Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Monday, June 02, 2008

Exploiting the Proteome for the Genome-Wide Genetic Analysis of Epistasis

Our review paper on "Exploiting the Proteome for the Genome-Wide Genetic Analysis of Epistasis in Common Human Diseases" has been accepted for publication in Human Genetics. The corrected online version of the paper can be found here. This paper explores protein-protein interaction databases as a source of expert knowledge that can be used to help guide stochastic search algorithms such as genetic programming in their effort to detect epistasis in genome-wide association studies.

Pattin, K.A., Moore, J.H. Exploiting the Proteome for the Genome-Wide Genetic Analysis of Epistasis in Common Human Diseases. Human Genetics, in press (2008).

Abstract

One of the central goals of human genetics is the identification of loci with alleles or genotypes that confer increased susceptibility. The availability of dense maps of single-nucleotide polymorphisms (SNPs) along with high-throughput genotyping technologies has set the stage for routine genome-wide association studies that are expected to significantly improve our ability to identify susceptibility loci. Before this promise can be realized, there are some significant challenges that need to be addressed. We address here the challenge of detecting epistasis or gene-gene interactions in genome-wide association studies. Discovering epistatic interactions in high dimensional datasets remains a challenge due to the computational complexity resulting from the analysis of all possible combinations of SNPs. One potential way to overcome the computational burden of a genome-wide epistasis analysis would be to devise a logical way to prioritize the many SNPs in a dataset so that the data may be analyzed more efficiently and yet still retain important biological information. One of the strongest demonstrations of the functional relationship between genes is protein-protein interaction. Thus, it is plausible that the expert knowledge extracted from protein interaction databases may allow for a more efficient analysis of genome-wide studies as well as facilitate the biological interpretation of the data. In this review we will discuss the challenges of detecting epistasis in genome-wide genetic studies and the means by which we propose to apply expert knowledge extracted from protein interaction databases to facilitate this process. We explore some of the fundamentals of protein interactions and the databases that are publicly available.

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