Abstract
To search a gene(s) conferring susceptibility to type 2 diabetes mellitus, we genotyped nearly 60,000 gene-based SNPs for Japanese patients and found evidence that the gene at chromosome 6p12 encoding transcription-factor-activating protein 2β (TFAP2B) was a likely candidate in view of significant association of polymorphism in this gene with type 2 diabetes. Extensive analysis of this region identified that several variations within TFAP2B were significantly associated with type 2 diabetes [a variable number of tandem repeat locus: χ2=10.9, P=0.0009; odds ratio=1.57, 95% CI 1.20–2.06, intron 1+774 (G/T); χ2=11.6, P=0.0006; odds ratio=1.60, 95% CI 1.22–2.09, intron 1+2093 (A/C); χ2=12.2, P=0.0004; odds ratio=1.61, 95% CI 1.23–2.11]. The association of TFAP2B with type 2 diabetes was also observed in the UK population. These results suggest that TFAP2B might be a new candidate for conferring susceptibility to type 2 diabetes and contribute to the pathogenesis of type 2 diabetes.
Similar content being viewed by others
Introduction
Type 2 diabetes mellitus (DM) affects more than one hundred million individuals worldwide (Zimmet et al. 2001). Its pathogenesis appears to be the consequence of insulin resistance in peripheral tissues combined with dysfunction of β cells in pancreatic islets although the precise mechanism is still not well known (Kahn 1998; Saltiel 2001).
That genetic factors contribute to the onset and progression of DM is undoubted, and several genes responsible for specific forms of the disease, such as maturity-onset diabetes of the young (MODY) or mitochondrial diabetes, have been identified (Fajans et al. 2001; Kadowaki et al. 1994). However, genetic alterations associated with these specific forms of diabetes account for only a small subset of cases; the gene or genes conferring susceptibility to type 2 diabetes in most patients remain to be identified.
Worldwide efforts to sequence the entire human genome have established a nearly complete blueprint (International Human Genome Sequencing Consortium 2001), providing a large body of information regarding genes whether their functions are already known or not. Single nucleotide polymorphisms (SNPs), the type of genetic variation found most frequently throughout the sequenced genome, have become useful markers for identifying genes involved in common diseases, such as DM. We developed a high-throughput SNP genotyping system that combined the Invader assay with multiplex polymerase chain reactions (PCRs) (Ohnishi et al. 2001) and undertook genome-wide association studies using SNPs to discover loci involved in susceptibility to common diseases.
In the study presented here, we show the results of a large-scale, case-control study using nearly 60,000 gene-based SNPs as genetic markers and provide the evidence that the gene encoding TFAP2B at chromosome 6p12 might be a novel candidate conferring susceptibility to type 2 diabetes.
Subjects and methods
Subjects and DNA preparations
DNA samples were obtained from patients with type 2 diabetes who come regularly to the outpatient clinics of Shiga University of Medical Science, Tokyo Women’s Medical University, Juntendo University, Kawasaki Medical School, Keio University School of Medicine or Iwate Medical University. Control individuals consisted of 470 members of the general population (control 1) and another set of the general population (control 2, n=889) who were recruited through several medical institutes in Japan. We also used a third set of control subjects with normal plasma glucose levels (HbA1c < 5.5% or fasting plasma glucose < 100 mg/dl and no family history for diabetes, control 3, n=598), for final analysis. Written informed consent was obtained from each patient, and DNA extraction was performed using a standard phenol-chloroform procedure. The UK samples comprised 590 cases with type 2 diabetes enriched for positive family history (probands from the Diabetes UK Warren 2 repository) (Wiltshire et al. 2001) and 549 UK population controls (the ECACC-HRC collection) (Groves et al. 2003).
Genotyping for gene-based SNPs
The SNPs for genotyping experiments were selected randomly from the IMS-JST Japanese SNPs database (http://snp.ims.u-tokyo.ac.jp) (Hirakawa et al. 2002; Haga et al. 2002). The genotype at each SNP locus was analyzed with the Invader assay, as previously described (Ohnishi et al. 2001). We screened 188 diabetic patients at first, and genotype and/or allele frequencies were compared with those of the general population. After evaluating the statistical data using 2×3 or 2×2 contingency tables, SNPs that showed significant differences in genotype or allele frequencies between diabetic patients and the general population were examined further in another larger set of diabetics (n=631). The protocol was approved by the ethics committee of the Institute of Physical and Chemical Research.
