Bipartite Community Structure of eQTLs.
Genome Wide Association Studies (GWAS) and expression quantitative trait locus (eQTL) analyses have identified genetic associations with a wide range of human phenotypes. However, many of these variants have weak effects and understanding their combined effect remains a challenge. One hypothesis is that multiple SNPs interact in complex networks to influence functional processes that ultimately lead to complex phenotypes, including disease states. Here we present CONDOR, a method that represents both cis- and trans-acting SNPs and the genes with which they are associated as a bipartite graph and then uses the modular structure of that graph to place SNPs into a functional context. In applying CONDOR to eQTLs in chronic obstructive pulmonary disease (COPD), we found the global network "hub" SNPs were devoid of disease associations through GWAS. However, the network was organized into 52 communities of SNPs and genes, many of which were enriched for genes in specific functional classes. We identified local hubs within each community ("core SNPs") and these were enriched for GWAS SNPs for COPD and many other diseases. These results speak to our intuition: rather than single SNPs influencing single genes, we see groups of SNPs associated with the expression of families of functionally related genes and that disease SNPs are associated with the perturbation of those functions. These methods are not limited in their application to COPD and can be used in the analysis of a wide variety of disease processes and other phenotypic traits.
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.|Department of Biostatistics, Harvard Chan School of Public Health, Boston, Massachusetts, United States of America.
John
J
Platig
Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.|Division of General Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.|Harvard Medical School, Boston, Massachusetts, United States of America.
Peter J
0000-0001-9920-4713
PJ
Castaldi
Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.|Harvard Medical School, Boston, Massachusetts, United States of America.|Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.
Dawn
D
DeMeo
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.|Department of Biostatistics, Harvard Chan School of Public Health, Boston, Massachusetts, United States of America.|Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.
John
J
Quackenbush
D000465:Algorithms; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D029424:Pulmonary Disease, Chronic Obstructive / Q000235:genetics; D040641:Quantitative Trait Loci / Q000235:genetics*