Data and Statistical R Code:
Integrating Genetics and Network Analysis to
Characterize Genes Related to Mouse Weight

Correspondence:     shorvath@mednet.ucla.edu

Method: Weighted Gene Co-Expression Network Analysis ( WGCNA )
                 Weighted Gene Coexpression Network Analysis


Here we provide statistical code and data for the paper:

Ghazalpour A, Doss S, Zhang B, Wang S, Plaisier C, Castellanos R, Brozell A, Schadt EE, Drake TA, Lusis AJ, Horvath S (2006)
Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight. PloS Genetics. Volume 2 | Issue 8 | AUGUST 2006

PLoS Genetics link

The following tutorials provide the statistical code used for generating the weighted gene co-expression network for mouse liver gene co-expression network analysis.

Abstract: Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight¡Vrelated module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest.


R Software Tutorials, Data and More

  1. Content of tutorial I
     A. Gene Co-expression Network Construction
     B. Module Definition Based on Average Linkage hierarchical clustering with the dynamic tree cut algorithm
     C. Relating Modules To Physiological Traits (module significance analysis)
     D. Comparing Weighted Network Results to Unweighted Network Results
     E. Studying the Clustering Coefficicient
                First Tutorial Word Document
                Microsoft word version
                PDF version
                Relevant Data (Zipped Tutorial1)

  2. Content of tutorial II
    A. Robustness of network modules and connectivity with respect to removing microrray samples
    B. Preservation of module and connectivity across multiple gender tissue combinations
                Second Tutorial Word Document
                Microsoft word version
                Relevant Data (Zipped Tutorial2)

  3. Content of tutorial III
    A. Relating the 22 clinical traits and the expression profiles to genetic SNP markers Quantitative trait locus, LODscore, QTL, linkage analysis, allelic association analysis, mQTL
    B. LOD score curve and mQTL graph (Figure 3)
    C. Regress a phenotype on multiple markers with the fitqtl package
                Third Tutorial Word Document
                Microsoft word version
                Relevant Data (Zipped Tutorial3)

  4. Content of tutorial IV
    A. Network properties are integrated with genetic information to characterize weight related genes of the blue module
    B. Multivariable regression models that regress the body weight based gene significance (GSweight) on SNP significance measures (GSmQTL) and intramodular connectivity (k)
                Fourth Tutorial Word Document
                Microsoft word version
                Relevant Data (Zipped Tutorial4)

  5. Content of tutorial V
    A. A simulated gene co-expression network to illustrate the use of the topological overlap matrix for module detection
                Fourth Tutorial Word Document
               
    Microsoft word version (recommended)
                PDF version

  6. Talk on this application
               
    PowerPoint version
                PDF version

Download the following R function file, which contains several R functions needed for Weighted Gene Co-Expression Network Analysis.
 Network R functions

Other material regarding weighted gene co-expression network analysis

             Weighted Gene Co-Expression Network Page

             The weighted gene co-expression network analysis method is described in Theory Paper 1: Zhang and Horvath (2005)

             For a more mathematical description of weighted gene co-expression networks consider Theory Papers: Dong and Horvath (2007, 2008)

 


2008-11-12

Please send your suggestions and comments to: shorvath@mednet.ucla.edu