OBJECTIVE

Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels.

RESEARCH DESIGN AND METHODS

We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening.

RESULTS

Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c.

CONCLUSIONS

GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c.

Glycated hemoglobin (HbA1c) results from glycation, the nonenzymatic and mostly irreversible chemical modification by glucose of hemoglobin molecules carried in erythrocytes. The rate of glycation directly depends on ambient blood glucose levels, so HbA1c reflects the average concentration of blood glucose over the average life span of a erythrocyte (in humans, ∼3 months), and represents a longer-term indicator of glycemic status compared to fasting glucose (FG) (1). In addition to ambient glycemia, it is known that medical conditions that change erythrocyte turnover (such as hemolytic anemias, chronic malaria, major blood loss, or blood transfusion), as well as genetic hereditary anemias and iron storage disorders (caused by rare variants in genes involved in erythrocyte membrane stability, hemoglobin function, erythrocyte glucose sensing, and membrane transport) may influence the variability of HbA1c in populations (2,4).

Common genetic variation also influences HbA1c variability. The heritability of HbA1c levels is relatively high (47–59%) when compared with FG (34–36%) or glucose levels as determined by 2-h postoral glucose tolerance test (33%) (5,6). Recent genome-wide association studies (GWAS) of FG have shown that single nucleotide polymorphisms (SNPs) near three loci (G6PC2, MTNR1B, and GCK) are also associated with HbA1c levels (7,,,,,,,15). A GWAS for HbA1c levels in 14,618 nondiabetic women found a suggestive association (P = 9.8 × 10−8) with SLC30A8 (a known type 2 diabetes locus) and genome-wide significant association (P < 5 × 10−8) at a novel locus, HK1, where rare variants are known to be associated with nonspherocytic hemolytic anemia (16). This suggests that both glycemic and erythrocyte genetic factors are associated with variation in HbA1c, but a more thorough accounting of common variants comprising the genetic architecture of HbA1c is needed.

In this study we tested the hypothesis that additional common genetic factors are associated with HbA1c. We conducted a meta-analysis of GWAS in up to 46,368 nondiabetic individuals of European ancestry as part of the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC) effort. In addition to seeking new common variants affecting HbA1c levels, we sought to place the size of the effect of novel genetic findings into the population perspective of diabetes screening and diagnosis. HbA1c levels have recently been recommended for this use based on high overlap between HbA1c distributions in populations without diabetes and those with subclinical (undiagnosed) diabetes, ease of measurement, and an established role as a treatment target in clinical diabetes (17,18). We estimated the degree to which these HbA1c-associated loci shifted the population level distribution of HbA1c, and thereby influenced diabetes screening using HbA1c.

Cohort description, study design, and genotyping.

The cohorts included in this study were part of MAGIC (19). The characteristics of the population samples used in this analysis are shown in Table 1. All participants were adults of European ancestry from Europe or the U.S., and free of diabetes as assessed by either clinical diagnosis, self-reported diabetes, diabetes treatment, or undiagnosed diabetes defined by FG ≥7.0 mmol/l. HbA1c (in percentages) was measured in all studies from fasting or nonfasting whole blood using NGSP-certified methods. We found remarkably consistent means and SD across studies, increasing confidence that laboratory variability had a minimal effect on the study results. A local research ethics committees approved all studies and all participants gave informed consent.

TABLE 1

Characteristics of 46,368 participants from 31 cohorts employed in the meta-analysis

CohortN males/femalesAge (years) men/womenBMI (kg/m2) men/womenHbA1C (%, NGSP) men/womenFasting plasma glucose (mmol/l) men/women
ARIC 3,106/3,671 57.4 (5.7)/56.7 (5.6) 27.33 (3.89)/26.63 (5.30) 5.41 (0.38)/5.37 (0.36) 5.75 (0.50)/5.52 (0.50) 
B58C-T1DGC 1,217/1,284 45.3 (0.3)/45.2 (0.3) 27.93 (4.12)/26.86 (5.5) 5.18 (0.48)/5.22 (0.51) — 
B58C-WTCCC 711/717 44.9 (0.4)/44.9 (0.4) 27.79 (4.21)/26.84 (5.41) 5.21 (0.68)/5.21 (0.51) — 
BLSA 253/235 72.2 (13.5)/67.2 (15.6) 26.99 (3.92)/25.87 (4.94) 5.44 (0.53)/5.45 (0.45) 5.25 (0.56)/4.99 (0.48) 
Croatia 275/384 54.8 (15.0)/55.2 (15.8) 27.43 (3.65)/26.94 (4.59) 5.25 (0.49)/5.31 (0.55) 5.40 (0.66)/5.26 (0.65) 
deCODE 170/172 66.1 (14.4)/63.8 (16.0) 28.20 (4.00)/28.0 (4.90) 5.80 (0.95)/5.77 (1.25) — 
DESIR 178/538 53.1 (5.6)/49.5 (8.5) 23.15 (1.16)/21.36 (1.85) 5.25 (0.38)/5.16 (0.38) 5.11 (0.32)/5.01 (0.38) 
DGI 218/262 59.1 (10.6)/59.5 (10.6) 26.42 (3.12)/26.29 (4.29) 5.73 (0.56)/5.61 (0.59) 5.50 (0.52)/5.39 (0.45) 
DIAGEN 429/571 59.0 (14.2)/59.0 (15.4) 27.08 (3.61)/26.80 (4.82) 5.59 (0.65)/5.50 (0.61) — 
Epic 5,000 1,732/1,627 57.6 (9.4)/54.0 (9.0) 25.75 (2.60)/24.84 (3.38) 5.19 (0.55)/5.08 (0.55) — 
EPIC cases 409/548 60.8 (8.9)/60.2 (9.1) 32.55 (2.53)/33.44 (3.24) 5.58 (0.97)/5.47 (0.62) — 
EPIC cohort 859/1,052 61.3 (9.3)/60.0 (9.2) 26.79 (3.31)/26.33 (4.36) 5.38 (0.56)/5.32 (0.57) — 
Fenland 606/772 44.4 (7.4)/45.4 (7.2) 27.56 (3.91)/26.59 (5.35) 5.42 (0.37)/5.37 (0.37) 5.01 (0.47)/4.74 (0.48) 
FHS 886/1,110 54.7 (10.0)/54.1 (9.9) 27.85 (3.92)/26.13 (4.97) 5.24 (0.62)/5.25 (0.61) 5.36 (0.48)/5.14 (0.49 
GenomeEUtwin 0/568 —/55.1 (21.8) —/24.6 (4.18) —/5.11 (0.68) —/5.24 (0.55) 
HEALTH2000 580/625 49.1 (10.4)/51.7 (11.4) 25.69 (3.26)/25.32 (4.19) 5.22 (0.29)/5.06 (0.32) 5.34 (0.48)/5.17 (0.42) 
Lolipop 582/188 53.2 (10.4)/51.2 (10.5) 27.49 (3.92)/26.74 (5.33) 5.05 (0.54)/5.34 (0.38) 5.51 (1.45)/5.35 (1.75) 
LURIC 215/195 54.1 (12.6)/61.1 (11.1) 26.90 (3.60)/26.60 (4.00) 5.80 (0.60)/5.90 (0.60) — 
KORA F3 711/751 62.3 (10.2)/61.6 (10.1) 27.89 (3.49)/27.70 (4.95) 5.30 (0.38)/5.33 (0.31) — 
KORA S4 844/892 53.9 (8.9)/53.4 (8.8) 27.91 (3.87)/27.25 (4.89) 5.57 (0.46)/5.59 (0.45) — 
METSIM 1,789/0 57.0 (7.3)/— 26.63 (3.76)/— 5.56 (0.32)/— — 
NHANES III 468/746 51.3 (20.6)/51.4 (20.3) 26.92 (4.85)/26.37 (5.81) 5.38 (0.50)/5.15 (0.55) — 
NTR 513/939 47.7 (14.4)/43.3 (13.7) 25.58 (3.28)/24.59 (4.02) 5.27 (0.46)/5.28 (0.45) 5.53 (0.51)/5.32 (0.51) 
ORCADES 298/353 53.7 (15.3)/52.2 (15.4) 27.79 (4.14)/27.30 (5.11) 5.40 (0.49)/5.41 (0.51) 5.45 (0.51)/5.18 (0.49) 
Partners/Roche 291/357 52.7 (12.9)/52.5 (12.7) 27.80 (5.20)/27.10 (7.30) 5.49 (0.48)/5.47 (0.45) — 
PROCARDIS 687/144 60.5 (6.7)/62.8 (6.3) 27.65 (3.58)/28.11 (4.98) 5.98 (1.07)/6.17 (1.10) — 
SardiNIA 1,418/1,928 46.5 (17.1)/45.2 (16.0) 26.36 (3.99)/24.65 (4.82) 5.47 (0.52)/5.39 (0.45) 4.96 (0.59)/4.67 (0.54) 
SHIP 1,696/1,842 49.0 (16.0)/47.0 (16.0) 27.30 (3.90)/26.60 (5.20) 5.3 (0.60)/5.2 (0.60) — 
Sorbs 254/376 46.6 (16.2)/46.4 (15.8) 26.90 (3.60)/26.7 (5.50) 5.35 (0.37)/5.36 (0.38) 5.47 (0.49)/5.21 (0.57) 
SardiNIA stage2 555/890 46.4 (15.1)/46.3 (15.8) 26.36 (3.48)/24.61 (4.60) 5.45 (0.85)/5.31 (0.86) — 
Segovia 274/309 53 (12)/55 (12) 27.35 (3.15)/27.41 (4.68) 5.17(0.49)/5.17 (0.45) — 
CohortN males/femalesAge (years) men/womenBMI (kg/m2) men/womenHbA1C (%, NGSP) men/womenFasting plasma glucose (mmol/l) men/women
ARIC 3,106/3,671 57.4 (5.7)/56.7 (5.6) 27.33 (3.89)/26.63 (5.30) 5.41 (0.38)/5.37 (0.36) 5.75 (0.50)/5.52 (0.50) 
B58C-T1DGC 1,217/1,284 45.3 (0.3)/45.2 (0.3) 27.93 (4.12)/26.86 (5.5) 5.18 (0.48)/5.22 (0.51) — 
B58C-WTCCC 711/717 44.9 (0.4)/44.9 (0.4) 27.79 (4.21)/26.84 (5.41) 5.21 (0.68)/5.21 (0.51) — 
BLSA 253/235 72.2 (13.5)/67.2 (15.6) 26.99 (3.92)/25.87 (4.94) 5.44 (0.53)/5.45 (0.45) 5.25 (0.56)/4.99 (0.48) 
Croatia 275/384 54.8 (15.0)/55.2 (15.8) 27.43 (3.65)/26.94 (4.59) 5.25 (0.49)/5.31 (0.55) 5.40 (0.66)/5.26 (0.65) 
deCODE 170/172 66.1 (14.4)/63.8 (16.0) 28.20 (4.00)/28.0 (4.90) 5.80 (0.95)/5.77 (1.25) — 
DESIR 178/538 53.1 (5.6)/49.5 (8.5) 23.15 (1.16)/21.36 (1.85) 5.25 (0.38)/5.16 (0.38) 5.11 (0.32)/5.01 (0.38) 
DGI 218/262 59.1 (10.6)/59.5 (10.6) 26.42 (3.12)/26.29 (4.29) 5.73 (0.56)/5.61 (0.59) 5.50 (0.52)/5.39 (0.45) 
DIAGEN 429/571 59.0 (14.2)/59.0 (15.4) 27.08 (3.61)/26.80 (4.82) 5.59 (0.65)/5.50 (0.61) — 
Epic 5,000 1,732/1,627 57.6 (9.4)/54.0 (9.0) 25.75 (2.60)/24.84 (3.38) 5.19 (0.55)/5.08 (0.55) — 
EPIC cases 409/548 60.8 (8.9)/60.2 (9.1) 32.55 (2.53)/33.44 (3.24) 5.58 (0.97)/5.47 (0.62) — 
EPIC cohort 859/1,052 61.3 (9.3)/60.0 (9.2) 26.79 (3.31)/26.33 (4.36) 5.38 (0.56)/5.32 (0.57) — 
Fenland 606/772 44.4 (7.4)/45.4 (7.2) 27.56 (3.91)/26.59 (5.35) 5.42 (0.37)/5.37 (0.37) 5.01 (0.47)/4.74 (0.48) 
FHS 886/1,110 54.7 (10.0)/54.1 (9.9) 27.85 (3.92)/26.13 (4.97) 5.24 (0.62)/5.25 (0.61) 5.36 (0.48)/5.14 (0.49 
GenomeEUtwin 0/568 —/55.1 (21.8) —/24.6 (4.18) —/5.11 (0.68) —/5.24 (0.55) 
HEALTH2000 580/625 49.1 (10.4)/51.7 (11.4) 25.69 (3.26)/25.32 (4.19) 5.22 (0.29)/5.06 (0.32) 5.34 (0.48)/5.17 (0.42) 
Lolipop 582/188 53.2 (10.4)/51.2 (10.5) 27.49 (3.92)/26.74 (5.33) 5.05 (0.54)/5.34 (0.38) 5.51 (1.45)/5.35 (1.75) 
LURIC 215/195 54.1 (12.6)/61.1 (11.1) 26.90 (3.60)/26.60 (4.00) 5.80 (0.60)/5.90 (0.60) — 
KORA F3 711/751 62.3 (10.2)/61.6 (10.1) 27.89 (3.49)/27.70 (4.95) 5.30 (0.38)/5.33 (0.31) — 
KORA S4 844/892 53.9 (8.9)/53.4 (8.8) 27.91 (3.87)/27.25 (4.89) 5.57 (0.46)/5.59 (0.45) — 
METSIM 1,789/0 57.0 (7.3)/— 26.63 (3.76)/— 5.56 (0.32)/— — 
NHANES III 468/746 51.3 (20.6)/51.4 (20.3) 26.92 (4.85)/26.37 (5.81) 5.38 (0.50)/5.15 (0.55) — 
NTR 513/939 47.7 (14.4)/43.3 (13.7) 25.58 (3.28)/24.59 (4.02) 5.27 (0.46)/5.28 (0.45) 5.53 (0.51)/5.32 (0.51) 
ORCADES 298/353 53.7 (15.3)/52.2 (15.4) 27.79 (4.14)/27.30 (5.11) 5.40 (0.49)/5.41 (0.51) 5.45 (0.51)/5.18 (0.49) 
Partners/Roche 291/357 52.7 (12.9)/52.5 (12.7) 27.80 (5.20)/27.10 (7.30) 5.49 (0.48)/5.47 (0.45) — 
PROCARDIS 687/144 60.5 (6.7)/62.8 (6.3) 27.65 (3.58)/28.11 (4.98) 5.98 (1.07)/6.17 (1.10) — 
SardiNIA 1,418/1,928 46.5 (17.1)/45.2 (16.0) 26.36 (3.99)/24.65 (4.82) 5.47 (0.52)/5.39 (0.45) 4.96 (0.59)/4.67 (0.54) 
SHIP 1,696/1,842 49.0 (16.0)/47.0 (16.0) 27.30 (3.90)/26.60 (5.20) 5.3 (0.60)/5.2 (0.60) — 
Sorbs 254/376 46.6 (16.2)/46.4 (15.8) 26.90 (3.60)/26.7 (5.50) 5.35 (0.37)/5.36 (0.38) 5.47 (0.49)/5.21 (0.57) 
SardiNIA stage2 555/890 46.4 (15.1)/46.3 (15.8) 26.36 (3.48)/24.61 (4.60) 5.45 (0.85)/5.31 (0.86) — 
Segovia 274/309 53 (12)/55 (12) 27.35 (3.15)/27.41 (4.68) 5.17(0.49)/5.17 (0.45) — 

