ABSTRACT: Although human leukocyte antigen (HLA) DQ and DR loci appear to confer the strongest genetic risk for type 1 diabetes, more detailed information is required for other loci within the HLA region to understand causality and stratify additional risk factors. The Type 1 Diabetes Genetics Consortium (T1DGC) study design included high-resolution genotyping of HLA-A, B, C, DRB1, DQ, and DP loci in all affected sibling pair and trio families, and cases and controls, recruited from four networks worldwide, for analysis with clinical phenotypes and immunological markers.In this article, we present the operational strategy of training, classification, reporting, and quality control of HLA genotyping in four laboratories on three continents over nearly 5 years.Methods to standardize HLA genotyping at eight loci included: central training and initial certification testing; the use of uniform reagents, protocols, instrumentation, and software versions; an automated data transfer; and the use of standardized nomenclature and allele databases. We implemented a rigorous and consistent quality control process, reinforced by repeated workshops, yearly meetings, and telephone conferences.A total of 15,246 samples have been HLA genotyped at eight loci to four-digit resolution; an additional 6797 samples have been HLA genotyped at two loci. The genotyping repeat rate decreased significantly over time, with an estimated unresolved Mendelian inconsistency rate of 0.21%. Annual quality control exercises tested 2192 genotypes (4384 alleles) and achieved 99.82% intra-laboratory and 99.68% inter-laboratory concordances.The chosen genotyping platform was unable to distinguish many allele combinations, which would require further multiple stepwise testing to resolve. For these combinations, a standard allele assignment was agreed upon, allowing further analysis if required.High-resolution HLA genotyping can be performed in multiple laboratories using standard equipment, reagents, protocols, software, and communication to produce consistent and reproducible data with minimal systematic error. Many of the strategies used in this study are generally applicable to other large multi-center studies.
