349 research outputs found

    CYP2C19 Genotype Prevalence and Association With Recurrent Myocardial Infarction in British–South Asians Treated With Clopidogrel

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    BACKGROUND: Cytochrome P450 family 2 subfamily C member 19 (CYP2C19) is a hepatic enzyme involved in the metabolism of clopidogrel from a prodrug to its active metabolite. Prior studies of genetic polymorphisms in CYP2C19 and their relationship with clinical efficacy have not included South Asian populations. OBJECTIVES: The objective of this study was to assess prevalence of common CYP2C19 genotype polymorphisms in a British-South Asian population and correlate these with recurrent myocardial infarction risk in participants prescribed clopidogrel. METHODS: The Genes & Health cohort of British Bangladeshi and Pakistani ancestry participants were studied. CYP2C19 diplotypes were assessed using array data. Multivariable logistic regression was used to test for association between genetically inferred CYP2C19 metabolizer status and recurrent myocardial infarction, controlling for known cardiovascular disease risk factors, percutaneous coronary intervention, age, sex, and population stratification. RESULTS: Genes & Health cohort participants (N = 44,396) have a high prevalence (57%) of intermediate or poor CYP2C19 metabolizers, with at least 1 loss-of-function CYP2C19 allele. The prevalence of poor metabolizers carrying 2 CYP2C19 loss-of-function alleles is 13%, which is higher than that in previously studied European (2.4%) and Central/South Asian populations (8.2%). Sixty-nine percent of the cohort who were diagnosed with an acute myocardial infarction were prescribed clopidogrel. Poor metabolizers were significantly more likely to have a recurrent myocardial infarction (OR: 3.1; P = 0.019). CONCLUSIONS: A pharmacogenomic-driven approach to clopidogrel prescribing has the potential to impact significantly on clinical management and outcomes in individuals of Bangladeshi and Pakistani ancestry

    Inference of disease associations with unmeasured genetic variants by combining results from genome-wide association studies with linkage disequilibrium patterns in a reference data set

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    Results from whole-genome association studies of many common diseases are now available. Increasingly, these are being incorporated into meta-analyses to increase the power to detect weak associations with measured single-nucleotide polymorphisms (SNPs). Imputation of genotypes at unmeasured loci has been widely applied using patterns of linkage disequilibrium (LD) observed in the HapMap panels, but there is a need for alternative methods that can utilize the pooled effect estimates from meta-analyses and explore possible associations with SNPs and haplotypes that are not included in HapMap

    Effective detection of human leukocyte antigen risk alleles in celiac disease using tag single nucleotide polymorphisms.

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    Background: The HLA genes, located in the MHC region on chromosome 6p21.3, play an important role in many autoimmune disorders, such as celiac disease (CD), type 1 diabetes (T1D), rheumatoid arthritis, multiple sclerosis, psoriasis and others. Known HLA variants that confer risk to CD, for example, include DQA1*05/DQB1*02 (DQ2.5) and DQA1*03/ DQB1*0302 (DQ8). To diagnose the majority of CD patients and to study disease susceptibility and progression, typing these strongly associated HLA risk factors is of utmost importance. However, current genotyping methods for HLA risk factors involve many reactions, and are complicated and expensive. We sought a simple experimental approach using tagging SNPs that predict the CD-associated HLA risk factors. Methodology: Our tagging approach exploits linkage disequilibrium between single nucleotide polymorphism (SNPs) and the CD-associated HLA risk factors DQ2.5 and DQ8 that indicate direct risk, and DQA1*0201/DQB1*0202 (DQ2.2) and DQA1*0505/DQB1*0301 (DQ7) that attribute to the risk of DQ2.5 to CD. To evaluate the predictive power of this approach, we performed an empirical comparison of the predicted DQ types, based on these six tag SNPs, with those executed with current validated laboratory typing methods of the HLA-DQA1 and -DQB1 genes in three large cohorts. The results were validated in three European celiac populations. Conclusion: Using this method, only six SNPs were needed to predict the risk types carried by .95% of CD patients. We determined that for this tagging approach the sensitivity was .0.991, specificity .0.996 and the predictive value .0.948. Our results show that this tag SNP method is very accurate an

    Complex nature of SNP genotype effects on gene expression in primary human leucocytes

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    <p>Abstract</p> <p>Background</p> <p>Genome wide association studies have been hugely successful in identifying disease risk variants, yet most variants do not lead to coding changes and how variants influence biological function is usually unknown.</p> <p>Methods</p> <p>We correlated gene expression and genetic variation in untouched primary leucocytes (n = 110) from individuals with celiac disease – a common condition with multiple risk variants identified. We compared our observations with an EBV-transformed HapMap B cell line dataset (n = 90), and performed a meta-analysis to increase power to detect non-tissue specific effects.</p> <p>Results</p> <p>In celiac peripheral blood, 2,315 SNP variants influenced gene expression at 765 different transcripts (< 250 kb from SNP, at FDR = 0.05, <it>cis </it>expression quantitative trait loci, eQTLs). 135 of the detected SNP-probe effects (reflecting 51 unique probes) were also detected in a HapMap B cell line published dataset, all with effects in the same allelic direction. Overall gene expression differences within the two datasets predominantly explain the limited overlap in observed <it>cis</it>-eQTLs. Celiac associated risk variants from two regions, containing genes <it>IL18RAP </it>and <it>CCR3</it>, showed significant <it>cis </it>genotype-expression correlations in the peripheral blood but not in the B cell line datasets. We identified 14 genes where a SNP affected the expression of different probes within the same gene, but in opposite allelic directions. By incorporating genetic variation in co-expression analyses, functional relationships between genes can be more significantly detected.</p> <p>Conclusion</p> <p>In conclusion, the complex nature of genotypic effects in human populations makes the use of a relevant tissue, large datasets, and analysis of different exons essential to enable the identification of the function for many genetic risk variants in common diseases.</p
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