Data are mean (SD). Fifteen cohorts were included in the fasting-glucose adjusted analysis shown in Table 2 (ARIC, BLSA, CROATIA, Fenland, FHS, DESIR, GENOMEUTWIN, Lolipop, NTR, ORCADES, SardiNIA, KORA F4, DGI, Sorbs and Health2000). BLSA, DGI, Fenland, FHS, KORA F4 and Sorbs were used for analyses that included 2-h glucose. The mean (mmol/l), SE and N for 2-h glucose levels for males and females, respectively, were: 6.96 (2.47) (236)/6.42 (2.04) (207) in BLSA; 5.75 (1.20) (209)/6.15 (1.25) (254) in DGI; 5.27 (1.41) (600)/5.16 (1.35) (757) in Fenland, 5.744 (1.614) (858)/5.992 (1.707) (1,067) in FHS, and 5.19 (2.02) (254)/5.54 (1.96) (376) in Sorbs. Fasting glucose was not available in KORA S4, thus conditional models were run in KORA F4, a follow-up visit of KORA S4 samples. Mean and SE 2-h glucose levels in males and females, respectively, were: 5.66 (0.67)/5.60 (0.57) for HbA1C and 5.82 (1.20)/5.40 (1.01) for glucose. Cohorts in italics provided only de novo genotyping data). The means for Hb (g/l, males/females) were 148.39 (10.29)/135.94 (9.55) (KORA F3), 148.21 (10.00)/134.51 (9.15) (KORA F4), 152.38 (11.33)/136.56 (10.38) (NHANES III) and 148.54 (12.12)/130.83 (11.60) (SardiNIA). The means for MCV (pg, males/females) were 92.32 (3.91)/90.74 (4.08) (KORA F3), 92.04 (4.23)/90.83 (4.38) (KORA F4), 89.69 (4.45)/89.40 (4.34) (NHANES III) and 87.29 (9.28)/85.64 (9.22) (SardiNIA). The means for MCH (fl, males/females) were 31.22 (1.51)/30.60 (1.64) (KORA F3), 31.50 (1.62)/30.89 (1.73) (KORA F4), 30.50 (1.74)/30.22 (1.67) (NHANES III) and 29.14 (3.60)/28.40 (3.69) (SardiNIA). The means for Iron (ìmol/l, males/females) were 17.66 (5.34)/16.29 (5.25) (KORA F3), 22.41 (6.87)/20.53 (6.53) (KORA F4), 18.78 (6.53)/17.03 (6.96) (NHANES III) and 18.01 (6.23)/15.30 (5.98) (SardiNIA). The means for Transferrin (g/l, males/females) were 2.45 (0.33)/2.56 (0.36) (KORA F3), 2.51 (0.35)/2.54 ( 0.38) (KORA F4), n.a. (NHANES III) and 1.96 (0.52)/2.07 (0.579) (SardiNIA).

We carried out a meta-analysis including 35,920 participants from 23 cohorts with available HbA1c measurements and genotype data including ∼2.5M genotyped and imputed autosomal SNPs. This sample size ensures 80% power to detect SNPs, explaining 0.12% of the trait variance at α = 5 × 10−8. For 5 SNPs (rs1046896, rs16926246, rs1799884, rs1800562, and rs552976) that had been previously selected from an interim analysis of the first 10 participating cohorts (n = 14,898), we obtained further data by genotyping up to 10,448 participants from 8 additional cohorts. The sample size for each SNP is thus related to the number of cohorts that were genotyped (up to 31) and to the specific call rate. Details on genotyping methodology, quality control metrics, and statistical analyses for each cohort are shown in supplementary Table S1 in the online appendix. Additional details on imputation and quality control applied by each study are given in the online supplementary methods.

Primary genome-wide association studies and meta-analysis.

In each cohort a linear regression model was fit using untransformed (percentage) HbA1c as the dependent variable to evaluate the additive effect of genotyped and imputed SNPs. HbA1c showed a mild deviation from normality in the majority of cohorts. Log-transformation did not significantly improve normality; nevertheless, such mild deviation did not result in an inflation of the test statistics suggestive of an excess of false positives, as indicated by a genomic correction λ very close to the expected value of 1.0; thus, we report untransformed (percentage) HbA1c results. The model was adjusted for age, sex, and other cohort-specific variables as applicable. Further details are given in the supplementary methods and supplementary Table S1. Regression estimates for each SNP were combined across studies in a meta-analysis using a fixed effect inverse-variance approach (justified by nonsignificant heterogeneity of effect sizes at all validated loci), as implemented in the METAL software. The individual cohort analysis results were corrected prior to performing the meta-analysis for residual inflation of the test statistic using the genomic control method if the λ coefficient was >1.0 (20). Cohort-specific results for each of the 10 loci are given in supplementary Table S2. Heterogeneity across study-specific effect sizes was assessed using the standard χ2 test implemented in METAL, Cochran's Q statistic and the I2 statistics (21).

Association with related traits and diseases.

Secondary analyses were carried out on 10 SNPs (rs2779116, rs552976, rs1800562, rs1799884, rs4737009, rs16926246, rs1387153, rs7998202, rs1046896, and rs855791) reaching genome-wide significance and including only the stronger of the 2 significant ANK1 SNPs (see supplementary methods for additional information). A first goal was to detect “pleiotropic” effects on potentially related traits for the 10 loci. To this end we tested them for association with correlated intermediate traits (BMI, and glycemic and hematologic parameters, supplementary Table S3).

Further, we carried out association analyses of HbA1c levels conditional on FG levels (Table 3) and hematologic parameters (supplementary Table S4) to formally test mediation by glycemia or erythrocyte traits. Mediation is used here to distinguish it from confounding. A confounder is a characteristic associated with both exposure and outcome but is not on the causal pathway linking the two together. By contrast, a mediator is also associated with both exposure and outcome, but is on the causal pathway that may explain the association between them. Our mediation analyses decompose the association between a SNP and HbA1c into two paths. The first path links the SNP directly to HbA1c, and the second path links the SNP to HbA1c through a mediator, e.g., FG or hematologic parameters. A marked attenuation of the size of effect on HbA1c of the SNP in the conditional “mediation” model implies that the SNP (e.g., rs552976) acts on the mediator (e.g., FG), which in turn acts on HbA1c levels. Further details on these analyses are provided in the on-line supplementary methods.

Finally, we tested associations of the 10 loci with risk of type 2 diabetes or coronary artery disease (CAD) using adequately powered case-controlled meta-analyses. Association statistics with type 2 diabetes were obtained from a previous analysis of the MAGIC datasets or from the DIAGRAM+ meta-analysis (22). CAD associations were tested in this study using cohorts described in supplementary Table S5. The CAD analytic sample size assembled for this study had 80% power to detect associations at an α level of 5 × 10−8 for a genotype relative risk of 1.14, and a risk allele frequency of 0.2.

Estimates of genetic effect size.

We used several methods to evaluate the size of the genetic effect of HbA1c-associated SNPs: 1) we used regression to estimate in percentages the total variance in HbA1c explained by the 10 loci; 2) we calculated an additive genotype score based on the number of risk alleles at 7 (nonglycemic) or 10 (all) loci and then calculated the difference in HbA1c (%) between individuals in the top 10% of the genotype score distribution and those in the bottom 10% (supplementary methods); and 3) we used net reclassification analysis to gauge the effect of individual genotype on HbA1c distributions at the population level.

Net reclassification analysis.

Variation in the measured level of HbA1c associated with nonglycemic genetic effects may affect the classification of individuals as diabetic or nondiabetic when screening general population samples using HbA1c. We used this relationship as a way to understand the clinical influence of the HbA1c loci when applied at the population level. We estimated the change in classification that occurred when accounting for effects of the seven loci presumed not to affect HbA1c via primarily glycemic mechanisms (SPTA1, HFE, ANK1, HK1, ATP11A/TUBGCP3, FN3K, and TMPRSS6) using the method of Pencina et al. (23). For this analysis we combined the Framingham Heart Study (FHS), and Atherosclerosis Risk In Communities (ARIC) European ancestry cohorts (N = 10,110). ARIC and FHS have several characteristics suitable for this analysis: 1) they are population-based samples, thus allowing a test of diabetes screening in a truly unselected sample; 2) they are of large sample size, thus maximizing the number of diabetic subjects that can readily be folded back for reclassification analysis; 3) they have both fasting glucose and HbA1c measured. We excluded as in previous analyses all individuals on diabetes treatment (diagnosed diabetes), but retained individuals with FG ≥7.0 mmol/l not on treatment (who we classified as having undiagnosed diabetes, N = 593) as well as all nondiabetic individuals (N = 9,517). We then sought to differentiate these individuals on the basis of their HbA1c levels, using ≥6.5% as the cutoff indicating diabetes. We counted the cumulative frequency distribution of measured HbA1c levels by diabetes status, then re-estimated the frequency distribution after regression analysis adjusting for the seven SNPs at the nonglycemic loci, recalibrating the distribution to have the same mean HbA1c as in each original cohort. We counted the proportion of undiagnosed diabetic individuals with unadjusted HbA1c ≥6.5% who had an adjusted HbA1c <6.5%, and the proportion of nondiabetic individuals with unadjusted HbA1c <6.5% who had an adjusted HbA1c ≥6.5%. The difference between these proportions is called “net reclassification” and in this instance indicates the overall proportion of a population whose diagnostic status would change based on the influence of these seven common, nonglycemic genetic variants.

