Predictive genetic panel for adult asthma using machine learning methods.

Background: Asthma is a chronic inflammatory disease of the airways that is heterogeneous and multifactorial, making its accurate characterization a complex process. Therefore, identifying the genetic variations associated with asthma and discovering the molecular interactions between the omics that confer risk of developing this disease will help us to unravel the biological pathways involved in its pathogenesis. Objective: We sought to develop a predictive genetic panel for asthma using machine learning methods. Methods: We tested 3 variable selection methods: Boruta's algorithm, the top 200 genome-wide association study markers according to their respective P values, and an elastic net regression. Ten different algorithms were chosen for the classification tests. A predictive panel was built on the basis of joint scores between the classification algorithms. Results: Two variable selection methods, Boruta and genome-wide association studies, were statistically similar in terms of the average accuracies generated, whereas elastic net had the worst overall performance. The predictive genetic panel was completed with 155 single-nucleotide variants, with 91.18% accuracy, 92.75% sensitivity, and 89.55% specificity using the support vector machine algorithm. The markers used range from known single-nucleotide variants to those not previously described in the literature. Our study shows potential in creating genetic prediction panels with tailored penalties per marker, aiding in the identification of optimal machine learning methods for intricate results. Conclusions: This method is able to classify asthma and nonasthma effectively, proving its potential utility in clinical prediction and diagnosis.

PDE4D gene variants and haplotypes are associated with asthma and atopy in Brazilian children.

PDE4D (Phosphodiesterase 4D) gene encodes a hydrolase of cyclic AMP. PDE4D genetic variants have been associated with asthma susceptibility. Therefore, this study aimed to investigate the association between PDE4D variants (and haplotypes) with asthma and atopy in a Brazilian population. The study comprised 1,246 unrelated participants from the SCAALA (Social Changes Asthma and Allergy in Latin America) program. Genotyping was performed using the Illumina 2.5 Human Omni bead chip. Multivariate logistic regression was used to investigate the association between PDE4D variants and asthma/atopy phenotypes in PLINK 1.09 software. Twenty-four SNVs in PDE4D were associated with atopy or asthma. The rs6898082 (A) variant increased asthma susceptibility (OR 2.76; CI 99% 1.26-6.03) and was also related to a greater PDE4D expression in the GTEx database. Also, the variant rs6870632 was further associated with asthma in meta-analysis with a replication cohort. In addition, the variants rs75699812 (C), rs8007656 (G), and rs958851 (T) were positively associated with atopy. Moreover, these variants formed an atopy risk haplotype (OR 1.82; CI 99% 1.15-2.88). Also, these variants were related to lower levels of IL-10. Functional in silico assessment showed that some PDE4D SNVs may have an impact on gene regulation and expression. Variants in the PDE4D are positively associated with asthma and allergy markers. It is possible that these variants lead to alteration in PDE4D expression and therefore impact immunity and pulmonary function.

The use of saliva as a practical and feasible alternative to urine in large-scale screening for congenital cytomegalovirus infection increases inclusion and detection rates.

INTRODUCTION: Although urine is considered the gold-standard material for the detection of congenital cytomegalovirus (CMV) infection, it can be difficult to obtain in newborns. The aim of this study was to compare the efficiency of detection of congenital CMV infection in saliva and urine samples. METHODS: One thousand newborns were included in the study. Congenital cytomegalovirus deoxyribonucleic acid (DNA) was detected by polymerase chain reaction (PCR). RESULTS: Saliva samples were obtained from all the newborns, whereas urine collection was successful in only 333 cases. There was no statistically significant difference between the use of saliva alone or saliva and urine collected simultaneously for the detection of CMV infection. CONCLUSIONS: Saliva samples can be used in large-scale neonatal screening for CMV infection.