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PubMed ID:
30655379
Public Release Type:
Journal
Publication Year: 2019
Affiliation: Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.; Center for Public Health Genomics, University of Virginia, Charlottesville, VA.; Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.; Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.; Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.; Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.; Pacific Northwest Diabetes Research Institute, Seattle, WA.; Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.; Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.; Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.; Pacific Northwest Diabetes Research Institute, Seattle, WA r.oram@exeter.ac.uk wah@uw.edu.; Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K. r.oram@exeter.ac.uk wah@uw.edu.
DOI:
https://doi.org/10.2337/dc18-1785
Authors:
Sharp Seth A, Rich Stephen S, Wood Andrew R, Jones Samuel E, Beaumont Robin N, Harrison James W, Schneider Darius A, Locke Jonathan M, Tyrrell Jess, Weedon Michael N, Hagopian William A, Oram Richard A
Request IDs:
20431
Studies:
Type 1 Diabetes Genetics Consortium
Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the "T1D GRS2") to better discriminate diabetes subtypes and to predict T1D in newborn screening studies.
