Network theory for data-driven epistasis networks.
One of the challenges of understanding the genetic basis of complex phenotypes is explaining variability not attributable to individual genes. While most existing methods that investigate variant mutations or differential gene expression focus on individual effects, a complex system of gene interactions (epistasis) and pathways is likely needed to explain phenotypic variation. Herein, we examine methods for treating the interactions in these biological data sets as edges in a network model of the phenotype and review relevant network theory methods for analyzing network structure and identifying important genes. In particular, we review methods for detecting community structure, describing the statistical properties of networks, and computing network centrality of genes that may reveal insights missed by individual genetic effects. We also discuss available tools to facilitate the construction and visualization of epistasis networks of GWAS data.
Department of Mathematics, University of Tulsa, 800 S. Tucker Drive, Rayzor Hall 2145, Tulsa, OK, 74104, USA.
Caleb A
CA
Lareau
Brett A
BA
McKinney
D000465:Algorithms; D016000:Cluster Analysis; D004843:Epistasis, Genetic; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012984:Software