Lack of association between genetic polymorphisms within DUSP12 - ATF6 locus and glucose metabolism related traits in a Chinese population.

Abstract

Genome-wide linkage studies in multiple ethnic populations found chromosome 1q21-q25 was the strongest and most replicable linkage signal in the human chromosome. Studies in Pima Indian, Caucasians and African Americans identified several SNPs in DUSP12 and ATF6, located in chromosome 1q21-q23, were associated with type 2 diabetes.
We selected 19 single nucleotide polymorphisms (SNPs) that could tag 98% of the SNPs with minor allele frequencies over 0.1 within DUSP12-ATF6 region. These SNPs were genotyped in a total of 3,700 Chinese Han subjects comprising 1,892 type 2 diabetes patients and 1,808 controls with normal glucose regulation.
None of the SNPs and haplotypes showed significant association to type 2 diabetes in our samples. No association between the SNPs and quantitative traits was observed either.
Our data suggests common SNPs within DUSP12-ATF6 locus may not play a major role in glucose metabolism in the Chinese.

Full text

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RESEARCH ARTICLEOpen Access
Lack of association between genetic polymorphisms
within DUSP12 - ATF6 locus and glucose metabolism
related traits in a Chinese population
Cheng Hu, Rong Zhang, Congrong Wang, Xiaojing Ma, Jie Wang, Yuqian Bao, Kunsan Xiang, Weiping Jia*
Abstract
Background: Genome-wide linkage studies in multiple ethnic populations found chromosome 1q21-q25 was the
strongest and most replicable linkage signal in the human chromosome. Studies in Pima Indian, Caucasians and
African Americans identified several SNPs in DUSP12 and ATF6, located in chromosome 1q21-q23, were associated
with type 2 diabetes.
Methods: We selected 19 single nucleotide polymorphisms (SNPs) that could tag 98% of the SNPs with minor
allele frequencies over 0.1 within DUSP12-ATF6 region. These SNPs were genotyped in a total of 3,700 Chinese Han
subjects comprising 1,892 type 2 diabetes patients and 1,808 controls with normal glucose regulation.
Results: None of the SNPs and haplotypes showed significant association to type 2 diabetes in our samples. No
association between the SNPs and quantitative traits was observed either.
Conclusions: Our data suggests common SNPs within DUSP12-ATF6 locus may not play a major role in glucose
metabolism in the Chinese.
Background
Type 2 diabetes is a complex disease caused by both
genetic and environmental factors. Although recent gen-
ome-wide association studies have identified several novel,
possibly causative genes, the contribution of them to dis-
ease risk is still very limited [1]. Thus the genetic architec-
ture of type 2 diabetes remained largely unknown.
Previous genome-wide linkage studies in multiple ethnic
populations, including Caucasians, Chinese and Pima
Indian, showed that chromosome 1q21-q25 was the stron-
gest and most replicable linkage signal in the human chro-
mosome [2-9]. Although genome-wide association studies
show no strong association signal in this region, whether
variants harbored in this region conferred modest effect to
the disease are worthy to be analyzed.
Dual specificity phosphatase 12 (DUSP12) and activating
transcription factor 6 (ATF6) were two neighbored genes
locating on the chromosome 1q21-q23. DUSP12 is a
glucokinase - associated protein identified from rat hepatic
cDNA library through yeast two-hybrid, using glucokinase
as bait. It may participate in glycolysis in the liver and pan-
creatic beta-cell through dephosphorylation of glucokinase
in the cytoplasm [10]. ATF6 is a key sensor of endoplasmic
reticulum stress. It activates unfolded protein response
through regulating a group of genes encoding molecular
chaperones and folding enzymes [11]. Previous studies
identified several single nucleotide polymorphisms (SNPs)
in this region associated with type 2 diabetes in different
populations. Among them, rs2070150 (P145A) was firstly
identified to be associated with type 2 diabetes in Pima
Indian, while rs4579731, rs3820449 and rs10918215 were
reported later in studies focusing on Caucasians and Afri-
can Americans [12-15]. However, International Type 2
Diabetes 1q Consortium failed to detect any association
signal on DUSP12 and ATF6 in a fine mapping study in
multiethnic samples [16]. Even though, only 285 East
Asian origin samples were included in the previous studies
and no one analyzed the association between SNPs from
this region and type 2 diabetes in large Asian samples.
Therefore, we performed the present study, aiming to test
* Correspondence: wpjia@sjtu.edu.cn
Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus,
Shanghai Clinical Center for Diabetes, Department of Endocrinology and
Metabolism, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital,
Shanghai, PR China
Hu et al. BMC Medical Genetics 2011, 12:3
http://www.biomedcentral.com/1471-2350/12/3
© 2011 Hu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.

