Association of Transcription Factor 7-Like 2 (TCF7L2) Variants With Type 2 Diabetes in a Finnish Sample
Transcription factor 7-like 2 (TCF7L2) is part of the Wnt signaling pathway. Genetic variants within TCF7L2 on chromosome 10q were recently reported to be associated with type 2 diabetes in Icelandic, Danish, and American (U.S.) samples. We previously observed a modest logarithm of odds score of 0.61 on chromosome 10q, ∼1 Mb from TCF7L2, in the Finland-United States Investigation of NIDDM Genetics study. We tested the five associated TCF7L2 single nucleotide polymorphism (SNP) variants in a Finnish sample of 1,151 type 2 diabetic patients and 953 control subjects. We confirmed the association with the same risk allele (P value <0.05) for all five SNPs. Our strongest results were for rs12255372 (odds ratio [OR] 1.36 [95% CI 1.15–1.61], P = 0.00026) and rs7903146 (1.33 [1.14–1.56], P = 0.00042). Based on the CEU HapMap data, we selected and tested 12 additional SNPs to tag SNPs in linkage disequilibrium with rs12255372. None of these SNPs showed stronger evidence of association than rs12255372 or rs7903146 (OR ≤1.26, P ≥ 0.0054). Our results strengthen the evidence that one or more variants in TCF7L2 are associated with increased risk of type 2 diabetes.
- FUSION, Finland-United States Investigation of NIDDM Genetics
- GIST, Genotype-IBD Sharing Test
- LD, linkage disequilibrium
- NGT, normal glucose tolerance
- SNP, single nucleotide polymorphism
Transcription factor 7-like 2 (TCF7L2) encodes a transcription factor that plays a role in the Wnt signaling pathway (1). A complex of TCF7L2, B-catenin, and other cofactors form a complex that is required for transcription of target genes (1). In a fine-scale association study of a 10.5-Mb type 2 diabetes linkage region on chromosome 10q, Grant et al. (2) identified a microsatellite marker, DG10S478, in intron 3 of TCF7L2 that was strongly associated with type 2 diabetes in an Icelandic sample of 1,185 case and 931 control subjects. The most strongly associated allele of DG10S478 was protective; the combination of all other alleles had an odds ratio (OR) of 1.50 (95% CI 1.31–1.71; P = 2.1 × 10−9). This result remained significant after correcting for all the alleles of the 228 microsatellite markers tested in the 10.5-Mb region. DG10S478 also was significantly associated with type 2 diabetes in samples of 228 case and 539 control subjects from Denmark, 361 case and 530 control subjects from the U.S., and in the combined Icelandic, Danish, and American (U.S.) samples (1.56 [1.42–1.73], P = 4.7 × 10−18) (2). Five nearby single nucleotide polymorphisms (SNPs) also showed strong evidence of type 2 diabetes association in these three case-control sample groups (2). These SNPs were in moderate to strong linkage disequilibrium (LD) (r2 = 0.43–0.95) with the most strongly associated DG10S478 allele and, therefore, also with the combined risk allele (2). We genotyped these 5 SNPs and 12 additional SNPs in Finnish case-control samples and found evidence to support the association of TCF7L2 with risk of type 2 diabetes.
RESEARCH DESIGN AND METHODS
We studied Finnish type 2 diabetic case subjects and normal glucose tolerant control subjects from the Finland-United States Investigation of NIDDM Genetics (FUSION) study (3,4) and from the Finrisk 2002 study, a population-based national risk factor survey in Finland (5). Diabetes was defined according to 1999 World Health Organization criteria (6) of fasting plasma glucose concentration ≥7.0 mmol/l or 2-h plasma glucose concentration ≥11.1 mmol/l, by report of diabetes medication use, or based on medical record review. Normal glucose tolerance (NGT) was defined as having fasting glucose <6.1 mmol/l and 2-h glucose <7.8 mmol/l. We selected 784 unrelated type 2 diabetic case subjects from the FUSION type 2 diabetes affected sibling pair families. We selected control subjects with NGT for the FUSION case subjects from three sources: 140 spouses of FUSION type 2 diabetic individuals, 217 subjects who had NGT by oral glucose tolerance tests at age 65 and 70 years, and 241 individuals from the Finrisk 2002 study sample. From the Finrisk 2002 study sample, we selected an additional set of 367 unrelated type 2 diabetic case subjects and 355 unrelated control subjects with NGT, approximately frequency matched for age, sex, and province of birth. Study protocols for the FUSION and Finrisk 2002 studies were approved by local ethics committees and/or institutional review boards of each participating recruitment or analysis site, and informed consent was obtained from all study participants.