Discovery of SNPs in the TFAP2B gene, and genotyping
On the basis of GenBank information about DNA sequences in the genomic region containing the TFAP2B gene (accession number: NT_007592), we designed PCR primers to amplify appropriate fragments of genomic DNA. Repetitive elements were excluded from the search by invoking the REPEAT MASKER computer program, in the manner described previously (Seki et al. 2000). PCR reactions and DNA sequencing were carried out, as previously described (Saito et al. 2001). The SNPs in this region were genotyped by means of Invader (Japanese samples) or Amplifluor assays (UK samples) (Bengra et al. 2002), and VNTR loci were analyzed with respect to allele sizes using the Applied Biosystems ABI PRISM 3700 Automated DNA Sequencer and GeneScan software (GenoTyper program).
Reverse transcription and polymerase chain reactions
First-strand cDNA was prepared by reverse transcription (RT) of total RNA extracted from the murine 3T3-L1 cells by oligo-dT priming using Superscript II reverse transcriptase (Invitrogen). Human cDNAs from multiple tissues were obtained from CLONTECH Inc. (Palo Allto, CA, USA). The first-strand or double-strand cDNAs were amplified by PCR experiments using primers mAP2RT-F (5′-GCG TCC TCA GAA GAG CCA AAT C-3′) and mAP2RT-R (5′-GTG CGT GAT GAG ACT GAA GTG C-3′) for murine TFAP2B and hAP2RT-F (5′-CCA AAT CTG TGA CTT CTC TAA TGA-3′) and hAP2RT-R (5′-GTA ACG TGA CAT TTG CTG CTT TG-3′) for human TFAP2B. Real time quantitative RT-PCR for human TFAP2B was performed by TaqMan assay using primers hAP2BTM-F (5′-TTG AAC CGG CAG CAC ACA-3′), hAP2BTM-R (5′-CTT GGT GGC CAA CAG CAT ATT-3′) and probe hAP2BTM-P (5′(FAM)-CCG AGT GAC CTG CAC TCC CGA AA-(TAMRA)3′).
Statistical analysis
Statistical methods for determining associations, haplotype frequencies and to calculate linkage disequilibrium (LD) coefficients (Δ) were described previously (Yamada et al. 2001). Analysis of haplotype structure was carried out by estimating haplotype phasing using the EM algorithm (Excoffier and Slatkin 1995) and by constructing haplotype blocks, as previously described (Ozaki et al. 2002; Daly et al.2001).
For the simulation approach, to calculate the actual type 1 error rate in our study, we simulated exactly the process of the third tests (first test: case 1 versus control 1; second test: case 2 versus control 1; third test: case 2 versus control 2) using the Monte-Carlo method. Since the two sets of cases (cases 1 and 2) and the two sets of controls (controls 1 and 2) were all collected independently, two alleles were independently drawn for each subject, assuming that the frequency of the minor allele was the same for all the four groups. This means that the simulations were performed under the null hypothesis. For the simulations, the frequency of the minor alleles was changed from 0.02 to 0.5. After the genotypes of all subjects were determined, the cases and controls were compared in four different ways (first and second tests). Thus, (1) the differences in allele frequencies were tested using the allele frequency 2×2 contingency tables; (2) the differences in the frequencies of the subjects with minor alleles were then tested using the 2×2 contingency tables; (3) the differences in the frequencies of the subjects with major alleles were tested; and (4) the frequencies of three genotypes were tested using 3×2 contingency tables. All tests were done using the Pearson’s chi-square test. The Monte-Carlo simulation was performed using Mersenne Twister uniform pseudo-random number generator (Matsumoto and Nishimura 1998).
