TCF7L2 Polymorphisms and Progression to Diabetes in the Diabetes Prevention Program
Jose C. Florez, M.D., Ph.D., Kathleen A. Jablonski, Ph.D., Nick Bayley, B.A., Toni I. Pollin, Ph.D., Paul I.W. de Bakker, Ph.D., Alan R. Shuldiner, M.D., William C. Knowler, M.D., Dr.P.H., David M. Nathan, M.D., David Altshuler, M.D., Ph.D., for the Diabetes Prevention Program Research Group
Background Common polymorphisms of the transcription factor7–like 2 gene (TCF7L2) have recently been associated withtype 2 diabetes. We examined whether the two most strongly associatedvariants (rs12255372 and rs7903146) predict the progressionto diabetes in persons with impaired glucose tolerance who wereenrolled in the Diabetes Prevention Program, in which lifestyleintervention or treatment with metformin was compared with placebo.
Methods We genotyped these variants in 3548 participants andperformed Cox regression analysis using genotype, intervention,and their interactions as predictors. We assessed the effectof genotype on measures of insulin secretion and insulin sensitivityat baseline and at one year.
Results Over an average period of three years, participantswith the risk-conferring TT genotype at rs7903146 were morelikely to have progression from impaired glucose tolerance todiabetes than were CC homozygotes (hazard ratio, 1.55; 95 percentconfidence interval, 1.20 to 2.01; P<0.001). The effect ofgenotype was stronger in the placebo group (hazard ratio, 1.81;95 percent confidence interval, 1.21 to 2.70; P=0.004) thanin the metformin and lifestyle-intervention groups (hazard ratios,1.62 and 1.15, respectively; P for the interaction between genotypeand intervention not significant). The TT genotype was associatedwith decreased insulin secretion but not increased insulin resistanceat baseline. Similar results were obtained for rs12255372.
Conclusions Common variants in TCF7L2 seem to be associatedwith an increased risk of diabetes among persons with impairedglucose tolerance. The risk-conferring genotypes in TCF7L2 areassociated with impaired beta-cell function but not with insulinresistance. (ClinicalTrials.gov number, NCT00004992
[ClinicalTrials.gov]
.)
The risk of type 2 diabetes is strongly influenced by inheritance.1Genetic susceptibility to the common form of type 2 diabetesappears polygenic — that is, it involves a number of variants,each with a modest effect on the risk of disease in an individualperson.2 Despite important advances in understanding the geneticdeterminants of the relatively rare monogenic forms of diabetes,3the pace of definitive identification of genes that increasethe risk of common type 2 diabetes has been slow.
Recently, Grant and colleagues4 reported on the associationof a common microsatellite (DG10S478) within intron 3 of thetranscription factor 7–like 2 gene (TCF7L2) with type2 diabetes in an Icelandic case–control sample and replicatedthis result in two additional case–control cohorts ofwhite patients. The noncoding single-nucleotide polymorphismsrs12255372 and rs7903146 were in strong linkage disequilibriumwith DG10S478 (r2=0.95 and r2=0.78, respectively) and showedsimilarly robust associations with type 2 diabetes (P<10–15).The authors recommended that these two single-nucleotide polymorphismsbe genotyped in all attempts at replication.
Reproducibility of reported genetic associations is essentialin complex human genetics, especially among populations of differentraces, ethnic backgrounds, and environmental exposures.5,6,7,8Furthermore, the effect that these polymorphisms have on therisk of type 2 diabetes and on validated preventive interventionshas not been prospectively ascertained. Finally, the pathophysiologicalmechanism by which variation in TCF7L2 might influence glycemictraits is not clear. Therefore, we genotyped the rs12255372and rs7903146 variants in 3548 participants in the DiabetesPrevention Program (DPP) to try to confirm this association,to assess the effect of these variants on the lifestyle andpharmacologic interventions used in the DPP,9 and to explorethe effect of these variants on insulin secretion, insulin sensitivity,or both.
Methods
The Diabetes Prevention Program
The DPP Research Group9 conducted a multicenter, randomizedclinical trial from 1996 to 2001 at 27 centers in the UnitedStates. The institutional review board at each center approvedthe protocol, and all participants gave written informed consent.The trial was designed to test whether a lifestyle interventionor pharmacologic treatment with metformin would prevent or delaythe development of diabetes in persons at increased risk forthe disease.9,10 The DPP enrolled 3234 overweight persons inthe United States without diabetes who had elevated plasma glucoseconcentrations after an overnight fast and impaired glucosetolerance. As compared with placebo, the lifestyle interventionsand treatment with metformin reduced the incidence of diabetesby 58 percent (95 percent confidence interval, 48 to 66 percent)and 31 percent (95 percent confidence interval, 17 to 43 percent),respectively, over an average follow-up period of approximatelythree years.9 An additional group of 585 participants was treatedwith troglitazone, but this treatment was halted during theDPP trial owing to its toxic effects on the liver.10
Participants
The 3548 participants included in this study (92.9 percent ofthe participants in the DPP trial; 2994 subjects randomly assignedto placebo, lifestyle intervention, or metformin, plus 554 participantsinitially randomly assigned to troglitazone, in whom only quantitativetraits were analyzed) each provided written informed consentfor the genetic investigation. Of these participants, 66.8 percentwere women, 56.4 percent white, 20.2 percent African American,16.8 percent Hispanic, 4.3 percent Asian, and 2.4 percent AmericanIndian, according to self-report. The mean (±SD) agewas 51±11 years, and the mean body-mass index (BMI) (theweight in kilograms divided by the square of the height in meters)was 34.0±6.7. The development of diabetes was assessedon the basis of semiannual measurements of fasting plasma glucoseconcentrations and annual oral glucose-tolerance tests (witha 75-g oral glucose load).9,10
Genotyping
DNA was extracted from peripheral-blood leukocytes and quantitatedwith the use of PicoGreen analysis (Molecular Probes). Genotypingwas performed by allele-specific primer extension of singleplexamplified products, with detection by matrix-assisted laserdesorption–ionization time-of-flight mass spectroscopyon a Sequenom platform.11,12 The genotyping success rate was99.3 percent, and the consensus rate (on the basis of 222 duplicategenotypes) was 99.1 percent. The allele frequencies for bothsingle-nucleotide polymorphisms in each of the five races orethnic groups were in Hardy–Weinberg equilibrium (P>0.05).
