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Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge

Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge

Kristine A. Pattin, Jason H. Moore
Copyright: © 2011 |Pages: 20
ISBN13: 9781609604912|ISBN10: 1609604911|EISBN13: 9781609604929
DOI: 10.4018/978-1-60960-491-2.ch006
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MLA

Pattin, Kristine A., and Jason H. Moore. "Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge." Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., IGI Global, 2011, pp. 128-147. https://doi.org/10.4018/978-1-60960-491-2.ch006

APA

Pattin, K. A. & Moore, J. H. (2011). Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge. In L. Liu, D. Wei, Y. Li, & H. Lei (Eds.), Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications (pp. 128-147). IGI Global. https://doi.org/10.4018/978-1-60960-491-2.ch006

Chicago

Pattin, Kristine A., and Jason H. Moore. "Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge." In Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., 128-147. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-491-2.ch006

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Abstract

Recent technological developments in the field of genetics have given rise to an abundance of research tools, such as genome-wide genotyping, that allow researchers to conduct genome-wide association studies (GWAS) for detecting genetic variants that confer increased or decreased susceptibility to disease. However, discovering epistatic, or gene-gene, interactions in high dimensional datasets is a problem due to the computational complexity that results from the analysis of all possible combinations of single-nucleotide polymorphisms (SNPs). A recently explored approach to this problem employs biological expert knowledge, such as pathway or protein-protein interaction information, to guide an analysis by the selection or weighting of SNPs based on this knowledge. Narrowing the evaluation to gene combinations that have been shown to interact experimentally provides a biologically concise reason why those two genes may be detected together statistically. This chapter discusses the challenges of discovering epistatic interactions in GWAS and how biological expert knowledge can be used to facilitate genome-wide genetic studies.

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