Chu Jen-hwa, Hersh Craig P, Castaldi Peter J, Cho Michael H, Raby Benjamin A, Laird Nan, Bowler Russell, Rennard Stephen, Loscalzo Joseph, Quackenbush John, Silverman Edwin K
Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
BMC Syst Biol. 2014 Jun 25;8:78. doi: 10.1186/1752-0509-8-78.
The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes.
We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches.
Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.
复杂疾病异质性的研究一直具有挑战性。在此，我们引入一种基于网络的方法，利用偏相关性来分析多种疾病相关表型之间的关系。
我们将此方法应用于两项关于慢性阻塞性肺疾病（COPD）的大型、特征明确的研究。我们还研究了这些COPD表型网络与其他因素之间的关联，包括病例对照状态、疾病严重程度和基因变异。利用这些表型网络，我们发现了一些使用传统流行病学方法无法观察到的表型之间的新关系。
复杂疾病的表型网络分析可为疾病易感性、疾病严重程度和遗传机制提供新的见解。
