Project description:The Type 1 Diabetes Genetics Consortium (T1DGC) was formed to address issues of limited sample size and consistency of phenotyping that had limited genetic investigations on risk of type 1 diabetes (T1D). The T1DGC first collected affected sib pair (ASP) families from four geographic networks (Asia-Pacific, Europe, North America, United Kingdom). In addition, T1D cases and controls were ascertained from existing and de novo collections. The T1DGC assembled 2,601 T1D ASP families and 69 Parent-T1D offspring trios, T1D cases from the UK Genetic Resource Investigating Diabetes (UKGRID, N=6,670), and controls from the British 1958 Birth Cohort (B58BC, N=6,523), the UK National Blood Services collection (NBS, N=2,893) and the NIHR Cambridge Biomedical Research Centre BioResource (CBR, N=2,846). All samples included in this series have reported or self-declared European ancestry. All DNA samples were collected after approval from relevant institutional research ethics committees. Genotyping was performed using a custom Illumina Infinium high-density genotyping array, ImmunoChip (Illumina, Inc; CA) according to manufacturer's protocols. The ImmunoChip was designed to densely genotype, using 1000 Genomes and any other available disease specific resequencing data, immune-mediated disease loci identified by common variant GWAS. The ImmunoChip Consortium selected 186 distinct loci containing markers meeting genome wide significance criteria (P<5x10-8) from twelve such diseases (autoimmune thyroid disease, ankylosing spondylitis, Crohn's disease, celiac disease, IgA deficiency, multiple sclerosis, primary biliary cirrhosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, T1D and ulcerative colitis). All 1000 Genomes Project pilot phase CEU population variants (Sept 2009 release) within 0.1cM (HapMap3 CEU) recombination blocks around each GWAS region lead marker were submitted for array design. No filtering on correlated variants (linkage disequilibrium) was applied. Additional content included regional resequencing data (submitted by several groups) as well as a small proportion of investigator-specific undisclosed content including intermediate GWAS results. After data cleaning and quality control, a total of 154,939 single nucleotide polymorphisms (SNPs) from 186 loci on ImmunoChip were scored. Case-control and family data were analyzed independently and combined by meta-analysis.

Project description:Type 2 diabetes (T2D) is among the leading causes of death in the U.S. Ethnic differences in T2D prevalence are evident, including especially for Native Hawaiians (NHs) who remain disproportionately affected by it. This difference in T2D susceptibility involves an interplay between genetic and environmental factors, of which epigenetic mechanisms, including DNA methylation (DNAm) provide a novel approach to investigating gene-environment interactions of health and disease. Monocytes, an innate immune cell intrinsic to the inflammatory response, are a fundamental immune cell component that likely underlies T2D pathogenesis, given their involvement in inflammation and inflammation-associated insulin resistance and metabolic dysfunction. From participants enrolled into the Multiethnic Cohort Study (MEC), who self-identified as NH (n=152), Japanese American (JA; n=119), or White (n=121), we investigated monocyte-specific DNAm patterns in participants at a baseline visit, when free of T2D, using the HumanMethylation850K (850K) to determine whether monocytes harbor an ethnic-specific epigenetic signature of T2D risk that precedes T2D diagnosis. Using an epigenome-wide association study (EWAS), we found 904 significantly (q < 0.01) differentially methylated loci (DML) at a 5% difference in DNAm between participants who remained T2D free at a 15-year follow-up (i.e., controls) and those that would be diagnosed with T2D by follow-up (i.e., incident T2D) after adjusted for age, sex, and education level. These methylation differences were enriched at regulatory regions of the genome, including intergenic and intragenic regions. Notably, these DML were able to distinctly stratify NHs by T2D risk groups, however, this signature was inapparent in Whites and JAs. Likewise, NHs in the incident T2D group displayed a higher degree of DNAm variability. Sensitivity analysis with traditional risk factors (fasting glucose and body mass index [BMI]) and neighborhood socioeconomic status (nSES) found these risk factors had minimal effect on T2D risk-associated DML. Next, using a similar approach we found ethnic-specific DML in monocytes that was able to uniquely stratify NHs, JAs, and Whites, however, in NHs this epigenetic landscape displayed a higher degree of DNAm variability. Similarly, these DML were enriched at regulatory regions of the genome. In both cases, we found genes associated with biological functions and pathways relevant to monocyte functionality, including immune activation and cellular metabolism. Due to the higher degree of epigenetic variability in NHs, including especially NHs with T2D risk, we investigated differentially variable CpGs associated with T2D risk and ethnic-differences. We found differentially variable CpGs between T2D risk groups were able to stratify NHs with and without T2D risk, whereas it was unresolvable in JAs and Whites. Ethnic-specific DNAm variability clustered each ethnic population distinctly, and NH controls were further partitioned from NHs with T2D risk. Our findings suggest monocytes harbor a unique DNAm signature associated with T2D risk, which may be related to ethnic differences that underlie DNAm variability in monocytes. The increased epigenetic variability in monocytes from NHs may underlie epigenetic plasticity that could allow these cells to readily respond to adverse environmental conditions throughout the life course that may underlie long-term disease risk, whereas in other less susceptible populations with similar environmental exposure, this epigenetic plasticity may not be apparent in monocytes.

