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.
|Original language||English (US)|
|Number of pages||12|
|Journal||IEEE/ACM Transactions on Computational Biology and Bioinformatics|
|State||Published - Apr 4 2012|
- Biology and genetics
- evolutionary computing and genetic algorithms
- graphs and networks.
ASJC Scopus subject areas
- Applied Mathematics