Association of GWAS-supported noncoding area loci rs404860, rs3117098, and rs7775228 with asthma in Chinese Zhuang population

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.

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.

Association of GWAS-supported noncoding area loci rs404860, rs3117098, and rs7775228 with asthma in Chinese Zhuang population

Background

Asthma is a complicated and polygenic inheritance disease, and its prevalence increases worldwide. Recent genome‐wide association studies (GWASs) identified a significant association of single nucleotide polymorphism with asthma in the Japanese population. This study aimed to examine the association of GWAS‐supported noncoding area loci, namely rs404860, rs3117098, and rs7775228, with asthma in Chinese Zhuang population.

Methods

A case‐control study involving 223 individuals, comprising 123 patients with asthma and 100 healthy controls, was conducted. Genotypes were determined by polymerase chain reaction (PCR)/ligase detection reaction assay. The association between gene polymorphisms and asthma risk was calculated by logistic regression analysis using different genetic models through comparisons of alleles (A vs a), homozygote genotypes (AA vs aa), heterozygote genotypes (Aa vs aa), dominant models (AA+Aa vs aa), and recessive models (AA vs. Aa+aa).

Results

The distribution of the genotype frequency of rs3117098 was statistically different between the case and control groups. For rs3117098, significant associations were observed through comparisons of alleles (OR: 1.832, 95% CI: 1.048‐3.204, P = .034) and dominant models (OR: 2.065, 95% CI: 1.001‐4.260, P = .050). The statistical analysis showed no significant difference for loci rs404860 and rs7775228 between patients with asthma and controls.

Conclusion

rs3117098 may be the risk factor for asthma in Chinese Zhuang population.