CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2012 vol.9 Issue No.03 - May-June
Issue No.03 - May-June (2012 vol.9)
M. A. Mooney , Dept. of Med. Inf. & Clinical Epidemiology, Oregon Health & Sci. Univ., Portland, OR, USA
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.145
Enormous data collection efforts and improvements in technology have made large genome-wide association studies a promising approach for better understanding the genetics of common diseases. Still, the knowledge gained from these studies may be extended even further by testing the hypothesis that genetic susceptibility is due to the combined effect of multiple variants or interactions between variants. Here, we explore and evaluate the use of a genetic algorithm to discover groups of SNPs (of size 2, 3, or 4) that are jointly associated with bipolar disorder. The algorithm is guided by the structure of a gene interaction network, and is able to find groups of SNPs that are strongly associated with the disease, while performing far fewer statistical tests than other methods.
Bioinformatics, Educational institutions, Genetic algorithms, Genomics, Diseases, Materials,graphs and networks., Biology and genetics, evolutionary computing and genetic algorithms
M. A. Mooney, B. Wilmot, S. K. McWeeney, "The GA and the GWAS: Using Genetic Algorithms to Search for Multilocus Associations", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 3, pp. 899-910, May-June 2012, doi:10.1109/TCBB.2011.145