A parallel genetic algorithm to discover patterns in genetic markers that indicate predisposition to multifactorial disease

Authors:
Tobias Rausch;Alun Thomas;Nicola J. Camp;Lisa A. Cannon-Albright;Julio C. Facelli
Affiliations:
Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA and Hasso-Plattner-Institute, University of Potsdam, 14482 Potsdam, Germany;Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA;Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA;Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA;Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA and Center for High Performance Computing, University of Utah, Salt Lake City, UT 84112, ...
Venue:
Computers in Biology and Medicine
Year:
2008

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Abstract

This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise.