A highly sensitive and specific genetic marker to diagnose aspirin-exacerbated respiratory disease using a genome-wide association study

Lung Function and Asthma Clinical Control in N-ERD Patients, Three-Year Follow-Up in the Context of Real-World Evidence

Purpose

To describe the lung function and clinical control of asthma in patients with N-ERD during three years of medical follow-up using GINA guidelines.

Methods

We evaluated 75 N-ERD and 68 asthma patients (AG). Clinical control, lung function, and asthma treatment were evaluated according to GINA-2014. We compared all variables at baseline and one, two, and three years after treatment.

Results

At baseline, the N-ERD group had better basal lung function (LF) than the AG group (p<0.01), and the AG group used higher doses of inhaled corticosteroids than the N-ERD group (52.4% vs 30.5%, p=0.01) and short-term oral corticosteroid (OCS) use (52.4% vs 30.5%, p<0.01). Instead, N-ERD patients needed more use of leukotriene receptor antagonists (LTRA) (29.3% vs 5.9%, p<0.01). This group had better clinical control than the AG group (62.1% vs 34.1%, p<0.01). During the medical follow-up, the LF of the N-ERD group remained at normal values; however, these parameters improved in AG from one year (p<0.01). Likewise, there was a diminished use of high doses of ICS (52.4% vs 33%, p<0.05) and short-term OCS (67.6% vs 20.6%, p<0.01) in asthma patients. However, N-ERD patients still needed more use of LTRAs (p<0.02) during the study. In this context, one-third of N-ERD patients had to use a combination of two drugs to maintain this control. From the second year on, clinical control of asthma was similar in both groups (p>0.05).

Conclusion

According to GINA guidelines, only one-third of patients with N-ERD can gradually achieve adequate lung function and good asthma control with a high ICS dosage. Only a very small portion of patients will require the continued use of a second medication as an LTRA to keep their asthma under control.

Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients

BACKGROUND AND PURPOSE

Late radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria.

MATERIALS AND METHODS

We applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria.

RESULTS

The PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract.

CONCLUSION

The risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.

Main Polymorphisms in Aspirin-Exacerbated Respiratory Disease

Aspirin exacerbated respiratory disease (AERD) is a condition caused by increased bronchoconstriction in people with asthma after taking aspirin or another NSAID. Molecular analysis of the human genome has opened up new perspectives on human polymorphisms and disease. This study was conducted to identify the genetic factors that influence this disease due to its unknown genetic factors. We evaluated research studies, letters, comments, editorials, eBooks, and reviews. PubMed/MEDLINE, Web of Sciences, Cochrane Library, and Scopus were searched for information. We used the keywords polymorphisms, aspirin-exacerbated respiratory disease, asthma, allergy as search terms. This study included 38 studies. AERD complications were associated with polymorphisms in ALOX15, EP2, ADRB2, SLC6A12, CCR3, CRTH2, CysLTs, DPCR1, DPP10, FPR2, HSP70, IL8, IL1B, IL5RA, IL-13, IL17RA, ILVBL, TBXA2R, TLR3, HLA-DRB and HLA-DQ, HLA-DR7, HLA-DP. AERD was associated with heterogeneity in gene polymorphisms, making it difficult to pinpoint specific gene changes. Therefore, diagnosing and treating AERD may be facilitated by examining common variants involving the disease.

Genome-wide association study identifies TNFSF15 associated with childhood asthma

BACKGROUND

Genome-wide association studies (GWASs) of asthma have identified several risk alleles and loci, but most have been conducted in individuals with European-ancestry. Studies in Asians, especially children, are still lacking. We aimed to identify susceptibility loci by performing the first GWAS of asthma in Korean children with persistent asthma.

METHODS

We used a discovery set of 741 children with persistent asthma as cases and 589 healthy children and 551 healthy adults as controls to perform a GWAS. We validated our GWAS findings using UK Biobank data. We then used the Genotype-Tissue Expression database to identify expression quantitative trait loci of candidate variants. Finally, we quantified proteins of genes associated with asthma.

