Author Summary Type 2 diabetes is a leading cause of morbidity and mortality in both the developed and developing world. Because the main metabolic characteristic of diabetes is increased blood glucose concentration, we sought to uncover the genetic determinants of glycated hemoglo- bin, an index of the mean blood glucose concentration of the preceding 8–12 weeks. Taking advantage of new technologies allowing us to interrogate genetic variation on a whole-genome basis, we found that variations in the GCK, SLC30A8, G6PC2, and HK1 genes are important determinants of glycated hemoglobin concentrations. While associations with the GCK, SLC30A8, and G6PC2 genes have previously been identified in genetic studies of diabetes and blood glucose concentration, the findings at HK1 are novel. HK1 encodes the enzyme hexokinase, responsible for the first step in glucose utilization and a likely candidate for the control of glucose metabolism. This observed genetic association between glycated hemoglo- bin levels and HK1 genetic variants paves the way for further studies of the role of HK1 in glucose metabolism and diabetes. Materials and Methods WGHS Study Participants The study population derived from the Women’s Genome Health Study (WGHS) [18]. Briefly, participants in the WGHS include women from the United States Women’s Health Study (WHS) with no prior history of cardiovascular disease, cancer, or other major chronic illness [19]. All participants provided a blood sample at the time of study enrollment from which genomic DNA was extracted. For all WGHS participants, baseline glycated hemoglobin (HbA1c) was measured using the Tina-Quant turbidimetric inhibition immunoassay (Roche Diagnostics, Indianapolis, Ind) on a Hitachi 911 autoanalyzer using packed red blood cells. This assay is standardized against the approved International Federa- tion of Clinical Chemists reference method and has a coefficient of variation of 7.2%. In addition to excluding women with self- reported diabetes at baseline, individuals with glycated hemoglo- bin of 7.0% or more were removed from analysis as this level has been proposed as diagnostic of drug-requiring diabetes [20]. The study was approved by the institutional review board of the Brigham and Women’s Hospital. Genotyping DNA samples were genotyped with the Infinium II technology from Illumina. Either the HumanHap300 Duo-Plus chip or the combination of the HumanHap300 Duo and I-Select chips was used. In either case, the custom content was identical and consisted of candidate SNPs chosen without regard to allele frequency to increase coverage of genetic variation with impact on biological function including metabolism, inflammation or cardio- vascular diseases. Genotyping at 318,237 HumanHap300 Duo SNPs and 45,571 custom content SNPs was attempted, for a total of 363,808 SNPs. Genetic context for all annotations are derived from human genome build 36.1 and dbSNP build 126. SNPs with call rates ,90% were excluded from further analysis. Likewise, all samples with percentage of missing genotypes higher than 2% were removed (N = 1426). No obvious bias distinguished failed from successful samples and failures are therefore assumed to be the result of random technical artifacts. Among retained Association of HK1 with Glycated Hemoglobin samples, SNPs were evaluated for deviation from Hardy-Weinberg equilibrium using an exact method [21] and were excluded when the P-value was lower than 1026. Samples were further validated by comparison of genotypes at 44 SNPs that had been previously ascertained using alternative technologies. SNPs with minor allele frequency .1% in Caucasians were used for analysis. After quality control, 308,625 HumanHap300 Duo SNPs and 28,718 custom content SNPs were left, for a total of 337,343 SNPs. Population Stratification Because population stratification can result in inflated type I error in genome-wide association analysis, a principal component analysis using 1443 ancestry informative SNPs was performed using PLINK [22] in order to confirm self-reported ancestry. Briefly, these SNPs were chosen based on Fst.0.4 in HapMap populations (YRB, CEU, CHB+JPT) and inter-SNP distance at least 500 kb in order to minimize linkage disequilibrium. Different ethnic groups were clearly distinguished with the two first components. Based on this analysis, 58 individuals were excluded from further evaluation as they did not cluster with other Caucasians, leaving 14,618 participants for the current study population. Two additional steps were taken to rule out the possibility that residual stratification within Caucasians was responsible for the associations observed. First, association analysis was done with correction by genomic control. Second, a principal component analysis [23] was performed in previously identified Caucasians (only) using 124,931 SNPs chosen to have pair-wise linkage disequilibrium lower than r2 = 0.4. The first ten compo- nents were then used as covariates in the association analysis. As adjustment by these covariates did not change the conclusions, we present analysis among Caucasian participants without further correction for sub-Caucasian ancestry. The value of the genomic control was 1.03916 before adjustment for the first ten components and 1.03328 after adjustment. Association Analysis To identify common genetic variants influencing glycated hemoglobin levels, we first attempted to discover which loci significantly contributed to glycated hemoglobin concentration. Glycated hemoglobin concentrations were adjusted for age, menopause and body mass index using a linear regression model in R to reduce the impact of clinical covariates on glycated hemoglobin variance. The normal distribution of adjusted glycated hemoglobin values was checked by visual inspection of the normal Quantile-Quantile plot. Adjusted glycated hemoglobin values were then tested for association with SNP genotypes by linear regression in PLINK [22], assuming an additive contribu- tion of each minor allele [24]. Beta coefficients are given using the major allele as the reference allele (major and minor alleles are presented in Supplementary Table S2). We also tested for association using genotypic, recessive and dominant genetic models. Since no additional locus was identified using these models we only present the results of the additive genetic model. A conservative P-value cut-off of 561028 was used to correct for the maximum of 1,000,000 independent statistical tests thought to correspond to all the common genetic variation of the human genome [25]. The false discovery rate was also calculated as described by Benjamini and Hochberg [26]. Once any locus with genome-wide significance was identified, a forward selection linear multiple regression model was used to further define the extent of the genetic association. Briefly, all genotyped SNPs within 100 kb of the most significantly associated SNP at each locus and passing quality control requirements were tested for possible incorporation into a multiple regression model. PLoS Genetics | www.plosgenetics.org 2 December 2008 | Volume 4 | Issue 12 | e1000312