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if variants from DUSP12 and ATF6 played a role in the
genetic susceptibility of type 2 diabetes in the Chinese.
Methods
Participants
In this case-control study, we recruited 3,700 unrelated
Chinese Han subjects, including 1,892 case and 1,808
controls. All the subjects were eastern Chinese Han
ancestry, residing in Shanghai and nearby region. In the
present study, all case subjects were type 2 diabetes
patients selected from Shanghai Diabetes Institute inpa-
tient database. Control subjects were community-based
populations enrolled from the Shanghai Diabetes Studies
[17]. The inclusion and exclusion criteria for the cases
and controls were described previously [17]. Briefly, all
cases were type 2 diabetes patients defined according to
1999 WHO criteria (fasting plasma glucose ≥7.0 mmol/l
and/or 2-h plasma glucose ≥11.1 mmol/l) and were trea-
ted with oral hypoglycemic agents and/or insulin. The
control subjects were normal glucose tolerance defined
based on fasting plasma glucose <6.1 mmol/l and 2-h
plasma glucose <7.8 mmol/l. This study was approved
by the institutional review board of Shanghai Jiao Tong
University Affiliated Sixth People’s Hospital. Written
informed consent was obtained from each participant.
Clinical measurement
All subjects underwent detailed clinical investigations, as
described previously [17]. Briefly, anthropometric para-
meters such as height, weight, waist and hip circumfer-
ence (for the control subjects only) were measured. For
the control subjects, blood samples were obtained at 0
and 120 min during the oral glucose tolerance tests
(OGTTs) to measure plasma glucose and serum insulin
levels. Lipid profiles such as total cholesterol and trigly-
ceride were also obtained. Insulin resistance and pan-
creatic b-cell function were assessed by homeostasis
model assessment (HOMA) [18]. HOMA-IR = fasting
insulin × fasting plasma glucose ÷ 22.5, HOMA-B = 20
× fasting insulin ÷ (fasting plasma glucose ÷ 3.5).
SNPs selection, genotyping and quality control
We selected 19 SNPs that spanning 197 kb of DUPS12
and ATF6 region, from 10 kb 5’ upstream the DUSP12 to
2 kb 3’ downstream the ATF6. These SNPs could tag
98% of the SNPs with MAF over 0.1 derived from Hap-
Map Phase III Chinese Han database under the threshold
of r2≥0.7. Among them, 7 SNPs located in the coding
region. The SNPs previously reported were either directly
genotyped or in linkage disequilibrium (LD) with geno-
typed SNPs. All the SNPs were genotyped using Seque-
nom’s MassARRAY iPLEX system (MassARRAY
Compact Analyzer, Sequenom, San Diego, CA, USA).
The key quality control requirements were: 1) sample
call rates ≥75%; 2) SNP call rate ≥85%; 3) less than two
discrepant genotypes of 100 duplicate samples; and 4)
Hardy-Weinberg equilibrium test ≥0.05 in controls and
cases respectively. After the quality control procedures of
the genotypes, 71 individuals were excluded. And one
SNP (rs3767635) failed Hardy-Weinberg equilibrium test.
The average call rate for the remaining 18 SNPs was
97.5%, and the average concordance rate based on 100
duplicate comparisons for each SNP was 99.4%. Detailed
information of the call rates and concordance rates for
the SNPs was shown in the Additional file 1 Table S1.
Statistical analyses
Observed genotypes were tested for fit to the expectation
of Hardy-Weinberg equilibrium using c2test. Pairwise
LD was estimated from the combined data of cases and
controls calulating |D’| and r2using Haploview (version
4.1) http://www.broadinstitute.org/haploview/haploview
[19]. Haplotype block structure was determined using
confidence interval algorithm [20] and haplotype fre-
quencies were estimated by Expectation-Maximization
algorithm [21] using Haploview (v 4.1). Allele, genotype
and haplotype frequencies for cases and controls were
compared using c2test or Fisher’s exact test. Odds ratios
(ORs) with 95% confidence intervals (CIs) were pre-
sented. The genotype - disease association analyses were
performed under the additive model adjusting age, gen-
der and BMI as confounding factors by logistic regres-
sion. Quantitative traits with skewed distribution were
natural logarithmically transformed to approximate uni-
variate normality. Quantitative traits were analyzed under
an additive genetic model by linear regression adjusted
for age, sex, and BMI. All statistical analyses were per-
formed by SAS (version 8.0; SAS Institute Inc., Cary, NC,
USA) unless specified otherwise. A two-tailed P value
<0.05 was considered significant. The allele frequencies
in HapMap populations and statistic power of the SNPs
were shown in the Additional file 2 Table S2.
