Carolina G Downie1, Sofia F Dimos2, Stephanie A Bien3, Yao Hu3, Burcu F Darst4, Linda M Polfus4,5, Yujie Wang2, Genevieve L Wojcik6, Ran Tao7,8, Laura M Raffield9, Nicole D Armstrong10, Hannah G Polikowsky11, Jennifer E Below11, Adolfo Correa12, Marguerite R Irvin10, Laura J F Rasmussen-Torvik13, Christopher S Carlson3, Lawrence S Phillips14,15, Simin Liu16,17, James S Pankow18, Stephen S Rich19, Jerome I Rotter20, Steven Buyske21, Tara C Matise22, Kari E North2, Christy L Avery2, Christopher A Haiman4, Ruth J F Loos23, Charles Kooperberg3, Mariaelisa Graff2, Heather M Highland2. 1. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. cdownie@live.unc.edu. 2. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 3. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 4. Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA. 5. Ambry Genetics, Aliso Viejo, CA, USA. 6. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 7. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA. 8. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA. 9. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 10. Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA. 11. Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. 12. Department of Medicine, Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA. 13. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 14. Atlanta VA Medical Center, Decatur, GA, USA. 15. Department of Medicine, Division of Endocrinology, Emory University School of Medicine, Atlanta, GA, USA. 16. Department of Medicine, Division of Endocrinology, Warren Alpert School of Medicine, Brown University, Providence, RI, USA. 17. Department of Epidemiology, Brown School of Public Health, Providence, RI, USA. 18. Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA. 19. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA. 20. Department of Pediatrics, Genome Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA. 21. Department of Statistics, Rutgers University, Piscataway, NJ, USA. 22. Department of Genetics, Rutgers University, Piscataway, NJ, USA. 23. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Abstract
AIMS/HYPOTHESIS: Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study. METHODS: We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci. RESULTS: Four novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis. CONCLUSIONS/ INTERPRETATION: Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations. DATA AVAILABILITY: Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).
AIMS/HYPOTHESIS: Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study. METHODS: We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci. RESULTS: Four novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis. CONCLUSIONS/ INTERPRETATION: Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations. DATA AVAILABILITY: Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).
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