Quantitative Measures
Data from the baseline oral glucose-tolerance test were usedto calculate two measures of insulin secretion13,14 and twomeasures of insulin sensitivity15,16 as previously described.17We used glucose and insulin measured in conventional units (milligramsper deciliter and microunits per milliliter, respectively) unlessotherwise specified. Measures of insulin secretion includedthe fasting insulin:glucose ratio, calculated by dividing (insulinat 30 minutes–insulin at 0 minutes) by (glucose at 30minutes–glucose at 0 minutes), and the corrected insulinresponse, calculated by means of the following equation: (100xinsulinat 30 minutes)÷[glucose at 30 minutesx(glucose at 30minutes–70 mg per deciliter)]. Measures of insulin sensitivityincluded the reciprocal of the fasting insulin level and theinsulin-sensitivity index, which is the reciprocal of insulinresistance according to the homeostasis model assessment16 andis calculated by the following equation: 22.5÷[fastinginsulinx(fasting glucose÷18.01)]. We have previouslyshown that both measures of insulin secretion strongly correlatewith each other, as do both measures of insulin sensitivity.17
Statistical Analysis
We examined Cox regression models according to genotype, intervention,and interactions between genotype and intervention as the independentvariables predicting the incidence of diabetes. To test forinteractions, we contrasted the likelihood of the model thatincluded all two-way treatment and genotype interaction termswith the likelihood of the model without any interaction terms;the ratio has a chi-square distribution. If this was significant,we tested each interaction term with the use of the Wald test.18Models were adjusted for risk factors for diabetes at enrollment.Because there was no evidence of differences between race orethnic groups in the incidence of diabetes for any of the interventions,initial analyses of the effects of genotype on incidence wereperformed for all races and ethnic groups combined; they werealso repeated only in populations that had similar allele frequencies(whites and African Americans together). No significant interactionbetween ethnicity and genotype was detected in any of our analyses.The population attributable risk was estimated with data fromthe placebo group for each ethnic group, calculated as follows:1–(1÷[p2HRhom+2p(1–p)HRhet+(1–p)2]),where p is the risk-allele frequency, HRhom is the hazard ratiofor homozygotes, and HRhet is the hazard ratio for heterozygotes.
For the quantitative trait analyses, baseline measures in theentire cohort (obtained in 3436 participants, including thoserandomly assigned to troglitazone) were log-transformed fornon-normality and a generalized linear model was performed comparingvalues according to genotype. In cases in which log transformationdid not result in a normal distribution, differences betweenmeans were compared with the use of a nonparametric Wilcoxontest. We further obtained an estimate of "composite beta-cellfunction" by adjusting baseline insulin secretion to insulinsensitivity through linear regression of log-transformed variables.17For the one-year analyses, we focused on the insulin:glucoseratio as a measure of insulin secretion and the insulin-sensitivityindex as a measure of insulin sensitivity, and we used a generalizedlinear model with interaction terms of genotype and treatment.Means were adjusted for baseline measures. Nominal two-sidedP values are reported and were adjusted for multiple comparisons(three genotypic groups within each trait) with the use of theHolm procedure.19 Analyses were done with the use of SAS software,version 8.2 (SAS Institute).
Results
Distribution of Allele Frequencies
Baseline demographic and anthropomorphic characteristics areshown in Table 1. For rs12255372, the frequency of the minorT allele in whites enrolled in the DPP (0.32) was similar tothat previously reported in European populations.4 The frequencyof minor alleles was similar in African Americans (0.31) butlower in Hispanics (0.23), Asians (0.14), and American Indians(0.05). Similar distributions were noted for rs7903146, withminor allele frequencies of 0.33 in whites, 0.31 in AfricanAmericans, 0.24 in Hispanics, 0.17 in Asians, and 0.12 in AmericanIndians. Linkage disequilibrium between both variants was strongin people of European descent (D'=0.90, r2=0.78) but nearlyabsent in African Americans (D'=0.11, r2=0.01). Allele frequencieswere similar across treatment groups.
Table 1. Baseline Characteristics of the Cohort According to Genotype at the rs12255372 and rs7903146 Variants.
Incidence of Diabetes
Grant et al.4 identified the T alleles at both single-nucleotidepolymorphisms as the risk variants. In the DPP, participantswho were homozygous for the T allele at rs7903146 were morelikely to have progression to diabetes than were those who werehomozygous for the C allele (hazard ratio, 1.55; 95 percentconfidence interval, 1.20 to 2.01; P<0.001). No excess riskwas conferred by the heterozygous genotype (Table 2). The effectof the risk-conferring genotype was greatest in the placebogroup (hazard ratio, 1.81; 95 percent confidence interval, 1.21to 2.70; P=0.004) and less in the metformin group (hazard ratio,1.62; 95 percent confidence interval, 1.03 to 2.54; P=0.04)and in the lifestyle-intervention group (hazard ratio, 1.15;95 percent confidence interval, 0.68 to 1.94; P=0.60). In theplacebo group, the incidence of diabetes for the TT, CT, andCC genotypes at rs7903146 was 18.5, 10.7, and 10.8 per 100 person-years,respectively (Figure 1). The results were similar for the risk-conferringTT genotype at rs12255372 as compared with the GG genotype,both in the overall group (hazard ratio, 1.53; 95 percent confidenceinterval, 1.17 to 2.01; P=0.002) and in the placebo group (hazardratio, 1.81; 95 percent confidence interval, 1.19 to 2.75; P=0.005).Results for rs12255372 in the metformin and lifestyle-interventiongroups were similar to those for rs7903146 but not significant(hazard ratio, 1.45 and 1.24, respectively; 95 percent confidenceintervals, 0.90 to 2.35 and 0.73 to 2.12; P=0.13 and P=0.43).
Figure 1. Incidence of Diabetes According to Treatment Group and Genotype at Variant rs7903146.
The P values were determined by the log-rank test.