RESULTS

Variants at the 17q12-21 locus and SNPs in CYBRD1 and TNFSF15 genes were associated with persistent childhood asthma at genome-wide thresholds of significance. Four SNPs in the TNFSF15 gene were also associated with childhood-onset asthma in British white participants in the UK Biobank data. The asthma-associated rs7856856-C allele, the lead SNP, was associated with decreased TNFSF15 expression in whole blood and in arteries. Korean children with asthma had lower serum TNFSF15 levels than controls, and those with the asthma risk rs7856856-CC genotype exhibited the lowest serum TNFSF15 levels overall, especially asthmatic children.

CONCLUSIONS

Our GWAS of persistent childhood asthma with allergic sensitization identified a new susceptibility gene, TNFSF15, and replicated associations at the 17q12-21 childhood-onset asthma locus. This novel association may be mediated by reduced expression of serum TNFSF15 and loss of suppression of angiogenesis.

Genome-wide association study identifies BTNL2 associated with atopic asthma in children

Asthma is a heterogeneous disease characterized by chronic airway inflammation with a genetic predisposition. Butyrophilin-like 2 (BTNL2) is a member of the immunoglobulin superfamily that plays an important role in regulating T cell activation and immune homeostasis. Here, we aimed to investigate the association of the genetic variants of BTNL2 with childhood asthma and asthma-related traits by utilizing extreme asthma phenotypes and employing a genome-wide association study. Our study included 243 children with well-defined moderate to severe atopic asthma and 134 healthy children with no history of allergic diseases and allergic sensitization. DNA from these subjects was genotyped using AxiomTM Genome-Wide Array Plates. Although no single nucleotide polymorphisms (SNPs) reached a genome-wide threshold of significance, 3 SNPs, rs3817971, rs41355746, and rs41441651, at BTNL2 were significantly associated with moderate to severe atopic asthma after performing Bonferroni correction. These SNPs were also associated with the risk of allergic sensitization toward house dust mites and the presence and degree of bronchial hyperresponsiveness. Thus, we identified that BTNL2 was associated with atopic moderate to severe persistent asthma in Korean children, and this may play an important role in disease development and susceptibility.

Aspirin exacerbated respiratory disease (AERD): molecular and cellular diagnostic & prognostic approaches

Aspirin-exacerbated respiratory disease (AERD) is characterized by immune cells dysfunction. This study aimed to investigate the molecular mechanisms involved in AERD pathogenesis. Relevant literatures were identified by a PubMed search (2005-2019) of english language papers using the terms "Aspirin-exacerbated respiratory disease", "Allergic inflammation", "molecular mechanism" and "mutation". According to the significant role of inflammation in AERD development, ILC-2 is known as the most important cell in disease progression. ILC-2 produces cytokines that induce allergic reactions and also cause lipid mediators production, which activates mast cells and basophils, ultimately. Finally, Monoclonal antibody and Aspirin desensitization in patients can be a useful treatment strategy for prevention and treatment.

Genome-Wide Association Study of Korean Asthmatics: A Comparison With UK Asthmatics

PURPOSE

Although genome-wide association studies (GWASs) represent the most powerful approach for identifying genes that influence asthma, to date, no studies have established genetic susceptibility to asthma in the Korean population. This study aimed to identify genetic variants associated with adult Korean asthmatics and compare them with the significant single nucleotide polymorphisms (SNPs) of UK asthmatics from the UK Biobank.

METHODS

Patients were defined as having asthma if they were diagnosed by a doctor or taking medications for asthma. Controls were defined as individuals without asthma or chronic obstructive pulmonary disease. We performed quality control, genotype imputation, GWAS, and PrediXcan analyses. In the GWAS, a P value of < 5 × 10^-8 was considered significant. We compared significant SNPs between Korean and UK patients with asthma.

RESULTS

A total of 1,386 asthmatic patients and 5,205 controls were analyzed. The SNP rs1770, located near the human leukocyte antigen (HLA)-DQB1, was the most significant SNP (P = 4.5 × 10^-10). In comparison with 24 SNPs in a GWAS of UK asthmatics, six SNPs were significant with the same odds ratio (OR) direction, including signals related to type 2 inflammation (e.g., IL1RL1, TSLP, and GATA3) and mucus plugging (e.g., MUC5AC). HLA-DQA1 showed an opposite OR direction. The HLA-DQB1 gene demonstrated significantly imputed mRNA expression in the lung tissue and whole blood.

