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Bioinformatics 2007 23(13):i167-i174; doi:10.1093/bioinformatics/btm205
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

GPDTI: A Genetic Programming Decision Tree Induction method to find epistatic effects in common complex diseases

Jesús K. Estrada-Gil 1,2,*, Juan C. Fernández-López 1,2, Enrique Hernández-Lemus 2, Irma Silva-Zolezzi 3, Alfredo Hidalgo-Miranda 3, Gerardo Jiménez-Sánchez 3 and Edgar E. Vallejo-Clemente 1

1Computer Science Department, Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Estado de Mexico, Mexico, 2Department of Computational Genomics and 3Department of Basic Research; Instituto Nacional de Medicina Genómica, Mexico

*To whom correspondence should be addressed.


  Abstract

Motivation: The identification of risk-associated genetic variants in common diseases remains a challenge to the biomedical research community. It has been suggested that common statistical approaches that exclusively measure main effects are often unable to detect interactions between some of these variants. Detecting and interpreting interactions is a challenging open problem from the statistical and computational perspectives. Methods in computing science may improve our understanding on the mechanisms of genetic disease by detecting interactions even in the presence of very low heritabilities.

Results: We have implemented a method using Genetic Programming that is able to induce a Decision Tree to detect interactions in genetic variants. This method has a cross-validation strategy for estimating classification and prediction errors and tests for consistencies in the results. To have better estimates, a new consistency measure that takes into account interactions and can be used in a genetic programming environment is proposed. This method detected five different interaction models with heritabilities as low as 0.008 and with prediction errors similar to the generated errors.

Availability: Information on the generated data sets and executable code is available upon request.

Contact: jestrada{at}inmegen.gob.mx


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