Although the effect of genotype at both single-nucleotide polymorphismswas stronger in the placebo group than in the metformin andlifestyle-intervention groups, there were no significant interactionsbetween genotype and intervention at either locus. Similarly,there were no significant interactions between genotype andrace or ethnic group or between genotype and BMI on diabetesincidence (Table 2). When we restricted our analysis to thepopulations with similar allele frequencies, the effect of theTT genotype, as compared with the CC genotype, at rs7903146on the risk of diabetes was indistinguishable from the overallresult (hazard ratio, 1.63; 95 percent confidence interval,1.17 to 2.27; P=0.004 in 2276 whites and African Americans together);we found similar results for rs12255372.
Quantitative Measures
We examined whether the risk allele at either single-nucleotidepolymorphism affected quantitative glycemic traits. At baseline,carriers of the T allele at rs7903146 had significantly lowerlevels of insulin secretion than did CC homozygotes, as measuredby both the insulin:glucose ratio and the corrected insulinresponse (Figure 2). Similar results were obtained for the Tallele at rs12255372.
Figure 2. Effects of Genotype at rs7903146 on Insulin Secretion at Baseline as Measured by the Mean (±SE) Insulin:Glucose Ratio (Panel A) and the Corrected Insulin Response (Panel B).
To convert the insulin:glucose ratio to picomoles per liter÷millimoles per liter, multiply by 125.1; to convert the corrected insulin response to picomoles per liter÷(millimoles per liter)2, multiply by 2254.9. Statistical comparisons were made on log-transformed values where appropriate. All pairwise comparisons are significant at a P value of less than 0.02; in comparisons between the CC and TT genotypes, the P value is less than 0.001 for both measures of insulin secretion. Although baseline glucose concentrations did not differ significantly across genotypic groups at 0 and 30 minutes, TT homozygotes had significantly lower insulin concentrations at both time points — at 30 minutes, CC, CT, and TT participants had mean insulin concentrations (±SD) of 106.0±69.7, 96.0±56.1, and 89.1±57.2 µU per milliliter, respectively (P<0.001). To convert microunits per milliliter to picomoles per liter, multiply by 6.94.
Surprisingly, two mean baseline measures of insulin sensitivitywere significantly higher in the presence of T alleles and inproportion to the number of T alleles (Figure 3). Compositebeta-cell function seemed to be impaired in TT homozygotes,as indicated by a shift in the regression curve downward andto the left (Figure 3). The greater mean insulin sensitivityin carriers of T alleles at rs7903146 correlated with a concomitantlower mean BMI and waist circumference at baseline and persistedafter adjustment for these traits; a similar trend was notedfor rs12255372 (Table 1).
Figure 3. Insulin Secretion and Insulin Sensitivity at Baseline for Each Genotype at rs7903146.
Composite beta-cell function is estimated by the relationship between insulin secretion (the insulin:glucose ratio and the corrected insulin response) and insulin sensitivity (insulin-sensitivity index and 1÷fasting insulin). The curves represent the regression line of the logarithm of estimated insulin secretion as a linear function of the logarithm of estimated insulin sensitivity for all participants at baseline, distributed according to genotype at rs7903146. The mean for each group is indicated by the point estimate in each curve. Carriers of the T allele have decreased insulin secretion accompanied by an increase in insulin sensitivity. The shift of the curve downward and to the left in TT homozygotes suggests a defect in composite beta-cell function. To convert insulin-sensitivity index and 1÷fasting insulin to (picomoles per literxmillimoles per liter)-1, multiply by 0.144.
Given these results of quantitative traits, we adjusted ourmodels of the incidence of diabetes for the presence of knownrisk factors. Initial adjustment for age and BMI at baselinedid not alter the results; full adjustment for sex and age,BMI, waist circumference, and the fasting plasma glucose concentrationat baseline reduced the hazard ratios slightly as compared withhomozygous genotypes, but they remained significant (hazardratio for the TT genotype as compared with the CC genotype atrs7903146, 1.39; 95 percent confidence interval, 1.06 to 1.82;P=0.02). When the influence of covariates was assessed withthe inclusion of interaction terms in the model, only waistcircumference showed a nominally significant interaction withgenotype. Including the baseline insulin:glucose ratio in themodel as a measure of insulin secretion also minimally decreasedthe observed effect (hazard ratio for the TT genotype as comparedwith the CC genotype at rs7903146, 1.41; 95 percent confidenceinterval, 1.08 to 1.83; P=0.01). Similar results were obtainedfor rs12255372.
At one year from baseline, we detected no significant effectsof genotype on the changes in any of the insulin-secretion orinsulin-sensitivity indexes associated with the three interventions,17,20consistent with the absence of interactions between genotypeand treatment group.
Discussion
Inconsistent reproducibility has been a vexing problem for geneticassociation studies in complex diseases.6,7,21 False positivereports of association, false negative attempts at replication,and genetic heterogeneity often complicate the picture, andthus a true genetic association usually emerges only after carefullyconducted, large-scale association studies confirm the originalreport.8
A limited number of common genetic variants meet that high standardin type 2 diabetes.22 In most cases, the genotypic risk is modest(1.15 to 1.25), requiring very large sample sizes for detection.The recent identification of a common allele in the TCF7L2 genethat increases the risk of type 2 diabetes by approximately1.45 in heterozygotes and 2.41 in homozygotes4 is thereforequite provocative. Despite these significant results in a cross-sectionalstudy,4 it was essential to replicate this genetic associationin other cohorts and to do so prospectively. In addition, ourevaluation of a potential mechanism by which the risk of diabetesis increased and the determination of whether these variantscause differential responses to validated preventive strategiesrepresent important next steps in exploring the association.
The DPP is a unique study in which to carry out such analyses.The large sample size and the cohort of several races and ethnicgroups, reflecting the diversity of the U.S. population withtype 2 diabetes, make it possible to test the role of geneticvariants in different races or ethnic groups, even if they conferonly modest risk. The DPP study is different from other largeobservational studies23 because of its interventional designand exclusive enrollment of overweight or obese persons withelevated fasting plasma glucose concentrations and impairedglucose tolerance, which indicate a high risk of diabetes atbaseline.