New common variants associated with HbA1c.

We carried out a meta-analysis of SNP associations with HbA1c levels in up to 46,368 participants of European ancestry from 31 cohorts. We identified 10 genomic regions associated with HbA1c levels (Table 2, Figs. 1 and 2). Six associated regions were new, including FN3K (rs1046896, P = 1.57 × 10−26), HFE (rs1800562, P = 2.59 × 10−20), TMPRSS6 (rs855791, P = 2.74 × 10−4), ANK1 (rs4737009, P = 6.11 × 10−12), SPTA1 (rs2779116, P = 2.75 × 10−9), and ATP11A/TUBGCP3 (rs7998202, P = 5.24 × 10−9). A second, independent SNP near ANK1 was also associated with HbA1c (rs6474359, P = 1.18 × 10−8; r2 with rs4737009 = 0.0001; see also supplementary methods). In addition, SNPs in or near HK1 (rs16926246, P = 3.11 × 10−54), MTNR1B (rs1387153, P = 3.96 × 10−11), GCK (rs1799884, P = 1.45 × 10−20), and G6PC2/ABCB11 (rs552976, P = 8.16 × 10−18) were associated with HbA1c levels. These loci had previously been associated with HbA1c (15,16), FG (9,,12,14,15) and/or type 2 diabetes risk (9,,12,15,16,19). Associations were generally similar across cohorts, showing no significant heterogeneity (Table 2). This lack of heterogeneity suggests that there is good consistency in trait measurement across different cohorts.

TABLE 2

Associations with HbA1C of 10 independent loci identified in the meta-analysis

SNPChrPos (B36)Nearest locusEffect/other alleleCEU freq (effect)HbA1C (%) association
Heterogeneity
Freq (effect)Nβ (SE)Pχ2P valueQ PI2 (%)
rs2779116 156,852,039 SPTA1 T/C 0.32 0.27 34,663 0.024 (0.004) 2.75 × 10−9 0.673 0.606 
rs552976 169,616,945 G6PC2/ABCB11 G/A 0.66 0.64 40,420* 0.047 (0.003) 8.16 × 10−18 0.596 0.591 
rs1800562 26,201,120 HFE G/A 0.96 0.94 43,778* 0.063 (0.007) 2.59 × 10−20 0.661 0.300 11 
rs1799884 44,002,308 GCK T/C 0.20 0.18 45,591* 0.038 (0.004) 1.45 × 10−20 0.187 0.120 24 
rs6474359 41,668,351 ANK1 T/C 0.97 0.97 29,997 0.058 (0.011) 1.18 × 10−8 0.328 0.267 15 
rs4737009 41,749,562 ANK1 A/G 0.28 0.24 36,862 0.027 (0.004) 6.11 × 10−12 0.182 0.182 21 
rs16926246 10 70,763,398 HK1 C/T 0.89 0.90 42,707* 0.089 (0.004) 3.11 × 10−54 0.329 0.162 21 
rs1387153 11 92,313,476 MTNR1B T/C 0.28 0.28 32,293 0.028 (0.004) 3.96 × 10−11 0.867 0.857 
rs7998202 13 112,379,869 ATP11A/TUBGCP3 G/A 0.15 0.14 34,724 0.031 (0.005) 5.24 × 10−9 0.415 0.383 
rs1046896 17 78,278,822 FN3K T/C 0.25 0.31 45,953* 0.035 (0.003) 1.57 × 10−26 0.450 0.440 
rs855791 22 35,792,882 TMPRSS6 A/G 0.39 0.42 34,562 0.027 (0.004) 2.74 × 10−14 0.970 0.962 
SNPChrPos (B36)Nearest locusEffect/other alleleCEU freq (effect)HbA1C (%) association
Heterogeneity
Freq (effect)Nβ (SE)Pχ2P valueQ PI2 (%)
rs2779116 156,852,039 SPTA1 T/C 0.32 0.27 34,663 0.024 (0.004) 2.75 × 10−9 0.673 0.606 
rs552976 169,616,945 G6PC2/ABCB11 G/A 0.66 0.64 40,420* 0.047 (0.003) 8.16 × 10−18 0.596 0.591 
rs1800562 26,201,120 HFE G/A 0.96 0.94 43,778* 0.063 (0.007) 2.59 × 10−20 0.661 0.300 11 
rs1799884 44,002,308 GCK T/C 0.20 0.18 45,591* 0.038 (0.004) 1.45 × 10−20 0.187 0.120 24 
rs6474359 41,668,351 ANK1 T/C 0.97 0.97 29,997 0.058 (0.011) 1.18 × 10−8 0.328 0.267 15 
rs4737009 41,749,562 ANK1 A/G 0.28 0.24 36,862 0.027 (0.004) 6.11 × 10−12 0.182 0.182 21 
rs16926246 10 70,763,398 HK1 C/T 0.89 0.90 42,707* 0.089 (0.004) 3.11 × 10−54 0.329 0.162 21 
rs1387153 11 92,313,476 MTNR1B T/C 0.28 0.28 32,293 0.028 (0.004) 3.96 × 10−11 0.867 0.857 
rs7998202 13 112,379,869 ATP11A/TUBGCP3 G/A 0.15 0.14 34,724 0.031 (0.005) 5.24 × 10−9 0.415 0.383 
rs1046896 17 78,278,822 FN3K T/C 0.25 0.31 45,953* 0.035 (0.003) 1.57 × 10−26 0.450 0.440 
rs855791 22 35,792,882 TMPRSS6 A/G 0.39 0.42 34,562 0.027 (0.004) 2.74 × 10−14 0.970 0.962 

*Indicates SNPs for which additional de novo genotyping was performed in eight cohorts. The β coefficient denotes the per-effect allele increase in HbA1C (%) at that locus.

FIG. 1.

Manhattan plot and quantile-quantile (QQ) plot of association findings. The figure summarizes the genome-wide association scan results combined across all studies by inverse variance weighting. The blue dotted line marks the threshold for genome-wide significance (5 × 10−8). SNPs in loci exceeding this threshold are highlighted in green. A QQ plot is shown in the inset panel, where the red line corresponds to all test statistics, and the blue line to results after excluding statistics at all associated loci (highlighted in green in the Manhattan plot). The gray area corresponds to the 90% confidence region from a null distribution of P values (generated from 100 simulations). (A high-quality color representation of this figure is available in the online issue.)

FIG. 1.

Manhattan plot and quantile-quantile (QQ) plot of association findings. The figure summarizes the genome-wide association scan results combined across all studies by inverse variance weighting. The blue dotted line marks the threshold for genome-wide significance (5 × 10−8). SNPs in loci exceeding this threshold are highlighted in green. A QQ plot is shown in the inset panel, where the red line corresponds to all test statistics, and the blue line to results after excluding statistics at all associated loci (highlighted in green in the Manhattan plot). The gray area corresponds to the 90% confidence region from a null distribution of P values (generated from 100 simulations). (A high-quality color representation of this figure is available in the online issue.)

Close modal
FIG. 2.

Regional association plots at the HbA1c loci. Each panel spans ± 250 kb around the most significant associated SNP in the region, which is highlighted with a blue square (panel C spans ± 300 kb). At the top of each panel, comb diagrams indicate the location of SNPs in the Illumina HumanHap 550K and Affymetrix 500K chips, and of SNPs imputed. The SNPs are colored according to their linkage disequilibrium with the top variant based on the CEU HapMap population (http://www.hapmap.org). Gene transcripts are annotated in the lower box, with the most likely biologic candidate highlighted in blue; ± indicates the direction of transcription. In panel C, a few gene names were omitted for clarity. Here, genes are, from left to right, SCGN, HIST1H2AA, HIST1H2BA, SLC17A4, SLC17A1, SLC17A3, SLC17A2, TRIM38, HIST1H1A, HIST1H3A, HIST1H4A, HIST1H4B, HIST1H3B, HIST1H2AB, HIST1H2BB, HIST1H3C, HIST1H1C, HFE, HIST1H4C, HIST1H1T, HIST1H2BC, HIST1H2AC, HIST1H1E, HIST1H2BD, HIST1H2BD, HIST1H2BE, HIST1H4D, HIST1H3D, HIST1H2AD, HIST1H2BF, HIST1H4E, HIST1H2BG, HIST1H2AE, HIST1H3E, HIST1H1D, HIST1H4F, HIST1H4G, HIST1H3F, HIST1H2BH, HIST1H3G, HIST1H2BI, and HIST1H4H. In panel D, the names of the first two genes, UBE2D4 and WBSCR19, were also omitted for clarity. (A high-quality color representation of this figure is available in the online issue.)

FIG. 2.

Regional association plots at the HbA1c loci. Each panel spans ± 250 kb around the most significant associated SNP in the region, which is highlighted with a blue square (panel C spans ± 300 kb). At the top of each panel, comb diagrams indicate the location of SNPs in the Illumina HumanHap 550K and Affymetrix 500K chips, and of SNPs imputed. The SNPs are colored according to their linkage disequilibrium with the top variant based on the CEU HapMap population (http://www.hapmap.org). Gene transcripts are annotated in the lower box, with the most likely biologic candidate highlighted in blue; ± indicates the direction of transcription. In panel C, a few gene names were omitted for clarity. Here, genes are, from left to right, SCGN, HIST1H2AA, HIST1H2BA, SLC17A4, SLC17A1, SLC17A3, SLC17A2, TRIM38, HIST1H1A, HIST1H3A, HIST1H4A, HIST1H4B, HIST1H3B, HIST1H2AB, HIST1H2BB, HIST1H3C, HIST1H1C, HFE, HIST1H4C, HIST1H1T, HIST1H2BC, HIST1H2AC, HIST1H1E, HIST1H2BD, HIST1H2BD, HIST1H2BE, HIST1H4D, HIST1H3D, HIST1H2AD, HIST1H2BF, HIST1H4E, HIST1H2BG, HIST1H2AE, HIST1H3E, HIST1H1D, HIST1H4F, HIST1H4G, HIST1H3F, HIST1H2BH, HIST1H3G, HIST1H2BI, and HIST1H4H. In panel D, the names of the first two genes, UBE2D4 and WBSCR19, were also omitted for clarity. (A high-quality color representation of this figure is available in the online issue.)

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Pleiotropy and mediation of SNP-HbA1c associations.

HbA1c levels are influenced by average ambient glycemia over the preceding 3 months, and possibly by erythrocyte turnover. We therefore investigated the novel HbA1c loci for associations with several diabetes-related and hematologic quantitative parameters in the MAGIC cohorts (19,24) (supplementary Table S4). As previously shown (19), 3 of 10 loci, GCK, MTNR1B, and G6PC2, were associated with FG and HOMA-B (an index of β-cell function, Table 3 and supplementary Table S3), and GCK was additionally associated with 2-h glucose. In all cases, the allele associated with increased HbA1c was also associated with increased FG and 2-h glucose. No HbA1c-associated SNP was significantly associated with measures of insulin (supplementary Table S3). We further used conditional models to investigate whether FG levels mediated associations of SNPs with HbA1c. In these analyses a marked attenuation of the effect size of the SNP in a model adjusted for FG compared with the original main effects model would be consistent with the hypothesis that glycemic pathways primarily account for, or mediate, the HbA1c association. For the three loci associated with FG (GCK, MTNR1B, and G6PC2/ABCB11), effect sizes were substantially decreased in FG-conditioned models, whereas at the other seven loci, effect sizes remained essentially unchanged (Table 3), indicating that associations with HbA1c at these loci are unlikely to be mediated by glycemic factors.

