Biobanking, consent, and commercialization in international genetics research: the Type 1 Diabetes Genetics Consortium.

BACKGROUND: and PURPOSE: This article describes several ethical, legal, and social issues typical of international genetics biobanking, as encountered in the Type 1 Diabetes Genetics Consortium (T1DGC). METHODS: 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. CONCLUSIONS: 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.

Measurement of islet cell antibodies in the Type 1 Diabetes Genetics Consortium: efforts to harmonize procedures among the laboratories.

BACKGROUND: and PURPOSE: 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). METHODS: 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. RESULTS: 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. LIMITATIONS: With three laboratories using different assay methods (including calibrators), consistent values among them could not be attained. CONCLUSIONS: 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.

Quality control of phenotypic forms data in the Type 1 Diabetes Genetics Consortium.

BACKGROUND: 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. PURPOSE: 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. METHODS: 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. RESULTS: 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%). CONCLUSIONS: It is critical to provide immediate follow-up in order to reinforce training and ensure the quality of the data collected and entered.

Designing and implementing sample and data collection for an international genetics study: the Type 1 Diabetes Genetics Consortium (T1DGC).

BACKGROUND AND PURPOSE: 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. METHODS: 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. RESULTS: 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. LIMITATIONS: 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. CONCLUSIONS: 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.

Collection and processing of whole blood for transformation of peripheral blood mononuclear cells and extraction of DNA: the Type 1 Diabetes Genetics Consortium.

BACKGROUND: and PURPOSE: 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. METHODS: 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. RESULTS: 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. LIMITATIONS: Although the isolation of PBMCs was consistent among sites, the measurement of DNA was difficult to harmonize. CONCLUSIONS: 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.

HLA genotyping in the international Type 1 Diabetes Genetics Consortium.

BACKGROUND: 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. PURPOSE: 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: 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. RESULTS: 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. LIMITATIONS: 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. CONCLUSIONS: 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.

Serum adiponectin in young adults--interactions with central adiposity, circulating levels of glucose, and insulin resistance: the CARDIA study.

PURPOSE: To study adiponectin, a circulating adipocytokine secreted by adipocytes inversely associated with diabetes and insulin resistance, and factors affecting its levels in the Coronary Artery Risk Development in Young Adults (CARDIA) study. METHODS: Adiponectin in serum was measured by radioimmunoassay in 3355 participants (ages: 23-45 years) categorized by fasting glucose levels as normal, impaired fasting glucose, or diabetes mellitus. RESULTS: Levels of adiponectin were higher in women, in white participants, and with age. Waist circumference, estimating visceral fat, strongly and inversely correlated with levels of adiponectin, more than body mass index. Adiponectin values adjusted for gender and race were lower with higher fasting glucose values in the normal range and still lower with impaired fasting glucose and untreated diabetes mellitus, even further adjusting for waist circumference and fasting insulin levels (p < 0.0001). Gender- and race-adjusted adiponectin levels were inversely associated with insulin resistance at year 15 and with insulin resistance measured 15 years previously and with its change from baseline to year 15 (p < 0.0001). CONCLUSIONS: These data suggest complex and significant physiologic interactions among circulating levels of adiponectin and measures of insulin action throughout young adulthood, even from several years earlier. Central obesity, as measured with waist circumference, is a primary factor affecting levels of circulating adiponectin. Furthermore, levels of glucose and levels of adiponectin may directly influence one another.

Tests for genetic interactions in type 1 diabetes: linkage and stratification analyses of 4,422 affected sib-pairs.

OBJECTIVE: Interactions between genetic and environmental factors lead to immune dysregulation causing type 1 diabetes and other autoimmune disorders. Recently, many common genetic variants have been associated with type 1 diabetes risk, but each has modest individual effects. Familial clustering of type 1 diabetes has not been explained fully and could arise from many factors, including undetected genetic variation and gene interactions. RESEARCH DESIGN AND METHODS: To address this issue, the Type 1 Diabetes Genetics Consortium recruited 3,892 families, including 4,422 affected sib-pairs. After genotyping 6,090 markers, linkage analyses of these families were performed, using a novel method and taking into account factors such as genotype at known susceptibility loci. RESULTS: Evidence for linkage was robust at the HLA and INS loci, with logarithm of odds (LOD) scores of 398.6 and 5.5, respectively. There was suggestive support for five other loci. Stratification by other risk factors (including HLA and age at diagnosis) identified one convincing region on chromosome 6q14 showing linkage in male subjects (corrected LOD = 4.49; replication P = 0.0002), a locus on chromosome 19q in HLA identical siblings (replication P = 0.006), and four other suggestive loci. CONCLUSIONS: This is the largest linkage study reported for any disease. Our data indicate there are no major type 1 diabetes subtypes definable by linkage analyses; susceptibility is caused by actions of HLA and an apparently random selection from a large number of modest-effect loci; and apart from HLA and INS, there is no important susceptibility factor discoverable by linkage methods.

Genome-wide scan for linkage to type 1 diabetes in 2,496 multiplex families from the Type 1 Diabetes Genetics Consortium.

