Mutagenesis vol. 28 no. 1 pp. 25–37, 2013

doi:10.1093/mutage/ges048
TCF7L2 gene polymorphisms and type 2 diabetes risk: a comprehensive and updated
meta-analysis involving 121 174 subjects
Sihua Peng1, Yimin Zhu2, Bingjian Lü3, Fangying Xu1,
Xiaobo Li1 and Maode Lai1,*
Department of Pathology, Zhejiang University School of Medicine,
Hangzhou 310058, P.R.China, 2Department of Public Health, Zhejiang
University School of Medicine, Hangzhou 310058, P.R.China, 3Department of
Surgical Pathology, Affiliated Women’s Hospital, Zhejiang University School
of Medicine, Hangzhou 310003, P.R.China
1
*To whom correspondence should be addressed. 388# Yuhangtang Road,
Hangzhou, Zhejiang 310058, P.R.China. Tel: +86 571 88208197;
Fax: +86 571 88208199; Email: [email protected]
Recently, many new loci associated with type 2 diabetes
have been uncovered by genetic association studies and
genome-wide association studies. As more reports are made,
particularly with respect to varying ethnicities, there is
a need to determine more precisely the effect sizes in each
major racial group. In addition, some reports have claimed
ethnic-specific associations with alternative single-nucleotide
polymorphisms (SNPs), and to that end there has been a
degree of confusion. We conducted a meta-analysis using an
additive genetic model. Eight polymorphisms in 155 studies
with 121 174 subjects (53 385 cases and 67 789 controls) were
addressed in this meta-analysis. Significant associations were
found between type 2 diabetes and rs7903146, rs12255372,
rs11196205, rs7901695, rs7895340 and rs4506565, with
summary odds ratios (ORs) (95% confidence interval) of
1.39 (1.34–1.45), 1.33 (1.27–1.40), 1.20 (1.14–1.26), 1.32
(1.25–1.39), 1.21 (1.13–1.29) and 1.39 (1.29–1.49), respectively. In addition, no significant associations were found
between the two polymorphisms (rs290487 and rs11196218)
and type 2 diabetes. The summary ORs for the six statistically significant associations (P < 0.05) were further evaluated by estimating the false-positive report probability, with
results indicating that all of the six significant associations
were considered noteworthy, and may plausibly be true
associations. Significant associations were found between
the six polymorphisms (rs7903146, rs12255372, rs11196205,
rs7901695, rs7895340 and rs4506565) in the TCF7L2 gene
and type 2 diabetes risk, and the other two polymorphisms
(rs11196218 and rs290487) were not found to be significantly associated with type 2 diabetes. Subgroups analyses
show that significant associations are not found between the
six SNPs (rs7903146, rs12255372, rs11196205, rs7901695,
rs7895340, and rs4506565) and the type 2 diabetes in some
ethnic populations.
Introduction
More than 170 million individuals suffer from type 2 diabetes worldwide, and the rapid increase in the prevalence of
type 2 diabetes is believed to be a result of environmental
factors, such as increased food intake and decreased physical
activity, acting on genetically susceptible individuals (1–3).
Recently, many new loci associated with type 2 diabetes have
been uncovered by genome-wide association study (GWAS)
Materials and methods
The meta-analysis was performed according to the PRISMA guidelines (supplementary Table I, available at Mutagenesis Online, Figure 1) (20).
Literature search
Scopus, PubMed, and Web of Science databases were searched (to May
2012) for studies describing the association between TCF7L2 polymorphisms
© The Author 2012. Published by Oxford University Press on behalf of the UK Environmental Mutagen Society.
All rights reserved. For permissions, please e-mail: [email protected].
25
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Received on August 26, 2011; revised on June 25, 2012; accepted July 10, 2012
screening (4,5), making the genetic risk factors for type 2 diabetes a major focus of research (6). There is an indication that
the pathogenesis of type 2 diabetes is related to variation in
the transcription factor 7-like 2 (TCF7L2) gene, which spans
a 215 863 base-pair region on chromosome 10q25.3 (7); its
product is a high-mobility group box-containing transcription
factor previously implicated in blood glucose homeostasis,
which plays a significant role in the Wnt signalling pathway
(8). Lyssenko et al. (9) found that the alteration of TCF7L2
expression and function disrupts pancreatic islet function, perhaps through dysregulation of proglucagon gene expression,
causing reduced insulin secretion and increased risk of type
2 diabetes. Recently, Gaulton et al. (10) published extensive
functional studies on rs7903146 in TCF7L2, indicating that
genetic variation at this locus acts in cis with local chromatin
and regulatory changes.
TCF7L2 has the largest effect among the various susceptibility genes on type 2 diabetes, such as KCNQ1, CDKN2B,
FTO and HHEX/IDE (11). The associations between various
single-nucleotide polymorphisms (SNPs) in TCF7L2 (12–14),
such as rs7903146, rs7901695 and rs12255372, were investigated. However, some results were found to be inconsistent. To this end, Cauchi et al. (15) conducted a meta-analysis
on TCF7L2 rs7903146 and type 2 diabetes in early 2007 and
suggested that rs7903146 is associated with type 2 diabetes
in various ethnic groups. In 2009, Tong et al. (16) conducted
another meta-analysis in which four SNPs in TCF7L2, including rs7903146, rs12255372, rs7901695 and rs11196205, were
reported to be associated with type 2 diabetes. Also in 2009,
Luo et al. (17) published a meta-analysis in which five SNPs
in TCF7L2, including rs7903146, rs12255372, rs11196205,
rs11196218 and rs290487, were addressed, but the study only
investigated the associations between the five SNPs and type 2
diabetes risk in an East Asian population. Thus, six SNPs were
evaluated in the three meta-analyses described above. After
the meta-analyses were published, more association studies
were performed to address the associations between these six
SNPs and type 2 diabetes risk. In particular, novel SNPs, e.g.
rs7895340 (18) and rs4506565 (19), were revealed to be associated with type 2 diabetes risks. However, as more and more
reports are published, particularly with respect to varying ethnicities, there is a need to determine more precisely the effect
sizes in each major ethnic group. In addition, some reports have
claimed ethnic-specific associations with alternative SNPs, and
to that end there has been a degree of confusion. Thus, a systematic and updated meta-analysis is arguably necessary to further
investigate the associations between the various SNPs and the
type 2 diabetes risk.
