使用全基因组关联研究已经发现单核苷酸多态性与肺功能相关。然而,肺功能是一个复杂的性状,除了单个基因外,还可能受到多个基因-基因相互作用的影响。我们的目标是建立一个细胞网络,通过结合SNP水平和使用Framingham心脏研究的纵向肺功能数据的网络分析来探索肺功能与基因型之间的关系。我们分析了来自后代队列的2,698名基因型参与者,他们平均每人进行了3.35次肺活量测定,平均长度为13年。1秒内重复用力呼气量(FEV1)和FEV1与用力肺活量(FVC)之比被用作结果。使用线性混合模型分析肺功能与等位基因之间的关联,方法是考虑同一受试者随时间的重复测量之间的相关性和家庭内相关性。使用dmGWAS进行网络分析,并使用来自第三代队列的数据进行验证。分析确定SMAD3,TGFBR2,CD44,CTGF,VCAN,CTNNB1,SCGB1A1,PDE4D,NRG1、EPHB1和LYN是肺功能的贡献者。这些基因中的大多数是新的,以前仅使用SNP水平分析未发现。这些新基因涉及转化生长因子β(TGFB)-SMAD途径,Wnt/β-catenin通路,等。因此,结合使用纵向肺功能数据的SNP水平和网络分析是识别风险基因的有用替代策略。
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.