A bioinformatics strategy for detecting the complexity of Chronic Obstructive Pulmonary Disease in Northern Chinese Han Population

Chronic Obstructive Pulmonary Disease (COPD) is a complex human disease which is driven not only by genetic factors, but also by various environmental variables, such as gender, age and smoking. Therefore, there is a demand for investigating the complexity among various risk factors involved in COPD. In this study, 44 tagging SNPs from EPHX1, GSTP1, SERPINE2 and TGFB1 were selected and genotyped in 310 COPD cases and 203 controls, all of which belong to the Han from North China. We integrated functional prediction algorithms of nonsynonymous SNPs (nsSNPs) into Bayesian network to explore the complex regulatory relationships among disease traits and various risk factors. The results showed that three basic variables (age, sex and smoking) were risk factors of COPD-related trait and phenotype. Besides these environmental risk factors, deleterious nsSNPs were found to perform better than those of significant synonymous SNPs when used as variables to make risk prediction of disease outcome. This study provides further evidences for detecting the complexity of COPD in Northern Chinese Han Population.