CONCLUSIONS

The SNP rs1770 of HLA-DQB1 was the most significant in Korean asthmatics. Similarities and discrepancies were found in the genetic variants between Korean and UK asthmatics. GWAS of Korean asthmatics should be replicated and compared with those of GWAS of other ethnicities.

Association of CYP2C19 and HSP70 Genes Polymorphism with Aspirin- Exacerbated Respiratory Disease in a Kurd Population

BACKGROUND

CYP2C19 a metabolizing enzyme and Heat Shock Proteins (HSP) are induced in stress conditions, such as hypoxia and ischemia. Recently, polymorphism in the CYP2C19 and HSP genes has been established in Aspirin-Exacerbated Respiratory Disease (AERD).

OBJECTIVE

We investigated the polymorphism of these two genes in Kurdish patients with AERD.

METHODS

This study involved 306 subjects, referred to the Be'sat hospital in Kurdistan Province, which were divided into three groups, (i) Aspirin Induced Asthma (AIA), (ii) Aspirin Tolerant Asthma (ATA), and (iii) healthy subjects as control. The subjects as control and ATA\AIA groups were verified by the physician. The demographic data of each subject with respect to age, sex, parental education, and residence was collected. Spirometry was performed on subjects and blood samples were collected for serum Immunoglobulin E (IgE) estimation and molecular tests. Genotyping was done for CYP2C19 681G>A، CYP2C19 636G>A, and HSPA1B1267A>G by using PCR- Restriction Fragment Length Polymorphism (RFLP) and for HSPA1B-179C>T by High Resolution Melting (HRM).

RESULTS

Demographic statistics were not significantly different between the three groups (p>0.05). Further, genotypes were also not observed to be significantly different in the genes of CYP2C19 681G>A, CYP2C19 636G>A and HSPA1B1267A>G (p>0.05). However, the heterozygote genotype in HSPA1B-179 C>T in AIA group was higher than the control group (p<0.05). Notably, 92.8 % of the subjects showed heterozygote genotype in HSPA1B1267 A>G. In clinical tests, FEV-1, FVC, and asthma severity in the AIA group were higher than control and additionally IgE levels were lower in this group (p<0.05).

CONCLUSION

The results confirm the association of polymorphism in the HSPA1B-179C>T and HSPA1B1267A>G with AERD in the Kurdish population.

Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study

The purpose of this study was to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two women with contralateral breast cancer and 153 women with unilateral breast cancer were identified within the Women’s Environmental Cancer and Radiation Epidemiology (WECARE) Study who were at increased risk of RCBC because they were ≤ 40 years of age at first diagnosis of breast cancer and received a scatter radiation dose > 1 Gy to the contralateral breast. A previously reported algorithm, preconditioned random forest regression, was applied to predict the risk of developing RCBC. The resulting model produced an area under the curve (AUC) of 0.62 (p = 0.04) on hold-out validation data. The biological analysis identified the cyclic AMP-mediated signaling and Ephrin-A as significant biological correlates, which were previously shown to influence cell survival after radiation in an ATM-dependent manner. The key connected genes and proteins that are identified in this analysis were previously identified as relevant to breast cancer, radiation response, or both. In summary, machine learning/bioinformatics methods applied to genome-wide genotyping data have great potential to reveal plausible biological correlates associated with the risk of RCBC.

Clinical Implications of Single Nucleotide Polymorphisms in Diagnosis of Asthma and its Subtypes

For the past three decades, a large number of genetic studies have been performed to examine genetic variants associated with asthma and its subtypes in hopes of gaining better understanding of the mechanisms underlying disease pathology and to identify genetic biomarkers predictive of disease outcomes. Various methods have been used to achieve these objectives, including linkage analysis, candidate gene polymorphism analysis, and genome-wide association studies (GWAS); however, the degree to which genetic variants contribute to asthma pathogenesis has proven to be much less significant than originally expected. Subsequent application of GWAS to well-defined phenotypes, such as occupational asthma and non-steroidal anti-inflammatory drugexacerbated respiratory diseases, has overcome some of these limitations, although with only partial success. Recently, a combinatorial analysis of single nucleotide polymorphisms (SNPs) identified by GWAS has been used to develop sets of genetic markers able to more accurately stratify asthma subtypes. In this review, we discuss the implications of the identified SNPs in diagnosis of asthma and its subtypes and the progress being made in combinatorial analysis of genetic variants.

