Analysis of the high affinity IgE receptor genes reveals epistatic effects of FCER1A variants on eczema risk

The Associations between Exposure to Multiple Heavy Metals and Total Immunoglobulin E in U.S. Adults

Immunoglobulin E (IgE) is a type of immunoglobulin, and elevated serum total IgE is often present in allergic diseases. Exposure to environmental heavy metals has been markedly linked to allergic diseases, leading to elevated total IgE levels. However, studies concerning the effects of multiple metal exposures on total IgE levels are limited. Therefore, the current study seeks to explore the correlation between heavy-metal co-exposure and total IgE levels based on the National Health and Nutrition Examination Survey (NHANES, 2005-2006). Participants possessed complete data on total IgE levels, 11 urinary metal concentrations and other covariates. The correlations between 11 metals and total IgE levels were analyzed using multiple linear regression, and total IgE levels were a continuous variable. Total IgE levels exceeding 150 kU/L were considered sensitized. Binary logistic regression analyses were employed to assess the correlation between metal exposure and the occurrence of an allergic state. Then, the association between co-exposure to the 11 metals and total IgE levels or the occurrence of sensitization status was further analyzed by Bayesian kernel machine regression (BKMR), a multi-contaminant model. There were 1429 adults with complete data included. Based on the median concentration, molybdenum (Mo) had the highest concentration (46.60 μg/L), followed by cesium (Cs), barium (Ba), lead (Pb), and mercury (Hg). And the median (interquartile range) for total IgE levels was 43.7 (17.3, 126.0) kU/L. Multiple linear regression results showed that Pb was significantly and positively associated with total IgE levels (β = 0.165; 95% CI: 0.046, 0.284). Binary logistic regression showed a significant positive correlation between urinary Pb (OR: 1.258; 95% CI: 1.052, 1.510) and tungsten (W) (OR: 1.251; 95% CI: 1.082, 1.447). Importantly, the BKMR model found a positive correlation between combined-metal exposure and total IgE levels and the occurrence of sensitization status. The mixed heavy-metal exposure was associated with increased total IgE levels, and this association may be driven primarily by the exposure of Pb and W. This study provides new insights into the relationship between heavy-metal exposure and allergic diseases. More research is needed to confirm these findings.

Transfluthrin is Associated with High Susceptibility to Asthma in Children with Promoter Variants of Beta Chain of High-Affinity Receptor IgE and Tumour Necrosis Factors-α Genes

This study investigates the genetic variations in FcεR1β-109 C/T (rs512555) and TNF-α-308 G/A (rs1800629) genes and examines whether the mosquito repellent transfluthrin (TFT) modifies the risk for asthmatic children. A case-control study was conducted involving 130 asthmatic children and 123 age-sex matched controls. Differential leukocyte counts, IgE, and hs-CRP levels were estimated using a five-part haematology analyzer and Beckman Coulter (AU480), respectively. Genetic variations in FcεR1β-109 and TNF-α-308 were analysed using restriction fragment length polymorphism. Serum TFT levels were measured using gas chromatography-tandem mass spectrometry. Asthmatic children had significantly increased total leukocyte, neutrophil, lymphocyte, eosinophil, and basophil counts (p < 0.0001), while their monocyte counts were lower compared to controls (p < 0.0001). TFT levels were higher in asthmatic children (1.38 ± 0.91 vs. control 0.69 ± 0.41µg/L, p < 0.0001), which predominantly induced wheezing. Elevated TFT levels were associated with an increased risk of childhood asthma (OR: 3.08, p < 0.0001). Children with the FcεRIβ TT (OR: 2.39, p < 0.017) and TNF-α GG genotypes (OR: 7.17, p < 0.0001) were more susceptible to asthma. TFT synergistically enhanced the risk of asthma in both FcεRIβ-109 TT (OR: 5.3, p = 0.001) and TNF-α-308 GG (OR: 17.18, p < 0.0001) genotypes. TFT levels were correlated with IgE (r = 0.363; p = 0.006), hs-CRP (r = 0.324; p = 0.049) and eosinophil (r = 0.300; p = 0.038), respectively. IgE and eosinophils were correlated (r = 0.599, p = 0.001) in the FcεRIβ TT genotype-carrying asthmatic children. Similarly, neutrophils and hs-CRP were correlated (r = 0.768, p < 0.0001) in asthmatic children with TNF-α GG genotype. The risk of asthma is inherently higher in children with FcεRIβ TT and TNF-α GG variants. TFT exposure amplifies the risk of asthma in children among all the subgenotypes of both genes. TFT influences IgE and eosinophil in FcεRIβ TT genotype while it influences neutrophils and hs-CRP in TNF-α GG genotypes.

