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Ann Nutr Metab. 2010 February; 56(1): 59–64.
Published online 2009 December 18. doi:  10.1159/000268019
PMCID: PMC2855271

Association of a Common G6PC2 Variant with Fasting Plasma Glucose Levels in Non-Diabetic Individuals



Fasting plasma glucose (FPG) levels correlate with cardiovascular disease and mortality in both diabetic and non-diabetic subjects. G6PC2 encodes a pancreatic islet-specific glucose-6-phosphatase-related protein and G6pc2-null mice were reported to exhibit decreased blood glucose levels. Two recent genome-wide association studies have implicated a role for G6PC2 in regulation of FPGlevels in the general European population and reported the strongest association with the rs560887 SNP. The purpose of this study was to replicate this association in our independent epidemiological samples.


DNA samples from non-Hispanic white Americans (NHWs; n = 623), Hispanic Americans (n = 410) and black Africans (n = 787) were genotyped for rs560887 using TaqMan allelic discrimination.


While no minor allele A of rs560887 was observed among blacks, its frequency was 33% in NHWs and 17.5% in Hispanics. The rs560887 minor allele was associated with reduced FPG levels in non-diabetic NHWs (p = 0.002 under an additive model). A similar trend of association was observed in non-diabetic Hispanics (p = 0.076 under a dominant model), which was more pronounced in normoglycemic subjects (p = 0.036).


Our results independently confirm the robust association of G6PC2/rs560887 with FPG levels in non-diabetic NHWs. The observed evidence for association in Hispanics warrants further studies in larger samples.

Key Words: Blood glucose, Plasma glucose, Fasting plasma glucose, G6PC2, Single nucleotide polymorphism, Polymorphism, rs560887


Blood glucose levels are maintained within narrow limits by hormonal and other regulators to prevent the harmful effects of hypo- or hyperglycemia. Acquired factors such as age and obesity appear to contribute to, but cannot fully explain, the inter-individual variation in blood glucose concentration [1]. Fasting plasma glucose (FPG) levels are under significant genetic influence (heritability estimates >25%) and have been shown to correlate with cardiovascular disease and mortality in both diabetic and non-diabetic individuals [2,3,4,5,6,7,8].

Glucose-6-phosphatase catalytic 2 gene (G6PC2, also known as IGRP, on chromosome 2q24.3) encodes a pancreatic islet-specific glucose-6-phosphatase catalytic subunit-related protein. G6PC2 had initially drawn attention as a major autoantigen for T cells that play an essential role in the development of type 1 diabetes in non-obese mice and humans [9,10,11]. G6pc2-null mice exhibit a mild metabolic phenotype with decreased blood glucose levels but unchanged insulin, glucagon, glycerol and cholesterol concentrations [12]. Recently, 2 genome-wide association studies (GWAS) have implicated a role for G6PC2 in contributing to inter-individual variation in FPG levels in the general population [13,14]. Both GWAS reported G6PC2/rs560887 [G>A, minor allele frequency (MAF) = 0.30 in Europeans] as the SNP most strongly associated with FPG levels among non-diabetic Europeans.

As replication of new findings by independent groups in independent samples is crucial for establishing the genetic associations for complex diseases, we sought to replicate the association of G6PC2/rs560887 with FPG levels in independent samples of non-Hispanic white (NHW) and Hispanic Americans. We also tried to assess the effect of rs560887 in black Africans, but the SNP turned out to be monomorphic in our sample.

Subjects and Methods


The study samples consisted of 623 NHWs and 410 Hispanics who were non-diabetic unrelated Americans from the San Luis Valley Diabetes Study, a population-based case-control study of type 2 diabetes (T2D) in the San Luis Valley, Southern Colorado [15,16,17,18]. A standard oral glucose tolerance test and World Health Organization criteria ( were used to identify the subjects with normal glucose tolerance, impaired glucose tolerance [2-hour oral glucose tolerance test plasma glucose levels of 140–199 mg/dl (7.8–11 mmol/l)], or previously unknown diabetes. Individuals with FPG levels below 100 mg/dl (5.6 mmol/l) were considered normoglycemic, those with levels of 100–125 mg/dl (5.6–6.9 mmol/l) were considered to have impaired fasting glucose, and those with levels of 126 mg/dl (7 mmol/l) and above were considered to have diabetes [19]. Non-diabetic individuals [blood glucose levels below 126 mg/dl (7 mmol/l)] included normoglycemic subjects as well as those with impaired glucose levels. The present study included a subset of non-diabetic participants; although some individuals had impaired fasting glucose, all subjects had normal glucose tolerance test results. The demographic features and characteristics of NHWs and Hispanics are presented in table table1.1. Detailed information on the black African sample (from Benin City, Nigeria), from which 787 individuals were included in this study, can be found elsewhere [20]. The study was approved by the University of Pittsburgh Institutional Review Board.