SNP selection and genotyping.
We genotyped the 5 TCF7L2 SNPs described by Grant et al. (2) and 12 SNPs that tagged all 63 SNPs in LD of r2 > 0.2 or D′ > 0.9 with rs12255372 based on the CEPH (Utah residents with ancestry from northern and western Europe) samples from CEU HapMap (October 2005 release). We genotyped the 17 TCF7L2 SNPs using the Sequenom homogeneous MassEXTEND assay. We achieved an average genotype call rate of 97.0% and call rates ≥95.6% for each SNP. Our genotyping was 99.72% consistent based on six inconsistencies among 2,148 duplicate genotype pairs. Genotype data for all 17 SNPs were consistent with Hardy-Weinberg equilibrium in case subjects, in control subjects, and in combined case and control subjects (P > 0.01). All samples were also genotyped for PPARG P12A and KCNJ11 E23K as described by Douglas et al. (7) or Willer et al. (personal communication).
Those individuals (n = 34) who were missing birthplace information were excluded from all analysis except LD estimation and Genotype-IBD Sharing Test (GIST; see below). We tested for type 2 diabetes–SNP association using logistic regression under the additive genetic model that is multiplicative on the OR scale, a dominant model, and a recessive model, with adjustment for 5-year age category, sex, and birth province. Within the additive model framework for each SNP with association P value <0.05, we tested the ability of any other genotyped SNP to significantly improve the model fit by comparing the fit of a model for the associated SNP to a model containing the associated SNP and one other SNP using a likelihood ratio test (8). To assess the joint risk conferred by the presence of risk alleles from three type 2 diabetes–associated genes, PPARG (P12A), KCNJ11 (E23K), and TCF7L2 (rs12255372), we counted the number of risk alleles for each individual and used the number of risk alleles to predict case/control status using logistic regression. We also directly calculated the OR for each number of risk alleles relative to three risk alleles.
We estimated pairwise LD measures using LDmax (9) and performed haplotype analysis using FAMHAP (10). We used a χ2 test of homogeneity to compare allele frequencies among selected groups. We tested for heterogeneity of ORs using a χ2 goodness-of-fit test (11).
We genotyped the five SNPs originally reported to be associated with type 2 diabetes by Grant et al. (2) in a Finnish sample of 1,151 type 2 diabetic case subjects and 953 control subjects with NGT from the FUSION (3,4) and Finrisk 2002 (5) studies (Table 1). We found significant evidence for type 2 diabetes association for all five of these SNPs (P < 0.05) with the same risk allele as the original report (2) (Table 2 and online appendix Table 1 [available at http://diabetes.diabetesjournals.org]). Our strongest evidence for type 2 diabetes association was for rs12255372, located in intron 4 (OR 1.36 [95% CI 1.15–1.61], P = 0.00026), and rs7903146, located in intron 3 (1.33 [1.14–1.56], P = 0.00042). These two SNPs are separated by 50 kb and were in moderately strong LD in our Finnish control subjects (r2 = 0.70, D′ = 0.91) (online appendix Table 2). Our strongest evidence for association was for an additive model (online appendix Table 1), which was consistent with the findings of Grant et al. (2). We combined three separate controls groups, two from FUSION and one from Finrisk, for comparison to the FUSION case subjects. We tested for heterogeneity of allele frequencies among these three control groups and found no significant differences (data not shown). The SNP–type 2 diabetes association OR estimates between the Finrisk and FUSION samples did not differ more frequently than expected by chance given the number of tests performed (online appendix Table 3).
