Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases
- 13 June 2008
- journal article
- review article
- Published by Springer Science and Business Media LLC in Human Genetics
- Vol. 124 (1), 19-29
- https://doi.org/10.1007/s00439-008-0522-8
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.Keywords
This publication has 53 references indexed in Scilit:
- The BioGRID Interaction Database: 2008 updateNucleic Acids Research, 2007
- Replicating genotype–phenotype associationsNature, 2007
- Epistatic effects of polymorphisms in genes from the renin-angiotensin, bradykinin, and fibrinolytic systems on plasma t-PA and PAI-1 levelsGenomics, 2007
- UniHI: an entry gate to the human protein interactomeNucleic Acids Research, 2006
- MINT: the Molecular INTeraction databaseNucleic Acids Research, 2006
- Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate GenesAmerican Journal of Human Genetics, 2006
- BioGRID: a general repository for interaction datasetsNucleic Acids Research, 2006
- A haplotype map of the human genomeNature, 2005
- Towards a proteome-scale map of the human protein–protein interaction networkNature, 2005
- Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneityGenetic Epidemiology, 2003