Results
A total of 18 SNPs were successfully genotyped in 3,629
individuals in the present study. The LD pattern of
these SNPs was shown in Figure 1. Three haplotype
blocks were constructed in this region.
The single SNP association analysis showed that no
SNP was significantly associated with type 2 diabetes in
our samples. The minimum P value was 0.0954 for
rs10799941. Logistic regression analysis adjusting age,
gender and BMI as confounding factors also suggested
no association between SNPs and type 2 diabetes
(Table 1). For the haplotype analysis, we compared the
haplotype distributions between cases and controls and
observed that no haplotype was nominally associated
with type 2 diabetes (Table 2).
Hu et al. BMC Medical Genetics 2011, 12:3
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We then analyzed the association between SNPs and
quantitative traits related to glucose metabolism in the
individuals with normal glucose regulation. No SNP was
associated with plasma glucose and serum insulin levels
at fasting status as well as 2-h after glucose stimulation.
No significant association was detected between insulin
sensitivity and beta cell function either (Table 3).
Discussion
Genome-wide linkage studies in various populations
suggested the existence of multiple susceptibility gene(s)
for type 2 diabetes at chromosome 1q21-q24 [2-9]. Sev-
eral specific genes in this region, such as LMNA,
NOS1AP and ATF6, were identified that they might con-
fer risk for diabetes in some populations [12-16,22-24].
Figure 1 Linkage disequilibrium plots for SNPs genotyped in DUSP12 - ATF6 locus in the Chinese samples. Shades of pink indicate the
strength of pairwise LD based on |D’|. Number shown are |D’| of each SNP pair.
Table 1 Allele frequencies and association to type 2 diabetes for SNPs in the DUSP12-ATF6 locus
GeneSNP Chromosome position Major/minor allele Risk allele Risk allele frequency OR (95%CI)
Pallele Pgenotype
CasesControls
DUSP12 rs10799941
DUSP12 rs1503814
DUSP12 rs12021510
DUSP12 rs12121310
DUSP12 rs1063178
DUSP12 rs1063179
DUSP12 rs3820449
ATF6
rs2070151
ATF6
rs2271013
ATF6
rs2271012
ATF6
rs2070150
ATF6
rs1135983
ATF6
rs10918029
ATF6
rs2340721
ATF6
rs2341475
ATF6
rs10918215
ATF6
rs7522210
ATF6
rs2499855
159974818 T,G
159975743 C,T
159975835 A,G
159985839 A,C
159988331 C,T
159988828 C,T
159993796 C,T
160014680 C,T
160020426 A,G
160020465 C,T
160027900 G,C
160027936 C,T
160062520 G,A
160116009 A,C
160145232 G,A
160166355 A,G
160193803 C,G
160196385 A,G
T
T
A
C
T
C
C
T
G
T
C
T
G
C
G
G
G
A
0.540
0.329
0.930
0.379
0.460
0.772
0.694
0.311
0.310
0.317
0.312
0.309
0.780
0.349
0.658
0.432
0.433
0.897
0.520 1.0821(0.9863-1.1873)
0.316 1.0613(0.9616-1.1714)
0.929 1.0141(0.8466-1.2147)
0.367 1.0546(0.9572-1.1620)
0.458 1.0074(0.9177-1.1060)
0.762 1.0594(0.9498-1.1817)
0.694 1.0002(0.9049-1.1056)
0.300 1.0512(0.9511-1.1619)
0.300 1.0506(0.9505-1.1612)
0.302 1.0706(0.9666-1.1858)
0.301 1.0568(0.9559-1.1685)
0.297 1.0563(0.9553-1.1680)
0.774 1.0360(0.9272-1.1575)
0.344 1.0197(0.9247-1.1244)
0.646 1.0531(0.9497-1.1677)
0.427 1.0223(0.9304-1.1234)
0.427 1.0258(0.9341-1.1264)
0.890 1.0753(0.9203-1.2563)
0.0954
0.2373
0.8794
0.2823
0.8766
0.3002
0.9964
0.3282
0.3338
0.1908
0.2805
0.2856
0.5326
0.6959
0.3268
0.6460
0.5940
0.3606
0.1122
0.3788
0.9472
0.5793
0.7165
0.3300
0.6854
0.2568
0.2515
0.1373
0.2015
0.2305
0.6230
0.9399
0.4704
0.4317
0.3425
0.5117
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Among these genes, ATF6 is a strong candidate by its
biological function in endoplasmic reticulum stress and
unfolded protein response, which linked insulin demand
with beta cell failure and diabetes. ATF6 is also the
binding target of WFS1, a known type 2 diabetes sus-
ceptible gene, and mediates its effect on endoplasmic
reticulum stress [25]. However, although we performed
the association study by analyzing 18 SNPs in 3700 Chi-
nese Han, we failed to find any evidence of association
between SNPs from this locus and traits related to glu-
cose metabolism in our samples. One possible explana-
tion might be that the statistical power of our samples
was not enough to detect the effects of this locus in the
Chinese population. Although we had over 80% power
to detect the association at the 0.05 level based on the
previously reported ORs in non-Asian populations (1.2
~ 1.3) and allele frequencies of reported SNPs in our
Chinese samples, we could not exclude the possibility
the reported effect size was overestimated due to the
“winner’s curse” effect or novel associated SNPs with
lower minor allele frequencies in the Chinese existed, in
this case our samples may not have sufficient power.
