Background: Currently, the majority of genetic association studies on chronic obstructive pulmonary disease (COPD) risk focused on identifying the individual effects of single nucleotide polymorphisms (SNPs) as well as their interaction effects on the disease. However, conventional genetic studies often use binary disease status as the primary phenotype, but for COPD, many quantitative traits have the potential correlation with the disease status and closely reflect pathological changes.
Method: Here, we genotyped 44 SNPs from four genes (EPHX1, GSTP1, SERPINE2, and TGFB1) in 310 patients and 203 controls which belonged to the Chinese Han population to test the two-way and three-way genetic interactions with COPD-related quantitative traits using recently developed generalized multifactor dimensionality reduction (GMDR) and quantitative multifactor dimensionality reduction (QMDR) algorithms.
Results: Based on the 310 patients and the whole samples of 513 subjects, the best gene-gene interactions models were detected for four lung-function-related quantitative traits. For the forced expiratory volume in 1 s (FEV1), the best interaction was seen from EPHX1, SERPINE2, and GSTP1. For FEV1%pre, the forced vital capacity (FVC), and FEV1/FVC, the best interactions were seen from SERPINE2 and TGFB1.
Conclusion: The results of this study provide further evidence for the genotype combinations at risk of developing COPD in Chinese Han population and improve the understanding on the genetic etiology of COPD and COPD-related quantitative traits.
Keywords: COPD; Interaction; MDR; Quantitative traits; SNP.