Journal of Human Genetics (2010) 55, 738–742& 2010 The Japan Society of Human Genetics All rights reserved 1434-5161/10 $32.00
www.nature.com/jhg

ORIGINAL ARTICLE

Polymorphisms in NRXN3, TFAP2B, MSRA, LYPLAL1,
FTO and MC4R and their effect on visceral fat areain the Japanese population
Kikuko Hotta1, Michihiro Nakamura1, Takahiro Nakamura2,3, Tomoaki Matsuo4, Yoshio Nakata4,
Seika Kamohara5, Nobuyuki Miyatake6, Kazuaki Kotani7, Ryoya Komatsu8, Naoto Itoh9, Ikuo Mineo10,
Jun Wada11, Masato Yoneda12, Atsushi Nakajima12, Tohru Funahashi7, Shigeru Miyazaki13,
Katsuto Tokunaga14, Manabu Kawamoto15, Hiroaki Masuzaki16, Takato Ueno17, Kazuyuki Hamaguchi18,
Kiyoji Tanaka4, Kentaro Yamada19, Toshiaki Hanafusa20, Shinichi Oikawa21, Hironobu Yoshimatsu22,
Kazuwa Nakao23, Toshiie Sakata22, Yuji Matsuzawa7, Yusuke Nakamura24 and Naoyuki Kamatani3
The predominant risk factor of metabolic syndrome is intra-abdominal fat accumulation, which is determined by waist
circumference and waist–hip ratio measurements and visceral fat area (VFA) that is measured by computed tomography (CT).
There is evidence that waist circumference and waist–hip ratio in the Caucasian population are associated with variations inseveral genes, including neurexin 3 (NRXN3), transcription factor AP-2b (TFAP2B), methionine sulfoxide reductase A (MSRA),
lysophospholipase-like-1 (LYPLAL1), fat mass and obesity associated (FTO) and melanocortin 4 receptor (MC4R) genes. To
investigate the relationship between VFA and subcutaneous fat area (SFA) and these genes in the recruited Japanese population,
we genotyped 8 single-nucleotide polymorphisms (SNPs) in these 6 genes from 1228 subjects. Multiple regression analysis
revealed that gender, age, and rs1558902 and rs1421085 genotypes (additive model) in FTO were significantly associated withbody mass index (BMI; P¼0.0039 and 0.0039, respectively), SFA (P¼0.0027 and 0.0023, respectively) and VFA (P¼0.045
and 0.040, respectively). However, SNPs in other genes, namely, NRXN3, TFAP2B, MSRA, LYPLAL1 and MC4R were notsignificantly associated with BMI, SFA or VFA. Our data suggest that some SNPs, which were identified in genome-wide studies
in the Caucasians, also confer susceptibility to fat distribution in the Japanese subjects.
Journal of Human Genetics (2010) 55, 738–742; doi:10.1038/jhg.2010.99; published online 12 August 2010
Keywords: fat mass and obesity associated (FTO); lysophospholipase-like 1 (LYPLAL1); melanocortin 4 receptor (MC4R); methionine
sulfoxide reductase A (MSRA); neurexin 3 (NRXN3); subcutaneous fat area; transcription factor AP-2-b (TFAP2B); visceral fat area

INTRODUCTION
Metabolic syndrome is a common clinical phenotype that is manifested as concurrent metabolic abnormalities, including central obesity, glucose intolerance, dyslipidemia and hypertension.1 Because
several other definitions also exist,2 the adequacy of this concept
remains debatable. Recently, metabolic syndrome has attracted

considerable interest because of increasing the number of patients.
Although the pathogenesis of metabolic syndrome is not fully understood, the predominant underlying risk factor is considered to bevisceral obesity due to an atherogenic diet and physical inactivity, in
addition to certain genetic factors.1,2 Adipose tissue, especially thevisceral fat, secretes various adipocytokines. An increase in the adipose