S. Peng et al.
Table I. Characteristics included in this meta-analysis of the association between the TCF7L2 polymorphisms and type 2 diabetes
Year
Country
Ethnicity
Sex (male/
female)
Subjects
Mean age (year) BMI (kg/m2) Source of controls
(case/control) (case/control)
(case/control)
SNPs involved
Grant_a
(12)a
2006
Iceland
Caucasian
1172/944
1185/931
66.7/50.7
29.7/26.7
Unrelated population,
non-diabetic controls
rs7903146,
rs12255372,
rs7901695,
rs11196205,
rs7895340
Grant_b
2006
Denmark
Caucasian
0/767
228/539
73.7/72.0
28.1/25.7
Unrelated population,
non-diabetic controls
rs7903146,
rs12255372,
rs7901695,
rs11196205,
rs7895340
Grant_c
2006
USA
Caucasian
578/313
361/530
63.3/60.0
29.7/28.2
Unrelated population,
non-diabetic controls
rs7903146,
rs12255372,
rs7901695,
rs11196205,
rs7895340
Scott (13)
2006
Finland
Caucasian
1173/931
1151/953
53.6/ NR
29.8/26.8
Age-, sex-, regional- matched
normal glucose-tolerant
controls
rs7903146,
rs12255372,
rs7901695,
rs11196205,
rs7895340
Cauchi
(31)
2006
French
Caucasian
2422/2444
2367/2499
57.1/58.6
30.0/22.8
Normoglycemic middle-aged
nonobese controls
rs7903146,
rs12255372
Groves
(33)
2006
UK
Caucasian
2305/1877
2158/2024
61.2/NR
28.4/NR
British Birth Cohort of 1958
and human random control
rs7903146,
rs12255372,
rs4506565
Zhang
(66)
2006
USA
Caucasian
1738/1782
1573/1947
56.6/56.5
30.6/27.4
Age-, race-, and BMI-matched rs12255372
controls without type 2
diabetes
Humphries
_a (41)b
2006
UK
Caucasian
NR/NRk
1459/2518
64.9/56.0
29.4/26.1
Healthy white men registered
with nine primary care practices in the UK
rs7903146,
rs12255372
Humphries
_b
2006
UK
Indian
NR/NR
919/312
58.7/NR
28.4/26.1
Non-diabetic ethically
matched controls from south
London
rs7903146,
rs12255372
Humphries
_c
2006
UK
African
NR/NR
385/331
61.6/NR
27.4/26.1
Non-diabetic ethically
matched controls from south
London
rs7903146,
rs12255372
Sale (29)
2007
USA
African
517/656
577/596
62.2/49.3
NR/NR
Regional matched controls
without type 2 diabetes
rs7903146,
rs12255372,
rs7901695,
rs11196205,
rs7895340
Guo (18)
2007
USA
American
1482/2019
Pima Indian
1561/1940
37.2/31.1
33.4/-
Racially matched non-diabetic rs7903146,
controls
rs12255372,
rs7901695,
rs11196205,
rs7895340
Ng (30)
2007
China
East Asian
332/520
433/419
39.4/40.6
25.8/22.5
Non-diabetic controls
Hayashi
(34)
2007
Japan
East Asian
1616/1078
1630/1064
61.5/45.5
23.7/22.9
Non-diabetic controls were
rs7903146,
enrolled from an annual health rs12255372,
check
rs7901695,
rs11196205
Horikoshi
_a (36)c
2007
Japan
East Asian
251/212
192/272
62.8/68.6
23.9/24.0
Non-diabetic controls from the rs7903146,
Health Management Center in rs12255372,
Hiroshima, Japan
rs7901695,
rs11196205,
rs7895340
Horikoshi
_b
2007
Japan
East Asian
562/455
657/360
63.2/70.5
24.4/23.6
Non-diabetic controls from the rs7903146,
Health Management Center in rs12255372,
Hiroshima, Japan
rs7901695,
rs11196205,
rs7895340
26
rs7903146,
rs11196205
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Study
TCF7L2 polymorphisms and type 2 diabetes
Table I. Continued
Study
Year
Country
Ethnicity
Sex (male/
female)
Subjects
Mean age (year) BMI (kg/m2) Source of controls
(case/control)
(case/control) (case/control)
Horikoshi
_c
2007
Japan
East Asian
310/238
356/192
70.6/68.6
24.0/23.8
Non-diabetic controls from the rs7903146,
Health Management Center in rs12255372,
Hiroshima, Japan
rs7901695,
rs11196205,
rs7895340
Kimber
(37)
2007
UK
Caucasian
3541/2975
3225/3291
60.7/65.3
31.3/26.8
Non-diabetic controls from the rs 12255372,
Go-DARTS2 study
rs7903146
Marzi
(38)
2007
Germany
Caucasian
1263/1106
678/1691
62.5/60.8
30.3/28.1
Non-diabetic controls
Mayans
(39)
2007
Sweden
Caucasian
NR/NR
872/857
NR/NR
NR/NR
Age-, sex- and geographically rs7903146,
matched controls without type rs12255372,
2 diabetes
rs7901695,
rs11196205,
van V-O
(40)
2007
Dutch
Caucasian
626/786
502/920
70.8/47.8
27.4/NR
Healthy blood bank donors of
white Dutch origin
Parra
(42)
2007
Mexican
Mexican
American
284/277
286/275
55.5/69.2
29.7/27.6
Normal glucose-tolerant
rs7903146,
controls with no family history rs12255372
of diabetes
Bodhini
(43)
2007
India
Indian Asian 900/1169
1031/1038
41.0/49.0
25.1/23.6
Normal glucose-tolerant
controls
Chandak
(44)
2007
India
Indian Asian 699/655
955/399
47.2/30.9
25.4/20.5
Ethnically matched controls
rs7903146,
with normal glucose tolerance rs12255372,
rs4506565
Chang
(45)
2007
China
East Asian
811/709
760/760
60.0/64.5
24.7/23.6
Normal glucose tolerant
controls in routine health
examination in Taiwan
rs 4506565,
rs7895340,
rs 12255372,
rs7903146
Scott_a
(5)d
2007
USA
Caucasian
1227/1108
1161/1174
63.4/64.0
29.8/26.8
Normal glucose-tolerant
(NGT) controls
rs7903146
Scott_b
2007
USA
Caucasian
1492/981
1215/1258
60.0/59.0
30.1/26.4
Normal glucose-tolerant
(NGT) controls
rs7903146
Sladek_a
(4)e
2007
France
Caucasian
679/676
686/669
60.2/53.4
25.8/23.2
Normal glucose-tolerant
(NGT) with BMI<27 kg/m2
rs 7903146,
rs12255372
Sladek_b
2007
France
Caucasian
2868/2643
2617/2894
62.2/56.4
28.9/25.3
Normal glucose-tolerant
(NGT) with BMI<35kg/m2
rs7903146,
rs12255372
Cauchi_a
(15)f
2007
North
African
African
277/633
504/406
58.0/55.0
28.0/27.2
Age at examination over 45
yr, normal fasting glucose
according to 1997 American
DiabetesAssociation (ADA),
and BMI <27 kg/m2.