TABLE 3

Associations with HbA1C of 10 independent loci conditioned on levels of fasting or 2-h glucose

SNPNearest locusEffect/OtherFasting glucose
2-h glucose
HbA1C (%) adjusted for sex, ageHbA1C (%) adjusted for glucose, sex, ageFasting glucose (mmol/l) adjusted for sex, ageHbA1C (%) adjusted for sex. ageHbA1C (%) adjusted for 2 h-glucose, sex, age2-h glucose (mmol/l) adjusted for sex, age
rs2779116 SPTA1 T/C β (SE) 0.019 (0.004) 0.017 (0.004) −0.001 (0.005) 0.026 (0.008) 0.029 (0.008) 0.029 (0.037) 
   P 2.4 × 106 1.7 × 106 0.900 9.6 × 104 2.9 × 104 0.432 
   N 20,700 21,359 21,505 6,394 6,347 6,347 
rs552976 G6PC2 /ABCB11 G/A β (SE) 0.028 (0.004) 0.013 (0.003) 0.060 (0.005) 0.029 (0.007) 0.027 (0.007) −0.021 (0.034) 
   P 4.5 × 1015 2.0 × 105 6.3 × 1036 6.1 × 105 1.4 × 104 0.538 
   N 23,496 23,496 23,642 6,393 6,346 6,346 
rs1800562 HFE G/A β (SE) 0.054 (0.007) 0.048 (0.006) −0.008 (0.010) 0.095 (0.016) 0.096 (0.016) 0.086 (0.073) 
   P 3.1 × 1013 2.5 × 1014 0.419 1.1 × 109 1.1 × 109 0.239 
   N 23,503 23,503 23,649 6,389 6,342 6,342 
rs1799884 GCK T/C β (SE) 0.030 (0.005) 0.018 (0.004) 0.053 (0.0063) 0.037 (0.010) 0.039 (0.010) 0.111 (0.046) 
   P 5.6 × 1011 7.3 × 106 4.7 × 1017 1.3 × 104 6.2 × 105 0.0143 
   N 23,497 23,497 23,643 6,394 6,347 6,347 
rs4737009 ANK1 A/G β (SE) 0.023 (0.004) 0.017 (0.004) 0.010 (0.006) 0.023 (0.008) 0.025 (0.008) −0.049 (0.038) 
   P 3.2 × 108 2.7 × 106 0.072 4.4 × 103 2.3 × 103 0.197 
   N 21,355 21,355 21,501 6,390 6,343 6,343 
rs16926246 HK1 C/T β (SE) 0.073 (0.007) 0.069 (0.006) −0.013 (0.009) 0.010 (0.017) 0.097 (0.017) 0.012 (0.092) 
   P 4.8 × 1026 6.4 × 1030 0.178 1.6 × 109 1.3 × 108 0.899 
   N 22,404 22,404 22,550 5,301 5,254 5,254 
rs1387153 MTNR1B T/C β (SE) 0.027 (0.004) 0.013 (0.004) 0.056 (0.006) 0.035 (0.008) 0.032 (0.009) 0.036 (0.040) 
   P 1.9 × 1011 2.2 × 104 1.8 × 1023 3.1 × 105 1.5 × 104 0.362 
   N 20,162 20,162 20,308 6,394 6,347 6,347 
rs7998202 ATP11A/TUBGCP3 G/A β (SE) 0.027 (0.006) 0.023 (0.005) 0.013 (0.008) 0.041 (0.012) 0.035 (0.012) −0.035 (0.054) 
   P 3.4 × 106 1.3 × 105 0.108 4.0 × 104 2.6 × 103 0.512 
   N 21,359 21,359 21,505 6,394 6,347 6,347 
rs1046896 FN3K T/C β (SE) 0.030 (0.004) 0.026 (0.003) 0.005 (0.005) 0.045 (0.008) 0.043 (0.008) −0.011 (0.036) 
   P 2.0 × 1016 1.0 × 1015 0.343 3.0 × 109 2.0 × 108 0.753 
   N 23,496 23,496 23,642 6,393 6,346 6,346 
rs855791 TMPRSS6 A/G β (SE) 0.020 (0.004) 0.019 (0.003) −0.006 (0.005) 0.024 (0.008) 0.022 (0.008) 0.009 (0.036) 
   P 6.7 × 108 8.3 × 109 0.223 1.7 × 103 6.1 × 103 0.815 
   N 23,508 23,508 23,654 6,394 6,347 6,347 
SNPNearest locusEffect/OtherFasting glucose
2-h glucose
HbA1C (%) adjusted for sex, ageHbA1C (%) adjusted for glucose, sex, ageFasting glucose (mmol/l) adjusted for sex, ageHbA1C (%) adjusted for sex. ageHbA1C (%) adjusted for 2 h-glucose, sex, age2-h glucose (mmol/l) adjusted for sex, age
rs2779116 SPTA1 T/C β (SE) 0.019 (0.004) 0.017 (0.004) −0.001 (0.005) 0.026 (0.008) 0.029 (0.008) 0.029 (0.037) 
   P 2.4 × 106 1.7 × 106 0.900 9.6 × 104 2.9 × 104 0.432 
   N 20,700 21,359 21,505 6,394 6,347 6,347 
rs552976 G6PC2 /ABCB11 G/A β (SE) 0.028 (0.004) 0.013 (0.003) 0.060 (0.005) 0.029 (0.007) 0.027 (0.007) −0.021 (0.034) 
   P 4.5 × 1015 2.0 × 105 6.3 × 1036 6.1 × 105 1.4 × 104 0.538 
   N 23,496 23,496 23,642 6,393 6,346 6,346 
rs1800562 HFE G/A β (SE) 0.054 (0.007) 0.048 (0.006) −0.008 (0.010) 0.095 (0.016) 0.096 (0.016) 0.086 (0.073) 
   P 3.1 × 1013 2.5 × 1014 0.419 1.1 × 109 1.1 × 109 0.239 
   N 23,503 23,503 23,649 6,389 6,342 6,342 
rs1799884 GCK T/C β (SE) 0.030 (0.005) 0.018 (0.004) 0.053 (0.0063) 0.037 (0.010) 0.039 (0.010) 0.111 (0.046) 
   P 5.6 × 1011 7.3 × 106 4.7 × 1017 1.3 × 104 6.2 × 105 0.0143 
   N 23,497 23,497 23,643 6,394 6,347 6,347 
rs4737009 ANK1 A/G β (SE) 0.023 (0.004) 0.017 (0.004) 0.010 (0.006) 0.023 (0.008) 0.025 (0.008) −0.049 (0.038) 
   P 3.2 × 108 2.7 × 106 0.072 4.4 × 103 2.3 × 103 0.197 
   N 21,355 21,355 21,501 6,390 6,343 6,343 
rs16926246 HK1 C/T β (SE) 0.073 (0.007) 0.069 (0.006) −0.013 (0.009) 0.010 (0.017) 0.097 (0.017) 0.012 (0.092) 
   P 4.8 × 1026 6.4 × 1030 0.178 1.6 × 109 1.3 × 108 0.899 
   N 22,404 22,404 22,550 5,301 5,254 5,254 
rs1387153 MTNR1B T/C β (SE) 0.027 (0.004) 0.013 (0.004) 0.056 (0.006) 0.035 (0.008) 0.032 (0.009) 0.036 (0.040) 
   P 1.9 × 1011 2.2 × 104 1.8 × 1023 3.1 × 105 1.5 × 104 0.362 
   N 20,162 20,162 20,308 6,394 6,347 6,347 
rs7998202 ATP11A/TUBGCP3 G/A β (SE) 0.027 (0.006) 0.023 (0.005) 0.013 (0.008) 0.041 (0.012) 0.035 (0.012) −0.035 (0.054) 
   P 3.4 × 106 1.3 × 105 0.108 4.0 × 104 2.6 × 103 0.512 
   N 21,359 21,359 21,505 6,394 6,347 6,347 
rs1046896 FN3K T/C β (SE) 0.030 (0.004) 0.026 (0.003) 0.005 (0.005) 0.045 (0.008) 0.043 (0.008) −0.011 (0.036) 
   P 2.0 × 1016 1.0 × 1015 0.343 3.0 × 109 2.0 × 108 0.753 
   N 23,496 23,496 23,642 6,393 6,346 6,346 
rs855791 TMPRSS6 A/G β (SE) 0.020 (0.004) 0.019 (0.003) −0.006 (0.005) 0.024 (0.008) 0.022 (0.008) 0.009 (0.036) 
   P 6.7 × 108 8.3 × 109 0.223 1.7 × 103 6.1 × 103 0.815 
   N 23,508 23,508 23,654 6,394 6,347 6,347 

β (SE) is the per-effect allele increase in HbA1C (%) as in Table 2. For analyses conditional on fasting glucose, data were available for up to 23,654 samples from 15 cohorts (ARIC, BLSA, CROATIA, Fenland, FHS, DESIR, GENOMEUTWIN, Lolipop, NTR, ORCADES, SardiNIA, KORA F4, DGI, Sorbs and Health2000). For analyses conditional on 2-h glucose, data were available for only a smaller set of six cohorts totaling up to 6,394 samples (BLSA, Fenland, FHS, KORA F4, DGI and Sorbs). The SNP association with HbA1C after adjusting for fasting glucose is attenuated most at the G6PC2/ABCB11, GCK and MTNR1B loci. Associations at ANK1 are given for rs4737009, with the ANK1 SNP showing the strongest association with HbA1C.

We also investigated associations of HbA1c loci with several hematologic parameters in a subset of four populations with available data (KORA F3, KORA F4, SardiNIA, and NHANES III, supplementary Table S3). Two HbA1c loci (encoding for functional alleles at HFE and TMPRSS6) showed genome-wide significant association with erythrocyte indexes, consistent with an influence of erythrocyte physiology on HbA1c variability. The HbA1c-raising alleles had diverse effects, including associations with lower hemoglobin, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH) and iron, and higher transferrin (HFE and TMPRSS6). In addition, three loci (SPTA1, ANK1, and HK1) showed suggestive associations (P < 5 × 10−3) with erythrocyte indexes, with HbA1c-raising alleles associated with increased MCV (SPTA1, ANK1), or lower hemoglobin (HK1).

We used these same four cohorts where those parameters were available to carry out a meta-analysis on HbA1c levels, this time conditioning for the hematologic traits. We did not observe any difference at the three “glycemic” loci, although attenuation of β estimates was observed at HFE, TMPRSS6, and HK1 (supplementary Table S4). However, the sample size used for this analysis was relatively underpowered, resulting in nonsignificant differences (P value > 0.1) and we lacked power for other loci, indicating the need for future analysis in larger sample collections.

Associations with disease: type 2 diabetes and CAD risk.

HbA1c has been shown to have strong epidemiologic associations with type 2 diabetes risk and with CAD risk in persons without diabetes. To ascertain if the novel loci affected type 2 diabetes risk, we tested associations in well-powered datasets. In a previous meta-analysis of 40,655 type 2 diabetes cases and 87,022 controls in MAGIC (19), MTNRB1, and GCK showed significant evidence of association (rs1387153 OR = 1.09, 95% CI 1.06–1.12, P = 8.0 × 10−13; rs1799884 OR = 1.07, 95% CI 1.05–1.10, P = 5.0 × 10−8), whereas G6PC2/ABCB11 did not (rs552976 OR = 0.97, 95% CI 0.95–0.99, P = 0.012). We tested the other novel loci reported here for associations with type 2 diabetes in a partly overlapping study of 8,130 cases and 38,987 controls from the DIAGRAM+ consortium (22) (supplementary Table S3). No other locus associated with HbA1c was associated with type 2 diabetes risk.

We also tested for associations with CAD using data from nine case/control studies of European descent (13,925 cases and 14,590 controls, supplementary Table S5). None of the SNPs associated with HbA1c were associated with CAD in the combined sample of 28,515 participants (supplementary Table S6).

Effect size estimates for HbA1c-associated loci.

In a regression model, the 10 loci combined explained ∼2.4% of the total variance in HbA1c levels, or about 5% of estimated HbA1c heritability. We calculated a genotype score using four of the largest population-based studies (ARIC, SardiNIA, KORA F4, and FHS). Using the 10 HbA1c loci, we estimated cohort-specific differences between the top and bottom 10% of the genotype score distribution (mean [SE] % HbA1c) to be: 5.25% (0.01) and 5.50% (0.004), respectively (P = 3.61 × 10−33) for ARIC; 5.37% (0.027) and 5.49% (0.027) (P = 1.36 × 10−3) for SardiNIA; 5.32% (0.024) and 5.58% (0.027) (P = 4.64 × 10−12) for KORA F4; and 5.07% (0.046) and 5.38% (0.046) (P = 1.45 × 10−6) for FHS. The corresponding weighted average difference between the top and bottom 10% of the HbA1c distributions was 0.21%. For a genotype score using only the seven nonglycemic loci (FN3K, HFE, TMPRSS6, ANK1, SPTA1, ATP11A/TUBGCP3, and HK1), the weighted average difference between the top and bottom 10% of the HbA1c distributions was 0.19%.

Net reclassification in diabetes screening with HbA1c.

We used net reclassification analysis to estimate the population-level impact of the seven nonglycemic loci when HbA1c ≥6.5 (%) is used as the reference cutoff for diabetes diagnosis, as recently proposed (18). We calculated the net reclassification around this threshold attributable to effects of the seven nonglycemic HbA1c loci that might be expected when screening a general European ancestry population for undiagnosed diabetes using HbA1c. We studied the FHS and ARIC cohorts combined (N = 10,110), and included individuals with undiagnosed diabetes for detection by screening. We compared the measured distribution of HbA1c to the distribution adjusted for the seven nonglycemic SNPs (Fig. 3). The net reclassification was −1.86% (P = 0.002), indicating that the population-level effect size of the 7 nonglycemic HbA1c-associated SNPs is equivalent to reclassification of about 2% of an European ancestry population sample according to HbA1c-determined diabetes status.

