14 research outputs found

    Influence of human leukocyte antigen (HLA) alleles and killer cell immunoglobulin-like receptors (KIR) types on heparin-induced thrombocytopenia (HIT)

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    Objectives Heparin-induced thrombocytopenia (HIT) is an unpredictable, life-threatening, immune-mediated reaction to heparin. Variation in human leukocyte antigen (HLA) genes is now used to prevent immune-mediated adverse drug reactions. Combinations of HLA alleles and killer cell immunoglobulin-like receptors (KIR) are associated with multiple autoimmune diseases and infections. The objective of this study is to evaluate the association of HLA alleles and KIR types, alone or in the presence of different HLA ligands, with HIT.\ud Methods HIT cases and heparin-exposed controls were identified in BioVU, an electronic health record coupled to a DNA biobank. HLA sequencing and KIR type imputation using Illumina® OMNI-Quad data were performed. Odds ratios for HLA alleles and KIR types and HLA*KIR interactions using conditional logistic regressions were determined in the overall population and by race/ethnicity. Analysis was restricted to KIR types and HLA alleles with a frequency greater than 0.01. P values for HLA and KIR association were corrected using a false discovery rate (FDR) q<0.05 and HLA*KIR interactions were considered significant at p<0.05. Results Sixty-five HIT cases and 350 matched controls were identified. No statistical differences in baseline characteristics were observed between cases and controls. The HLA-DRB3*01:01 allele was significantly associated with HIT in the overall population (odds ratio 2.81[1.57-5.02], p=2.1x10-4, q=0.02) and in individuals with European ancestry, independent of other alleles. No KIR types were associated with HIT, although a significant interaction was observed between KIR2DS5 and the HLA-C1 KIR binding group (p=0.03). Conclusions The HLA-DRB3*01:01 allele was identified as a potential risk factor for HIT. This class II HLA gene and allele represent biologically plausible candidates for influencing HIT pathogenesis. We found limited evidence of the role of KIR types in HIT pathogenesis. Replication and further study of the HLA-DRB3*01:01 association is necessary

    Immunopharmacogenomics: Mechanisms of HLA‐Associated Drug Reactions

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    The human leukocyte antigen (HLA) system is the most polymorphic in the human genome that has been associated with protection and predisposition to a broad array of infectious, autoimmune, and malignant diseases. More recently over the last two decades, HLA class I alleles have been strongly associated with T-cell-mediated drug hypersensitivity reactions. In the case of abacavir hypersensitivity and HLA-B*57:01, the 100% negative predictive value and low number needed to test to prevent a single case has led to a durable and effective global preprescription screening strategy. However, HLA associations are still undefined for most drugs clinically associated with different delayed drug hypersensitivity phenotypes, and an HLA association relevant to one population is not generalizable across ethnicities. Furthermore, while a specific risk HLA allele is necessary for drug-induced T-cell activation, it is not sufficient. The low and incomplete positive predictive value has hindered efforts at clinical implementation for many drugs but has provided the impetus to understand the mechanisms of HLA class I restricted T-cell-mediated drug hypersensitivity reactions. Current research has focused on defining the contribution of additional elements of the adaptive immune response and other genetic and ecologic risk factors that contribute to drug hypersensitivity risk. In this review we focus on new insights into immunological, pharmacological, and genetic mechanisms underpinning HLA-associated drug reactions and the implications for future translation into clinical care

    Identifying genetically driven clinical phenotypes using linear mixed models

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    We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q<0.05) phenotypes associated with HLA SNP variation and show that hypothyroidism is genetically correlated with Type I diabetes (rG=0.31, s.e. 0.12, P=0.003). We also report novel SNP associations for hypothyroidism near HLA-DQA1/HLA-DQB1 at rs6906021 (combined odds ratio (OR)=1.2 (95% confidence interval (CI): 1.1–1.2), P=9.8 × 10−11) and for polymyalgia rheumatica near C6orf10 at rs6910071 (OR=1.5 (95% CI: 1.3–1.6), P=1.3 × 10−10). Phenome-wide application of GLMMs identifies phenotypes with important genetic drivers, and focusing on these phenotypes can identify novel genetic associations

