[Show abstract][Hide abstract] ABSTRACT: Although human leukocyte antigen (HLA) DQ and DR loci appear to confer the strongest genetic risk for type 1 diabetes, more detailed information is required for other loci within the HLA region to understand causality and stratify additional risk factors. The Type 1 Diabetes Genetics Consortium (T1DGC) study design included high-resolution genotyping of HLA-A, B, C, DRB1, DQ, and DP loci in all affected sibling pair and trio families, and cases and controls, recruited from four networks worldwide, for analysis with clinical phenotypes and immunological markers.
In this article, we present the operational strategy of training, classification, reporting, and quality control of HLA genotyping in four laboratories on three continents over nearly 5 years.
Methods to standardize HLA genotyping at eight loci included: central training and initial certification testing; the use of uniform reagents, protocols, instrumentation, and software versions; an automated data transfer; and the use of standardized nomenclature and allele databases. We implemented a rigorous and consistent quality control process, reinforced by repeated workshops, yearly meetings, and telephone conferences.
A total of 15,246 samples have been HLA genotyped at eight loci to four-digit resolution; an additional 6797 samples have been HLA genotyped at two loci. The genotyping repeat rate decreased significantly over time, with an estimated unresolved Mendelian inconsistency rate of 0.21%. Annual quality control exercises tested 2192 genotypes (4384 alleles) and achieved 99.82% intra-laboratory and 99.68% inter-laboratory concordances.
The chosen genotyping platform was unable to distinguish many allele combinations, which would require further multiple stepwise testing to resolve. For these combinations, a standard allele assignment was agreed upon, allowing further analysis if required.
High-resolution HLA genotyping can be performed in multiple laboratories using standard equipment, reagents, protocols, software, and communication to produce consistent and reproducible data with minimal systematic error. Many of the strategies used in this study are generally applicable to other large multi-center studies.
[Show abstract][Hide abstract] ABSTRACT: When collecting phenotypic data in clinics across the globe, the Type 1 Diabetes Genetics Consortium (T1DGC) used several techniques that ensured consistency, completeness, and accuracy of the data.
The aim of this article is to describe the procedures used for collection, entry, processing, and management of the phenotypic data in this international study.
The T1DGC ensured the collection of high quality data using the following procedures throughout the entire study period. The T1DGC used centralized and localized training, required a pilot study, certified all data entry personnel, created standardized data collection forms, reviewed a sample of form sets quarterly throughout the duration of the study, and used a data entry system that provided immediate feedback to those entering the data.
Due to the intensive procedures in developing the forms, the study was able to uphold consistency among all clinics and minimal changes were required after implementation of the forms. The train-the-trainer model was efficient and only a small number of clinics had to repeat a pilot study. The study was able to maintain a low percentage of missing data (<0.001%) and low duplicate data entry error rate (0.10%).
It is critical to provide immediate follow-up in order to reinforce training and ensure the quality of the data collected and entered.
[Show abstract][Hide abstract] ABSTRACT: The Type 1 Diabetes Genetics Consortium (T1DGC) is an international project whose primary aims are to: (a) discover genes that modify type 1 diabetes risk; and (b) expand upon the existing genetic resources for type 1 diabetes research. The initial goal was to collect 2500 affected sibling pair (ASP) families worldwide.
T1DGC was organized into four regional networks (Asia-Pacific, Europe, North America, and the United Kingdom) and a Coordinating Center. A Steering Committee, with representatives from each network, the Coordinating Center, and the funding organizations, was responsible for T1DGC operations. The Coordinating Center, with regional network representatives, developed study documents and data systems. Each network established laboratories for: DNA extraction and cell line production; human leukocyte antigen genotyping; and autoantibody measurement. Samples were tracked from the point of collection, processed at network laboratories and stored for deposit at National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repositories. Phenotypic data were collected and entered into the study database maintained by the Coordinating Center.
T1DGC achieved its original ASP recruitment goal. In response to research design changes, the T1DGC infrastructure also recruited trios, cases, and controls. Results of genetic analyses have identified many novel regions that affect susceptibility to type 1 diabetes. T1DGC created a resource of data and samples that is accessible to the research community.
Participation in T1DGC was declined by some countries due to study requirements for the processing of samples at network laboratories and/or final deposition of samples in NIDDK Central Repositories. Re-contact of participants was not included in informed consent templates, preventing collection of additional samples for functional studies.
T1DGC implemented a distributed, regional network structure to reach ASP recruitment targets. The infrastructure proved robust and flexible enough to accommodate additional recruitment. T1DGC has established significant resources that provide a basis for future discovery in the study of type 1 diabetes genetics.
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To yield large amounts of DNA for many genotype analyses and to provide a renewable source of DNA, the Type 1 Diabetes Genetics Consortium (T1DGC) harvested DNA and peripheral blood mononuclear cells (PBMCs) from individuals with type 1 diabetes and their family members in several regions of the world.
DNA repositories were established in Asia-Pacific, Europe, North America, and the United Kingdom. To address region-specific needs, different methods and sample processing techniques were used among the laboratories to extract and to quantify DNA and to establish Epstein-Barr virus transformed cell lines.
More than 98% of the samples of PBMCs were successfully transformed. Approximately 20-25 microg of DNA were extracted per mL of whole blood. Extraction of DNA from the cell pack ranged from 92 to 165 microg per cell pack. In addition, the extracted DNA from whole blood or transformed cells was successfully utilized in each regional human leukocyte antigen genotyping laboratory and by several additional laboratories performing consortium-wide genotyping projects.
Although the isolation of PBMCs was consistent among sites, the measurement of DNA was difficult to harmonize.