rs7903146
Cauchi_b
2007
Austria
Caucasian
1010/551
486/1075
56.5/51.5
30.7/26.4
Age at examination over 45
yr, normal fasting glucose
according to 1997 American
DiabetesAssociation (ADA),
and BMI <27 kg/m2.
rs7903146
De Silva
(46)
2007
UK
Caucasian
1329/1257
487/2099
67.4/63.9
29.7/27.5
Normal glucose tolerant
controls
rs7903146
Miyake_a
(47)g
2007
Japan
East Asian
425/363
465/323
60.5/75.6
24.3/21.4
Hospital patients for annual
medical checkup or unrelated
disorders, with no past history
of diabetes and HbA1c values
<5.8%.
rs7903146,
rs11196205,
rs12255372,
rs11196218,
rs290487
Miyake_b
2007
Japan
East Asian
556/596
576/576
60.2/67.3
23.9/23.0
Hospital patients presenting
for annual medical checkup or
unrelated disorders, with no
past history of diabetes, and
HbA1c values <5.8%.
rs7903146,
rs11196205,
rs12255372,
rs11196218,
rs290487
SNPs involved
rs7903146,
rs12255372
rs7903146,
rs12255372
27
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rs7903146,
rs12255372
S. Peng et al.
Table I. Continued
Year
Country
Ethnicity
Sex (male/
female)
Subjects
Mean age (year) BMI (kg/m2) Source of controls
(case/control)
(case/control) (case/control)
SNPs involved
Miyake_c
2007
Japan
East Asian
1084/1063
1173/974
62.5/69.2
23.1/22.6
Hospital patients presenting
for annual medical checkup or
unrelated disorders, >60 years
of age, HbA1c values <5.8%,
and no family history of
type 2 diabetes in first- or
second-degree relatives.
rs7903146,
rs11196205,
rs12255372,
rs11196218,
290487
Rees (49)
2008
India
Indian
669/599
831/437
56.9/55.0
28.3/28.1
Ethnically-matched normogly- rs7901695,
cemic controls
rs7903146,
rs11196205,
rs12255372
Kunika
(48)
2008
Japan
East Asian
1442/1435
1444/1433
39.9/63.2
22.1/23.6
Healthy adult with <5.8%
HbA1c and no other diseases
rs7903146,
rs12255372,
rs11196205,
rs7901695
Mahurkar
(50)
2008
India
Indian
652/487
478/661
NR
NR
Healthy adults without any
complaints or evidence of
pancreatitis
rs7903146,
1rs2255372
Sanghera
(51)
2008
India
Indian
569/524
556/537
55.7/51.63
27.78/27.03
204 control subjects were
non-diabetic spouses of
diabetic patients and the
remaining 333 were unrelated
non-T2DM individuals, who
had no family history of
T2DM
rs7903146,
rs12255372,
rs11196205,
rs7901695
Alsmadi
(53)
2008
Saudi
Arabia
Arab
501/367
522/346
67.2/68.6
28.9/NR
Random unrelated anonymous rs7903146,
individuals, aged between
rs12255372
60–95 years with a fasting
plasma glucose <7.0 mmol/l.
Ren (52)
2008
China
East Asian
483/517
500/500
49.4/59.1
24.9/24.4
≥45 years old, no family his- rs7903146,
tory of diabetes, with a normal rs12255372,
OGTT and HbA1c <6%.
rs290487
Marquezine
_MS2 (54)h
2008
Brazil
Brazilian
423/188
173/386
59.8 (all
subjects)
27.1 (all
subjects)
Diagnosis of diabetes was
based on fasting glycaemia ≥
126 mg/dl (7 mmol/l).
rs7903146
Marquezine
_VIT
2008
Brazil
Brazilian
718/859
112/1295
44.8 (all
subjects)
26.3 (all
subjects)
Diagnosis of diabetes was
based on fasting glycaemia ≥
126 mg/dl (7 mmol/l).
rs7903146
Saadi (55)
2008
United
Arab
Emirates
Arab
96/187
95/188
53.2/34.5
30.4/27.41
Non-diabetic controls
rs7903146,
rs12255372
Yan_AA
(62)i
2009
USA
African
American
1028/1699
485/2242
53.0 (Base line) 29.12 (Base
line)
Non-diabetic controls
rs7903146
Yan_Cau
2009
USA
Caucasian
4313/4989
923/8379
54.0 (Base line) 26.63 (Base
line)
Non-diabetic controls
rs7903146
Ezzidi (56)
2009
Tunisia
Arab
664/733
884/513
59.4/60.0
27.82/24.83
Non-diabetic controls
rs7903146
Tabara (57)
2009
Japan
East Asian
494/414
506/402
60.0/59.0
24.0/23.0
Non-diabetic control subjects, rs7903146,
normal glucose tolerance,
rs12255372
absence of a history of diabetes in the subject and among
first-degree relatives
Takeuchi
_ stage 1
(58)j
2009
Japan
East Asian
579/443
519/503
66.6/64.7
24.5/23.3
Non-diabetic controls
diagnosed according to 1999
World Health Organization
criteria.
rs7903146,
rs11196218
Takeuchi
_ stage 2
2009
Japan
East Asian
1155/969
1110/1014
62.7/71.1
23.3/23.0
Non-diabetic controls
diagnosed according to 1999
World Health Organization
criteria
rs7903146
Takeuchi
_rep.