FIG. 3.

Net reclassification when screening for undiagnosed diabetes, using HbA1c as a population-level measure of genetic effect size. The figure shows the distribution of HbA1c in the FHS and ARIC cohorts combined (N = 10,110), stratified by individuals with undiagnosed type 2 diabetes (UnDx DM, N = 593, black lines) or without diabetes (Non DM, N = 9,517, gray lines), and by HbA1c without adjustment (solid lines) or after adjustment for seven nonglycemic SNPs (dashed lines). The vertical dashed line is the diabetes diagnostic threshold at HbA1c ≥6.5(%). Net reclassification is the overall proportion of the population appropriately moved above or below this line by considering the genetic information. For instance, among individuals with undiagnosed diabetes, 39.5% had an unadjusted HbA1c level ≥6.5 (%) and 37.4% had a seven SNP-adjusted HbA1c level ≥6.5 (%), and among those with undiagnosed diabetes, 2.02% of those with undiagnosed diabetes were misclassified by the influence of the seven SNPs. The net reclassification is calculated as the difference −2.02% − (−0.17%) = −1.86%.

FIG. 3.

Net reclassification when screening for undiagnosed diabetes, using HbA1c as a population-level measure of genetic effect size. The figure shows the distribution of HbA1c in the FHS and ARIC cohorts combined (N = 10,110), stratified by individuals with undiagnosed type 2 diabetes (UnDx DM, N = 593, black lines) or without diabetes (Non DM, N = 9,517, gray lines), and by HbA1c without adjustment (solid lines) or after adjustment for seven nonglycemic SNPs (dashed lines). The vertical dashed line is the diabetes diagnostic threshold at HbA1c ≥6.5(%). Net reclassification is the overall proportion of the population appropriately moved above or below this line by considering the genetic information. For instance, among individuals with undiagnosed diabetes, 39.5% had an unadjusted HbA1c level ≥6.5 (%) and 37.4% had a seven SNP-adjusted HbA1c level ≥6.5 (%), and among those with undiagnosed diabetes, 2.02% of those with undiagnosed diabetes were misclassified by the influence of the seven SNPs. The net reclassification is calculated as the difference −2.02% − (−0.17%) = −1.86%.

Close modal

HbA1c levels are influenced by ambient glycemia, and also by erythrocyte biology, as seen in hereditary anemias and iron storage disorders caused by rare, highly-penetrant genetic variants. We analyzed associations of HbA1c levels with common genetic variants associated in a meta-analysis of up to 46,000 nondiabetic individuals of European descent from 31 cohorts. We identified 10 loci associated with HbA1c at genome-wide levels of significance, with 1 locus, ANK1, showing 2 independent signals. Of these, six (in or near FN3K, HFE, TMPRSS6, ATP11A/TUBGCP3, ANK1, and SPTA1) represent new common genetic determinants of HbA1c, and four (GCK, G6PC2/ABCB11, MTNR1B, and HK1) are confirmatory (9,11; 13,,16; and 25).

Fasting and postprandial glucose levels are key determinants of HbA1c. Of the 10 loci identified, those in GCK, G6PC2, and MTNR1B were strongly associated with levels of FG in this and previous studies (8; 10; 12,,,16; 19). Two of them (GCK and MTNR1B) were also associated with type 2 diabetes (19). Analyses conditioned on FG further supported an effect on HbA1c via regulation of systemic glucose concentrations for GCK, G6PC2, and MTNR1B loci alone. No other HbA1c locus was associated with type 2 diabetes risk or quantitative type 2 diabetes risk factors, suggesting that associations with HbA1c levels were not likely to be mediated by ambient glycemia. Rare variants at some of these loci (HK1, encoding hexokinase 1; ANK1, ankyrin; SPTA1, spectrin) cause hereditary anemias, and common variants at some loci are associated with quantitative hematologic traits as well as HbA1c (25,26). This is consistent with the hypothesis that these common variants influence HbA1c levels via erythrocyte physiology. Specific mechanisms are suggested by existing knowledge on the function of leading candidate genes in each region (see the supplemental on-line appendix).

HK1 is a good example to consider mechanism of action of common variants, as it has confirmed support as a true-positive HbA1c-associated locus (16,27) and rare variants in HK1 are associated with nonspherocytic hemolytic anemia (MIM 142600) (28,29). HK1 encodes the erythrocyte isoform of hexokinase, which determines the intracellular commitment of glucose to the glycolytic pathway by catalyzing the conversion of intracellular glucose to glucose-6-phosphate. One plausible explanation for the observed association lies in the potential dissociation between ambient plasma glucose and intracellular cytoplasmic glucose that might be induced by functional variants at HK1; since the enzyme is preferentially active in erythrocytes, the intracellular utilization (metabolism) of glucose may not be reflective of systemic levels of glycemia. In support of this notion, the HbA1c-raising allele was not associated with any glycemic traits in another recent study of European cohorts, but had robust associations with lower hemoglobin and hematocrit (27). In the CHARGE consortium, common variants in HK1 were associated with decreased hemoglobin (25). We postulate, therefore, that the hemoglobin-lowering variant may affect the overall percentage of HbA1c through an increased glucose/hemoglobin molar ratio, which in turn could increase the rate of hemoglobin that is glycated at a given glucose level. Variation in rates of deglycation and of erythrocyte turnover also are likely to play an important role in measured HbA1c levels. These hypotheses require further testing. A possible role of erythrocyte membrane stability and altered erythrocyte life span (ANK1, SPTA1) and hemoglobin deglycation (FN3K) may be postulated based on the known function of the respective gene products (supplementary online appendix).

A role for iron homeostasis influencing HbA1c is suggested by the HFE and TMPRSS6 loci, where associations were observed at known functional variants in two complementary and directionally consistent pathways (30). At HFE the A allele at rs1800562 (Cys262Tyr), which is responsible for hereditary hemochromatosis (MIM 235200), was associated with lower levels of HbA1c, rather than the higher levels one would predict from epidemiologic observations of the increased HFE mutation prevalence in patients with type 2 diabetes (31,32). This apparently paradoxical relationship may be due to a shift in glucose to hemoglobin molar ratio associated with higher overall hemoglobin (supplementary Table S3), leading to consequent decrease in the percentage of glycated hemoglobin. The reciprocal observation is seen for TMPRSS6, where the A allele at SNP rs855791 (Val736Ala) was associated with lower hemoglobin levels and higher HbA1c levels, as one would predict in a state of iron deficiency and disproportionately lower total hemoglobin concentrations.

It is known that conditions characterized by altered erythrocyte physiology may influence the utility of HbA1c in diabetes diagnosis (2,4,18), although this has generally been attributed to specific pathologies, such as inherited hemoglobinopathies, rather than to physiologic variation in the general population. We show here for the first time that the common genetic variation resulting in subtler but more widespread alteration of iron levels or hemoglobin concentration can also affect HbA1c levels. The absolute size of the genetic effect of 7 to 10 common SNPs associated with HbA1c is about 0.2%, comparing the extremes of the HbA1c-raising allele distribution. This is smaller than the 0.5% HbA1c average intralaboratory variation for HbA1c-certified labs reported as of 2000 (33). We sought to frame these genetic effects in population-level terms by comparing HbA1c distributions without and with adjustment for the seven nonglycemic SNPs and calculating net reclassification around the 6.5% HbA1c diagnostic threshold. We found the overall effect of the nonglycemic loci identified in this study to be small but detectable, potentially affecting about 2% of white individuals likely reclassified by diabetes status. This estimate represents an upper boundary for the effect of these common variants, as most people (the majority in the center of the distribution) are expected to have a smaller individual genotype effect size.

Our findings are therefore directly relevant to recent initiatives to focus diabetes diagnosis and care more centrally on HbA1c. Although the 10 loci described here likely represent the strongest common association signals found in Europeans, they account for a relatively small proportion of total variance of HbA1c and have minimal effect on diagnosis or misclassification of diabetes. Therefore, our study achieves a significant result in quantifying, for the first time, the misclassification risk associated with the top tier of HbA1c-associated common genetic variation. Future research will be required to explore two main areas not addressed in this study. First, genetic association studies in diabetic individuals will be important to assess the contribution of HbA1c-associated variants to its application in diabetes control. These analyses require different study designs to ours, and are beyond the scope of current datasets. Second, it will be important to explore associations of HbA1c with low to intermediate frequency variants through imputation from the 1,000 Genomes Project, direct association using whole-genome sequencing data, and in-depth replication and locus fine-mapping through custom arrays.

Finally, it will be important to evaluate reclassification rates in different populations, because the allele frequencies of some SNPs shown to be associated with HbA1c are known to vary substantially among populations with different ethnic ancestries. For instance, the A allele frequency at rs1800562 (HFE) in populations of European ancestry is 5% (CEU), but the A allele is absent in populations of African or East Asian ancestry (YRI, CHB/JPT). The T allele frequency at rs855791 (TMPRSS6) is 39% in CEU samples, but only 11 and 5% in the YRI and CHB/JPT samples, respectively. It will therefore be important to assess how variation in frequency and effect size influence the impact of HbA1c-associated variants in diverse populations.

In summary, in a meta-analysis of GWAS in a large number of individuals of European ancestry, we identified 10 common genetic loci associated with HbA1c levels. Six of these loci are novel, and seven appear to influence HbA1c via nonglycemic erythrocyte and iron biologic pathways. The genetic effect size of this set of loci on variations in HbA1c levels is small, but carries a detectable reclassification risk that will need to be refined by the discovery of additional variants and testing in diverse ancestral populations.

URLs. METAL, http://www.sph.umich.edu/csg/abecasis/Metal/index.html; HapMap, http://www.hapmap.org; R- project, http://www.r-project.org; 1,000 Genomes Project, http://www.1000genomes.org.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

