Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD

BackgroundThe 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.ResultsWe 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.ConclusionPhenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.

[1]  Korbinian Strimmer,et al.  An empirical Bayes approach to inferring large-scale gene association networks , 2005, Bioinform..

[2]  Xiao-Lin Wu,et al.  Inferring causal phenotype networks using structural equation models , 2011, Genetics Selection Evolution.

[3]  Edwin K. Silverman,et al.  Chronic obstructive pulmonary disease , 2012, Nature Reviews Disease Primers.

[4]  Xin Yao,et al.  Modularity-based credible prediction of disease genes and detection of disease subtypes on the phenotype-gene heterogeneous network , 2011, BMC Systems Biology.

[5]  Korbinian Strimmer,et al.  From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data , 2007, BMC Systems Biology.

[6]  Edwin K Silverman,et al.  Computed Tomography Phenotypes in Severe, Early-Onset Chronic Obstructive Pulmonary Disease , 2007, COPD.

[7]  Edwin K Silverman,et al.  Characterisation of COPD heterogeneity in the ECLIPSE cohort , 2010, Respiratory research.

[8]  D. Postma,et al.  Chronic obstructive pulmonary disease. , 2002, Clinical evidence.

[9]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[10]  Ross Lazarus,et al.  Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes , 2011, BMC Systems Biology.

[11]  S. Horvath,et al.  Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology , 2011, Nature.

[12]  B. Celli,et al.  Addressing the complexity of chronic obstructive pulmonary disease: from phenotypes and biomarkers to scale-free networks, systems biology, and P4 medicine. , 2011, American journal of respiratory and critical care medicine.

[13]  E. Regan,et al.  Genetic Epidemiology of COPD (COPDGene) Study Design , 2011, COPD.

[14]  Edwin K Silverman,et al.  Exacerbations in chronic obstructive pulmonary disease: do they contribute to disease progression? , 2007, Proceedings of the American Thoracic Society.

[15]  Christoph Lange,et al.  Risk loci for chronic obstructive pulmonary disease: a genome-wide association study and meta-analysis. , 2014, The Lancet. Respiratory medicine.

[16]  K. Shianna,et al.  A Genome-Wide Association Study in Chronic Obstructive Pulmonary Disease (COPD): Identification of Two Major Susceptibility Loci , 2009, PLoS genetics.

[17]  Scott T. Weiss,et al.  A Genome-Wide Association Study of Pulmonary Function Measures in the Framingham Heart Study , 2009, PLoS genetics.

[18]  W. MacNee,et al.  Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) , 2008, European Respiratory Journal.

[19]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[20]  N. Morton Genetic epidemiology , 1997, International Journal of Obesity.

[21]  M. DePamphilis,et al.  HUMAN DISEASE , 1957, The Ulster Medical Journal.

[22]  John Quackenbush,et al.  Seeded Bayesian Networks: Constructing genetic networks from microarray data , 2008, BMC Systems Biology.

[23]  Nicola A Hanania,et al.  Chronic obstructive pulmonary disease exacerbations in the COPDGene study: associated radiologic phenotypes. , 2011, Radiology.

[24]  Jianxin Chen,et al.  Clinical Data Mining of Phenotypic Network in Angina Pectoris of Coronary Heart Disease , 2012, Evidence-based complementary and alternative medicine : eCAM.

[25]  Darcy A. Davis,et al.  Exploring and Exploiting Disease Interactions from Multi-Relational Gene and Phenotype Networks , 2011, PloS one.

[26]  Carla G. Wilson,et al.  Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease. , 2013, AJR. American journal of roentgenology.

[27]  A. Marie Fitch,et al.  Shortest path analysis using partial correlations for classifying gene functions from gene expression data , 2009, Bioinform..

[28]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[29]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[30]  Christoph Lange,et al.  Variants in FAM13A are associated with chronic obstructive pulmonary disease , 2010, Nature Genetics.

[31]  H. Hotelling New Light on the Correlation Coefficient and its Transforms , 1953 .

[32]  Albert-László Barabási,et al.  A Dynamic Network Approach for the Study of Human Phenotypes , 2009, PLoS Comput. Biol..