2009
Japan
East Asian
4785/4104
4000/4889
62.3/63.8
NR/ 22.6
Non-diabetic controls
diagnosed according to 1999
World Health Organization
criteria
rs7903146
28
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Study
TCF7L2 polymorphisms and type 2 diabetes
Table I. Continued
Study
Year
Country
Ethnicity
Sex (male/
female)
Subjects
Mean age (year) BMI (kg/m2) Source of controls
(case/control)
(case/control) (case/control)
Luo (17)
2009
China
East Asian
483/517
500/500
49.4/59.1
24.9/24.4
≥45 years old, no family his- rs11196205
tory of diabetes, with a normal
OGTT and HbA1c <6%.
Ereqat (59)
2010
Palestinian
108/225
219/114
58.9/50.3
31.40/24.8
rs7903146
Fasting glycaemia<115 mg/
dl, fasting total cholesterol
<220 mg/dl, age at study
>40 years and BMI<30 kg/m2.
Wen (61)
2010
China
East Asian
808/1493
1165/1136
60.3/59.1
25.2/24.1
The non-diabetic unrelated
rs7903146
control individuals were older
than 45 years, with no family
history of diabetes mellitus.
Lin (60)
2010
China
East Asian
1451/1517
1529/1439
60.2/58.1
23.9/23.5
Controls with a fasting plasma rs7903146
glucose concentration <
5.6 mmol were enrolled.
Gupta (19)
2010
India
Indian
240/163
219/184
58.0/53.76
24.8/22.9
Fasting glucose <126 mg/dl,
rs7903146,
without insulin treatment or
rs12255372,
any oral anti-diabetic medica- rs4506565
tion in their life-time, and age
> 40 years
SNPs involved
2010
China
East Asian
273/210
295/188
49.7/48.3
24.8/22.9
Healthy non-diabetic control
rs290487
2010
Iran
Middle East 126/272
258/168
53/52
28/26
Healthy control subjects were
age-matched with the case
population.
rs7903146
2011
Dabelea
_NHW (65)l
USA
NHW
338/356
86/608
15.5/14.4
34.8/22.3
Control group was healthy
subjects randomly selected
from the general population.
rs7903146,
rs12255372
Dabelea_AA 2011
(65)
USA
African–
American
224/321
154/391
15.914.8
37/23.8
Control group was healthy
subjects randomly selected
from the general population.
rs7903146,
rs12255372
Palizban (64) 2012
Iran
Middle East 96/94
110/80
50.8/NA
25.9/25.5
Control group was healthy
subjects randomly selected
from the general population.
rs7903146
China
East Asian
700/570
49.7/46.2
24.6/24.3
Control group was healthy
subjects randomly selected
from the general population.
rs290487,
rs11196218
Qiao (68)
2012
Total
121 174 (53 385/67 789)
796/474
Grant_a, Iceland; Grant_b, Denmark; Grant_c, USA.
Humphries_a, UK European Whites; Humphries_b, Indian Asians; Humphries_c, Afro-Caribbean.
Horikoshi_a, initial sample from Tokyo, Japan; Horikoshi_b, replication sample from Tokyo, Japan; Horikoshi_c, with subjects from Hiroshima, Japan.
d
Scott_a, Stage 1 of GWAS; Scott_b, Stage 2 of GWAS.
e
Sladek_a, Stage 1 of GWAS; Sladek_b, Stage 2 of GWAS.
f
Cauchi_a, Moroccan; Cauchi_b, Austrian.
g
Miyake_a, Kobe, Japan; Miyake_b, Gunma, Japan; Miyake_c, Consortium (collected from seven districts in Japan by the Study Group of the Millennium
Genome Project for Diabetes Mellitus).
h
Marquezine_MS2, Multi-vessel coronary artery disease patients (MASS II Study), Brazil; Marquezine_VIT, General population of Vitoria/ES, Brazil.
i
Yan_AA, African-American, USA; Yan_Cau, Caucasian, USA.
j
Takeuchi_ stage 1, Stage 1 of GWAS; Takeuchi_ stage 2, Stage 2 of GWAS; Takeuchi_rep, GWAS replication study.
k
Not reported.
l
AA, African-American; NHW, Non-Hispanic white.
a
b
c
and type 2 diabetes risk. There were no limits on language. Two search themes
were combined using the Boolean operator ‘and’. The first theme was ‘diabetes
mellitus’, ‘diabetes mellitus, type 2’, ‘T2D’, ‘T2DM’, ‘type 2 diabetes’ and the
second theme was ‘TCF7L2’ or ‘transcription factor 7-like 2’. As some original
data are always included in meta-analysis articles on genetic-association studies, meta-analysis articles were not excluded.
Selection
Two reviewers (SHP and YMZ) identified articles eligible for further review
by performing an initial screening of identified abstracts or titles. Articles were
considered for inclusion in this systematic review if they reported data from an
original study and reported the outcome of type 2 diabetes in genetic-association
studies or in GWAS studies. The observed agreement among reviewers with
regard to eligibility of articles in this first screening was 89.5%. Articles were
retained when either of the two reviewers believed they should be retained. The
second screening was based on full-text review. The inclusion and exclusion
criteria were described as follows.
Inclusion criteria: (i) association studies and GWAS with case–control subjects, (ii) subjects in cases and controls were adults (>16 years), (iii) cases were
type 2 diabetes, (iv) controls were healthy and (v) at least two studies could be
obtained for a specific SNP.
Exclusion criteria: (i) studies that included subjects (in the control group)
who suffered from other diseases, (ii) failure to report the genotyped data, (iii)
lack of sufficient information, (iv) family-based studies and (v) only a single
study to be found for a specific SNP.
29
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Yu (67)
Amoli (63)
S. Peng et al.
Downloaded from http://mutage.oxfordjournals.org/ at Zhejiang University on December 30, 2012
Fig. 1. PRISMA flow diagram (flow of included studies).
Validity assessment
Studies with less than 500 subjects were considered low-quality studies. To
ensure that the same samples were not used in more than one published study,
two authors (SHP and XBL) were responsible for the overlap identification of
the subjects across different studies.
