Confirmation of HLA class II independent type 1 diabetes associations in the major histocompatibility complex...

Until recently, human leucocyte antigen (HLA) class II-independent associations with type 1 diabetes (T1D) in the Major Histocompatibility Complex (MHC) region were not adequately characterized owing to insufficient map coverage, inadequate statistical approaches and strong linkage disequilibrium spanning the entire MHC. Here we test for HLA class II-independent associations in the MHC using fine mapping data generated by the Type 1 Diabetes Genetics Consortium (T1DGC).

We have applied recursive partitioning to the modelling of the class II loci and used stepwise conditional logistic regression to test approximately 1534 loci between 29 and 34 Mb on chromosome 6p21, typed in 2240 affected sibpair (ASP) families.

Preliminary analyses confirm that HLA-B (at 31.4 Mb), HLA-A (at 30.0 Mb) are associated with T1D independently of the class II genes HLA-DRB1 and HLA-DQB1 (P = 6.0 x 10(-17) and 8.8 x 10(-13), respectively). In addition, a second class II region of association containing the single-nucleotide polymorphism (SNP), rs439121, and the class II locus HLA-DPB1, was identified as a T1D susceptibility effect which is independent of HLA-DRB1, HLA-DQB1 and HLA-B (P = 9.2 x 10(-8)). A younger age-at-diagnosis of T1D was found for HLA-B*39 (P = 7.6 x 10(-6)), and HLA-B*38 was protective for T1D.

These analyses in the T1DGC families replicate our results obtained previously in approximately 2000 cases and controls and 850 families. Taking both studies together, there is evidence for four T1D-associated regions at 30.0 Mb (HLA-A), 31.4 Mb (HLA-B), 32.5 Mb (rs9268831/HLA-DRA) and 33.2 Mb (rs439121/HLA-DPB1) that are independent of HLA-DRB1/HLA-DQB1. Neither study found evidence of independent associations at HLA-C, HLA-DQA1 loci nor in the UBD/MAS1L or ITPR3 gene regions. These studies show that to find true class II-independent effects, large, well-powered sample collections are required and be genotyped with a dense map of markers. In addition, a robust statistical methodology that fully models the class II effects is necessary. Recursive partitioning is a useful tool for modelling these multiallelic systems.