1Laboratory for Endocrinology and Metabolism, Center for Genomic Medicine, RIKEN, Yokohama, Japan; 2Laboratory for Mathematics, National Defense Medical College,
Tokorozawa, Japan; 3Laboratory for Statistical Analysis, Center for Genomic Medicine, RIKEN, Tokyo, Japan; 4Graduate School of Comprehensive Human Sciences, University ofTsukuba, Tsukuba, Japan; 5Health Science University, Yamanashi, Japan; 6Okayama Southern Institute of Health, Okayama, Japan; 7Department of Metabolic Medicine,
Graduate School of Medicine, Osaka University, Osaka, Japan; 8Rinku General Medical Center, Osaka, Japan; 9Toyonaka Municipal Hospital, Osaka, Japan; 10Otemae Hospital,
Osaka, Japan; 11Department of Medicine and Clinical Science, Okayama University Graduate School of Medicine and Dentistry, Okayama, Japan; 12Division of Gastroenterology,
Yokohama City University Graduate School of Medicine, Yokohama, Japan; 13Tokyo Postal Services Agency Hospital, Tokyo, Japan; 14Itami City Hospital, Hyogo, Japan; 15Instituteof Rheumatology, Tokyo Women’s Medical University, Tokyo, Japan; 16Division of Endocrinology and Metabolism, Second Department of Internal Medicine, University of the
Ryukyus Faculty of Medicine, Okinawa, Japan; 17Research Center for Innovative Cancer Therapy, Kurume University, Kurume, Japan; 18Department of Community Health andGerontological Nursing, Faculty of Medicine, Oita University, Oita, Japan; 19Division of Endocrinology and Metabolism, Department of Medicine, Kurume University, Kurume,
Japan; 20First Department of Internal Medicine, Osaka Medical College, Osaka, Japan; 21Division of Endocrinology and Metabolism, Department of Medicine, Nippon Medical
School, Tokyo, Japan; 22Department of Internal Medicine I, Faculty of Medicine, Oita University, Oita, Japan; 23Department of Medicine and Clinical Science, Kyoto University
Graduate School of Medicine, Kyoto, Japan and 24Laboratory for Molecular Medicine, Human Genome Center, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
Correspondence: Dr K Hotta, Laboratory for Endocrinology and Metabolism, Center for Genomic Medicine, RIKEN, 1-7-22, Suehiro, Tsurumi-ku, Yokohama, Kanagawa 230-0045,
Japan.
E-mail: kikuko@src.riken.jp
Received 14 June 2010; revised and accepted 21 July 2010; published online 12 August 2010

Gene polymorphisms and body fat distribution
K Hotta et al
739

Table 1 Clinical characteristics of the subjects

Age (years)
BMI (kg 2)
VFA (cm2)
SFA (cm2)

Men (n¼518)

Women (n¼710)

Total (n¼1228)

49.7±11.930.3±6.2
160.9±66.8

52.1±11.3
28.2±5.3
102.5±54.5

51.1±11.6
29.1±5.8
127.1±66.5

213.0±110.8

246.4±98.6232.3±105.2

Abbreviations: BMI, body mass index; SFA, subcutaneous fat area; VFA, visceral fat area.
Data are shown as the mean ± s.d.

tissue mass leads to an alteration in the plasma adipocytokine level,
resulting in dyslipidemia, hypertension and insulin resistance.3,4 Intraabdominal fat accumulation (central adiposity) is determined in termsof the waist circumference and waist–hip ratio measurements andvisceral fat area (VFA) that is measured by computed tomography (CT).1,5,6 Recently, two genome-wide association studies had
been conducted to identify the loci that were linked with waist
circumference or waist–hip ratio.7,8
In this study, we investigated the association of single-nucleotide
polymorphisms (SNPs) in neurexin 3 (NRXN3), transcription factor
AP-2b (TFAP2B), methionine sulfoxide reductase A (MSRA), lysophospholipase-like-1 (LYPLAL1), fat mass and obesity associated(FTO) and melanocortin 4 receptor (MC4R) genes with VFA andsubcutaneous fat area (SFA) that were determined by CT.
MATERIALS AND METHODS
Study subjects
We recruited 1228 Japanese subjects from outpatient clinics after they agreed toundergo CT examinations (supine position) that were performed to determine
the VFA and SFA values at the level of the umbilicus (L4–L5). Both VFA andSFA were calculated using the FatScan software program (N2system, Osaka,
Japan).9 The clinical characteristics of the patients are summarized in Table 1.
All subjects provided their informed written consent, and the protocol wasapproved by the ethics committee of each institution and by that of RIKEN.