Data abstraction
Eligible studies were assessed independently by four reviewers (SHP, YMZ, BJL
and FYX). Study information on the sex of the subjects, author name, ethnicity of the subjects, year of publication, mean age of examination, mean body
mass index (BMI) (calculated as weight in kilograms divided by height in meters
squared) and genotypes (e.g., CC/TC/TT) were retrieved. The minor allele frequency (MAF) and P value of the Hardy–Weinberg equilibrium (HWE) were calculated from the above genotype data (in control subjects). Different criteria for
30
diagnosing type 2 diabetes were adopted in the studies included, including those
recommended by World Health Organization in 1985, 1998 and 1999 (21–23),
the American Diabetes Association in 1997 (24), and the International Diabetes
Federation for the metabolic syndrome for epidemiologic studies in 2006 (25).
Statistical analysis
All statistical tests were performed with Stata 12.0 software (Stata, College
Station, TX, USA). Cochran’s χ2 test (Q test) was used to evaluate heterogeneity between studies; a threshold P value of 0.1 was considered statistically significant, and the I2 test was also conducted to evaluate heterogeneity
between studies (26). To assess the effect of genotype on type 2 diabetes risk, a
random-effect model was used for rs7903146, rs12255372 and rs290487, and
a fixed-effect model for the other five SNPs, according to the heterogeneity
level of the studies. Egger’s tests were used to address publication bias with a
TCF7L2 polymorphisms and type 2 diabetes
Table II. Results of meta-analyses of the eight SNPs under the additive genetic model
Study
Subject (case/control)
OR (95% CI), P
Heterogeneity test (Q test)
Publication bias test Effect model
(Egger’s test P value)
rs7903146
60
115 093 (49 214/65 879)
1.39 (1.34−1.45), <10−3
τ²= 0.0147; χ² = 169.2, df = 59,
P < 10−3; I² = 65.1%
0.652
Random
rs12255372
40
67 398 (32 830/34 568)
1.33 (1.27−1.40), <10−3
τ² = 0.0121; χ² = 95.21, df = 39,
P < 10−3; I² = 59.0%
0.482
Random
rs11196205
18
25 982 (13 677/12 305)
1.20 (1.14−1.26), <10−3
χ² = 21.94, df = 17, P = 0.187;
I² = 22.5%
0.063
Fixed
rs7901695
12
19 627 (10 125/9502)
1.32 (1.25−1.39), <10−3
χ² = 16.07, df = 11, P = 0.138;
I² = 31.6%
0.375
Fixed
rs7895340
8
10 580 (5321/5259)
1.21 (1.13−1.29), <10−3
χ² = 7.91, df = 7, P = 0.341;
I² = 11.5%
0.663
Fixed
rs290487
7
8982 (4787/4185)
1.01 (0.93−1.11), 0.7450
τ² = 0.0059; χ² = 11.0, df = 6,
P = 0.0899; I² = 45.4%
0.492
Random
rs11196218
7
9466 (4979/4487)
0.99 (0.92−1.06), 0.7480
χ² = 4.36, df = 6, P = 0.628;
I² = 0.0%
0.595
Fixed
rs4506565
3
6237 (3184/3053)
1.39 (1.29−1.49), <10−3
χ² = 1.0, df = 2, P = 0.607;
I² = 0.0%
0.150
Fixed
P value <0.1 considered significant. Finetti diagram generator software (free
software from https://finetti.meb.uni-bonn.de/, Germany) was used to test the
significance of HWE with a threshold P value of 0.05. To address the source of
the between-studies heterogeneity, we performed sensitivity analysis through
inclusion and exclusion of certain studies.
As to the appropriate genetic model selection for the analysis of TCF7L2
SNPs with type 2 diabetes risk, Grant et al. (12) pointed out that dominant
and recessive genetic models are not appropriate, and Sladek et al. (4) verified that additive genetic model is more appropriate than both dominant and
recessive genetic models. So we adopted the additive genetic model in this
meta-analysis. For comparison, the meta-analyses for both dominant and recessive genetic models were also performed.
For each statistically significant reported association, we estimated the
false-positive report probability (FPRP) using methods described by Wacholder
et al. (27); Wacholder et al. suggest the estimation of statistical power based
on the ability to detect an odds ratio (OR) of 1.5, with an α level equal to the
observed P value. However, given the recent attention to much smaller ORs,
this estimate may be too conservative; thus, we present the ORs at both 1.5
and 1.2. To evaluate whether an association is noteworthy, we used an FPRP
cut-off value of 0.2, as suggested by the authors (27) for summary analyses.
Hence, FPRP values less than 0.2 indicate an association that remained robust
for a given prior probability and will be referred to as noteworthy in the present meta-analysis. Statistical power and FPRP were computed using the Excel
spreadsheet provided by Wacholder et al. (27).
LD (linkage disequilibrium) patterns of the SNPs were investigated using
the HapMap Database (http://hapmap.ncbi.nlm.nih.gov/) and HaploView software (28).
Results
Characteristics of studies
According to the search strategy, 356, 377 and 339 articles (a
total of 1072 articles) were retrieved from Scopus, PubMed and
Web of Science, respectively. Many articles were found to overlap
across the three databases. Seven articles were obtained through
other sources (five from Google Scholar and two from colleague).
About 105 duplicates of the three databases were removed. After
the screening by reading the titles, 336 articles were excluded.
Then 541 articles were excluded by reading the abstracts, yielding 97 articles for full-text review. After review of these articles,
49 articles were excluded. Finally, we obtained 48 articles for
inclusion in this meta-analysis: 44 articles, including 60 studies
on rs7903146 (4,5,12,13,15,18,19,29–65); 31 articles, including 40 studies on rs12255372 (4,12,13,18,19,29,31,33–45,47–
53,55,57,65,66); 12 articles, including 18 studies on rs11196205
(12,13,17,18,30,34,36,39,47–49,51); 10 articles, including 12
studies on rs7901695 (12,13,18,29,34,36,39,48,49,51); four articles, including eight studies on rs7895340 (12,13,18,36); five articles, including seven studies on rs290487 (47,52,61,67,68); five
articles, including seven studies on rs11196218 (17,47,58,61,68);
and three articles, including three studies on rs4506565 (19,33,44)
(Figure 1). In all of the included studies, the genotype distribution in control subjects was consistent with the HWE, except for
five studies on rs7903146 (51,52,54,58) (supplementary Table
II–IX, available at Mutagenesis Online). The studies that deviated
from HWE (with a P value of less than 0.05) were not excluded,
but sensitivity analysis was performed by exclusion and inclusion of these five studies. Details on sensitivity analysis will be
discussed below.
