Polymorphisms and haplotypes of the chromosome locus 17q12-17q21.1 contribute to adult asthma susceptibility in Slovenian patients

Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches

INTRODUCTION

To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches.

METHODS

A case-control study with 123 asthmatics and 100 controls was conducted in the Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches were used to identify the major factors that contribute to asthma.

RESULTS

A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times the 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances exhibited area under the curve (AUC) values of 64.3% and 71.4%, respectively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found the top six risk factors for predicting asthma to be rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index.

CONCLUSION

Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma, and thus provide insights into the disease pathogenesis.

CRISPR/Cas9 genome editing demonstrates functionality of the autoimmunity-associated SNP rs12946510

Genome-wide association studies (GWAS) map genetic associations of complex traits with precision limited to a linkage disequilibrium group. To translate GWAS results into new understanding of disease mechanisms, individual causative polymorphisms and their target genes should be identified. CRISPR/Cas9 genome editing can be used to create isogenic cell lines bearing alternative genotypes of candidate single-nucleotide polymorphisms to test their causality and to reveal gene targets. An intergenic polymorphism rs12946510 is associated with multiple sclerosis, inflammatory bowel disease and asthma. We created sublines of the T-helper cell line bearing alternative genotypes of rs12946510 and showed that its risk ("T") allele is associated with lower expression of IKZF3 and ORMDL3 genes and reduced cell activation. Our editing procedure can become an effective tool for discovering new genes involved in pathogenesis of complex diseases.

Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms

In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.

Targeting IL-17A/glucocorticoid synergy to CSF3 expression in neutrophilic airway diseases

IL-17A plays a critical role in the pathogenesis of steroid-resistant neutrophilic airway inflammation, which is a hallmark of severe asthma and chronic obstructive pulmonary disease (COPD). Through RNA sequencing analysis of transcriptomes of human airway smooth muscle cells treated with IL-17A, dexamethasone (DEX, a synthetic glucocorticoid drug), alone or in combination, we identified a group of genes that are synergistically induced by IL-17A and DEX, including the neutrophil-promoting cytokine CSF3. In type-17 (Th17/IL-17Ahi) preclinical models of neutrophilic severe asthma (acute and chronic) and COPD, although DEX treatment was able to reduce the expression of neutrophil-mobilizing CXCL1 and CXCL2 in lung tissue, CSF3 expression was upregulated by DEX treatment. We found that DEX treatment alone failed to alleviate neutrophilic airway inflammation and pathology, and even exacerbated the disease phenotype when CSF3 was highly induced. Disruption of the IL-17A/DEX synergy by IL-17A inhibition with anti-IL-17A mAb or cyanidin-3-glucoside (C3G, a small-molecule IL-17A blocker) or depletion of CSF3 effectively rendered DEX sensitivity in type-17 preclinical models of neutrophilic airway diseases. Our study elucidates what we believe is a novel mechanism of steroid resistance in type-17 neutrophilic airway inflammation and offers an effective steroid-sparing therapeutic strategy (combined low-dose DEX and C3G) for treating neutrophilic airway diseases.

Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine

Asthma is a common complex disorder and has been subject to intensive omics research for disease susceptibility and therapeutic innovation. Candidate biomarkers of asthma and its precision treatment demand that they stand the test of multiomics data triangulation before they can be prioritized for clinical applications. We classified the biomarkers of asthma after a search of the literature and based on whether or not a given biomarker candidate is reported in multiple omics platforms and methodologies, using PubMed and Web of Science, we identified omics studies of asthma conducted on diverse platforms using keywords, such as asthma, genomics, metabolomics, and epigenomics. We extracted data about asthma candidate biomarkers from 73 articles and developed a catalog of 190 potential asthma biomarkers (167 human, 23 animal data), comprising DNA loci, transcripts, proteins, metabolites, epimutations, and noncoding RNAs. The data were sorted according to 13 omics types: genomics, epigenomics, transcriptomics, proteomics, interactomics, metabolomics, ncRNAomics, glycomics, lipidomics, environmental omics, pharmacogenomics, phenomics, and integrative omics. Importantly, we found that 10 candidate biomarkers were apparent in at least two or more omics levels, thus promising potential for further biomarker research and development and precision medicine applications. This multiomics catalog reported herein for the first time contributes to future decision-making on prioritization of biomarkers and validation efforts for precision medicine in asthma. The findings may also facilitate meta-analyses and integrative omics studies in the future.

Asthma MicroRNA Regulome Development Using Validated miRNA-Target Interaction Visualization

Asthma is a common multifactorial complex disease caused by an interaction of genetic and environmental factors. There are no robust biomarkers or molecular diagnostics for asthma or its detailed phenotypic stratification in the clinic. Regulatory and epigenomic factors are priority candidates for asthma biomarker discovery and translational research because this common disease emerges in association with host/environment interactions. In this context, epigenomic molecular events such as microRNA (miRNA) silencing affect asthma susceptibility and severity. We report here an analysis of the miRNAs in the literature, their targets associated with asthma, and present the findings organized as an miRNA-target network, an miRNA regulome of asthma. The miRNA-target interactions in asthma were extracted from the PubMed and the Web of Science databases, while the miRNA-target network was visualized with the Cytoscape tool. Genomic locations of miRNA and target genes were displayed using the Ensembl Whole Genome tool. We cataloged miRNAs associated with asthma and their experimentally validated targets, retrieving 48 miRNAs associated with asthma, and 54 experimentally validated miRNA targets. Four central molecules involved in 34.5% of all interactions were identified in the network. The miRNA-target pairs were constructed as an asthma-associated miRNA-target regulatory network. The network revealed subnetworks pointing toward potential asthma biomarker candidates. The asthma miRNA regulome reported here offers a strong foundation for future translational research and systems medicine applications for asthma diagnostic and therapeutic innovation. Developed protocol for constructing miRNA regulome could now be used for biomarker development in multifactorial diseases.

Genetic variants and risk of asthma in an American Indian population

BACKGROUND

Asthma is recognized as a complex, multifactorial disease with a genetic component that is well recognized. Certain genetic variants are associated with asthma in a number of populations.

OBJECTIVE

To determine whether the same variants increase the risk of asthma among American Indian children.

METHODS

The electronic medical records of an Indian Health Service facility identified all children between 6 and 17 years of age with case-defining criteria for asthma (n = 108). Control children (n = 216), matched for age, were also identified. Real-time polymerase chain reaction assays were used to genotype 10 single-nucleotide polymorphisms (SNPs) at 6 genetic loci. Genotypic distributions among cases and controls were evaluated by χ² and logistic regression methods.

RESULTS

A variant at 5q22.1 revealed a statistically significant imbalance in the distribution of genotypes between case-control pairs (rs10056340, P < .001). In logistic regression analyses, the same variant at 5q22.1 and a variant at 17q21 were associated with asthma at P < .05 (rs10056340 and rs9303277). Inclusions of age, body mass index, and atopy in multivariate models revealed significant associations between rs10056340 (odds ratio, 2.020; 95% confidence interval, 1.283-3.180; P = .002) and all 5 17q21 SNPs and asthma in this population. In analyses restricted to atopic individuals, the association of rs10056340 was essentially unchanged, whereas among nonatopic individuals the trend was in the same direction but nonsignificant. The reverse was true for the 17q21 SNPs.

CONCLUSION

These findings demonstrate that many variants commonly associated with asthma in other populations also accompany this condition among American Indian children. American Indian children also appear to have an increased risk of asthma associated with obesity.