FCER1G Gene Hypomethylation in Patients with Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease that, when improperly treated, leads to disability in patients. Various factors that may cause the development and activity of RA are being considered. Epigenetic factors are also receiving increasing attention. In our study, we analyzed the association between FCER1G gene methylation and RA activity. We conducted our study in 50 RA patients and 24 controls. The patients were divided into two groups in terms of high disease activity and remission. Quantitative real-time methylation-specific PCR was used to analyze the methylation status of the investigated genes. We observed that RA patients have lower levels of methylation of the FCER1G gene compared to controls, but we did not find any difference in the methylation status of this gene between patients with high disease activity and remission. The results of this study suggest that FCER1G gene methylation may be a new potential epigenetic marker of RA that is independent of disease activity.

Identification of FCER1G related to Activated Memory CD4+ T Cells Infiltration by Gene Co-expression Network and Construction of a Risk Prediction Module in Diffuse Large B-Cell Lymphoma

Diffuse large B cell lymphoma (DLBCL) is a group of biologically heterogeneous tumors with different prognoses. The tumor microenvironment plays a vital role in the tumorigenesis and development of DLBCL, and activated memory CD4⁺ T cells are an essential component of immunological cells in the lymphoma microenvironment. So far, there are few reports about activated memory CD4+T cells infiltration and related genes in the DLBCL tumor microenvironment. This study obtained the mRNA expression profile information of the testing GSE87371 dataset and another six validation datasets (GSE53786, GSE181063, GSE10846, GSE32918, GSE32018, GSE9327, GSE3892, TCGA-DLBC) from the GEO and TCGA databases. Weighted Gene Co-expression Network Analysis (WGCNA) screened gene module associated with activated memory CD4⁺ T cells infiltration. CIBERSORT and TIMER (immune cells infiltrating estimation analysis tools) were used to identify the relationship between activated memory CD4⁺ T cells and genes associated with immune infiltrating cells in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) built the risk prediction model and verified it using nomogram and Kaplan-Meier analysis. Further functional characterization includes Gene Ontology, KEGG pathway analysis and Gene Set Enrichment Analysis (GSEA) to investigate the role and underlying mechanisms of these genes. These results suggest that the expression of FCER1G can reflect the invasion of activated memory CD4⁺ T cells in DLBCL, which provides a new idea for studying the tumor microenvironment and may become a potential predictive biomarker for the assessment of DLBCL.

Prognostic and predictive value of FCER1G in glioma outcomes and response to immunotherapy

PURPOSE

Glioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma.

METHODS

The expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups.

RESULTS

FCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P < 0.001), whereas age (HR = 1.26, 95 % CI  1.04-1.52), grade (HR = 2.75, 95 % CI 2.06-3.68), tumor recurrence (HR = 2.17, 95 % CI  1.81-2.62), IDH mutant (HR = 2.46, 95 % CI 1.97-3.01) and chemotherapeutic status (HR = 1.4, 95 % CI  1.20-1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients.

CONCLUSIONS

This study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy.

Genetic Modifiers and Rare Mendelian Disease

Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it is evident that no genetic variant acts alone. In other words, some other variants in the genome (genetic modifiers) may alleviate (suppress) or exacerbate (enhance) the severity of the disease, resulting in the variability of phenotypic outcomes. Thus, to truly understand the disease, we need to consider how the disease-causing variants interact with the rest of the genome in an individual. Here, we review the current state-of-the-field in the identification of genetic modifiers in rare Mendelian diseases and discuss the potential for future approaches that could bridge the existing gap.

