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54. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

07. bis 10.09.2009, Essen

Genome-wide Interaction Analysis Guided by A Priori Information

Meeting Abstract

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  • Tim Becker - Institut für Biometrie, Medizinische Informatik und Epidemiologie, Bonn
  • Christine Herold - Institut für Biometrie, Medizinische Informatik und Epidemiologie, Bonn

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 54. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds). Essen, 07.-10.09.2009. Düsseldorf: German Medical Science GMS Publishing House; 2009. Doc09gmds142

DOI: 10.3205/09gmds142, URN: urn:nbn:de:0183-09gmds1423

Published: September 2, 2009

© 2009 Becker et al.
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Outline

Text

Complex diseases are caused by interacting genetic and environmental factors. Due to computational burden, genome-wide association studies (GWAS) are typically limited to single-marker analysis. We present an approach for genome-wide interaction analysis that overcomes the computational issue by prioritizing SNPs for interaction analysis using a priori information. Sources of information can be biological relevance (gene location, function class ...) or statistical evidence (single marker association at a moderate level). We present a respective software product that implements different approaches to joint analysis of multiple SNPs (full modelling of marginal and interaction effects, as well as explicit testing for interaction with a log-linear model). The software implements various methods to account for multiple testing and to judge genome-wide significance. In particular, genome-wide Monte-Carlo simulations are feasible. We present the results from an application to a GWAS.