To further investigate the type 2 diabetes TCF7L2 association in this region, we genotyped 12 additional TCF7L2 SNPs that tagged all 63 SNPs in LD of r2 > 0.2 or D′ > 0.9 with rs12255372 based on the CEU HapMap sample; these 12 SNPs all were within introns 3 (88 kb in length) and 4 (101 kb in length). Using this tag SNP set, we observed a region of high LD based on D′ that extended ∼56 kb from the middle of intron 3 (rs17747324) to the first part of intron 4 (rs12255372) and contained the two most strongly associated SNPs, rs7903146 and rs12255372 (online appendix Table 2). In our sample, all but 2 of the 12 additional SNPs were in lower r2 with rs12255372 than the other 4 SNPs from Grant et al. (2), and the 3 most distal SNPs were in lower LD with rs12255372 than predicted by the CEU HapMap sample (online appendix Table 2). Under an additive model, each of the 12 additional SNPs was less strongly associated with type 2 diabetes (OR ≤1.26, P ≥ 0.0054) than rs12255372 or rs7903146 (Table 2).
Haplotype analysis did not reveal a risk haplotype that explained the association substantially more than any individual SNP. Within a logistic regression model framework, either of the two most strongly associated SNPs was, in our sample, sufficient to explain the observed association, since adding a second SNP did not significantly improve model fit.
Risk allele frequencies for the two SNPs with strongest type 2 diabetes associations, rs12255372 and rs7903146, were 8–12% (10–19%) lower in our Finnish control subjects (cases) than in the Icelandic, Danish, and American (U.S.) control subjects (cases) (Table 3). Allele frequencies for these SNPs did not vary significantly among the 13 historical Finnish provinces in control subjects (P ≥ 0.23, data not shown).
In a linkage genome scan of 737 FUSION type 2 diabetic families, we observed modest evidence for type 2 diabetes linkage on chromosome 10q (logarithm of odds = 0.61) at 131.5 cM on the FUSION linkage map near microsatellite marker D10S1237, ∼1 Mb distal to TCF7L2 (12). Using GIST, we found evidence for an association between the presence of the risk allele in the FUSION type 2 diabetic case subjects and increased allele sharing identity by descent within FUSION type 2 diabetes sibships for the four most strongly associated SNPs by case/control analysis, rs7903146, rs7901695, rs17747224, and rs12255372 (P = 0.036, 0.033, 0.055, and 0.064, respectively), suggesting that any of these SNPs may partially explain the observed linkage signal.
One of the underlying goals of type 2 diabetes genetic research is to identify individuals at higher or lower risk for disease based on the presence or absence of risk variants from multiple genes. Of SNPs that have been tested for type 2 diabetes association by multiple groups, PPARG P12A and KCNJ11 E23K have been shown to be associated with type 2 diabetes in multiple studies (14–16,17) including FUSION (7) (Willer et al., personal communication). We examined the combined risk of type 2 diabetes from variants in PPARG, KCNJ11, and TCF7L2 in our Finnish sample. Within our sample, the ORs from additive models for PPARG P12A (1.27 [95% CI 1.07–1.50]), KCNJ11 E23K (1.23 [1.08–1.40]), and the TCF7L2 rs12255372 allele T (1.35 [1.16–1.38]) did not differ significantly, and thus, we analyzed the data in terms of the total number of risk alleles for these three SNPs (0–6 risk alleles). Each risk allele increased the odds of type 2 diabetes, which approximates the increase in risk, by a factor of 1.26 (95% CI 1.15–1.38). For example, compared with individuals with the median number of three risk alleles, individuals with one risk allele have 0.63-fold–higher risk of type 2 diabetes, and individuals with five risk alleles have 1.59-fold–higher risk of type 2 diabetes (Table 4). When we do not assume equal allele effects but instead calculate the OR for each number of risk alleles compared with the reference count of three risk alleles, we obtain similar OR estimates (Table 4).