Enhanced expression of FCER1G predicts positive prognosis in multiple myeloma

Background: Multiple myeloma (MM) is the second most common hematologic malignancy worldwide and does not have sufficient prognostic indicators. FCER1G (Fc fragment Of IgE receptor Ig) is located on chromosome 1q23.3 and is involved in the innate immunity. Early studies have shown that FCER1G participates in many immune-related pathways encompassing multiple cell types. Meanwhile, it is associated with many malignancies. However, the relationship between MM and FCER1G has not been studied.

Methods: In this study, we integrated nine independent gene expression omnibus (GEO) datasets and analyzed the associations of FCER1G expression and myeloma progression, ISS stage, 1q21 amplification and survival in 2296 myeloma patients and 48 healthy donors.

Results: The expression of FCER1G showed a decreasing trend with the advance of myeloma. As ISS stage and 1q21 amplification level increased, the expression of FCER1G decreased (P = 0.0012 and 0.0036, respectively). MM patients with high FCER1G expression consistently had longer EFS and OS across three large sample datasets (EFS: P = 0.0057, 0.0049, OS: P = 0.0014, 0.00065, 0.0019 and 0.0029, respectively). Meanwhile, univariate and multivariate analysis indicated that high FCER1G expression was an independent favorable prognostic factor for EFS and OS in MM patients (EFS: P = 0.006, 0.027, OS: P =0.002,0.025, respectively).

Conclusions: The expression level of FCER1G negatively correlated with myeloma progression, and high FCER1G expression may be applied as a favorable biomarker in MM patients.

The Genetics and Epigenetics of Atopic Dermatitis-Filaggrin and Other Polymorphisms

Atopic dermatitis (AD) is a chronic inflammatory skin disease caused by a combination of genetic and environmental factors. Genetic evidences depict a complex network comprising by epidermal barrier dysfunctions and dysregulation of innate and adaptive immunity in the pathogenesis of AD. Mutations in the human filaggrin gene (FLG) are the most significant and well-replicated genetic mutation associated with AD, and other mutations associated with epidermal barriers such as SPINK5, FLG-2, SPRR3, and CLDN1 have all been linked to AD. Gene variants may also contribute to the abnormal innate and adaptive responses found in AD, including mutations in PRRs and AMPs, TSLP and TSLPR, IL-1 family cytokines and receptors genes, vitamin D pathway genes, FCER1A, and Th2 and other cytokines genes. GWAS and Immunochip analysis have identified a total of 19 susceptibility loci for AD. Candidate genes at these susceptibility loci identified by GWAS and Immunochip analysis also suggest roles for epidermal barrier functions, innate and adaptive immunity, interleukin-1 family signaling, regulatory T cells, the vitamin D pathway, and the nerve growth factor pathway in the pathogenesis of AD. Increasing evidences show the modern lifestyle (i.e., the hygiene hypothesis, Western diet) and other environmental factors such as pollution and environmental tobacco smoke (ETS) lead to the increasing prevalence of AD with the development of industrialization. Epigenetic alterations in response to these environmental factors, including DNA methylation and microRNA related to immune system and skin barriers, have been found to contribute to the pathogenesis of AD. Genetic variants and epigenetic alteration might be the key tools for the molecular taxonomy of AD and provide the background for the personalized management.

Identification of Differentially Expressed Genes in Kawasaki Disease Patients as Potential Biomarkers for IVIG Sensitivity by Bioinformatics Analysis

Kawasaki disease (KD) is a leading cause of acquired heart disease predominantly affecting infants and young children. Intravenous immunoglobulin (IVIG) is applied as the most favorable treatment against KD, but IVIG resistant remains exist. Although several clinical scoring systems have been developed to identify children at highest risk of IVIG resistance, there is a need to identify sufficiently sensitive biomarkers for IVIG treatment. Some differentially expressed genes (DEGs) could be the promising potential biomarkers for IVIG-related sensitivity diagnosis. We employed a systematic and integrative bioinformatics framework to identify such kind of genes. The performance of the candidate genes was evaluated by hierarchical clustering, ROC analysis and literature mining. By analyzing three datasets of KD patients, 34 DEGs of the three groups have been found to be associated with IVIG-related sensitivity. A module of 12 genes could predict resistant group patients with high accuracy, and a module of ten genes could predict responsive group patients effectively with accuracy of 96 %. And three of them are most likely to serve as drug targets or diagnostic biomarkers in the future. Compared with unsupervised hierarchical clustering analysis, our modules could distinct IVIG-resistant patients efficiently. Two groups of DEGs could predict IVIG-related sensitivity with high accuracy, which are potential biomarkers for the clinical diagnosis and prediction of IVIG treatment response in KD patients, improving the prognosis of patients.

