Dataset Information

Can Non-HLA Single Nucleotide Polymorphisms Help Stratify Risk in TrialNet Relatives at Risk for Type 1 Diabetes?

ABSTRACT:

Context

Genome-wide association studies identified >50 type 1 diabetes (T1D) associated non-human leukocyte antigens (non-HLA) loci.

Objective

The purpose of this study was to assess the contribution of non-HLA single nucleotide polymorphisms (SNPs) to risk of disease progression.

Design and setting

The TrialNet Pathway to Prevention Study follows relatives of T1D patients for development of autoantibodies (Abs) and T1D.

Participants

Using the Immunochip, we analyzed 53 diabetes-associated, non-HLA SNPs in 1016 Ab-positive, at-risk non-Hispanic white relatives.

Main outcome measure

Effect of SNPs on the development of multiple Abs and T1D.

Results

Cox proportional analyses included all substantial non-HLA SNPs, HLA genotypes, relationship to proband, sex, age at initial screening, initial Ab type, and number. Factors involved in progression from single to multiple Abs included age at screening, relationship to proband, HLA genotypes, and rs3087243 (cytotoxic T lymphocyte antigen-4). Significant factors for diabetes progression included age at screening, Ab number, HLA genotypes, rs6476839 [GLIS family zinc finger 3 (GLIS3)], and rs3184504 [SH2B adaptor protein 3 (SH2B3)]. When glucose area under the curve (AUC) was included, factors involved in disease progression included glucose AUC, age at screening, Ab number, relationship to proband, HLA genotypes, rs6476839 (GLIS3), and rs7221109 (CCR7). In stratified analyses by age, glucose AUC, age at screening, sibling, HLA genotypes, rs6476839 (GLIS3), and rs4900384 (C14orf64) were significantly associated with progression to diabetes in participants <12 years old, whereas glucose AUC, sibling, rs3184504 (SH2B3), and rs4900384 (C14orf64) were significant in those ≥12.

Conclusions

In conclusion, we identified five non-HLA SNPs associated with increased risk of progression from Ab positivity to disease that may improve risk stratification for prevention trials.

SUBMITTER: Steck AK

PROVIDER: S-EPMC5546868 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

Can Non-HLA Single Nucleotide Polymorphisms Help Stratify Risk in TrialNet Relatives at Risk for Type 1 Diabetes?

Steck Andrea K AK Xu Ping P Geyer Susan S Redondo Maria J MJ Antinozzi Peter P Wentworth John M JM Sosenko Jay J Onengut-Gumuscu Suna S Chen Wei-Min WM Rich Stephen S SS Pugliese Alberto A

The Journal of clinical endocrinology and metabolism 20170801 8

<h4>Context</h4>Genome-wide association studies identified >50 type 1 diabetes (T1D) associated non-human leukocyte antigens (non-HLA) loci.<h4>Objective</h4>The purpose of this study was to assess the contribution of non-HLA single nucleotide polymorphisms (SNPs) to risk of disease progression.<h4>Design and setting</h4>The TrialNet Pathway to Prevention Study follows relatives of T1D patients for development of autoantibodies (Abs) and T1D.<h4>Participants</h4>Using the Immunochip, we analyzed ...[more]