    Applications of Immunopharmacogenomics: Predicting, Preventing, and Understanding Immune-Mediated Adverse Drug Reactions

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    Adverse drug reactions (ADRs) are a significant health care burden. Immunemediated adverse drug reactions (IM-ADRs) are responsible for one-fifth of ADRs but contribute a disproportionately high amount of that burden due to their severity. Variation in human leukocyte antigen (HLA) genes has emerged as a potential preprescription screening strategy for the prevention of previously unpredictable IM-ADRs. Immunopharmacogenomics combines the disciplines of immunogenomics and pharmacogenomics and focuses on the effects of immune-specific variation on drug disposition and IM-ADRs. In this review, we present the latest evidence for HLA associations with IM-ADRs, ongoing research into biological mechanisms of IM-ADRs, and the translation of clinical actionable biomarkers for IM-ADRs, with a focus on T cell-mediated ADRs

    Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2C9 and HLA-B genotypes and phenytoin dosing: 2020 update

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    First published: 11 August 2020Phenytoin is an antiepileptic drug with a narrow therapeutic index and large inter-patient pharmacokinetic variability, partly due to genetic variation in CYP2C9. Furthermore, the variant allele HLA-B*15:02 is associated with an increased risk of Stevens-Johnson syndrome and toxic epidermal necrolysis in response to phenytoin treatment. We summarize evidence from the published literature supporting these associations and provide therapeutic recommendations for the use of phenytoin based on CYP2C9 and/or HLA-B genotypes (updates on cpicpgx.org).Jason H. Karnes, Allan E. Rettie, Andrew A. Somogyi, Rachel Huddart, Alison E. Fohner, Christine M. Formea ... et al

    Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants

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    Although many phenotypes have been associated with variants in human leukocyte antigen (HLA) genes, the full phenotypic impact of HLA variants across all diseases is unknown. We imputed HLA genomic variation from two populations of 28,839 and 8431 European ancestry individuals and tested association of HLA variation with 1368 phenotypes. A total of 104 four-digit and 92 two-digit HLA allele phenotype associations were significant in both discovery and replication cohorts, the strongest being HLA-DQB1*03:02 and type 1 diabetes. Four previously unidentified associations were identified across the spectrum of disease with two- and four-digit HLA alleles and 10 with nonsynonymous variants. Some conditions associated with multiple HLA variants and stronger associations with more severe disease manifestations were identified. A comprehensive, publicly available catalog of clinical phenotypes associated with HLA variation is provided. Examining HLA variant disease associations in this large data set allows comprehensive definition of disease associations to drive further mechanistic insights

    Racial, ethnic, and gender differences in obesity and body fat distribution: An All of Us Research Program demonstration project

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    Differences in obesity and body fat distribution across gender and race/ethnicity have been extensively described. We sought to replicate these differences and evaluate newly emerging data from the All of Us Research Program (AoU). We compared body mass index (BMI), waist circumference, and waist-to-hip ratio from the baseline physical examination, and alanine aminotransferase (ALT) from the electronic health record in up to 88,195 Non-Hispanic White (NHW), 40,770 Non-Hispanic Black (NHB), 35,640 Hispanic, and 5,648 Asian participants. We compared AoU sociodemographic variable distribution to National Health and Nutrition Examination Survey (NHANES) data and applied the pseudo-weighting method for adjusting selection biases of AoU recruitment. Our findings replicate previous observations with respect to gender differences in BMI. In particular, we replicate the large gender disparity in obesity rates among NHB participants, in which obesity and mean BMI are much higher in NHB women than NHB men (33.34 kg/m2 versus 28.40 kg/m2 respectively; p<2.22x10-308). The overall age-adjusted obesity prevalence in AoU participants is similar overall but lower than the prevalence found in NHANES for NHW participants. ALT was higher in men than women, and lower among NHB participants compared to other racial/ethnic groups, consistent with previous findings. Our data suggest consistency of AoU with national averages related to obesity and suggest this resource is likely to be a major source of scientific inquiry and discovery in diverse populations. © 2021 Karnes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
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