We found type 2 diabetes association in Finns with the five type 2 diabetes–associated TCF7L2 SNPs identified by Grant et al. (2). Our two most strongly associated SNPs had the strongest evidence of association in the combined Icelandic, Danish, and American (U.S.) samples. The ORs based on our Finnish sample were consistently lower than those reported by Grant et al. (2), but there was no evidence of heterogeneity in the ORs between our Finnish sample and the original Icelandic, Danish, and American (U.S.) samples for any of the five SNPs (P = 0.21−0.45) (Table 2).
We assayed 12 additional TCF7L2 SNPs chosen to tag all known SNPs in LD with rs12255372 and did not find evidence of a more strongly associated variant. However, we have not assayed SNPs that cover the remainder of the gene, and other TCF7L2 variants that increase of risk of type 2 diabetes may exist.
The risk allele frequencies for our two most strongly associated SNPs were substantially lower in Finnish case and control subjects than in the Icelandic, Danish, or American (U.S.) case and control subjects of Grant et al. (2), suggesting that there are underlying differences in the population allele frequencies rather than differences due to case or control sampling criteria.
We found evidence from GIST (13) that the associated variants partially explain the excess allele sharing identity by descent in the region of our modest linkage signal (logarithm of odds = 0.61) (12) on chromosome 10q, again suggesting that a variant or variants that increase the risk of type 2 diabetes are present in this region. However, in the Icelandic sample, these SNPs did not explain the observed linkage signal (2).
We found additive effects on the risk of type 2 diabetes for each additional risk allele from TCF7L2, PPARG (P12A), and KCNJ11 (E23K). Hansen et al. (18) observed additive effects for the number of risk alleles from PPARG (P12A) and KCNJ11 (E23K), and Hattersley et al. (19) observed additive effects for these alleles in combination with alleles from additional risk loci. These SNPs likely represent only a small proportion of the total set of genetic variants associated with the risk of type 2 diabetes; thus, an individual with multiple risk alleles from this particular set of three variants could still have a low risk of type 2 diabetes compared with others in the population. These OR estimates also may be biased relative to those that would be observed in the general population because a large proportion of our case subjects are from type 2 diabetes affected sibpair families and because we have excluded individuals with impaired glucose tolerance or impaired fasting glucose from our control group.
We tested the TCF7L2 SNPs for quantitative trait association, including weight-related traits, blood pressure, fasting levels of insulin, glucose, lipids, and free fatty acids in FUSION case subjects; 2-h oral glucose tolerance test in FUSION control subjects; insulin and glucose in control subjects; and glucose effectiveness, acute insulin response, and insulin sensitivity and disposition index in spouse control subjects (3,4). We found no significant SNP trait associations after taking into account the number of tests. The most significant result was observed in the FUSION spouse group with lower disposition index in individuals with the G (putative risk) allele of rs11196192 (P = 0.0020) (data not shown).
In summary, we have confirmed the association of variants in TCF7L2 with type 2 diabetes observed in the Icelandic, Danish, and American (U.S.) samples of Grant et al. (2). TCF7L2 joins a growing list of transcription factors that are involved in the growth, development, and metabolism of type 2 diabetes and contain genetic variants that increase the risk of type 2 diabetes.
The FUSION study was funded by National Institutes of Health (NIH) Grants DK62370 (to M.B.), DK72193 (to K.L.M.), and U54 DA021519 and by National Human Genome Research Institute intramural funds under project number 1 Z01 HG000024 and NIH Grant DA021519. K.L.M. is supported by a Burroughs Wellcome Career Award in the Biomedical Sciences. J.T. has been partially supported by the Academy of Finland (38387 and 46558). R.N.B. is supported by NIH Grants DK27619 and DK29867.
We thank the Finnish citizens who generously participated in the FUSION study.
Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org.
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- Received March 14, 2006.
- Accepted June 15, 2006.