Our data indicate that the risk alleles in rs7903146 and rs12255372predict the risk of diabetes prospectively, beyond that conferredby the clinical risk factors reflected by the DPP eligibilitycriteria. The genotypic relative risk may differ slightly fromthe odds ratio documented by Grant et al.4 owing to a differentstudy design, an overestimate of the initial finding,6 populationheterogeneity in the DPP, various degrees of linkage disequilibriumbetween the DG10S478 microsatellite and the single-nucleotidepolymorphisms evaluated here, or our limited temporal window(three years on average) for the clinical transition from impairedglucose tolerance to diabetes. The size of the effect seemsrobust for a sample of this size; given the higher probabilityconferred by the initial report, our finding is a strong confirmationof the original genetic association.
Grant and coworkers4 exhaustively assessed coding variationin whites by a variety of deep resequencing methods in the region,suggesting that other functional variants in this gene are unlikelyto have been missed. In addition to rs12255372 and rs7903146,other single-nucleotide polymorphisms in linkage disequilibriumwith them were also strongly associated with diabetes. Whichof these single-nucleotide polymorphisms is responsible forthe observed association requires further study in adequatelypowered samples. In particular, the absence of linkage disequilibriumbetween rs7903146 and rs12255372 in African Americans may helpdistinguish whether one of the two single-nucleotide polymorphisms(or the haplotype formed by the risk alleles at both loci) isthe sole source of the association signal; we could not makethis distinction after initial exploratory analyses in our dataset, perhaps because of inadequate sample size. Given theirallele frequencies and assuming an overall genotypic relativerisk of 1.54 (Table 2) and diabetes prevalence of 10 percentamong African Americans, we estimate that the enrollment ofapproximately 1400 persons of African ancestry would be necessaryfor the case–control study to have 80 percent power (witha P value of less than 0.05 considered to indicate statisticalsignificance) to distinguish between rs7903146 and rs12255372as the source of the association. Sample sizes at least twiceas large would be needed if one intended to detect a signalarising solely from the haplotype formed by the minor allelesat both loci.
We studied the TCF7L2 single-nucleotide polymorphisms in nonwhitepopulations. Subgroup analysis according to race or ethnic groupshowed similar effect sizes at this locus, but these effectsizes were not individually significant, possibly because ofinadequate sample size. However, we cannot rule out effectsof genotype on risk that were specific to race or ethnic group;with the results we have obtained here, a cohort of more than20,000 persons would be needed to detect an interaction betweengenotype and African-American ethnicity, at a nominal (unadjusted)P value of less than 0.05. Similarly, our sample size may nothave been sufficient to detect a significant effect of the heterozygousgenotype on the risk of diabetes. Nevertheless, the resultsof the report by Grant et al.4 and our findings of a specificeffect of a single copy of the T allele on quantitative glycemictraits suggest that heterozygosity at this locus may have phenotypicconsequences.
The original study speculated that genetic variation in TCF7L2might impair the expression of glucagon-like peptide 1 in enteroendocrinecells, possibly by interfering with -catenin–mediatedtranscriptional activation of its gene GCG.24 Our finding thatinsulin secretion is decreased in carriers of the risk-conferringgenotype, which is consistent with an increased incidence ofdiabetes, lends indirect support to this model. However, itis not readily apparent how rs12255372 and rs7903146, whichlie in short interspersed repeat elements approximately 41 kbupstream and 9 kb downstream, respectively, of exon 4 in TCF7L2,might affect TCF7L2 expression or the function of its proteinproduct. Fine mapping of the association signal and directedfunctional studies should help determine the molecular consequencesof genetic variation at this locus.
The enhanced insulin sensitivity (and the lower BMI and smallerwaist circumference) in carriers of the T allele at both single-nucleotidepolymorphisms was unexpected and may be an artifact of our requirementthat patients have high-risk characteristics for diabetes atenrollment. Specifically, if the T allele leads to decreasedinsulin secretion and a higher risk of diabetes, subjects withadditional insulin resistance would be more likely to have diabetesat baseline (all other factors being equal), precluding theirenrollment in this trial. Conversely, at-risk participants carryingthe T allele may not have had diabetes at the time of enrollmentbecause of enhanced insulin sensitivity owing to other geneticor environmental factors. In either case, our results stronglysuggest that these variants do not cause insulin resistancein persons with impaired glucose tolerance and support the notionthat variants in TCF7L2 lead to diabetes by means of defectsin insulin secretion. Like KCNJ11, TCF7L2 seems to be a diabetes-associatedgene in which common polymorphisms primarily affect the betacell.23,25,26
Finally, we did not detect significant interactions betweengenotypes at either single-nucleotide polymorphism and the DPPinterventions. The absence of an effect may not be surprising,since these interventions succeeded primarily by improving insulinsensitivity17 and these variants affect insulin secretion. However,we did not observe any effect of genotype at these loci in thelifestyle-intervention group, raising the possibility that abehavioral intervention can mitigate the risk conferred by geneticbackground. Conversely, the intervention groups may have beenunderpowered for these analyses. Whether the response to drugsdesigned to improve insulin secretion (e.g., sulfonylureas,meglitinides, or incretins) will be affected by these commonvariants requires specific testing in pharmacogenetic trials.
In summary, our results from this large prospective study confirmand extend the finding that the transcription factor gene TCF7L2is associated with susceptibility to type 2 diabetes. Furtherunderstanding of the mechanisms by which variation in this geneaffects glucose homeostasis may provide new insights into themolecular basis of diabetes and opportunities for more targetedinterventions for prevention and therapy.
Supported by the National Institutes of Health (NIH) throughthe National Institute of Diabetes and Digestive and KidneyDiseases (NIDDK), the Office of Research on Minority Health,the National Institute of Child Health and Human Development,the Office of Women's Health, and the National Institute onAging; the Indian Health Service; the Centers for Disease Controland Prevention; the American Diabetes Association; Bristol-MyersSquibb; and Parke-Davis; an NIH grant (R01DK072041-01, to Drs.Altshuler, Florez, Pollin, and Shuldiner); and in part by theIntramural Research Program of the NIDDK. Many of the clinicalcenters were supported by the General Clinical Research CenterProgram, National Center for Research Resources. The clinicalcenters and the coordinating center were supported by the NIDDKthrough a cooperative agreement, except the Southwestern AmericanIndian Centers, which were supported directly by the NIDDK andthe Indian Health Service. Dr. Florez is supported by an NIHResearch Career Award (K23 DK65978-03).
