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.

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.

Beta-2 adrenergic receptor (ADRB2) gene polymorphisms and the risk of asthma: a meta-analysis of case-control studies.

BACKGROUND AND OBJECTIVE: A number of studies have assessed the relationship between beta-2 adrenergic receptor (ADRB2) gene polymorphisms and asthma risk. However, the results are inconsistent. A meta-analysis that focused on the association between asthma and all ADRB2 polymorphisms with at least three case-control studies was thus performed. METHODS: A literature search of the PubMed, Embase, Web of Science, CNKI, and Wangfang databases was conducted. Odds ratios with 95% confidence intervals were used to assess the strength of associations. RESULTS: Arg16Gly, Gln27Glu, Thr164Ile, and Arg19Cys single nucleotide polymorphisms (SNPs) were identified in 46 case-control studies. The results showed that not all of the SNPs were associated with asthma in the overall population. Significant associations were found for the Arg16Gly polymorphism in the South American population via dominant model comparison (OR = 1.754, 95% CI = 1.179-2.609, I2 = 16.9%, studies  = 2, case  = 314, control  = 237) in an analysis stratified by ethnicity. For the Gln27Glu polymorphism, a protective association was found in children via recessive model comparison (OR = 0.566, 95% CI = 0.417-0.769, I2 = 0.0%, studies  = 11, case  = 1693, control  =  502) and homozygote genotype comparison (OR = 0.610, 95% CI = 0.434-0.856, I2 = 0.0%, studies  = 11, case  = 1693, control  = 1502), and in adults via dominant model comparison (OR = 0.864, 95% CI = 0.768-0.971, I2 = 46.9%, n = 18, case  = 3160, control  = 3433). CONCLUSIONS: None of the ADRB2 gene polymorphisms were reproducibly associated with a risk of asthma across ethnic groups in the general population.