SNP-SNP Interactions: Focusing on Variable Coding for Complex Models of EpistasisFernando Pires Hartwig*
Postgraduate Program in Epidemiology, Department of Social Medicine, Faculty of Medicine, Federal University of Pelotas, Brazil
- *Corresponding Author:
- Fernando Pires Hartwig
Postgraduate Program in Epidemiology
Department of Social Medicine
Faculty of Medicine
Federal University of Pelotas, Brazil
E-mail: [email protected]
Received date: August 24, 2013; Accepted date:October 14, 2013; Published date: October 24, 2013
Citation: Hartwig FP (2013) SNP-SNP Interactions: Focusing on Variable Coding for Complex Models of Epistasis. J Genet Syndr Gene Ther 4:189. doi:10.4172/2157-7412.1000189
Copyright: © 2013 Hartwig FP. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Genetic epidemiology is a promising field to identify patterns of disease susceptibility that can be explored in personalized medicine. However, especially for complex traits, the genetic component is likely to be composed of several loci and/or of interactions between them. The last is addressed in this manuscript, which aims to provide an overview of the advantages and disadvantages of statistically-oriented and biologically-oriented approaches for two-SNP interactions. Eight biologically-oriented models of epistasis are discussed, focusing on their implementation, which is exemplified with real data. Additionally, some key technical points (such as reducing statistical power due to multiple testing and use of conceptual considerations) are discussed, and an exploratory step prior to the analysis is proposed to pre-select the models of epistasis to be actually tested. A function (written in R) is provided (under request) to facilitate the implementation of such models (and can be easily modified to implement others). It is stressed that, regardless of the method choice, the biological meaning of the model being tested is critical for correct interpretation of the results.