APPENDIX

Nicole Soranzo,1,2 Serena Sanna,3 Eleanor Wheeler,1 Christian Gieger,4 Dörte Radke,5 Josée Dupuis,6,7 Nabila Bouatia-Naji,8 Claudia Langenberg,9 Inga Prokopenko,10,11 Elliot Stolerman,12,13,14 Manjinder S. Sandhu,9,15,16 Matthew M. Heeney,17 Joseph M. Devaney,18 Muredach P. Reilly,19,20 Sally L. Ricketts,15 Alexandre F.R. Stewart,21 Benjamin F. Voight,12,13,22 Christina Willenborg,23,24 Benjamin Wright,25 David Altshuler,12,13,14 Dan Arking,26 Beverley Balkau,27,28 Daniel Barnes,9 Eric Boerwinkle,29 Bernhard Böhm,30 Amélie Bonnefond,8 Lori L. Bonnycastle,31 Dorret I. Boomsma,32 Stefan R. Bornstein,33 Yvonne Böttcher,34 Suzannah Bumpstead,1 Mary Susan Burnett-Miller,18 Harry Campbell,35 Antonio Cao,3 John Chambers,36 Robert Clark,37 Francis S. Collins,31 Josef Coresh,38 Eco J.C. de Geus,32 Mariano Dei,3 Panos Deloukas,1 Angela Döring,4 Josephine M. Egan,39 Roberto Elosua,40 Luigi Ferrucci,41 Nita Forouhi,9 Caroline S. Fox,7,42 Christopher Franklin,35 Maria Grazia Franzosi,43 Sophie Gallina,8 Anuj Goel,11,44 Jürgen Graessler,33 Harald Grallert,4 Andreas Greinacher,45 David Hadley,46 Alistair Hall,47 Anders Hamsten on behalf of Procardis Consortium,48 Caroline Hayward,49 Simon Heath,50 Christian Herder,51 Georg Homuth,52 Jouke-Jan Hottenga,32 Rachel Hunter-Merrill,6 Thomas Illig,4 Anne U. Jackson,53 Antti Jula,54 Marcus Kleber,55 Christopher W. Knouff,56 Augustine Kong,57 Jaspal Kooner,58 Anna Köttgen,59 Peter Kovacs,60 Knut Krohn,60 Brigitte Kühnel,4 Johanna Kuusisto,61 Markku Laakso,61 Mark Lathrop,62 Cécile Lecoeur,8 Man Li,59 Mingyao Li,63 Ruth J.F. Loos,9 Jian'an Luan,9 Valeriya Lyssenko,64 Reedik Mägi,10,11 Patrik K.E. Magnusson,65 Anders Mälarstig,48 Massimo Mangino,2María Teresa Martínez-Larrad,66,67 Winfried März,55 Wendy L. McArdle,68 Ruth McPherson,21 Christa Meisinger,4 Thomas Meitinger,69,70 Olle Melander,64 Karen L. Mohlke,71 Vincent E. Mooser,56 Mario A. Morken,31 Narisu Narisu,31 David M. Nathan,14,72 Matthias Nauck,73 Chris O'Donnell,7 Konrad Oexle,69 Nazario Olla,3 James S. Pankow,74 Felicity Payne,1 John F. Peden,11,44 Nancy L. Pedersen,65 Leena Peltonen,1,75,76 Markus Perola,76,77 Ozren Polasek,78,79 Eleonora Porcu,3 Daniel J. Rader,19,20 Wolfgang Rathmann,80 Samuli Ripatti,76,77 Ghislain Rocheleau,81,82 Michael Roden,51,83 Igor Rudan,35,84 Veikko Salomaa,77 Richa Saxena,12,13 David Schlessinger,85 Heribert Schunkert,24 Peter Schwarz,33 Udo Seedorf,86 Elizabeth Selvin,38 Manuel Serrano-Ríos,66,67 Peter Shrader,87 Angela Silveira,48 David Siscovick,88 Kjioung Song,56 Timothy D. Spector,2Kari Stefansson,89,90 Valgerdur Steinthorsdottir,89 David P. Strachan,46 Rona Strawbridge,48 Michael Stumvoll,34,91 Ida Surakka,76,77 Amy J. Swift,31 Toshiko Tanaka,41,92 Alexander Teumer,52 Gudmar Thorleifsson,57 Unnur Thorsteinsdottir,89,90 Anke Tönjes,34 Gianluca Usala,3 Veronique Vitart,49 Henry Völzke,5 Henri Wallaschofski,73 Dawn M. Waterworth,56 Hugh Watkins,11,44 H-Erich Wichmann,4,93,94 Sarah H. Wild,35 Gonneke Willemsen,32 Gordon H. Williams,14,42 James F. Wilson,35 Juliane Winkelmann,69,70,95 Alan F. Wright,49 WTCCC,96 Carina Zabena,66,67 Jing Hua Zhao,9 Stephen E. Epstein,18 Jeanette Erdmann,24 Hakon H. Hakonarson,97 Sekar Kathiresan,12,13,14,98 Kay-Tee Khaw,99 Robert Roberts,21 Nilesh J. Samani,47 Mark D. Fleming,100 Robert Sladek,81,82 Gonçalo Abecasis,53 Michael Boehnke,53 Philippe Froguel,8,101 Leif Groop,64 Mark I. McCarthy,10,11,102 W.H. Linda Kao,103 Jose C. Florez,12,13,14,72 Manuela Uda,3 Nicholas J. Wareham,9 Inês Barroso,1 and James B. Meigs.14,87

Disclosures are listed in the online appendix.

Parts of this study were presented in abstract form at the 70th Scientific Sessions of the American Diabetes Association, Orlando, Florida, 25–29 June 2010.

Acknowledgments are listed in the online appendix.