Clinical characteristics of the included studies are shown in
Table I. The genotype data, MAFs, and the P values of HWE are
shown in supplementary Table II–IX, available at Mutagenesis
Online. These studies were published between 2006 and 2012.
Across all eight SNPs, 155 studies were included. In total, 121
174 subjects (53 385 type 2 diabetes cases and 67789 healthy
controls) were involved.
We estimated the MAFs in the eight polymorphisms from the
control subjects. Across all studies, the MAF ranged between
2.4 and 41.6% (mean: 21.3%) for rs7903146, between 0.4
and 37.2% (mean: 19.5%) for rs12255372, between 1.3 and
47.5% (mean: 20.2%) for rs11196205, between 3.9 and 45.5%
(mean: 21.9%) for rs7901695, between 4.7 and 46.7% (mean:
25.4%) for rs7895340, between 33.6 and 62.0% (mean: 43.0%)
for rs290487, between 21.6 and 26.5% (mean: 24.0%) for
rs11196218 and between 28.8 and 37.3% (mean: 32.8%) for
rs4506565. There were ethnic variations of the MAF in all eight
SNPs. Caucasian, Indian, Arab, Palestinian, Brazilian and African
populations exhibited higher MAFs at rs7903146, rs12255372,
rs11196205, rs7901695 and rs7895340 (between 9.0 and 47.5%,
mean: 30.4%), whereas East Asian populations (e.g. Chinese and
Japanese) and American Pima Indians had lower MAFs (between
0.4 and 8.9%, with a mean value of 3.9%). All of the studies of
rs290487 and rs11196218 were performed in East Asian populations; the MAFs were between 33.6 and 62.0% for rs290487
and between 21.6 and 26.5% for rs11196218. For rs4506565, the
MAFs were between 28.8 and 37.3%.
31
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SNP
S. Peng et al.
Assessment of study quality
Due to the limited sample size of the study population (case
and control), 12 studies were considered to be low-quality studies, including six studies of rs7903146 (19,36,55,59,63,64);
three of rs12255372 (19,36,55) and one for each of the five
other SNPs (rs7901695 (36), rs11196205 (36), rs7895340 (36),
rs290487 (67) and rs4506565 (19)).
After going through the overlap identification procedure, we
confirmed that the same samples were not used in more than
one published study.
Meta-analysis results
Table II summarises the associations of the SNPs with type
2 diabetes under the additive genetic model. For the 115 093
32
subjects, Figure 2 presents the results of the 60 studies examining the association between rs7903146 and type 2 diabetes
risk. Of the 60 studies, 42 (70.0%) reported significant positive
associations. Overall, the summary OR showed evidence of
significant association of rs7903146 and risk of type 2 diabetes
(OR: 1.39, 95% CI: 1.34–1.45) in a random-effect model. But
in some ethnic populations, no significant associations were
found, including American Pima Indian, Mexican and Brazil
(Figure 2).
Results of the 40 studies that examined the association between
the rs12255372 polymorphism and the type 2 diabetes, involving
67 398 subjects, are shown in Figure 3. Twenty-four (60.0%) of
the 40 individual studies showed significant positive associations.
The combined analysis, using a random-effect model, showed
an effect of rs12255372 on type 2 diabetes risk (OR: 1.33, 95%
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Fig. 2. Forest plot representing the association between rs7903146 and type 2 diabetes risk under the additive genetic model. The OR of each study is represented
by a square, and the size of the square represents the weight of each study with respect to the overall estimate. 95% CIs are represented by the horizontal lines,
and the diamond represents the overall estimate and its 95% CI.
TCF7L2 polymorphisms and type 2 diabetes
CI: 1.27–1.40; Table II). Whereas in some ethnic populations, no
significant associations were found, including African, American
Pima Indian, Mexican American and Arab (Figure 3).
Results of the 18 studies that examined the associations
between risk of type 2 diabetes and rs11196205 are shown
in supplementary Figure 1, available at Mutagenesis Online.
Eight (44.4%) of the 18 individual studies showed statistically
significant positive associations, involving 25 982 subjects.
Overall, the summary odds ratio showed evidence of a significant association of rs11196205 and risk of type 2 diabetes (OR
1.20, 95% CI: 1.14–1.26) in a fixed-effect model. Whereas in
American Pima Indian, no significant associations were found
(supplementary Figure 1, available at Mutagenesis Online).
Of the 12 studies on rs7901695, nine (75.0%) reported significant positive associations (supplementary Figure 2, available at Mutagenesis Online), involving 19 627 subjects. The
meta-analysis produced an overall OR of 1.32 (95% CI: 1.25–
1.39) in a fixed-effect model. Also in American Pima Indian, no
significant associations were found (supplementary Figure 2,
available at Mutagenesis Online).
Supplementary Figure 3, available at Mutagenesis Online,
presents the results of the association between rs7895340 and
type 2 diabetes. Of the eight studies involving 10 580 subjects,
only three reported significant positive associations with 3411
subjects. Overall, the summary odds ratio showed evidence of
significant association of rs7895340 and risk of type 2 diabetes
(OR: 1.21, 95% CI: 1.13–1.29) in a fixed-effect model. In this
case, more case–control studies with big sample size should be
done to confirm the association.
Of the seven studies on rs290487, involving 8982 subjects,
none reported a significant positive association (supplementary Figure 4, available at Mutagenesis Online). Overall, the
summary OR showed no evidence of significant association
between rs290487 and risk of type 2 diabetes (OR: 1.01, 95%
CI: 0.93–1.11) in a random-effect model. For this SNP, only
East Asian studies were reported.
Based on evidence for rs11196218 provided by 9466 subjects, none of the seven studies reported a significant positive
association (supplementary Figure 5, available at Mutagenesis
Online). Overall, the summary OR showed no evidence of a
33
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Fig. 3. Forest plot representing the association between rs12255372 and type 2 diabetes risk under the additive genetic model. The OR of each study is
represented by a square, and the size of the square represents the weight of each study with respect to the overall estimate. 95% CIs are represented by the
horizontal lines, and the diamond represents the overall estimate and its 95% CI.