Dr. Shuldiner reports having received consulting fees from Merck,Sanofi-Aventis, and Sensors for Medicine, as well as lecturefees from Sanofi-Aventis. Dr. Altshuler reports having receivedconsulting fees from Rosetta Inpharmatics (a subsidiary of Merck)and serving on the advisory board of Medical Portfolio Management.No other potential conflict of interest relevant to this articlewas reported.
We are indebted to Santica Marcovina and Greg Strylewicz fortheir careful processing of the DNA samples; to Mark Daly, NoëlBurt, and our colleagues at the Broad Institute Genetic AnalysisPlatform for their assistance; and to the participants of theDPP for their commitment and dedication.
Source Information
From the Diabetes Prevention Program Outcomes Study Coordinating Center, George Washington University, Rockville, Md.
Address reprint requests to Dr. Florez at the Diabetes Prevention Program Coordinating Center, the Biostatistics Center, George Washington University, 6110 Executive Blvd., Suite 750, Rockville, MD 20852, or at dppmail{at}biostat.bsc.gwu.edu.
Florez JC, Hirschhorn JN, Altshuler D. The inherited basis of diabetes mellitus: implications for the genetic analysis of complex traits. Annu Rev Genomics Hum Genet 2003;4:257-291. [CrossRef][Web of Science][Medline]
Fajans SS, Bell GI, Polonsky KS. Molecular mechanisms and clinical pathophysiology of maturity-onset diabetes of the young. N Engl J Med 2001;345:971-980. [Free Full Text]
Grant SFA, Thorleifsson G, Reynisdottir I, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet 2006;38:320-323. [CrossRef][Web of Science][Medline]
Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG. Replication validity of genetic association studies. Nat Genet 2001;29:306-309. [CrossRef][Web of Science][Medline]
Lohmueller K, Pearce CL, Pike M, Lander ES, Hirschhorn JN. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet 2003;33:177-182. [CrossRef][Web of Science][Medline]
Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346:393-403. [Free Full Text]
Diabetes Prevention Program Research Group. The Diabetes Prevention Program: design and methods for a clinical trial in the prevention of type 2 diabetes. Diabetes Care 1999;22:623-634. [Erratum, Diabetes Care 1999;22:1389.] [Abstract]
Gabriel SB, Schaffner SF, Nguyen H, et al. The structure of haplotype blocks in the human genome. Science 2002;296:2225-2229. [Free Full Text]
Tang K, Fu DJ, Julien D, Braun A, Cantor CR, Koster H. Chip-based genotyping by mass spectrometry. Proc Natl Acad Sci U S A 1999;96:10016-10020. [Free Full Text]
Sluiter WJ, Erkelens DW, Reitsma WD, Doorenbos H. Glucose tolerance and insulin release, a mathematical approach. I. Assay of the -cell response after oral glucose loading. Diabetes 1976;25:241-244. [Abstract]
Phillips DI, Clark PM, Hales CN, Osmond C. Understanding oral glucose tolerance: comparison of glucose or insulin measurements during the oral glucose tolerance test with specific measurements of insulin resistance and insulin secretion. Diabet Med 1994;11:286-292. [Web of Science][Medline]
Hanson RL, Pratley RE, Bogardus C, et al. Evaluation of simple indices of insulin sensitivity and insulin secretion for use in epidemiologic studies. Am J Epidemiol 2000;151:190-198. [Free Full Text]
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and -cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-419. [CrossRef][Web of Science][Medline]
The Diabetes Prevention Program Research Group. Role of insulin secretion and sensitivity in the evolution of type 2 diabetes in the Diabetes Prevention Program: effects of lifestyle intervention and metformin. Diabetes 2005;54:2404-2414. [Free Full Text]
Greenberg RS, Kleinbaum DG. Mathematical modeling strategies for the analysis of epidemiologic research. Annu Rev Public Health 1985;6:223-245. [CrossRef][Web of Science][Medline]
Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat 1979;6:65-70.
The Diabetes Prevention Program Research Group. Prevention of type 2 diabetes with troglitazone in the Diabetes Prevention Program. Diabetes 2005;54:1150-1156. [Free Full Text]
Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K. A comprehensive review of genetic association studies. Genet Med 2002;4:45-61. [Web of Science][Medline]
O'Rahilly S, Barroso I, Wareham NJ. Genetic factors in type 2 diabetes: the end of the beginning? Science 2005;307:370-373. [Free Full Text]
Lyssenko V, Almgren P, Anevski D, et al. Genetic prediction of future type 2 diabetes. PLoS Med 2005;2:e345-e345. [Medline]
Yi F, Brubaker PL, Jin T. TCF-4 mediates cell type-specific regulation of proglucagon gene expression by -catenin and glycogen synthase kinase-3. J Biol Chem 2005;280:1457-1464. [Free Full Text]
Nielsen E-MD, Hansen L, Carstensen B, et al. The E23K variant of Kir6.2 associates with impaired post-OGTT serum insulin response and increased risk of type 2 diabetes. Diabetes 2003;52:573-577. [Free Full Text]
Florez JC, Burtt N, de Bakker PIW, et al. Haplotype structure and genotype-phenotype correlations of the sulfonylurea receptor and the islet ATP-sensitive potassium channel gene region. Diabetes 2004;53:1360-1368. [Free Full Text]
Bo, S., Gambino, R., Ciccone, G., Rosato, R., Milanesio, N., Villois, P., Pagano, G., Cassader, M., Gentile, L., Durazzo, M., Cavallo-Perin, P.
(2009). Effects of TCF7L2 polymorphisms on glucose values after a lifestyle intervention. Am. J. Clin. Nutr.
90: 1502-1508
[Abstract][Full Text]
Staiger, H., Machicao, F., Fritsche, A., Haring, H.-U.
(2009). Pathomechanisms of Type 2 Diabetes Genes. Endocr. Rev.
30: 557-585
[Abstract][Full Text]
Liu, P.-H., Chang, Y.-C., Jiang, Y.-D., Chen, W. J., Chang, T.-J., Kuo, S.-S., Lee, K.-C., Hsiao, P.-C., Chiu, K. C., Chuang, L.-M.
(2009). Genetic Variants of TCF7L2 Are Associated with Insulin Resistance and Related Metabolic Phenotypes in Taiwanese Adolescents and Caucasian Young Adults. J. Clin. Endocrinol. Metab.
