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Publication Detail

Title: Layers of epistasis: genome-wide regulatory networks and network approaches to genome-wide association studies.

Authors: Cowper-Sal lari, Richard; Cole, Michael D; Karagas, Margaret R; Lupien, Mathieu; Moore, Jason H

Published In Wiley Interdiscip Rev Syst Biol Med, (2011 Sep-Oct)

Abstract: The conceptual foundation of the genome-wide association study (GWAS) has advanced unchecked since its conception. A revision might seem premature as the potential of GWAS has not been fully realized. Multiple technical and practical limitations need to be overcome before GWAS can be fairly criticized. But with the completion of hundreds of studies and a deeper understanding of the genetic architecture of disease, warnings are being raised. The results compiled to date indicate that risk-associated variants lie predominantly in noncoding regions of the genome. Additionally, alternative methodologies are uncovering large and heterogeneous sets of rare variants underlying disease. The fear is that, even in its fulfillment, the current GWAS paradigm might be incapable of dissecting all kinds of phenotypes. In the following text, we review several initiatives that aim to overcome these limitations. The overarching theme of these studies is the inclusion of biological knowledge to both the analysis and interpretation of genotyping data. GWAS is uninformed of biology by design and although there is some virtue in its simplicity, it is also its most conspicuous deficiency. We propose a framework in which to integrate these novel approaches, both empirical and theoretical, in the form of a genome-wide regulatory network (GWRN). By processing experimental data into networks, emerging data types based on chromatin immunoprecipitation are made computationally tractable. This will give GWAS re-analysis efforts the most current and relevant substrates, and root them firmly on our knowledge of human disease.

PubMed ID: 21197657 Exiting the NIEHS site

MeSH Terms: Epistasis, Genetic/genetics*; Gene Regulatory Networks*; Genetic Variation; Genome-Wide Association Study*; Linkage Disequilibrium

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