DNA extraction 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 rs1558902 and rs1421085 in
FTO, rs489693 and rs17700144 in MC4R, rs1046997 in NRXN3, rs987237 in
TFAP2B, rs782622 (rs545854) in MSRA and rs2605100 in LYPLAL1. The SNPs
were genotyped using Invader assays as described previously.10 The success rateof this assays was 499.0%.

Statistical analysis
Differences in the quantitative clinical data between case and control groups
were tested by the Mann–Whitney U-test. Differences in the quantities ofclinical data between the different genotypes were assessed by the Kruskal–
Wallis test. We coded genotypes as 0, 1 or 2 depending on the number of copiesof the risk alleles. Multiple linear regression analysis was performed to test theindependent effect of risk alleles on body mass index (BMI), VFA or SFA byconsidering the effects of other variables (age and gender), which were assumed
to be independent of the effect of the SNPs. The significance of the association
between an independent variable and dependent variable was determined using
a t-test. Hardy–Weinberg equilibrium was assessed using the w2-test.11

RESULTS
BMI, VFA and SFA are known to be affected by gender, and an
association between rs2605100 in LYPLAL1 and the waist–hip ratio
has been reported only in women.7 Therefore, we first compared theanthropometric parameters (BMI, VFA and SFA) among the different
genotypes in the men and women. One SNP—rs10146997 in
NRXN3—was found to be monomorphic, as reported in the HapMap

database. Two SNPs (rs1558902 and rs1421085) in FTO were significantly associated with BMI in women (Table 2). The association of
rs489693 and rs17700144 in MC4R with BMI has been reported in the
Caucasian population;7,8 however, such association was not observedin our study. The SNPs in TFAP2B, MSRA and LYPLAL1 were notassociated with BMI, which is a finding consistent with that of
Lindgren et al.7 No SNPs in the five genes were associated with VFA
in either men or women (Table 3). Although rs489693 was marginallyassociated with VFA in men, the risk allele was different from thatreported in the Caucasian population.7 Two SNPs (rs1558902 andrs1421085) in FTO were significantly associated with SFA both in men(P¼0.034 and 0.040, respectively) and women (P¼0.010 and 0.0083,
respectively) (Table 4). SNPs in other genes were not significantly
associated with SFA. All SNPs were found to exhibit Hardy–Weinberg
equilibrium (P40.10).
Next, we attempted to perform multiple linear regression analysis
by using BMI, VFA and SFA as the dependent variables, and with age,gender or genotype as an explanatory variable. We transformed
genotypes to 0, 1 or 2 depending on the number of copies of therisk alleles. The A-allele of rs1558902 and the C-allele of rs1421085 in
FTO were significantly associated with increases in BMI (P¼0.0039
and 0.0039, respectively), VFA (P¼0.045 and 0.040, respectively) andSFA (P¼0.0027 and 0.0023, respectively) even after age and genderwere included in the model (Supplementary Tables 1, 2, and 3).
Multiple linear regression analysis showed that the SNPs of othergenes were not significantly associated with BMI, VFA and SFA.
We also conducted power analysis of linear regression (additivemodel) with a significance level of 0.05, considering the effect size ofthe parameters. For rs1558902, the estimated effect sizes per allele(regression coefficients) for VFA and SFA were 5.8 and 14.4 cm2,
respectively (Supplementary Tables 2 and 3). The power of ourstatistical test was calculated on the basis of these estimated effectsizes and by performing 10 000 simulations. When the allele frequency
was assumed to be 0.2, the power was estimated to be 0.32 for VFA
and 0.81 for SFA; however, when the allele frequency was assumed tobe 0.1, the respective powers were estimated to be 0.20 and 0.56.
DISCUSSION
The most important risk factor for metabolic syndrome is visceral fat
obesity. According to the criteria released by the Japanese Committee
for the Diagnostic Criteria of Metabolic Syndrome in April 2005,5
metabolic syndrome is defined by the presence of two or moreabnormalities (dyslipidemia, impaired glucose tolerance or diabetes
and hypertension), in addition to visceral fat obesity. The cutoff points
for visceral fat obesity (waist circumference, 85 cm in men and 90 cm
in women) are based on the cutoff point for VFA (100 cm2) that isdetermined by CT.5,6 Visceral fat mass measurement by CT is more
precise than that derived from BMI or waist circumference measurements. Furthermore, for predicting metabolic risk-factor clustering,
VFA is superior to waist circumference or BMI.12 Therefore, we
examined the association of VFA and SFA with the SNPs related to
waist circumference and waist–hip ratio, and identified in a genomewide study.7,8 We found that the SNPs in FTO were significantly
associated with BMI, as we have previously reported.13 We also foundthat these SNPs were associated with VFA and SFA; however, theassociation between these SNPs and VFA was marginal because VFA
was not significantly different among the genotypes in men. We did not
find any association between other SNPs and BMI, VFA or SFA. Thesefindings may be due to the low power of this study. Therefore, further
studies with more subjects should be conducted to conclude that SNPs
in genes other than FTO are not associated with VFA or SFA.
Journal of Human Genetics