1.
Mortensen
HB
,
Christophersen
C
:
Glucosylation of human haemoglobin a in red blood cells studied in vitro. Kinetics of the formation and dissociation of haemoglobin HbA1c
.
Clin Chim Acta
1983
; 
134
:
317
326
2.
Panzer
S
,
Kronik
G
,
Lechner
K
,
Bettelheim
P
,
Neumann
E
,
Dudczak
R
:
Glycosylated hemoglobins (GHb): an index of red cell survival
.
Blood
1982
; 
59
:
1348
1350
3.
Cohen
RM
,
Franco
RS
,
Khera
PK
,
Smith
EP
,
Lindsell
CJ
,
Ciraolo
PJ
,
Palascak
MB
,
Joiner
CH
:
Red cell life span heterogeneity in hematologically normal people is sufficient to alter HbHbA1c
.
Blood
2008
; 
112
:
4284
4291
4.
Roberts
WL
,
Safar-Pour
S
,
De
BK
,
Rohlfing
CL
,
Weykamp
CW
,
Little
RR
:
Effects of hemoglobin C and S traits on glycohemoglobin measurements by eleven methods
.
Clin Chem
2005
; 
51
:
776
778
5.
Meigs
JB
,
Panhuysen
CI
,
Myers
RH
,
Wilson
PW
,
Cupples
LA
:
A genome-wide scan for loci linked to plasma levels of glucose and HbA(1C) in a community-based sample of Caucasian pedigrees: the Framingham Offspring Study
.
Diabetes
2002
; 
51
:
833
840
6.
Pilia
G
,
Chen
WM
,
Scuteri
A
,
Orru
M
,
Albai
G
,
Dei
M
,
Lai
S
,
Usala
G
,
Lai
M
,
Loi
P
,
Mameli
C
,
Vacca
L
,
Deiana
M
,
Olla
N
,
Masala
M
,
Cao
A
,
Najjar
SS
,
Terracciano
A
,
Nedorezov
T
,
Sharov
A
,
Zonderman
AB
,
Abecasis
GR
,
Costa
P
,
Lakatta
E
,
Schlessinger
D
:
Heritability of cardiovascular and personality traits in 6,148 Sardinians
.
PLoS Genet
2006
; 
2
:
e132
7.
McCarthy
MI
,
Zeggini
E
:
Genome-wide association studies in type 2 diabetes
.
Curr Diab Rep
2009
; 
9
:
164
171
8.
Weedon
MN
,
Clark
VJ
,
Qian
Y
,
Ben-Shlomo
Y
,
Timpson
N
,
Ebrahim
S
,
Lawlor
DA
,
Pembrey
ME
,
Ring
S
,
Wilkin
TJ
,
Voss
LD
,
Jeffery
AN
,
Metcalf
B
,
Ferrucci
L
,
Corsi
AM
,
Murray
A
,
Melzer
D
,
Knight
B
,
Shields
B
,
Smith
GD
,
Hattersley
AT
,
Di Rienzo
A
,
Frayling
TM
:
A common haplotype of the glucokinase gene alters fasting glucose and birth weight: association in six studies and population-genetics analyses
.
Am J Hum Genet
2006
; 
79
:
991
1001
9.
Sparso
T
,
Andersen
G
,
Nielsen
T
,
Burgdorf
KS
,
Gjesing
AP
,
Nielsen
AL
,
Albrechtsen
A
,
Rasmussen
SS
,
Jorgensen
T
,
Borch-Johnsen
K
,
Sandbaek
A
,
Lauritzen
T
,
Madsbad
S
,
Hansen
T
,
Pedersen
O
:
The GCKR rs780094 polymorphism is associated with elevated fasting serum triacylglycerol, reduced fasting and OGTT-related insulinaemia, and reduced risk of type 2 diabetes
.
Diabetologia
2008
; 
51
:
70
75
10.
Prokopenko
I
,
Langenberg
C
,
Florez
JC
,
Saxena
R
,
Soranzo
N
,
Thorleifsson
G
,
Loos
RJ
,
Manning
AK
,
Jackson
AU
,
Aulchenko
Y
,
Potter
SC
,
Erdos
MR
,
Sanna
S
,
Hottenga
JJ
,
Wheeler
E
,
Kaakinen
M
,
Lyssenko
V
,
Chen
WM
,
Ahmadi
K
,
Beckmann
JS
,
Bergman
RN
,
Bochud
M
,
Bonnycastle
LL
,
Buchanan
TA
,
Cao
A
,
Cervino
A
,
Coin
L
,
Collins
FS
,
Crisponi
L
,
de Geus
EJ
,
Dehghan
A
,
Deloukas
P
,
Doney
AS
,
Elliott
P
,
Freimer
N
,
Gateva
V
,
Herder
C
,
Hofman
A
,
Hughes
TE
,
Hunt
S
,
Illig
T
,
Inouye
M
,
Isomaa
B
,
Johnson
T
,
Kong
A
,
Krestyaninova
M
,
Kuusisto
J
,
Laakso
M
,
Lim
N
,
Lindblad
U
,
Lindgren
CM
,
McCann
OT
,
Mohlke
KL
,
Morris
AD
,
Naitza
S
,
Orru
M
,
Palmer
CN
,
Pouta
A
,
Randall
J
,
Rathmann
W
,
Saramies
J
,
Scheet
P
,
Scott
LJ
,
Scuteri
A
,
Sharp
S
,
Sijbrands
E
,
Smit
JH
,
Song
K
,
Steinthorsdottir
V
,
Stringham
HM
,
Tuomi
T
,
Tuomilehto
J
,
Uitterlinden
AG
,
Voight
BF
,
Waterworth
D
,
Wichmann
HE
,
Willemsen
G
,
Witteman
JC
,
Yuan
X
,
Zhao
JH
,
Zeggini
E
,
Schlessinger
D
,
Sandhu
M
,
Boomsma
DI
,
Uda
M
,
Spector
TD
,
Penninx
BW
,
Altshuler
D
,
Vollenweider
P
,
Jarvelin
MR
,
Lakatta
E
,
Waeber
G
,
Fox
CS
,
Peltonen
L
,
Groop
LC
,
Mooser
V
,
Cupples
LA
,
Thorsteinsdottir
U
,
Boehnke
M
,
Barroso
I
,
Van Duijn
C
,
Dupuis
J
,
Watanabe
RM
,
Stefansson
K
,
McCarthy
MI
,
Wareham
NJ
,
Meigs
JB
,
Abecasis
GR
:
Variants in MTNR1B influence fasting glucose levels
.
Nat Genet
2009
; 
41
:
77
81
11.
Orho-Melander
M
,
Melander
O
,
Guiducci
C
,
Perez-Martinez
P
,
Corella
D
,
Roos
C
,
Tewhey
R
,
Rieder
MJ
,
Hall
J
,
Abecasis
G
,
Tai
ES
,
Welch
C
,
Arnett
DK
,
Lyssenko
V
,
Lindholm
E
,
Saxena
R
,
de Bakker
PI
,
Burtt
N
,
Voight
BF
,
Hirschhorn
JN
,
Tucker
KL
,
Hedner
T
,
Tuomi
T
,
Isomaa
B
,
Eriksson
KF
,
Taskinen
MR
,
Wahlstrand
B
,
Hughes
TE
,
Parnell
LD
,
Lai
CQ
,
Berglund
G
,
Peltonen
L
,
Vartiainen
E
,
Jousilahti
P
,
Havulinna
AS
,
Salomaa
V
,
Nilsson
P
,
Groop
L
,
Altshuler
D
,
Ordovas
JM
,
Kathiresan
S
:
Common missense variant in the glucokinase regulatory protein gene is associated with increased plasma triglyceride and C-reactive protein but lower fasting glucose concentrations
.
Diabetes
2008
; 
57
:
3112
3121
12.
Lyssenko
V
,
Nagorny
CL
,
Erdos
MR
,
Wierup
N
,
Jonsson
A
,
Spegel
P
,
Bugliani
M
,
Saxena
R
,
Fex
M
,
Pulizzi
N
,
Isomaa
B
,
Tuomi
T
,
Nilsson
P
,
Kuusisto
J
,
Tuomilehto
J
,
Boehnke
M
,
Altshuler
D
,
Sundler
F
,
Eriksson
JG
,
Jackson
AU
,
Laakso
M
,
Marchetti
P
,
Watanabe
RM
,
Mulder
H
,
Groop
L
:
Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion
.
Nat Genet
2009
; 
41
:
82
88
13.
Chen
WM
,
Erdos
MR
,
Jackson
AU
,
Saxena
R
,
Sanna
S
,
Silver
KD
,
Timpson
NJ
,
Hansen
T
,
Orru
M
,
Grazia Piras
M
,
Bonnycastle
LL
,
Willer
CJ
,
Lyssenko
V
,
Shen
H
,
Kuusisto
J
,
Ebrahim
S
,
Sestu
N
,
Duren
WL
,
Spada
MC
,
Stringham
HM
,
Scott
LJ
,
Olla
N
,
Swift
AJ
,
Najjar
S
,
Mitchell
BD
,
Lawlor
DA
,
Smith
GD
,
Ben-Shlomo
Y
,
Andersen
G
,
Borch-Johnsen
K
,
Jorgensen
T
,
Saramies
J
,
Valle
TT
,
Buchanan
TA
,
Shuldiner
AR
,
Lakatta
E
,
Bergman
RN
,
Uda
M
,
Tuomilehto
J
,
Pedersen
O
,
Cao
A
,
Groop
L
,
Mohlke
KL
,
Laakso
M
,
Schlessinger
D
,
Collins
FS
,
Altshuler
D
,
Abecasis
GR
,
Boehnke
M
,
Scuteri
A
,
Watanabe
RM
:
Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels
.
J Clin Invest
2008
; 
118
:
2620
2628
14.
Bouatia-Naji
N
,
Rocheleau
G
,
Van Lommel
L
,
Lemaire
K
,
Schuit
F
,
Cavalcanti-Proenca
C
,
Marchand
M
,
Hartikainen
AL
,
Sovio
U
,
De Graeve
F
,
Rung
J
,
Vaxillaire
M
,
Tichet
J
,
Marre
M
,
Balkau
B
,
Weill
J
,
Elliott
P
,
Jarvelin
MR
,
Meyre
D
,
Polychronakos
C
,
Dina
C
,
Sladek
R
,
Froguel
P
:
A polymorphism within the G6PC2 gene is associated with fasting plasma glucose levels
.
Science
2008
; 
320
:
1085
1088
15.
Bouatia-Naji
N
,
Bonnefond
A
,
Cavalcanti-Proenca
C
,
Sparso
T
,
Holmkvist
J
,
Marchand
M
,
Delplanque
J
,
Lobbens
S
,
Rocheleau
G
,
Durand
E
,
De Graeve
F
,
Chevre
JC
,
Borch-Johnsen
K
,
Hartikainen
AL
,
Ruokonen
A
,
Tichet
J
,
Marre
M
,
Weill
J
,
Heude
B
,
Tauber
M
,
Lemaire
K
,
Schuit
F
,
Elliott
P
,
Jorgensen
T
,
Charpentier
G
,
Hadjadj
S
,
Cauchi
S
,
Vaxillaire
M
,
Sladek
R
,
Visvikis-Siest
S
,
Balkau
B
,
Levy-Marchal
C
,
Pattou
F
,
Meyre
D
,
Blakemore
AI
,
Jarvelin
MR
,
Walley
AJ
,
Hansen
T
,
Dina
C
,
Pedersen
O
,
Froguel
P
:
A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk
.
Nat Genet
2009
; 
41
:
89
94
16.
Pare
G
,
Chasman
DI
,
Parker
AN
,
Nathan
DM
,
Miletich
JP
,
Zee
RY
,
Ridker
PM
:
Novel association of HK1 with glycated hemoglobin in a non-diabetic population: a genome-wide evaluation of 14,618 participants in the Women's Genome Health Study
.
PLoS Genet
2008
; 
4
:
e1000312
17.
Selvin
E
,
Zhu
H
,
Brancati
FL
:
Elevated HbA1c in adults without a history of diabetes in the U.S
.
Diabetes Care
2009
; 
32
:
828
833
18.
International Expert Committee report on the role of the HbA1c assay in the diagnosis of diabetes
.
Diabetes Care
2009
; 
32
:
1327
1334
19.
Dupuis
J
,
Langenberg
C
,
Prokopenko
I
,
Saxena
R
,
Soranzo
N
,
Jackson
AU
,
Wheeler
E
,
Glazer
NL
,
Bouatia-Naji
N
,
Gloyn
AL
,
Lindgren
CM
,
Mägi
R
,
Morris
AP
,
Randall
J
,
Johnson
T
,
Elliott
P
,
Rybin
D
,
Thorleifsson
G
,
Steinthorsdottir
V
,
Henneman
P
,
Grallert
H
,
Dehghan
A
,
Hottenga
JJ
,
Franklin
CS
,
Navarro
P
,
Song
K
,
Goel
A
,
Perry
JRB
,
Egan
JM
,
Lajunen
T
,
Grarup
N
,
Sparsø
T
,
Doney
A
,
Voight
B
,
Stringham
HM
,
Li
M
,
Kanoni
S
,
Shrader
P
,
Cavalcanti-Proença
C
,
Kumari
M
,
Qi
L
,
Timpson
NJ
,
Gieger
C
,
Zabena
C
,
Rocheleau
G
,
Ingelsson
E
,
An
P
,
O'Connell
J
,
Luan
J
,
Elliott
A
,
McCarroll
SA
,
Payne
F
,
Roccasecca
RM
,
Pattou
F
,
Sethupathy
P
,
Ardlie
K
,
Ariyurek
Y
,
Balkau
B
,
Barter
P
,
Beilby
JP
,
Ben-Shlomo
Y
,
Benediktsson
R
,
Bennett
AJ
,
Bergmann
S
,
Bochud
M
,
Boerwinkle
E
,
Bonnefond
A
,
Bonnycastle
LL
,
Borch-Johnsen
K
,
Böttcher
Y
,
Brunner
E
,
Bumpstead
SJ
,
Charpentier
G
,
Chen
Y
,
Chines
P
,
Clarke
R
,
Coin
LJM
,
Cooper
MN
,
Cornelis
M
,
Crawford
G
,
Crisponi
L
,
Day
INM
,
de G
E.
,
Delplanque
J
,
Dina
C
,
Erdos
MR
,
Fedson
AC
,
Fischer-Rosinsky
A
,
Forouhi
NG
,
Fox
CS
,
Frants
R
,
Franzosi
MG
,
Galan
P
,
Goodarzi
MO
,
Graessler
J
,
Groves
C
,
Grundy
S
,
Gwilliam
R
,
Gyllensten
U
,
Hadjadj
S
,
Hallmans
G
,
Hammond
N
,
Han
X
,
Hartikainen
A
,
Hassanali
N
,
Hayward
C
,
Heath
SC
,
Hercberg
S
,
Herder
C
,
Hicks
AA
,
Hillman
DR
,
Hingorani
AD
,
Hofman
A
,
Hui
J
,
Hung
J
,
Isomaa
B
,
Johnson
PRV
,
Jørgensen
T
,
Jula
A
,
Kaakinen
M
,
Kaprio
J
,
Kesaniemi
YA
,
Kivimaki
M
,
Knight
B
,
Koskinen
S
,
Kovacs
P
,
Kyvik
KO
,
Lathrop
GM
,
Lawlor
D
,
Bacquer
OL
,
Lecoeur
C
,
Li
Y
,
Lyssenko
V
,
Mahley
R
,
Mangino
M
,
Manning
AK
,
Martínez-Larrad
MT
,
McAteer
JB
,
McCulloch
LJ
,
McPherson
R
,
Meisinger
C
,
Melzer
D
,
Meyre
D
,
Mitchell
BD
,
Morken
MA
,
Mukherjee
S
,
Naitza
S
,
Narisu
N
,
Neville
MJ
,
Oostra
BA
,
Orrù
M
,
Pakyz
R
,
Palmer
CNA
,
Paolisso
G
,
Pattaro
C
,
Pearson
D
,
Peden
JF
,
Pedersen
NL
,
Perola
M
,
Pfeiffer
AFH
,
Pichler
I
,
Polasek
O
,
Posthuma
D
,
Potter
SC
,
Pouta
A
,
Province
MA
,
Psaty
BM
,
Rathmann
W
,
Rayner
NW
,
Rice
K
,
Ripatti
S
,
Rivadeneira
F
,
Roden
M
,
Rolandsson
O
,
Sandbaek
A
,
Sandhu
M
,
Sanna
S
,
Sayer
AA
,
Scheet
P
,
Scott
L
,
Seedorf
U
,
Sharp
SJ
,
Shields
B
,
Sigurðsson
G
,
Sijbrands
EJG
,
Silveira
A
,
Simpson
L
,
Singleton
A
,
Smith
N
,
Sovio
U
,
Swift
A
,
Syddall
H
,
Syvänen
A
,
Tanaka
