Improved Risk Assessment for
Insulin-Dependent Diabetes
mellitus by Analysis of Amino
Acids in HLA-DQ and DRB1 Loci

Ghabanbasani, M. Zamani; Spaepen, Marijke; Buyse, Inge M.; Marynen, Peter; Bex, Mieke; Bouillon, Roger; Jj, Cassiman

doi:10.1159/000472361

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…We have selected a set of residues in DRB1 and DQB1 that have either been implicated in T1D susceptibility by various authors or are in linkage disequilibrium with DQ-alpha residues that have, as DQA1 genotyping was not available in most studies (6,12,(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) (Table 5). We do not claim that the residues selected can explain susceptibility to T1D, but rather we wanted to explore how consistent such combinations would be across haplotypes and ethnic boundaries.…”

Section: Amino Acid Sitesmentioning

confidence: 99%

See 1 more Smart Citation

Relative predispositional effects of HLA class II DRB1‐DQB1 haplotypes and genotypes on type 1 diabetes: a meta‐analysis

Thomson

Valdes

Noble³

et al. 2007

Tissue Antigens

158

153

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The direct involvement of the human leukocyte antigen class II DR-DQ genes in type 1 diabetes (T1D) is well established, and these genes display a complex hierarchy of risk effects at the genotype and haplotype levels. We investigated, using data from 38 studies, whether the DR-DQ haplotypes and genotypes show the same relative predispositional effects across populations and ethnic groups. Significant differences in risk within a population were considered, as well as comparisons across populations using the patient/control (P/C) ratio. Within a population, the ratio of the P/C ratios for two different genotypes or haplotypes is a function only of the absolute penetrance values, allowing ranking of risk effects. Categories of consistent predisposing, intermediate ('neutral'), and protective haplotypes were identified and found to correlate with disease prevalence and the marked ethnic differences in DRB1-DQB1 frequencies. Specific effects were identified, for example for predisposing haplotypes, there was a statistically significant and consistent hierarchy for DR4 DQB1*0302s: DRB1*0405 ¼ *0401 ¼ *0402 > *0404 > *0403, with DRB1*0301 DQB1*0200 (DR3) being significantly less predisposing than DRB1*0402 and more than DRB1*0404. The predisposing DRB1*0401 DQB1*0302 haplotype was relatively increased compared with the protective haplotype DRB1*0401 DQB1*0301 in heterozygotes with DR3 compared with heterozygotes with DRB1*0101 DQB1*0501 (DR1). Our results show that metaanalyses and use of the P/C ratio and rankings thereof can be valuable in determining T1D risk factors at the haplotype and amino acid residue levels.

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Section: Amino Acid Sitesmentioning

confidence: 99%

“…Cross ethnic studies have been particularly informative in determining both the direct role of HLA DR-DQ genes in T1D and their RPEs (5,6,(18)(19)(20)(21)(22)(23). Also, various HLA DRB1, DQA1 and DQB1 amino acid sites, either individually or in various combinations, have been suggested as T1D components (6,12,(15)(16)(24)(25)(26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

Relative predispositional effects of HLA class II DRB1‐DQB1 haplotypes and genotypes on type 1 diabetes: a meta‐analysis

Thomson

Valdes

Noble³

et al. 2007

Tissue Antigens

158

153

View full text Add to dashboard Cite

show abstract

“…Although multiple genes have been implicated, HLA-linked genes are the major type 1 diabetes susceptibility markers known to date, and, under a multiplicative model, constitute ~53% (2) of the type 1 diabetes genetic component (3,4). It is widely recognized that the major component of HLA type 1 diabetes susceptibility involves DRB1, DQA1, and DQB1 genes (5)(6)(7)(8)(9).…”

Section: Association Between Type 1 Diabetes Age Of Onset and Hla Amomentioning

confidence: 99%

Association between type 1 diabetes age of onset and HLA among sibling pairs.

et al. 1999

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In this study, we report type 1 diabetes age-of-onset association with HLA class II (DRB1, DQB1, and DPB1) and class I (A) genes in 222 multiplex families from the Human Biological Data Interchange. Linear regression showed a small (R2 = 0.26) but significant correlation in the ages of onset among sib pairs. A strong association in age of onset between members of sib pairs was observed when the analysis was performed using contingency tables that split sibs into three age-at-onset ranges (0-10, 11-20, and 21-36 years). The association is strongest for sib pairs that share both haplotypes and is nonsignificant for sib pairs that do not share any DR-DQ haplotypes. A goodness-of-fit test revealed that DR-DQ haplotype sharing cannot account for all the association in age of onset among sib pairs. The age-of-onset distribution of DR-DQ haplotypes is affected by the DPB1 and A alleles present. The strongest associations were found with the A locus: DR3/DR4 genotype frequency decreases with age of onset in this data set only among A*0101- individuals, and A*2402 is strongly associated with younger age of onset in many DR-DQ haplotypes.

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“…However, this combination by itself is insufficient to define the role of HLA in IDDM, and does not explain the known genetic heterogeneity of IDDM (Thomson et al, 1988;Klitz, 1988;Erlich, 1991;R0nningen et al, 1991). DRßl position 71 has also been reported as involved in increased susceptibility to IDDM (Ghabanbasani et al, 1994). There is overwhelming evidence that other amino acid sites in HLA molecules must also be involved in the disease process.…”

Section: Performance Of the Algorithmmentioning

confidence: 99%

On Distinguishing Unique Combinations in Biological Sequences

Salamon

Tarhio²,

Rønningen³

et al. 1996

Journal of Computational Biology

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The problem of defining combinations of variants unique to a sequence is efficiently addressed as a set covering computation. The unique-combinations method is introduced, which identifies patterns in biological sequence data that distinguish a sequence from a group of other sequences. This method is further developed to describe features consistently present in one group of sequences but not in a second group. The approach is incorporated into a novel analytical tool, designed for use in studies of polymorphic sequence data, such as mitochondrial, human leukocyte antigen (HLA), or viral pathogen sequences. The unique combinations method is well suited to applications in medical genetics and evolutionary genetics. An example implementation of the unique-combinations method yields greatly improved risk assessment for insulin-dependent diabetes mellitus (IDDM) from amino acid patterns isolated in an analysis of HLA class II DQA1-DQB1 patient and control genotypes.

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Improved Risk Assessment for Insulin-Dependent Diabetes mellitus by Analysis of Amino Acids in HLA-DQ and DRB1 Loci

Cited by 7 publications

References 16 publications

Relative predispositional effects of HLA class II DRB1‐DQB1 haplotypes and genotypes on type 1 diabetes: a meta‐analysis

Relative predispositional effects of HLA class II DRB1‐DQB1 haplotypes and genotypes on type 1 diabetes: a meta‐analysis

Association between type 1 diabetes age of onset and HLA among sibling pairs.

On Distinguishing Unique Combinations in Biological Sequences

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