94: 3575-3582
[Abstract][Full Text]
Biyasheva, A., Legro, R. S., Dunaif, A., Urbanek, M.
(2009). Evidence for Association between Polycystic Ovary Syndrome (PCOS) and TCF7L2 and Glucose Intolerance in Women with PCOS and TCF7L2. J. Clin. Endocrinol. Metab.
94: 2617-2625
[Abstract][Full Text]
Grant, R. W., Moore, A. F., Florez, J. C.
(2009). Genetic Architecture of Type 2 Diabetes: Recent Progress and Clinical Implications. Diabetes Care
32: 1107-1114
[Full Text]
Schinner, S., Willenberg, H. S, Schott, M., Scherbaum, W. A
(2009). Pathophysiological aspects of Wnt-signaling in endocrine disease. Eur J Endocrinol
160: 731-737
[Abstract][Full Text]
Shin, J., Kayser, S. R., Langaee, T. Y.
(2009). Pharmacogenetics: from discovery to patient care. Am J Health Syst Pharm
66: 625-637
[Abstract][Full Text]
Cornelis, M. C, Qi, L., Kraft, P., Hu, F. B
(2009). TCF7L2, dietary carbohydrate, and risk of type 2 diabetes in US women. Am. J. Clin. Nutr.
89: 1256-1262
[Abstract][Full Text]
Warodomwichit, D., Arnett, D. K., Kabagambe, E. K., Tsai, M. Y., Hixson, J. E., Straka, R. J., Province, M., An, P., Lai, C.-Q., Borecki, I., Ordovas, J. M.
(2009). Polyunsaturated Fatty Acids Modulate the Effect of TCF7L2 Gene Variants on Postprandial Lipemia. J. Nutr.
139: 439-446
[Abstract][Full Text]
Yu, J., Steck, A. K., Babu, S., Yu, L., Miao, D., McFann, K., Hutton, J., Eisenbarth, G. S., Klingensmith, G.
(2009). Single Nucleotide Transcription Factor 7-Like 2 (TCF7L2) Gene Polymorphisms in Antiislet Autoantibody-Negative Patients at Onset of Diabetes. J. Clin. Endocrinol. Metab.
94: 504-510
[Abstract][Full Text]
Utzschneider, K. M., Prigeon, R. L., Faulenbach, M. V., Tong, J., Carr, D. B., Boyko, E. J., Leonetti, D. L., McNeely, M. J., Fujimoto, W. Y., Kahn, S. E.
(2009). Oral Disposition Index Predicts the Development of Future Diabetes Above and Beyond Fasting and 2-h Glucose Levels. Diabetes Care
32: 335-341
[Abstract][Full Text]
Yan, Y., North, K. E., Ballantyne, C. M., Brancati, F. L., Chambless, L. E., Franceschini, N., Heiss, G., Kottgen, A., Pankow, J. S., Selvin, E., West, S. L., Boerwinkle, E.
(2009). Transcription Factor 7-Like 2 (TCF7L2) Polymorphism and Context-Specific Risk of Type 2 Diabetes in African American and Caucasian Adults: The Atherosclerosis Risk in Communities Study. Diabetes
58: 285-289
[Abstract][Full Text]
Florez, J. C.
(2008). The Genetics of Type 2 Diabetes: A Realistic Appraisal in 2008. J. Clin. Endocrinol. Metab.
93: 4633-4642
[Abstract][Full Text]
Meigs, J. B., Shrader, P., Sullivan, L. M., McAteer, J. B., Fox, C. S., Dupuis, J., Manning, A. K., Florez, J. C., Wilson, P. W.F., D'Agostino, R. B. Sr., Cupples, L. A.
(2008). Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes. NEJM
359: 2208-2219
[Abstract][Full Text]
Altshuler, D., Daly, M. J., Lander, E. S.
(2008). Genetic Mapping in Human Disease. Science
322: 881-888
[Abstract][Full Text]
Jin, T., Liu, L.
(2008). Minireview: The Wnt Signaling Pathway Effector TCF7L2 and Type 2 Diabetes Mellitus. Mol. Endocrinol.
22: 2383-2392
[Abstract][Full Text]
Lango, H., the U.K. Type 2 Diabetes Genetics Consortium, , Palmer, C. N.A., Morris, A. D., Zeggini, E., Hattersley, A. T., McCarthy, M. I., Frayling, T. M., Weedon, M. N.
(2008). Assessing the Combined Impact of 18 Common Genetic Variants of Modest Effect Sizes on Type 2 Diabetes Risk. Diabetes
57: 3129-3135
[Abstract][Full Text]
Moore, A. F., Jablonski, K. A., McAteer, J. B., Saxena, R., Pollin, T. I., Franks, P. W., Hanson, R. L., Shuldiner, A. R., Knowler, W. C., Altshuler, D., Florez, J. C., for the Diabetes Prevention Program Research Group,
(2008). Extension of Type 2 Diabetes Genome-Wide Association Scan Results in the Diabetes Prevention Program. Diabetes
57: 2503-2510
[Abstract][Full Text]
Grarup, N., Andersen, G., Krarup, N. T., Albrechtsen, A., Schmitz, O., Jorgensen, T., Borch-Johnsen, K., Hansen, T., Pedersen, O.
(2008). Association Testing of Novel Type 2 Diabetes Risk Alleles in the JAZF1, CDC123/CAMK1D, TSPAN8, THADA, ADAMTS9, and NOTCH2 Loci With Insulin Release, Insulin Sensitivity, and Obesity in a Population-Based Sample of 4,516 Glucose-Tolerant Middle-Aged Danes. Diabetes
57: 2534-2540
[Abstract][Full Text]
Horikawa, Y., Miyake, K., Yasuda, K., Enya, M., Hirota, Y., Yamagata, K., Hinokio, Y., Oka, Y., Iwasaki, N., Iwamoto, Y., Yamada, Y., Seino, Y., Maegawa, H., Kashiwagi, A., Yamamoto, K., Tokunaga, K., Takeda, J., Kasuga, M.
(2008). Replication of Genome-Wide Association Studies of Type 2 Diabetes Susceptibility in Japan. J. Clin. Endocrinol. Metab.
