Open Access Highly Accessed Research article

Genetic variants associated with fasting glucose and insulin concentrations in an ethnically diverse population: results from the Population Architecture using Genomics and Epidemiology (PAGE) study

Megan D Fesinmeyer1, James B Meigs2, Kari E North34, Fredrick R Schumacher5, Petra Bůžková6, Nora Franceschini4, Jeffrey Haessler1, Robert Goodloe7, Kylee L Spencer7, Venkata Saroja Voruganti8, Barbara V Howard9, Rebecca Jackson10, Laurence N Kolonel11, Simin Liu12, JoAnn E Manson13, Kristine R Monroe5, Kenneth Mukamal14, Holli H Dilks7, Sarah A Pendergrass15, Andrew Nato16, Peggy Wan5, Lynne R Wilkens11, Loic Le Marchand11, José Luis Ambite17, Steven Buyske1618, Jose C Florez2, Dana C Crawford7, Lucia A Hindorff19, Christopher A Haiman5, Ulrike Peters1 and James S Pankow20*

Author Affiliations

1 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle WA, USA

2 General Medicine Division, Department of Medicine, Harvard Medical School, Boston MA, USA

3 Carolina Center for Genome Sciences, School of Public Health, University of North Carolina, Chapel Hill, NC, USA

4 Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

5 Department of Preventive Medicine, Keck School of Medicine / Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA

6 Department of Biostatistics, University of Washington, Seattle, WA, USA

7 Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA

8 Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA

9 MedStar Research Institute, Georgetown University, Hyattsville, MD, USA

10 Department of Internal Medicine, Ohio State Medical Center, Columbus, OH, USA

11 Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA

12 Department of Epidemiology, University of California, Los Angeles, CA, USA

13 Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

14 Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

15 Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA, USA

16 Department of Genetics, Rutgers University, Piscataway, NJ, USA

17 Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA

18 Department of Statistics & Biostatistics, Rutgers University, Piscataway, NJ, USA

19 Office of Population Genomics, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

20 Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis MN, USA

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BMC Medical Genetics 2013, 14:98  doi:10.1186/1471-2350-14-98

Published: 25 September 2013



Multiple genome-wide association studies (GWAS) within European populations have implicated common genetic variants associated with insulin and glucose concentrations. In contrast, few studies have been conducted within minority groups, which carry the highest burden of impaired glucose homeostasis and type 2 diabetes in the U.S.


As part of the 'Population Architecture using Genomics and Epidemiology (PAGE) Consortium, we investigated the association of up to 10 GWAS-identified single nucleotide polymorphisms (SNPs) in 8 genetic regions with glucose or insulin concentrations in up to 36,579 non-diabetic subjects including 23,323 European Americans (EA) and 7,526 African Americans (AA), 3,140 Hispanics, 1,779 American Indians (AI), and 811 Asians. We estimated the association between each SNP and fasting glucose or log-transformed fasting insulin, followed by meta-analysis to combine results across PAGE sites.


Overall, our results show that 9/9 GWAS SNPs are associated with glucose in EA (p = 0.04 to 9 × 10-15), versus 3/9 in AA (p= 0.03 to 6 × 10-5), 3/4 SNPs in Hispanics, 2/4 SNPs in AI, and 1/2 SNPs in Asians. For insulin we observed a significant association with rs780094/GCKR in EA, Hispanics and AI only.


Generalization of results across multiple racial/ethnic groups helps confirm the relevance of some of these loci for glucose and insulin metabolism. Lack of association in non-EA groups may be due to insufficient power, or to unique patterns of linkage disequilibrium.