Lessons Learned From GWAS of Asthma

Asthma is a common complex disease of the airways. Genome-wide association studies (GWASs) of asthma have identified many risk alleles and loci that have been replicated in worldwide populations. Although the risk alleles identified by GWAS have small effects and explain only a small portion of prevalence, the discovery of asthma loci can provide an understanding of its genetic architecture and the molecular pathways involved in disease pathogenesis. These discoveries can translate into advances in clinical care by identifying therapeutic targets, preventive strategies and ultimately approaches for personalized medicine. In this review, we summarize results from GWAS of asthma from the past 10 years and the insights gleaned from these discoveries.

Genetic and Epigenetic Components of Aspirin-Exacerbated Respiratory Disease

Aspirin-exacerbated respiratory disease (AERD) severity and its clinical phenotypes are characterized by genetic variation within pathways for arachidonic acid metabolism, inflammation, and immune responses. Epigenetic effects, including DNA methylation and histone protein modification, contribute to regulation of many genes that contribute to inflammatory states in AERD. The development of noninvasive, predictive clinical tests using data from genetic, epigenetic, pharmacogenetic, and biomarker studies will improve precision medicine efforts for AERD and asthma treatment.

Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population

BACKGROUND

Aspirin Exacerbated Respiratory Disease (AERD) is a chronic medical condition that encompasses asthma, nasal polyposis, and hypersensitivity to aspirin and other non-steroidal anti-inflammatory drugs. Several previous studies have shown that part of the genetic effects of the disease may be induced by the interaction of multiple genetic variants. However, heavy computational cost as well as the complexity of the underlying biological mechanism has prevented a thorough investigation of epistatic interactions and thus most previous studies have typically considered only a small number of genetic variants at a time.

METHODS

In this study, we propose a gene network based analysis framework to identify genetic risk factors from a genome-wide association study dataset. We first derive multiple single nucleotide polymorphisms (SNP)-based epistasis networks that consider marginal and epistatic effects by using different information theoretic measures. Each SNP epistasis network is converted into a gene-gene interaction network, and the resulting gene networks are combined as one for downstream analysis. The integrated network is validated on existing knowledgebase of DisGeNET for known gene-disease associations and GeneMANIA for biological function prediction.

RESULTS

We demonstrated our proposed method on a Korean GWAS dataset, which has genotype information of 440,094 SNPs for 188 cases and 247 controls. The topological properties of the generated networks are examined for scale-freeness, and we further performed various statistical analyses in the Allergy and Asthma Portal (AAP) using the selected genes from our integrated network.

CONCLUSIONS

Our result reveals that there are several gene modules in the network that are of biological significance and have evidence for controlling susceptibility and being related to the treatment of AERD.

Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

Genetic basis of hypersensitivity reactions to nonsteroidal anti-inflammatory drugs

PURPOSE OF REVIEW

NSAIDs are the main triggers of hypersensitivity reactions to drugs. However, the full genetic and molecular basis of these reactions has yet to be uncovered. In this article, we have summarized research from recent years into the effects of genetic variants on the different clinical entities induced by NSAID hypersensitivity, focusing on prostaglandin and leukotriene-related genes as well as others beyond the arachidonic acid pathway.

RECENT FINDINGS

We introduce recent contributions of high-throughput approaches including genome-wide association studies as well as available information from epigenetics and next-generation sequencing. Finally, we give our thoughts on future directions in this field, including the scope for bioinformatics and systems biology and the need for clear patient phenotyping.

SUMMARY

The full genetic and molecular basis of clinical entities induced by NSAIDs hypersensitivity has yet to be uncovered, and despite commendable efforts over recent years, no clinically proven genetic markers currently exist for these disorders. It is clear that we will continue to find more about these reactions in the coming years, concurrently with improvements in technology and experimental techniques, and a precise definition of different phenotypes.

HLA-DRB and HLA-DQ genetic variability in patients with aspirin-exacerbated respiratory disease

BACKGROUND

Major histocompatibility complex (MHC) class II is involved in T-cell activation, cytokine secretion, and induction of immune responses. Cytokines, staphylococcus super antigens, and eosinophil activation are proposed to play important roles in aspirin-exacerbated respiratory disease (AERD).