T
,
Thorand
B
,
Tichet
J
,
Tönjes
A
,
Tuomi
T
,
Uitterlinden
AG
,
van D
K. W.
,
van H
M.
,
Varma
D
,
Visvikis-Siest
S
,
Vitart
V
,
Vogelzangs
N
,
Waeber
G
,
Wagner
PJ
,
Walley
A
,
Walters
GB
,
Ward
KL
,
Watkins
H
,
Weedon
MN
,
Wild
SH
,
Willemsen
G
,
Witteman
JCM
,
Yarnell
JWG
,
Zeggini
E
,
Zelenika
D
,
Zethelius
B
,
Zhai
G
,
Zhao
JH
,
Zillikens
MC
,
Consortium.
D
,
Consortium.
G
,
Consortium.
GB
,
Borecki
IB
,
Loos
RJF
,
Meneton
P
,
Magnusson
PKE
,
Nathan
DM
,
Williams
GH
,
Hattersley
AT
,
Silander
K
,
Salomaa
V
,
Smith
GD
,
Bornstein
SR
,
Schwarz
P
,
Spranger
J
,
Karpe
F
,
Shuldiner
AR
,
Cooper
C
,
Dedoussis
GV
,
Serrano-Ríos
M
,
Morris
AD
,
Lind
L
,
Palmer
LJ
,
Hu
F
,
Franks
PW
,
Ebrahim
S
,
Marmot
M
,
Kao
WHL
,
Pankow
JS
,
Sampson
MJ
,
Kuusisto
J
,
Laakso
M
,
Hansen
T
,
Pedersen
O
,
Pramstaller
PP
,
Wichmann
H-E
,
Illig
T
,
Rudan
I
,
Wright
AF
,
Stumvoll
M
,
Campbell
H
,
Wilson
JF
,
Hamsten
A
on, behalf, of, Procardis, consortium
.,
Bergman
RN
,
Buchanan
TA
,
Collins
FS
,
Mohlke
KL
,
Tuomilehto
J
,
Valle
TT
,
Altshuler
D
,
Rotter
JI
,
Siscovick
DS
,
Penninx
BWJH
,
Boomsma
D
,
Deloukas
P
,
Spector
TD
,
Frayling
TM
,
Ferrucci
L
,
Kong
A
,
Thorsteinsdottir
U
,
Stefansson
K
,
van Duijn
CM
,
Aulchenko
YS
,
Cao
A
,
Scuteri
A
,
Schlessinger
D
,
Uda
M
,
Ruokonen
A
,
Jarvelin
MR
,
Waterworth
DM
,
Vollenweider
P
,
Peltonen
L
,
Mooser
V
,
Abecasis
GR
,
Wareham
NJ
,
Sladek
R
,
Froguel
P
,
Watanabe
RM
,
Meigs
JB
,
Groop
L
,
Boehnke
M
,
McCarthy
MI
,
Florez
JC
,
Barroso
I
:
Novel genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk
.
Nat Genet
2010
; 
42
:
105
116
20.
Devlin
B
,
Roeder
K
:
Genomic control for association studies
.
Biometrics
1999
; 
55
:
997
1004
21.
Ioannidis
JP
,
Patsopoulos
NA
,
Evangelou
E
:
Heterogeneity in meta-analyses of genome-wide association investigations
.
PLoS One
2007
; 
2
:
e841
22.
Voight
BF
,
Scott
LJ
,
Steinthorsdottir
V
,
Morris
AP
,
Dina
C
,
Welch
RP
,
Zeggini
E
,
Huth
C
,
Aulchenko
YS
,
Thorleifsson
G
,
McCulloch
LJ
,
Ferreira
T
,
Grallert
H
,
Amin
N
,
Wu
G
,
Willer
CJ
,
Raychaudhuri
S
,
McCarroll
SA
,
Langenberg
C
,
Hofmann
OM
,
Dupuis
J
,
Qi
L
,
Segrè
AV
,
Hoek
Mv
,
Navarro
P
,
Ardlie
K
,
Balkau
B
,
Benediktsson
R
,
Bennett
AJ
,
Blagieva
R
,
Boerwinkle
E
,
Bonnycastle
LL
,
Boström
KB
,
Bravenboer
B
,
Bumpstead
S
,
Burtt
NP
,
Charpentier
G
,
Chines
PS
,
Cornelis
M
,
Couper
DJ
,
Crawford
G
,
Doney
AS
,
Elliott
KS
,
Elliott
AL
,
Erdos
MR
,
Fox
CS
,
Franklin
CS
,
Ganser
M
,
Gieger
C
,
Grarup
N
,
Green
T
,
Griffin
S
,
Groves
CJ
,
Guiducci
C
,
Hadjadj
S
,
Hassanali
N
,
Herder
C
,
Isomaa
B
,
Jackson
AU
,
Johnson
PR
,
Jørgensen
T
,
Kao
WH
,
Klopp
N
,
Kong
A
,
Kraft
P
,
Kuusisto
J
,
Lauritzen
T
,
Li
M
,
Lieverse
A
,
Lindgren
CM
,
Lyssenko
V
,
Marre
M
,
Meitinger
T
,
Midthjell
K
,
Morken
MA
,
Narisu
N
,
Nilsson
P
,
Owen
KR
,
Payne
F
,
Perry
JR
,
Petersen
A
,
Platou
C
,
Proença
C
,
Prokopenko
I
,
Rathmann
W
,
Rayner
NW
,
Robertson
NR
,
Rocheleau
G
,
Roden
M
,
Sampson
MJ
,
Saxena
R
,
Shields
BM
,
Shrader
P
,
Sigurdsson
G
,
Sparsø
T
,
Strassburger
K
,
Stringham
HM
,
Sun
Q
,
Swift
AJ
,
Thorand
B
,
Tichet
J
,
Tuomi
T
,
van Dam
RM
,
van Haeften
TW
,
Herpt
Tv
,
van Vliet-Ostaptchouk
JV
,
Walters
GB
,
Weedon
MN
,
Wijmenga
C
,
Witteman
J
investigators TM, consortium TG
Bergman
RN
,
Cauchi
S
,
Collins
FS
,
Gloyn
AL
,
Gyllensten
U
,
Hansen
T
,
Hide
WA
,
Hitman
GA
,
Hofman
A
,
Hunter
DJ
,
Hveem
K
,
Laakso
M
,
Mohlke
KL
,
Morris
AD
,
Palmer
CN
,
Pramstaller
PP
,
Rudan
I
,
Sijbrands
E
,
Stein
LD
,
Tuomilehto
J
,
Uitterlinden
A
,
Walker
M
,
Wareham
NJ
,
Watanabe
RM
,
Abecasis
GR
,
Boehm
BO
,
Campbell
H
,
Daly
MJ
,
Hattersley
AT
,
Hu
FB
,
Meigs
JB
,
Pankow
JS
,
Pedersen
O
,
Wichmann
H
,
Barroso
I
,
Florez
JC
,
Frayling
TM
,
Groop
L
,
Sladek
R
,
Thorsteinsdottir
U
,
Wilson
JF
,
Illig
T
,
Froguel
P
,
van Duijn
CM
,
Stefansson
K
,
Altshuler
D
,
Boehnke
M
,
McCarthy
MI
:
Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis
.
Nat Genet
2010
; 
42
:
579
589
23.
Pencina
MJ
,
D'Agostino
RB
 Sr
,
D'Agostino
RB
 Jr
,
Vasan
RS
:
Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond
.
Stat Med
2008
; 
27
:
157
172
;
discussion 207–112
24.
Saxena
R
,
Hivert
M
,
Langenberg
C
,
Tanaka
T
,
Pankow
JS
,
Vollenweider
P
,
Lyssenko
V
,
Bouatia-Naji
N
,
Dupuis
J
,
Jackson
AU
,
Kao
WHL
,
Li
M
,
Glazer
NL
,
Manning
AK
,
Luan
J
,
Stringham
HM
,
Prokopenko
I
,
Johnson
T
,
Grarup
N
,
Lecoeur
C
,
Shrader
P
,
O'Connell
J
,
Ingelsson
E
,
Couper
DJ
,
Rice
K
,
Song
K
,
Andreasen
CH
,
Dina
C
,
Kottgen
A
,
Bacquer
OL
,
Pattou
F
,
Taneera
J
,
Steinthorsdottir
V
,
Rybin
D
,
Ardlie
K
,
Sampson
M
,
Qi
L
,
Hoek
MV
,
Weedon
MN
,
Aulchenko
YS
,
Voight
BF
,
Grallert
H
,
Balkau
B
,
Bergman
RN
,
Bielinski
SJ
,
Bonnefond
A
,
Bonnycastle
LL
,
Borch-Johnsen
K
,
Bttcher
Y
,
Brunner
E
,
Buchanan
TA
,
Bumpstead
SJ
,
Cavalcanti-Proena
C
,
Charpentier
G
,
Chen
YI
,
Chines
PS
,
Collins
FS
,
Cornelis
M
,
Crawford
GJ
,
Delplanque
J
,
Doney
A
,
Egan
JM
,
Erdos
MR
,
Firmann
M
,
Forouhi
NG
,
Fox
CS
,
Goodarzi
MO
,
Graessler
J
,
Hingorani
A
,
Isomaa
B
,
Jrgensen
T
,
Kivimaki
M
,
Kovacs
P
,
Krohn
K
,
Kumari
M
,
Lauritzen
T
,
Levy-Marchal
C
,
Mayor
V
,
McAteer
JB
,
Meyre
D
,
Mitchell
BD
,
Mohlke
KL
,
Morken
MA
,
Narisu
N
,
Palmer
CNA
,
Pakyz
R
,
Pascoe
L
,
Payne
F
,
Pearson
D
,
Rathmann
W
,
Sandbaek
A
,
Sayer
AA
,
Scott
LJ
,
Sharp
SJ
,
Sijbrands
E
,
Singleton
A
,
Siscovick
DS
,
Smith
NL
,
Sparso
T
,
Swift
A
,
Syddall
H
,
Thorleifsson
G
,
Tnjes
A
,
Tuomi
T
,
Tuomilehto
J
,
Valle
TT
,
Waeber
G
,
Walley
A
,
Waterworth
DM
,
Zeggini
E
,
Zhao
JH
,
consortium
G
,
Illig
T
,
Wichmann
HE
,
Wilson
JF
,
Duijn
Cv
,
Hu
FB
,
Morris
AD
,
Frayling
TM
,
Hattersley
AT
,
Thorsteinsdottir
U
,
Stefansson
K
,
Nilsson
P
,
Syvnen
A
,
Shuldiner
AR
,
Walker
M
,
Bornstein
SR
,
Schwarz
P
,
Williams
GH
,
Nathan
DM
,
Kuusisto
J
,
Laakso
M
,
Cooper
C
,
Hansen
T
,
Pedersen
O
,
Marmot
M
,
Ferrucci
L
,
Mooser
V
,
Stumvoll
M
,
Loos
RJ
,
Altshuler
D
,
Psaty
BM
,
Rotter
JI
,
Boerwinkle
E
,
Florez
JC
,
McCarthy
MI
,
Boehnke
M
,
Barroso
I
,
Sladek
R
,
Froguel
P
,
Meigs
JB
,
Groop
L
,
Wareham
NJ
,
Watanabe
RM
:
Genetic variation in GIPR impacts the glucose and insulin responses to an oral glucose challenge
.
Nat Genet
2010
; 
42
:
142
148
25.
Ganesh
SK
,
Zakai
NA
,
van Rooij
FJ
,
Soranzo
N
,
Smith
AV
,
Nalls
MA
,
Chen
MH
,
Kottgen
A
,
Glazer
NL
,
Dehghan
A
,
Kuhnel
B
,
Aspelund
T
,
Yang
Q
,
Tanaka
T
,
Jaffe
A
,
Bis
JC
,
Verwoert
GC
,
Teumer
A
,
Fox
CS
,
Guralnik
JM
,
Ehret
GB
,
Rice
K
,
Felix
JF
,
Rendon
A
,
Eiriksdottir
G
,
Levy
D
,
Patel
KV
,
Boerwinkle
E
,
Rotter
JI
,
Hofman
A
,
Sambrook
JG
,
Hernandez
DG
,
Zheng
G
,
Bandinelli
S
,
Singleton
AB
,
Coresh
J
,
Lumley
T
,
Uitterlinden
AG
,
Vangils
JM
,
Launer
LJ
,
Cupples
LA
,
Oostra
BA
,
Zwaginga
JJ
,
Ouwehand
WH
,
Thein
SL
,
Meisinger
C
,
Deloukas
P
,
Nauck
M
,
Spector
TD
,
Gieger
C
,
Gudnason
V
,
van Duijn
CM
,
Psaty
BM
,
Ferrucci
L
,
Chakravarti
A
,
Greinacher
A
,
O'Donnell
CJ
,
Witteman
JC
,
Furth
S
,
Cushman
M
,
Harris
TB
,
Lin
JP
:
Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium
.
Nat Genet
2009
; 
41
:
1191
1198
26.
Soranzo
N
,
Spector
TD
,
Mangino
M
,
Kuhnel
B
,
Rendon
A
,
Teumer
A
,
Willenborg
C
,
Wright
B
,
Chen
L
,
Li
M
,
Salo
P
,
Voight
BF
,
Burns
P
,
Laskowski
RA
,
Xue
Y
,
Menzel
S
,
Altshuler
D
,
Bradley
JR
,
Bumpstead
S
,
Burnett
MS
,
Devaney
J
,
Doring
A
,
Elosua
R
,
Epstein
SE
,
Erber
W
,
Falchi
M
,
Garner
SF
,
Ghori
MJ
,
Goodall
AH
,
Gwilliam
R
,
Hakonarson
HH
,
Hall
AS
,
Hammond
N
,
Hengstenberg
C
,
Illig
T
,
Konig
IR
,
Knouff
CW
,
McPherson
R
,
Melander
O
,
Mooser
V
,
Nauck
M
,
Nieminen
MS
,
O'Donnell
CJ
,
Peltonen
L
,
Potter
SC
,
Prokisch
H
,
Rader
DJ
,
Rice
CM
,
Roberts
R
,
Salomaa
V
,
Sambrook
J
,
Schreiber
S
,
Schunkert
H
,
Schwartz
SM
,
Serbanovic-Canic
J
,
Sinisalo
J
,
Siscovick
DS
,
Stark
K
,
Surakka
I
,
Stephens
J
,
Thompson
JR
,
Volker
U
,
Volzke
H
,
Watkins
NA
,
Wells
GA
,
Wichmann
HE
,
Van Heel
DA
,
Tyler-Smith
C
,
Thein
SL
,
Kathiresan
S
,
Perola
M
,
Reilly
MP
,
Stewart
AF
,
Erdmann
J
,
Samani
NJ
,
Meisinger
C
,
Greinacher
A
,
Deloukas
P
,
Ouwehand
WH
,
Gieger
C
:
A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium
.
Nat Genet
2009
;
41
:
1182
1190
27.
Bonnefond
A
,
Vaxillaire
M
,
Labrune
Y
,
Lecoeur
C
,
Chevre
JC
,
Bouatia-Naji
N
,
Cauchi
S
,
Balkau
B
,
Marre
M
,
Tichet
J
,
Riveline
JP
,
Hadjadj
S
,
Gallois
Y
,
Czernichow
S
,
Hercberg
S
,
Kaakinen
M
,
Wiesner
S
,
Charpentier
G
,
Levy-Marchal
C
,
Elliott
P
,
Jarvelin
MR
,
Horber
F
,
Dina
C
,
Pedersen
O
,
Sladek
R
,
Meyre
D
,
Froguel
P
:
Genetic variant in HK1 is associated with a proanemic state and HbA1c but not other glycemic control-related traits
.
Diabetes
2009
;
58
:
2687
2697
28.
Rijksen
G
,
Akkerman
JW
,
van den Wall Bake
AW
,
Hofstede
DP
,
Staal
GE
:
Generalized hexokinase deficiency in the blood cells of a patient with nonspherocytic hemolytic anemia
.
Blood
1983
;
61
:
12
18
29.
Bianchi
M
,
Magnani
M
:
Hexokinase mutations that produce nonspherocytic hemolytic anemia
.
Blood Cells Mol Dis
1995
;
21
:
2
8
30.
Schmidt
PJ
,
Toran
PT
,
Giannetti
AM
,
Bjorkman
PJ
,
Andrews
NC
:
The transferrin receptor modulates Hfe-dependent regulation of hepcidin expression
.
Cell Metab
2008
;
7
:
205
214
31.
Conte
D
,
Manachino
D
,
Colli
A
,
Guala
A
,
Aimo
G
,
Andreoletti
M
,
Corsetti
M
,
Fraquelli
M
:
Prevalence of genetic hemochromatosis in a cohort of Italian patients with diabetes mellitus
.
Ann Intern Med
1998
;
128
:
370
373
32.
Phelps
G
,
Chapman
I
,
Hall
P
,
Braund
W
,
Mackinnon
M
:
Prevalence of genetic haemochromatosis among diabetic patients
.
Lancet
1989
;
2
:
233
234
33.
Little
RR
,
Rohlfing
CL
,
Wiedmeyer
HM
,
Myers
GL
,
Sacks
DB
,
Goldstein
DE
:
The national glycohemoglobin standardization program: a five-year progress report
.
Clin Chem
2001
;
47
:
1985
1992

Supplementary data