Table 1
Biometric and quantitative data of 2 non-diabetic US samples; non-Hispanic whites (NHWs) and Hispanics

Laboratory Methods

Blood samples were drawn after overnight fasting. Glucose concentration was determined using glucose oxidase method and total immunoreactive insulin concentration using double-antibody radioimmunoassay [15,16,17,18]. Total cholesterol, HDL cholesterol, and triglycerides were measured using enzymatic methods, and LDL cholesterol was calculated by the Friedewald equation when triglyceride levels were less than 400 mg/dl (4.5 mmol/l) [15,16,17,18].


DNA samples were genotyped for rs560887 using a TaqMan SNP genotyping assay (C____323082_10; Applied Biosystems, Foster City, Calif., USA) and end-point fluorescence readings were performed on an ABI Prism 7900HT instrument (Applied Biosystems). The overall genotype call rate was 99.8% in NHWs, 98.3% in Hispanics, and 98.5% in blacks. The discrepancy rate was 0% in all samples for 10% random repeats.

Statistical Methods

Allele and genotype frequencies were determined by direct counting. Deviation of the observed genotype frequencies from those predicted by Hardy-Weinberg equilibrium was evaluated using the χ2 goodness-of-fit test. Dependent quantitative variables with skewed distributions were transformed (using log transformation) to reduce the effects of non-normality. After initial removal of extreme outliers, additional outliers falling outside of the transformed mean ± 3.5 SD range were excluded in each population, separately in males and females. This excluded a maximum of 4 values for any trait in any population sample. Analysis of variance and linear regression were used to test for the effects of genotypes on the quantitative traits transformed (if necessary) and adjusted for 4 covariates (gender, age, BMI and smoking) included in the model. When BMI was treated as a dependent variable, 3 covariates (gender, age, and smoking) were used in the model. Genotype associations were tested under the ‘additive’ model in NHWs in order to replicate the findings of original GWAS in Europeans. Due to the smaller size of the Hispanic sample, the tests were performed under the ‘dominant’ model in this sample. All computations were performed using the R statistical software (version 2.3.1; Because we were testing a specific hypothesis regarding the relationship between the G6PC2 genotypes and FPG levels and other metabolic traits, a nominal p value of less than 5% was considered as evidence of association.


The rs560887 genotype frequencies were in Hardy-Weinberg equilibrium and the frequency of the minor allele A was 33% in NHWs (similar to that previously reported for Europeans in the literature and dbSNP; and less common (17.5%) in Hispanics. Data from 775 successfully genotyped black Africans did not show any example of allele A, suggesting that if it exists in this population, its frequency is extremely low. It is unlikely that heterozygotes in black Africans were overlooked as a result of technical issues since the TaqMan genotype clusters were compact and well-separated. Among the 3 submissions in the dbSNP, 2 submitters reported a MAF of 0 or 0.8% in ‘black Africans’ while 1 reported a higher MAF of 4.3% in ‘black Americans’; the latter is most likely due to Caucasian admixture.

Table Table22 summarizes the association analysis results for FPG levels and other quantitative metabolic traits in ‘non-diabetic’ NHWs and Hispanics. G6PC2/rs560887 minor allele was significantly associated with reduced FPG levels in NHWs after adjusting for gender, age, BMI and smoking (p = 0.002 under an additive model; major allele homozygote = 0, heterozygote = 1, minor allele homozygote = 2). The effect size of rs560887 was small and explained only ~1.4% of the variation in FPG levels. Both the direction and size of this effect were consistent with GWAS in Europeans. We observed a similar relationship between G6PC2/rs560887 and FPG levels in ‘non-diabetic’ Hispanics, however, it was not statistically significant (adjusted p = 0.076 under a dominant model; major allele homozygotes vs. heterozygotes + minor allele homozygotes). When the association with FPG levels was tested in only ‘normoglycemic’ subjects (by excluding non-diabetic individuals with impaired fasting glucose levels; table table1),1), it did not improve in NHWs (data not shown), however, it became modestly significant in Hispanics (adjusted p = 0.036 under the dominant model; adjusted mean values ± SE were 91.54 ± 0.37 for GG vs. 90.18 ± 0.51 for GA + AA). Using the same genetic models (additive in NHWs and dominant in Hispanics), we next tested whether G6PC2/rs560887 was associated with other quantitative traits in non-diabetic NHWs and Hispanics. We did not observe a significant association with BMI, fasting insulin levels or lipid measurements in either sample.

Table 2
Adjusteda mean values ± SE of quantitative metabolic traits among three G6PC2/rs560887 genotypes in 2 non-diabetic US populations; non-Hispanic whites (NHWs) and Hispanics


Epidemiological studies suggested a relationship between blood glucose levels and risk for cardiovascular disease and all-cause mortality in both diabetic and non-diabetic subjects [2,3,4,5,6,7,8]. A systematic overview and meta-analysis of non-diabetic cohort studies revealed a graded relationship between glucose levels and cardiovascular risk, extending below the diabetic threshold [6]. A U-shaped relation between glucose levels and mortality was reported [7]; that is, both high (diabetes or impaired glucose levels) and low [<70 or 80 mg/dl (3.9 or 4.4 mmol/l)] FPG levels were associated with increased mortality risk from cardiovascular disease and all causes. The DECODE study [8] also reported increased mortality risk with both high and very low glucose concentrations. These observations highlight the importance of investigating not only the genetic determinants of T2D but also those involved in regulation of FPG levels in non-diabetic individuals.