OBJECTIVES

This study is aimed at investigating the association of HLA-DRB and DQ genetic variabilities in patients with AERD.

METHODS

A genetic association analysis in three different groups, including 33 patients with AERD, 17 patients with aspirin-tolerant asthma (ATA), and 100 healthy controls was performed. Oral aspirin challenge (OAC) test was performed to identify aspirin hypersensitivity. Pulmonary function test (PFT) was performed for all patients. Eosinophil percentage in nasal smear and peripheral blood and serum immunoglobin (Ig)E were investigated. HLA-DRB, HLA-DQA1, and HLA-DQB1 were genotyped using polymerase chain reaction.

RESULTS

HLA-DQB1*0302 (OR, 5.49, 95% confidence interval [CI],(2.40-12.59)), HLA-DQA1*0301 (OR, 2.90, 95% CI, (1.49-5.67)), HLA-DRB4 (OR, 2.94, 95% CI, (1.61-5.36)), and HLA-DRB1*04 (OR, 3.19, 95% CI, (1.57-6.47)) were higher in patients with AERD compared with controls. In patients with AERD, HLA-DQB1*0301 (OR,0.22, 95% CI, (0.09-0.54)), HLA-DQA1*0501 (OR, 0.42, 95% CI, (0.21-0.81)), HLA-DRB1*11 (OR, 0.30, 95% CI, (0.12-0.73)), and HLA-DRB3 (OR, 0.38, 95% CI, (0.21-0.70)) were significantly lower compared with healthy controls. Patients with AERD had lower frequencies of HLA-DQB1*0301 (OR, 0.27, 95% CI, (0.08-0.86)), and HLA-DRB1*011 (OR, 0.27, 95% CI, (0.08-0.86)) compared with ATA. Haplotypes of HLA-DRB1*04/ DQA1*0301/ DQB1*0302 (OR, 4.25, 95% CI, (1.94-9.29)) and HLA-DRB1*07 /DQA1*0201/ DQB1*0201 (OR, 3.52, 95% CI, (1.54-8.06)) were higher in patients with AERD compared with controls (all p < 0.05).

CONCLUSIONS

Results of this study suggest that HLA-DQB1*0302 and HLA-DRB1*04 and their related haplotypes are genes involved in predisposing patients to AERD, whereas HLA-DQB1*0301 and HLA-DRB1*011 have negative association with AERD.

Pharmacogenetic tests to predict the efficacy of aspirin desensitization in patients with aspirin-exacerbated respiratory diseases; HLA-DQB302

This study is aimed at investigating the association of HLA-DRB1, HLA-DQA1, and HLA-DQB1 variability with the response to aspirin desensitization (AD). A total of 16 patients with aspirin-exacerbated respiratory diseases (AERD, 81.3% were female) with median age of 29 ± 4.3 years were included in this study. Following 6 months, Sino-Nasal Outcome Test-22 (SNOT-22), medication, symptom scores, and forced expiratory volume in 1 s (FEV1) (all p < 0.001) improved significantly. However, only seven patients (43.7%) had clinically significant improvement in all of the medication and symptom scores and FEV1, who were considered responders to AD. Responders to AD had significantly higher symptom scores compared with non-responders at baseline (20 ± 1.18 vs 10 ± 1.27; p = 0.003). HLADQB1*0302 was significantly lower in non-responders than in responders to AD (0.12 [0.02-0.76]; p = 0.022). Sensitivity and specificity of HLA-DQB1*0302 to predict response to AD was 71.4% (95% CI: 35.8-91.7) and 81.8% (95% CI: 52.3-94.8). This study introduces HLA-DQB1*0302 as a genetic marker for favorable response to AD.

Review: High-performance computing to detect epistasis in genome scale data sets

It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations; the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.

Systems biology approaches to enhance our understanding of drug hypersensitivity reactions

Hypersensitivity drug reactions (HDRs) encompass a wide spectrum of unpredictable clinical entities. They represent an important health problem, affecting people of all ages, and lead to a large strain on the public health system. Here, we summarize experiments that use high-throughput genomics technologies to investigate HDRs. We also introduce the field of systems biology as a relatively recent discipline concerned with the integration and analysis of high-throughput data sets such as DNA microarrays and next-generation sequencing data. We describe previous studies that have applied systems biology techniques to related fields such as allergy and asthma. Finally, we present a number of potential applications of systems biology to the study of HDRs, in order to make the reader aware of the types of analyses that can be performed and the insights that can be gained through their application.