For step-wise logistic regression analysis, the probability Pc of an individual of being a case rather than a control was assumed to be affected by a set of SNPs according to the logistic model: logit(Pc)=a0 + a1 x1 + a2 x2 for single SNP, for example. Here, we used a coding scheme x1=−1, 0, 1, and x2=−0.5, 0.5, −0.5 for genotypes 1/1, 1/2 and 2/2, respectively, for representing an additive effect by x1 and a dominance/recessive effect by x2 (Cordell and Clayton 2002). The weights were estimated by the maximum-likelihood method and tested by comparison with the null-hypothesis logit (Pc)=a0 (constant). For multiple SNPs, interaction effects were added further in addition to the main effects of additional SNPs and tested step-wise whether their effects were significant or not. The tests were performed using R. We applied both strategies of forward selection (starting from one SNP) and backward selection (starting from all SNPs) until the most significant SNP set were obtained (Cordell and Clayton 2002).
Results
Association study
We first genotyped 188 Japanese patients with type 2 diabetes (case 1) at 58,266 SNP loci and compared their allelic or genotype frequencies at these loci with those in the general population (control 1) (first test). At each locus, we tested the differences between the two populations by the four ways described in the Methods section; 1,496 SNP loci revealed a P value of <0.01 by at least one of the four tests (Table 1). We then analyzed these 1,496 loci for the second test using another larger group of patients (case 2). When case 2 and control 1 were compared by the four ways described in the Methods section, we found that the distribution of genotypes at a landmark SNP locus in the second intron of the TFAP2B gene on chromosome 6p12 was most strongly associated with type 2 diabetes [GG versus GC+CC: χ2=15.9, P=0.00007, odds ratio=1.65, 95% CI 1.29–2.11, G versus C: χ2=11.3, P=0.0007, odds ratio=1.38, 95% CI 1.14–1.66, (Tables 2 and 3)]. Furthermore, we compared the frequency of the alleles at this locus in type 2 diabetic subjects (case 2) with that in a different set of controls (control 2) (third test) and identified significant association with type 2 diabetes (GG versus GC+CC: P=0.04, odds ratio=1.21, 95% CI: 1.00–1.48, G versus C: P=0.03, odds ratio=1.18, 95% CI: 1.01–1.37).
We further examined the difference in the allele frequencies at this locus using 349 cases (case 3) and 598 controls (control 3), both of which were unused for the first three tests (fourth test) and confirmed a significant difference with the P value of 0.03.
Since the above three tests (first through third) used the overlapped materials, the P values from the three tests might not be correct. Therefore, the overall empirical type I error rate of these three tests was calculated by the simulation, as described in the Methods section. The simulation was iterated two hundred million times for each given minor allele frequency. As shown in Fig. 1, the type I error rate increased according to the increase of the minor allele frequency from 0.02 to 0.5. Under all conditions tested, the upper limits of the 95% CI of the type I error rates were lower than 1.61×10−5 (Fig. 1). This empirical P value obtained by the simulations was multiplied by the P value for the fourth test (P=0.03) because the later test was independent from the former. The resulting P value of <4.67×10−7 is considered to be the probability of the SNP association to pass the four tests. When we test 58,266 SNPs in this way, the probability to judge at least one of the SNPs to be significant was P<0.028, as calculated by Bonferroni’s correction for the multiple comparisons.
Thus, the difference in the minor allele frequencies of the selected SNPs between cases and controls is significant at the type I error rate of lower than 0.05.
Subsequent LD mapping of this region using 26 SNPs around the landmark SNP in the TFAP2B gene revealed that the LD of this region seemed to extend to an approximately 300-kb region (200 kb upstream and 100 kb downstream to the landmark SNP). Therefore, we thought the critical region for susceptibility to type 2 diabetes lay within this 300-kb block that contained three genes (two confirmed and one predicted), in addition to TFAP2B. We further genotyped 188 patients for 33 additional SNP loci present within these three genes but found no significant association between any of the 33 SNPs and type 2 diabetes (P>0.05, data not shown), suggesting the TFAP2B gene itself to be the most likely candidate for susceptibility to type 2 diabetes.