Secondly, the relatively loose criteria for tagging SNP
selection, which is the limitation of the current study,
missed information for a group of SNPs in this region.
As we used r2over 0.7 and minor allele frequency over
0.1 as SNPs selection criterion, we failed to capture 15
(6.8%) SNPs if the stringent criterion r2over 0.8 and
minor allele frequency over 0.05 was adopted. Thirdly,
the LD pattern and allele frequencies differed between
Chinese Hans and previously studied populations, which
suggested population differences in the genetic architec-
ture between Chinese and other ethnic populations, may
also partly explain the lack of association between this
locus and previously reported phenotypes. Finally we
cannot exclude the possibility that rare variants within
Table 3 Association between SNPs from DUSP12-ATF6 and clinical features related to glucose metabolism in the
normal glucose regulation subjects
SNP Fasting glucose2 h glucose Fasting insulin HOMA-IRHOMA-B
Beta SE
P
Beta SE
P
Beta SE
P
Beta SE
P
Beta SE
P
rs10799941
rs1503814
rs12021510
rs12121310
rs1063178
rs1063179
rs3820449
rs2070151
rs2271013
rs2271012
rs2070150
rs1135983
rs10918029
rs2340721
rs2341475
rs10918215
rs7522210
rs2499855
0.0065
0.0098
0.0073
0.0288
0.0298
-0.0178
0.0216
-0.0187
-0.0203
-0.0196
-0.0177
-0.0168
0.0075
-0.0023
0.0202
0.0022
0.0001
0.0153
0.0164
0.0179
0.0322
0.0173
0.0165
0.0192
0.0178
0.0184
0.0184
0.0186
0.0185
0.0185
0.0197
0.0174
0.0187
0.0167
0.0166
0.0266
0.6933
0.5833
0.8203
0.0963
0.0722
0.3541
0.2256
0.3094
0.2704
0.2934
0.3397
0.3633
0.7027
0.8958
0.2810
0.8941
0.9945
0.5654
0.0325
0.0247
-0.0795
0.0740
0.0356
0.0082
0.0739
-0.0112
-0.0126
-0.0193
-0.0154
-0.0192
0.0282
0.0488
-0.0207
-0.0441
-0.0425
-0.0842
0.0387
0.0425
0.0760
0.0411
0.0392
0.0454
0.0421
0.0435
0.0435
0.0440
0.0438
0.0438
0.0467
0.0410
0.0445
0.0396
0.0394
0.0630
0.4024
0.5613
0.2953
0.0719
0.3639
0.8561
0.0791
0.7970
0.7728
0.6615
0.7249
0.6609
0.5451
0.2342
0.6419
0.2647
0.2809
0.1814
-0.0087
0.0023
0.0039
-0.0066
0.0083
-0.0241
-0.0179
0.0023
0.0014
0.0086
0.0062
0.0128
-0.0102
-0.0074
0.0021
0.0120
0.0095
0.0172
0.0226
0.0251
0.0452
0.0241
0.0230
0.0262
0.0247
0.0254
0.0252
0.0257
0.0257
0.0256
0.0271
0.0239
0.0257
0.0232
0.0230
0.0367
0.7019
0.9273
0.9319
0.7858
0.7192
0.3590
0.4687
0.9295
0.9559
0.7376
0.8106
0.6180
0.7066
0.7581
0.9350
0.6054
0.6810
0.6391
-0.0076
0.0065
0.0054
0.0010
0.0162
-0.0258
-0.0113
-0.0061
-0.0073
0.0004
-0.0020
0.0046
-0.0071
-0.0064
0.0093
0.0093
0.0061
0.0199
0.0237
0.0262
0.0473
0.0253
0.0240
0.0274
0.0259
0.0266
0.0264
0.0269
0.0269
0.0268
0.0284
0.0250
0.0269
0.0243
0.0241
0.0384
0.7492
0.8036
0.9086
0.9678
0.5008
0.3483
0.6627
0.8182
0.7822
0.9883
0.9405
0.8642
0.8029
0.7988
0.7299
0.7006
0.8001
0.6045
-0.0164
-0.0135
-0.0021
-0.0400
-0.0259
-0.0090
-0.0489
0.0328
0.0330
0.0376
0.0349
0.0427
-0.0177
-0.0125
-0.0226
0.0193
0.0213
0.0023
0.0249
0.0277
0.0499
0.0267
0.0254
0.0288
0.0273
0.0280
0.0278
0.0285
0.0283
0.0283
0.0300
0.0264
0.0289
0.0256
0.0253
0.0407
0.5116
0.6264
0.9670
0.1346
0.3068
0.7552
0.0737
0.2426
0.2367
0.1866
0.2178
0.1314
0.5561
0.6346
0.4332
0.4501
0.3991
0.9546
