Contract grant sponsor: Spanish Science and Innovation Ministry Strategic Project; Contract grant numbers: PSE-010000-2006-6, IPT-010000-2010-36.
Unveiling Case-Control Relationships in Designing a Simple and Powerful Method for Detecting Gene-Gene Interactions
Article first published online: 7 AUG 2012
© 2012 Wiley Periodicals, Inc.
Volume 36, Issue 7, pages 710–716, November 2012
How to Cite
Canela-Xandri, O., Julià, A., Gelpí, J. L. and Marsal, S. (2012), Unveiling Case-Control Relationships in Designing a Simple and Powerful Method for Detecting Gene-Gene Interactions. Genet. Epidemiol., 36: 710–716. doi: 10.1002/gepi.21665
- Issue published online: 12 OCT 2012
- Article first published online: 7 AUG 2012
- Manuscript Accepted: 14 JUN 2012
- Manuscript Revised: 1 JUN 2012
- Manuscript Received: 31 JAN 2012
- Spanish Science and Innovation Ministry Strategic. Grant Numbers: PSE-010000-2006-6, IPT-010000-2010-36
- statistical method;
- binary trait;
- genome-wide association study
The detection of gene-gene interactions (i.e., epistasis) in the human genome is becoming decisive for the complete characterization of the genetic factors associated with complex binary traits. Despite the fact that many methods have been developed to address this challenging issue, their performance still remains insufficient. We will show how case and control groups store complementary information regarding interactions, and the use of this fundamental property in the design of a new, rapid, and highly powerful epistasis analysis method. Unlike previous approaches where statistical methods are tested over a very limited range of situations, we have performed an exhaustive evaluation of the power of our new method. To this end, we also propose a more comprehensive interpretation of epistasis in which genotype interactions may be of risk, protective, or neutral. In this extended view of genetic interactions, we demonstrate that our method has superior performance than existing approaches, thus, providing a highly powerful tool for the identification of gene-gene interactions associated with binary traits.