Development of a genetic marker set to diagnose aspirin-exacerbated respiratory disease in a genome-wide association study

We developed a genetic marker set of single nucleotide polymorphisms (SNPs) by summing risk scores of 14 SNPs showing a significant association with aspirin-exacerbated respiratory disease (AERD) from our previous 660 W genome-wide association data. The summed scores were higher in the AERD than in the aspirin-tolerant asthma (ATA) group (P=8.58 × 10(-37)), and were correlated with the percent decrease in forced expiratory volume in 1 s after aspirin challenge (r(2)=0.150, P=5.84 × 10(-30)). The area under the curve of the scores for AERD in the receiver operating characteristic curve was 0.821. The best cutoff value of the summed risk scores was 1.01328 (P=1.38 × 10(-32)). The sensitivity and specificity of the best scores were 64.7% and 85.0%, respectively, with 42.1% positive and 93.4% negative predictive values. The summed risk score may be used as a genetic marker with good discriminative power for distinguishing AERD from ATA.

Differential gene expression profiles of peripheral blood mononuclear cells in childhood asthma

OBJECTIVE

Asthma is a common childhood disease with strong genetic components. This study compared whole-genome expression differences between asthmatic young children and healthy controls to identify gene signatures of childhood asthma.

METHODS

Total RNA extracted from peripheral blood mononuclear cells (PBMC) was subjected to microarray analysis. QRT-PCR was performed to verify the microarray results. Classification and functional characterization of differential genes were illustrated by hierarchical clustering and gene ontology analysis. Multiple logistic regression (MLR) analysis, receiver operating characteristic (ROC) curve analysis, and discriminate power were used to scan asthma-specific diagnostic markers.

RESULTS

For fold-change>2 and p < 0.05, there were 758 named differential genes. The results of QRT-PCR confirmed successfully the array data. Hierarchical clustering divided 29 highly possible genes into seven categories and the genes in the same cluster were likely to possess similar expression patterns or functions. Gene ontology analysis presented that differential genes primarily enriched in immune response, response to stress or stimulus, and regulation of apoptosis in biological process. MLR and ROC curve analysis revealed that the combination of ADAM33, Smad7, and LIGHT possessed excellent discriminating power.

CONCLUSIONS

The combination of ADAM33, Smad7, and LIGHT would be a reliable and useful childhood asthma model for prediction and diagnosis.

Exonic variants associated with development of aspirin exacerbated respiratory diseases

Aspirin-exacerbated respiratory disease (AERD) is one phenotype of asthma, often occurring in the form of a severe and sudden attack. Due to the time-consuming nature and difficulty of oral aspirin challenge (OAC) for AERD diagnosis, non-invasive biomarkers have been sought. The aim of this study was to identify AERD-associated exonic SNPs and examine the diagnostic potential of a combination of these candidate SNPs to predict AERD. DNA from 165 AERD patients, 397 subjects with aspirin-tolerant asthma (ATA), and 398 normal controls were subjected to an Exome BeadChip assay containing 240K SNPs. 1,023 models (2¹⁰-1) were generated from combinations of the top 10 SNPs, selected by the p-values in association with AERD. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was calculated for each model. SNP Function Portal and PolyPhen-2 were used to validate the functional significance of candidate SNPs. An exonic SNP, exm537513 in HLA-DPB1, showed the lowest p-value (p = 3.40×10⁻⁸) in its association with AERD risk. From the top 10 SNPs, a combination model of 7 SNPs (exm537513, exm83523, exm1884673, exm538564, exm2264237, exm396794, and exm791954) showed the best AUC of 0.75 (asymptotic p-value of 7.94×10⁻²¹), with 34% sensitivity and 93% specificity to discriminate AERD from ATA. Amino acid changes due to exm83523 in CHIA were predicted to be “probably damaging” to the structure and function of the protein, with a high score of ‘1’. A combination model of seven SNPs may provide a useful, non-invasive genetic marker combination for predicting AERD.