S. Peng et al.
Table III. Comparison of the results between present meta-analysis and the three previously published meta-analyses
SNP
Meta-analysis
Cauchi et al. (15)
Tong et al. (16)
This study
Luo et al. (17)
Study
Subject
OR (95% CI)
Study Subject OR (95% CI) Study Subject
rs7903146
28
46 397
1.46
(1.42–1.51)
35
69 451
1.42
(1.35–1.49)
Study
Subject
OR (95% CI)
-
-
60
115 093
1.39 (1.34–1.45)
For East
Asian
-
-
-
-
-
-
10
19 454
1.40
(1.25−1.56)
17
67 002
1.50 (1.39–1.62)
rs12255372
-
-
-
29
57 165
1.38
(1.31–1.45)
-
-
-
40
67 398
1.33 (1.27–1.40)
For East
Asian
-
-
-
-
-
-
-
11 330
1.56
(1.30−1.87)
11
29 860
1.60 (1.39–1.84)
rs11196205
-
-
-
15
21 161
1.24
(1.15–1.33)
-
-
-
18
25 982
1.20 (1.14–1.26)
5
10 662
1.31
(1.19−1.52)
10
26 606
1.27 (1.15–1.41)
For East
Asian
rs7901695
-
-
-
10
15 718
1.32
(1.23–1.42)
-
-
-
12
19 627
1.32 (1.25–1.39)
rs11196218
(East Asian)
-
-
-
-
-
-
3
5 939
1.09
(1.00−1.19)
7
9 466
0.99 (0.92–1.06)
rs290487 (East Asian)
-
-
-
-
-
3
6 607
1.11
(1.03−1.19)
7
8 982
1.01 (0.93–1.11)
rs7895340
-
-
-
-
-
-
-
-
-
8
10 580
1.21 (1.13–1.29)
rs4506565
-
-
-
-
-
-
-
-
-
3
6 237
1.39 (1.29–1.49)
significant association of rs11196218 and risk of type 2 diabetes (OR: 0.99, 95% CI: 0.92–1.06) in a fixed-effect model. For
this SNP, only East Asian studies were reported.
Finally, all of the three studies, involving 6237 subjects,
reported a significant positive association between type 2 diabetes and rs4506565 (supplementary Figure 6, available at
Mutagenesis Online). The meta-analysis produced an overall
OR of 1.39 (95% CI: 1.29–1.49) in a fixed-effect model. For
this SNP, only Caucasian and Indian studies were reported and
other ethnic population.
No evidence of heterogeneities was found in the associations between the type 2 diabetes and the SNPs, except for
rs7903146, rs12255372 and rs290487. The funnel plots (data
not shown) and Egger’s tests provided no evidence of publication biases, except for rs11196205 and rs4506565 (Table II,
Figures 2 and 3 and supplementary Figures 1–6, available at
Mutagenesis Online).
In addition, we found that associations between each of the
eight SNPs and type 2 diabetes did not vary significantly when
analyses were performed using recessive or dominant genetic
models, e.g. for rs7903146, (OR: 1.47, 95% CI: 1.39–1.55) in
dominant genetic model; (OR: 1.65, 95% CI: 1.52–1.79) in
recessive genetic model (supplementary Figures 9–24, available at Mutagenesis Online).
LD pattern of the SNPs
We investigated the LD pattern of the eight SNPs, obtaining LD
pattern results for these SNPs in all of the 11 ethnic populations
in the HapMap database (supplementary Figure 7, available at
Mutagenesis Online).
We found that three SNPs (rs7903146, rs7901695 and
rs4506565) were in strong LD with each other across various
populations, with r2 ≥ 0.8, whereas other SNPs (rs12255372,
34
rs11196205, rs290487 and rs11196218) were in weaker LD
with each other in different populations, with r2 < 0.8. Thus,
rs7903146 could be considered the tag SNP of rs7901695 and
rs4506565, because the effect size of rs7903146 is the strongest among the three SNPs. On the other hand, rs7903146 were
detected as a risk SNP in as many as 60 studies compared
with rs7901695 in 12 studies and rs4606565 in only 3 studies.
Further, rs7903146 consistently ranked top in GWAS studies
(4,5), and both functional work (10) and re-sequencing studies
(48,69) confirmed that it is likely the signal source.
Sensitivity analysis
Higher between-study heterogeneities were found in the studies concerning rs7903146 and rs12255372. We addressed these
heterogeneities through sensitivity analysis.
In the studies concerning rs7903146, heterogeneities existed
in the Caucasian, Indian and African subgroups. After exclusion of the three studies (4,31) performed in Caucasians, including Cauchi_2006, Sladk_a and Sladk_b, an obvious decrease
in heterogeneity was observed in this subgroup (I2 = 71.9%,
P < 10−3 vs. I2 = 14.8%, P = 0.276). After exclusion of one
study (50) in Indians, the I2 value decreased to zero (I2 = 52.2%,
P < 0.051 vs. I2 = 0%, P = 0.816) in this subgroup. After exclusion of one study performed in an African population (41), the
I2 value decreased to zero (I2 = 64.5%, P < 0.024 vs. I2 = 0%,
P = 0.551) in this subgroup. These results suggested that the
three studies in Caucasians, the one study in an Indian population, and the one study in an African population were the
sources of between-study heterogeneity.
With regard to rs12255372, heterogeneities were found in
the Caucasian, Indian and African subgroups. After exclusion
of the two studies by Cauchi et al. (31) and Sladek et al. (4), a
sharp, obvious decrease in heterogeneity was observed in the
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OR (95% CI)
-
TCF7L2 polymorphisms and type 2 diabetes
Table IV. Estimation of false-positive report probability in the six significant
associations
SNP
OR (95% CI)
Power
OR, 1.5
Power
OR, 1.2
rs7903146
rs12255372
rs11196205
rs7901695
rs7895340
rs4506565
1.39 (1.34–1.45)
1.33 (1.27–1.40)
1.20 (1.14–1.26)
1.32 (1.25–1.39)
1.21 (1.13–1.29)
1.39 (1.29–1.49)
1.0
1.0
1.0
1.0
1.0
1.0
10e-3
10e-3
0.5
10e-3
0.400
10e-3
FPRP values at prior
probability of 0.001
OR, 1.5
OR, 1.2
10e-3
10e-3
10e-3
10e-3
10e-3
10e-3
10e-3
10e-3
10e-3
10e-3
10e-3
10e-3
Discussion
A comparison between the present meta-analysis and the other
three meta-analyses is shown in Table III. Generally, the present
study is an updated meta-analysis of the other three meta-analyses that included more subjects, e.g. 115 093 (present metaanalysis) vs. 69 451 (the meta-analysis by Tong et al. (16)) for
rs7903146. We did not find significant associations between
either of the two SNPs (rs11196218 and rs290487) and type
2 diabetes risk, which differs from the results obtained by Luo
et al. (17). Two new statistically significant associations were
found between two SNPs (rs7895340 and rs4506565) and type
2 diabetes risk, which were not addressed in the other three
meta-analyses.