Two recent GWAS reported an association of small effect size (~1%) between the G6PC2/rs560887 SNP and reduced FPG levels in the general European population [13,14]. The first GWAS [13] evaluated 392,935 SNPs in a discovery sample of 654 ‘normoglycemic’ French subjects and was able to replicate the strongest association observed with FPG levels (G6PC2/rs560887; p = 4 × 10–7 adjusted for age, gender and BMI under the additive model) in additional French and Finnish samples. The second GWAS [14] investigated the SNPs from 2 genome-wide scans in a total of 5,088 ‘non-diabetic’ individuals from Finland and Sardinia (Italy) and found a strong association between FPG levels and a SNP (rs563694) that resides between G6PC2 and ABCB11 (p = 3.5 × 10–7). The same study [14] was able to confirm the significant association of rs563694 in 6 of 7 follow-up samples of mixed European descent. Subsequent genotyping/analyses of the G6PC2-ABCB11 region by the second GWAS [14] also revealed that G6PC2/rs560887 [in high linkage disequilibrium (LD) with rs563694; D’ = 0.99, r2 = 0.84] showed the strongest association with fasting glucose (meta p = 2.8 × 10–10). Interestingly, rs560887 did not show evidence of association with T2D in either GWAS [13,14], leading the authors to hypothesize that either the genetic determinants of FPG levels in physiological states are different than those associated with T2D risk or the effect of G6PC2 variation on T2D susceptibility is too small to be easily detected.

Our results in ‘non-diabetic’ NHWs support the findings of recent GWAS with a significant association (adjusted p = 0.002 under the additive model) of G6PC2/rs560887 with FPG levels in the same direction (minor allele A associated with reduced FPG levels). Consistent with GWAS, the effect size of rs560887 was small and accounted for only ~1.4% of inter-individual variation in FPG levels. Although it was not statistically significant, we observed a similar trend of association in ‘non-diabetic’ Hispanics (adjusted p = 0.076 under the dominant model), which was more pronounced and modestly significant when analyzed only in ‘normoglycemic’ subjects after excluding non-diabetic individuals with impaired fasting glucose levels (adjusted p = 0.036 under the dominant model). It is important to note that we had less power to detect the association in Hispanics due to the smaller sample size (410 Hispanics vs. 623 NHWs) and lower MAF of rs560887 (17.5% in Hispanics vs. 33% in NHWs). In addition, there may be LD structure differences between the two ethnic groups; such that the true causative genetic variant(s) may be in high LD with rs560887 in NHWs but in low LD in Hispanics. Consistent with recent GWAS and mouse studies, we did not detect significant association with BMI, fasting insulin levels, or lipid measurements in either population, suggesting that the observed association with FPG levels is independent of these traits. GWAS also concluded that the effect of the G6PC2 variation on FPG levels might be independent from adiposity-induced insulin resistance.

The rs560887 SNP is located close to the acceptor splice-site in the third intron of G6PC2 and predicted to affect pre-mRNA splicing by alternative usage of exon 4 [13,14]. Due to tissue-specific expression of various G6PC2 splice forms [21], the pancreatic RNA samples from individuals with different rs560887 genotypes (which are not readily available) would be necessary to directly test the latter hypothesis.

In conclusion, our results independently confirm the robust association of G6PC2/rs560887 (or another tightly linked, yet undiscovered variant) with FPG levels in non-diabetic NHWs and further support the relevance of the glucose phosphorylation pathway to blood glucose homeostasis in the general population. Although additional GWAS and meta-analysis increasingly support the association of G6PC2/rs560887 with FPG levels in individuals of European descent [22], our results further extend this observation to the Hispanic population by providing evidence for association that warrants replication in larger samples due to lower MAF of rs560887 in this population. The small effect sizes of G6PC2/rs560887 and other relevant genetic variants in GCKR, GCK and MTNR1B[22] suggest that the majority of genetic factors that regulate FPG levels remain to be identified. The absence (or very low MAF) of this SNP in our black African sample supports the presence of additional common variants (in G6PC2 and/or other genes) that contribute to blood glucose regulation in individuals from various ethnic origins. In fact, the rs560887 variant is also rare among Asians and a recent study demonstrated that another G6PC2 variant common in Chinese was associated with FPG levels in this population [23]. Resequencing of G6PC2 in selected individuals with upper and lower range of FPG levels may help to determine the extent to which common and rare variation of G6PC2 explains the variation in FPG levels and whether rs560887 is the most important G6PC2-linked genetic determinant of glucoregulation.


We are grateful to the subjects who participated in this study. This study was supported in part by grants HL 84613 and HL 444313 from the National Heart, Lung, and Blood Institute.


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