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

Type 2 diabetes is a leading cause of morbidity and mortality. While genetic variants have been found to influence the risk of type 2 diabetes mellitus, relatively few studies have focused on genes associated with glycated hemoglobin, an index of the mean blood glucose concentration of the preceding 8–12 weeks. Epidemiologic studies and randomized clinical trials have documented the relationship between glycated hemoglobin levels and the development of long-term complications in diabetes; moreover, higher glycated hemoglobin levels in the subdiabetic range have been shown to predict type 2 diabetes risk and cardiovascular disease. To examine the common genetic determinants of glycated hemoglobin levels, we performed a genome-wide association study that evaluated 337,343 SNPs in 14,618 apparently healthy Caucasian women. The results show that glycated hemoglobin levels are associated with genetic variation at the GCK (rs730497; P = 2.8×10−12), SLC30A8 (rs13266634; P = 9.8×10−8), G6PC2 (rs1402837; P = 6.8×10−10), and HK1 (rs7072268; P = 6.4×10−9) loci. While associations at the GCK, SLC30A8, and G6PC2 loci are confirmatory, the findings at HK1 are novel. We were able to replicate this novel association in an independent validation sample of 455 additional non-diabetic men and women. HK1 encodes the enzyme hexokinase, the first step in glycolysis and a likely candidate for the control of glucose metabolism. This observed genetic association between glycated hemoglobin levels and HK1 polymorphisms paves the way for further studies of the role of HK1 in hemoglobin glycation, glucose metabolism, and diabetes.

[1]  D. Lawlor,et al.  Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels. , 2008, The Journal of clinical investigation.

[2]  Jean Tichet,et al.  A Polymorphism Within the G6PC2 Gene Is Associated with Fasting Plasma Glucose Levels , 2008, Science.

[3]  P. Elliott,et al.  Common Variation in the FTO Gene Alters Diabetes-Related Metabolic Traits to the Extent Expected Given Its Effect on BMI , 2008, Diabetes.

[4]  P. Ridker,et al.  C-reactive protein gene variation and type 2 diabetes mellitus: a case-control study. , 2008, Atherosclerosis.

[5]  M. McCarthy,et al.  Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes , 2008, Nature Genetics.

[6]  Swneke D. Bailey,et al.  Genetic Variants of FTO Influence Adiposity, Insulin Sensitivity, Leptin Levels, and Resting Metabolic Rate in the Quebec Family Study , 2008, Diabetes.

[7]  Yusuke Nakamura,et al.  Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 With Susceptibility to Type 2 Diabetes in a Japanese Population , 2008, Diabetes.

[8]  N. Cook,et al.  Rationale, design, and methodology of the Women's Genome Health Study: a genome-wide association study of more than 25,000 initially healthy american women. , 2008, Clinical chemistry.

[9]  M. Haluzík,et al.  Adiponectin and resistin gene polymorphisms in patients with anorexia nervosa and obesity and its influence on metabolic phenotype. , 2008, Physiological research.

[10]  T. Nielsen,et al.  The GCKR rs780094 polymorphism is associated with elevated fasting serum triacylglycerol, reduced fasting and OGTT-related insulinaemia, and reduced risk of type 2 diabetes , 2007, Diabetologia.

[11]  Zhaohui S. Qin,et al.  A second generation human haplotype map of over 3.1 million SNPs , 2007, Nature.

[12]  D. Nathan,et al.  Relationship between glycated haemoglobin levels and mean glucose levels over time , 2007, Diabetologia.

[13]  N. Stefan,et al.  Polymorphisms within Novel Risk Loci for Type 2 Diabetes Determine β-Cell Function , 2007, PloS one.

[14]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[15]  T. Frayling.  Genome–wide association studies provide new insights into type 2 diabetes aetiology , 2007, Nature Reviews Genetics.

[16]  P. Ridker,et al.  Hemoglobin A1c predicts diabetes but not cardiovascular disease in nondiabetic women. , 2007, The American journal of medicine.

[17]  D. Lawlor,et al.  Independent Associations of Fasting Insulin, Glucose, and Glycated Haemoglobin with Stroke and Coronary Heart Disease in Older Women , 2007, PLoS medicine.

[18]  M. McCarthy,et al.  Replication of Genome-Wide Association Signals in UK Samples Reveals Risk Loci for Type 2 Diabetes , 2007, Science.

[19]  Marcia M. Nizzari,et al.  Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels , 2007, Science.