Compared with the results by Tong et al (16), our findings
showed that the trends of these associations were not altered.
However, in the meta-analysis by Luo et al. (17), which only
addressed the studies performed among the East Asian population, significant associations were found between two SNPs
(rs11196218 and rs290487) and type 2 diabetes risk, whereas
in present meta-analysis, trends of these associations were
altered (Table III).
With regard to the two SNPs (rs4506565 and rs7895340)
that were not addressed in the previous three meta-analyses,
both of these two SNPs showed significant associations with
type 2 diabetes. In particular, rs4506565 was in strong LD with
rs7903146 and rs7901695.
We found that East Asian populations and American Pima
Indians typically had very low MAFs compared with the
other populations. Furthermore, for rs7903146, rs12255372,
rs11196205, rs7901695 and rs7895340, the MAFs in East
Asian and American Pima Indians were less than 10%, whereas
the MAFs in other populations were >15% except for the study
by Scott et al. (13), which yielded an MAF value of 9% in
control subjects. With regard to the SNPs of rs290487 and
Limitations
Inherent limitations of this meta-analysis should be pointed
out: (i) because the available information is incomplete, the
ORs were not adjusted by age, gender and/or other factors; (ii)
the heterogeneities in rs7903146 and rs12255372 were higher;
(iii) some studies were excluded because of the lack of genotyped data and (iv) some low-quality studies were included in
this meta-analysis.
Considering the lower statistical power of the ORs, which was
mainly due to the small sample sizes employed in the published
studies, some association studies are still worth carrying out with
a larger sample size. This larger sample size should be determined
based on statistical analysis, especially for East Asian ethnic populations, which are typically characterised by MAF < 5%.
In summary, we have carried out an extensive, up-to-date and
unbiased meta-analysis comprising 121 174 subjects (53 385
35
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Caucasian subgroup (I2 = 64.4%, P = 0.P<10−3 vs. I2 = 0.0%,
P = 0.474). After exclusion of one study (50) performed in an
Indian population, the I2 value decreased to zero (I2 = 25.0%,
P = 0.238 vs. I2 = 0%, P = 0.621) in this subgroup. These
results suggested that the two studies performed in Caucasians
and the one study performed in Indians were the sources of
between-study heterogeneity.
After exclusion of the five studies that deviated from HWE
concerning rs7903146, both of the OR and the heterogeneity
level were almost unchanged (OR: 1.39 (95% CI: 1.34–1.45)
vs. OR: 1.39 (95% CI: 1.33–1.45) and I2 = 65.1%, P < 10−3
vs. I2 = 67.2%, P < 10−3) (supplementary Figure 8, available at
Mutagenesis Online), suggesting that inclusion of these studies
might not induce bias due to the deviation from HWE.
rs11196218, however, the MAFs in East Asian were between
21.6 and 62.0% in control subjects.
Recent evidence indicates that if sample sizes are sufficiently
large, it is possible to identify and replicate genetic associations
with common complex diseases. However, it is still unclear
what is truly meant by ‘sufficiently large’ (70,71). Therefore,
statistical power analyses constitute a crucial step in the design
of these studies. Conventional statistical power calculations for
case–control studies disregard many basic elements of analytic
complexity and can seriously underestimate true sample size
requirements. Many methods have been proposed to address
the sample size issue (70,72,73). However, owing to many factors, such as financial pressure, difficulties in sample collection
or neglect of the initial design of sample size, too few samples were employed in some genetic-association studies. In this
meta-analysis, there were 12 studies with less than 500 subjects, which were considered to be low-quality studies. Among
the 12 low-quality studies with less than 500 subjects, eight
(66.7%) reported no-significant associations; two reported a
very large effect sizes. In the latter cases, the effect size was
clearly overestimated.
We found that the associations are different across various ethnic population. For example, American Pima Indian
showed no significant associations between the type 2 diabetes
and the SNPs, including rs7903146, rs12255372, rs11196205
and rs7901695, but the overall associations were statistically
significant.
With the continuous progress in genotyping technologies, a
large number of genetic variants can be tested. Many false-positive results are likely to be published due to the widely used significance threshold of P < 0.05. Therefore, this review adopted
FPRP methodology, which is based not only on the observed
P value but also on both the power and prior probability of the
hypothesis, allowing the user to incorporate prior knowledge,
including functional information, of the specifically tested variants (74). As suggested by Thomas and Clayton (75), the prior
probability for studies evaluating candidate genes will usually
exceed 0.001. Thus, at a prior probability of 0.001, all of the six
associations (rs7903146, rs12255372, rs11196205, rs7903146,
rs7895340 and rs4506565) were noteworthy and may plausibly
be true associations (Table IV).
As the confirmation of the associations between the SNPs
and the type 2 diabetes, further functional characterisation of
these genetic variants are necessary to determine the mechanisms of their association.
S. Peng et al.
cases and 67 789 controls) to investigate the association of
eight SNPs in the TCF7L2 gene with type 2 diabetes risk. We
found statistically significant evidence for a modest increase
in the risk of type 2 diabetes with rs7903146, rs12255372,
rs11196205, rs7901695, rs7895340 and rs4506565 but not with
rs11196218 or rs290487. But subgroups analyses show that
significant associations are not found between the six SNPs
(rs7903146, rs12255372, rs11196205, rs7901695, rs7895340
and rs4506565) and type 2 diabetes in some ethnic populations.
Supplementary data
Supplementary Tables I–IX and Figures 1–24 are available at
Mutagenesis Online.
Funding
Acknowledgement
We thank Xiaoning Peng, Xiaomin Zeng and Yizhen Lu for helpful discussion.
Conflict of interest statement: None declared.
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