Six-locus high resolution HLA haplotype frequencies derived from mixed-resolution DNA typing for the entire US donor registry

• Published in: Human immunology Vol. 74; no. 10; pp. 1313 - 1320
• Main Authors: Gragert, Loren, Madbouly, Abeer, Freeman, John, Maiers, Martin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2013
• Subjects: Alleles; Allergy and Immunology; Cluster Analysis; Donor Selection; Ethnic Groups - genetics; Evolution, Molecular; Gene Frequency; Genetic Loci; Genetic Variation; Genetics, Population; Haplotypes; Hematopoietic Stem Cell Transplantation; Histocompatibility Testing; HLA Antigens - genetics; Humans; Multilocus Sequence Typing; Registries; Tissue Donors; United States; United States
• ISSN: 0198-8859; 1879-1166; 1879-1166
• DOI: 10.1016/j.humimm.2013.06.025

We have calculated six-locus high resolution HLA A∼C∼B∼DRB3/4/5∼DRB1∼DQB1 haplotype frequencies using all Be The Match® Registry volunteer donors typed by DNA methods at recruitment. Mixed resolution HLA typing data was inputted to a modified expectation–maximization (EM) algorithm in the form of genotype lists generated by interpretation of primary genomic typing data to the IMGT/HLA v3.4.0 allele list. The full cohort consists of 6.59million subjects categorized at a broad race level. Overall 25.8% of the individuals were typed at the C locus, and 5.2% typed at the DQB1 locus, while all individuals were typed for A, B, DRB1. We also present a subset of 2.90million subjects with detailed race/ethnic information mapped to 21 population subgroups, 64.1% of which have primary DNA typing data across at least A, B, and DRB1 loci. Sample sizes at the detailed race level range from 1,242,890 for European Caucasian to 1,376 Alaskan Native or Aleut. Genetic distance measurements show high levels of HLA genetic divergence among the 21 detailed race categories, especially among the eight Asian–American populations. These haplotype frequencies will be used to improve match predictions for donor selection algorithms for hematopoietic stem cell transplantation and improve the accuracy in modeling registry match rates.