Single nucleotide polymorphisms have been found to be associated with pulmonary function using genome-wide association studies. However, lung function is a complex trait that is likely to be influenced by multiple gene-gene interactions besides individual genes. Our goal is to build a cellular network to explore the relationship between pulmonary function and genotypes by combining SNP level and network analyses using longitudinal lung function data from the Framingham Heart Study. We analyzed 2,698 genotyped participants from the Offspring cohort that had an average of 3.35 spirometry measurements per person for a mean length of 13 years. Repeated forced expiratory volume in one second (FEV1 ) and the ratio of FEV1 to forced vital capacity (FVC) were used as outcomes. Data were analyzed using linear-mixed models for the association between lung function and alleles by accounting for the correlation among repeated measures over time within the same subject and within-family correlation. Network analyses were performed using dmGWAS and validated with data from the Third Generation cohort. Analyses identified SMAD3, TGFBR2, CD44, CTGF, VCAN, CTNNB1, SCGB1A1, PDE4D, NRG1, EPHB1, and LYN as contributors to pulmonary function. Most of these genes were novel that were not found previously using solely SNP-level analysis. These novel genes are involving the transforming growth factor beta (TGFB)-SMAD pathway, Wnt/beta-catenin pathway, etc. Therefore, combining SNP-level and network analyses using longitudinal lung function data is a useful alternative strategy to identify risk genes.
Keywords: CD44; CTGF; CTNNB1; EPHB1; GWAS; LYN; NRG1; PDE4D; SCGB1A1; SMAD3; TGFBR2; VCAN; network analysis; pulmonary function.
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