Gene polymorphisms and body fat distribution
K Hotta et al
740

Table 2 Comparison of BMI among the different genotypes
BMI (kg m2)
SNP ID

Closest gene

Allele1/allele2

Risk allele

rs1558902

FTO

A/T

A

rs1421085

rs489693

rs17700144

FTO

MC4R

MC4R

C/T

C

C/A

A

A/G

A

rs987237

TFAP2B

A/G

G

rs7826222

MSRA

C/G

C

rs2605100

LYPLAL1

A/G

G

Gender

GenotypeMen

25/190/302

Women
Total

39/239/430
64/429/732

Men
Women

25/189/304
39/238/431

Total
Men

64/427/735
308/184/26

Women
Total

422/238/50
730/422/76

11

12

22

P-value30.3±5.1
29.6±6.7
29.9±6.1
30.3±5.1

30.9±5.928.8±5.629.9±6.4
27.7±5.0

0.058

29.7±5.830.9±5.928.6±5.730.0±6.4

29.6±6.7
29.9±6.1

28.8±5.6
29.7±5.8

27.7±5.0
28.6±5.730.8±6.928.4±5.2

29.5±4.6
27.8±5.530.2±6.6
28.3±6.2

29.4±6.1

28.9±6.4
30.3±6.2

0.012
0.000210.076
0.00930.00022
0.24
0.13
0.045

Men

0/30/488—28.5±5.2
30.7±5.6

Women
Total

2/42/662
2/72/1150
330/168/19
434/242/32

27.1±3.828.6±4.930.6±6.1
28.0±5.2

28.3±5.429.1±5.930.1±6.3
28.8±4.7

0.34
0.62

Men
Women

25.0, 26.2
25.0, 26.2
30.2±6.2
28.2±5.5

Total
Men

764/410/51
77/235/205
117/321/271
194/556/476

29.1±5.7
30.5±5.928.2±4.829.3±5.430.0±6.928.2±5.9

0.80
0.22

Women
Total

29.1±5.930.7±5.1
28.0±5.3

Men
Women

22/145/351
14/200/495

29.1±5.4
30.5±5.229.2±5.430.9±6.6

29.0±6.4
30.1±6.1

Total

36/345/846

26.6±4.729.0±5.3

28.7±6.0
29.6±6.3

28.0±5.0
28.9±5.6

0.48

0.56
0.47

0.68
0.44
0.23
0.24
0.16

Abbreviations: BMI, body mass index; SNP, single-nucleotide polymorphism.
P-values were analyzed using the Kruskal–Wallis test. Pvalues for 12 vs 22 at rs17700144 were analyzed using the Mann–Whitney U-test. Data are presented as the mean±s.d.