SNP discovery in TFAP2B gene and genotyping
We screened genetic polymorphisms in an entire region of the TFAP2B gene except repetitive sequences and found 40 additional variations, including 28 SNPs, eight insertion/deletion polymorphisms and four tandem-repeat polymorphisms although no SNP was found in the coding region of the TFAP2B gene. We then genotyped these polymorphisms for 349 patients [case 3, lean subjects (BMI<25) were not included in case 3] and 598 healthy controls (control 3). The clinical characteristics of these patients are shown in Table 4. The several variations also revealed a significant association with type 2 diabetes. Among them, the stronger association was observed at a variable number of tandem repeat (VNTR) loci (χ2=10.9, P=0.0009; odds ratio=1.57, 95% CI 1.20–2.06) and two SNPs in the first intron (χ2=11.6, P=0.0006; odds ratio=1.60, 95% CI 1.22–2.09, and χ2=12.2, P=0.0004; odds ratio=1.61, 95% CI 1.23–2.11) (Table 5). We also analyzed haplotype structure using the EM algorithm and found that 12 SNPs with the allelic frequency of >0.15 in the TFAP2B gene constituted one haplotype block, and five common haplotypes could cover more than 90% of the population (Fig. 2). Subsequent association study for each haplotype with type 2 diabetes identified a significant association of haplotype 4 with type 2 diabetes. However, the association of this haplotype was not stronger than found at the single locus. We also applied a step-wise logistic regression analysis to the SNPs in the block to get the subset of SNPs most significantly associated to the disease. The analysis was based on a full genotype model that included all effects of additive, dominance/recessive, and interaction between SNPs (see Methods). Applying both forward and backward selection strategies, we found that the original SNP itself (SNP at intron 1) was most significantly associated to the disease; other combinations of SNPs revealed less significance, and any additional effects of other SNPs to this original SNP were not significant. Therefore, the variations in intron1 (VNTR and SNPs) seemed to be able to explain most of the positive association of TFAP2B with type 2 diabetes.
We next examined the association of this gene with type 2 diabetes in a different ethnic group. As shown in Table 6, the association of this gene with type 2 diabetes could be observed also in UK population. The results indicated that the T allele of SNP at intron 1+774 was shown as a risk allele, which was consistent with the result in the Japanese population although some difference in the allele frequency and in the pattern of LD within this region seemed to be present between these two populations.
Reverse transcription polymerase chain reactions
To investigate the possible biological mechanism of TFAP2B involvement in this disease, we examined the expression pattern of this gene by RT-PCR using RNAs from various human tissues and found the pattern similar to that reported previously (Moser et al. 1995). However, we identified a high level of TFAP2B expression in the adipose tissue that had not been examined in the previous studies (Fig. 3a, b). Furthermore, we interestingly found that expression of m TFAP2B increased in mouse 3T3-L1 cells according to the degree of differentiation (Fig. 3c).
Discussion
In the report presented here, we performed a genome-wide, case-control association study using gene-based SNPs and identified the TFAP2B gene as a candidate gene conferring susceptibility to type 2 diabetes.
The contribution of genetic factors to pathogenesis of type 2 diabetes is well accepted, but only a few genes have been implicated in playing significant roles in susceptibility to type 2 diabetes so far (Horikawa et al. 2000; Ong et al. 1999; Altshuler et al. 2000). The difficulty of identifying alleles responsible for common diseases is explained by the fact that effects of individual genes in a complex genetic and environmental background are often too small to be identified with classical approaches. Our successful results presented here, as well as the recent publication for the susceptibility genes for myocardial infarction (Ozaki et al. 2002) and rheumatoid arthritis (Suzuki et al. 2003), provide solid evidences that a genome-wide approach using SNPs as genetic markers is a useful and powerful tool to identify genes conferring susceptibility to common diseases, such as DM.
In a large-scale, genome-wide association study like our present one, the type 1 error should be minimized. The design of this study to select a particular SNP associated with diabetes was complicated because the same controls or cases were used in the first (case 1 versus control 1), the second (case 2 versus control 1), and the third (case 2 versus control 2) tests. Hence, the ordinary methods for the statistical tests could not be applied. Since these three tests were not independent of each other, one could easily overestimate the significance. To estimate the probability of an SNP passing the three tests under the conditions used in this study, we simulated exactly the process of the three tests using the Monte-Carlo method, and the final P value of <4.67×10−7 was obtained as the probability of the particular SNP to pass the four tests (P<0.028 after Bonferroni’s correction).
