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BioMed Research International
Volume 2015 (2015), Article ID 523641, 10 pages
http://dx.doi.org/10.1155/2015/523641
Research Article

Detecting Genetic Interactions for Quantitative Traits Using -Spacing Entropy Measure

1Department of Physiology and Biophysics, Eulji University, Daejeon, Republic of Korea
2Department of Bioinformatics, Seoul National University, Seoul, Republic of Korea
3Department of Informational Statistics, Korea University, Jochiwon, Republic of Korea
4Department of Statistics, Seoul National University, Seoul, Republic of Korea
5Department of Preventive Medicine, Eulji University, Daejeon, Republic of Korea

Received 14 November 2014; Revised 4 February 2015; Accepted 8 March 2015

Academic Editor: Xiang-Yang Lou

Copyright © 2015 Jaeyong Yee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A number of statistical methods for detecting gene-gene interactions have been developed in genetic association studies with binary traits. However, many phenotype measures are intrinsically quantitative and categorizing continuous traits may not always be straightforward and meaningful. Association of gene-gene interactions with an observed distribution of such phenotypes needs to be investigated directly without categorization. Information gain based on entropy measure has previously been successful in identifying genetic associations with binary traits. We extend the usefulness of this information gain by proposing a nonparametric evaluation method of conditional entropy of a quantitative phenotype associated with a given genotype. Hence, the information gain can be obtained for any phenotype distribution. Because any functional form, such as Gaussian, is not assumed for the entire distribution of a trait or a given genotype, this method is expected to be robust enough to be applied to any phenotypic association data. Here, we show its use to successfully identify the main effect, as well as the genetic interactions, associated with a quantitative trait.