An Exploratory Pilot Study of Genetic Marker for IgE-Mediated Allergic Diseases with Expressions of FcεR1α and Cε

The high affinity immunoglobulin E (IgE) receptor-FcεR1 is mainly expressed on the surface of effector cells. Cross-linking of IgE Abs bound to FcεR1 by multi-valent antigens can induce the activation of these cells and the secretion of inflammatory mediators. Since FcεR1 plays a central role in the induction and maintenance of allergic responses, this study aimed to investigate the association of FcεR1 with the allergic phenotype of Cε expression and cytokine and histamine release from peripheral leukocytes. Peripheral leukocytes from 67 allergic and 50 non-allergic subjects were used for genotyping analysis. Peripheral mononuclear cells (PBMCs) were used for Cε expression and ELISpot analysis, while polymorphonuclear cells (PMNs) were used for histamine release. The association between genotype polymorphism of the FcεR1α promoter region (rs2427827 and rs2251746) and allergic features of Cε expression and histamine were analyzed, and their effects on leukocytes function were compared with wild type. The genotype polymorphisms of FcεR1α promoter region with CT and TT in rs2427827 and TC in rs2251746 were significantly higher in allergic patients than in non-allergic controls. Patients with single nucleotide polymorphism (SNP) of FcεR1α promoter region had high levels of total IgE, mite-specific Der p 2 (Group 2 allergen of Dermatophagoides pteronyssinus)-specific IgE and IgE secretion B cells. The mRNA expression of FcεR1α was significantly increased after Der p2 stimulation in PBMCs with SNPs of the FcεR1α promoter region. Despite the increased Cε mRNA expression in PBMCs and histamine release from PMNs and the up-regulated mRNA expression of interleukin (IL)-6 and IL-8 secretions after Der p2 stimulation, there was no statistically significant difference between SNPs of the FcεR1α promoter region and the wild type. SNPs of FcεR1α promoter region were associated with IgE expression, IgE producing B cells, and increased Der p2-induced FcεR1α mRNA expression. These SNPs may be used as a disease marker for IgE-mediated allergic inflammation caused by Dermatophagoides pteronyssinus.

Different FCER1A polymorphisms influence IgE levels in asthmatics and non-asthmatics

BACKGROUND

Recently, three genome-wide association studies (GWAS) demonstrated FCER1A, the gene encoding a ligand-binding subunit of the high-affinity IgE receptor, to be a major susceptibility locus for serum IgE levels. The top association signal differed between the two studies from the general population and the one based on an asthma case-control design. In this study, we investigated whether different FCER1A polymorphisms are associated with total serum IgE in the general population and asthmatics specifically.

METHODS

Nineteen polymorphisms were studied in FCER1A based on a detailed literature search and a tagging approach. Polymorphisms were genotyped by the Illumina HumanHap300Chip (6 polymorphisms) or MALDI-TOF MS (13 polymorphisms) in at least 1303 children (651 asthmatics) derived from the German International Study of Asthma and Allergies in Childhood II and Multicentre Asthma Genetics in Childhood Study.

RESULTS

Similar to two population-based GWAS, the peak association with total serum IgE was observed for SNPs rs2511211, rs2427837, and rs2251746 (mean r(2) > 0.8), with the lowest p-value of 4.37 × 10(-6). The same 3 polymorphisms showed the strongest association in non-asthmatics (lowest p = 0.0003). While these polymorphisms were also associated with total serum IgE in asthmatics (lowest p = 0.003), additional polymorphisms (rs3845625, rs7522607, and rs2427829) demonstrated associations with total serum IgE in asthmatics only (lowest p = 0.01).