To evaluate the association of TFAP2B with type 2 diabetes further, we examined the association of this gene with type 2 diabetes in a different ethnic group. The results indicated that the SNP at the first intron of TFAP2B (intron 1+774) was significantly associated with type 2 diabetes in the UK population also (GG 0.791, GT 0.186, TT 0.023 in type 2 DM, GG 0.824, TG 0.174, TT 0.002 in control, P=0.002, Table 6). Subsequent haplotype analysis revealed a significant difference in haplotype frequency between type 2 DM and controls (P=0.007, Table 6), with an increase in the TCC haplotype in the case. This result in the UK population was almost consistent with that in the Japanese population, further supporting a positive association of the TFAP2B gene with type 2 diabetes although there seemed to be some differences in the allele frequency and in the pattern of LD in this region between these two populations.
TFAP2B is a well-known transcription factor and has been reported to play an important role in embryonic development. In mice, expression of m TFAP2B decreases significantly after birth (Moser et al. 1995). Mice lacking TFAP2B die within 1 or 2 days after birth from renal failure due to polycystic kidney disease (Moser et al. 1997). In humans, mutation of TFAP2B causes Char syndrome, a condition characterized by patent ductus arteriosus and variable degrees of facial dysmorphism and hand abnormalities (Satoda et al. 2000); those features suggest that TFAP2B plays an important role in the embryonic development of various tissues. However, until now, no evidence has emerged to suggest a role of TFAP2B in the pathogenesis of type 2 diabetes. To investigate its possible roles in this disease, we examined the expression pattern of this gene by RT-PCR using RNAs from various human tissues.
Our report is the first to show that TFAP2B is expressed in differentiated adipocytes that are well known as a target of insulin and as cells associated with insulin resistance. Differentiated adipocytes can function in an endocrine manner to secrete several cytokines, called “adipokines,” which include TNF-α, IL-6, leptin, adiponectin, and others (Spiegelman and Flier 1996; Matsuzawa et al. 1999). These genes are found to contain binding sites for TFAP2 in their promoter (Kroeger and Abraham 1996; Isse et al. 1995; Takahashi et al. 2000). Given such observations, we suggest that TFAP2B plays a key role in the pathogenesis of type 2 diabetes by affecting insulin responsiveness through the transcriptional regulation of genes involved in insulin response of differentiated adipocytes.
In summary, by means of a large-scale, gene-based SNP approach, we have identified TFAP2B as a novel susceptibility gene for type 2 diabetes. These results suggest that TFAP2B itself, as well as molecules upstream or downstream of its function, might represent novel targets for treatment or prevention of this common disorder.
References
Altshuler D, Hirschhorn JN, Klannemark M, Lindgren CM, Vohl MC, Nemesh J, Lane CR, Schaffner SF, Bolk S, Brewer C, Tuomi T, Gaudet D, Hudson TJ, Daly M, Groop L, Lander ES (2000) The common PPARγ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet 26:76–80
Bengra C, Mifflin TE, Khripin Y, Manunta P, Williams SM, Jose PA, Felder RA (2002) Genotyping of essential hypertension single-nucleotide polymorphisms by a homogeneous PCR method with universal energy transfer primers. Clin Chem 48:2131–2140
Cordell HJ, Clayton DG (2002) A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes. Am J Hum Genet 70:124–141
Daly MJ, Rioux JD, Schaffner SF, Hudson TJ, Lander ES (2001) High-resolution haplotype structure in the human genome. Nat Genet 29:229–232
Excoffier L, Slatkin M (1995) Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Mol Biol Evol 12:921–927