DNA repositories can be established in different regions of the world and produce similar amounts of high-quality DNA for a variety of high-throughput genotyping techniques. Furthermore, even with the distances and time necessary for transportation, highly efficient transformation of PBMCs is possible. For future studies/trials involving several laboratories in different locations, the T1DGC experience includes examples of protocols that may be applicable. In summary, T1DGC has developed protocols that would be of interest to any scientific organization attempting to overcome the logistical problems associated with studies/trials spanning multiple research facilities, located in different regions of the world.
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This article describes several ethical, legal, and social issues typical of international genetics biobanking, as encountered in the Type 1 Diabetes Genetics Consortium (T1DGC).
By studying the examples set and lessons learned from other international biobanking studies and by devoting considerable time and resources to identifying, addressing, and continually monitoring ethical and regulatory concerns, T1DGC was able to minimize the problems reported by some earlier studies.
Several important conclusions can be drawn based on the experience in this study: (1) Basic international standards for research ethics review and informed consent are broadly consistent across developed countries. (2) When consent forms are adapted locally and translated into different languages, discrepancies are inevitable and therefore require prompt central review and resolution before research is initiated. (3) Providing separate 'check-box' consent for different elements of a study creates confusion and may not be essential. (4) Creating immortalized cell lines to aid future research is broadly acceptable, both in the US and internationally. (5) Imposing some limits on the use of stored samples aids in obtaining ethics approvals worldwide. (6) Allowing potential commercial uses of donated samples is controversial in some Asian countries. (7) Obtaining government approvals can be labor-intensive and time-consuming, and can require legal and diplomatic skills.
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Three network laboratories measured antibodies to islet autoantigens. Antibodies to glutamic acid decarboxylase (GAD65 [GADA]) and the intracellular portion of protein tyrosine phosphatase (IA-2(ic) [IA-2A]) were measured by similar, but not identical, methods in samples from participants in the Type 1 Diabetes Genetics Consortium (T1DGC).
All laboratories used radiobinding assays to detect antibodies to in vitro transcribed and translated antigen, but with different local standards, calibrated against the World Health Organization (WHO) reference reagent. Using a common method to calculate WHO units/mL, we compared results reported on samples included in the Diabetes Autoantibody Standardization Program (DASP), and developed standard methods for reporting in WHO units/mL. We evaluated intra-assay and inter-assay coefficient of variation (CV) in blind duplicate samples and assay comparability in four DASP workshops.
Values were linearly related in the three laboratories for both GADA and IA-2A, and intra-assay technical errors for values within the standard curve were below 13% for GADA and below 8.5% for IA-2A. Correlations in samples tested 1-2 years apart were >97%. Over the course of the study, internal CVs were 10-20% with one exception, and the laboratories concordantly called samples GADA or IA-2A positive or negative in 96.7% and 99.6% of duplicates within the standard curve. Despite acceptable CVs and general concordance in ranking samples, the laboratories differed markedly in absolute values for GADA and IA-2A reported in WHO units/mL in DASP over a large range of values.
With three laboratories using different assay methods (including calibrators), consistent values among them could not be attained.
Modifications in the assays are needed to improve comparability of results expressed as WHO units/mL across laboratories. It will be essential to retain high intra- and inter-assay precision, sensitivity and specificity and to confirm the accuracy of harmonized methods.
[Show abstract][Hide abstract] ABSTRACT: The Type I Diabetes Genetics Consortium (T1DGC) Rapid Response Workshop was established to evaluate published candidate gene associations in a large collection of affected sib-pair (ASP) families. We report on our quality control (QC) and preliminary family-based association analyses. A random sample of blind duplicates was analyzed for QC. Quality checks, including examination of plate-panel yield, marker yield, Hardy-Weinberg equilibrium, mismatch error rate, Mendelian error rate, and allele distribution across plates, were performed. Genotypes from 2324 families within nine cohorts were obtained from a panel of 21 candidate genes, including 384 single-nucleotide polymorphisms on two genotyping platforms performed at the Broad Institute Center for Genotyping and Analysis (Cambridge, MA, USA). The T1DGC Rapid Response project, following rigorous QC procedures, resulted in a 2297 family, 9688 genotyped individual database on a single-candidate gene panel. The available data include 9005 individuals with genotype data from both platforms and 683 individuals genotyped (276 in Illumina; 407 in Sequenom) on only one platform.
[Show abstract][Hide abstract] ABSTRACT: Aim: The aim of this study was to perform quality control (QC) and initial family-based association analyses on the major histocompatibility complex (MHC) single nucleotide polymorphism (SNP) and microsatellite marker data for the MHC Fine Mapping Workshop through the Type 1 Diabetes Genetics Consortium (T1DGC).Methods: A random sample of blind duplicates was sent for analysis of QC. DNA samples collected from participants were shipped to the genotyping laboratory from several T1DGC DNA Repository sites. Quality checks including examination of plate-panel yield, marker yield, Hardy–Weinberg equilibrium, mismatch error rate, Mendelian error rate and allele distribution across plates were performed.Results: Genotypes from 2325 families within nine cohorts were obtained and subjected to QC procedures. The MHC project consisted of three marker panels – two 1536 SNP sets (Illumina Golden Gate platform performed at the Wellcome Trust Sanger Institute, Cambridge, UK) and one 66 microsatellite marker panel (performed at deCODE). In the raw SNP data, the overall concordance rate was 99.1% (±0.02).Conclusions: The T1DGC MHC Fine Mapping project resulted in a 2300 family, 9992 genotyped individuals database comprising of two 1536 SNP panels and a 66 microsatellite panel to densely cover the 4 Mb MHC core region for use in statistical genetic analyses.