CONCLUSIONS

These data suggest that FCER1A polymorphisms not only drive IgE levels in the general population but that specific polymorphisms may also influence IgE in association with asthma, suggesting that disease-specific mechanisms in IgE regulation exist.

Genome-wide scan on total serum IgE levels identifies no common variants in a healthy Chinese male population

Immunoglobulin E (IgE) provides important information on the humoral immune status, and the IgE level is routinely detected in clinical practice. There are many diseases associated with IgE, such as atopic disease, autoimmune diseases, and so on. IgE is a genetically complex trait, but comprehensive genetic assessment of the variability in serum IgE levels is lacking. Previous genome-wide association studies (GWAS) on total serum IgE levels have identified FCER1A as the susceptibility locus; however, the candidate gene association study in southern Chinese patients reported no association. Given the genetic difference in different populations, we firstly conducted this two-stage GWAS in a Chinese population of 3,495 men, including 1,999 unrelated subjects in the first stage and 1,496 independent individuals replicated in the second stage. In the first stage, we totally identified three single nucleotide polymorphisms (SNPs) which reached a P value of 1.0 × 10⁻⁵. Rs17090302 on chromosome 3 and Rs28708846 on chromosome 13 are intergenic. Rs432085 from chromosome 3p28 is located in the gene CCDC50. When the two-stage data was combined, none of the SNPs reached the genome-wide significant level. Collectively, we did not identify novel loci associated with the serum IgE level in Chinese males, but we hypothesized that CCDC50 was a candidate gene in regulation on IgE level.

Current concepts of IgE regulation and impact of genetic determinants

Immunoglobulin E (IgE) mediated immune responses seem to be directed against parasites and neoplasms, but are best known for their involvement in allergies. The IgE network is tightly controlled at different levels as outlined in this review. Genetic determinants were suspected to influence IgE regulation and IgE levels considerably for many years. Linkage and candidate gene studies suggested a number of loci and genes to correlate with total serum IgE levels, and recently genome-wide association studies (GWAS) provided the power to identify genetic determinants for total serum IgE levels: 1q23 (FCER1A), 5q31 (RAD50, IL13, IL4), 12q13 (STAT6), 6p21.3 (HLA-DRB1) and 16p12 (IL4R, IL21R). In this review, we analyse the potential role of these GWAS hits in the IgE network and suggest mechanisms of how genes and genetic variants in these loci may influence IgE regulation.

Atopic dermatitis 2.0: from the clinical phenotype to the molecular taxonomy and stratified medicine

Atopic dermatitis (AD) is a paradigmatic inflammatory chronic skin disease. As for other chronic skin diseases, (i) the spectrum of the clinical phenotype and severity as well as (ii) the genetic background and (iii) the underlying mechanisms strongly suggest a high degree of pathophysiological heterogeneity yet leading to a similar clinical pattern, that is, the eczematous skin lesion, but showing distinct progression patterns. This review suggests to exploit the recent knowledge about AD for a novel approach proposing a tentative first molecular taxonomy of this disease based on the genotype and endophenotype. The consequences in terms of personalized prevention and management are delineated.

Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction

Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic effects. These may hamper the identification of genuine epistasis. If lower-order genetic effects contribute to the genetic variance of a trait, identified statistical interactions may simply be due to a signal boost of these effects. In this study, we restrict attention to quantitative traits and bi-allelic SNPs as genetic markers. Moreover, our interaction study focuses on 2-way SNP-SNP interactions. Via simulations, we assess the performance of different corrective measures for lower-order genetic effects in Model-Based Multifactor Dimensionality Reduction epistasis detection, using additive and co-dominant coding schemes. Performance is evaluated in terms of power and familywise error rate. Our simulations indicate that empirical power estimates are reduced with correction of lower-order effects, likewise familywise error rates. Easy-to-use automatic SNP selection procedures, SNP selection based on “top” findings, or SNP selection based on p-value criterion for interesting main effects result in reduced power but also almost zero false positive rates. Always accounting for main effects in the SNP-SNP pair under investigation during Model-Based Multifactor Dimensionality Reduction analysis adequately controls false positive epistasis findings. This is particularly true when adopting a co-dominant corrective coding scheme. In conclusion, automatic search procedures to identify lower-order effects to correct for during epistasis screening should be avoided. The same is true for procedures that adjust for lower-order effects prior to Model-Based Multifactor Dimensionality Reduction and involve using residuals as the new trait. We advocate using “on-the-fly” lower-order effects adjusting when screening for SNP-SNP interactions using Model-Based Multifactor Dimensionality Reduction analysis.