Fajans SS, Bell GI, Polonsky KS (2001) Molecular mechanisms and clinical pathophysiology of maturity-onset diabetes of the young. N Engl J Med 345:971–980
Groves CJ, Wiltshire S, Smedley D, Owen KR, Frayling TM, Walker M, Hitman GA, Levy JC, O’Rahilly S, Menzel S, Hattersley AT, McCarthy MI (2003) Association and haplotype analysis of the insulin-degrading enzyme (IDE) gene, a strong positional and biological candidate for type 2 diabetes susceptibility. Diabetes 52:1300–1305
Haga H, Yamada R, Ohnishi Y, Nakamura Y, Tanaka T (2002) Gene-based SNP discovery as part of the japanese millennium genome project: identification of 190,562 genetic variations in the human genome. J Hum Genet 47:605–610
Hirakawa M, Tanaka T, Hashimoto Y, Kuroda M, Takagi T, Nakamura Y (2002) JSNP: a database of common gene variations in the japanese population. Nucleic Acids Res 30:158–162
Horikawa Y, Oda N, Cox NJ, Li X, Orho-Melander M, Hara M, Hinokio Y et al (2000) Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet 26:163–175
International Human Genome Sequencing Consortium (2001) Initial sequencing and the analysis of human genome. Nature 409:860–921
Isse N, Ogawa Y, Tamura N, Masuzaki H, Mori K, Okazaki T, Satoh N, Shigemoto M, Yoshimasa Y, Nishi S, Hosoda K, Inazawa J, Nakao K (1995) Structural oganization and chromosomal assignment of the human obese gene. J Biol Chem 270:27728–27733
Kadowaki T, Kadowaki H, Mori Y, Tobe K, Sakuta R, Suzuki Y, Tanabe Y, Sakura H, Awata T, Goto Y, Hayakawa T, Matsuoka K, Kawamori R, Kamada T, Horai S, Nonaka I, Hagura R, Akanuma Y, Yazaki Y (1994) A subtype of diabetes mellitus associated with a mutation of mitochondrial DNA. N Engl J Med 330:962–968
Kahn BB (1998) Type 2 diabetes: when insulin secretion fails to compensate for insulin resistance. Cell 92:593–596
Kroeger KM, Abraham LJ (1996) Identification of an AP-2 element in the −323 to −285 region of the TNF-alpha gene. Biochem Mol Biol Int 40:43–51
Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans Model Comput Simul 8:3–30
Matsuzawa Y, Funahashi T, Nakamura T (1999) Molecular mechanism of metabolic syndrome X: contribution of adipocytokines, adipocyte-derived bioactive substances. Ann NY Acad Sci 892:146–154
Moser M, Imhof A, Pscherer A, Bauer R, Amselgruber W, Sinowatz F, Hofstadter F, Schule R, Buettner R (1995) Cloning and characterization of a second AP-2 transcription factor: AP-2β. Development 121:2779–2788
Moser M, Pscherer A, Roth C, Becker J, Mucher G, Zerres K, Dixkens C, Weis J, Guay-Woodford L, Buettner R, Reinhard Fassler (1997) Enhanced apoptotic cell death of renal epithelial cells in mice lacking transcription factor AP-2β. Genes Dev 11:1938–1948
Ohnishi Y, Tanaka T, Ozaki K, Yamada R, Suzuki H, Nakamura Y (2001) A high-throughput SNP typing system for genome-wide association studies. J Hum Genet 46:471–477
Ong KKL, Phillips DI, Fall C, Poulton J, Bennett ST, Golding J, Todd JA, Dunger DB (1999) The insulin gene VNTR, type 2 diabetes and birth weight. Nat Genet 21:262–263
Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T, Sato H, Sato H, Hori M, Nakamura Y, Tanaka T (2002) Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat Genet 32:650–654
Saito S, Iida A, Sekine A, Miura Y, Sakamoto T, Ogawa C, Kawauchi S, Higuchi S, Nakamura Y (2001) Identification of 197 genetic variations in six human methyltransferase genes in the Japanese population. J Hum Genet 46:529–537
Saltiel AR (2001) New perspectives into the molecular pathogenesis and treatment of type 2 diabetes. Cell 104:517–529
Satoda M, Zhao F, Diaz GA, Burn J, Goodship J, Davidson HR, Pierpont ME, Gelb BD (2000) Mutation in TFAP2B cause Char syndrome, a familial form of patent ductus arteriosus. Nat Genet 25:42–46