93: 3136-3141
[Abstract][Full Text]
Ng, M. C.Y., Park, K. S., Oh, B., Tam, C. H.T., Cho, Y. M., Shin, H. D., Lam, V. K.L., Ma, R. C.W., So, W. Y., Cho, Y. S., Kim, H.-L., Lee, H. K., Chan, J. C.N., Cho, N. H.
(2008). Implication of Genetic Variants Near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in Type 2 Diabetes and Obesity in 6,719 Asians. Diabetes
57: 2226-2233
[Abstract][Full Text]
Yi, F., Sun, J., Lim, G. E., Fantus, I. G., Brubaker, P. L., Jin, T.
(2008). Cross Talk between the Insulin and Wnt Signaling Pathways: Evidence from Intestinal Endocrine L Cells. Endocrinology
149: 2341-2351
[Abstract][Full Text]
Folsom, A. R., Pankow, J. S., Peacock, J. M., Bielinski, S. J., Heiss, G., Boerwinkle, E.
(2008). Variation in TCF7L2 and Increased Risk of Colon Cancer: The Atherosclerosis Risk in Communities (ARIC) Study. Diabetes Care
31: 905-909
[Abstract][Full Text]
Cervin, C., Lyssenko, V., Bakhtadze, E., Lindholm, E., Nilsson, P., Tuomi, T., Cilio, C. M., Groop, L.
(2008). Genetic Similarities Between Latent Autoimmune Diabetes in Adults, Type 1 Diabetes, and Type 2 Diabetes. Diabetes
57: 1433-1437
[Abstract][Full Text]
Liu, Z., Habener, J. F.
(2008). Glucagon-like Peptide-1 Activation of TCF7L2-dependent Wnt Signaling Enhances Pancreatic Beta Cell Proliferation. J. Biol. Chem.
283: 8723-8735
[Abstract][Full Text]
Shu, L., Sauter, N. S., Schulthess, F. T., Matveyenko, A. V., Oberholzer, J., Maedler, K.
(2008). Transcription Factor 7-Like 2 Regulates {beta}-Cell Survival and Function in Human Pancreatic Islets. Diabetes
57: 645-653
[Abstract][Full Text]
Omori, S., Tanaka, Y., Takahashi, A., Hirose, H., Kashiwagi, A., Kaku, K., Kawamori, R., Nakamura, Y., Maeda, S.
(2008). Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 With Susceptibility to Type 2 Diabetes in a Japanese Population. Diabetes
57: 791-795
[Abstract][Full Text]
Iakoubova, O. A., Sabatine, M. S., Rowland, C. M., Tong, C. H., Catanese, J. J., Ranade, K., Simonsen, K. L., Kirchgessner, T. G., Cannon, C. P., Devlin, J. J., Braunwald, E.
(2008). Polymorphism in KIF6 gene and benefit from statins after acute coronary syndromes: results from the PROVE IT-TIMI 22 study.. J Am Coll Cardiol
51: 449-455
[Abstract][Full Text]
Palmer, N. D., Lehtinen, A. B., Langefeld, C. D., Campbell, J. K., Haffner, S. M., Norris, J. M., Bergman, R. N., Goodarzi, M. O., Rotter, J. I., Bowden, D. W.
(2008). Association of TCF7L2 Gene Polymorphisms with Reduced Acute Insulin Response in Hispanic Americans. J. Clin. Endocrinol. Metab.
93: 304-309
[Abstract][Full Text]
Kang, E. S., Kim, M. S., Kim, Y. S., Hur, K. Y., Han, S. J., Nam, C. M., Ahn, C. W., Cha, B. S., Kim, S. I., Lee, H. C.
(2008). A Variant of the Transcription Factor 7-Like 2 (TCF7L2) Gene and the Risk of Posttransplantation Diabetes Mellitus in Renal Allograft Recipients. Diabetes Care
31: 63-68
[Abstract][Full Text]
Vaxillaire, M., Veslot, J., Dina, C., Proenca, C., Cauchi, S., Charpentier, G., Tichet, J., Fumeron, F., Marre, M., Meyre, D., Balkau, B., Froguel, P., for the DESIR Study Group,
(2008). Impact of Common Type 2 Diabetes Risk Polymorphisms in the DESIR Prospective Study. Diabetes
57: 244-254
[Abstract][Full Text]
Bloomgarden, Z. T.
(2007). Diabetes and Obesity: Part 1. Diabetes Care
30: 3145-3151
[Full Text]
Taylor, K. D., Norris, J. M., Rotter, J. I.
(2007). Genome-Wide Association: Which Do You Want First: the Good News, the Bad News, or the Good News?. Diabetes
56: 2844-2848
[Full Text]
Hanson, R. L., Bogardus, C., Duggan, D., Kobes, S., Knowlton, M., Infante, A. M., Marovich, L., Benitez, D., Baier, L. J., Knowler, W. C.
(2007). A Search for Variants Associated With Young-Onset Type 2 Diabetes in American Indians in a 100K Genotyping Array. Diabetes
56: 3045-3052
[Abstract][Full Text]
Florez, J. C., Manning, A. K., Dupuis, J., McAteer, J., Irenze, K., Gianniny, L., Mirel, D. B., Fox, C. S., Cupples, L. A., Meigs, J. B.
(2007). A 100K Genome-Wide Association Scan for Diabetes and Related Traits in the Framingham Heart Study: Replication and Integration With Other Genome-Wide Datasets. Diabetes
56: 3063-3074
[Abstract][Full Text]
Guo, T., Hanson, R. L., Traurig, M., Li Muller, Y., Ma, L., Mack, J., Kobes, S., Knowler, W. C., Bogardus, C., Baier, L. J.
(2007). TCF7L2 Is Not a Major Susceptibility Gene for Type 2 Diabetes in Pima Indians: Analysis of 3,501 Individuals. Diabetes
56: 3082-3088
[Abstract][Full Text]
Dunnick, J. K., Thayer, K. A., Travlos, G. S.
(2007). Inclusion of Biomarkers for Detecting Perturbations in the Heart and Lung and Lipid/Carbohydrate Metabolism in National Toxicology Program Studies. Toxicol Sci
100: 29-35
[Abstract][Full Text]
Goodarzi, M. O., Rotter, J. I.
