Table 4. Partition of Glycated Hemoglobin Variance According to Genetic and Clinical Variables. Category Clinical Covariates 2q24.3 (G6PC2) Locus 7p13 (GCK) Locus 8q24 (SLC30A8) Locus 10q21.3 (HK1) Locus TOTAL Variable Age rs1402837 rs730497 rs13266634 rs7072268 rs2305198 Body Mass Index Menopause Status 0.0006 Variable R2 Category R2 0.0354 0.0585 0.0024 0.0029 0.0019 0.0021 0.0049 0.0946 0.0024 0.0029 0.0019 0.0070 0.1088 doi:10.1371/journal.pgen.1000312.t004 erythrocytes (or act as proxies for other HK1 functional variants). Since intra-erythrocyte glucose concentration is thought to be the main determinant of hemoglobin glycation rate [39] and hexokinase is the initial and rate-limiting enzymatic step in erythrocyte glucose metabolism, after glucose transporter mediat- ed diffusion, it is likely that variations in HK1 activity would affect glycation. Alternatively, genetic variations at HK1 might modulate systemic glucose metabolism, raising the possibility that these variations are associated with the risk of incident diabetes. Indeed, HK1 is expressed in muscle, an insulin-sensitive tissue recognized for its importance in glucose metabolism. Association of HK1 with Glycated Hemoglobin SLC30A8 functions as a zinc transporter in secretory vesicles of pancreatic beta cells, providing zinc for insulin maturation and storage [40,41]. A coding non-synonymous polymorphism of SLC30A8 (R325W; rs13266634) has been demonstrated to be protective for type 2 diabetes [32,42–45] and was shown to influence insulin secretion following an intravenous glucose challenge [7]. In accordance with these results, that same coding SNP was associated with lower glycated hemoglobin in our study. Because many genetic variants have been previously associated with the risk of type 2 diabetes, we also tested these for association with glycated hemoglobin. Although not genome-wide significant, all of these associations had a consistent direction of effect (as compared with their association with diabetes). The low combined P-value shows that it is very unlikely that these glycated hemoglobin associations had such a consistent direction of effect by chance alone, suggesting that they likely reflect underlying ‘‘true’’ associations. Furthermore, it also suggests that if we had analyzed a much bigger sample, some of these associations might have become genome-wide significant. To address the possibility that the very low P-value from the analysis of all 19 SNPs stems solely from the inclusion of the very significant SLC30A8 and GCK SNPs, we repeated the procedure using only the weakly associated SNPs as a sensitivity analysis, with similar results. It therefore appears that genetic variations affecting the risk of future diabetes do not necessarily have a marked impact on glycated hemoglobin in healthy women, pointing to different, yet partially overlapping, genetic architectures for glycated hemoglobin and diabetes. The limitations of this study include less than certain elimination of diabetic subjects in the WGHS cohort, since we did not perform oral glucose tolerance testing. Therefore, some Table 5. Association of Glycated Hemoglobin with Type II Diabetes Candidate SNPs. Candidate SNP Best WGHS Proxy r2 Beta (%HbA1c) P-Value (one-sided) Direction of Effect in Agreement Gene(s) ADAMTS9 CDC123,CAMK1D CDKAL1 CDKN2A-2B FTO GCK GCKR HHEX-IDE IGF2BP2 JAZF1 KCNJ11 NOTCH2 PPARG SLC30A8 TCF2 TCF7L2 THADA TSPAN8,LGR5 WFS1 rs4607103 rs12779790 rs10946398 rs10811661 rs8050136 rs1799884 rs780094 rs1111875 rs4402960 rs864745 rs5215 rs10923931 rs1801282 rs13266634 rs4430796 rs7901695 rs7578597 rs7961581 rs10010131 rs4132228 rs11257622 rs4712523 rs2383208 rs8050136 rs730497 rs780094 rs1111875 rs1470579 rs1635852 rs5215 rs2793831 rs1899951 rs13266634 rs7501939 rs7903146 rs6708660 rs1353362 rs10012946 0.78 0.79 1 1 1 1 1 1 1 1 1 1 1 0.97 0.76 0.80 0.16 0.96 1 D’ 0.95 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.95 20.004 0.006 0.004 20.008 0.003 0.030 20.007 20.008 0.008 20.001 0.002 0.001 20.008 20.019 0.007 0.009 20.007 0.005 20.004 0.11 0.07 0.11 0.02 0.18 0.01 0.34 0.28 0.44 0.05 1.4E-12 0.01 4.8E-03 4.9E-08 0.02 3.5E-03 0.01 0.10 0.12 YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES All analyses were performed using adjusted glycated hemoglobin values (see text for details). Linkage disequilibrium between candidate SNPs and their best WGHS proxies (r2 and D’) were derived from HapMap data build 35 (CEU individuals). One-sided P-values equal or lower than 0.05 are shown in bold. doi:10.1371/journal.pgen.1000312.t005 PLoS Genetics | www.plosgenetics.org 6 December 2008 | Volume 4 | Issue 12 | e1000312