Biomedical subjects

Jeffrey C Barrett

Publications and source records attributed to Jeffrey C Barrett.

4 recordsLinked to original sources

PubMed2006

Association of PTPN22 haplotypes with Graves' disease.

CONTEXT: A recent study reported associations of a series of single nucleotide polymorphisms (SNPs) within PTPN22, including rs2476601, with rheumatoid arthritis. OBJECTIVE: Having previously reported significant association of the T allele of rs2476601 in a Graves' disease (GD) cohort, we sought to determine whether novel rheumatoid arthritis-associated SNPs were also contributing to susceptibility to GD. DESIGN: Case control and family-based studies of five PTPN22 tag SNPs were performed. SETTING: An United Kingdom academic department of medicine was the setting for the study. PATIENTS OR OTHER PARTICIPANTS: A total of 768 GD patients, 768 control subjects, and 313 families with autoimmune thyroid disease participated. MAIN OUTCOME MEASURE: Tests for association with disease were the main outcome measure. RESULTS: No association with disease of any of the individual SNPs and no correlation between genotype and clinical phenotype were seen. However, haplotype analysis of the SNP markers with addition of rs2476601 did reveal a strong association of a haplotype containing the T allele, in both the case control (chi2 = 29.13; P = 6.77 x 10(-8)) and family data sets (chi2 = 5.24; P = 0.02). Furthermore, a novel protective effect of a haplotype containing all six SNPs was observed (chi2 = 17.02; P = 3.7 x 10(-5)). CONCLUSIONS: These data suggest that the association of SNPs within the PTPN22 region differs between autoimmune diseases, occurring individually and/or as part of a haplotype, indicating that the mechanisms by which PTPN22 confers susceptibility to GD may, in part, be disease specific.

Arthritis, Rheumatoid↗

PubMed2006

Evaluating coverage of genome-wide association studies.

Genome-wide association studies involving hundreds of thousands of SNPs in thousands of cases and controls are now underway. The first of many analytical challenges in these studies involves the choice of SNPs to genotype. It is not practical to construct a different panel of tag SNPs for each study, so the first generation of genome-wide scans will use predefined, commercially available marker panels, which will in part dictate their success or failure. We compare different approaches in use today, and show that although many of them provide substantial coverage of common variation in non-African populations, the precise extent is strongly dependent on the frequencies of alleles of interest and on specific considerations of study design. Overall, despite substantial differences in genotyping technologies, marker selection strategies and number of markers assayed, the first-generation high-throughput platforms all offer similar levels of genome coverage.

Case-Control Studies↗

PubMed2006

Biases and reconciliation in estimates of linkage disequilibrium in the human genome.

Genetic association studies of common disease often rely on linkage disequilibrium (LD) along the human genome and in the population under study. Although understanding the characteristics of this correlation has been the focus of many large-scale surveys (culminating in genomewide haplotype maps), the results of different studies have yielded wide-ranging estimates. Since understanding these differences (and whether they can be reconciled) has important implications for whole-genome association studies, in this article we dissect biases in these estimations that are due to known aspects of study design and analytic methodology. In particular, we document in the empirical data that the long-known complicating effects of allele frequency, marker density, and sample size largely reconcile all large-scale surveys. Two exceptions are an underappraisal of redundancy among single-nucleotide polymorphisms (SNPs) when evaluation is limited to short regions (as in candidate-gene resequencing studies) and an inflation in the extent of LD in HapMap phase I, which is likely due to oversampling of specific haplotypes in the creation of the public SNP map. Understanding these factors can guide the understanding of empirical LD surveys and has implications for genetic association studies.

Genome, Human↗

PubMed2006

Common deletion polymorphisms in the human genome.

The locations and properties of common deletion variants in the human genome are largely unknown. We describe a systematic method for using dense SNP genotype data to discover deletions and its application to data from the International HapMap Consortium to characterize and catalogue segregating deletion variants across the human genome. We identified 541 deletion variants (94% novel) ranging from 1 kb to 745 kb in size; 278 of these variants were observed in multiple, unrelated individuals, 120 in the homozygous state. The coding exons of ten expressed genes were found to be commonly deleted, including multiple genes with roles in sex steroid metabolism, olfaction and drug response. These common deletion polymorphisms typically represent ancestral mutations that are in linkage disequilibrium with nearby SNPs, meaning that their association to disease can often be evaluated in the course of SNP-based whole-genome association studies.

Databases, Genetic↗