Leveraging gene co-expression networks to pinpoint the regulation of complex traits and disease, with a focus on cardiovascular traits.

Abstract

Over the past decade, the number of genome-scale transcriptional datasets in publicly available databases has climbed to nearly one million, providing an unprecedented opportunity for extensive analyses of gene co-expression networks. In systems-genetic studies of complex diseases researchers increasingly focus on groups of highly interconnected genes within complex transcriptional networks (referred to as clusters, modules or subnetworks) to uncover specific molecular processes that can inform functional disease mechanisms and pathological pathways. Here, we outline the basic paradigms underlying gene co-expression network analysis and critically review the most commonly used computational methods. Finally, we discuss specific applications of network-based approaches to the study of cardiovascular traits, which highlight the power of integrated analyses of networks, genetic and gene-regulation data to elucidate the complex mechanisms underlying cardiovascular disease.