Table 3 Comparison of VFA among the different genotypes
VFA (cm2)
SNP ID

Closest gene

Allele1/allele2

Risk allele

Genderrs1558902

FTO

A/T

A

Men

25/190/302

Women
Total

39/239/430
64/429/732

Men
Women

25/189/304
39/238/431

Total
Men

64/427/735
308/184/26

Women
Total

422/238/50
730/422/76

Men
Women

0/30/488
2/42/662—
68.3, 90.2

Total
Men

2/72/1150
330/168/19

Women
Total

434/242/32
764/410/51

68.3, 90.2
158.4±65.7
102.7±53.4

Men
Women

77/235/205
117/321/271

Total
Men

194/556/476
22/145/351

Women
Total

14/200/495
36/345/846

rs1421085

rs489693

rs17700144

rs987237

rs7826222

rs2605100

FTO

MC4R

MC4R

TFAP2BMSRA

LYPLAL1

C/T

C/A

A/G

A/G

C/G

A/G

C

A

A

G

C

G

Genotype

11

12

22

P-value

163.2±52.1
105.8±69.3
128.2±68.7163.2±52.1

166.9±67.5
106.2±53.1

157.1±67.4
100.1±53.70.22

133.0±67.0
167.2±67.6

123.6±66.0
156.8±67.2

105.8±69.3
128.2±68.7

106.1±53.2
133.1±67.2

100.0±53.4
123.5±65.7

166.7±67.2
105.6±55.1

154.7±66.2
95.5±51.2

135.5±56.7
109.1±61.8131.4±67.6

121.3±65.2
161.5±62.4

118.2±61.0
160.9±67.1

104.8±50.8128.4±62.2

102.4±54.8127.2±66.9

169.0±69.1
101.3±55.7132.6±56.7
109.4±61.5

126.7±65.2161.7±65.6

129.0±69.9
162.9±66.6

118.0±60.3
158.5±67.7

105.4±57.1
127.7±66.5

103.3±54.8128.5±66.9

100.5±53.1
125.4±66.3

153.2±65.4
94.1±41.1

167.9±74.5
101.7±53.0

158.5±63.4
103.1±55.5130.2±63.6

129.5±70.9126.1±64.9

0.23
0.039
0.18
0.25
0.037
0.047
0.089
0.035
0.87
0.60
0.69
0.06
0.71
0.65
0.80
0.74
0.71
0.51
0.92
0.86

Abbreviations: VFA, visceral fat area; SNP, single-nucleotide polymorphism.
P-values were analyzed using the Kruskal–Wallis test. P-values for 12 vs 22 at rs17700144 were analyzed using the Mann–Whitney U-test. Data are presented as the mean±s.d.

SNPs in FTO were associated with SFA and VFA. Therefore, someof the SNPs, which were identified by genome-wide association studies
in the Caucasian population, were also found to confer susceptibility
Journal of Human Genetics

to body fat distribution in the Japanese subjects. On the other hand,
rs10146997 in NRXN3 was found to be monomorphic in our subjects,
and we could not find associations between SNPs in MC4R, TFAP2B,