Table 2 Association analyses of haplotypes in DUSP12-
ATF6 locus with type 2 diabetes
HaplotypeHaplotype frequencies
P value
CasesControls
Block 1 (rs10799941-rs1503814)
GC
TT
TC
GT
0.449
0.318
0.222
0.011
0.467
0.304
0.217
0.013
0.1199
0.1919
0.5799
0.5919
Block 2 (rs12021510-rs12121310-rs1063178-rs1063179-rs3820449)
ACTCT
AACCC
AACTC
AATCC
GACCC
ACTCC
0.307
0.242
0.228
0.080
0.070
0.074
0.306
0.236
0.237
0.089
0.069
0.064
0.8928
0.5579
0.3592
0.1618
0.8911
0.0897
Block 3 (rs2070151-rs10918029-rs7522210-rs2499855)
CGCA
TGGA
CACA
CGGG
CGGA
0.342
0.304
0.218
0.100
0.021
0.341
0.296
0.226
0.107
0.017
0.9407
0.4348
0.3917
0.3166
0.3030
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this region participated in the pathogenesis of diabetes
as we only focused on the common ones.
Conclusion
Our data suggests common variants within DUSP12 and
ATF6 genes may not play a major role in glucose meta-
bolism in the Chinese. However, due to the limitation of
the current study, the effects of SNPs from this locus on
type 2 diabetes need to be tested in further studies with
larger East Asian origin samples and higher marker
density.
Additional material
Additional file 1: Call rates and concordance rates of SNPs
genotyped. This file contains detailed information of quality control
analysis of the SNPs, including call rates and concordance rates.
Additional file 2: Allele frequencies and statistic power of the SNPs.
This file contains the allele frequencies of all SNPs in the HapMap
populations and our samples. The statistic power of the SNPs in our
samples was also shown in this file.
List of abbreviations
ATF6: activating transcription factor 6; CI: confidence interval; DUSP12: dual
specificity phosphatase 12; HOMA: homeostasis model assessment; LD:
linkage disequilibrium; OGTT: oral glucose tolerance test; OR: odds ratio; SNP:
single nucleotide polymorphism;
Acknowledgements
This work was supported by research grants from Project of National Natural
Science Foundation of China (30630061), Shanghai Rising-Star Program
(09QA1404400), the National 863 project of China (2006AA02A409), Key
Project of Science and Technology of Shanghai (09DZ1950202), Shanghai
Municipal Hospitals’ Project of Chronic Disease Prevention and Treatment
(SHDC12007316) and National Institutes of Health/National Institute of
Diabetes and Digestive and Kidney Diseases (R01-DK073490). We thank all
nursing and medical staff at Shanghai Clinical Center for Diabetes for their
dedication and professionalism.
Authors’ contributions
CH designed the study, participated in genotyping, performed statistical
analysis and drafted the manuscript. RZ prepared the DNA samples and
participated in genotyping. CW participated in genotyping. XM participated
in sample collection and clinical studies. JW participated in the clinical study
and revised the manuscript. YB participated in clinical study and contributed
to discussion. KX contributed to discussion. WJ supervised the study and
revised the manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 16 July 2010 Accepted: 6 January 2011
Published: 6 January 2011
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