Helen Rance

Identification of the sources of error in allele frequency estimations from pooled DNA indicates an optimal experimental design

Genotyping costs still preclude analysis of a comprehensive SNP map in thousands of individual subjects in the search for disease susceptibility loci. Allele frequency estimation in DNA pools from cases and controls offers a partial solution, but variance in these estimates will result in some loss of statistical power. However, there has been no systematic attempt to quantify the several sources of error in previous studies. We report an analysis of the magnitude of variance components of each experimental stage in DNA pooling studies, and find that a design based on the formation of numerous small pools of approximately 50 individuals is superior to the formation of fewer, larger pools and the replication of any of the experimental stages. We conclude that this approach may retain an effective sample size greater than 68% of the true sample size, whilst offering a 60‐fold reduction in DNA usage and a greater than 30‐fold saving in cost, compared to individual genotyping. The possibility of combining pooling with informed selection of haplotype tag SNPs is also considered. In this way further savings in efficiency may be possible by using pooled allele frequency estimates to infer haplotype frequencies and hence, allele frequencies at untyped markers.

Investigating the utility of combining Φ29 whole genome amplification and highly multiplexed single nucleotide polymorphism BeadArray™ genotyping

BackgroundSustainable DNA resources and reliable high-throughput genotyping methods are required for large-scale, long-term genetic association studies. In the genetic dissection of common disease it is now recognised that thousands of samples and hundreds of thousands of markers, mostly single nucleotide polymorphisms (SNPs), will have to be analysed. In order to achieve these aims, both an ability to boost quantities of archived DNA and to genotype at low costs are highly desirable. We have investigated Φ29 polymerase Multiple Displacement Amplification (MDA)-generated DNA product (MDA product), in combination with highly multiplexed BeadArray™ genotyping technology. As part of a large-scale BeadArray genotyping experiment we made a direct comparison of genotyping data generated from MDA product with that from genomic DNA (gDNA) templates.ResultsEighty-six MDA product and the corresponding 86 gDNA samples were genotyped at 345 SNPs and a concordance rate of 98.8% was achieved. The BeadArray sample exclusion rate, blind to sample type, was 10.5% for MDA product compared to 5.8% for gDNA.ConclusionsWe conclude that the BeadArray technology successfully produces high quality genotyping data from MDA product. The combination of these technologies improves the feasibility and efficiency of mapping common disease susceptibility genes despite limited stocks of gDNA samples.

Association of intercellular adhesion molecule-1 gene with type 1 diabetes

Intercellular adhesion molecule-1 (ICAM-1) functions via its ligands, the leucocyte integrins, in adhesion of immune cells to endothelial cells and in T cell activation. The third immunoglobulin-like extracellular domain binds integrin Mac-1 and contains a common non-conservative aminoacid polymorphism, G241R. Phenotypically, ICAM-1 has been associated with type 1 diabetes, a T-cell-mediated autoimmune disease. We assessed two independent datasets, and noted that R241 was associated with lower risk of type 1 diabetes than is G241 (3695 families, relative risk 0.91, p=0.03; 446 families, 0.60, p=0.006). Our data indicate an aetiological role for ICAM-1 in type 1 diabetes, which needs to be confirmed in future genetic and functional experiments.

Development of an integrated genome informatics, data management and workflow infrastructure: A toolbox for the study of complex disease genetics

The genetic dissection of complex disease remains a significant challenge. Sample-tracking and the recording, processing and storage of high-throughput laboratory data with public domain data, require integration of databases, genome informatics and genetic analyses in an easily updated and scaleable format. To find genes involved in multifactorial diseases such as type 1 diabetes (T1D), chromosome regions are defined based on functional candidate gene content, linkage information from humans and animal model mapping information. For each region, genomic information is extracted from Ensembl, converted and loaded into ACeDB for manual gene annotation. Homology information is examined using ACeDB tools and the gene structure verified. Manually curated genes are extracted from ACeDB and read into the feature database, which holds relevant local genomic feature data and an audit trail of laboratory investigations. Public domain information, manually curated genes, polymorphisms, primers, linkage and association analyses, with links to our genotyping database, are shown in Gbrowse. This system scales to include genetic, statistical, quality control (QC) and biological data such as expression analyses of RNA or protein, all linked from a genomics integrative display. Our system is applicable to any genetic study of complex disease, of either large or small scale.

Type 1 diabetes genetic susceptibility encoded by HLA DQB1 genes in Romania.

Most cases of type 1 diabetes (T1DM) are due to an immune‐mediated destruction of the pancreatic beta cells, a process that is conditioned by multiple genes and environmental factors. The main susceptibility genes are represented by the class II HLA‐DRB1 and DQB1 alleles. The aim of our study was to reconfirm the contribution of HLA‐DQB1 polymorphisms to T1DM genetic susceptibility for the Romanian population. For this, 219 Romanian T1DM families were genotyped at high resolution for HLA DQB1 using the PCR‐SSOP method (Polymerase Chain Reaction ‐ Sequence Specific Oligonucleotide Probes). Allele transmission to diabetics and unaffected siblings was studied using the Transmission Disequilibrium Test (TDT). We found an increased transmission of DQB1 *02 (77.94% transmission, PTDT= 7.18 × 10−11) and DQB1*0302 (80.95% transmission, PTDT= 2.25 × 10−10) alleles to diabetics, indicating the diabetogenic effect of these alleles. Conversely, DQB1*0301, DQB1*0603, DQB1*0602, DQB1*0601 and DQB1*05 alleles are protective, being significantly less transmitted to diabetics. In conclusion, our results confirmed the strong effect of HLA‐DQB1 alleles on diabetes risk In Romania, with some characteristics which can contribute to the low incidence of T1DM in this country.

Testing the possible negative association of type 1 diabetes and atopic disease by analysis of the interleukin 4 receptor gene

Variations in the interleukin 4 receptor A (IL4RA) gene have been reported to be associated with atopy, asthma, and allergy, which may occur less frequently in subjects with type 1 diabetes (T1D). Since atopy shows a humoral immune reactivity pattern, and T1D results from a cellular (T lymphocyte) response, we hypothesised that alleles predisposing to atopy could be protective for T1D and transmitted less often than the expected 50% from heterozygous parents to offspring with T1D. We genotyped seven exonic single nucleotide polymorphisms (SNPs) and the –3223 C>T SNP in the putative promoter region of IL4RA in up to 3475 T1D families, including 1244 Finnish T1D families. Only the −3223 C>T SNP showed evidence of negative association (P=0.014). There was some evidence for an interaction between −3233 C>T and the T1D locus IDDM2 in the insulin gene region (P=0.001 in the combined and P=0.02 in the Finnish data set). We, therefore, cannot rule out a genetic effect of IL4RA in T1D, but it is not a major one.