Analysis of gene-gene interactions

The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.

Genetics of atopic dermatitis

Atopic dermatitis (AD) is a multifactorial disease, with a strong genetic predisposition. Genome-wide studies as well as candidate gene studies revealed several susceptibility loci. Since the observation of a strong association of "loss of function" mutations in the filaggrin gene with AD, the epidermal barrier was rediscovered as important pathophysiological co-factor of this disease.

Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data

Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the power is lowest in the presence of phenotypic mixtures or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical power estimates reduce even further with main effects corrections, but at the same time, false-positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis.

Genome-wide association studies on IgE regulation: are genetics of IgE also genetics of atopic disease?

PURPOSE OF REVIEW

Total IgE levels are considered a useful endophenotype for studying the genetics of atopic diseases. However, the role and significance of genetic factors influencing IgE regulation for atopic diseases as endpoints is unclear.

RECENT FINDINGS

Recently, genome-wide association studies (GWASs) have been applied to atopic traits with considerable success. A total of seven published GWASs on asthma, one GWAS on eczema, and one GWAS on total IgE have reported 11 new loci. Most of these loci appear to be trait-specific. A notable exception is the Th2 cytokine cluster, where genetic variation seems to be relevant across atopic phenotypes.

SUMMARY

GWASs have identified several novel asthma and eczema loci as well as novel loci for IgE levels. In this review, we evaluate the interrelation between these loci and summarize to which degree recent findings on IgE reflect genetic vulnerability for atopic disease.

SNPs in the FCER1A gene region show no association with allergic rhinitis in a Han Chinese population

Background

Immunoglobulin E (IgE) is a central player in the allergic response, and raised total IgE levels are considered as an indicator of atopy or potential development of atopy. A recent genome-wide scan in a German population-based cohort of adults identified the gene encoding the alpha chain of the high affinity receptor for IgE (FCER1A) as a susceptibility locus influencing total serum IgE levels. The aim of this study was to investigate whether the polymorphisms in the FCER1A gene are associated with allergic rhinitis (AR) in a Han Chinese population.

Methodology/Principal Findings

A population of 378 patients with AR and 288 healthy controls was studied. Precise phenotyping of patients was accomplished by means of a questionnaire and clinical examination. Blood was drawn for DNA extraction and total serum immunoglobulin E (IgE) measurement. A total of 16 single nucleotide polymorphisms (SNPs) in FCER1A were selected and individually genotyped. None of the SNPs in the FCER1A showed an association with AR. Similarly, the lack of association was also evident in subgroup analysis for the presence of different allergen sensitivities. None of the selected SNPs in FCER1A was associated with total IgE level.

Conclusions

Although FCER1A presents itself as a good candidate for contributing to total serum IgE, this study failed to find an association between SNPs in the FCER1A gene region and IgE level or AR susceptibility.

Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise

Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.

mbmdr: an R package for exploring gene-gene interactions associated with binary or quantitative traits

SUMMARY

We describe mbmdr, an R package for implementing the model-based multifactor dimensionality reduction (MB-MDR) method. MB-MDR has been proposed by Calle et al. as a dimension reduction method for exploring gene-gene interactions in case-control association studies. It is an extension of the popular multifactor dimensionality reduction (MDR) method of Ritchie et al. allowing a more flexible definition of risk cells. In MB-MDR, risk categories are defined using a regression model which allows adjustment for covariates and main effects and, in addition to the classical low risk and high risk categories, MB-MDR considers a third category of indeterminate or not informative cells. An important improvement added to the current mbmdr algorithm with respect to the original MB-MDR formulation in Calle et al. and also to the classical MDR approach, is the extension of the methodology to different outcome types. While MB-MDR was initially proposed for binary traits in the context of case-control studies, the mbmdr package provides options to analyze both binary or quantitative traits for unrelated individuals.

AVAILABILITY

http://cran.r-project.org/.