[20]  G. Abecasis,et al.  A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants , 2007, Science.

[21]  J. Hirschhorn,et al.  Genetic model testing and statistical power in population‐based association studies of quantitative traits , 2007, Genetic epidemiology.

[22]  T. Hudson,et al.  A genome-wide association study identifies novel risk loci for type 2 diabetes , 2007, Nature.

[23]  Pak Chung Sham,et al.  WHAP: haplotype-based association analysis , 2007, Bioinform..

[24]  Yusuke Nakamura,et al.  Association of CDKAL 1 , IGF 2 BP 2 , CDKN 2 A / B , HHEX , SLC 30 A 8 and KCNJ 11 with susceptibility to type 2 diabetes in a Japanese population , 2007 .

[25]  D. Melzer,et al.  A common haplotype of the glucokinase gene alters fasting glucose and birth weight: association in six studies and population-genetics analyses. , 2006, American journal of human genetics.

[26]  N. Wareham,et al.  Glycated hemoglobin as a marker of cardiovascular risk , 2006, Current opinion in lipidology.

[27]  A. Favier,et al.  In vivo expression and functional characterization of the zinc transporter ZnT8 in glucose-induced insulin secretion , 2006, Journal of Cell Science.

[28]  D. Reich,et al.  Principal components analysis corrects for stratification in genome-wide association studies , 2006, Nature Genetics.

[29]  Karen Chuang,et al.  High-throughput SNP genotyping by single-tube PCR with Tm-shift primers. , 2005, BioTechniques.

[30]  Edward R. Ashwood,et al.  Tietz Textbook of Clinical Chemistry and Molecular Diagnostics , 2005 .

[31]  G. Abecasis,et al.  A note on exact tests of Hardy-Weinberg equilibrium. , 2005, American journal of human genetics.

[32]  P. Donnelly,et al.  Comparison of Fine-Scale Recombination Rates in Humans and Chimpanzees , 2005, Science.

[33]  J. Manson,et al.  A Randomized Trial of Low-Dose Aspirin in the Primary Prevention of Cardiovascular Disease in Women , 2005, The New England journal of medicine.

[34]  Richard W Jones,et al.  Genetic regulation of birth weight and fasting glucose by a common polymorphism in the islet cell promoter of the glucokinase gene. , 2005, Diabetes.

[35]  Wouter W van Solinge,et al.  The energy-less red blood cell is lost: erythrocyte enzyme abnormalities of glycolysis. , 2005, Blood.

[36]  N. Day,et al.  Association of Hemoglobin A1c with Cardiovascular Disease and Mortality in Adults: The European Prospective Investigation into Cancer in Norfolk , 2004, Annals of Internal Medicine.

[37]  A. Favier,et al.  Identification and cloning of a beta-cell-specific zinc transporter, ZnT-8, localized into insulin secretory granules. , 2004, Diabetes.

[38]  P. Donnelly,et al.  The Fine-Scale Structure of Recombination Rate Variation in the Human Genome , 2004, Science.

[39]  M. Odawara,et al.  Association of the SNP-19 genotype 22 in the calpain-10 gene with elevated body mass index and hemoglobin A1c levels in Japanese. , 2003, Clinica chimica acta; international journal of clinical chemistry.

[40]  D. Swinkels,et al.  Rapid genotyping of single nucleotide polymorphisms using novel minor groove binding DNA oligonucleotides (MGB probes) , 2002, Human mutation.

[41]  R. Holman,et al.  Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. , 1998 .

[42]  Uk-Prospective-Diabetes-Study-Group.  Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) , 1998, The Lancet.

[43]  S. Piomelli,et al.  Identification of the cDNA for human red blood cell-specific hexokinase isozyme. , 1997, Blood.

[44]  Vic Hasselblad,et al.  A clinical approach for the diagnosis of diabetes mellitus: an analysis using glycosylated hemoglobin levels. Meta-analysis Research Group on the Diagnosis of Diabetes Using Glycated Hemoglobin Levels. , 1996, JAMA.

[45]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[46]  S. Genuth,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.

[47]  D. Singer,et al.  Tests of glycemia in diabetes mellitus. Their use in establishing a diagnosis and in treatment. , 1989, Annals of internal medicine.

[48]  D. Singer,et al.  The clinical information value of the glycosylated hemoglobin assay. , 1984, The New England journal of medicine.

[49]  H. Bunn.  Nonenzymatic glycosylation of protein: relevance to diabetes. , 1981, The American journal of medicine.