(2007). Testing the Gene or Testing a Variant?: The Case of TCF7L2. Diabetes
56: 2417-2419
[Full Text]
Chang, Y.-C., Chang, T.-J., Jiang, Y.-D., Kuo, S.-S., Lee, K.-C., Chiu, K. C., Chuang, L.-M.
(2007). Association Study of the Genetic Polymorphisms of the Transcription Factor 7-Like 2 (TCF7L2) Gene and Type 2 Diabetes in the Chinese Population. Diabetes
56: 2631-2637
[Abstract][Full Text]
Sale, M. M., Smith, S. G., Mychaleckyj, J. C., Keene, K. L., Langefeld, C. D., Leak, T. S., Hicks, P. J., Bowden, D. W., Rich, S. S., Freedman, B. I.
(2007). Variants of the Transcription Factor 7-Like 2 (TCF7L2) Gene Are Associated With Type 2 Diabetes in an African-American Population Enriched for Nephropathy. Diabetes
56: 2638-2642
[Abstract][Full Text]
Ng, M. C. Y., Tam, C. H. T., Lam, V. K. L., So, W.-Y., Ma, R. C. W., Chan, J. C. N.
(2007). Replication and Identification of Novel Variants at TCF7L2 Associated with Type 2 Diabetes in Hong Kong Chinese. J. Clin. Endocrinol. Metab.
92: 3733-3737
[Abstract][Full Text]
Raitakari, O. T., Ronnemaa, T., Huupponen, R., Viikari, L., Fan, M., Marniemi, J., Hutri-Kahonen, N., Viikari, J. S.A., Lehtimakimd, T.
(2007). Variation of the Transcription Factor 7-Like 2 (TCF7L2) Gene Predicts Impaired Fasting Glucose in Healthy Young Adults: The Cardiovascular Risk in Young Finns Study. Diabetes Care
30: 2299-2301
[Full Text]
Valdez, R., Greenlund, K. J., Khoury, M. J., Yoon, P. W.
(2007). Is Family History a Useful Tool for Detecting Children at Risk for Diabetes and Cardiovascular Diseases? A Public Health Perspective. Pediatrics
120: S78-S86
[Abstract][Full Text]
Pearson, E. R., Donnelly, L. A., Kimber, C., Whitley, A., Doney, A. S.F., McCarthy, M. I., Hattersley, A. T., Morris, A. D., Palmer, C. N.A.
(2007). Variation in TCF7L2 Influences Therapeutic Response to Sulfonylureas: A GoDARTs Study. Diabetes
56: 2178-2182
[Abstract][Full Text]
Loos, R. J.F., Franks, P. W., Francis, R. W., Barroso, I., Gribble, F. M., Savage, D. B., Ong, K. K., O'Rahilly, S., Wareham, N. J.
(2007). TCF7L2 Polymorphisms Modulate Proinsulin Levels and {beta}-Cell Function in a British Europid Population. Diabetes
56: 1943-1947
[Abstract][Full Text]
Duan, Q. L., Dube, M.-P., Frasure-Smith, N., Barhdadi, A., Lesperance, F., Theroux, P., St-Onge, J., Rouleau, G. A., McCaffery, J. M.
(2007). Additive Effects of Obesity and TCF7L2 Variants on Risk for Type 2 Diabetes Among Cardiac Patients. Diabetes Care
30: 1621-1623
[Full Text]
Korner, A., Berndt, J., Stumvoll, M., Kiess, W., Kovacs, P.
(2007). TCF7L2 Gene Polymorphisms Confer an Increased Risk for Early Impairment of Glucose Metabolism and Increased Height in Obese Children. J. Clin. Endocrinol. Metab.
92: 1956-1960
[Abstract][Full Text]
Florez, J. C., Jablonski, K. A., Sun, M. W., Bayley, N., Kahn, S. E., Shamoon, H., Hamman, R. F., Knowler, W. C., Nathan, D. M., Altshuler, D., for the Diabetes Prevention Program Research Group,
(2007). Effects of the Type 2 Diabetes-Associated PPARG P12A Polymorphism on Progression to Diabetes and Response to Troglitazone. J. Clin. Endocrinol. Metab.
92: 1502-1509
[Abstract][Full Text]
Clee, S. M., Attie, A. D.
(2007). The Genetic Landscape of Type 2 Diabetes in Mice. Endocr. Rev.
28: 48-83
[Abstract][Full Text]
Lehman, D. M., Hunt, K. J., Leach, R. J., Hamlington, J., Arya, R., Abboud, H. E., Duggirala, R., Blangero, J., Goring, H. H.H., Stern, M. P.
(2007). Haplotypes of Transcription Factor 7-Like 2 (TCF7L2) Gene and Its Upstream Region Are Associated With Type 2 Diabetes and Age of Onset in Mexican Americans. Diabetes
56: 389-393
[Abstract][Full Text]
Schadt, E. E., Lum, P. Y.
(2006). Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J. Lipid Res.
47: 2601-2613
[Abstract][Full Text]
Florez, J. C., Saxena, R., Winckler, W., Burtt, N. P., Almgren, P., Bengtsson Bostrom, K., Tuomi, T., Gaudet, D., Ardlie, K. G., Daly, M. J., Altshuler, D., Hirschhorn, J. N., Groop, L.
(2006). The Kruppel-Like Factor 11 (KLF11) Q62R Polymorphism Is Not Associated With Type 2 Diabetes in 8,676 People. Diabetes
55: 3620-3624
[Abstract][Full Text]
(2006). Endocrinology & Metabolism News, September 2006. J. Clin. Endocrinol. Metab.
91: 17a-17a
[Full Text]
O'Rahilly, S., Wareham, N. J.
(2006). Genetic variants and common diseases--better late than never.. NEJM
355: 306-308
[Full Text]
-catenin–mediated transcriptional activation of its gene GCG.24 Our finding that insulin secretion is decreased in carriers of the risk-conferring genotype, which is consistent with an increased incidence of diabetes, lends indirect support to this model. However, it is not readily apparent how rs12255372 and rs7903146, which lie in short interspersed repeat elements approximately 41 kb upstream and 9 kb downstream, respectively, of exon 4 in TCF7L2, might affect TCF7L2 expression or the function of its protein product. Fine mapping of the association signal and directed functional studies should help determine the molecular consequences of genetic variation at this locus. 
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