Project description:Cases with Type 1 Diabetes (T1D) in the UK, were part of the Wellcome Trust Case Control Consortium (WTCCC) - <a href="http://www.wtccc.org.uk" target="_blank">http://www.wtccc.org.uk</a> - that first reported in 2007: Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661-678. [PubMed: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17554300" target="_blank">17554300</a>] In this genome-wide association study (GWAS), funded by the NIH and JDRF, and sponsored by the Type 1 Diabetes Genetics Consortium (T1DGC), we were able to extend the case and control groups used in the WTCCC, with the intention of performing a well-powered meta-analysis. The study is written up as: Barrett, J.C., Clayton, D.G., Concannon, P., Akolkar, B., Cooper, J.D., Erlich, H.A., Julier, C., Morahan, G., Nerup, J., Nierras, C., Plagnol, V., Pociot, F., Schuilenburg, H., Smyth, D.J., Stevens, H., Todd, J.A., Walker, N.M., Rich, S.S. and The Type 1 Diabetes Genetics Consortium Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nature Genetics, PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/19430480" target="_blank">19430480</a>. Resources - data: * Case data from this study is deposited here (i.e. in dbGaP) * Control data from this experiment - with subjects from the 1958 British Birth Cohort - is deposited with the European Genotype Archive (EGA): <a href="http://www.ebi.ac.uk/ega/" target="_blank">http://www.ebi.ac.uk/ega/</a> from where the WTCCC data is also available. * A complete description of how to request all components of the meta-analysis is available at: <a href="http://www.t1dbase.org/page/PosterView/display/poster_id/324" target="_blank">http://www.t1dbase.org/page/PosterView/display/poster_id/324</a> * Additional genetic data on the same case subjects, including some HLA types, are available from the Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory (JDRF/WT-DIL): <a href="http://www-gene.cimr.cam.ac.uk/todd/" target="_blank">http://www-gene.cimr.cam.ac.uk/todd/</a> Resources - samples: Case and control DNA samples are also available: * Case DNA samples are available from the JDRF/WT-DIL, as above, and will be available from the NIDDK Genetics Repository at Rutgers: <a href="https://www.niddkrepository.org" target="_blank">https://www.niddkrepository.org</a> * Control DNA samples are available from the 1958 British Birth Cohort (aka National Child Development Study): <a href="http://www.cls.ioe.ac.uk/studies.asp?section=000100020003" target="_blank">http://www.cls.ioe.ac.uk/studies.asp?section=000100020003</a> Use restrictions: There are access limitations to both data and samples, in order to keep their use in line with subjects' consent.

Dataset Information

Type 1 Diabetes Genetics Consortium (T1DGC): Multi-Ethnic ImmunoChip Study

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets