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Epistasis-list.org: A Curated Database of Gene-Gene and Gene-Environment Interactions in Human Epidemiology
Citation:
MOTSINGER-REIF, A. M., S. J. WOOD, S. OBEROI, AND D. REIF. Epistasis-list.org: A Curated Database of Gene-Gene and Gene-Environment Interactions in Human Epidemiology. Presented at American Society of Human Genetics Annual Meeting, Philadelphia, PA, November 01, 2008.
Impact/Purpose:
The success of these new methods, as well as the appropriate application of traditional approaches, in detecting such interactions is evidenced by the increasing number of epistatic models found in studies of human genetics. We have manually curated a list of such interactions from PubMed literature references, and have developed a searchable web-based tool compiling these results. This curated list is available at www.epistasis-list.org and will be updated regularly. The website is organized into a searchable list of all interactions found, as well as a searchable list of analytical tools available to detect such interactions. The organized list of interactions is searchable by disease, genetic or environmental factors detected, study design used, analytical method used, etc. The list of analytical methods is searchable by pertinent details such as study design or outcome and risk factor variable characteristics. Through the end of 2007, over 450 gene-gene and/or gene-environment interactions have been found. Breakdowns according to analytical methods and study designs used indicate some important trends. For example, ~63% of all interactions were discovered with traditional statistical methods, while ~37% used novel techniques; over 96% of studies used an association mapping approach, with only 10% using family-based data. It is our hope that this curated database will assist investigators in evaluation of epistasis in their own work by highlighting successful applications of appropriate experimental design and analysis.
Description:
The field of human genetics has experienced a paradigm shift in that common diseases are now thought to be due to the complex interactions among numerous genetic and environmental factors. This paradigm shift has prompted the development of myriad novel methods to detect such interactions in epidemiological studies.