OBJECTIVE: Type 1 diabetes arises from the actions of multiple genetic and environmental risk factors. Considerable success at identifying common genetic variants that contribute to type 1 diabetes risk has come from genetic association (primarily case-control) studies. However, such studies have limited power to detect genes containing multiple rare variants that contribute significantly to disease risk. RESEARCH DESIGN AND METHODS: The Type 1 Diabetes Genetics Consortium (T1DGC) has assembled a collection of 2,496 multiplex type 1 diabetic families from nine geographical regions containing 2,658 affected sib-pairs (ASPs). We describe the results of a genome-wide scan for linkage to type 1 diabetes in the T1DGC family collection. RESULTS: Significant evidence of linkage to type 1 diabetes was confirmed at the HLA region on chromosome 6p21.3 (logarithm of odds [LOD] = 213.2). There was further evidence of linkage to type 1 diabetes on 6q that could not be accounted for by the major linkage signal at the HLA class II loci on chromosome 6p21. Suggestive evidence of linkage (LOD > or =2.2) was observed near CTLA4 on chromosome 2q32.3 (LOD = 3.28) and near INS (LOD = 3.16) on chromosome 11p15.5. Some evidence for linkage was also detected at two regions on chromosome 19 (LOD = 2.84 and 2.54). CONCLUSIONS: Five non-HLA chromosome regions showed some evidence of linkage to type 1 diabetes. A number of previously proposed type 1 diabetes susceptibility loci, based on smaller ASP numbers, showed limited or no evidence of linkage to disease. Low-frequency susceptibility variants or clusters of loci with common alleles could contribute to the linkage signals observed.

A human type 1 diabetes susceptibility locus maps to chromosome 21q22.3.

OBJECTIVE: The Type 1 Diabetes Genetics Consortium (T1DGC) has assembled and genotyped a large collection of multiplex families for the purpose of mapping genomic regions linked to type 1 diabetes. In the current study, we tested for evidence of loci associated with type 1 diabetes utilizing genome-wide linkage scan data and family-based association methods. RESEARCH DESIGN AND METHODS: A total of 2,496 multiplex families with type 1 diabetes were genotyped with a panel of 6,090 single nucleotide polymorphisms (SNPs). Evidence of association to disease was evaluated by the pedigree disequilibrium test. Significant results were followed up by genotyping and analyses in two independent sets of samples: 2,214 parent-affected child trio families and a panel of 7,721 case and 9,679 control subjects. RESULTS- Three of the SNPs most strongly associated with type 1 diabetes localized to previously identified type 1 diabetes risk loci: INS, IFIH1, and KIAA0350. A fourth strongly associated SNP, rs876498 (P = 1.0 x 10(-4)), occurred in the sixth intron of the UBASH3A locus at chromosome 21q22.3. Support for this disease association was obtained in two additional independent sample sets: families with type 1 diabetes (odds ratio [OR] 1.06 [95% CI 1.00-1.11]; P = 0.023) and case and control subjects (1.14 [1.09-1.19]; P = 7.5 x 10(-8)). CONCLUSIONS: The T1DGC 6K SNP scan and follow-up studies reported here confirm previously reported type 1 diabetes associations at INS, IFIH1, and KIAA0350 and identify an additional disease association on chromosome 21q22.3 in the UBASH3A locus (OR 1.10 [95% CI 1.07-1.13]; P = 4.4 x 10(-12)). This gene and its flanking regions are now validated targets for further resequencing, genotyping, and functional studies in type 1 diabetes.

The Association of Whole Grain Intake and Fasting Insulin in a Biracial Cohort of Young Adults: The CARDIA Study.

BACKGROUND: Whole grain consumption may influence insulin through beneficial effects on satiety and body weight, intestinal absorption, or the action of specific nutrients or constituents. DESIGN AND METHODS: We examined the associations of whole grain intake, assessed by a diet history interview at baseline (year 0) and year 7, with body mass index (BMI), waist-hip ratio (WHR), and fasting insulin in 3,627 Black and White adults in the Coronary Artery Risk Development in Young Adults Study (CARDIA). We estimated year 0 and year 7 cross-sectional associations accounting for correlation between years using repeated measures regression. RESULTS: After adjustment for age, education, energy intake, CARDIA field center, physical activity, alcohol consumption, and cigarette smoking, whole grain consumption was unrelated to WHR and inversely related to body mass index only in Whites at year 7. Whole grain consumption was inversely related to fasting insulin in Whites at year 0 and Black men and Whites at year 7. Mean differences for year 7 fasting insulin between the least vs. most frequent categories of whole grain consumption (0-2 vs. > 9 times/week) were 2.2, 1.0 and 1.0 µU/mL in Black men, white men, and white women, respectively (p < 0.05). The inverse association of whole grain intake and fasting insulin remained significant (p < 0.05) after adjustment for body mass index. Potentially mediating nutrients explaining part of the relationship between whole grain intake and fasting insulin were dietary magnesium and fiber. CONCLUSIONS: The independent inverse relationship between whole grain consumption and fasting insulin levels may have important public health implications in light of the low consumption of whole grains and the increasing prevalence of obesity and diabetes in the US.