PMID: 28520980

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Project description:BACKGROUND: Genetic variation associated with human leukocyte antigen (HLA) genes has immunological functions and is associated with autoimmune diseases. To date, large-scale studies involving classical HLA genes have been limited by time-consuming and expensive HLA-typing technologies. To reduce these costs, single-nucleotide polymorphisms (SNPs) have been used to predict HLA-allele types. Although HLA allelic distributions differ among populations, most prediction model of HLA genes are based on Caucasian samples, with few reported studies involving non-Caucasians. RESULTS: Our sample consisted of 437 Han Chinese with Affymetrix 5.0 and Illumina 550 K SNPs, of whom 214 also had data on Affymetrix 6.0 SNPs. All individuals had HLA typings at a 4-digit resolution. Using these data, we have built prediction model of HLA genes that are specific for a Han Chinese population. To optimize our prediction model of HLA genes, we analyzed a number of critical parameters, including flanking-region size, genotyping platform, and imputation. Predictive accuracies generally increased both with sample size and SNP density. CONCLUSIONS: SNP data from the HapMap Project are about five times more dense than commercially available genotype chip data. Using chips to genotype our samples, however, only reduced the accuracy of our HLA predictions by only ~3%, while saving a great deal of time and expense. We demonstrated that classical HLA alleles can be predicted from SNP genotype data with a high level of accuracy (80.37% (HLA-B) ~95.79% (HLA-DQB1)) in a Han Chinese population. This finding offers new opportunities for researchers in obtaining HLA genotypes via prediction using their already existing chip datasets. Since the genetic variation structure (e.g. SNP, HLA, Linkage disequilibrium) is different between Han Chinese and Caucasians, and has strong impact in building prediction models for HLA genes, our findings emphasize the importance of building ethnic-specific models when analyzing human populations.

Project description:BACKGROUND:Asthma afflicts 6% to 8% of the United States population, and severe asthma represents approximately 10% of asthmatic patients. Several epidemiologic studies in the United States and Europe have linked Alternaria sensitivity to both persistence and severity of asthma. In order to begin to understand genetic risk factors underlying Alternaria sensitivity and asthma, in these studies we examined T cell responses to Alternaria antigens, HLA Class II restriction and HLA-DQ protection in children with severe asthma. METHODS:Sixty children with Alternaria-sensitive moderate-severe asthma were compared to 49 children with Alternaria-sensitive mild asthma. We examined HLA-DR and HLA-DQ frequencies in Alternaria-sensitive asthmatic by HLA typing. To determine ratios of Th1/Th2 Alternaria-specific T-cells, cultures were stimulated in media alone, Alternaria alternata extract and Alt a1. Sensitivity to IL-4 stimulation was measured by up-regulation of CD23 on B cells. RESULTS:Children with Alternaria-sensitive moderate-severe asthma trended to have increased sensitivities to Cladosporium (46% versus 35%), to Aspergillus (43% versus 28%), and significantly increased sensitivities to trees (78% versus 57%) and to weeds (68% versus 48%). The IL-4RA ile75val polymorphism was significantly increased in Alternaria-sensitive moderate-severe asthmatics, 83% (0.627 allele frequency) compared to Alternaria-sensitive mild asthmatics, 57% (0.388 allele frequency). This was associated with increased sensitivity to IL-4 stimulation measured by significantly increased IL-4 stimulated CD23 expression on CD19+ and CD86+CD19+ B cells of Alternaria-sensitive moderate-severe asthmatics. IL-5 and IL-13 synthesis was significantly increased in Alternaria-sensitive moderate-severe asthmatics compared to mild asthmatics to Alternaria extract and Alt a1 stimulation. The frequency of HLA-DQB1*03 allele was significantly decreased in Alternaria-sensitive moderate-severe asthmatics compared to mild asthmatics, 39% versus 63%, with significantly decreased allele frequency, 0.220 versus 0.398. SUMMARY:In children with Alternaria-sensitive moderate severe asthma, there was an increased Th2 response to Alternaria stimulation and increased sensitivity to IL-4 stimulation. This skewing towards a Th2 response was associated with an increased frequency of the IL-4RA ile75val polymorphism. In evaluating the HLA association, there was a decreased frequency of HLA-DQB1*03 in Alternaria-sensitive moderate severe asthmatic children consistent with previous studies suggest that HLA-DQB1*03 may be protective against the development of mold-sensitive severe asthma.

Dataset Information

Can Non-HLA Single Nucleotide Polymorphisms Help Stratify Risk in TrialNet Relatives at Risk for Type 1 Diabetes?

Context

Objective

Design and setting

Participants

Main outcome measure

Results

Conclusions

Publications

Can Non-HLA Single Nucleotide Polymorphisms Help Stratify Risk in TrialNet Relatives at Risk for Type 1 Diabetes?

Similar Datasets

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