Shraddha Piparia

Objective: Asthma is a complex and heterogeneous syndrome, making it hard to predict disease progression and suitable treatments. One strategy for reducing this uncertainty is to define genetic subtypes, or endophenotypes, that capture shared biological mechanisms. Most genome-wide studies, however, compare one subgroup against all others within a single cohort and rarely replicate their findings. We aimed to determine whether simultaneously modeling all asthma endophenotypes improves the discovery and replication of genetic associations compared with the standard one-versus-rest approach. Methods: We analyzed common single-nucleotide polymorphisms (SNPs) in the Childhood Asthma Management Program (CAMP) using an analysis of covariance (ANCOVA) across all severity-related endophenotypes, adjusting for age, sex, and ancestry principal components. SNPs showing genome-wide significance were tested for replication in the Genetics of Asthma in Costa Rican Children Study (GACRS). For comparison, we performed traditional one-versus-rest logistic regression analyses within each cohort, using identical covariates and endophenotype labels. Results: The ANCOVA identified 244 genome-wide significant SNPs in CAMP, of which six unique loci replicated in GACRS. In contrast, logistic regression recovered only four significant contrasts from those six loci in CAMP and replicated just one in GACRS. Conclusions: Our findings highlight genetic variants that are associated with asthma severity endophenotypes and demonstrate that modeling all clinical subtypes simultaneously can reveal biologically meaningful signals that are missed by standard pairwise design.

Introduction: MicroRNAs (miRNAs) have been linked to allergic diseases but their effects on sensitisation to allergens in individuals with asthma are unknown. We aimed to identify miRNAs associated with house dust mite (HDM) sensitisation in childhood asthma.Methods: Serum samples from 1126 children with asthma who participated in the Genetics of Asthma in Costa Rica Study (GACRS) were profiled for 304 miRNAs. We first divided according to HDM sensitisation and then tested whether miRNAs were differentially expressed (DE) between the two groups. Gene enrichment analysis for target genes of the DE miRNAs was then performed to identify potential causal pathways. Replication analysis was performed in the Childhood Asthma Management Program (CAMP), in which expression data of 258 miRNAs in 491 children were available. A mediation analysis was conducted to discern relationships between miRNA and phenotype differences according to HDM sensitisation in GACRS cohort.Results: There were 906 (80.5%) and 220 (19.5%) subjects in the GACRS HDM+ and HDM– groups. Compared with HDM– participants, those in the HDM+ group were more likely to be severe in variables including pulmonary function, oral corticosteroid use and blood tests. A total of 17 miRNAs were DE (p < 0.05) between the two groups, with miR‐642a‐3p, let‐7c‐5p and miR‐107 most significantly associated with HDM sensitisation. In CAMP, there were 39 DE miRNAs, and increased expression of miR‐107 in HDM+ children was replicated in this cohort. In both GACRS and CAMP, the cadherin‐binding pathway was enriched in an analysis of target genes for DE miRNA. In a mediation analysis, miR‐107 showed significant indirect effects on eosinophil count and total IgE that were mediated by HDM sensitisation.Conclusion: In children with asthma, miR‐107 is associated with HDM sensitisation. Furthermore, miR‐107 was indirectly associated with total IgE and eosinophil count through HDM sensitisation.

Asthma is a heterogeneous, multifactorial disease with multiple genetic and environmental risk factors playing a role in pathogenesis and therapeutic response. Understanding of pharmacogenetics can help with matching individualized treatments to specific genotypes of asthma to improve therapeutic outcomes especially in uncontrolled or severe asthma. In this review, we outline novel information about biology, pathways, and mechanisms related to interindividual variability in drug response (corticosteroids, bronchodilators, leukotriene modifiers, and biologics) for childhood asthma. We discuss candidate gene, genome-wide association studies and newer omics studies including epigenomics, transcriptomics, proteomics, and metabolomics as well as integrative genomics and systems biology methods related to childhood asthma. The articles were obtained after a series of searches, last updated November 2022, using database PubMed/CINAHL DB. Implementation of pharmacogenetic algorithms can improve therapeutic targeting in children with asthma, particularly with severe or uncontrolled asthma who typically have challenges in clinical management and carry considerable financial burden. Future studies focusing on potential biomarkers both clinical and pharmacogenetic can help formulate a prognostic test for asthma treatment response that would represent true bench to bedside clinical implementation.

Purpose: Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not. Methods: A total of 2572 chest impressions from 721 individuals under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection. Results: Five RFC models were implemented with incremental features and the best model achieved an F1-score of 0.79 with Area Under the ROC curve as 0.85 using all input features. Hyper parameter tuning and cross validation was performed using grid search cross validation and SHAP model was used to determine feature importance. The radiological features such as pneumonia, small airways disease, and atelectasis (confounded with catheter) were found to be highly associated with predicting the status of COVID-19 infection. Conclusions: In this sample, radiological X-ray films can predict the status of COVID-19 infection with good accuracy. The multivariate model including symptoms presented around the time of COVID-19 test yielded good prediction score.