Gene polymorphisms and body fat distribution
K Hotta et al
741

Table 4 Comparison of SFA among the different genotypes
SFA (cm2)
SNP ID

Closest gene

Allele1/allele2

Risk allele

rs1558902

FTO

A/T

A

rs1421085

rs489693

rs17700144

FTO

MC4R

MC4R

C/T

C/A

A/G

C

A

A

rs987237

TFAP2B

A/G

G

rs7826222

MSRA

C/G

C

rs2605100

LYPLAL1

A/G

G

Gender

GenotypeMen

25/190/302

Women
Total

39/239/430
64/429/732

Men
Women

25/189/304
39/238/431

Total
Men

64/427/735
308/184/26

Women
Total

422/238/50
730/422/76

11

12

22

P-value

215.9±110.5
279.6±129.9
254.3±125.7
215.9±110.5

223.0±104.4
255.9±96.9241.3±101.5
223.1±104.6

206.2±114.7
238.5±95.6

0.034

225.2±105.1
206.4±114.4

279.6±129.9
254.3±125.7

256.2±96.9
241.6±101.6

238.4±95.6
225.1±104.9

220.8±114.4
250.0±95.2

199.7±97.0
242.8±106.6

214.0±149.6
232.9±86.8

237.7±104.6

226.4±111.7
212.1±110.3

0.010
0.00240.040
0.00830.0021
0.16
0.29
0.051

Men

0/30/488—

223.9±104.6
227.6±119.8

Women
Total

2/42/662
2/72/1150
330/168/19
434/242/32

222.4±76.3
224.5±96.1
225.0±119.6
237.8±92.1

248.6±99.9233.1±105.9
195.0±96.8
246.2±88.7

0.15
0.67

Men
Women

179.3, 180.0
179.3, 180.0
207.8±106.8
251.5±102.6

Total
Men

764/410/51
77/235/205
117/321/271
194/556/476

232.6±104.3
211.9±109.2
247.1±91.3

227.1±94.2
207.9±109.3
247.9±109.0

0.96
0.44

Women
Total

232.6±106.6
230.0±119.7
241.9±93.0

Men
Women

22/145/351
14/200/495

237.1±104.3
217.3±106.5

232.3±100.7
216.2±112.5

230.7±110.8
211.4±110.7

Total

36/345/846

224.2±94.0
220.0±100.5

257.5±108.6
240.1±112.0

242.8±94.2
229.7±102.5

0.34

0.20
0.38

0.65
0.52
0.66
0.17
0.21

Abbreviations: SFA, subcutaneous fat area; SNP, single-nucleotide polymorphism.
P-values were analyzed using the Kruskal–Wallis test. P-values for 12 vs 22 at rs17700144 were analyzed using the Mann–Whitney U-test. Data are presented as the mean±s.d.

MSRA or LYPLAL1 and VFA or SFA. Thus, there is a possibility thatgenetic susceptibility to body fat distribution is likely to differ among
various ethnic groups.
rs1558902 and rs1421085 in FTO were in linkage disequilibriumwith rs9939609, which is associated with obesity and type II diabetes.14–16 Concentrations of circulating adipocytokines are affected by
the accumulation of adipose tissue, especially visceral adipose tissue.
Therefore, rs1558902 and rs1421085 probably affect subcutaneous andvisceral fat accumulation, leading to the development of type II
diabetes by altered adipocytokine secretion. The precise mechanismof how FTO affects adipose tissue accumulation is not clear yet;however, there is evidence that FTO is involved in the developmentof obesity. FTO is ubiquitously expressed, and a strong expression isobserved in the arcuate, paraventricular, dorsomedial and ventromedial
nuclei, which are critical energy-regulation sites.16–18 FTO also exists
in the nucleus and is reported to be a member of the Fe(II) and
2-oxoglutarate-dependent oxygenase superfamily.17,19 FTO-deficientand dominant-negative mutant FTO mice have shown reduced fat
mass and increased energy expenditure.20,21 These reports indicate that
FTO has an important role in energy homeostasis by regulating energy
expenditure. Although the effects of the SNPs rs1558902 and rs1421085
on gene expression need to be elucidated, variations in FTO probably
affect subcutaneous and visceral fat accumulation.
In summary, we showed that rs1558902 and rs1421085 in FTO may
be associated with SFA and VFA in the Japanese population. In ourstudy, rs489693 and rs17700144 in MC4R, rs1046997 in NRXN3,
rs987237 in TFAP2B, rs782622 (rs545854) in MSRA and rs2605100 in
LYPLAL1 were not associated with SFA or VFA.
ACKNOWLEDGEMENTS
We express our appreciation to Ms Ritsuko Kusano for her contribution to ourstudy. This work was supported by a grant from the Grants-in-Aid from the

Ministry of Education, Culture, Sports, Science and Technology of Japan(21591186) and by the Mitsui Life Science Social Welfare Foundation.

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