Suna Onengut-Gumuscu1,2, Wei-Min Chen1,2, Catherine C Robertson1, Jessica K Bonnie1, Emily Farber1, Zhennan Zhu1, Jorge R Oksenberg3, Steven R Brant4, S Louis Bridges5, Jeffrey C Edberg5, Robert P Kimberly5, Peter K Gregersen6, Marian J Rewers7, Andrea K Steck7, Mary H Black8, Dana Dabelea9, Catherine Pihoker10, Mark A Atkinson11, Lynne E Wagenknecht12, Jasmin Divers13, Ronny A Bell12, Henry A Erlich14, Patrick Concannon1, Stephen S Rich15,2. 1. Center for Public Health Genomics, University of Virginia, Charlottesville, VA. 2. Department of Public Health Sciences, University of Virginia, Charlottesville, VA. 3. Department of Neurology, School of Medicine, University of California, San Francisco, San Francisco, CA. 4. Meyerhoff Inflammatory Bowel Disease Center, Department of Medicine, School of Medicine, and Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD. 5. Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL. 6. Robert S. Boas Center for Genomics & Human Genetics, The Feinstein Institute for Medical Research, Manhasset, NY. 7. Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO. 8. Ambry Genetics, Aliso Viejo, CA. 9. Colorado School of Public Health, University of Colorado Denver, Aurora, CO. 10. Department of Pediatrics, University of Washington, Seattle, WA. 11. Diabetes Institute and Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL. 12. Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC. 13. Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC. 14. Center for Genetics, Children's Hospital Oakland Research Institute, Oakland, CA. 15. Center for Public Health Genomics, University of Virginia, Charlottesville, VA ssr4n@virginia.edu.
Abstract
OBJECTIVE: Genetic risk scores (GRS) have been developed that differentiate individuals with type 1 diabetes from those with other forms of diabetes and are starting to be used for population screening; however, most studies were conducted in European-ancestry populations. This study identifies novel genetic variants associated with type 1 diabetes risk in African-ancestry participants and develops an African-specific GRS. RESEARCH DESIGN AND METHODS: We generated single nucleotide polymorphism (SNP) data with the ImmunoChip on 1,021 African-ancestry participants with type 1 diabetes and 2,928 control participants. HLA class I and class II alleles were imputed using SNP2HLA. Logistic regression models were used to identify genome-wide significant (P < 5.0 × 10-8) SNPs associated with type 1 diabetes in the African-ancestry samples and validate SNPs associated with risk in known European-ancestry loci (P < 2.79 × 10-5). RESULTS: African-specific (HLA-DQA1*03:01-HLA-DQB1*02:01) and known European-ancestry HLA haplotypes (HLA-DRB1*03:01-HLA-DQA1*05:01-HLA-DQB1*02:01, HLA-DRB1*04:01-HLA-DQA1*03:01-HLA-DQB1*03:02) were significantly associated with type 1 diabetes risk. Among European-ancestry defined non-HLA risk loci, six risk loci were significantly associated with type 1 diabetes in subjects of African ancestry. An African-specific GRS provided strong prediction of type 1 diabetes risk (area under the curve 0.871), performing significantly better than a European-based GRS and two polygenic risk scores in independent discovery and validation cohorts. CONCLUSIONS: Genetic risk of type 1 diabetes includes ancestry-specific, disease-associated variants. The GRS developed here provides improved prediction of type 1 diabetes in African-ancestry subjects and a means to identify groups of individuals who would benefit from immune monitoring for early detection of islet autoimmunity.
OBJECTIVE: Genetic risk scores (GRS) have been developed that differentiate individuals with type 1 diabetes from those with other forms of diabetes and are starting to be used for population screening; however, most studies were conducted in European-ancestry populations. This study identifies novel genetic variants associated with type 1 diabetes risk in African-ancestry participants and develops an African-specific GRS. RESEARCH DESIGN AND METHODS: We generated single nucleotide polymorphism (SNP) data with the ImmunoChip on 1,021 African-ancestry participants with type 1 diabetes and 2,928 control participants. HLA class I and class II alleles were imputed using SNP2HLA. Logistic regression models were used to identify genome-wide significant (P < 5.0 × 10-8) SNPs associated with type 1 diabetes in the African-ancestry samples and validate SNPs associated with risk in known European-ancestry loci (P < 2.79 × 10-5). RESULTS: African-specific (HLA-DQA1*03:01-HLA-DQB1*02:01) and known European-ancestry HLA haplotypes (HLA-DRB1*03:01-HLA-DQA1*05:01-HLA-DQB1*02:01, HLA-DRB1*04:01-HLA-DQA1*03:01-HLA-DQB1*03:02) were significantly associated with type 1 diabetes risk. Among European-ancestry defined non-HLA risk loci, six risk loci were significantly associated with type 1 diabetes in subjects of African ancestry. An African-specific GRS provided strong prediction of type 1 diabetes risk (area under the curve 0.871), performing significantly better than a European-based GRS and two polygenic risk scores in independent discovery and validation cohorts. CONCLUSIONS: Genetic risk of type 1 diabetes includes ancestry-specific, disease-associated variants. The GRS developed here provides improved prediction of type 1 diabetes in African-ancestry subjects and a means to identify groups of individuals who would benefit from immune monitoring for early detection of islet autoimmunity.
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