Seki T, Tanaka T, Nakamura Y (2000) Genomic structure and multiple single-nucleotide polymorphisms (SNPs) of the thiopurine S-methyltransferase (TPMT) gene. J Hum Genet 45:299–302
Spiegelman BM, Flier JS (1996) Adipogenesis and Obesity: Rounding out the big picture. Cell 87:377–389
Suzuki A, Yamada R, Chang X, Tokuhiro S, Sawada T, Suzuki M, Nagasaki M, Nakayama-Hamada M, Kawaida R, Ono M, Ohtsuki M, Furukawa H, Yoshino S, Yukioka M, Tohma S, Matsubara T, Wakitani S, Teshima R, Nishioka Y, Sekine A, Iida A, Takahashi A, Tsunoda T, Nakamura Y, Yamamoto K (2003) Functional haplotypes of PADI4, encoding citrullinating enzyme peptidylarginine deiminase 4, are associated with rheumatoid arthritis. Nat Genet 34:395–402
Takahashi M, Arita Y, Yamagata K, Matsukawa Y, Okutomi K, Horie M, Shimomura I, Hotta K, Kuriyama H, Kihara S, Nakamura T, Yamashita S, Funahashi T, Matsuzawa Y (2000) Genomic structure and mutations in adipose-specific gene, adiponectin. Int J Obes 24:861–868
Wiltshire S, Hattersley AT, Hitman GA, Walker M, Levy JC, Sampson M, O’Rahilly S et al (2001) A genomewide scan for loci predisposing to type 2 diabetes in a U.K. population (the Diabetes UK Warren 2 Repository): analysis of 573 pedigrees provides independent replication of a susceptibility locus on chromosome 1q. Am J Hum Genet 69:553–569
Yamada R, Tanaka T, Unoki M, Nagai T, Sawada T, Ohnishi Y, Tsunoda T, Yukioka M, Maeda A, Suzuki, K, Tateishi H, Ochi T, Nakamura Y, Yamamoto K (2001) Association between a single-nucleotide polymorphism in the promoter of the human interleukin-3 gene and rheumatoid arthritis in Japanese patients, and maximum-likelihood estimation of combinational effect that two genetic loci have on susceptibility to the disease. Am J Hum Genet 68:674–685
Zimmet P, Alberti KGMM, Shaw J (2001) Global and societal implications of the diabetes epidemic. Nature 414:782–786
Acknowledgements
We thank Ms. K. Ohashi for her technical assistance and Drs. S. Saito, T. Tanaka, Y. Ohnishi, R. Yamada, S. Ikegawa, A. Takahashi, and K. Hotta for helpful discussions. This work was supported by a grant from the Japanese Millennium Project.
Author information
Authors and Affiliations
Corresponding author
Additional information
Accession numbers and URLs for data in this article are as follows: Genbank, http://www.ncbi.nlm.nih.gov/Genbank/ [for the TFAP2B gene (accession number NT_007592)]. For SNPs and primers, the IMS-JST Japanese SNP database (http://snp.ims.u-tokyo.ac.jp/). Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/OMIM [for type 2 diabetes (MIM 125853), TFAP2B (MIM 601601), MODY (MIM 606391), Char syndrome (MIM 169100)].
Rights and permissions
About this article
Cite this article
Maeda, S., Tsukada, S., Kanazawa, A. et al. Genetic variations in the gene encoding TFAP2B are associated with type 2 diabetes mellitus. J Hum Genet 50, 283–292 (2005). https://doi.org/10.1007/s10038-005-0253-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10038-005-0253-9
Keywords
This article is cited by
-
Integrative analysis of Mendelian randomization and Bayesian colocalization highlights four genes with putative BMI-mediated causal pathways to diabetes
Scientific Reports (2020)
-
Comprehensive analysis of the expression and prognosis for TFAP2 in human lung carcinoma
Genes & Genomics (2020)
-
Association between Transcription Factor AP-2B genotype, obesity, insulin resistance and dietary intake in a longitudinal birth cohort study
International Journal of Obesity (2019)
-
Mutation status coupled with RNA-sequencing data can efficiently identify important non-significantly mutated genes serving as diagnostic biomarkers of endometrial cancer
BMC Bioinformatics (2017)
-
Replication study of the association of rs7578597 in THADA, rs10886471 in GRK5, and rs7403531 in RASGRP1 with susceptibility to type 2 diabetes among a Japanese population
Diabetology International (2015)