HLA-DR4 is associated with insulin-dependent diabetes mellitus (IDDM) in many populations. Many recent studies suggest that the DR4 effect is really due to DQ3.2, an allele of the nearby DQB1 locus. We used T cell clones, MAb, and allele-specific oligonucleotides to test IDDM and control subjects for DR4 subtypes (Dw4, Dw10, Dw13, and Dw14) and for DR4-associated DQB1 alleles (DQ3.1 and DQ3.2). We find that (a) IDDM is approximately equally associated with alleles of the DRB1 locus (Dw4 and Dw10, combined relative risk, RR = 6.4) and the DQB1 locus (DQ3.2, RR = 5.9); and (b) there is significant interaction, in a statistical sense, between these DR and DQ alleles in IDDM. The only IDDM-associated DR4 haplotypes were those carrying the IDDM-associated alleles at both loci (RR = 12.1); haplotypes with Dw4 or 10 but not DQ3.2, or vice versa, had a RR less than 1. Alternative explanations include: (a) that susceptibility requires specific allelic products of both DR and DQ loci; (b) that the combination of certain DR and DQ alleles marks haplotypes with the true susceptibility allele at a third locus; or (c) that Dw4 and 10 mark haplotypes with an allele at another locus that interacts with DQ3.2. As discussed, this third locus is unlikely to be DQA1 (DQ alpha). The data thus are not easily reconciled with an exclusive effect of HLA-DQ. This information increases our ability to predict IDDM by genetic typing: in the population studied, heterozygotes DR3/[DQ3.2, Dw4] or DR3/[DQ3.2, Dw10] had a relative risk of 38.0 and an absolute risk of 1 in 15.
M J Sheehy, S J Scharf, J R Rowe, M H Neme de Gimenez, L M Meske, H A Erlich, B S Nepom
Usage data is cumulative from April 2023 through April 2024.
Usage | JCI | PMC |
---|---|---|
Text version | 190 | 0 |
65 | 17 | |
Scanned page | 85 | 4 |
Citation downloads | 14 | 0 |
Totals | 354 | 21 |
Total Views | 375 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.