPs in the MKKS
gene. SNPs in the FTO, SCG3 and MTMR9 genes had no SNPSNP
epistatic effects on metabolic syndrome. As metabolic syndrome is acomplex concept and the criteria are still controversial, further
investigations are necessary to elucidate the roles of SNPs in the
MKKS, FTO, SCG3 and MTMR9 genes in the development of
metabolic syndrome.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
This work was supported by a Grant-in-Aid from the Ministry of Education,
Science, Sports, and Culture of Japan (21591186) and by the Mitsui Life Science
Social Welfare Foundation.

FTO, SCG3, MTMR9 and metabolic syndrome
K Hotta et al651

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